49dd4a12034a58d8195abccf923cb1e9c2031af1a7963206e766ea35985d7fb7

microsoftexcel-controlnet/annotator/leres/leres/depthmap.py

@@ -0,0 +1,546 @@
+# Author: thygate
+# https://github.com/thygate/stable-diffusion-webui-depthmap-script
+
+from modules import devices
+from modules.shared import opts
+from torchvision.transforms import transforms
+from operator import getitem
+
+import torch, gc
+import cv2
+import numpy as np
+import skimage.measure
+
+whole_size_threshold = 1600  # R_max from the paper
+pix2pixsize = 1024
+
+def scale_torch(img):
+    """
+    Scale the image and output it in torch.tensor.
+    :param img: input rgb is in shape [H, W, C], input depth/disp is in shape [H, W]
+    :param scale: the scale factor. float
+    :return: img. [C, H, W]
+    """
+    if len(img.shape) == 2:
+        img = img[np.newaxis, :, :]
+    if img.shape[2] == 3:
+        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406) , (0.229, 0.224, 0.225) )])
+        img = transform(img.astype(np.float32))
+    else:
+        img = img.astype(np.float32)
+        img = torch.from_numpy(img)
+    return img
+    
+def estimateleres(img, model, w, h):
+    # leres transform input
+    rgb_c = img[:, :, ::-1].copy()
+    A_resize = cv2.resize(rgb_c, (w, h))
+    img_torch = scale_torch(A_resize)[None, :, :, :] 
+    
+    # compute
+    with torch.no_grad():
+        img_torch = img_torch.to(devices.get_device_for("controlnet"))
+        prediction = model.depth_model(img_torch)
+
+    prediction = prediction.squeeze().cpu().numpy()
+    prediction = cv2.resize(prediction, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_CUBIC)
+
+    return prediction
+
+def generatemask(size):
+    # Generates a Guassian mask
+    mask = np.zeros(size, dtype=np.float32)
+    sigma = int(size[0]/16)
+    k_size = int(2 * np.ceil(2 * int(size[0]/16)) + 1)
+    mask[int(0.15*size[0]):size[0] - int(0.15*size[0]), int(0.15*size[1]): size[1] - int(0.15*size[1])] = 1
+    mask = cv2.GaussianBlur(mask, (int(k_size), int(k_size)), sigma)
+    mask = (mask - mask.min()) / (mask.max() - mask.min())
+    mask = mask.astype(np.float32)
+    return mask
+
+def resizewithpool(img, size):
+    i_size = img.shape[0]
+    n = int(np.floor(i_size/size))
+
+    out = skimage.measure.block_reduce(img, (n, n), np.max)
+    return out
+
+def rgb2gray(rgb):
+    # Converts rgb to gray
+    return np.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140])
+
+def calculateprocessingres(img, basesize, confidence=0.1, scale_threshold=3, whole_size_threshold=3000):
+    # Returns the R_x resolution described in section 5 of the main paper.
+
+    # Parameters:
+    #    img :input rgb image
+    #    basesize : size the dilation kernel which is equal to receptive field of the network.
+    #    confidence: value of x in R_x; allowed percentage of pixels that are not getting any contextual cue.
+    #    scale_threshold: maximum allowed upscaling on the input image ; it has been set to 3.
+    #    whole_size_threshold: maximum allowed resolution. (R_max from section 6 of the main paper)
+
+    # Returns:
+    #    outputsize_scale*speed_scale :The computed R_x resolution
+    #    patch_scale: K parameter from section 6 of the paper
+
+    # speed scale parameter is to process every image in a smaller size to accelerate the R_x resolution search
+    speed_scale = 32
+    image_dim = int(min(img.shape[0:2]))
+
+    gray = rgb2gray(img)
+    grad = np.abs(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)) + np.abs(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3))
+    grad = cv2.resize(grad, (image_dim, image_dim), cv2.INTER_AREA)
+
+    # thresholding the gradient map to generate the edge-map as a proxy of the contextual cues
+    m = grad.min()
+    M = grad.max()
+    middle = m + (0.4 * (M - m))
+    grad[grad < middle] = 0
+    grad[grad >= middle] = 1
+
+    # dilation kernel with size of the receptive field
+    kernel = np.ones((int(basesize/speed_scale), int(basesize/speed_scale)), float)
+    # dilation kernel with size of the a quarter of receptive field used to compute k
+    # as described in section 6 of main paper
+    kernel2 = np.ones((int(basesize / (4*speed_scale)), int(basesize / (4*speed_scale))), float)
+
+    # Output resolution limit set by the whole_size_threshold and scale_threshold.
+    threshold = min(whole_size_threshold, scale_threshold * max(img.shape[:2]))
+
+    outputsize_scale = basesize / speed_scale
+    for p_size in range(int(basesize/speed_scale), int(threshold/speed_scale), int(basesize / (2*speed_scale))):
+        grad_resized = resizewithpool(grad, p_size)
+        grad_resized = cv2.resize(grad_resized, (p_size, p_size), cv2.INTER_NEAREST)
+        grad_resized[grad_resized >= 0.5] = 1
+        grad_resized[grad_resized < 0.5] = 0
+
+        dilated = cv2.dilate(grad_resized, kernel, iterations=1)
+        meanvalue = (1-dilated).mean()
+        if meanvalue > confidence:
+            break
+        else:
+            outputsize_scale = p_size
+
+    grad_region = cv2.dilate(grad_resized, kernel2, iterations=1)
+    patch_scale = grad_region.mean()
+
+    return int(outputsize_scale*speed_scale), patch_scale
+
+# Generate a double-input depth estimation
+def doubleestimate(img, size1, size2, pix2pixsize, model, net_type, pix2pixmodel):
+    # Generate the low resolution estimation
+    estimate1 = singleestimate(img, size1, model, net_type)
+    # Resize to the inference size of merge network.
+    estimate1 = cv2.resize(estimate1, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC)
+
+    # Generate the high resolution estimation
+    estimate2 = singleestimate(img, size2, model, net_type)
+    # Resize to the inference size of merge network.
+    estimate2 = cv2.resize(estimate2, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC)
+
+    # Inference on the merge model
+    pix2pixmodel.set_input(estimate1, estimate2)
+    pix2pixmodel.test()
+    visuals = pix2pixmodel.get_current_visuals()
+    prediction_mapped = visuals['fake_B']
+    prediction_mapped = (prediction_mapped+1)/2
+    prediction_mapped = (prediction_mapped - torch.min(prediction_mapped)) / (
+                torch.max(prediction_mapped) - torch.min(prediction_mapped))
+    prediction_mapped = prediction_mapped.squeeze().cpu().numpy()
+
+    return prediction_mapped
+
+# Generate a single-input depth estimation
+def singleestimate(img, msize, model, net_type):
+    # if net_type == 0:
+    return estimateleres(img, model, msize, msize)
+    # else:
+    # 	return estimatemidasBoost(img, model, msize, msize)
+
+def applyGridpatch(blsize, stride, img, box):
+    # Extract a simple grid patch.
+    counter1 = 0
+    patch_bound_list = {}
+    for k in range(blsize, img.shape[1] - blsize, stride):
+        for j in range(blsize, img.shape[0] - blsize, stride):
+            patch_bound_list[str(counter1)] = {}
+            patchbounds = [j - blsize, k - blsize, j - blsize + 2 * blsize, k - blsize + 2 * blsize]
+            patch_bound = [box[0] + patchbounds[1], box[1] + patchbounds[0], patchbounds[3] - patchbounds[1],
+                           patchbounds[2] - patchbounds[0]]
+            patch_bound_list[str(counter1)]['rect'] = patch_bound
+            patch_bound_list[str(counter1)]['size'] = patch_bound[2]
+            counter1 = counter1 + 1
+    return patch_bound_list
+
+# Generating local patches to perform the local refinement described in section 6 of the main paper.
+def generatepatchs(img, base_size):
+    
+    # Compute the gradients as a proxy of the contextual cues.
+    img_gray = rgb2gray(img)
+    whole_grad = np.abs(cv2.Sobel(img_gray, cv2.CV_64F, 0, 1, ksize=3)) +\
+        np.abs(cv2.Sobel(img_gray, cv2.CV_64F, 1, 0, ksize=3))
+
+    threshold = whole_grad[whole_grad > 0].mean()
+    whole_grad[whole_grad < threshold] = 0
+
+    # We use the integral image to speed-up the evaluation of the amount of gradients for each patch.
+    gf = whole_grad.sum()/len(whole_grad.reshape(-1))
+    grad_integral_image = cv2.integral(whole_grad)
+
+    # Variables are selected such that the initial patch size would be the receptive field size
+    # and the stride is set to 1/3 of the receptive field size.
+    blsize = int(round(base_size/2))
+    stride = int(round(blsize*0.75))
+
+    # Get initial Grid
+    patch_bound_list = applyGridpatch(blsize, stride, img, [0, 0, 0, 0])
+
+    # Refine initial Grid of patches by discarding the flat (in terms of gradients of the rgb image) ones. Refine
+    # each patch size to ensure that there will be enough depth cues for the network to generate a consistent depth map.
+    print("Selecting patches ...")
+    patch_bound_list = adaptiveselection(grad_integral_image, patch_bound_list, gf)
+
+    # Sort the patch list to make sure the merging operation will be done with the correct order: starting from biggest
+    # patch
+    patchset = sorted(patch_bound_list.items(), key=lambda x: getitem(x[1], 'size'), reverse=True)
+    return patchset
+
+def getGF_fromintegral(integralimage, rect):
+    # Computes the gradient density of a given patch from the gradient integral image.
+    x1 = rect[1]
+    x2 = rect[1]+rect[3]
+    y1 = rect[0]
+    y2 = rect[0]+rect[2]
+    value = integralimage[x2, y2]-integralimage[x1, y2]-integralimage[x2, y1]+integralimage[x1, y1]
+    return value
+
+# Adaptively select patches
+def adaptiveselection(integral_grad, patch_bound_list, gf):
+    patchlist = {}
+    count = 0
+    height, width = integral_grad.shape
+
+    search_step = int(32/factor)
+
+    # Go through all patches
+    for c in range(len(patch_bound_list)):
+        # Get patch
+        bbox = patch_bound_list[str(c)]['rect']
+
+        # Compute the amount of gradients present in the patch from the integral image.
+        cgf = getGF_fromintegral(integral_grad, bbox)/(bbox[2]*bbox[3])
+
+        # Check if patching is beneficial by comparing the gradient density of the patch to
+        # the gradient density of the whole image
+        if cgf >= gf:
+            bbox_test = bbox.copy()
+            patchlist[str(count)] = {}
+
+            # Enlarge each patch until the gradient density of the patch is equal
+            # to the whole image gradient density
+            while True:
+
+                bbox_test[0] = bbox_test[0] - int(search_step/2)
+                bbox_test[1] = bbox_test[1] - int(search_step/2)
+
+                bbox_test[2] = bbox_test[2] + search_step
+                bbox_test[3] = bbox_test[3] + search_step
+
+                # Check if we are still within the image
+                if bbox_test[0] < 0 or bbox_test[1] < 0 or bbox_test[1] + bbox_test[3] >= height \
+                        or bbox_test[0] + bbox_test[2] >= width:
+                    break
+
+                # Compare gradient density
+                cgf = getGF_fromintegral(integral_grad, bbox_test)/(bbox_test[2]*bbox_test[3])
+                if cgf < gf:
+                    break
+                bbox = bbox_test.copy()
+
+            # Add patch to selected patches
+            patchlist[str(count)]['rect'] = bbox
+            patchlist[str(count)]['size'] = bbox[2]
+            count = count + 1
+    
+    # Return selected patches
+    return patchlist
+
+def impatch(image, rect):
+    # Extract the given patch pixels from a given image.
+    w1 = rect[0]
+    h1 = rect[1]
+    w2 = w1 + rect[2]
+    h2 = h1 + rect[3]
+    image_patch = image[h1:h2, w1:w2]
+    return image_patch
+
+class ImageandPatchs:
+    def __init__(self, root_dir, name, patchsinfo, rgb_image, scale=1):
+        self.root_dir = root_dir
+        self.patchsinfo = patchsinfo
+        self.name = name
+        self.patchs = patchsinfo
+        self.scale = scale
+
+        self.rgb_image = cv2.resize(rgb_image, (round(rgb_image.shape[1]*scale), round(rgb_image.shape[0]*scale)),
+                                    interpolation=cv2.INTER_CUBIC)
+
+        self.do_have_estimate = False
+        self.estimation_updated_image = None
+        self.estimation_base_image = None
+
+    def __len__(self):
+        return len(self.patchs)
+
+    def set_base_estimate(self, est):
+        self.estimation_base_image = est
+        if self.estimation_updated_image is not None:
+            self.do_have_estimate = True
+
+    def set_updated_estimate(self, est):
+        self.estimation_updated_image = est
+        if self.estimation_base_image is not None:
+            self.do_have_estimate = True
+
+    def __getitem__(self, index):
+        patch_id = int(self.patchs[index][0])
+        rect = np.array(self.patchs[index][1]['rect'])
+        msize = self.patchs[index][1]['size']
+
+        ## applying scale to rect:
+        rect = np.round(rect * self.scale)
+        rect = rect.astype('int')
+        msize = round(msize * self.scale)
+
+        patch_rgb = impatch(self.rgb_image, rect)
+        if self.do_have_estimate:
+            patch_whole_estimate_base = impatch(self.estimation_base_image, rect)
+            patch_whole_estimate_updated = impatch(self.estimation_updated_image, rect)
+            return {'patch_rgb': patch_rgb, 'patch_whole_estimate_base': patch_whole_estimate_base,
+                    'patch_whole_estimate_updated': patch_whole_estimate_updated, 'rect': rect,
+                    'size': msize, 'id': patch_id}
+        else:
+            return {'patch_rgb': patch_rgb, 'rect': rect, 'size': msize, 'id': patch_id}
+
+    def print_options(self, opt):
+        """Print and save options
+
+        It will print both current options and default values(if different).
+        It will save options into a text file / [checkpoints_dir] / opt.txt
+        """
+        message = ''
+        message += '----------------- Options ---------------\n'
+        for k, v in sorted(vars(opt).items()):
+            comment = ''
+            default = self.parser.get_default(k)
+            if v != default:
+                comment = '\t[default: %s]' % str(default)
+            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
+        message += '----------------- End -------------------'
+        print(message)
+
+        # save to the disk
+        """
+        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
+        util.mkdirs(expr_dir)
+        file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase))
+        with open(file_name, 'wt') as opt_file:
+            opt_file.write(message)
+            opt_file.write('\n')
+        """
+
+    def parse(self):
+        """Parse our options, create checkpoints directory suffix, and set up gpu device."""
+        opt = self.gather_options()
+        opt.isTrain = self.isTrain   # train or test
+
+        # process opt.suffix
+        if opt.suffix:
+            suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
+            opt.name = opt.name + suffix
+
+        #self.print_options(opt)
+
+        # set gpu ids
+        str_ids = opt.gpu_ids.split(',')
+        opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                opt.gpu_ids.append(id)
+        #if len(opt.gpu_ids) > 0:
+        #    torch.cuda.set_device(opt.gpu_ids[0])
+
+        self.opt = opt
+        return self.opt
+
+
+def estimateboost(img, model, model_type, pix2pixmodel, max_res=512):
+    global whole_size_threshold
+    
+    # get settings
+    if hasattr(opts, 'depthmap_script_boost_rmax'):
+        whole_size_threshold = opts.depthmap_script_boost_rmax
+        
+    if model_type == 0: #leres
+        net_receptive_field_size = 448
+        patch_netsize = 2 * net_receptive_field_size
+    elif model_type == 1: #dpt_beit_large_512
+        net_receptive_field_size = 512
+        patch_netsize = 2 * net_receptive_field_size
+    else: #other midas
+        net_receptive_field_size = 384
+        patch_netsize = 2 * net_receptive_field_size
+
+    gc.collect()
+    devices.torch_gc()
+
+    # Generate mask used to smoothly blend the local pathc estimations to the base estimate.
+    # It is arbitrarily large to avoid artifacts during rescaling for each crop.
+    mask_org = generatemask((3000, 3000))
+    mask = mask_org.copy()
+
+    # Value x of R_x defined in the section 5 of the main paper.
+    r_threshold_value = 0.2
+    #if R0:
+    #	r_threshold_value = 0
+
+    input_resolution = img.shape
+    scale_threshold = 3  # Allows up-scaling with a scale up to 3
+
+    # Find the best input resolution R-x. The resolution search described in section 5-double estimation of the main paper and section B of the
+    # supplementary material.
+    whole_image_optimal_size, patch_scale = calculateprocessingres(img, net_receptive_field_size, r_threshold_value, scale_threshold, whole_size_threshold)
+
+    # print('wholeImage being processed in :', whole_image_optimal_size)
+
+    # Generate the base estimate using the double estimation.
+    whole_estimate = doubleestimate(img, net_receptive_field_size, whole_image_optimal_size, pix2pixsize, model, model_type, pix2pixmodel)
+
+    # Compute the multiplier described in section 6 of the main paper to make sure our initial patch can select
+    # small high-density regions of the image.
+    global factor
+    factor = max(min(1, 4 * patch_scale * whole_image_optimal_size / whole_size_threshold), 0.2)
+    # print('Adjust factor is:', 1/factor)
+        
+    # Check if Local boosting is beneficial.
+    if max_res < whole_image_optimal_size:
+        # print("No Local boosting. Specified Max Res is smaller than R20, Returning doubleestimate result")
+        return cv2.resize(whole_estimate, (input_resolution[1], input_resolution[0]), interpolation=cv2.INTER_CUBIC)
+
+    # Compute the default target resolution.
+    if img.shape[0] > img.shape[1]:
+        a = 2 * whole_image_optimal_size
+        b = round(2 * whole_image_optimal_size * img.shape[1] / img.shape[0])
+    else:
+        a = round(2 * whole_image_optimal_size * img.shape[0] / img.shape[1])
+        b = 2 * whole_image_optimal_size
+    b = int(round(b / factor))
+    a = int(round(a / factor))
+
+    """
+    # recompute a, b and saturate to max res.
+    if max(a,b) > max_res:
+        print('Default Res is higher than max-res: Reducing final resolution')
+        if img.shape[0] > img.shape[1]:
+            a = max_res
+            b = round(max_res * img.shape[1] / img.shape[0])
+        else:
+            a = round(max_res * img.shape[0] / img.shape[1])
+            b = max_res
+        b = int(b)
+        a = int(a)
+    """
+
+    img = cv2.resize(img, (b, a), interpolation=cv2.INTER_CUBIC)
+
+    # Extract selected patches for local refinement
+    base_size = net_receptive_field_size * 2
+    patchset = generatepatchs(img, base_size)
+
+    # print('Target resolution: ', img.shape)
+
+    # Computing a scale in case user prompted to generate the results as the same resolution of the input.
+    # Notice that our method output resolution is independent of the input resolution and this parameter will only
+    # enable a scaling operation during the local patch merge implementation to generate results with the same resolution
+    # as the input.
+    """
+    if output_resolution == 1:
+        mergein_scale = input_resolution[0] / img.shape[0]
+        print('Dynamicly change merged-in resolution; scale:', mergein_scale)
+    else:
+        mergein_scale = 1
+    """
+    # always rescale to input res for now
+    mergein_scale = input_resolution[0] / img.shape[0]
+
+    imageandpatchs = ImageandPatchs('', '', patchset, img, mergein_scale)
+    whole_estimate_resized = cv2.resize(whole_estimate, (round(img.shape[1]*mergein_scale),
+                                        round(img.shape[0]*mergein_scale)), interpolation=cv2.INTER_CUBIC)
+    imageandpatchs.set_base_estimate(whole_estimate_resized.copy())
+    imageandpatchs.set_updated_estimate(whole_estimate_resized.copy())
+
+    print('Resulting depthmap resolution will be :', whole_estimate_resized.shape[:2])
+    print('Patches to process: '+str(len(imageandpatchs)))
+
+    # Enumerate through all patches, generate their estimations and refining the base estimate.
+    for patch_ind in range(len(imageandpatchs)):
+        
+        # Get patch information
+        patch = imageandpatchs[patch_ind] # patch object
+        patch_rgb = patch['patch_rgb'] # rgb patch
+        patch_whole_estimate_base = patch['patch_whole_estimate_base'] # corresponding patch from base
+        rect = patch['rect'] # patch size and location
+        patch_id = patch['id'] # patch ID
+        org_size = patch_whole_estimate_base.shape # the original size from the unscaled input
+        print('\t Processing patch', patch_ind, '/', len(imageandpatchs)-1, '|', rect)
+
+        # We apply double estimation for patches. The high resolution value is fixed to twice the receptive
+        # field size of the network for patches to accelerate the process.
+        patch_estimation = doubleestimate(patch_rgb, net_receptive_field_size, patch_netsize, pix2pixsize, model, model_type, pix2pixmodel)
+        patch_estimation = cv2.resize(patch_estimation, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC)
+        patch_whole_estimate_base = cv2.resize(patch_whole_estimate_base, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC)
+
+        # Merging the patch estimation into the base estimate using our merge network:
+        # We feed the patch estimation and the same region from the updated base estimate to the merge network
+        # to generate the target estimate for the corresponding region.
+        pix2pixmodel.set_input(patch_whole_estimate_base, patch_estimation)
+
+        # Run merging network
+        pix2pixmodel.test()
+        visuals = pix2pixmodel.get_current_visuals()
+
+        prediction_mapped = visuals['fake_B']
+        prediction_mapped = (prediction_mapped+1)/2
+        prediction_mapped = prediction_mapped.squeeze().cpu().numpy()
+
+        mapped = prediction_mapped
+
+        # We use a simple linear polynomial to make sure the result of the merge network would match the values of
+        # base estimate
+        p_coef = np.polyfit(mapped.reshape(-1), patch_whole_estimate_base.reshape(-1), deg=1)
+        merged = np.polyval(p_coef, mapped.reshape(-1)).reshape(mapped.shape)
+
+        merged = cv2.resize(merged, (org_size[1],org_size[0]), interpolation=cv2.INTER_CUBIC)
+
+        # Get patch size and location
+        w1 = rect[0]
+        h1 = rect[1]
+        w2 = w1 + rect[2]
+        h2 = h1 + rect[3]
+
+        # To speed up the implementation, we only generate the Gaussian mask once with a sufficiently large size
+        # and resize it to our needed size while merging the patches.
+        if mask.shape != org_size:
+            mask = cv2.resize(mask_org, (org_size[1],org_size[0]), interpolation=cv2.INTER_LINEAR)
+
+        tobemergedto = imageandpatchs.estimation_updated_image
+
+        # Update the whole estimation:
+        # We use a simple Gaussian mask to blend the merged patch region with the base estimate to ensure seamless
+        # blending at the boundaries of the patch region.
+        tobemergedto[h1:h2, w1:w2] = np.multiply(tobemergedto[h1:h2, w1:w2], 1 - mask) + np.multiply(merged, mask)
+        imageandpatchs.set_updated_estimate(tobemergedto)
+
+    # output
+    return cv2.resize(imageandpatchs.estimation_updated_image, (input_resolution[1], input_resolution[0]), interpolation=cv2.INTER_CUBIC)

microsoftexcel-controlnet/annotator/leres/leres/multi_depth_model_woauxi.py

@@ -0,0 +1,34 @@
+from . import network_auxi as network
+from .net_tools import get_func
+import torch
+import torch.nn as nn
+from modules import devices
+
+class RelDepthModel(nn.Module):
+    def __init__(self, backbone='resnet50'):
+        super(RelDepthModel, self).__init__()
+        if backbone == 'resnet50':
+            encoder = 'resnet50_stride32'
+        elif backbone == 'resnext101':
+            encoder = 'resnext101_stride32x8d'
+        self.depth_model = DepthModel(encoder)
+
+    def inference(self, rgb):
+        with torch.no_grad():
+            input = rgb.to(self.depth_model.device)
+            depth = self.depth_model(input)
+            #pred_depth_out = depth - depth.min() + 0.01
+            return depth #pred_depth_out
+
+
+class DepthModel(nn.Module):
+    def __init__(self, encoder):
+        super(DepthModel, self).__init__()
+        backbone = network.__name__.split('.')[-1] + '.' + encoder
+        self.encoder_modules = get_func(backbone)()
+        self.decoder_modules = network.Decoder()
+
+    def forward(self, x):
+        lateral_out = self.encoder_modules(x)
+        out_logit = self.decoder_modules(lateral_out)
+        return out_logit

microsoftexcel-controlnet/annotator/leres/leres/net_tools.py

@@ -0,0 +1,54 @@
+import importlib
+import torch
+import os
+from collections import OrderedDict
+
+
+def get_func(func_name):
+    """Helper to return a function object by name. func_name must identify a
+    function in this module or the path to a function relative to the base
+    'modeling' module.
+    """
+    if func_name == '':
+        return None
+    try:
+        parts = func_name.split('.')
+        # Refers to a function in this module
+        if len(parts) == 1:
+            return globals()[parts[0]]
+        # Otherwise, assume we're referencing a module under modeling
+        module_name = 'annotator.leres.leres.' + '.'.join(parts[:-1])
+        module = importlib.import_module(module_name)
+        return getattr(module, parts[-1])
+    except Exception:
+        print('Failed to f1ind function: %s', func_name)
+        raise
+
+def load_ckpt(args, depth_model, shift_model, focal_model):
+    """
+    Load checkpoint.
+    """
+    if os.path.isfile(args.load_ckpt):
+        print("loading checkpoint %s" % args.load_ckpt)
+        checkpoint = torch.load(args.load_ckpt)
+        if shift_model is not None:
+            shift_model.load_state_dict(strip_prefix_if_present(checkpoint['shift_model'], 'module.'),
+                                    strict=True)
+        if focal_model is not None:
+            focal_model.load_state_dict(strip_prefix_if_present(checkpoint['focal_model'], 'module.'),
+                                    strict=True)
+        depth_model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."),
+                                    strict=True)
+        del checkpoint
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+
+def strip_prefix_if_present(state_dict, prefix):
+    keys = sorted(state_dict.keys())
+    if not all(key.startswith(prefix) for key in keys):
+        return state_dict
+    stripped_state_dict = OrderedDict()
+    for key, value in state_dict.items():
+        stripped_state_dict[key.replace(prefix, "")] = value
+    return stripped_state_dict

