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############################################################
# The contents below have been combined using files in the #
# following repository: #
# https://github.com/richzhang/PerceptualSimilarity #
############################################################
############################################################
# __init__.py #
############################################################
import numpy as np
from skimage.metrics import structural_similarity
import torch
from saicinpainting.utils import get_shape
class PerceptualLoss(torch.nn.Module):
def __init__(self, model='net-lin', net='alex', colorspace='rgb', model_path=None, spatial=False, use_gpu=True):
# VGG using our perceptually-learned weights (LPIPS metric)
# def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss
super(PerceptualLoss, self).__init__()
self.use_gpu = use_gpu
self.spatial = spatial
self.model = DistModel()
self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace,
model_path=model_path, spatial=self.spatial)
def forward(self, pred, target, normalize=True):
"""
Pred and target are Variables.
If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1]
If normalize is False, assumes the images are already between [-1,+1]
Inputs pred and target are Nx3xHxW
Output pytorch Variable N long
"""
if normalize:
target = 2 * target - 1
pred = 2 * pred - 1
return self.model(target, pred)
def normalize_tensor(in_feat, eps=1e-10):
norm_factor = torch.sqrt(torch.sum(in_feat ** 2, dim=1, keepdim=True))
return in_feat / (norm_factor + eps)
def l2(p0, p1, range=255.):
return .5 * np.mean((p0 / range - p1 / range) ** 2)
def psnr(p0, p1, peak=255.):
return 10 * np.log10(peak ** 2 / np.mean((1. * p0 - 1. * p1) ** 2))
def dssim(p0, p1, range=255.):
return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
def rgb2lab(in_img, mean_cent=False):
from skimage import color
img_lab = color.rgb2lab(in_img)
if (mean_cent):
img_lab[:, :, 0] = img_lab[:, :, 0] - 50
return img_lab
def tensor2np(tensor_obj):
# change dimension of a tensor object into a numpy array
return tensor_obj[0].cpu().float().numpy().transpose((1, 2, 0))
def np2tensor(np_obj):
# change dimenion of np array into tensor array
return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
def tensor2tensorlab(image_tensor, to_norm=True, mc_only=False):
# image tensor to lab tensor
from skimage import color
img = tensor2im(image_tensor)
img_lab = color.rgb2lab(img)
if (mc_only):
img_lab[:, :, 0] = img_lab[:, :, 0] - 50
if (to_norm and not mc_only):
img_lab[:, :, 0] = img_lab[:, :, 0] - 50
img_lab = img_lab / 100.
return np2tensor(img_lab)
def tensorlab2tensor(lab_tensor, return_inbnd=False):
from skimage import color
import warnings
warnings.filterwarnings("ignore")
lab = tensor2np(lab_tensor) * 100.
lab[:, :, 0] = lab[:, :, 0] + 50
rgb_back = 255. * np.clip(color.lab2rgb(lab.astype('float')), 0, 1)
if (return_inbnd):
# convert back to lab, see if we match
lab_back = color.rgb2lab(rgb_back.astype('uint8'))
mask = 1. * np.isclose(lab_back, lab, atol=2.)
mask = np2tensor(np.prod(mask, axis=2)[:, :, np.newaxis])
return (im2tensor(rgb_back), mask)
else:
return im2tensor(rgb_back)
def rgb2lab(input):
from skimage import color
return color.rgb2lab(input / 255.)
def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255. / 2.):
image_numpy = image_tensor[0].cpu().float().numpy()
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
return image_numpy.astype(imtype)
def im2tensor(image, imtype=np.uint8, cent=1., factor=255. / 2.):
return torch.Tensor((image / factor - cent)
[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
def tensor2vec(vector_tensor):
return vector_tensor.data.cpu().numpy()[:, :, 0, 0]
def voc_ap(rec, prec, use_07_metric=False):
""" ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
if use_07_metric:
# 11 point metric
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255. / 2.):
# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):
image_numpy = image_tensor[0].cpu().float().numpy()
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
return image_numpy.astype(imtype)
def im2tensor(image, imtype=np.uint8, cent=1., factor=255. / 2.):
# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.):
return torch.Tensor((image / factor - cent)
[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
############################################################
# base_model.py #
############################################################
class BaseModel(torch.nn.Module):
def __init__(self):
super().__init__()
def name(self):
return 'BaseModel'
def initialize(self, use_gpu=True):
self.use_gpu = use_gpu
def forward(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, path, network_label, epoch_label):
save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
save_path = os.path.join(path, save_filename)
torch.save(network.state_dict(), save_path)
# 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('Loading network from %s' % save_path)
network.load_state_dict(torch.load(save_path, map_location='cpu'))
def update_learning_rate():
pass
def get_image_paths(self):
return self.image_paths
def save_done(self, flag=False):
np.save(os.path.join(self.save_dir, 'done_flag'), flag)
np.savetxt(os.path.join(self.save_dir, 'done_flag'), [flag, ], fmt='%i')
############################################################
# dist_model.py #
############################################################
import os
from collections import OrderedDict
from scipy.ndimage import zoom
from tqdm import tqdm
class DistModel(BaseModel):
def name(self):
return self.model_name
def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False,
model_path=None,
use_gpu=True, printNet=False, spatial=False,
is_train=False, lr=.0001, beta1=0.5, version='0.1'):
'''
INPUTS
model - ['net-lin'] for linearly calibrated network
['net'] for off-the-shelf network
['L2'] for L2 distance in Lab colorspace
['SSIM'] for ssim in RGB colorspace
net - ['squeeze','alex','vgg']
model_path - if None, will look in weights/[NET_NAME].pth
colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM
use_gpu - bool - whether or not to use a GPU
printNet - bool - whether or not to print network architecture out
spatial - bool - whether to output an array containing varying distances across spatial dimensions
spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below).
spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images.
spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear).
is_train - bool - [True] for training mode
lr - float - initial learning rate
beta1 - float - initial momentum term for adam
version - 0.1 for latest, 0.0 was original (with a bug)
'''
BaseModel.initialize(self, use_gpu=use_gpu)
self.model = model
self.net = net
self.is_train = is_train
self.spatial = spatial
self.model_name = '%s [%s]' % (model, net)
if (self.model == 'net-lin'): # pretrained net + linear layer
self.net = PNetLin(pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net,
use_dropout=True, spatial=spatial, version=version, lpips=True)
kw = dict(map_location='cpu')
if (model_path is None):
import inspect
model_path = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', '..', 'models', 'lpips_models', f'{net}.pth'))
if (not is_train):
self.net.load_state_dict(torch.load(model_path, **kw), strict=False)
elif (self.model == 'net'): # pretrained network
self.net = PNetLin(pnet_rand=pnet_rand, pnet_type=net, lpips=False)
elif (self.model in ['L2', 'l2']):
self.net = L2(use_gpu=use_gpu, colorspace=colorspace) # not really a network, only for testing
self.model_name = 'L2'
elif (self.model in ['DSSIM', 'dssim', 'SSIM', 'ssim']):
self.net = DSSIM(use_gpu=use_gpu, colorspace=colorspace)
self.model_name = 'SSIM'
else:
raise ValueError("Model [%s] not recognized." % self.model)
self.trainable_parameters = list(self.net.parameters())
if self.is_train: # training mode
# extra network on top to go from distances (d0,d1) => predicted human judgment (h*)
self.rankLoss = BCERankingLoss()
self.trainable_parameters += list(self.rankLoss.net.parameters())
self.lr = lr
self.old_lr = lr
self.optimizer_net = torch.optim.Adam(self.trainable_parameters, lr=lr, betas=(beta1, 0.999))
else: # test mode
self.net.eval()
# if (use_gpu):
# self.net.to(gpu_ids[0])
# self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids)
# if (self.is_train):
# self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0
if (printNet):
print('---------- Networks initialized -------------')
print_network(self.net)
print('-----------------------------------------------')
def forward(self, in0, in1, retPerLayer=False):
''' Function computes the distance between image patches in0 and in1
INPUTS
in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1]
OUTPUT
computed distances between in0 and in1
'''
return self.net(in0, in1, retPerLayer=retPerLayer)
# ***** TRAINING FUNCTIONS *****
def optimize_parameters(self):
self.forward_train()
self.optimizer_net.zero_grad()
self.backward_train()
self.optimizer_net.step()
self.clamp_weights()
def clamp_weights(self):
for module in self.net.modules():
if (hasattr(module, 'weight') and module.kernel_size == (1, 1)):
module.weight.data = torch.clamp(module.weight.data, min=0)
def set_input(self, data):
self.input_ref = data['ref']
self.input_p0 = data['p0']
self.input_p1 = data['p1']
self.input_judge = data['judge']
# if (self.use_gpu):
# self.input_ref = self.input_ref.to(device=self.gpu_ids[0])
# self.input_p0 = self.input_p0.to(device=self.gpu_ids[0])
# self.input_p1 = self.input_p1.to(device=self.gpu_ids[0])
# self.input_judge = self.input_judge.to(device=self.gpu_ids[0])
# self.var_ref = Variable(self.input_ref, requires_grad=True)
# self.var_p0 = Variable(self.input_p0, requires_grad=True)
# self.var_p1 = Variable(self.input_p1, requires_grad=True)
def forward_train(self): # run forward pass
# print(self.net.module.scaling_layer.shift)
# print(torch.norm(self.net.module.net.slice1[0].weight).item(), torch.norm(self.net.module.lin0.model[1].weight).item())
assert False, "We shoud've not get here when using LPIPS as a metric"
self.d0 = self(self.var_ref, self.var_p0)
self.d1 = self(self.var_ref, self.var_p1)
self.acc_r = self.compute_accuracy(self.d0, self.d1, self.input_judge)
self.var_judge = Variable(1. * self.input_judge).view(self.d0.size())
self.loss_total = self.rankLoss(self.d0, self.d1, self.var_judge * 2. - 1.)
