import functools import os import warnings import cv2 import numpy as np import torch import torch.nn as nn from einops import rearrange from huggingface_hub import hf_hub_download from PIL import Image from ..util import HWC3, resize_image 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: def __init__(self, model): self.model = model @classmethod def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None, local_files_only=False): filename = filename or "netG.pth" if os.path.isdir(pretrained_model_or_path): model_path = os.path.join(pretrained_model_or_path, filename) else: model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only) 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(model_path) 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() return cls(net) def to(self, device): self.model.to(device) return self def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs): if "return_pil" in kwargs: warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning) output_type = "pil" if kwargs["return_pil"] else "np" if type(output_type) is bool: warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions") if output_type: output_type = "pil" device = next(iter(self.model.parameters())).device if not isinstance(input_image, np.ndarray): input_image = np.array(input_image, dtype=np.uint8) input_image = HWC3(input_image) input_image = resize_image(input_image, detect_resolution) 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(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) detected_map = line detected_map = HWC3(detected_map) img = resize_image(input_image, image_resolution) H, W, C = img.shape detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR) detected_map = 255 - detected_map if output_type == "pil": detected_map = Image.fromarray(detected_map) return detected_map