microsoftexcel-controlnet/annotator/leres/leres/network_auxi.py

@@ -0,0 +1,417 @@
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+
+from . import Resnet, Resnext_torch
+
+
+def resnet50_stride32():
+    return DepthNet(backbone='resnet', depth=50, upfactors=[2, 2, 2, 2])
+
+def resnext101_stride32x8d():
+    return DepthNet(backbone='resnext101_32x8d', depth=101, upfactors=[2, 2, 2, 2])
+
+
+class Decoder(nn.Module):
+    def __init__(self):
+        super(Decoder, self).__init__()
+        self.inchannels =  [256, 512, 1024, 2048]
+        self.midchannels = [256, 256, 256, 512]
+        self.upfactors = [2,2,2,2]
+        self.outchannels = 1
+
+        self.conv = FTB(inchannels=self.inchannels[3], midchannels=self.midchannels[3])
+        self.conv1 = nn.Conv2d(in_channels=self.midchannels[3], out_channels=self.midchannels[2], kernel_size=3, padding=1, stride=1, bias=True)
+        self.upsample = nn.Upsample(scale_factor=self.upfactors[3], mode='bilinear', align_corners=True)
+        
+        self.ffm2 = FFM(inchannels=self.inchannels[2], midchannels=self.midchannels[2], outchannels = self.midchannels[2], upfactor=self.upfactors[2])
+        self.ffm1 = FFM(inchannels=self.inchannels[1], midchannels=self.midchannels[1], outchannels = self.midchannels[1], upfactor=self.upfactors[1])
+        self.ffm0 = FFM(inchannels=self.inchannels[0], midchannels=self.midchannels[0], outchannels = self.midchannels[0], upfactor=self.upfactors[0])
+        
+        self.outconv = AO(inchannels=self.midchannels[0], outchannels=self.outchannels, upfactor=2)
+        self._init_params()
+        
+    def _init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                init.normal_(m.weight, std=0.01)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.ConvTranspose2d):
+                init.normal_(m.weight, std=0.01)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d): #NN.BatchNorm2d
+                init.constant_(m.weight, 1)
+                init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                init.normal_(m.weight, std=0.01)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+                    
+    def forward(self, features):
+        x_32x = self.conv(features[3])  # 1/32
+        x_32 = self.conv1(x_32x)
+        x_16 = self.upsample(x_32)  # 1/16
+
+        x_8 = self.ffm2(features[2], x_16)  # 1/8
+        x_4 = self.ffm1(features[1], x_8)  # 1/4
+        x_2 = self.ffm0(features[0], x_4)  # 1/2
+        #-----------------------------------------
+        x = self.outconv(x_2)  # original size
+        return x
+
+class DepthNet(nn.Module):
+    __factory = {
+        18: Resnet.resnet18,
+        34: Resnet.resnet34,
+        50: Resnet.resnet50,
+        101: Resnet.resnet101,
+        152: Resnet.resnet152
+    }
+    def __init__(self,
+                 backbone='resnet',
+                 depth=50,
+                 upfactors=[2, 2, 2, 2]):
+        super(DepthNet, self).__init__()
+        self.backbone = backbone
+        self.depth = depth
+        self.pretrained = False
+        self.inchannels = [256, 512, 1024, 2048]
+        self.midchannels = [256, 256, 256, 512]
+        self.upfactors = upfactors
+        self.outchannels = 1
+
+        # Build model
+        if self.backbone == 'resnet':
+            if self.depth not in DepthNet.__factory:
+                raise KeyError("Unsupported depth:", self.depth)
+            self.encoder = DepthNet.__factory[depth](pretrained=self.pretrained)
+        elif self.backbone == 'resnext101_32x8d':
+            self.encoder = Resnext_torch.resnext101_32x8d(pretrained=self.pretrained)
+        else:
+            self.encoder = Resnext_torch.resnext101(pretrained=self.pretrained)
+
+    def forward(self, x):
+        x = self.encoder(x)  # 1/32, 1/16, 1/8, 1/4
+        return x
+
+
+class FTB(nn.Module):
+    def __init__(self, inchannels, midchannels=512):
+        super(FTB, self).__init__()
+        self.in1 = inchannels
+        self.mid = midchannels
+        self.conv1 = nn.Conv2d(in_channels=self.in1, out_channels=self.mid, kernel_size=3, padding=1, stride=1,
+                               bias=True)
+        # NN.BatchNorm2d
+        self.conv_branch = nn.Sequential(nn.ReLU(inplace=True), \
+                                         nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3,
+                                                   padding=1, stride=1, bias=True), \
+                                         nn.BatchNorm2d(num_features=self.mid), \
+                                         nn.ReLU(inplace=True), \
+                                         nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3,
+                                                   padding=1, stride=1, bias=True))
+        self.relu = nn.ReLU(inplace=True)
+
+        self.init_params()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = x + self.conv_branch(x)
+        x = self.relu(x)
+
+        return x
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                init.normal_(m.weight, std=0.01)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.ConvTranspose2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                init.normal_(m.weight, std=0.01)
+                # init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):  # NN.BatchNorm2d
+                init.constant_(m.weight, 1)
+                init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                init.normal_(m.weight, std=0.01)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+
+
+class ATA(nn.Module):
+    def __init__(self, inchannels, reduction=8):
+        super(ATA, self).__init__()
+        self.inchannels = inchannels
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(nn.Linear(self.inchannels * 2, self.inchannels // reduction),
+                                nn.ReLU(inplace=True),
+                                nn.Linear(self.inchannels // reduction, self.inchannels),
+                                nn.Sigmoid())
+        self.init_params()
+
+    def forward(self, low_x, high_x):
+        n, c, _, _ = low_x.size()
+        x = torch.cat([low_x, high_x], 1)
+        x = self.avg_pool(x)
+        x = x.view(n, -1)
+        x = self.fc(x).view(n, c, 1, 1)
+        x = low_x * x + high_x
+
+        return x
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                # init.normal(m.weight, std=0.01)
+                init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.ConvTranspose2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                # init.normal_(m.weight, std=0.01)
+                init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):  # NN.BatchNorm2d
+                init.constant_(m.weight, 1)
+                init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                init.normal_(m.weight, std=0.01)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+
+
+class FFM(nn.Module):
+    def __init__(self, inchannels, midchannels, outchannels, upfactor=2):
+        super(FFM, self).__init__()
+        self.inchannels = inchannels
+        self.midchannels = midchannels
+        self.outchannels = outchannels
+        self.upfactor = upfactor
+
+        self.ftb1 = FTB(inchannels=self.inchannels, midchannels=self.midchannels)
+        # self.ata = ATA(inchannels = self.midchannels)
+        self.ftb2 = FTB(inchannels=self.midchannels, midchannels=self.outchannels)
+
+        self.upsample = nn.Upsample(scale_factor=self.upfactor, mode='bilinear', align_corners=True)
+
+        self.init_params()
+
+    def forward(self, low_x, high_x):
+        x = self.ftb1(low_x)
+        x = x + high_x
+        x = self.ftb2(x)
+        x = self.upsample(x)
+
+        return x
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                init.normal_(m.weight, std=0.01)
+                # init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.ConvTranspose2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                init.normal_(m.weight, std=0.01)
+                # init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):  # NN.Batchnorm2d
+                init.constant_(m.weight, 1)
+                init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                init.normal_(m.weight, std=0.01)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+
+
+class AO(nn.Module):
+    # Adaptive output module
+    def __init__(self, inchannels, outchannels, upfactor=2):
+        super(AO, self).__init__()
+        self.inchannels = inchannels
+        self.outchannels = outchannels
+        self.upfactor = upfactor
+
+        self.adapt_conv = nn.Sequential(
+            nn.Conv2d(in_channels=self.inchannels, out_channels=self.inchannels // 2, kernel_size=3, padding=1,
+                      stride=1, bias=True), \
+            nn.BatchNorm2d(num_features=self.inchannels // 2), \
+            nn.ReLU(inplace=True), \
+            nn.Conv2d(in_channels=self.inchannels // 2, out_channels=self.outchannels, kernel_size=3, padding=1,
+                      stride=1, bias=True), \
+            nn.Upsample(scale_factor=self.upfactor, mode='bilinear', align_corners=True))
+
+        self.init_params()
+
+    def forward(self, x):
+        x = self.adapt_conv(x)
+        return x
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                init.normal_(m.weight, std=0.01)
+                # init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.ConvTranspose2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                init.normal_(m.weight, std=0.01)
+                # init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):  # NN.Batchnorm2d
+                init.constant_(m.weight, 1)
+                init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                init.normal_(m.weight, std=0.01)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+
+
+
+# ==============================================================================================================
+
+
+class ResidualConv(nn.Module):
+    def __init__(self, inchannels):
+        super(ResidualConv, self).__init__()
+        # NN.BatchNorm2d
+        self.conv = nn.Sequential(
+            # nn.BatchNorm2d(num_features=inchannels),
+            nn.ReLU(inplace=False),
+            # nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=3, padding=1, stride=1, groups=inchannels,bias=True),
+            # nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=1, padding=0, stride=1, groups=1,bias=True)
+            nn.Conv2d(in_channels=inchannels, out_channels=inchannels / 2, kernel_size=3, padding=1, stride=1,
+                      bias=False),
+            nn.BatchNorm2d(num_features=inchannels / 2),
+            nn.ReLU(inplace=False),
+            nn.Conv2d(in_channels=inchannels / 2, out_channels=inchannels, kernel_size=3, padding=1, stride=1,
+                      bias=False)
+        )
+        self.init_params()
+
+    def forward(self, x):
+        x = self.conv(x) + x
+        return x
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                init.normal_(m.weight, std=0.01)
+                # init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.ConvTranspose2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                init.normal_(m.weight, std=0.01)
+                # init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):  # NN.BatchNorm2d
+                init.constant_(m.weight, 1)
+                init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                init.normal_(m.weight, std=0.01)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+
+
+class FeatureFusion(nn.Module):
+    def __init__(self, inchannels, outchannels):
+        super(FeatureFusion, self).__init__()
+        self.conv = ResidualConv(inchannels=inchannels)
+        # NN.BatchNorm2d
+        self.up = nn.Sequential(ResidualConv(inchannels=inchannels),
+                                nn.ConvTranspose2d(in_channels=inchannels, out_channels=outchannels, kernel_size=3,
+                                                   stride=2, padding=1, output_padding=1),
+                                nn.BatchNorm2d(num_features=outchannels),
+                                nn.ReLU(inplace=True))
+
+    def forward(self, lowfeat, highfeat):
+        return self.up(highfeat + self.conv(lowfeat))
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                init.normal_(m.weight, std=0.01)
+                # init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.ConvTranspose2d):
+                # init.kaiming_normal_(m.weight, mode='fan_out')
+                init.normal_(m.weight, std=0.01)
+                # init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):  # NN.BatchNorm2d
+                init.constant_(m.weight, 1)
+                init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                init.normal_(m.weight, std=0.01)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+
+
+class SenceUnderstand(nn.Module):
+    def __init__(self, channels):
+        super(SenceUnderstand, self).__init__()
+        self.channels = channels
+        self.conv1 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
+                                   nn.ReLU(inplace=True))
+        self.pool = nn.AdaptiveAvgPool2d(8)
+        self.fc = nn.Sequential(nn.Linear(512 * 8 * 8, self.channels),
+                                nn.ReLU(inplace=True))
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=1, padding=0),
+            nn.ReLU(inplace=True))
+        self.initial_params()
+
+    def forward(self, x):
+        n, c, h, w = x.size()
+        x = self.conv1(x)
+        x = self.pool(x)
+        x = x.view(n, -1)
+        x = self.fc(x)
+        x = x.view(n, self.channels, 1, 1)
+        x = self.conv2(x)
+        x = x.repeat(1, 1, h, w)
+        return x
+
+    def initial_params(self, dev=0.01):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                # print torch.sum(m.weight)
+                m.weight.data.normal_(0, dev)
+                if m.bias is not None:
+                    m.bias.data.fill_(0)
+            elif isinstance(m, nn.ConvTranspose2d):
+                # print torch.sum(m.weight)
+                m.weight.data.normal_(0, dev)
+                if m.bias is not None:
+                    m.bias.data.fill_(0)
+            elif isinstance(m, nn.Linear):
+                m.weight.data.normal_(0, dev)
+
+
+if __name__ == '__main__':
+    net = DepthNet(depth=50, pretrained=True)
+    print(net)
+    inputs = torch.ones(4,3,128,128)
+    out = net(inputs)
+    print(out.size())
+

microsoftexcel-controlnet/annotator/leres/pix2pix/LICENSE

@@ -0,0 +1,19 @@
+https://github.com/compphoto/BoostingMonocularDepth
+
+Copyright 2021, Seyed Mahdi Hosseini Miangoleh, Sebastian Dille, Computational Photography Laboratory. All rights reserved.
+
+This software is for academic use only. A redistribution of this 
+software, with or  without modifications, has to be for academic 
+use only, while giving the appropriate credit to the original 
+authors of the software. The methods implemented as a part of 
+this software may be covered under patents or patent applications.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR ''AS IS'' AND ANY EXPRESS OR IMPLIED
+WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
+ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

microsoftexcel-controlnet/annotator/leres/pix2pix/models/__init__.py

@@ -0,0 +1,67 @@
+"""This package contains modules related to objective functions, optimizations, and network architectures.
+
+To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel.
+You need to implement the following five functions:
+    -- <__init__>:                      initialize the class; first call BaseModel.__init__(self, opt).
+    -- <set_input>:                     unpack data from dataset and apply preprocessing.
+    -- <forward>:                       produce intermediate results.
+    -- <optimize_parameters>:           calculate loss, gradients, and update network weights.
+    -- <modify_commandline_options>:    (optionally) add model-specific options and set default options.
+
+In the function <__init__>, you need to define four lists:
+    -- self.loss_names (str list):          specify the training losses that you want to plot and save.
+    -- self.model_names (str list):         define networks used in our training.
+    -- self.visual_names (str list):        specify the images that you want to display and save.
+    -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage.
+
+Now you can use the model class by specifying flag '--model dummy'.
+See our template model class 'template_model.py' for more details.
+"""
+
+import importlib
+from .base_model import BaseModel
+
+
+def find_model_using_name(model_name):
+    """Import the module "models/[model_name]_model.py".
+
+    In the file, the class called DatasetNameModel() will
+    be instantiated. It has to be a subclass of BaseModel,
+    and it is case-insensitive.
+    """
+    model_filename = "annotator.leres.pix2pix.models." + model_name + "_model"
+    modellib = importlib.import_module(model_filename)
+    model = None
+    target_model_name = model_name.replace('_', '') + 'model'
+    for name, cls in modellib.__dict__.items():
+        if name.lower() == target_model_name.lower() \
+           and issubclass(cls, BaseModel):
+            model = cls
+
+    if model is None:
+        print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name))
+        exit(0)
+
+    return model
+
+
+def get_option_setter(model_name):
+    """Return the static method <modify_commandline_options> of the model class."""
+    model_class = find_model_using_name(model_name)
+    return model_class.modify_commandline_options
+
+
+def create_model(opt):
+    """Create a model given the option.
+
+    This function warps the class CustomDatasetDataLoader.
+    This is the main interface between this package and 'train.py'/'test.py'
+
+    Example:
+        >>> from models import create_model
+        >>> model = create_model(opt)
+    """
+    model = find_model_using_name(opt.model)
+    instance = model(opt)
+    print("model [%s] was created" % type(instance).__name__)
+    return instance

microsoftexcel-controlnet/annotator/leres/pix2pix/models/base_model.py

@@ -0,0 +1,241 @@
+import os
+import torch, gc
+from modules import devices
+from collections import OrderedDict
+from abc import ABC, abstractmethod
+from . import networks
+
+
+class BaseModel(ABC):
+    """This class is an abstract base class (ABC) for models.
+    To create a subclass, you need to implement the following five functions:
+        -- <__init__>:                      initialize the class; first call BaseModel.__init__(self, opt).
+        -- <set_input>:                     unpack data from dataset and apply preprocessing.
+        -- <forward>:                       produce intermediate results.
+        -- <optimize_parameters>:           calculate losses, gradients, and update network weights.
+        -- <modify_commandline_options>:    (optionally) add model-specific options and set default options.
+    """
+
+    def __init__(self, opt):
+        """Initialize the BaseModel class.
+
+        Parameters:
+            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
+
+        When creating your custom class, you need to implement your own initialization.
+        In this function, you should first call <BaseModel.__init__(self, opt)>
+        Then, you need to define four lists:
+            -- self.loss_names (str list):          specify the training losses that you want to plot and save.
+            -- self.model_names (str list):         define networks used in our training.
+            -- self.visual_names (str list):        specify the images that you want to display and save.
+            -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
+        """
+        self.opt = opt
+        self.gpu_ids = opt.gpu_ids
+        self.isTrain = opt.isTrain
+        self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')  # get device name: CPU or GPU
+        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)  # save all the checkpoints to save_dir
+        if opt.preprocess != 'scale_width':  # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark.
+            torch.backends.cudnn.benchmark = True
+        self.loss_names = []
+        self.model_names = []
+        self.visual_names = []
+        self.optimizers = []
+        self.image_paths = []
+        self.metric = 0  # used for learning rate policy 'plateau'
+
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        """Add new model-specific options, and rewrite default values for existing options.
+
+        Parameters:
+            parser          -- original option parser
+            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
+
+        Returns:
+            the modified parser.
+        """
+        return parser
+
+    @abstractmethod
+    def set_input(self, input):
+        """Unpack input data from the dataloader and perform necessary pre-processing steps.
+
+        Parameters:
+            input (dict): includes the data itself and its metadata information.
+        """
+        pass
+
+    @abstractmethod
+    def forward(self):
+        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
+        pass
+
+    @abstractmethod
+    def optimize_parameters(self):
+        """Calculate losses, gradients, and update network weights; called in every training iteration"""
+        pass
+
+    def setup(self, opt):
+        """Load and print networks; create schedulers
+
+        Parameters:
+            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
+        """
+        if self.isTrain:
+            self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
+        if not self.isTrain or opt.continue_train:
+            load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch
+            self.load_networks(load_suffix)
+        self.print_networks(opt.verbose)
+
+    def eval(self):
+        """Make models eval mode during test time"""
+        for name in self.model_names:
+            if isinstance(name, str):
+                net = getattr(self, 'net' + name)
+                net.eval()
+
+    def test(self):
+        """Forward function used in test time.
+
+        This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop
+        It also calls <compute_visuals> to produce additional visualization results
+        """
+        with torch.no_grad():
+            self.forward()
+            self.compute_visuals()
+
+    def compute_visuals(self):
+        """Calculate additional output images for visdom and HTML visualization"""
+        pass
+
+    def get_image_paths(self):
+        """ Return image paths that are used to load current data"""
+        return self.image_paths
+
+    def update_learning_rate(self):
+        """Update learning rates for all the networks; called at the end of every epoch"""
+        old_lr = self.optimizers[0].param_groups[0]['lr']
+        for scheduler in self.schedulers:
+            if self.opt.lr_policy == 'plateau':
+                scheduler.step(self.metric)
+            else:
+                scheduler.step()
+
+        lr = self.optimizers[0].param_groups[0]['lr']
+        print('learning rate %.7f -> %.7f' % (old_lr, lr))
+
+    def get_current_visuals(self):
+        """Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
+        visual_ret = OrderedDict()
+        for name in self.visual_names:
+            if isinstance(name, str):
+                visual_ret[name] = getattr(self, name)
+        return visual_ret
+
+    def get_current_losses(self):
+        """Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
+        errors_ret = OrderedDict()
+        for name in self.loss_names:
+            if isinstance(name, str):
+                errors_ret[name] = float(getattr(self, 'loss_' + name))  # float(...) works for both scalar tensor and float number
+        return errors_ret
+
+    def save_networks(self, epoch):
+        """Save all the networks to the disk.
+
+        Parameters:
+            epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
+        """
+        for name in self.model_names:
+            if isinstance(name, str):
+                save_filename = '%s_net_%s.pth' % (epoch, name)
+                save_path = os.path.join(self.save_dir, save_filename)
+                net = getattr(self, 'net' + name)
+
+                if len(self.gpu_ids) > 0 and torch.cuda.is_available():
+                    torch.save(net.module.cpu().state_dict(), save_path)
+                    net.cuda(self.gpu_ids[0])
+                else:
+                    torch.save(net.cpu().state_dict(), save_path)
+
+    def unload_network(self, name):
+        """Unload network and gc.
+        """
+        if isinstance(name, str):
+            net = getattr(self, 'net' + name)
+            del net
+            gc.collect()
+            devices.torch_gc()
+            return None
+
+    def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
+        """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
+        key = keys[i]
+        if i + 1 == len(keys):  # at the end, pointing to a parameter/buffer
+            if module.__class__.__name__.startswith('InstanceNorm') and \
+                    (key == 'running_mean' or key == 'running_var'):
+                if getattr(module, key) is None:
+                    state_dict.pop('.'.join(keys))
+            if module.__class__.__name__.startswith('InstanceNorm') and \
+               (key == 'num_batches_tracked'):
+                state_dict.pop('.'.join(keys))
+        else:
+            self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
+
+    def load_networks(self, epoch):
+        """Load all the networks from the disk.
+
+        Parameters:
+            epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
+        """
+        for name in self.model_names:
+            if isinstance(name, str):
+                load_filename = '%s_net_%s.pth' % (epoch, name)
+                load_path = os.path.join(self.save_dir, load_filename)
+                net = getattr(self, 'net' + name)
+                if isinstance(net, torch.nn.DataParallel):
+                    net = net.module
+                # print('Loading depth boost model from %s' % load_path)
+                # if you are using PyTorch newer than 0.4 (e.g., built from
+                # GitHub source), you can remove str() on self.device
+                state_dict = torch.load(load_path, map_location=str(self.device))
+                if hasattr(state_dict, '_metadata'):
+                    del state_dict._metadata
+
+                # patch InstanceNorm checkpoints prior to 0.4
+                for key in list(state_dict.keys()):  # need to copy keys here because we mutate in loop
+                    self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))
+                net.load_state_dict(state_dict)
+
+    def print_networks(self, verbose):
+        """Print the total number of parameters in the network and (if verbose) network architecture
+
+        Parameters:
+            verbose (bool) -- if verbose: print the network architecture
+        """
+        print('---------- Networks initialized -------------')
+        for name in self.model_names:
+            if isinstance(name, str):
+                net = getattr(self, 'net' + name)
+                num_params = 0
+                for param in net.parameters():
+                    num_params += param.numel()
+                if verbose:
+                    print(net)
+                print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
+        print('-----------------------------------------------')
+
+    def set_requires_grad(self, nets, requires_grad=False):
+        """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
+        Parameters:
+            nets (network list)   -- a list of networks
+            requires_grad (bool)  -- whether the networks require gradients or not
+        """
+        if not isinstance(nets, list):
+            nets = [nets]
+        for net in nets:
+            if net is not None:
+                for param in net.parameters():
+                    param.requires_grad = requires_grad

microsoftexcel-controlnet/annotator/leres/pix2pix/models/base_model_hg.py

@@ -0,0 +1,58 @@
+import os
+import torch
+
+class BaseModelHG():
+    def name(self):
+        return 'BaseModel'
+
+    def initialize(self, opt):
+        self.opt = opt
+        self.gpu_ids = opt.gpu_ids
+        self.isTrain = opt.isTrain
+        self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
+        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
+
+    def set_input(self, input):
+        self.input = input
+
+    def forward(self):
+        pass
+
+    # used in test time, no backprop
+    def test(self):
+        pass
+
+    def get_image_paths(self):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        return self.input
+
+    def get_current_errors(self):
+        return {}
+
+    def save(self, label):
+        pass
+
+    # helper saving function that can be used by subclasses
+    def save_network(self, network, network_label, epoch_label, gpu_ids):
+        save_filename = '_%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        torch.save(network.cpu().state_dict(), save_path)
+        if len(gpu_ids) and torch.cuda.is_available():
+            network.cuda(device_id=gpu_ids[0])
+
+    # helper loading function that can be used by subclasses
+    def load_network(self, network, network_label, epoch_label):
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        print(save_path)
+        model = torch.load(save_path)
+        return model
+        # network.load_state_dict(torch.load(save_path))
+
+    def update_learning_rate():
+        pass

microsoftexcel-controlnet/annotator/leres/pix2pix/models/networks.py

@@ -0,0 +1,623 @@
+import torch
+import torch.nn as nn
+from torch.nn import init
+import functools
+from torch.optim import lr_scheduler
+
+
+###############################################################################
+# Helper Functions
+###############################################################################
+
+
+class Identity(nn.Module):
+    def forward(self, x):
+        return x
+
+
+def get_norm_layer(norm_type='instance'):
+    """Return a normalization layer
+
+    Parameters:
+        norm_type (str) -- the name of the normalization layer: batch | instance | none
+
+    For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
+    For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
+    """
+    if norm_type == 'batch':
+        norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
+    elif norm_type == 'instance':
+        norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
+    elif norm_type == 'none':
+        def norm_layer(x): return Identity()
+    else:
+        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
+    return norm_layer
+
+
+def get_scheduler(optimizer, opt):
+    """Return a learning rate scheduler
+
+    Parameters:
+        optimizer          -- the optimizer of the network
+        opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．　
+                              opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
+
+    For 'linear', we keep the same learning rate for the first <opt.n_epochs> epochs
+    and linearly decay the rate to zero over the next <opt.n_epochs_decay> epochs.
+    For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
+    See https://pytorch.org/docs/stable/optim.html for more details.
+    """
+    if opt.lr_policy == 'linear':
+        def lambda_rule(epoch):
+            lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)
+            return lr_l
+        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
+    elif opt.lr_policy == 'step':
+        scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
+    elif opt.lr_policy == 'plateau':
+        scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
+    elif opt.lr_policy == 'cosine':
+        scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
+    else:
+        return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
+    return scheduler
+
+
+def init_weights(net, init_type='normal', init_gain=0.02):
+    """Initialize network weights.
+
+    Parameters:
+        net (network)   -- network to be initialized
+        init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
+        init_gain (float)    -- scaling factor for normal, xavier and orthogonal.
+
+    We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
+    work better for some applications. Feel free to try yourself.
+    """
+    def init_func(m):  # define the initialization function
+        classname = m.__class__.__name__
+        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
+            if init_type == 'normal':
+                init.normal_(m.weight.data, 0.0, init_gain)
+            elif init_type == 'xavier':
+                init.xavier_normal_(m.weight.data, gain=init_gain)
+            elif init_type == 'kaiming':
+                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
+            elif init_type == 'orthogonal':
+                init.orthogonal_(m.weight.data, gain=init_gain)
+            else:
+                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
+            if hasattr(m, 'bias') and m.bias is not None:
+                init.constant_(m.bias.data, 0.0)
+        elif classname.find('BatchNorm2d') != -1:  # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
+            init.normal_(m.weight.data, 1.0, init_gain)
+            init.constant_(m.bias.data, 0.0)
+
+    # print('initialize network with %s' % init_type)
+    net.apply(init_func)  # apply the initialization function <init_func>
+
+
+def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
+    """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
+    Parameters:
+        net (network)      -- the network to be initialized
+        init_type (str)    -- the name of an initialization method: normal | xavier | kaiming | orthogonal
+        gain (float)       -- scaling factor for normal, xavier and orthogonal.
+        gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
+
+    Return an initialized network.
+    """
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        net.to(gpu_ids[0])
+        net = torch.nn.DataParallel(net, gpu_ids)  # multi-GPUs
+    init_weights(net, init_type, init_gain=init_gain)
+    return net
+
+
+def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]):
+    """Create a generator
+
+    Parameters:
+        input_nc (int) -- the number of channels in input images
+        output_nc (int) -- the number of channels in output images
+        ngf (int) -- the number of filters in the last conv layer
+        netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128
+        norm (str) -- the name of normalization layers used in the network: batch | instance | none
+        use_dropout (bool) -- if use dropout layers.
+        init_type (str)    -- the name of our initialization method.
+        init_gain (float)  -- scaling factor for normal, xavier and orthogonal.
+        gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
+
+    Returns a generator
+
+    Our current implementation provides two types of generators:
+        U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images)
+        The original U-Net paper: https://arxiv.org/abs/1505.04597
+
+        Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks)
+        Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations.
+        We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style).
+
+
+    The generator has been initialized by <init_net>. It uses RELU for non-linearity.
+    """
+    net = None
+    norm_layer = get_norm_layer(norm_type=norm)
+
+    if netG == 'resnet_9blocks':
+        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
+    elif netG == 'resnet_6blocks':
+        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
+    elif netG == 'resnet_12blocks':
+        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=12)
+    elif netG == 'unet_128':
+        net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
+    elif netG == 'unet_256':
+        net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
+    elif netG == 'unet_672':
+        net = UnetGenerator(input_nc, output_nc, 5, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
+    elif netG == 'unet_960':
+        net = UnetGenerator(input_nc, output_nc, 6, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
+    elif netG == 'unet_1024':
+        net = UnetGenerator(input_nc, output_nc, 10, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
+    else:
+        raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
+    return init_net(net, init_type, init_gain, gpu_ids)
+
+
+def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]):
+    """Create a discriminator
+
+    Parameters:
+        input_nc (int)     -- the number of channels in input images
+        ndf (int)          -- the number of filters in the first conv layer
+        netD (str)         -- the architecture's name: basic | n_layers | pixel
+        n_layers_D (int)   -- the number of conv layers in the discriminator; effective when netD=='n_layers'
+        norm (str)         -- the type of normalization layers used in the network.
+        init_type (str)    -- the name of the initialization method.
+        init_gain (float)  -- scaling factor for normal, xavier and orthogonal.
+        gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
+
+    Returns a discriminator
+
+    Our current implementation provides three types of discriminators:
+        [basic]: 'PatchGAN' classifier described in the original pix2pix paper.
+        It can classify whether 70×70 overlapping patches are real or fake.
+        Such a patch-level discriminator architecture has fewer parameters
+        than a full-image discriminator and can work on arbitrarily-sized images
+        in a fully convolutional fashion.
+
+        [n_layers]: With this mode, you can specify the number of conv layers in the discriminator
+        with the parameter <n_layers_D> (default=3 as used in [basic] (PatchGAN).)
+
+        [pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not.
+        It encourages greater color diversity but has no effect on spatial statistics.
+
+    The discriminator has been initialized by <init_net>. It uses Leakly RELU for non-linearity.
+    """
+    net = None
+    norm_layer = get_norm_layer(norm_type=norm)
+
+    if netD == 'basic':  # default PatchGAN classifier
+        net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer)
+    elif netD == 'n_layers':  # more options
+        net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer)
+    elif netD == 'pixel':     # classify if each pixel is real or fake
+        net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
+    else:
+        raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
+    return init_net(net, init_type, init_gain, gpu_ids)
+
+
+##############################################################################
+# Classes
+##############################################################################
+class GANLoss(nn.Module):
+    """Define different GAN objectives.
+
+    The GANLoss class abstracts away the need to create the target label tensor
+    that has the same size as the input.
+    """
+
+    def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
+        """ Initialize the GANLoss class.
+
+        Parameters:
+            gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
+            target_real_label (bool) - - label for a real image
+            target_fake_label (bool) - - label of a fake image
+
+        Note: Do not use sigmoid as the last layer of Discriminator.
+        LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
+        """
+        super(GANLoss, self).__init__()
+        self.register_buffer('real_label', torch.tensor(target_real_label))
+        self.register_buffer('fake_label', torch.tensor(target_fake_label))
+        self.gan_mode = gan_mode
+        if gan_mode == 'lsgan':
+            self.loss = nn.MSELoss()
+        elif gan_mode == 'vanilla':
+            self.loss = nn.BCEWithLogitsLoss()
+        elif gan_mode in ['wgangp']:
+            self.loss = None
+        else:
+            raise NotImplementedError('gan mode %s not implemented' % gan_mode)
+
+    def get_target_tensor(self, prediction, target_is_real):
+        """Create label tensors with the same size as the input.
+
+        Parameters:
+            prediction (tensor) - - tpyically the prediction from a discriminator
+            target_is_real (bool) - - if the ground truth label is for real images or fake images
+
+        Returns:
+            A label tensor filled with ground truth label, and with the size of the input
+        """
+
+        if target_is_real:
+            target_tensor = self.real_label
+        else:
+            target_tensor = self.fake_label
+        return target_tensor.expand_as(prediction)
+
+    def __call__(self, prediction, target_is_real):
+        """Calculate loss given Discriminator's output and grount truth labels.
+
+        Parameters:
+            prediction (tensor) - - tpyically the prediction output from a discriminator
+            target_is_real (bool) - - if the ground truth label is for real images or fake images
+
+        Returns:
+            the calculated loss.
+        """
+        if self.gan_mode in ['lsgan', 'vanilla']:
+            target_tensor = self.get_target_tensor(prediction, target_is_real)
+            loss = self.loss(prediction, target_tensor)
+        elif self.gan_mode == 'wgangp':
+            if target_is_real:
+                loss = -prediction.mean()
+            else:
+                loss = prediction.mean()
+        return loss
+
+
+def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0):
+    """Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
+
+    Arguments:
+        netD (network)              -- discriminator network
+        real_data (tensor array)    -- real images
+        fake_data (tensor array)    -- generated images from the generator
+        device (str)                -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
+        type (str)                  -- if we mix real and fake data or not [real | fake | mixed].
+        constant (float)            -- the constant used in formula ( ||gradient||_2 - constant)^2
+        lambda_gp (float)           -- weight for this loss
+
+    Returns the gradient penalty loss
+    """
+    if lambda_gp > 0.0:
+        if type == 'real':   # either use real images, fake images, or a linear interpolation of two.
+            interpolatesv = real_data
+        elif type == 'fake':
+            interpolatesv = fake_data
+        elif type == 'mixed':
+            alpha = torch.rand(real_data.shape[0], 1, device=device)
+            alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape)
+            interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
+        else:
+            raise NotImplementedError('{} not implemented'.format(type))
+        interpolatesv.requires_grad_(True)
+        disc_interpolates = netD(interpolatesv)
+        gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv,
+                                        grad_outputs=torch.ones(disc_interpolates.size()).to(device),
+                                        create_graph=True, retain_graph=True, only_inputs=True)
+        gradients = gradients[0].view(real_data.size(0), -1)  # flat the data
+        gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp        # added eps
+        return gradient_penalty, gradients
+    else:
+        return 0.0, None
+
+
+class ResnetGenerator(nn.Module):
+    """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
+
+    We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
+    """
+
+    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
+        """Construct a Resnet-based generator
+
+        Parameters:
+            input_nc (int)      -- the number of channels in input images
+            output_nc (int)     -- the number of channels in output images
+            ngf (int)           -- the number of filters in the last conv layer
+            norm_layer          -- normalization layer
+            use_dropout (bool)  -- if use dropout layers
+            n_blocks (int)      -- the number of ResNet blocks
+            padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero
+        """
+        assert(n_blocks >= 0)
+        super(ResnetGenerator, self).__init__()
+        if type(norm_layer) == functools.partial:
+            use_bias = norm_layer.func == nn.InstanceNorm2d
+        else:
+            use_bias = norm_layer == nn.InstanceNorm2d
+
+        model = [nn.ReflectionPad2d(3),
+                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
+                 norm_layer(ngf),
+                 nn.ReLU(True)]
+
+        n_downsampling = 2
+        for i in range(n_downsampling):  # add downsampling layers
+            mult = 2 ** i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
+                      norm_layer(ngf * mult * 2),
+                      nn.ReLU(True)]
+
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):       # add ResNet blocks
+
+            model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
+
+        for i in range(n_downsampling):  # add upsampling layers
+            mult = 2 ** (n_downsampling - i)
+            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
+                                         kernel_size=3, stride=2,
+                                         padding=1, output_padding=1,
+                                         bias=use_bias),
+                      norm_layer(int(ngf * mult / 2)),
+                      nn.ReLU(True)]
+        model += [nn.ReflectionPad2d(3)]
+        model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
+        model += [nn.Tanh()]
+
+        self.model = nn.Sequential(*model)
+
+    def forward(self, input):
+        """Standard forward"""
+        return self.model(input)
+
+
+class ResnetBlock(nn.Module):
+    """Define a Resnet block"""
+
+    def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
+        """Initialize the Resnet block
+
+        A resnet block is a conv block with skip connections
+        We construct a conv block with build_conv_block function,
+        and implement skip connections in <forward> function.
+        Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
+        """
+        super(ResnetBlock, self).__init__()
+        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)
+
+    def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
+        """Construct a convolutional block.
+
+        Parameters:
+            dim (int)           -- the number of channels in the conv layer.
+            padding_type (str)  -- the name of padding layer: reflect | replicate | zero
+            norm_layer          -- normalization layer
+            use_dropout (bool)  -- if use dropout layers.
+            use_bias (bool)     -- if the conv layer uses bias or not
+
+        Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
+        """
+        conv_block = []
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]
+        if use_dropout:
+            conv_block += [nn.Dropout(0.5)]
+
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]
+
+        return nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        """Forward function (with skip connections)"""
+        out = x + self.conv_block(x)  # add skip connections
+        return out
+
+
+class UnetGenerator(nn.Module):
+    """Create a Unet-based generator"""
+
+    def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):
+        """Construct a Unet generator
+        Parameters:
+            input_nc (int)  -- the number of channels in input images
+            output_nc (int) -- the number of channels in output images
+            num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,
+                                image of size 128x128 will become of size 1x1 # at the bottleneck
+            ngf (int)       -- the number of filters in the last conv layer
+            norm_layer      -- normalization layer
+
+        We construct the U-Net from the innermost layer to the outermost layer.
+        It is a recursive process.
+        """
+        super(UnetGenerator, self).__init__()
+        # construct unet structure
+        unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True)  # add the innermost layer
+        for i in range(num_downs - 5):          # add intermediate layers with ngf * 8 filters
+            unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
+        # gradually reduce the number of filters from ngf * 8 to ngf
+        unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer)  # add the outermost layer
+
+    def forward(self, input):
+        """Standard forward"""
+        return self.model(input)
+
+
+class UnetSkipConnectionBlock(nn.Module):
+    """Defines the Unet submodule with skip connection.
+        X -------------------identity----------------------
+        |-- downsampling -- |submodule| -- upsampling --|
+    """
+
+    def __init__(self, outer_nc, inner_nc, input_nc=None,
+                 submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
+        """Construct a Unet submodule with skip connections.
+
+        Parameters:
+            outer_nc (int) -- the number of filters in the outer conv layer
+            inner_nc (int) -- the number of filters in the inner conv layer
+            input_nc (int) -- the number of channels in input images/features
+            submodule (UnetSkipConnectionBlock) -- previously defined submodules
+            outermost (bool)    -- if this module is the outermost module
+            innermost (bool)    -- if this module is the innermost module
+            norm_layer          -- normalization layer
+            use_dropout (bool)  -- if use dropout layers.
+        """
+        super(UnetSkipConnectionBlock, self).__init__()
+        self.outermost = outermost
+        if type(norm_layer) == functools.partial:
+            use_bias = norm_layer.func == nn.InstanceNorm2d
+        else:
+            use_bias = norm_layer == nn.InstanceNorm2d
+        if input_nc is None:
+            input_nc = outer_nc
+        downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
+                             stride=2, padding=1, bias=use_bias)
+        downrelu = nn.LeakyReLU(0.2, True)
+        downnorm = norm_layer(inner_nc)
+        uprelu = nn.ReLU(True)
+        upnorm = norm_layer(outer_nc)
+
+        if outermost:
+            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1)
+            down = [downconv]
+            up = [uprelu, upconv, nn.Tanh()]
+            model = down + [submodule] + up
+        elif innermost:
+            upconv = nn.ConvTranspose2d(inner_nc, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1, bias=use_bias)
+            down = [downrelu, downconv]
+            up = [uprelu, upconv, upnorm]
+            model = down + up
+        else:
+            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1, bias=use_bias)
+            down = [downrelu, downconv, downnorm]
+            up = [uprelu, upconv, upnorm]
+
+            if use_dropout:
+                model = down + [submodule] + up + [nn.Dropout(0.5)]
+            else:
+                model = down + [submodule] + up
+
+        self.model = nn.Sequential(*model)
+
+    def forward(self, x):
+        if self.outermost:
+            return self.model(x)
+        else:   # add skip connections
+            return torch.cat([x, self.model(x)], 1)
+
+
+class NLayerDiscriminator(nn.Module):
+    """Defines a PatchGAN discriminator"""
+
+    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):
+        """Construct a PatchGAN discriminator
+
+        Parameters:
+            input_nc (int)  -- the number of channels in input images
+            ndf (int)       -- the number of filters in the last conv layer
+            n_layers (int)  -- the number of conv layers in the discriminator
+            norm_layer      -- normalization layer
+        """
+        super(NLayerDiscriminator, self).__init__()
+        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
+            use_bias = norm_layer.func == nn.InstanceNorm2d
+        else:
+            use_bias = norm_layer == nn.InstanceNorm2d
+
+        kw = 4
+        padw = 1
+        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
+        nf_mult = 1
+        nf_mult_prev = 1
+        for n in range(1, n_layers):  # gradually increase the number of filters
+            nf_mult_prev = nf_mult
+            nf_mult = min(2 ** n, 8)
+            sequence += [
+                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
+                norm_layer(ndf * nf_mult),
+                nn.LeakyReLU(0.2, True)
+            ]
+
+        nf_mult_prev = nf_mult
+        nf_mult = min(2 ** n_layers, 8)
+        sequence += [
+            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
+            norm_layer(ndf * nf_mult),
+            nn.LeakyReLU(0.2, True)
+        ]
+
+        sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]  # output 1 channel prediction map
+        self.model = nn.Sequential(*sequence)
+
+    def forward(self, input):
+        """Standard forward."""
+        return self.model(input)
+
+
+class PixelDiscriminator(nn.Module):
+    """Defines a 1x1 PatchGAN discriminator (pixelGAN)"""
+
+    def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d):
+        """Construct a 1x1 PatchGAN discriminator
+
+        Parameters:
+            input_nc (int)  -- the number of channels in input images
+            ndf (int)       -- the number of filters in the last conv layer
+            norm_layer      -- normalization layer
+        """
+        super(PixelDiscriminator, self).__init__()
+        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
+            use_bias = norm_layer.func == nn.InstanceNorm2d
+        else:
+            use_bias = norm_layer == nn.InstanceNorm2d
+
+        self.net = [
+            nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias),
+            norm_layer(ndf * 2),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)]
+
+        self.net = nn.Sequential(*self.net)
+
+    def forward(self, input):
+        """Standard forward."""
+        return self.net(input)

microsoftexcel-controlnet/annotator/leres/pix2pix/models/pix2pix4depth_model.py

@@ -0,0 +1,155 @@
+import torch
+from .base_model import BaseModel
+from . import networks
+
+
+class Pix2Pix4DepthModel(BaseModel):
+    """ This class implements the pix2pix model, for learning a mapping from input images to output images given paired data.
+
+    The model training requires '--dataset_mode aligned' dataset.
+    By default, it uses a '--netG unet256' U-Net generator,
+    a '--netD basic' discriminator (PatchGAN),
+    and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the orignal GAN paper).
+
+    pix2pix paper: https://arxiv.org/pdf/1611.07004.pdf
+    """
+    @staticmethod
+    def modify_commandline_options(parser, is_train=True):
+        """Add new dataset-specific options, and rewrite default values for existing options.
+
+        Parameters:
+            parser          -- original option parser
+            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
+
+        Returns:
+            the modified parser.
+
+        For pix2pix, we do not use image buffer
+        The training objective is: GAN Loss + lambda_L1 * ||G(A)-B||_1
+        By default, we use vanilla GAN loss, UNet with batchnorm, and aligned datasets.
+        """
+        # changing the default values to match the pix2pix paper (https://phillipi.github.io/pix2pix/)
+        parser.set_defaults(input_nc=2,output_nc=1,norm='none', netG='unet_1024', dataset_mode='depthmerge')
+        if is_train:
+            parser.set_defaults(pool_size=0, gan_mode='vanilla',)
+            parser.add_argument('--lambda_L1', type=float, default=1000, help='weight for L1 loss')
+        return parser
+
+    def __init__(self, opt):
+        """Initialize the pix2pix class.
+
+        Parameters:
+            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
+        """
+        BaseModel.__init__(self, opt)
+        # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
+
+        self.loss_names = ['G_GAN', 'G_L1', 'D_real', 'D_fake']
+        # self.loss_names = ['G_L1']
+
+        # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
+        if self.isTrain:
+            self.visual_names = ['outer','inner', 'fake_B', 'real_B']
+        else:
+            self.visual_names = ['fake_B']
+
+        # specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
+        if self.isTrain:
+            self.model_names = ['G','D']
+        else:  # during test time, only load G
+            self.model_names = ['G']
+
+        # define networks (both generator and discriminator)
+        self.netG = networks.define_G(opt.input_nc, opt.output_nc, 64, 'unet_1024', 'none',
+                                      False, 'normal', 0.02, self.gpu_ids)
+
+        if self.isTrain:  # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
+            self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
+                                          opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
+
+        if self.isTrain:
+            # define loss functions
+            self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
+            self.criterionL1 = torch.nn.L1Loss()
+            # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
+            self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=1e-4, betas=(opt.beta1, 0.999))
+            self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=2e-06, betas=(opt.beta1, 0.999))
+            self.optimizers.append(self.optimizer_G)
+            self.optimizers.append(self.optimizer_D)
+
+    def set_input_train(self, input):
+        self.outer = input['data_outer'].to(self.device)
+        self.outer = torch.nn.functional.interpolate(self.outer,(1024,1024),mode='bilinear',align_corners=False)
+
+        self.inner = input['data_inner'].to(self.device)
+        self.inner = torch.nn.functional.interpolate(self.inner,(1024,1024),mode='bilinear',align_corners=False)
+
+        self.image_paths = input['image_path']
+
+        if self.isTrain:
+            self.gtfake = input['data_gtfake'].to(self.device)
+            self.gtfake = torch.nn.functional.interpolate(self.gtfake, (1024, 1024), mode='bilinear', align_corners=False)
+            self.real_B = self.gtfake
+
+        self.real_A = torch.cat((self.outer, self.inner), 1)
+
+    def set_input(self, outer, inner):
+        inner = torch.from_numpy(inner).unsqueeze(0).unsqueeze(0)
+        outer = torch.from_numpy(outer).unsqueeze(0).unsqueeze(0)
+
+        inner = (inner - torch.min(inner))/(torch.max(inner)-torch.min(inner))
+        outer = (outer - torch.min(outer))/(torch.max(outer)-torch.min(outer))
+
+        inner = self.normalize(inner)
+        outer = self.normalize(outer)
+
+        self.real_A = torch.cat((outer, inner), 1).to(self.device)
+
+
+    def normalize(self, input):
+        input = input * 2
+        input = input - 1
+        return input
+
+    def forward(self):
+        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
+        self.fake_B = self.netG(self.real_A)  # G(A)
+
+    def backward_D(self):
+        """Calculate GAN loss for the discriminator"""
+        # Fake; stop backprop to the generator by detaching fake_B
+        fake_AB = torch.cat((self.real_A, self.fake_B), 1)  # we use conditional GANs; we need to feed both input and output to the discriminator
+        pred_fake = self.netD(fake_AB.detach())
+        self.loss_D_fake = self.criterionGAN(pred_fake, False)
+        # Real
+        real_AB = torch.cat((self.real_A, self.real_B), 1)
+        pred_real = self.netD(real_AB)
+        self.loss_D_real = self.criterionGAN(pred_real, True)
+        # combine loss and calculate gradients
+        self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
+        self.loss_D.backward()
+
+    def backward_G(self):
+        """Calculate GAN and L1 loss for the generator"""
+        # First, G(A) should fake the discriminator
+        fake_AB = torch.cat((self.real_A, self.fake_B), 1)
+        pred_fake = self.netD(fake_AB)
+        self.loss_G_GAN = self.criterionGAN(pred_fake, True)
+        # Second, G(A) = B
+        self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1
+        # combine loss and calculate gradients
+        self.loss_G = self.loss_G_L1 + self.loss_G_GAN
+        self.loss_G.backward()
+
+    def optimize_parameters(self):
+        self.forward()                   # compute fake images: G(A)
+        # update D
+        self.set_requires_grad(self.netD, True)  # enable backprop for D
+        self.optimizer_D.zero_grad()     # set D's gradients to zero
+        self.backward_D()                # calculate gradients for D
+        self.optimizer_D.step()          # update D's weights
+        # update G
+        self.set_requires_grad(self.netD, False)  # D requires no gradients when optimizing G
+        self.optimizer_G.zero_grad()        # set G's gradients to zero
+        self.backward_G()                   # calculate graidents for G
+        self.optimizer_G.step()             # udpate G's weights

microsoftexcel-controlnet/annotator/leres/pix2pix/options/__init__.py

@@ -0,0 +1 @@
+"""This package options includes option modules: training options, test options, and basic options (used in both training and test)."""

microsoftexcel-controlnet/annotator/leres/pix2pix/options/base_options.py

@@ -0,0 +1,156 @@
+import argparse
+import os
+from ...pix2pix.util import util
+# import torch
+from ...pix2pix import models
+# import pix2pix.data
+import numpy as np
+
+class BaseOptions():
+    """This class defines options used during both training and test time.
+
+    It also implements several helper functions such as parsing, printing, and saving the options.
+    It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class.
+    """
+
+    def __init__(self):
+        """Reset the class; indicates the class hasn't been initailized"""
+        self.initialized = False
+
+    def initialize(self, parser):
+        """Define the common options that are used in both training and test."""
+        # basic parameters
+        parser.add_argument('--dataroot', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
+        parser.add_argument('--name', type=str, default='void', help='mahdi_unet_new, scaled_unet')
+        parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
+        parser.add_argument('--checkpoints_dir', type=str, default='./pix2pix/checkpoints', help='models are saved here')
+        # model parameters
+        parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. [cycle_gan | pix2pix | test | colorization]')
+        parser.add_argument('--input_nc', type=int, default=2, help='# of input image channels: 3 for RGB and 1 for grayscale')
+        parser.add_argument('--output_nc', type=int, default=1, help='# of output image channels: 3 for RGB and 1 for grayscale')
+        parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')
+        parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer')
+        parser.add_argument('--netD', type=str, default='basic', help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
+        parser.add_argument('--netG', type=str, default='resnet_9blocks', help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]')
+        parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
+        parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [instance | batch | none]')
+        parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]')
+        parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
+        parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
+        # dataset parameters
+        parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
+        parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
+        parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
+        parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
+        parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
+        parser.add_argument('--load_size', type=int, default=672, help='scale images to this size')
+        parser.add_argument('--crop_size', type=int, default=672, help='then crop to this size')
+        parser.add_argument('--max_dataset_size', type=int, default=10000, help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
+        parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]')
+        parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
+        parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
+        # additional parameters
+        parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]')
+        parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
+        parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')
+
+        parser.add_argument('--data_dir', type=str, required=False,
+                            help='input files directory images can be .png .jpg .tiff')
+        parser.add_argument('--output_dir', type=str, required=False,
+                            help='result dir. result depth will be png. vides are JMPG as avi')
+        parser.add_argument('--savecrops', type=int, required=False)
+        parser.add_argument('--savewholeest', type=int, required=False)
+        parser.add_argument('--output_resolution', type=int, required=False,
+                            help='0 for no restriction 1 for resize to input size')
+        parser.add_argument('--net_receptive_field_size', type=int, required=False)
+        parser.add_argument('--pix2pixsize', type=int, required=False)
+        parser.add_argument('--generatevideo', type=int, required=False)
+        parser.add_argument('--depthNet', type=int, required=False, help='0: midas 1:strurturedRL')
+        parser.add_argument('--R0', action='store_true')
+        parser.add_argument('--R20', action='store_true')
+        parser.add_argument('--Final', action='store_true')
+        parser.add_argument('--colorize_results', action='store_true')
+        parser.add_argument('--max_res', type=float, default=np.inf)
+
+        self.initialized = True
+        return parser
+
+    def gather_options(self):
+        """Initialize our parser with basic options(only once).
+        Add additional model-specific and dataset-specific options.
+        These options are defined in the <modify_commandline_options> function
+        in model and dataset classes.
+        """
+        if not self.initialized:  # check if it has been initialized
+            parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+            parser = self.initialize(parser)
+
+        # get the basic options
+        opt, _ = parser.parse_known_args()
+
+        # modify model-related parser options
+        model_name = opt.model
+        model_option_setter = models.get_option_setter(model_name)
+        parser = model_option_setter(parser, self.isTrain)
+        opt, _ = parser.parse_known_args()  # parse again with new defaults
+
+        # modify dataset-related parser options
+        # dataset_name = opt.dataset_mode
+        # dataset_option_setter = pix2pix.data.get_option_setter(dataset_name)
+        # parser = dataset_option_setter(parser, self.isTrain)
+
+        # save and return the parser
+        self.parser = parser
+        #return parser.parse_args() #EVIL
+        return opt
+
+    def print_options(self, opt):
+        """Print and save options
+
+        It will print both current options and default values(if different).
+        It will save options into a text file / [checkpoints_dir] / opt.txt
+        """
+        message = ''
+        message += '----------------- Options ---------------\n'
+        for k, v in sorted(vars(opt).items()):
+            comment = ''
+            default = self.parser.get_default(k)
+            if v != default:
+                comment = '\t[default: %s]' % str(default)
+            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
+        message += '----------------- End -------------------'
+        print(message)
+
+        # save to the disk
+        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
+        util.mkdirs(expr_dir)
+        file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase))
+        with open(file_name, 'wt') as opt_file:
+            opt_file.write(message)
+            opt_file.write('\n')
+
+    def parse(self):
+        """Parse our options, create checkpoints directory suffix, and set up gpu device."""
+        opt = self.gather_options()
+        opt.isTrain = self.isTrain   # train or test
+
+        # process opt.suffix
+        if opt.suffix:
+            suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
+            opt.name = opt.name + suffix
+
+        #self.print_options(opt)
+
+        # set gpu ids
+        str_ids = opt.gpu_ids.split(',')
+        opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                opt.gpu_ids.append(id)
+        #if len(opt.gpu_ids) > 0:
+        #    torch.cuda.set_device(opt.gpu_ids[0])
+
+        self.opt = opt
+        return self.opt

microsoftexcel-controlnet/annotator/leres/pix2pix/options/test_options.py

@@ -0,0 +1,22 @@
+from .base_options import BaseOptions
+
+
+class TestOptions(BaseOptions):
+    """This class includes test options.
+
+    It also includes shared options defined in BaseOptions.
+    """
+
+    def initialize(self, parser):
+        parser = BaseOptions.initialize(self, parser)  # define shared options
+        parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
+        parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
+        # Dropout and Batchnorm has different behavioir during training and test.
+        parser.add_argument('--eval', action='store_true', help='use eval mode during test time.')
+        parser.add_argument('--num_test', type=int, default=50, help='how many test images to run')
+        # rewrite devalue values
+        parser.set_defaults(model='pix2pix4depth')
+        # To avoid cropping, the load_size should be the same as crop_size
+        parser.set_defaults(load_size=parser.get_default('crop_size'))
+        self.isTrain = False
+        return parser

microsoftexcel-controlnet/annotator/leres/pix2pix/util/__init__.py

@@ -0,0 +1 @@
+"""This package includes a miscellaneous collection of useful helper functions."""

microsoftexcel-controlnet/annotator/leres/pix2pix/util/get_data.py

@@ -0,0 +1,110 @@
+from __future__ import print_function
+import os
+import tarfile
+import requests
+from warnings import warn
+from zipfile import ZipFile
+from bs4 import BeautifulSoup
+from os.path import abspath, isdir, join, basename
+
+
+class GetData(object):
+    """A Python script for downloading CycleGAN or pix2pix datasets.
+
+    Parameters:
+        technique (str) -- One of: 'cyclegan' or 'pix2pix'.
+        verbose (bool)  -- If True, print additional information.
+
+    Examples:
+        >>> from util.get_data import GetData
+        >>> gd = GetData(technique='cyclegan')
+        >>> new_data_path = gd.get(save_path='./datasets')  # options will be displayed.
+
+    Alternatively, You can use bash scripts: 'scripts/download_pix2pix_model.sh'
+    and 'scripts/download_cyclegan_model.sh'.
+    """
+
+    def __init__(self, technique='cyclegan', verbose=True):
+        url_dict = {
+            'pix2pix': 'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/',
+            'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets'
+        }
+        self.url = url_dict.get(technique.lower())
+        self._verbose = verbose
+
+    def _print(self, text):
+        if self._verbose:
+            print(text)
+
+    @staticmethod
+    def _get_options(r):
+        soup = BeautifulSoup(r.text, 'lxml')
+        options = [h.text for h in soup.find_all('a', href=True)
+                   if h.text.endswith(('.zip', 'tar.gz'))]
+        return options
+
+    def _present_options(self):
+        r = requests.get(self.url)
+        options = self._get_options(r)
+        print('Options:\n')
+        for i, o in enumerate(options):
+            print("{0}: {1}".format(i, o))
+        choice = input("\nPlease enter the number of the "
+                       "dataset above you wish to download:")
+        return options[int(choice)]
+
+    def _download_data(self, dataset_url, save_path):
+        if not isdir(save_path):
+            os.makedirs(save_path)
+
+        base = basename(dataset_url)
+        temp_save_path = join(save_path, base)
+
+        with open(temp_save_path, "wb") as f:
+            r = requests.get(dataset_url)
+            f.write(r.content)
+
+        if base.endswith('.tar.gz'):
+            obj = tarfile.open(temp_save_path)
+        elif base.endswith('.zip'):
+            obj = ZipFile(temp_save_path, 'r')
+        else:
+            raise ValueError("Unknown File Type: {0}.".format(base))
+
+        self._print("Unpacking Data...")
+        obj.extractall(save_path)
+        obj.close()
+        os.remove(temp_save_path)
+
+    def get(self, save_path, dataset=None):
+        """
+
+        Download a dataset.
+
+        Parameters:
+            save_path (str) -- A directory to save the data to.
+            dataset (str)   -- (optional). A specific dataset to download.
+                            Note: this must include the file extension.
+                            If None, options will be presented for you
+                            to choose from.
+
+        Returns:
+            save_path_full (str) -- the absolute path to the downloaded data.
+
+        """
+        if dataset is None:
+            selected_dataset = self._present_options()
+        else:
+            selected_dataset = dataset
+
+        save_path_full = join(save_path, selected_dataset.split('.')[0])
+
+        if isdir(save_path_full):
+            warn("\n'{0}' already exists. Voiding Download.".format(
+                save_path_full))
+        else:
+            self._print('Downloading Data...')
+            url = "{0}/{1}".format(self.url, selected_dataset)
+            self._download_data(url, save_path=save_path)
+
+        return abspath(save_path_full)

microsoftexcel-controlnet/annotator/leres/pix2pix/util/guidedfilter.py

@@ -0,0 +1,47 @@
+import numpy as np
+
+class GuidedFilter():
+    def __init__(self, source, reference, r=64, eps= 0.05**2):
+        self.source = source;
+        self.reference = reference;
+        self.r = r
+        self.eps = eps
+
+        self.smooth = self.guidedfilter(self.source,self.reference,self.r,self.eps)
+
+    def boxfilter(self,img, r):
+        (rows, cols) = img.shape
+        imDst = np.zeros_like(img)
+
+        imCum = np.cumsum(img, 0)
+        imDst[0 : r+1, :] = imCum[r : 2*r+1, :]
+        imDst[r+1 : rows-r, :] = imCum[2*r+1 : rows, :] - imCum[0 : rows-2*r-1, :]
+        imDst[rows-r: rows, :] = np.tile(imCum[rows-1, :], [r, 1]) - imCum[rows-2*r-1 : rows-r-1, :]
+
+        imCum = np.cumsum(imDst, 1)
+        imDst[:, 0 : r+1] = imCum[:, r : 2*r+1]
+        imDst[:, r+1 : cols-r] = imCum[:, 2*r+1 : cols] - imCum[:, 0 : cols-2*r-1]
+        imDst[:, cols-r: cols] = np.tile(imCum[:, cols-1], [r, 1]).T - imCum[:, cols-2*r-1 : cols-r-1]
+
+        return imDst
+
+    def guidedfilter(self,I, p, r, eps):
+        (rows, cols) = I.shape
+        N = self.boxfilter(np.ones([rows, cols]), r)
+
+        meanI = self.boxfilter(I, r) / N
+        meanP = self.boxfilter(p, r) / N
+        meanIp = self.boxfilter(I * p, r) / N
+        covIp = meanIp - meanI * meanP
+
+        meanII = self.boxfilter(I * I, r) / N
+        varI = meanII - meanI * meanI
+
+        a = covIp / (varI + eps)
+        b = meanP - a * meanI
+
+        meanA = self.boxfilter(a, r) / N
+        meanB = self.boxfilter(b, r) / N
+
+        q = meanA * I + meanB
+        return q

microsoftexcel-controlnet/annotator/leres/pix2pix/util/html.py

@@ -0,0 +1,86 @@
+import dominate
+from dominate.tags import meta, h3, table, tr, td, p, a, img, br
+import os
+
+
+class HTML:
+    """This HTML class allows us to save images and write texts into a single HTML file.
+
+     It consists of functions such as <add_header> (add a text header to the HTML file),
+     <add_images> (add a row of images to the HTML file), and <save> (save the HTML to the disk).
+     It is based on Python library 'dominate', a Python library for creating and manipulating HTML documents using a DOM API.
+    """
+
+    def __init__(self, web_dir, title, refresh=0):
+        """Initialize the HTML classes
+
+        Parameters:
+            web_dir (str) -- a directory that stores the webpage. HTML file will be created at <web_dir>/index.html; images will be saved at <web_dir/images/
+            title (str)   -- the webpage name
+            refresh (int) -- how often the website refresh itself; if 0; no refreshing
+        """
+        self.title = title
+        self.web_dir = web_dir
+        self.img_dir = os.path.join(self.web_dir, 'images')
+        if not os.path.exists(self.web_dir):
+            os.makedirs(self.web_dir)
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+
+        self.doc = dominate.document(title=title)
+        if refresh > 0:
+            with self.doc.head:
+                meta(http_equiv="refresh", content=str(refresh))
+
+    def get_image_dir(self):
+        """Return the directory that stores images"""
+        return self.img_dir
+
+    def add_header(self, text):
+        """Insert a header to the HTML file
+
+        Parameters:
+            text (str) -- the header text
+        """
+        with self.doc:
+            h3(text)
+
+    def add_images(self, ims, txts, links, width=400):
+        """add images to the HTML file
+
+        Parameters:
+            ims (str list)   -- a list of image paths
+            txts (str list)  -- a list of image names shown on the website
+            links (str list) --  a list of hyperref links; when you click an image, it will redirect you to a new page
+        """
+        self.t = table(border=1, style="table-layout: fixed;")  # Insert a table
+        self.doc.add(self.t)
+        with self.t:
+            with tr():
+                for im, txt, link in zip(ims, txts, links):
+                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
+                        with p():
+                            with a(href=os.path.join('images', link)):
+                                img(style="width:%dpx" % width, src=os.path.join('images', im))
+                            br()
+                            p(txt)
+
+    def save(self):
+        """save the current content to the HMTL file"""
+        html_file = '%s/index.html' % self.web_dir
+        f = open(html_file, 'wt')
+        f.write(self.doc.render())
+        f.close()
+
+
+if __name__ == '__main__':  # we show an example usage here.
+    html = HTML('web/', 'test_html')
+    html.add_header('hello world')
+
+    ims, txts, links = [], [], []
+    for n in range(4):
+        ims.append('image_%d.png' % n)
+        txts.append('text_%d' % n)
+        links.append('image_%d.png' % n)
+    html.add_images(ims, txts, links)
+    html.save()

microsoftexcel-controlnet/annotator/leres/pix2pix/util/image_pool.py

@@ -0,0 +1,54 @@
+import random
+import torch
+
+
+class ImagePool():
+    """This class implements an image buffer that stores previously generated images.
+
+    This buffer enables us to update discriminators using a history of generated images
+    rather than the ones produced by the latest generators.
+    """
+
+    def __init__(self, pool_size):
+        """Initialize the ImagePool class
+
+        Parameters:
+            pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created
+        """
+        self.pool_size = pool_size
+        if self.pool_size > 0:  # create an empty pool
+            self.num_imgs = 0
+            self.images = []
+
+    def query(self, images):
+        """Return an image from the pool.
+
+        Parameters:
+            images: the latest generated images from the generator
+
+        Returns images from the buffer.
+
+        By 50/100, the buffer will return input images.
+        By 50/100, the buffer will return images previously stored in the buffer,
+        and insert the current images to the buffer.
+        """
+        if self.pool_size == 0:  # if the buffer size is 0, do nothing
+            return images
+        return_images = []
+        for image in images:
+            image = torch.unsqueeze(image.data, 0)
+            if self.num_imgs < self.pool_size:   # if the buffer is not full; keep inserting current images to the buffer
+                self.num_imgs = self.num_imgs + 1
+                self.images.append(image)
+                return_images.append(image)
+            else:
+                p = random.uniform(0, 1)
+                if p > 0.5:  # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer
+                    random_id = random.randint(0, self.pool_size - 1)  # randint is inclusive
+                    tmp = self.images[random_id].clone()
+                    self.images[random_id] = image
+                    return_images.append(tmp)
+                else:       # by another 50% chance, the buffer will return the current image
+                    return_images.append(image)
+        return_images = torch.cat(return_images, 0)   # collect all the images and return
+        return return_images

microsoftexcel-controlnet/annotator/leres/pix2pix/util/util.py

@@ -0,0 +1,105 @@
+"""This module contains simple helper functions """
+from __future__ import print_function
+import torch
+import numpy as np
+from PIL import Image
+import os
+
+
+def tensor2im(input_image, imtype=np.uint16):
+    """"Converts a Tensor array into a numpy image array.
+
+    Parameters:
+        input_image (tensor) --  the input image tensor array
+        imtype (type)        --  the desired type of the converted numpy array
+    """
+    if not isinstance(input_image, np.ndarray):
+        if isinstance(input_image, torch.Tensor):  # get the data from a variable
+            image_tensor = input_image.data
+        else:
+            return input_image
+        image_numpy = torch.squeeze(image_tensor).cpu().numpy()  # convert it into a numpy array
+        image_numpy = (image_numpy + 1) / 2.0 * (2**16-1) #
+    else:  # if it is a numpy array, do nothing
+        image_numpy = input_image
+    return image_numpy.astype(imtype)
+
+
+def diagnose_network(net, name='network'):
+    """Calculate and print the mean of average absolute(gradients)
+
+    Parameters:
+        net (torch network) -- Torch network
+        name (str) -- the name of the network
+    """
+    mean = 0.0
+    count = 0
+    for param in net.parameters():
+        if param.grad is not None:
+            mean += torch.mean(torch.abs(param.grad.data))
+            count += 1
+    if count > 0:
+        mean = mean / count
+    print(name)
+    print(mean)
+
+
+def save_image(image_numpy, image_path, aspect_ratio=1.0):
+    """Save a numpy image to the disk
+
+    Parameters:
+        image_numpy (numpy array) -- input numpy array
+        image_path (str)          -- the path of the image
+    """
+    image_pil = Image.fromarray(image_numpy)
+
+    image_pil = image_pil.convert('I;16')
+
+    # image_pil = Image.fromarray(image_numpy)
+    # h, w, _ = image_numpy.shape
+    #
+    # if aspect_ratio > 1.0:
+    #     image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)
+    # if aspect_ratio < 1.0:
+    #     image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)
+
+    image_pil.save(image_path)
+
+
+def print_numpy(x, val=True, shp=False):
+    """Print the mean, min, max, median, std, and size of a numpy array
+
+    Parameters:
+        val (bool) -- if print the values of the numpy array
+        shp (bool) -- if print the shape of the numpy array
+    """
+    x = x.astype(np.float64)
+    if shp:
+        print('shape,', x.shape)
+    if val:
+        x = x.flatten()
+        print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
+            np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
+
+
+def mkdirs(paths):
+    """create empty directories if they don't exist
+
+    Parameters:
+        paths (str list) -- a list of directory paths
+    """
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+
+def mkdir(path):
+    """create a single empty directory if it didn't exist
+
+    Parameters:
+        path (str) -- a single directory path
+    """
+    if not os.path.exists(path):
+        os.makedirs(path)

microsoftexcel-controlnet/annotator/leres/pix2pix/util/visualizer.py

@@ -0,0 +1,166 @@
+import numpy as np
+import os
+import sys
+import ntpath
+import time
+from . import util, html
+from subprocess import Popen, PIPE
+import torch
+
+
+if sys.version_info[0] == 2:
+    VisdomExceptionBase = Exception
+else:
+    VisdomExceptionBase = ConnectionError
+
+
+def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
+    """Save images to the disk.
+
+    Parameters:
+        webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
+        visuals (OrderedDict)    -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs
+        image_path (str)         -- the string is used to create image paths
+        aspect_ratio (float)     -- the aspect ratio of saved images
+        width (int)              -- the images will be resized to width x width
+
+    This function will save images stored in 'visuals' to the HTML file specified by 'webpage'.
+    """
+    image_dir = webpage.get_image_dir()
+    short_path = ntpath.basename(image_path[0])
+    name = os.path.splitext(short_path)[0]
+
+    webpage.add_header(name)
+    ims, txts, links = [], [], []
+
+    for label, im_data in visuals.items():
+        im = util.tensor2im(im_data)
+        image_name = '%s_%s.png' % (name, label)
+        save_path = os.path.join(image_dir, image_name)
+        util.save_image(im, save_path, aspect_ratio=aspect_ratio)
+        ims.append(image_name)
+        txts.append(label)
+        links.append(image_name)
+    webpage.add_images(ims, txts, links, width=width)
+
+
+class Visualizer():
+    """This class includes several functions that can display/save images and print/save logging information.
+
+    It uses a Python library 'visdom' for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with images.
+    """
+
+    def __init__(self, opt):
+        """Initialize the Visualizer class
+
+        Parameters:
+            opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
+        Step 1: Cache the training/test options
+        Step 2: connect to a visdom server
+        Step 3: create an HTML object for saveing HTML filters
+        Step 4: create a logging file to store training losses
+        """
+        self.opt = opt  # cache the option
+        self.display_id = opt.display_id
+        self.use_html = opt.isTrain and not opt.no_html
+        self.win_size = opt.display_winsize
+        self.name = opt.name
+        self.port = opt.display_port
+        self.saved = False
+
+        if self.use_html:  # create an HTML object at <checkpoints_dir>/web/; images will be saved under <checkpoints_dir>/web/images/
+            self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
+            self.img_dir = os.path.join(self.web_dir, 'images')
+            print('create web directory %s...' % self.web_dir)
+            util.mkdirs([self.web_dir, self.img_dir])
+        # create a logging file to store training losses
+        self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
+        with open(self.log_name, "a") as log_file:
+            now = time.strftime("%c")
+            log_file.write('================ Training Loss (%s) ================\n' % now)
+
+    def reset(self):
+        """Reset the self.saved status"""
+        self.saved = False
+
+    def create_visdom_connections(self):
+        """If the program could not connect to Visdom server, this function will start a new server at port < self.port > """
+        cmd = sys.executable + ' -m visdom.server -p %d &>/dev/null &' % self.port
+        print('\n\nCould not connect to Visdom server. \n Trying to start a server....')
+        print('Command: %s' % cmd)
+        Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
+
+    def display_current_results(self, visuals, epoch, save_result):
+        """Display current results on visdom; save current results to an HTML file.
+
+        Parameters:
+            visuals (OrderedDict) - - dictionary of images to display or save
+            epoch (int) - - the current epoch
+            save_result (bool) - - if save the current results to an HTML file
+        """
+        if self.use_html and (save_result or not self.saved):  # save images to an HTML file if they haven't been saved.
+            self.saved = True
+            # save images to the disk
+            for label, image in visuals.items():
+                image_numpy = util.tensor2im(image)
+                img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
+                util.save_image(image_numpy, img_path)
+
+            # update website
+            webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=1)
+            for n in range(epoch, 0, -1):
+                webpage.add_header('epoch [%d]' % n)
+                ims, txts, links = [], [], []
+
+                for label, image_numpy in visuals.items():
+                    # image_numpy = util.tensor2im(image)
+                    img_path = 'epoch%.3d_%s.png' % (n, label)
+                    ims.append(img_path)
+                    txts.append(label)
+                    links.append(img_path)
+                webpage.add_images(ims, txts, links, width=self.win_size)
+            webpage.save()
+
+    # def plot_current_losses(self, epoch, counter_ratio, losses):
+        # """display the current losses on visdom display: dictionary of error labels and values
+        #
+        # Parameters:
+        #     epoch (int)           -- current epoch
+        #     counter_ratio (float) -- progress (percentage) in the current epoch, between 0 to 1
+        #     losses (OrderedDict)  -- training losses stored in the format of (name, float) pairs
+        # """
+        # if not hasattr(self, 'plot_data'):
+        #     self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())}
+        # self.plot_data['X'].append(epoch + counter_ratio)
+        # self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']])
+        # try:
+        #     self.vis.line(
+        #         X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1),
+        #         Y=np.array(self.plot_data['Y']),
+        #         opts={
+        #             'title': self.name + ' loss over time',
+        #             'legend': self.plot_data['legend'],
+        #             'xlabel': 'epoch',
+        #             'ylabel': 'loss'},
+        #         win=self.display_id)
+        # except VisdomExceptionBase:
+        #     self.create_visdom_connections()
+
+    # losses: same format as |losses| of plot_current_losses
+    def print_current_losses(self, epoch, iters, losses, t_comp, t_data):
+        """print current losses on console; also save the losses to the disk
+
+        Parameters:
+            epoch (int) -- current epoch
+            iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
+            losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
+            t_comp (float) -- computational time per data point (normalized by batch_size)
+            t_data (float) -- data loading time per data point (normalized by batch_size)
+        """
+        message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data)
+        for k, v in losses.items():
+            message += '%s: %.3f ' % (k, v)
+
+        print(message)  # print the message
+        with open(self.log_name, "a") as log_file:
+            log_file.write('%s\n' % message)  # save the message

microsoftexcel-controlnet/annotator/lineart/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Caroline Chan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

microsoftexcel-controlnet/annotator/lineart/__init__.py

@@ -0,0 +1,133 @@
+import os
+import cv2
+import torch
+import numpy as np
+
+import torch.nn as nn
+from einops import rearrange
+from modules import devices
+from annotator.annotator_path import models_path
+
+
+norm_layer = nn.InstanceNorm2d
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, in_features):
+        super(ResidualBlock, self).__init__()
+
+        conv_block = [  nn.ReflectionPad2d(1),
+                        nn.Conv2d(in_features, in_features, 3),
+                        norm_layer(in_features),
+                        nn.ReLU(inplace=True),
+                        nn.ReflectionPad2d(1),
+                        nn.Conv2d(in_features, in_features, 3),
+                        norm_layer(in_features)
+                        ]
+
+        self.conv_block = nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        return x + self.conv_block(x)
+
+
+class Generator(nn.Module):
+    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
+        super(Generator, self).__init__()
+
+        # Initial convolution block
+        model0 = [   nn.ReflectionPad2d(3),
+                    nn.Conv2d(input_nc, 64, 7),
+                    norm_layer(64),
+                    nn.ReLU(inplace=True) ]
+        self.model0 = nn.Sequential(*model0)
+
+        # Downsampling
+        model1 = []
+        in_features = 64
+        out_features = in_features*2
+        for _ in range(2):
+            model1 += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
+                        norm_layer(out_features),
+                        nn.ReLU(inplace=True) ]
+            in_features = out_features
+            out_features = in_features*2
+        self.model1 = nn.Sequential(*model1)
+
+        model2 = []
+        # Residual blocks
+        for _ in range(n_residual_blocks):
+            model2 += [ResidualBlock(in_features)]
+        self.model2 = nn.Sequential(*model2)
+
+        # Upsampling
+        model3 = []
+        out_features = in_features//2
+        for _ in range(2):
+            model3 += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
+                        norm_layer(out_features),
+                        nn.ReLU(inplace=True) ]
+            in_features = out_features
+            out_features = in_features//2
+        self.model3 = nn.Sequential(*model3)
+
+        # Output layer
+        model4 = [  nn.ReflectionPad2d(3),
+                        nn.Conv2d(64, output_nc, 7)]
+        if sigmoid:
+            model4 += [nn.Sigmoid()]
+
+        self.model4 = nn.Sequential(*model4)
+
+    def forward(self, x, cond=None):
+        out = self.model0(x)
+        out = self.model1(out)
+        out = self.model2(out)
+        out = self.model3(out)
+        out = self.model4(out)
+
+        return out
+
+
+class LineartDetector:
+    model_dir = os.path.join(models_path, "lineart")
+    model_default = 'sk_model.pth'
+    model_coarse = 'sk_model2.pth'
+
+    def __init__(self, model_name):
+        self.model = None
+        self.model_name = model_name
+        self.device = devices.get_device_for("controlnet")
+
+    def load_model(self, name):
+        remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/" + name
+        model_path = os.path.join(self.model_dir, name)
+        if not os.path.exists(model_path):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=self.model_dir)
+        model = Generator(3, 1, 3)
+        model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+        model.eval()
+        self.model = model.to(self.device)
+
+    def unload_model(self):
+        if self.model is not None:
+            self.model.cpu()
+
+    def __call__(self, input_image):
+        if self.model is None:
+            self.load_model(self.model_name)
+        self.model.to(self.device)
+
+        assert input_image.ndim == 3
+        image = input_image
+        with torch.no_grad():
+            image = torch.from_numpy(image).float().to(self.device)
+            image = image / 255.0
+            image = rearrange(image, 'h w c -> 1 c h w')
+            line = self.model(image)[0][0]
+
+            line = line.cpu().numpy()
+            line = (line * 255.0).clip(0, 255).astype(np.uint8)
+
+            return line

microsoftexcel-controlnet/annotator/lineart_anime/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Caroline Chan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

microsoftexcel-controlnet/annotator/lineart_anime/__init__.py

@@ -0,0 +1,161 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import functools
+
+import os
+import cv2
+from einops import rearrange
+from modules import devices
+from annotator.annotator_path import models_path
+
+
+class UnetGenerator(nn.Module):
+    """Create a Unet-based generator"""
+
+    def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):
+        """Construct a Unet generator
+        Parameters:
+            input_nc (int)  -- the number of channels in input images
+            output_nc (int) -- the number of channels in output images
+            num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,
+                                image of size 128x128 will become of size 1x1 # at the bottleneck
+            ngf (int)       -- the number of filters in the last conv layer
+            norm_layer      -- normalization layer
+        We construct the U-Net from the innermost layer to the outermost layer.
+        It is a recursive process.
+        """
+        super(UnetGenerator, self).__init__()
+        # construct unet structure
+        unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True)  # add the innermost layer
+        for _ in range(num_downs - 5):          # add intermediate layers with ngf * 8 filters
+            unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
+        # gradually reduce the number of filters from ngf * 8 to ngf
+        unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer)  # add the outermost layer
+
+    def forward(self, input):
+        """Standard forward"""
+        return self.model(input)
+
+
+class UnetSkipConnectionBlock(nn.Module):
+    """Defines the Unet submodule with skip connection.
+        X -------------------identity----------------------
+        |-- downsampling -- |submodule| -- upsampling --|
+    """
+
+    def __init__(self, outer_nc, inner_nc, input_nc=None,
+                 submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
+        """Construct a Unet submodule with skip connections.
+        Parameters:
+            outer_nc (int) -- the number of filters in the outer conv layer
+            inner_nc (int) -- the number of filters in the inner conv layer
+            input_nc (int) -- the number of channels in input images/features
+            submodule (UnetSkipConnectionBlock) -- previously defined submodules
+            outermost (bool)    -- if this module is the outermost module
+            innermost (bool)    -- if this module is the innermost module
+            norm_layer          -- normalization layer
+            use_dropout (bool)  -- if use dropout layers.
+        """
+        super(UnetSkipConnectionBlock, self).__init__()
+        self.outermost = outermost
+        if type(norm_layer) == functools.partial:
+            use_bias = norm_layer.func == nn.InstanceNorm2d
+        else:
+            use_bias = norm_layer == nn.InstanceNorm2d
+        if input_nc is None:
+            input_nc = outer_nc
+        downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
+                             stride=2, padding=1, bias=use_bias)
+        downrelu = nn.LeakyReLU(0.2, True)
+        downnorm = norm_layer(inner_nc)
+        uprelu = nn.ReLU(True)
+        upnorm = norm_layer(outer_nc)
+
+        if outermost:
+            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1)
+            down = [downconv]
+            up = [uprelu, upconv, nn.Tanh()]
+            model = down + [submodule] + up
+        elif innermost:
+            upconv = nn.ConvTranspose2d(inner_nc, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1, bias=use_bias)
+            down = [downrelu, downconv]
+            up = [uprelu, upconv, upnorm]
+            model = down + up
+        else:
+            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1, bias=use_bias)
+            down = [downrelu, downconv, downnorm]
+            up = [uprelu, upconv, upnorm]
+
+            if use_dropout:
+                model = down + [submodule] + up + [nn.Dropout(0.5)]
+            else:
+                model = down + [submodule] + up
+
+        self.model = nn.Sequential(*model)
+
+    def forward(self, x):
+        if self.outermost:
+            return self.model(x)
+        else:   # add skip connections
+            return torch.cat([x, self.model(x)], 1)
+
+
+class LineartAnimeDetector:
+    model_dir = os.path.join(models_path, "lineart_anime")
+
+    def __init__(self):
+        self.model = None
+        self.device = devices.get_device_for("controlnet")
+
+    def load_model(self):
+        remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/netG.pth"
+        modelpath = os.path.join(self.model_dir, "netG.pth")
+        if not os.path.exists(modelpath):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=self.model_dir)
+        norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
+        net = UnetGenerator(3, 1, 8, 64, norm_layer=norm_layer, use_dropout=False)
+        ckpt = torch.load(modelpath)
+        for key in list(ckpt.keys()):
+            if 'module.' in key:
+                ckpt[key.replace('module.', '')] = ckpt[key]
+                del ckpt[key]
+        net.load_state_dict(ckpt)
+        net.eval()
+        self.model = net.to(self.device)
+
+    def unload_model(self):
+        if self.model is not None:
+            self.model.cpu()
+
+    def __call__(self, input_image):
+        if self.model is None:
+            self.load_model()
+        self.model.to(self.device)
+
+        H, W, C = input_image.shape
+        Hn = 256 * int(np.ceil(float(H) / 256.0))
+        Wn = 256 * int(np.ceil(float(W) / 256.0))
+        img = cv2.resize(input_image, (Wn, Hn), interpolation=cv2.INTER_CUBIC)
+        with torch.no_grad():
+            image_feed = torch.from_numpy(img).float().to(self.device)
+            image_feed = image_feed / 127.5 - 1.0
+            image_feed = rearrange(image_feed, 'h w c -> 1 c h w')
+
+            line = self.model(image_feed)[0, 0] * 127.5 + 127.5
+            line = line.cpu().numpy()
+
+            line = cv2.resize(line, (W, H), interpolation=cv2.INTER_CUBIC)
+            line = line.clip(0, 255).astype(np.uint8)
+            return line
+

microsoftexcel-controlnet/annotator/manga_line/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Miaomiao Li
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

microsoftexcel-controlnet/annotator/manga_line/__init__.py

@@ -0,0 +1,248 @@
+import os
+import torch
+import torch.nn as nn
+from PIL import Image
+import fnmatch
+import cv2
+
+import sys
+
+import numpy as np
+from einops import rearrange
+from modules import devices
+from annotator.annotator_path import models_path
+
+
+class _bn_relu_conv(nn.Module):
+    def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
+        super(_bn_relu_conv, self).__init__()
+        self.model = nn.Sequential(
+            nn.BatchNorm2d(in_filters, eps=1e-3),
+            nn.LeakyReLU(0.2),
+            nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2), padding_mode='zeros')
+        )
+
+    def forward(self, x):
+        return self.model(x)
+
+        # the following are for debugs
+        print("****", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape)
+        for i,layer in enumerate(self.model):
+            if i != 2:
+                x = layer(x)
+            else:
+                x = layer(x)
+                #x = nn.functional.pad(x, (1, 1, 1, 1), mode='constant', value=0)
+            print("____", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape)
+            print(x[0])
+        return x
+
+class _u_bn_relu_conv(nn.Module):
+    def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
+        super(_u_bn_relu_conv, self).__init__()
+        self.model = nn.Sequential(
+            nn.BatchNorm2d(in_filters, eps=1e-3),
+            nn.LeakyReLU(0.2),
+            nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2)),
+            nn.Upsample(scale_factor=2, mode='nearest')
+        )
+
+    def forward(self, x):
+        return self.model(x)
+
+
+
+class _shortcut(nn.Module):
+    def __init__(self, in_filters, nb_filters, subsample=1):
+        super(_shortcut, self).__init__()
+        self.process = False
+        self.model = None
+        if in_filters != nb_filters or subsample != 1:
+            self.process = True
+            self.model = nn.Sequential(
+                    nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample)
+                )
+
+    def forward(self, x, y):
+        #print(x.size(), y.size(), self.process)
+        if self.process:
+            y0 = self.model(x)
+            #print("merge+", torch.max(y0+y), torch.min(y0+y),torch.mean(y0+y), torch.std(y0+y), y0.shape)
+            return y0 + y
+        else:
+            #print("merge", torch.max(x+y), torch.min(x+y),torch.mean(x+y), torch.std(x+y), y.shape)
+            return x + y
+
+class _u_shortcut(nn.Module):
+    def __init__(self, in_filters, nb_filters, subsample):
+        super(_u_shortcut, self).__init__()
+        self.process = False
+        self.model = None
+        if in_filters != nb_filters:
+            self.process = True
+            self.model = nn.Sequential(
+                nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample, padding_mode='zeros'),
+                nn.Upsample(scale_factor=2, mode='nearest')
+            )
+
+    def forward(self, x, y):
+        if self.process:
+            return self.model(x) + y
+        else:
+            return x + y
+
+
+class basic_block(nn.Module):
+    def __init__(self, in_filters, nb_filters, init_subsample=1):
+        super(basic_block, self).__init__()
+        self.conv1 = _bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample)
+        self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
+        self.shortcut = _shortcut(in_filters, nb_filters, subsample=init_subsample)
+
+    def forward(self, x):
+        x1 = self.conv1(x)
+        x2 = self.residual(x1)
+        return self.shortcut(x, x2)
+
+class _u_basic_block(nn.Module):
+    def __init__(self, in_filters, nb_filters, init_subsample=1):
+        super(_u_basic_block, self).__init__()
+        self.conv1 = _u_bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample)
+        self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
+        self.shortcut = _u_shortcut(in_filters, nb_filters, subsample=init_subsample)
+
+    def forward(self, x):
+        y = self.residual(self.conv1(x))
+        return self.shortcut(x, y)
+
+
+class _residual_block(nn.Module):
+    def __init__(self, in_filters, nb_filters, repetitions, is_first_layer=False):
+        super(_residual_block, self).__init__()
+        layers = []
+        for i in range(repetitions):
+            init_subsample = 1
+            if i == repetitions - 1 and not is_first_layer:
+                init_subsample = 2
+            if i == 0:
+                l = basic_block(in_filters=in_filters, nb_filters=nb_filters, init_subsample=init_subsample)
+            else:
+                l = basic_block(in_filters=nb_filters, nb_filters=nb_filters, init_subsample=init_subsample)
+            layers.append(l)
+
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class _upsampling_residual_block(nn.Module):
+    def __init__(self, in_filters, nb_filters, repetitions):
+        super(_upsampling_residual_block, self).__init__()
+        layers = []
+        for i in range(repetitions):
+            l = None
+            if i == 0: 
+                l = _u_basic_block(in_filters=in_filters, nb_filters=nb_filters)#(input)
+            else:
+                l = basic_block(in_filters=nb_filters, nb_filters=nb_filters)#(input)
+            layers.append(l)
+
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class res_skip(nn.Module):
+
+    def __init__(self):
+        super(res_skip, self).__init__()
+        self.block0 = _residual_block(in_filters=1, nb_filters=24, repetitions=2, is_first_layer=True)#(input)
+        self.block1 = _residual_block(in_filters=24, nb_filters=48, repetitions=3)#(block0)
+        self.block2 = _residual_block(in_filters=48, nb_filters=96, repetitions=5)#(block1)
+        self.block3 = _residual_block(in_filters=96, nb_filters=192, repetitions=7)#(block2)
+        self.block4 = _residual_block(in_filters=192, nb_filters=384, repetitions=12)#(block3)
+        
+        self.block5 = _upsampling_residual_block(in_filters=384, nb_filters=192, repetitions=7)#(block4)
+        self.res1 = _shortcut(in_filters=192, nb_filters=192)#(block3, block5, subsample=(1,1))
+
+        self.block6 = _upsampling_residual_block(in_filters=192, nb_filters=96, repetitions=5)#(res1)
+        self.res2 = _shortcut(in_filters=96, nb_filters=96)#(block2, block6, subsample=(1,1))
+
+        self.block7 = _upsampling_residual_block(in_filters=96, nb_filters=48, repetitions=3)#(res2)
+        self.res3 = _shortcut(in_filters=48, nb_filters=48)#(block1, block7, subsample=(1,1))
+
+        self.block8 = _upsampling_residual_block(in_filters=48, nb_filters=24, repetitions=2)#(res3)
+        self.res4 = _shortcut(in_filters=24, nb_filters=24)#(block0,block8, subsample=(1,1))
+
+        self.block9 = _residual_block(in_filters=24, nb_filters=16, repetitions=2, is_first_layer=True)#(res4)
+        self.conv15 = _bn_relu_conv(in_filters=16, nb_filters=1, fh=1, fw=1, subsample=1)#(block7)
+
+    def forward(self, x):
+        x0 = self.block0(x)
+        x1 = self.block1(x0)
+        x2 = self.block2(x1)
+        x3 = self.block3(x2)
+        x4 = self.block4(x3)
+
+        x5 = self.block5(x4)
+        res1 = self.res1(x3, x5)
+
+        x6 = self.block6(res1)
+        res2 = self.res2(x2, x6)
+
+        x7 = self.block7(res2)
+        res3 = self.res3(x1, x7)
+
+        x8 = self.block8(res3)
+        res4 = self.res4(x0, x8)
+
+        x9 = self.block9(res4)
+        y = self.conv15(x9)
+
+        return y
+
+
+class MangaLineExtration:
+    model_dir = os.path.join(models_path, "manga_line")
+
+    def __init__(self):
+        self.model = None
+        self.device = devices.get_device_for("controlnet")
+
+    def load_model(self):
+        remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/erika.pth"
+        modelpath = os.path.join(self.model_dir, "erika.pth")
+        if not os.path.exists(modelpath):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=self.model_dir)
+        #norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
+        net = res_skip()
+        ckpt = torch.load(modelpath)
+        for key in list(ckpt.keys()):
+            if 'module.' in key:
+                ckpt[key.replace('module.', '')] = ckpt[key]
+                del ckpt[key]
+        net.load_state_dict(ckpt)
+        net.eval()
+        self.model = net.to(self.device)
+
+    def unload_model(self):
+        if self.model is not None:
+            self.model.cpu()
+
+    def __call__(self, input_image):
+        if self.model is None:
+            self.load_model()
+        self.model.to(self.device)
+        img = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)
+        img = np.ascontiguousarray(img.copy()).copy()
+        with torch.no_grad():
+            image_feed = torch.from_numpy(img).float().to(self.device)
+            image_feed = rearrange(image_feed, 'h w -> 1 1 h w')
+            line = self.model(image_feed)
+            line = 255 - line.cpu().numpy()[0, 0]
+            return line.clip(0, 255).astype(np.uint8)
+
+    

microsoftexcel-controlnet/annotator/mediapipe_face/__init__.py

@@ -0,0 +1,5 @@
+from .mediapipe_face_common import generate_annotation
+
+
+def apply_mediapipe_face(image, max_faces: int = 1, min_confidence: float = 0.5):
+    return generate_annotation(image, max_faces, min_confidence)

microsoftexcel-controlnet/annotator/mediapipe_face/mediapipe_face_common.py

@@ -0,0 +1,155 @@
+from typing import Mapping
+
+import mediapipe as mp
+import numpy
+
+
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_face_detection = mp.solutions.face_detection  # Only for counting faces.
+mp_face_mesh = mp.solutions.face_mesh
+mp_face_connections = mp.solutions.face_mesh_connections.FACEMESH_TESSELATION
+mp_hand_connections = mp.solutions.hands_connections.HAND_CONNECTIONS
+mp_body_connections = mp.solutions.pose_connections.POSE_CONNECTIONS
+
+DrawingSpec = mp.solutions.drawing_styles.DrawingSpec
+PoseLandmark = mp.solutions.drawing_styles.PoseLandmark
+
+min_face_size_pixels: int = 64
+f_thick = 2
+f_rad = 1
+right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad)
+right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad)
+right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad)
+left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad)
+left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad)
+left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad)
+mouth_draw = DrawingSpec(color=(10, 180, 10), thickness=f_thick, circle_radius=f_rad)
+head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad)
+
+# mp_face_mesh.FACEMESH_CONTOURS has all the items we care about.
+face_connection_spec = {}
+for edge in mp_face_mesh.FACEMESH_FACE_OVAL:
+    face_connection_spec[edge] = head_draw
+for edge in mp_face_mesh.FACEMESH_LEFT_EYE:
+    face_connection_spec[edge] = left_eye_draw
+for edge in mp_face_mesh.FACEMESH_LEFT_EYEBROW:
+    face_connection_spec[edge] = left_eyebrow_draw
+# for edge in mp_face_mesh.FACEMESH_LEFT_IRIS:
+#    face_connection_spec[edge] = left_iris_draw
+for edge in mp_face_mesh.FACEMESH_RIGHT_EYE:
+    face_connection_spec[edge] = right_eye_draw
+for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW:
+    face_connection_spec[edge] = right_eyebrow_draw
+# for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS:
+#    face_connection_spec[edge] = right_iris_draw
+for edge in mp_face_mesh.FACEMESH_LIPS:
+    face_connection_spec[edge] = mouth_draw
+iris_landmark_spec = {468: right_iris_draw, 473: left_iris_draw}
+
+
+def draw_pupils(image, landmark_list, drawing_spec, halfwidth: int = 2):
+    """We have a custom function to draw the pupils because the mp.draw_landmarks method requires a parameter for all
+    landmarks.  Until our PR is merged into mediapipe, we need this separate method."""
+    if len(image.shape) != 3:
+        raise ValueError("Input image must be H,W,C.")
+    image_rows, image_cols, image_channels = image.shape
+    if image_channels != 3:  # BGR channels
+        raise ValueError('Input image must contain three channel bgr data.')
+    for idx, landmark in enumerate(landmark_list.landmark):
+        if (
+                (landmark.HasField('visibility') and landmark.visibility < 0.9) or
+                (landmark.HasField('presence') and landmark.presence < 0.5)
+        ):
+            continue
+        if landmark.x >= 1.0 or landmark.x < 0 or landmark.y >= 1.0 or landmark.y < 0:
+            continue
+        image_x = int(image_cols*landmark.x)
+        image_y = int(image_rows*landmark.y)
+        draw_color = None
+        if isinstance(drawing_spec, Mapping):
+            if drawing_spec.get(idx) is None:
+                continue
+            else:
+                draw_color = drawing_spec[idx].color
+        elif isinstance(drawing_spec, DrawingSpec):
+            draw_color = drawing_spec.color
+        image[image_y-halfwidth:image_y+halfwidth, image_x-halfwidth:image_x+halfwidth, :] = draw_color
+
+
+def reverse_channels(image):
+    """Given a numpy array in RGB form, convert to BGR.  Will also convert from BGR to RGB."""
+    # im[:,:,::-1] is a neat hack to convert BGR to RGB by reversing the indexing order.
+    # im[:,:,::[2,1,0]] would also work but makes a copy of the data.
+    return image[:, :, ::-1]
+
+
+def generate_annotation(
+        img_rgb,
+        max_faces: int,
+        min_confidence: float
+):
+    """
+    Find up to 'max_faces' inside the provided input image.
+    If min_face_size_pixels is provided and nonzero it will be used to filter faces that occupy less than this many
+    pixels in the image.
+    """
+    with mp_face_mesh.FaceMesh(
+            static_image_mode=True,
+            max_num_faces=max_faces,
+            refine_landmarks=True,
+            min_detection_confidence=min_confidence,
+    ) as facemesh:
+        img_height, img_width, img_channels = img_rgb.shape
+        assert(img_channels == 3)
+
+        results = facemesh.process(img_rgb).multi_face_landmarks
+
+        if results is None:
+            print("No faces detected in controlnet image for Mediapipe face annotator.")
+            return numpy.zeros_like(img_rgb)
+
+        # Filter faces that are too small
+        filtered_landmarks = []
+        for lm in results:
+            landmarks = lm.landmark
+            face_rect = [
+                landmarks[0].x,
+                landmarks[0].y,
+                landmarks[0].x,
+                landmarks[0].y,
+            ]  # Left, up, right, down.
+            for i in range(len(landmarks)):
+                face_rect[0] = min(face_rect[0], landmarks[i].x)
+                face_rect[1] = min(face_rect[1], landmarks[i].y)
+                face_rect[2] = max(face_rect[2], landmarks[i].x)
+                face_rect[3] = max(face_rect[3], landmarks[i].y)
+            if min_face_size_pixels > 0:
+                face_width = abs(face_rect[2] - face_rect[0])
+                face_height = abs(face_rect[3] - face_rect[1])
+                face_width_pixels = face_width * img_width
+                face_height_pixels = face_height * img_height
+                face_size = min(face_width_pixels, face_height_pixels)
+                if face_size >= min_face_size_pixels:
+                    filtered_landmarks.append(lm)
+            else:
+                filtered_landmarks.append(lm)
+
+        # Annotations are drawn in BGR for some reason, but we don't need to flip a zero-filled image at the start.
+        empty = numpy.zeros_like(img_rgb)
+
+        # Draw detected faces:
+        for face_landmarks in filtered_landmarks:
+            mp_drawing.draw_landmarks(
+                empty,
+                face_landmarks,
+                connections=face_connection_spec.keys(),
+                landmark_drawing_spec=None,
+                connection_drawing_spec=face_connection_spec
+            )
+            draw_pupils(empty, face_landmarks, iris_landmark_spec, 2)
+
+        # Flip BGR back to RGB.
+        empty = reverse_channels(empty).copy()
+
+        return empty

microsoftexcel-controlnet/annotator/midas/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Intel ISL (Intel Intelligent Systems Lab)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

microsoftexcel-controlnet/annotator/midas/__init__.py

@@ -0,0 +1,49 @@
+import cv2
+import numpy as np
+import torch
+
+from einops import rearrange
+from .api import MiDaSInference
+from modules import devices
+
+model = None
+
+def unload_midas_model():
+    global model
+    if model is not None:
+        model = model.cpu()
+
+def apply_midas(input_image, a=np.pi * 2.0, bg_th=0.1):
+    global model
+    if model is None:
+        model = MiDaSInference(model_type="dpt_hybrid")
+    if devices.get_device_for("controlnet").type != 'mps':
+        model = model.to(devices.get_device_for("controlnet"))
+    
+    assert input_image.ndim == 3
+    image_depth = input_image
+    with torch.no_grad():
+        image_depth = torch.from_numpy(image_depth).float()
+        if devices.get_device_for("controlnet").type != 'mps':
+            image_depth = image_depth.to(devices.get_device_for("controlnet"))
+        image_depth = image_depth / 127.5 - 1.0
+        image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
+        depth = model(image_depth)[0]
+
+        depth_pt = depth.clone()
+        depth_pt -= torch.min(depth_pt)
+        depth_pt /= torch.max(depth_pt)
+        depth_pt = depth_pt.cpu().numpy()
+        depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
+
+        depth_np = depth.cpu().numpy()
+        x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3)
+        y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3)
+        z = np.ones_like(x) * a
+        x[depth_pt < bg_th] = 0
+        y[depth_pt < bg_th] = 0
+        normal = np.stack([x, y, z], axis=2)
+        normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
+        normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)[:, :, ::-1]
+
+        return depth_image, normal_image

microsoftexcel-controlnet/annotator/midas/api.py

@@ -0,0 +1,181 @@
+# based on https://github.com/isl-org/MiDaS
+
+import cv2
+import torch
+import torch.nn as nn
+import os
+from annotator.annotator_path import models_path
+
+from torchvision.transforms import Compose
+
+from .midas.dpt_depth import DPTDepthModel
+from .midas.midas_net import MidasNet
+from .midas.midas_net_custom import MidasNet_small
+from .midas.transforms import Resize, NormalizeImage, PrepareForNet
+
+base_model_path = os.path.join(models_path, "midas")
+old_modeldir = os.path.dirname(os.path.realpath(__file__))
+remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt"
+
+ISL_PATHS = {
+    "dpt_large": os.path.join(base_model_path, "dpt_large-midas-2f21e586.pt"),
+    "dpt_hybrid": os.path.join(base_model_path, "dpt_hybrid-midas-501f0c75.pt"),
+    "midas_v21": "",
+    "midas_v21_small": "",
+}
+
+OLD_ISL_PATHS = {
+    "dpt_large": os.path.join(old_modeldir, "dpt_large-midas-2f21e586.pt"),
+    "dpt_hybrid": os.path.join(old_modeldir, "dpt_hybrid-midas-501f0c75.pt"),
+    "midas_v21": "",
+    "midas_v21_small": "",
+}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def load_midas_transform(model_type):
+    # https://github.com/isl-org/MiDaS/blob/master/run.py
+    # load transform only
+    if model_type == "dpt_large":  # DPT-Large
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "dpt_hybrid":  # DPT-Hybrid
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "midas_v21":
+        net_w, net_h = 384, 384
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    elif model_type == "midas_v21_small":
+        net_w, net_h = 256, 256
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    else:
+        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
+
+    transform = Compose(
+        [
+            Resize(
+                net_w,
+                net_h,
+                resize_target=None,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=32,
+                resize_method=resize_mode,
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            normalization,
+            PrepareForNet(),
+        ]
+    )
+
+    return transform
+
+
+def load_model(model_type):
+    # https://github.com/isl-org/MiDaS/blob/master/run.py
+    # load network
+    model_path = ISL_PATHS[model_type]
+    old_model_path = OLD_ISL_PATHS[model_type]
+    if model_type == "dpt_large":  # DPT-Large
+        model = DPTDepthModel(
+            path=model_path,
+            backbone="vitl16_384",
+            non_negative=True,
+        )
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "dpt_hybrid":  # DPT-Hybrid
+        if os.path.exists(old_model_path):
+            model_path = old_model_path
+        elif not os.path.exists(model_path):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=base_model_path)
+        
+        model = DPTDepthModel(
+            path=model_path,
+            backbone="vitb_rn50_384",
+            non_negative=True,
+        )
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "midas_v21":
+        model = MidasNet(model_path, non_negative=True)
+        net_w, net_h = 384, 384
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )
+
+    elif model_type == "midas_v21_small":
+        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
+                               non_negative=True, blocks={'expand': True})
+        net_w, net_h = 256, 256
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )
+
+    else:
+        print(f"model_type '{model_type}' not implemented, use: --model_type large")
+        assert False
+
+    transform = Compose(
+        [
+            Resize(
+                net_w,
+                net_h,
+                resize_target=None,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=32,
+                resize_method=resize_mode,
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            normalization,
+            PrepareForNet(),
+        ]
+    )
+
+    return model.eval(), transform
+
+
+class MiDaSInference(nn.Module):
+    MODEL_TYPES_TORCH_HUB = [
+        "DPT_Large",
+        "DPT_Hybrid",
+        "MiDaS_small"
+    ]
+    MODEL_TYPES_ISL = [
+        "dpt_large",
+        "dpt_hybrid",
+        "midas_v21",
+        "midas_v21_small",
+    ]
+
+    def __init__(self, model_type):
+        super().__init__()
+        assert (model_type in self.MODEL_TYPES_ISL)
+        model, _ = load_model(model_type)
+        self.model = model
+        self.model.train = disabled_train
+
+    def forward(self, x):
+        with torch.no_grad():
+            prediction = self.model(x)
+        return prediction
+

microsoftexcel-controlnet/annotator/midas/midas/base_model.py

@@ -0,0 +1,16 @@
+import torch
+
+
+class BaseModel(torch.nn.Module):
+    def load(self, path):
+        """Load model from file.
+
+        Args:
+            path (str): file path
+        """
+        parameters = torch.load(path, map_location=torch.device('cpu'))
+
+        if "optimizer" in parameters:
+            parameters = parameters["model"]
+
+        self.load_state_dict(parameters)

microsoftexcel-controlnet/annotator/midas/midas/blocks.py

@@ -0,0 +1,342 @@
+import torch
+import torch.nn as nn
+
+from .vit import (
+    _make_pretrained_vitb_rn50_384,
+    _make_pretrained_vitl16_384,
+    _make_pretrained_vitb16_384,
+    forward_vit,
+)
+
+def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
+    if backbone == "vitl16_384":
+        pretrained = _make_pretrained_vitl16_384(
+            use_pretrained, hooks=hooks, use_readout=use_readout
+        )
+        scratch = _make_scratch(
+            [256, 512, 1024, 1024], features, groups=groups, expand=expand
+        )  # ViT-L/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb_rn50_384":
+        pretrained = _make_pretrained_vitb_rn50_384(
+            use_pretrained,
+            hooks=hooks,
+            use_vit_only=use_vit_only,
+            use_readout=use_readout,
+        )
+        scratch = _make_scratch(
+            [256, 512, 768, 768], features, groups=groups, expand=expand
+        )  # ViT-H/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb16_384":
+        pretrained = _make_pretrained_vitb16_384(
+            use_pretrained, hooks=hooks, use_readout=use_readout
+        )
+        scratch = _make_scratch(
+            [96, 192, 384, 768], features, groups=groups, expand=expand
+        )  # ViT-B/16 - 84.6% Top1 (backbone)
+    elif backbone == "resnext101_wsl":
+        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
+        scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand)     # efficientnet_lite3  
+    elif backbone == "efficientnet_lite3":
+        pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
+        scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand)  # efficientnet_lite3     
+    else:
+        print(f"Backbone '{backbone}' not implemented")
+        assert False
+        
+    return pretrained, scratch
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    out_shape4 = out_shape
+    if expand==True:
+        out_shape1 = out_shape
+        out_shape2 = out_shape*2
+        out_shape3 = out_shape*4
+        out_shape4 = out_shape*8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer4_rn = nn.Conv2d(
+        in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+
+    return scratch
+
+
+def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
+    efficientnet = torch.hub.load(
+        "rwightman/gen-efficientnet-pytorch",
+        "tf_efficientnet_lite3",
+        pretrained=use_pretrained,
+        exportable=exportable
+    )
+    return _make_efficientnet_backbone(efficientnet)
+
+
+def _make_efficientnet_backbone(effnet):
+    pretrained = nn.Module()
+
+    pretrained.layer1 = nn.Sequential(
+        effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
+    )
+    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
+    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
+    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
+
+    return pretrained
+    
+
+def _make_resnet_backbone(resnet):
+    pretrained = nn.Module()
+    pretrained.layer1 = nn.Sequential(
+        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
+    )
+
+    pretrained.layer2 = resnet.layer2
+    pretrained.layer3 = resnet.layer3
+    pretrained.layer4 = resnet.layer4
+
+    return pretrained
+
+
+def _make_pretrained_resnext101_wsl(use_pretrained):
+    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
+    return _make_resnet_backbone(resnet)
+
+
+
+class Interpolate(nn.Module):
+    """Interpolation module.
+    """
+
+    def __init__(self, scale_factor, mode, align_corners=False):
+        """Init.
+
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
+        )
+
+        return x
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        out = self.relu(x)
+        out = self.conv1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+
+        return out + x
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.resConfUnit1 = ResidualConvUnit(features)
+        self.resConfUnit2 = ResidualConvUnit(features)
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            output += self.resConfUnit1(xs[1])
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=True
+        )
+
+        return output
+
+
+
+
+class ResidualConvUnit_custom(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups=1
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+        
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+
+        if self.bn==True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn==True:
+            out = self.bn1(out)
+       
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn==True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+        # return out + x
+
+
+class FeatureFusionBlock_custom(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock_custom, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups=1
+
+        self.expand = expand
+        out_features = features
+        if self.expand==True:
+            out_features = features//2
+        
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
+        
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+            # output += res
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output
+

microsoftexcel-controlnet/annotator/midas/midas/dpt_depth.py

@@ -0,0 +1,109 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .base_model import BaseModel
+from .blocks import (
+    FeatureFusionBlock,
+    FeatureFusionBlock_custom,
+    Interpolate,
+    _make_encoder,
+    forward_vit,
+)
+
+
+def _make_fusion_block(features, use_bn):
+    return FeatureFusionBlock_custom(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+    )
+
+
+class DPT(BaseModel):
+    def __init__(
+        self,
+        head,
+        features=256,
+        backbone="vitb_rn50_384",
+        readout="project",
+        channels_last=False,
+        use_bn=False,
+    ):
+
+        super(DPT, self).__init__()
+
+        self.channels_last = channels_last
+
+        hooks = {
+            "vitb_rn50_384": [0, 1, 8, 11],
+            "vitb16_384": [2, 5, 8, 11],
+            "vitl16_384": [5, 11, 17, 23],
+        }
+
+        # Instantiate backbone and reassemble blocks
+        self.pretrained, self.scratch = _make_encoder(
+            backbone,
+            features,
+            False, # Set to true of you want to train from scratch, uses ImageNet weights
+            groups=1,
+            expand=False,
+            exportable=False,
+            hooks=hooks[backbone],
+            use_readout=readout,
+        )
+
+        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+
+        self.scratch.output_conv = head
+
+
+    def forward(self, x):
+        if self.channels_last == True:
+            x.contiguous(memory_format=torch.channels_last)
+
+        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        out = self.scratch.output_conv(path_1)
+
+        return out
+
+
+class DPTDepthModel(DPT):
+    def __init__(self, path=None, non_negative=True, **kwargs):
+        features = kwargs["features"] if "features" in kwargs else 256
+
+        head = nn.Sequential(
+            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+            nn.Identity(),
+        )
+
+        super().__init__(head, **kwargs)
+
+        if path is not None:
+           self.load(path)
+
+    def forward(self, x):
+        return super().forward(x).squeeze(dim=1)
+

microsoftexcel-controlnet/annotator/midas/midas/midas_net.py

@@ -0,0 +1,76 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
+
+
+class MidasNet(BaseModel):
+    """Network for monocular depth estimation.
+    """
+
+    def __init__(self, path=None, features=256, non_negative=True):
+        """Init.
+
+        Args:
+            path (str, optional): Path to saved model. Defaults to None.
+            features (int, optional): Number of features. Defaults to 256.
+            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+        """
+        print("Loading weights: ", path)
+
+        super(MidasNet, self).__init__()
+
+        use_pretrained = False if path is None else True
+
+        self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
+
+        self.scratch.refinenet4 = FeatureFusionBlock(features)
+        self.scratch.refinenet3 = FeatureFusionBlock(features)
+        self.scratch.refinenet2 = FeatureFusionBlock(features)
+        self.scratch.refinenet1 = FeatureFusionBlock(features)
+
+        self.scratch.output_conv = nn.Sequential(
+            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear"),
+            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+        )
+
+        if path:
+            self.load(path)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input data (image)
+
+        Returns:
+            tensor: depth
+        """
+
+        layer_1 = self.pretrained.layer1(x)
+        layer_2 = self.pretrained.layer2(layer_1)
+        layer_3 = self.pretrained.layer3(layer_2)
+        layer_4 = self.pretrained.layer4(layer_3)
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        out = self.scratch.output_conv(path_1)
+
+        return torch.squeeze(out, dim=1)

microsoftexcel-controlnet/annotator/midas/midas/midas_net_custom.py

@@ -0,0 +1,128 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
+
+
+class MidasNet_small(BaseModel):
+    """Network for monocular depth estimation.
+    """
+
+    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
+        blocks={'expand': True}):
+        """Init.
+
+        Args:
+            path (str, optional): Path to saved model. Defaults to None.
+            features (int, optional): Number of features. Defaults to 256.
+            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+        """
+        print("Loading weights: ", path)
+
+        super(MidasNet_small, self).__init__()
+
+        use_pretrained = False if path else True
+                
+        self.channels_last = channels_last
+        self.blocks = blocks
+        self.backbone = backbone
+
+        self.groups = 1
+
+        features1=features
+        features2=features
+        features3=features
+        features4=features
+        self.expand = False
+        if "expand" in self.blocks and self.blocks['expand'] == True:
+            self.expand = True
+            features1=features
+            features2=features*2
+            features3=features*4
+            features4=features*8
+
+        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
+  
+        self.scratch.activation = nn.ReLU(False)    
+
+        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
+
+        
+        self.scratch.output_conv = nn.Sequential(
+            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
+            Interpolate(scale_factor=2, mode="bilinear"),
+            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
+            self.scratch.activation,
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+            nn.Identity(),
+        )
+        
+        if path:
+            self.load(path)
+
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input data (image)
+
+        Returns:
+            tensor: depth
+        """
+        if self.channels_last==True:
+            print("self.channels_last = ", self.channels_last)
+            x.contiguous(memory_format=torch.channels_last)
+
+
+        layer_1 = self.pretrained.layer1(x)
+        layer_2 = self.pretrained.layer2(layer_1)
+        layer_3 = self.pretrained.layer3(layer_2)
+        layer_4 = self.pretrained.layer4(layer_3)
+        
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+        
+        out = self.scratch.output_conv(path_1)
+
+        return torch.squeeze(out, dim=1)
+
+
+
+def fuse_model(m):
+    prev_previous_type = nn.Identity()
+    prev_previous_name = ''
+    previous_type = nn.Identity()
+    previous_name = ''
+    for name, module in m.named_modules():
+        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
+            # print("FUSED ", prev_previous_name, previous_name, name)
+            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
+        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
+            # print("FUSED ", prev_previous_name, previous_name)
+            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
+        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
+        #    print("FUSED ", previous_name, name)
+        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
+
+        prev_previous_type = previous_type
+        prev_previous_name = previous_name
+        previous_type = type(module)
+        previous_name = name

microsoftexcel-controlnet/annotator/midas/midas/transforms.py

@@ -0,0 +1,234 @@
+import numpy as np
+import cv2
+import math
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+    """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+    Args:
+        sample (dict): sample
+        size (tuple): image size
+
+    Returns:
+        tuple: new size
+    """
+    shape = list(sample["disparity"].shape)
+
+    if shape[0] >= size[0] and shape[1] >= size[1]:
+        return sample
+
+    scale = [0, 0]
+    scale[0] = size[0] / shape[0]
+    scale[1] = size[1] / shape[1]
+
+    scale = max(scale)
+
+    shape[0] = math.ceil(scale * shape[0])
+    shape[1] = math.ceil(scale * shape[1])
+
+    # resize
+    sample["image"] = cv2.resize(
+        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
+    )
+
+    sample["disparity"] = cv2.resize(
+        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
+    )
+    sample["mask"] = cv2.resize(
+        sample["mask"].astype(np.float32),
+        tuple(shape[::-1]),
+        interpolation=cv2.INTER_NEAREST,
+    )
+    sample["mask"] = sample["mask"].astype(bool)
+
+    return tuple(shape)
+
+
+class Resize(object):
+    """Resize sample to given size (width, height).
+    """
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(
+                    f"resize_method {self.__resize_method} not implemented"
+                )
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, min_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, min_val=self.__width
+            )
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, max_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, max_val=self.__width
+            )
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(
+            sample["image"].shape[1], sample["image"].shape[0]
+        )
+
+        # resize sample
+        sample["image"] = cv2.resize(
+            sample["image"],
+            (width, height),
+            interpolation=self.__image_interpolation_method,
+        )
+
+        if self.__resize_target:
+            if "disparity" in sample:
+                sample["disparity"] = cv2.resize(
+                    sample["disparity"],
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(
+                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+                )
+
+            sample["mask"] = cv2.resize(
+                sample["mask"].astype(np.float32),
+                (width, height),
+                interpolation=cv2.INTER_NEAREST,
+            )
+            sample["mask"] = sample["mask"].astype(bool)
+
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std.
+    """
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input.
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+
+        if "disparity" in sample:
+            disparity = sample["disparity"].astype(np.float32)
+            sample["disparity"] = np.ascontiguousarray(disparity)
+
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+
+        return sample

microsoftexcel-controlnet/annotator/midas/midas/vit.py

@@ -0,0 +1,491 @@
+import torch
+import torch.nn as nn
+import timm
+import types
+import math
+import torch.nn.functional as F
+
+
+class Slice(nn.Module):
+    def __init__(self, start_index=1):
+        super(Slice, self).__init__()
+        self.start_index = start_index
+
+    def forward(self, x):
+        return x[:, self.start_index :]
+
+
+class AddReadout(nn.Module):
+    def __init__(self, start_index=1):
+        super(AddReadout, self).__init__()
+        self.start_index = start_index
+
+    def forward(self, x):
+        if self.start_index == 2:
+            readout = (x[:, 0] + x[:, 1]) / 2
+        else:
+            readout = x[:, 0]
+        return x[:, self.start_index :] + readout.unsqueeze(1)
+
+
+class ProjectReadout(nn.Module):
+    def __init__(self, in_features, start_index=1):
+        super(ProjectReadout, self).__init__()
+        self.start_index = start_index
+
+        self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
+
+    def forward(self, x):
+        readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
+        features = torch.cat((x[:, self.start_index :], readout), -1)
+
+        return self.project(features)
+
+
+class Transpose(nn.Module):
+    def __init__(self, dim0, dim1):
+        super(Transpose, self).__init__()
+        self.dim0 = dim0
+        self.dim1 = dim1
+
+    def forward(self, x):
+        x = x.transpose(self.dim0, self.dim1)
+        return x
+
+
+def forward_vit(pretrained, x):
+    b, c, h, w = x.shape
+
+    glob = pretrained.model.forward_flex(x)
+
+    layer_1 = pretrained.activations["1"]
+    layer_2 = pretrained.activations["2"]
+    layer_3 = pretrained.activations["3"]
+    layer_4 = pretrained.activations["4"]
+
+    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
+    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
+    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
+    layer_4 = pretrained.act_postprocess4[0:2](layer_4)
+
+    unflatten = nn.Sequential(
+        nn.Unflatten(
+            2,
+            torch.Size(
+                [
+                    h // pretrained.model.patch_size[1],
+                    w // pretrained.model.patch_size[0],
+                ]
+            ),
+        )
+    )
+
+    if layer_1.ndim == 3:
+        layer_1 = unflatten(layer_1)
+    if layer_2.ndim == 3:
+        layer_2 = unflatten(layer_2)
+    if layer_3.ndim == 3:
+        layer_3 = unflatten(layer_3)
+    if layer_4.ndim == 3:
+        layer_4 = unflatten(layer_4)
+
+    layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
+    layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
+    layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
+    layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
+
+    return layer_1, layer_2, layer_3, layer_4
+
+
+def _resize_pos_embed(self, posemb, gs_h, gs_w):
+    posemb_tok, posemb_grid = (
+        posemb[:, : self.start_index],
+        posemb[0, self.start_index :],
+    )
+
+    gs_old = int(math.sqrt(len(posemb_grid)))
+
+    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
+    posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
+    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
+
+    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
+
+    return posemb
+
+
+def forward_flex(self, x):
+    b, c, h, w = x.shape
+
+    pos_embed = self._resize_pos_embed(
+        self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
+    )
+
+    B = x.shape[0]
+
+    if hasattr(self.patch_embed, "backbone"):
+        x = self.patch_embed.backbone(x)
+        if isinstance(x, (list, tuple)):
+            x = x[-1]  # last feature if backbone outputs list/tuple of features
+
+    x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
+
+    if getattr(self, "dist_token", None) is not None:
+        cls_tokens = self.cls_token.expand(
+            B, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        dist_token = self.dist_token.expand(B, -1, -1)
+        x = torch.cat((cls_tokens, dist_token, x), dim=1)
+    else:
+        cls_tokens = self.cls_token.expand(
+            B, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+
+    x = x + pos_embed
+    x = self.pos_drop(x)
+
+    for blk in self.blocks:
+        x = blk(x)
+
+    x = self.norm(x)
+
+    return x
+
+
+activations = {}
+
+
+def get_activation(name):
+    def hook(model, input, output):
+        activations[name] = output
+
+    return hook
+
+
+def get_readout_oper(vit_features, features, use_readout, start_index=1):
+    if use_readout == "ignore":
+        readout_oper = [Slice(start_index)] * len(features)
+    elif use_readout == "add":
+        readout_oper = [AddReadout(start_index)] * len(features)
+    elif use_readout == "project":
+        readout_oper = [
+            ProjectReadout(vit_features, start_index) for out_feat in features
+        ]
+    else:
+        assert (
+            False
+        ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
+
+    return readout_oper
+
+
+def _make_vit_b16_backbone(
+    model,
+    features=[96, 192, 384, 768],
+    size=[384, 384],
+    hooks=[2, 5, 8, 11],
+    vit_features=768,
+    use_readout="ignore",
+    start_index=1,
+):
+    pretrained = nn.Module()
+
+    pretrained.model = model
+    pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+    pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+    pretrained.activations = activations
+
+    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+    # 32, 48, 136, 384
+    pretrained.act_postprocess1 = nn.Sequential(
+        readout_oper[0],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[0],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.ConvTranspose2d(
+            in_channels=features[0],
+            out_channels=features[0],
+            kernel_size=4,
+            stride=4,
+            padding=0,
+            bias=True,
+            dilation=1,
+            groups=1,
+        ),
+    )
+
+    pretrained.act_postprocess2 = nn.Sequential(
+        readout_oper[1],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[1],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.ConvTranspose2d(
+            in_channels=features[1],
+            out_channels=features[1],
+            kernel_size=2,
+            stride=2,
+            padding=0,
+            bias=True,
+            dilation=1,
+            groups=1,
+        ),
+    )
+
+    pretrained.act_postprocess3 = nn.Sequential(
+        readout_oper[2],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[2],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+    )
+
+    pretrained.act_postprocess4 = nn.Sequential(
+        readout_oper[3],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[3],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.Conv2d(
+            in_channels=features[3],
+            out_channels=features[3],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        ),
+    )
+
+    pretrained.model.start_index = start_index
+    pretrained.model.patch_size = [16, 16]
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+    pretrained.model._resize_pos_embed = types.MethodType(
+        _resize_pos_embed, pretrained.model
+    )
+
+    return pretrained
+
+
+def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
+
+    hooks = [5, 11, 17, 23] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[256, 512, 1024, 1024],
+        hooks=hooks,
+        vit_features=1024,
+        use_readout=use_readout,
+    )
+
+
+def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
+    )
+
+
+def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
+    )
+
+
+def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model(
+        "vit_deit_base_distilled_patch16_384", pretrained=pretrained
+    )
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[96, 192, 384, 768],
+        hooks=hooks,
+        use_readout=use_readout,
+        start_index=2,
+    )
+
+
+def _make_vit_b_rn50_backbone(
+    model,
+    features=[256, 512, 768, 768],
+    size=[384, 384],
+    hooks=[0, 1, 8, 11],
+    vit_features=768,
+    use_vit_only=False,
+    use_readout="ignore",
+    start_index=1,
+):
+    pretrained = nn.Module()
+
+    pretrained.model = model
+
+    if use_vit_only == True:
+        pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+        pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+    else:
+        pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
+            get_activation("1")
+        )
+        pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
+            get_activation("2")
+        )
+
+    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+    pretrained.activations = activations
+
+    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+    if use_vit_only == True:
+        pretrained.act_postprocess1 = nn.Sequential(
+            readout_oper[0],
+            Transpose(1, 2),
+            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+            nn.Conv2d(
+                in_channels=vit_features,
+                out_channels=features[0],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=features[0],
+                out_channels=features[0],
+                kernel_size=4,
+                stride=4,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ),
+        )
+
+        pretrained.act_postprocess2 = nn.Sequential(
+            readout_oper[1],
+            Transpose(1, 2),
+            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+            nn.Conv2d(
+                in_channels=vit_features,
+                out_channels=features[1],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=features[1],
+                out_channels=features[1],
+                kernel_size=2,
+                stride=2,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ),
+        )
+    else:
+        pretrained.act_postprocess1 = nn.Sequential(
+            nn.Identity(), nn.Identity(), nn.Identity()
+        )
+        pretrained.act_postprocess2 = nn.Sequential(
+            nn.Identity(), nn.Identity(), nn.Identity()
+        )
+
+    pretrained.act_postprocess3 = nn.Sequential(
+        readout_oper[2],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[2],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+    )
+
+    pretrained.act_postprocess4 = nn.Sequential(
+        readout_oper[3],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[3],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.Conv2d(
+            in_channels=features[3],
+            out_channels=features[3],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        ),
+    )
+
+    pretrained.model.start_index = start_index
+    pretrained.model.patch_size = [16, 16]
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model._resize_pos_embed = types.MethodType(
+        _resize_pos_embed, pretrained.model
+    )
+
+    return pretrained
+
+
+def _make_pretrained_vitb_rn50_384(
+    pretrained, use_readout="ignore", hooks=None, use_vit_only=False
+):
+    model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
+
+    hooks = [0, 1, 8, 11] if hooks == None else hooks
+    return _make_vit_b_rn50_backbone(
+        model,
+        features=[256, 512, 768, 768],
+        size=[384, 384],
+        hooks=hooks,
+        use_vit_only=use_vit_only,
+        use_readout=use_readout,
+    )

microsoftexcel-controlnet/annotator/midas/utils.py

@@ -0,0 +1,189 @@
+"""Utils for monoDepth."""
+import sys
+import re
+import numpy as np
+import cv2
+import torch
+
+
+def read_pfm(path):
+    """Read pfm file.
+
+    Args:
+        path (str): path to file
+
+    Returns:
+        tuple: (data, scale)
+    """
+    with open(path, "rb") as file:
+
+        color = None
+        width = None
+        height = None
+        scale = None
+        endian = None
+
+        header = file.readline().rstrip()
+        if header.decode("ascii") == "PF":
+            color = True
+        elif header.decode("ascii") == "Pf":
+            color = False
+        else:
+            raise Exception("Not a PFM file: " + path)
+
+        dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
+        if dim_match:
+            width, height = list(map(int, dim_match.groups()))
+        else:
+            raise Exception("Malformed PFM header.")
+
+        scale = float(file.readline().decode("ascii").rstrip())
+        if scale < 0:
+            # little-endian
+            endian = "<"
+            scale = -scale
+        else:
+            # big-endian
+            endian = ">"
+
+        data = np.fromfile(file, endian + "f")
+        shape = (height, width, 3) if color else (height, width)
+
+        data = np.reshape(data, shape)
+        data = np.flipud(data)
+
+        return data, scale
+
+
+def write_pfm(path, image, scale=1):
+    """Write pfm file.
+
+    Args:
+        path (str): pathto file
+        image (array): data
+        scale (int, optional): Scale. Defaults to 1.
+    """
+
+    with open(path, "wb") as file:
+        color = None
+
+        if image.dtype.name != "float32":
+            raise Exception("Image dtype must be float32.")
+
+        image = np.flipud(image)
+
+        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
+            color = True
+        elif (
+            len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
+        ):  # greyscale
+            color = False
+        else:
+            raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
+
+        file.write("PF\n" if color else "Pf\n".encode())
+        file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
+
+        endian = image.dtype.byteorder
+
+        if endian == "<" or endian == "=" and sys.byteorder == "little":
+            scale = -scale
+
+        file.write("%f\n".encode() % scale)
+
+        image.tofile(file)
+
+
+def read_image(path):
+    """Read image and output RGB image (0-1).
+
+    Args:
+        path (str): path to file
+
+    Returns:
+        array: RGB image (0-1)
+    """
+    img = cv2.imread(path)
+
+    if img.ndim == 2:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
+
+    return img
+
+
+def resize_image(img):
+    """Resize image and make it fit for network.
+
+    Args:
+        img (array): image
+
+    Returns:
+        tensor: data ready for network
+    """
+    height_orig = img.shape[0]
+    width_orig = img.shape[1]
+
+    if width_orig > height_orig:
+        scale = width_orig / 384
+    else:
+        scale = height_orig / 384
+
+    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
+    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
+
+    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
+
+    img_resized = (
+        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
+    )
+    img_resized = img_resized.unsqueeze(0)
+
+    return img_resized
+
+
+def resize_depth(depth, width, height):
+    """Resize depth map and bring to CPU (numpy).
+
+    Args:
+        depth (tensor): depth
+        width (int): image width
+        height (int): image height
+
+    Returns:
+        array: processed depth
+    """
+    depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
+
+    depth_resized = cv2.resize(
+        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
+    )
+
+    return depth_resized
+
+def write_depth(path, depth, bits=1):
+    """Write depth map to pfm and png file.
+
+    Args:
+        path (str): filepath without extension
+        depth (array): depth
+    """
+    write_pfm(path + ".pfm", depth.astype(np.float32))
+
+    depth_min = depth.min()
+    depth_max = depth.max()
+
+    max_val = (2**(8*bits))-1
+
+    if depth_max - depth_min > np.finfo("float").eps:
+        out = max_val * (depth - depth_min) / (depth_max - depth_min)
+    else:
+        out = np.zeros(depth.shape, dtype=depth.type)
+
+    if bits == 1:
+        cv2.imwrite(path + ".png", out.astype("uint8"))
+    elif bits == 2:
+        cv2.imwrite(path + ".png", out.astype("uint16"))
+
+    return

microsoftexcel-controlnet/annotator/mlsd/LICENSE

@@ -0,0 +1,201 @@
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+
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microsoftexcel-controlnet/annotator/mlsd/__init__.py

@@ -0,0 +1,49 @@
+import cv2
+import numpy as np
+import torch
+import os
+
+from einops import rearrange
+from .models.mbv2_mlsd_tiny import  MobileV2_MLSD_Tiny
+from .models.mbv2_mlsd_large import  MobileV2_MLSD_Large
+from .utils import  pred_lines
+from modules import devices
+from annotator.annotator_path import models_path
+
+mlsdmodel = None
+remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/mlsd_large_512_fp32.pth"
+old_modeldir = os.path.dirname(os.path.realpath(__file__))
+modeldir = os.path.join(models_path, "mlsd")
+
+def unload_mlsd_model():
+    global mlsdmodel
+    if mlsdmodel is not None:
+        mlsdmodel = mlsdmodel.cpu()
+
+def apply_mlsd(input_image, thr_v, thr_d):
+    global modelpath, mlsdmodel
+    if mlsdmodel is None:
+        modelpath = os.path.join(modeldir, "mlsd_large_512_fp32.pth")
+        old_modelpath = os.path.join(old_modeldir, "mlsd_large_512_fp32.pth")
+        if os.path.exists(old_modelpath):
+            modelpath = old_modelpath
+        elif not os.path.exists(modelpath):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=modeldir)
+        mlsdmodel = MobileV2_MLSD_Large()
+        mlsdmodel.load_state_dict(torch.load(modelpath), strict=True)
+    mlsdmodel = mlsdmodel.to(devices.get_device_for("controlnet")).eval()
+        
+    model = mlsdmodel
+    assert input_image.ndim == 3
+    img = input_image
+    img_output = np.zeros_like(img)
+    try:
+        with torch.no_grad():
+            lines = pred_lines(img, model, [img.shape[0], img.shape[1]], thr_v, thr_d)
+            for line in lines:
+                x_start, y_start, x_end, y_end = [int(val) for val in line]
+                cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1)
+    except Exception as e:
+        pass
+    return img_output[:, :, 0]

microsoftexcel-controlnet/annotator/mlsd/models/mbv2_mlsd_large.py

@@ -0,0 +1,292 @@
+import os
+import sys
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+from  torch.nn import  functional as F
+
+
+class BlockTypeA(nn.Module):
+    def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
+        super(BlockTypeA, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c2, out_c2, kernel_size=1),
+            nn.BatchNorm2d(out_c2),
+            nn.ReLU(inplace=True)
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c1, out_c1, kernel_size=1),
+            nn.BatchNorm2d(out_c1),
+            nn.ReLU(inplace=True)
+        )
+        self.upscale = upscale
+
+    def forward(self, a, b):
+        b = self.conv1(b)
+        a = self.conv2(a)
+        if self.upscale:
+             b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True)
+        return torch.cat((a, b), dim=1)
+
+
+class BlockTypeB(nn.Module):
+    def __init__(self, in_c, out_c):
+        super(BlockTypeB, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=1),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_c),
+            nn.ReLU()
+        )
+
+    def forward(self, x):
+        x = self.conv1(x) + x
+        x = self.conv2(x)
+        return x
+
+class BlockTypeC(nn.Module):
+    def __init__(self, in_c, out_c):
+        super(BlockTypeC, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=5, dilation=5),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=1),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        return x
+
+def _make_divisible(v, divisor, min_value=None):
+    """
+    This function is taken from the original tf repo.
+    It ensures that all layers have a channel number that is divisible by 8
+    It can be seen here:
+    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+    :param v:
+    :param divisor:
+    :param min_value:
+    :return:
+    """
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+class ConvBNReLU(nn.Sequential):
+    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
+        self.channel_pad = out_planes - in_planes
+        self.stride = stride
+        #padding = (kernel_size - 1) // 2
+
+        # TFLite uses slightly different padding than PyTorch
+        if stride == 2:
+            padding = 0
+        else:
+            padding = (kernel_size - 1) // 2
+
+        super(ConvBNReLU, self).__init__(
+            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
+            nn.BatchNorm2d(out_planes),
+            nn.ReLU6(inplace=True)
+        )
+        self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
+
+
+    def forward(self, x):
+        # TFLite uses  different padding
+        if self.stride == 2:
+            x = F.pad(x, (0, 1, 0, 1), "constant", 0)
+            #print(x.shape)
+
+        for module in self:
+            if not isinstance(module, nn.MaxPool2d):
+                x = module(x)
+        return x
+
+
+class InvertedResidual(nn.Module):
+    def __init__(self, inp, oup, stride, expand_ratio):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = int(round(inp * expand_ratio))
+        self.use_res_connect = self.stride == 1 and inp == oup
+
+        layers = []
+        if expand_ratio != 1:
+            # pw
+            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
+        layers.extend([
+            # dw
+            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
+            # pw-linear
+            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(oup),
+        ])
+        self.conv = nn.Sequential(*layers)
+
+    def forward(self, x):
+        if self.use_res_connect:
+            return x + self.conv(x)
+        else:
+            return self.conv(x)
+
+
+class MobileNetV2(nn.Module):
+    def __init__(self, pretrained=True):
+        """
+        MobileNet V2 main class
+        Args:
+            num_classes (int): Number of classes
+            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
+            inverted_residual_setting: Network structure
+            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
+            Set to 1 to turn off rounding
+            block: Module specifying inverted residual building block for mobilenet
+        """
+        super(MobileNetV2, self).__init__()
+
+        block = InvertedResidual
+        input_channel = 32
+        last_channel = 1280
+        width_mult = 1.0
+        round_nearest = 8
+
+        inverted_residual_setting = [
+            # t, c, n, s
+            [1, 16, 1, 1],
+            [6, 24, 2, 2],
+            [6, 32, 3, 2],
+            [6, 64, 4, 2],
+            [6, 96, 3, 1],
+            #[6, 160, 3, 2],
+            #[6, 320, 1, 1],
+        ]
+
+        # only check the first element, assuming user knows t,c,n,s are required
+        if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
+            raise ValueError("inverted_residual_setting should be non-empty "
+                             "or a 4-element list, got {}".format(inverted_residual_setting))
+
+        # building first layer
+        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
+        self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
+        features = [ConvBNReLU(4, input_channel, stride=2)]
+        # building inverted residual blocks
+        for t, c, n, s in inverted_residual_setting:
+            output_channel = _make_divisible(c * width_mult, round_nearest)
+            for i in range(n):
+                stride = s if i == 0 else 1
+                features.append(block(input_channel, output_channel, stride, expand_ratio=t))
+                input_channel = output_channel
+
+        self.features = nn.Sequential(*features)
+        self.fpn_selected = [1, 3, 6, 10, 13]
+        # weight initialization
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out')
+                if m.bias is not None:
+                    nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.ones_(m.weight)
+                nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                nn.init.zeros_(m.bias)
+        if pretrained:
+           self._load_pretrained_model()
+
+    def _forward_impl(self, x):
+        # This exists since TorchScript doesn't support inheritance, so the superclass method
+        # (this one) needs to have a name other than `forward` that can be accessed in a subclass
+        fpn_features = []
+        for i, f in enumerate(self.features):
+            if i > self.fpn_selected[-1]:
+                break
+            x = f(x)
+            if i in self.fpn_selected:
+                fpn_features.append(x)
+
+        c1, c2, c3, c4, c5 = fpn_features
+        return c1, c2, c3, c4, c5
+
+
+    def forward(self, x):
+        return self._forward_impl(x)
+
+    def _load_pretrained_model(self):
+        pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth')
+        model_dict = {}
+        state_dict = self.state_dict()
+        for k, v in pretrain_dict.items():
+            if k in state_dict:
+                model_dict[k] = v
+        state_dict.update(model_dict)
+        self.load_state_dict(state_dict)
+
+
+class MobileV2_MLSD_Large(nn.Module):
+    def __init__(self):
+        super(MobileV2_MLSD_Large, self).__init__()
+
+        self.backbone = MobileNetV2(pretrained=False)
+        ## A, B
+        self.block15 = BlockTypeA(in_c1= 64, in_c2= 96,
+                                  out_c1= 64, out_c2=64,
+                                  upscale=False)
+        self.block16 = BlockTypeB(128, 64)
+
+        ## A, B
+        self.block17 = BlockTypeA(in_c1 = 32,  in_c2 = 64,
+                                  out_c1= 64,  out_c2= 64)
+        self.block18 = BlockTypeB(128, 64)
+
+        ## A, B
+        self.block19 = BlockTypeA(in_c1=24, in_c2=64,
+                                  out_c1=64, out_c2=64)
+        self.block20 = BlockTypeB(128, 64)
+
+        ## A, B, C
+        self.block21 = BlockTypeA(in_c1=16, in_c2=64,
+                                  out_c1=64, out_c2=64)
+        self.block22 = BlockTypeB(128, 64)
+
+        self.block23 = BlockTypeC(64, 16)
+
+    def forward(self, x):
+        c1, c2, c3, c4, c5 = self.backbone(x)
+
+        x = self.block15(c4, c5)
+        x = self.block16(x)
+
+        x = self.block17(c3, x)
+        x = self.block18(x)
+
+        x = self.block19(c2, x)
+        x = self.block20(x)
+
+        x = self.block21(c1, x)
+        x = self.block22(x)
+        x = self.block23(x)
+        x = x[:, 7:, :, :]
+
+        return x

microsoftexcel-controlnet/annotator/mlsd/models/mbv2_mlsd_tiny.py

@@ -0,0 +1,275 @@
+import os
+import sys
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+from  torch.nn import  functional as F
+
+
+class BlockTypeA(nn.Module):
+    def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
+        super(BlockTypeA, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c2, out_c2, kernel_size=1),
+            nn.BatchNorm2d(out_c2),
+            nn.ReLU(inplace=True)
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c1, out_c1, kernel_size=1),
+            nn.BatchNorm2d(out_c1),
+            nn.ReLU(inplace=True)
+        )
+        self.upscale = upscale
+
+    def forward(self, a, b):
+        b = self.conv1(b)
+        a = self.conv2(a)
+        b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True)
+        return torch.cat((a, b), dim=1)
+
+
+class BlockTypeB(nn.Module):
+    def __init__(self, in_c, out_c):
+        super(BlockTypeB, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=1),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_c),
+            nn.ReLU()
+        )
+
+    def forward(self, x):
+        x = self.conv1(x) + x
+        x = self.conv2(x)
+        return x
+
+class BlockTypeC(nn.Module):
+    def __init__(self, in_c, out_c):
+        super(BlockTypeC, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=5, dilation=5),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=1),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        return x
+
+def _make_divisible(v, divisor, min_value=None):
+    """
+    This function is taken from the original tf repo.
+    It ensures that all layers have a channel number that is divisible by 8
+    It can be seen here:
+    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+    :param v:
+    :param divisor:
+    :param min_value:
+    :return:
+    """
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+class ConvBNReLU(nn.Sequential):
+    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
+        self.channel_pad = out_planes - in_planes
+        self.stride = stride
+        #padding = (kernel_size - 1) // 2
+
+        # TFLite uses slightly different padding than PyTorch
+        if stride == 2:
+            padding = 0
+        else:
+            padding = (kernel_size - 1) // 2
+
+        super(ConvBNReLU, self).__init__(
+            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
+            nn.BatchNorm2d(out_planes),
+            nn.ReLU6(inplace=True)
+        )
+        self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
+
+
+    def forward(self, x):
+        # TFLite uses  different padding
+        if self.stride == 2:
+            x = F.pad(x, (0, 1, 0, 1), "constant", 0)
+            #print(x.shape)
+
+        for module in self:
+            if not isinstance(module, nn.MaxPool2d):
+                x = module(x)
+        return x
+
+
+class InvertedResidual(nn.Module):
+    def __init__(self, inp, oup, stride, expand_ratio):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = int(round(inp * expand_ratio))
+        self.use_res_connect = self.stride == 1 and inp == oup
+
+        layers = []
+        if expand_ratio != 1:
+            # pw
+            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
+        layers.extend([
+            # dw
+            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
+            # pw-linear
+            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(oup),
+        ])
+        self.conv = nn.Sequential(*layers)
+
+    def forward(self, x):
+        if self.use_res_connect:
+            return x + self.conv(x)
+        else:
+            return self.conv(x)
+
+
+class MobileNetV2(nn.Module):
+    def __init__(self, pretrained=True):
+        """
+        MobileNet V2 main class
+        Args:
+            num_classes (int): Number of classes
+            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
+            inverted_residual_setting: Network structure
+            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
+            Set to 1 to turn off rounding
+            block: Module specifying inverted residual building block for mobilenet
+        """
+        super(MobileNetV2, self).__init__()
+
+        block = InvertedResidual
+        input_channel = 32
+        last_channel = 1280
+        width_mult = 1.0
+        round_nearest = 8
+
+        inverted_residual_setting = [
+            # t, c, n, s
+            [1, 16, 1, 1],
+            [6, 24, 2, 2],
+            [6, 32, 3, 2],
+            [6, 64, 4, 2],
+            #[6, 96, 3, 1],
+            #[6, 160, 3, 2],
+            #[6, 320, 1, 1],
+        ]
+
+        # only check the first element, assuming user knows t,c,n,s are required
+        if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
+            raise ValueError("inverted_residual_setting should be non-empty "
+                             "or a 4-element list, got {}".format(inverted_residual_setting))
+
+        # building first layer
+        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
+        self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
+        features = [ConvBNReLU(4, input_channel, stride=2)]
+        # building inverted residual blocks
+        for t, c, n, s in inverted_residual_setting:
+            output_channel = _make_divisible(c * width_mult, round_nearest)
+            for i in range(n):
+                stride = s if i == 0 else 1
+                features.append(block(input_channel, output_channel, stride, expand_ratio=t))
+                input_channel = output_channel
+        self.features = nn.Sequential(*features)
+
+        self.fpn_selected = [3, 6, 10]
+        # weight initialization
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out')
+                if m.bias is not None:
+                    nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.ones_(m.weight)
+                nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                nn.init.zeros_(m.bias)
+
+        #if pretrained:
+        #    self._load_pretrained_model()
+
+    def _forward_impl(self, x):
+        # This exists since TorchScript doesn't support inheritance, so the superclass method
+        # (this one) needs to have a name other than `forward` that can be accessed in a subclass
+        fpn_features = []
+        for i, f in enumerate(self.features):
+            if i > self.fpn_selected[-1]:
+                break
+            x = f(x)
+            if i in self.fpn_selected:
+                fpn_features.append(x)
+
+        c2, c3, c4 = fpn_features
+        return c2, c3, c4
+
+
+    def forward(self, x):
+        return self._forward_impl(x)
+
+    def _load_pretrained_model(self):
+        pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth')
+        model_dict = {}
+        state_dict = self.state_dict()
+        for k, v in pretrain_dict.items():
+            if k in state_dict:
+                model_dict[k] = v
+        state_dict.update(model_dict)
+        self.load_state_dict(state_dict)
+
+
+class MobileV2_MLSD_Tiny(nn.Module):
+    def __init__(self):
+        super(MobileV2_MLSD_Tiny, self).__init__()
+
+        self.backbone = MobileNetV2(pretrained=True)
+
+        self.block12 = BlockTypeA(in_c1= 32, in_c2= 64,
+                                  out_c1= 64, out_c2=64)
+        self.block13 = BlockTypeB(128, 64)
+
+        self.block14 = BlockTypeA(in_c1 = 24,  in_c2 = 64,
+                                  out_c1= 32,  out_c2= 32)
+        self.block15 = BlockTypeB(64, 64)
+
+        self.block16 = BlockTypeC(64, 16)
+
+    def forward(self, x):
+        c2, c3, c4 = self.backbone(x)
+
+        x = self.block12(c3, c4)
+        x = self.block13(x)
+        x = self.block14(c2, x)
+        x = self.block15(x)
+        x = self.block16(x)
+        x = x[:, 7:, :, :]
+        #print(x.shape)
+        x = F.interpolate(x, scale_factor=2.0, mode='bilinear', align_corners=True)
+
+        return x

microsoftexcel-controlnet/annotator/mlsd/utils.py

@@ -0,0 +1,581 @@
+'''
+modified by  lihaoweicv
+pytorch version
+'''
+
+'''
+M-LSD
+Copyright 2021-present NAVER Corp.
+Apache License v2.0
+'''
+
+import os
+import numpy as np
+import cv2
+import torch
+from  torch.nn import  functional as F
+from modules import devices
+
+
+def deccode_output_score_and_ptss(tpMap, topk_n = 200, ksize = 5):
+    '''
+    tpMap:
+    center: tpMap[1, 0, :, :]
+    displacement: tpMap[1, 1:5, :, :]
+    '''
+    b, c, h, w = tpMap.shape
+    assert  b==1, 'only support bsize==1'
+    displacement = tpMap[:, 1:5, :, :][0]
+    center = tpMap[:, 0, :, :]
+    heat = torch.sigmoid(center)
+    hmax = F.max_pool2d( heat, (ksize, ksize), stride=1, padding=(ksize-1)//2)
+    keep = (hmax == heat).float()
+    heat = heat * keep
+    heat = heat.reshape(-1, )
+
+    scores, indices = torch.topk(heat, topk_n, dim=-1, largest=True)
+    yy = torch.floor_divide(indices, w).unsqueeze(-1)
+    xx = torch.fmod(indices, w).unsqueeze(-1)
+    ptss = torch.cat((yy, xx),dim=-1)
+
+    ptss   = ptss.detach().cpu().numpy()
+    scores = scores.detach().cpu().numpy()
+    displacement = displacement.detach().cpu().numpy()
+    displacement = displacement.transpose((1,2,0))
+    return  ptss, scores, displacement
+
+
+def pred_lines(image, model,
+               input_shape=[512, 512],
+               score_thr=0.10,
+               dist_thr=20.0):
+    h, w, _ = image.shape
+    h_ratio, w_ratio = [h / input_shape[0], w / input_shape[1]]
+
+    resized_image = np.concatenate([cv2.resize(image, (input_shape[1], input_shape[0]), interpolation=cv2.INTER_AREA),
+                                    np.ones([input_shape[0], input_shape[1], 1])], axis=-1)
+
+    resized_image = resized_image.transpose((2,0,1))
+    batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
+    batch_image = (batch_image / 127.5) - 1.0
+
+    batch_image = torch.from_numpy(batch_image).float().to(devices.get_device_for("controlnet"))
+    outputs = model(batch_image)
+    pts, pts_score, vmap = deccode_output_score_and_ptss(outputs, 200, 3)
+    start = vmap[:, :, :2]
+    end = vmap[:, :, 2:]
+    dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
+
+    segments_list = []
+    for center, score in zip(pts, pts_score):
+        y, x = center
+        distance = dist_map[y, x]
+        if score > score_thr and distance > dist_thr:
+            disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
+            x_start = x + disp_x_start
+            y_start = y + disp_y_start
+            x_end = x + disp_x_end
+            y_end = y + disp_y_end
+            segments_list.append([x_start, y_start, x_end, y_end])
+
+    lines = 2 * np.array(segments_list)  # 256 > 512
+    lines[:, 0] = lines[:, 0] * w_ratio
+    lines[:, 1] = lines[:, 1] * h_ratio
+    lines[:, 2] = lines[:, 2] * w_ratio
+    lines[:, 3] = lines[:, 3] * h_ratio
+
+    return lines
+
+
+def pred_squares(image,
+                 model,
+                 input_shape=[512, 512],
+                 params={'score': 0.06,
+                         'outside_ratio': 0.28,
+                         'inside_ratio': 0.45,
+                         'w_overlap': 0.0,
+                         'w_degree': 1.95,
+                         'w_length': 0.0,
+                         'w_area': 1.86,
+                         'w_center': 0.14}):
+    '''
+    shape = [height, width]
+    '''
+    h, w, _ = image.shape
+    original_shape = [h, w]
+
+    resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA),
+                                    np.ones([input_shape[0], input_shape[1], 1])], axis=-1)
+    resized_image = resized_image.transpose((2, 0, 1))
+    batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
+    batch_image = (batch_image / 127.5) - 1.0
+
+    batch_image = torch.from_numpy(batch_image).float().to(devices.get_device_for("controlnet"))
+    outputs = model(batch_image)
+
+    pts, pts_score, vmap = deccode_output_score_and_ptss(outputs, 200, 3)
+    start = vmap[:, :, :2]  # (x, y)
+    end = vmap[:, :, 2:]  # (x, y)
+    dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
+
+    junc_list = []
+    segments_list = []
+    for junc, score in zip(pts, pts_score):
+        y, x = junc
+        distance = dist_map[y, x]
+        if score > params['score'] and distance > 20.0:
+            junc_list.append([x, y])
+            disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
+            d_arrow = 1.0
+            x_start = x + d_arrow * disp_x_start
+            y_start = y + d_arrow * disp_y_start
+            x_end = x + d_arrow * disp_x_end
+            y_end = y + d_arrow * disp_y_end
+            segments_list.append([x_start, y_start, x_end, y_end])
+
+    segments = np.array(segments_list)
+
+    ####### post processing for squares
+    # 1. get unique lines
+    point = np.array([[0, 0]])
+    point = point[0]
+    start = segments[:, :2]
+    end = segments[:, 2:]
+    diff = start - end
+    a = diff[:, 1]
+    b = -diff[:, 0]
+    c = a * start[:, 0] + b * start[:, 1]
+
+    d = np.abs(a * point[0] + b * point[1] - c) / np.sqrt(a ** 2 + b ** 2 + 1e-10)
+    theta = np.arctan2(diff[:, 0], diff[:, 1]) * 180 / np.pi
+    theta[theta < 0.0] += 180
+    hough = np.concatenate([d[:, None], theta[:, None]], axis=-1)
+
+    d_quant = 1
+    theta_quant = 2
+    hough[:, 0] //= d_quant
+    hough[:, 1] //= theta_quant
+    _, indices, counts = np.unique(hough, axis=0, return_index=True, return_counts=True)
+
+    acc_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='float32')
+    idx_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='int32') - 1
+    yx_indices = hough[indices, :].astype('int32')
+    acc_map[yx_indices[:, 0], yx_indices[:, 1]] = counts
+    idx_map[yx_indices[:, 0], yx_indices[:, 1]] = indices
+
+    acc_map_np = acc_map
+    # acc_map = acc_map[None, :, :, None]
+    #
+    # ### fast suppression using tensorflow op
+    # acc_map = tf.constant(acc_map, dtype=tf.float32)
+    # max_acc_map = tf.keras.layers.MaxPool2D(pool_size=(5, 5), strides=1, padding='same')(acc_map)
+    # acc_map = acc_map * tf.cast(tf.math.equal(acc_map, max_acc_map), tf.float32)
+    # flatten_acc_map = tf.reshape(acc_map, [1, -1])
+    # topk_values, topk_indices = tf.math.top_k(flatten_acc_map, k=len(pts))
+    # _, h, w, _ = acc_map.shape
+    # y = tf.expand_dims(topk_indices // w, axis=-1)
+    # x = tf.expand_dims(topk_indices % w, axis=-1)
+    # yx = tf.concat([y, x], axis=-1)
+
+    ### fast suppression using pytorch op
+    acc_map = torch.from_numpy(acc_map_np).unsqueeze(0).unsqueeze(0)
+    _,_, h, w = acc_map.shape
+    max_acc_map = F.max_pool2d(acc_map,kernel_size=5, stride=1, padding=2)
+    acc_map = acc_map * ( (acc_map == max_acc_map).float() )
+    flatten_acc_map = acc_map.reshape([-1, ])
+
+    scores, indices = torch.topk(flatten_acc_map, len(pts), dim=-1, largest=True)
+    yy = torch.div(indices, w, rounding_mode='floor').unsqueeze(-1)
+    xx = torch.fmod(indices, w).unsqueeze(-1)
+    yx = torch.cat((yy, xx), dim=-1)
+
+    yx = yx.detach().cpu().numpy()
+
+    topk_values = scores.detach().cpu().numpy()
+    indices = idx_map[yx[:, 0], yx[:, 1]]
+    basis = 5 // 2
+
+    merged_segments = []
+    for yx_pt, max_indice, value in zip(yx, indices, topk_values):
+        y, x = yx_pt
+        if max_indice == -1 or value == 0:
+            continue
+        segment_list = []
+        for y_offset in range(-basis, basis + 1):
+            for x_offset in range(-basis, basis + 1):
+                indice = idx_map[y + y_offset, x + x_offset]
+                cnt = int(acc_map_np[y + y_offset, x + x_offset])
+                if indice != -1:
+                    segment_list.append(segments[indice])
+                if cnt > 1:
+                    check_cnt = 1
+                    current_hough = hough[indice]
+                    for new_indice, new_hough in enumerate(hough):
+                        if (current_hough == new_hough).all() and indice != new_indice:
+                            segment_list.append(segments[new_indice])
+                            check_cnt += 1
+                        if check_cnt == cnt:
+                            break
+        group_segments = np.array(segment_list).reshape([-1, 2])
+        sorted_group_segments = np.sort(group_segments, axis=0)
+        x_min, y_min = sorted_group_segments[0, :]
+        x_max, y_max = sorted_group_segments[-1, :]
+
+        deg = theta[max_indice]
+        if deg >= 90:
+            merged_segments.append([x_min, y_max, x_max, y_min])
+        else:
+            merged_segments.append([x_min, y_min, x_max, y_max])
+
+    # 2. get intersections
+    new_segments = np.array(merged_segments)  # (x1, y1, x2, y2)
+    start = new_segments[:, :2]  # (x1, y1)
+    end = new_segments[:, 2:]  # (x2, y2)
+    new_centers = (start + end) / 2.0
+    diff = start - end
+    dist_segments = np.sqrt(np.sum(diff ** 2, axis=-1))
+
+    # ax + by = c
+    a = diff[:, 1]
+    b = -diff[:, 0]
+    c = a * start[:, 0] + b * start[:, 1]
+    pre_det = a[:, None] * b[None, :]
+    det = pre_det - np.transpose(pre_det)
+
+    pre_inter_y = a[:, None] * c[None, :]
+    inter_y = (pre_inter_y - np.transpose(pre_inter_y)) / (det + 1e-10)
+    pre_inter_x = c[:, None] * b[None, :]
+    inter_x = (pre_inter_x - np.transpose(pre_inter_x)) / (det + 1e-10)
+    inter_pts = np.concatenate([inter_x[:, :, None], inter_y[:, :, None]], axis=-1).astype('int32')
+
+    # 3. get corner information
+    # 3.1 get distance
+    '''
+    dist_segments:
+        | dist(0), dist(1), dist(2), ...|
+    dist_inter_to_segment1:
+        | dist(inter,0), dist(inter,0), dist(inter,0), ... |
+        | dist(inter,1), dist(inter,1), dist(inter,1), ... |
+        ...
+    dist_inter_to_semgnet2:
+        | dist(inter,0), dist(inter,1), dist(inter,2), ... |
+        | dist(inter,0), dist(inter,1), dist(inter,2), ... |
+        ...
+    '''
+
+    dist_inter_to_segment1_start = np.sqrt(
+        np.sum(((inter_pts - start[:, None, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
+    dist_inter_to_segment1_end = np.sqrt(
+        np.sum(((inter_pts - end[:, None, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
+    dist_inter_to_segment2_start = np.sqrt(
+        np.sum(((inter_pts - start[None, :, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
+    dist_inter_to_segment2_end = np.sqrt(
+        np.sum(((inter_pts - end[None, :, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
+
+    # sort ascending
+    dist_inter_to_segment1 = np.sort(
+        np.concatenate([dist_inter_to_segment1_start, dist_inter_to_segment1_end], axis=-1),
+        axis=-1)  # [n_batch, n_batch, 2]
+    dist_inter_to_segment2 = np.sort(
+        np.concatenate([dist_inter_to_segment2_start, dist_inter_to_segment2_end], axis=-1),
+        axis=-1)  # [n_batch, n_batch, 2]
+
+    # 3.2 get degree
+    inter_to_start = new_centers[:, None, :] - inter_pts
+    deg_inter_to_start = np.arctan2(inter_to_start[:, :, 1], inter_to_start[:, :, 0]) * 180 / np.pi
+    deg_inter_to_start[deg_inter_to_start < 0.0] += 360
+    inter_to_end = new_centers[None, :, :] - inter_pts
+    deg_inter_to_end = np.arctan2(inter_to_end[:, :, 1], inter_to_end[:, :, 0]) * 180 / np.pi
+    deg_inter_to_end[deg_inter_to_end < 0.0] += 360
+
+    '''
+    B -- G
+    |    |
+    C -- R
+    B : blue / G: green / C: cyan / R: red
+
+    0 -- 1
+    |    |
+    3 -- 2
+    '''
+    # rename variables
+    deg1_map, deg2_map = deg_inter_to_start, deg_inter_to_end
+    # sort deg ascending
+    deg_sort = np.sort(np.concatenate([deg1_map[:, :, None], deg2_map[:, :, None]], axis=-1), axis=-1)
+
+    deg_diff_map = np.abs(deg1_map - deg2_map)
+    # we only consider the smallest degree of intersect
+    deg_diff_map[deg_diff_map > 180] = 360 - deg_diff_map[deg_diff_map > 180]
+
+    # define available degree range
+    deg_range = [60, 120]
+
+    corner_dict = {corner_info: [] for corner_info in range(4)}
+    inter_points = []
+    for i in range(inter_pts.shape[0]):
+        for j in range(i + 1, inter_pts.shape[1]):
+            # i, j > line index, always i < j
+            x, y = inter_pts[i, j, :]
+            deg1, deg2 = deg_sort[i, j, :]
+            deg_diff = deg_diff_map[i, j]
+
+            check_degree = deg_diff > deg_range[0] and deg_diff < deg_range[1]
+
+            outside_ratio = params['outside_ratio']  # over ratio >>> drop it!
+            inside_ratio = params['inside_ratio']  # over ratio >>> drop it!
+            check_distance = ((dist_inter_to_segment1[i, j, 1] >= dist_segments[i] and \
+                               dist_inter_to_segment1[i, j, 0] <= dist_segments[i] * outside_ratio) or \
+                              (dist_inter_to_segment1[i, j, 1] <= dist_segments[i] and \
+                               dist_inter_to_segment1[i, j, 0] <= dist_segments[i] * inside_ratio)) and \
+                             ((dist_inter_to_segment2[i, j, 1] >= dist_segments[j] and \
+                               dist_inter_to_segment2[i, j, 0] <= dist_segments[j] * outside_ratio) or \
+                              (dist_inter_to_segment2[i, j, 1] <= dist_segments[j] and \
+                               dist_inter_to_segment2[i, j, 0] <= dist_segments[j] * inside_ratio))
+
+            if check_degree and check_distance:
+                corner_info = None
+
+                if (deg1 >= 0 and deg1 <= 45 and deg2 >= 45 and deg2 <= 120) or \
+                        (deg2 >= 315 and deg1 >= 45 and deg1 <= 120):
+                    corner_info, color_info = 0, 'blue'
+                elif (deg1 >= 45 and deg1 <= 125 and deg2 >= 125 and deg2 <= 225):
+                    corner_info, color_info = 1, 'green'
+                elif (deg1 >= 125 and deg1 <= 225 and deg2 >= 225 and deg2 <= 315):
+                    corner_info, color_info = 2, 'black'
+                elif (deg1 >= 0 and deg1 <= 45 and deg2 >= 225 and deg2 <= 315) or \
+                        (deg2 >= 315 and deg1 >= 225 and deg1 <= 315):
+                    corner_info, color_info = 3, 'cyan'
+                else:
+                    corner_info, color_info = 4, 'red'  # we don't use it
+                    continue
+
+                corner_dict[corner_info].append([x, y, i, j])
+                inter_points.append([x, y])
+
+    square_list = []
+    connect_list = []
+    segments_list = []
+    for corner0 in corner_dict[0]:
+        for corner1 in corner_dict[1]:
+            connect01 = False
+            for corner0_line in corner0[2:]:
+                if corner0_line in corner1[2:]:
+                    connect01 = True
+                    break
+            if connect01:
+                for corner2 in corner_dict[2]:
+                    connect12 = False
+                    for corner1_line in corner1[2:]:
+                        if corner1_line in corner2[2:]:
+                            connect12 = True
+                            break
+                    if connect12:
+                        for corner3 in corner_dict[3]:
+                            connect23 = False
+                            for corner2_line in corner2[2:]:
+                                if corner2_line in corner3[2:]:
+                                    connect23 = True
+                                    break
+                            if connect23:
+                                for corner3_line in corner3[2:]:
+                                    if corner3_line in corner0[2:]:
+                                        # SQUARE!!!
+                                        '''
+                                        0 -- 1
+                                        |    |
+                                        3 -- 2
+                                        square_list:
+                                            order: 0 > 1 > 2 > 3
+                                            | x0, y0, x1, y1, x2, y2, x3, y3 |
+                                            | x0, y0, x1, y1, x2, y2, x3, y3 |
+                                            ...
+                                        connect_list:
+                                            order: 01 > 12 > 23 > 30
+                                            | line_idx01, line_idx12, line_idx23, line_idx30 |
+                                            | line_idx01, line_idx12, line_idx23, line_idx30 |
+                                            ...
+                                        segments_list:
+                                            order: 0 > 1 > 2 > 3
+                                            | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
+                                            | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
+                                            ...
+                                        '''
+                                        square_list.append(corner0[:2] + corner1[:2] + corner2[:2] + corner3[:2])
+                                        connect_list.append([corner0_line, corner1_line, corner2_line, corner3_line])
+                                        segments_list.append(corner0[2:] + corner1[2:] + corner2[2:] + corner3[2:])
+
+    def check_outside_inside(segments_info, connect_idx):
+        # return 'outside or inside', min distance, cover_param, peri_param
+        if connect_idx == segments_info[0]:
+            check_dist_mat = dist_inter_to_segment1
+        else:
+            check_dist_mat = dist_inter_to_segment2
+
+        i, j = segments_info
+        min_dist, max_dist = check_dist_mat[i, j, :]
+        connect_dist = dist_segments[connect_idx]
+        if max_dist > connect_dist:
+            return 'outside', min_dist, 0, 1
+        else:
+            return 'inside', min_dist, -1, -1
+
+    top_square = None
+
+    try:
+        map_size = input_shape[0] / 2
+        squares = np.array(square_list).reshape([-1, 4, 2])
+        score_array = []
+        connect_array = np.array(connect_list)
+        segments_array = np.array(segments_list).reshape([-1, 4, 2])
+
+        # get degree of corners:
+        squares_rollup = np.roll(squares, 1, axis=1)
+        squares_rolldown = np.roll(squares, -1, axis=1)
+        vec1 = squares_rollup - squares
+        normalized_vec1 = vec1 / (np.linalg.norm(vec1, axis=-1, keepdims=True) + 1e-10)
+        vec2 = squares_rolldown - squares
+        normalized_vec2 = vec2 / (np.linalg.norm(vec2, axis=-1, keepdims=True) + 1e-10)
+        inner_products = np.sum(normalized_vec1 * normalized_vec2, axis=-1)  # [n_squares, 4]
+        squares_degree = np.arccos(inner_products) * 180 / np.pi  # [n_squares, 4]
+
+        # get square score
+        overlap_scores = []
+        degree_scores = []
+        length_scores = []
+
+        for connects, segments, square, degree in zip(connect_array, segments_array, squares, squares_degree):
+            '''
+            0 -- 1
+            |    |
+            3 -- 2
+
+            # segments: [4, 2]
+            # connects: [4]
+            '''
+
+            ###################################### OVERLAP SCORES
+            cover = 0
+            perimeter = 0
+            # check 0 > 1 > 2 > 3
+            square_length = []
+
+            for start_idx in range(4):
+                end_idx = (start_idx + 1) % 4
+
+                connect_idx = connects[start_idx]  # segment idx of segment01
+                start_segments = segments[start_idx]
+                end_segments = segments[end_idx]
+
+                start_point = square[start_idx]
+                end_point = square[end_idx]
+
+                # check whether outside or inside
+                start_position, start_min, start_cover_param, start_peri_param = check_outside_inside(start_segments,
+                                                                                                      connect_idx)
+                end_position, end_min, end_cover_param, end_peri_param = check_outside_inside(end_segments, connect_idx)
+
+                cover += dist_segments[connect_idx] + start_cover_param * start_min + end_cover_param * end_min
+                perimeter += dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min
+
+                square_length.append(
+                    dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min)
+
+            overlap_scores.append(cover / perimeter)
+            ######################################
+            ###################################### DEGREE SCORES
+            '''
+            deg0 vs deg2
+            deg1 vs deg3
+            '''
+            deg0, deg1, deg2, deg3 = degree
+            deg_ratio1 = deg0 / deg2
+            if deg_ratio1 > 1.0:
+                deg_ratio1 = 1 / deg_ratio1
+            deg_ratio2 = deg1 / deg3
+            if deg_ratio2 > 1.0:
+                deg_ratio2 = 1 / deg_ratio2
+            degree_scores.append((deg_ratio1 + deg_ratio2) / 2)
+            ######################################
+            ###################################### LENGTH SCORES
+            '''
+            len0 vs len2
+            len1 vs len3
+            '''
+            len0, len1, len2, len3 = square_length
+            len_ratio1 = len0 / len2 if len2 > len0 else len2 / len0
+            len_ratio2 = len1 / len3 if len3 > len1 else len3 / len1
+            length_scores.append((len_ratio1 + len_ratio2) / 2)
+
+            ######################################
+
+        overlap_scores = np.array(overlap_scores)
+        overlap_scores /= np.max(overlap_scores)
+
+        degree_scores = np.array(degree_scores)
+        # degree_scores /= np.max(degree_scores)
+
+        length_scores = np.array(length_scores)
+
+        ###################################### AREA SCORES
+        area_scores = np.reshape(squares, [-1, 4, 2])
+        area_x = area_scores[:, :, 0]
+        area_y = area_scores[:, :, 1]
+        correction = area_x[:, -1] * area_y[:, 0] - area_y[:, -1] * area_x[:, 0]
+        area_scores = np.sum(area_x[:, :-1] * area_y[:, 1:], axis=-1) - np.sum(area_y[:, :-1] * area_x[:, 1:], axis=-1)
+        area_scores = 0.5 * np.abs(area_scores + correction)
+        area_scores /= (map_size * map_size)  # np.max(area_scores)
+        ######################################
+
+        ###################################### CENTER SCORES
+        centers = np.array([[256 // 2, 256 // 2]], dtype='float32')  # [1, 2]
+        # squares: [n, 4, 2]
+        square_centers = np.mean(squares, axis=1)  # [n, 2]
+        center2center = np.sqrt(np.sum((centers - square_centers) ** 2))
+        center_scores = center2center / (map_size / np.sqrt(2.0))
+
+        '''
+        score_w = [overlap, degree, area, center, length]
+        '''
+        score_w = [0.0, 1.0, 10.0, 0.5, 1.0]
+        score_array = params['w_overlap'] * overlap_scores \
+                      + params['w_degree'] * degree_scores \
+                      + params['w_area'] * area_scores \
+                      - params['w_center'] * center_scores \
+                      + params['w_length'] * length_scores
+
+        best_square = []
+
+        sorted_idx = np.argsort(score_array)[::-1]
+        score_array = score_array[sorted_idx]
+        squares = squares[sorted_idx]
+
+    except Exception as e:
+        pass
+
+    '''return list
+    merged_lines, squares, scores
+    '''
+
+    try:
+        new_segments[:, 0] = new_segments[:, 0] * 2 / input_shape[1] * original_shape[1]
+        new_segments[:, 1] = new_segments[:, 1] * 2 / input_shape[0] * original_shape[0]
+        new_segments[:, 2] = new_segments[:, 2] * 2 / input_shape[1] * original_shape[1]
+        new_segments[:, 3] = new_segments[:, 3] * 2 / input_shape[0] * original_shape[0]
+    except:
+        new_segments = []
+
+    try:
+        squares[:, :, 0] = squares[:, :, 0] * 2 / input_shape[1] * original_shape[1]
+        squares[:, :, 1] = squares[:, :, 1] * 2 / input_shape[0] * original_shape[0]
+    except:
+        squares = []
+        score_array = []
+
+    try:
+        inter_points = np.array(inter_points)
+        inter_points[:, 0] = inter_points[:, 0] * 2 / input_shape[1] * original_shape[1]
+        inter_points[:, 1] = inter_points[:, 1] * 2 / input_shape[0] * original_shape[0]
+    except:
+        inter_points = []
+
+    return new_segments, squares, score_array, inter_points

microsoftexcel-controlnet/annotator/mmpkg/mmcv/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# flake8: noqa
+from .arraymisc import *
+from .fileio import *
+from .image import *
+from .utils import *
+from .version import *
+from .video import *
+from .visualization import *
+
+# The following modules are not imported to this level, so mmcv may be used
+# without PyTorch.
+# - runner
+# - parallel
+# - op

microsoftexcel-controlnet/annotator/mmpkg/mmcv/arraymisc/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .quantization import dequantize, quantize
+
+__all__ = ['quantize', 'dequantize']

microsoftexcel-controlnet/annotator/mmpkg/mmcv/arraymisc/quantization.py

@@ -0,0 +1,55 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+
+
+def quantize(arr, min_val, max_val, levels, dtype=np.int64):
+    """Quantize an array of (-inf, inf) to [0, levels-1].
+
+    Args:
+        arr (ndarray): Input array.
+        min_val (scalar): Minimum value to be clipped.
+        max_val (scalar): Maximum value to be clipped.
+        levels (int): Quantization levels.
+        dtype (np.type): The type of the quantized array.
+
+    Returns:
+        tuple: Quantized array.
+    """
+    if not (isinstance(levels, int) and levels > 1):
+        raise ValueError(
+            f'levels must be a positive integer, but got {levels}')
+    if min_val >= max_val:
+        raise ValueError(
+            f'min_val ({min_val}) must be smaller than max_val ({max_val})')
+
+    arr = np.clip(arr, min_val, max_val) - min_val
+    quantized_arr = np.minimum(
+        np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1)
+
+    return quantized_arr
+
+
+def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
+    """Dequantize an array.
+
+    Args:
+        arr (ndarray): Input array.
+        min_val (scalar): Minimum value to be clipped.
+        max_val (scalar): Maximum value to be clipped.
+        levels (int): Quantization levels.
+        dtype (np.type): The type of the dequantized array.
+
+    Returns:
+        tuple: Dequantized array.
+    """
+    if not (isinstance(levels, int) and levels > 1):
+        raise ValueError(
+            f'levels must be a positive integer, but got {levels}')
+    if min_val >= max_val:
+        raise ValueError(
+            f'min_val ({min_val}) must be smaller than max_val ({max_val})')
+
+    dequantized_arr = (arr + 0.5).astype(dtype) * (max_val -
+                                                   min_val) / levels + min_val
+
+    return dequantized_arr

microsoftexcel-controlnet/annotator/mmpkg/mmcv/cnn/__init__.py

@@ -0,0 +1,41 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .alexnet import AlexNet
+# yapf: disable
+from .bricks import (ACTIVATION_LAYERS, CONV_LAYERS, NORM_LAYERS,
+                     PADDING_LAYERS, PLUGIN_LAYERS, UPSAMPLE_LAYERS,
+                     ContextBlock, Conv2d, Conv3d, ConvAWS2d, ConvModule,
+                     ConvTranspose2d, ConvTranspose3d, ConvWS2d,
+                     DepthwiseSeparableConvModule, GeneralizedAttention,
+                     HSigmoid, HSwish, Linear, MaxPool2d, MaxPool3d,
+                     NonLocal1d, NonLocal2d, NonLocal3d, Scale, Swish,
+                     build_activation_layer, build_conv_layer,
+                     build_norm_layer, build_padding_layer, build_plugin_layer,
+                     build_upsample_layer, conv_ws_2d, is_norm)
+from .builder import MODELS, build_model_from_cfg
+# yapf: enable
+from .resnet import ResNet, make_res_layer
+from .utils import (INITIALIZERS, Caffe2XavierInit, ConstantInit, KaimingInit,
+                    NormalInit, PretrainedInit, TruncNormalInit, UniformInit,
+                    XavierInit, bias_init_with_prob, caffe2_xavier_init,
+                    constant_init, fuse_conv_bn, get_model_complexity_info,
+                    initialize, kaiming_init, normal_init, trunc_normal_init,
+                    uniform_init, xavier_init)
+from .vgg import VGG, make_vgg_layer
+
+__all__ = [
+    'AlexNet', 'VGG', 'make_vgg_layer', 'ResNet', 'make_res_layer',
+    'constant_init', 'xavier_init', 'normal_init', 'trunc_normal_init',
+    'uniform_init', 'kaiming_init', 'caffe2_xavier_init',
+    'bias_init_with_prob', 'ConvModule', 'build_activation_layer',
+    'build_conv_layer', 'build_norm_layer', 'build_padding_layer',
+    'build_upsample_layer', 'build_plugin_layer', 'is_norm', 'NonLocal1d',
+    'NonLocal2d', 'NonLocal3d', 'ContextBlock', 'HSigmoid', 'Swish', 'HSwish',
+    'GeneralizedAttention', 'ACTIVATION_LAYERS', 'CONV_LAYERS', 'NORM_LAYERS',
+    'PADDING_LAYERS', 'UPSAMPLE_LAYERS', 'PLUGIN_LAYERS', 'Scale',
+    'get_model_complexity_info', 'conv_ws_2d', 'ConvAWS2d', 'ConvWS2d',
+    'fuse_conv_bn', 'DepthwiseSeparableConvModule', 'Linear', 'Conv2d',
+    'ConvTranspose2d', 'MaxPool2d', 'ConvTranspose3d', 'MaxPool3d', 'Conv3d',
+    'initialize', 'INITIALIZERS', 'ConstantInit', 'XavierInit', 'NormalInit',
+    'TruncNormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit',
+    'Caffe2XavierInit', 'MODELS', 'build_model_from_cfg'
+]

microsoftexcel-controlnet/annotator/mmpkg/mmcv/cnn/alexnet.py

@@ -0,0 +1,61 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+
+import torch.nn as nn
+
+
+class AlexNet(nn.Module):
+    """AlexNet backbone.
+
+    Args:
+        num_classes (int): number of classes for classification.
+    """
+
+    def __init__(self, num_classes=-1):
+        super(AlexNet, self).__init__()
+        self.num_classes = num_classes
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+            nn.Conv2d(64, 192, kernel_size=5, padding=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+            nn.Conv2d(192, 384, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(384, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+        )
+        if self.num_classes > 0:
+            self.classifier = nn.Sequential(
+                nn.Dropout(),
+                nn.Linear(256 * 6 * 6, 4096),
+                nn.ReLU(inplace=True),
+                nn.Dropout(),
+                nn.Linear(4096, 4096),
+                nn.ReLU(inplace=True),
+                nn.Linear(4096, num_classes),
+            )
+
+    def init_weights(self, pretrained=None):
+        if isinstance(pretrained, str):
+            logger = logging.getLogger()
+            from ..runner import load_checkpoint
+            load_checkpoint(self, pretrained, strict=False, logger=logger)
+        elif pretrained is None:
+            # use default initializer
+            pass
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    def forward(self, x):
+
+        x = self.features(x)
+        if self.num_classes > 0:
+            x = x.view(x.size(0), 256 * 6 * 6)
+            x = self.classifier(x)
+
+        return x