return self.loss_total
def backward_train(self):
torch.mean(self.loss_total).backward()
def compute_accuracy(self, d0, d1, judge):
''' d0, d1 are Variables, judge is a Tensor '''
d1_lt_d0 = (d1 < d0).cpu().data.numpy().flatten()
judge_per = judge.cpu().numpy().flatten()
return d1_lt_d0 * judge_per + (1 - d1_lt_d0) * (1 - judge_per)
def get_current_errors(self):
retDict = OrderedDict([('loss_total', self.loss_total.data.cpu().numpy()),
('acc_r', self.acc_r)])
for key in retDict.keys():
retDict[key] = np.mean(retDict[key])
return retDict
def get_current_visuals(self):
zoom_factor = 256 / self.var_ref.data.size()[2]
ref_img = tensor2im(self.var_ref.data)
p0_img = tensor2im(self.var_p0.data)
p1_img = tensor2im(self.var_p1.data)
ref_img_vis = zoom(ref_img, [zoom_factor, zoom_factor, 1], order=0)
p0_img_vis = zoom(p0_img, [zoom_factor, zoom_factor, 1], order=0)
p1_img_vis = zoom(p1_img, [zoom_factor, zoom_factor, 1], order=0)
return OrderedDict([('ref', ref_img_vis),
('p0', p0_img_vis),
('p1', p1_img_vis)])
def save(self, path, label):
if (self.use_gpu):
self.save_network(self.net.module, path, '', label)
else:
self.save_network(self.net, path, '', label)
self.save_network(self.rankLoss.net, path, 'rank', label)
def update_learning_rate(self, nepoch_decay):
lrd = self.lr / nepoch_decay
lr = self.old_lr - lrd
for param_group in self.optimizer_net.param_groups:
param_group['lr'] = lr
print('update lr [%s] decay: %f -> %f' % (type, self.old_lr, lr))
self.old_lr = lr
def score_2afc_dataset(data_loader, func, name=''):
''' Function computes Two Alternative Forced Choice (2AFC) score using
distance function 'func' in dataset 'data_loader'
INPUTS
data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside
func - callable distance function - calling d=func(in0,in1) should take 2
pytorch tensors with shape Nx3xXxY, and return numpy array of length N
OUTPUTS
[0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators
[1] - dictionary with following elements
d0s,d1s - N arrays containing distances between reference patch to perturbed patches
gts - N array in [0,1], preferred patch selected by human evaluators
(closer to "0" for left patch p0, "1" for right patch p1,
"0.6" means 60pct people preferred right patch, 40pct preferred left)
scores - N array in [0,1], corresponding to what percentage function agreed with humans
CONSTS
N - number of test triplets in data_loader
'''
d0s = []
d1s = []
gts = []
for data in tqdm(data_loader.load_data(), desc=name):
d0s += func(data['ref'], data['p0']).data.cpu().numpy().flatten().tolist()
d1s += func(data['ref'], data['p1']).data.cpu().numpy().flatten().tolist()
gts += data['judge'].cpu().numpy().flatten().tolist()
d0s = np.array(d0s)
d1s = np.array(d1s)
gts = np.array(gts)
scores = (d0s < d1s) * (1. - gts) + (d1s < d0s) * gts + (d1s == d0s) * .5
return (np.mean(scores), dict(d0s=d0s, d1s=d1s, gts=gts, scores=scores))
def score_jnd_dataset(data_loader, func, name=''):
''' Function computes JND score using distance function 'func' in dataset 'data_loader'
INPUTS
data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside
func - callable distance function - calling d=func(in0,in1) should take 2
pytorch tensors with shape Nx3xXxY, and return pytorch array of length N
OUTPUTS
[0] - JND score in [0,1], mAP score (area under precision-recall curve)
[1] - dictionary with following elements
ds - N array containing distances between two patches shown to human evaluator
sames - N array containing fraction of people who thought the two patches were identical
CONSTS
N - number of test triplets in data_loader
'''
ds = []
gts = []
for data in tqdm(data_loader.load_data(), desc=name):
ds += func(data['p0'], data['p1']).data.cpu().numpy().tolist()
gts += data['same'].cpu().numpy().flatten().tolist()
sames = np.array(gts)
ds = np.array(ds)
sorted_inds = np.argsort(ds)
ds_sorted = ds[sorted_inds]
sames_sorted = sames[sorted_inds]
TPs = np.cumsum(sames_sorted)
FPs = np.cumsum(1 - sames_sorted)
FNs = np.sum(sames_sorted) - TPs
precs = TPs / (TPs + FPs)
recs = TPs / (TPs + FNs)
score = voc_ap(recs, precs)
return (score, dict(ds=ds, sames=sames))
############################################################
# networks_basic.py #
############################################################
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
def spatial_average(in_tens, keepdim=True):
return in_tens.mean([2, 3], keepdim=keepdim)
def upsample(in_tens, out_H=64): # assumes scale factor is same for H and W
in_H = in_tens.shape[2]
scale_factor = 1. * out_H / in_H
return nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False)(in_tens)
# Learned perceptual metric
class PNetLin(nn.Module):
def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False,
version='0.1', lpips=True):
super(PNetLin, self).__init__()
self.pnet_type = pnet_type
self.pnet_tune = pnet_tune
self.pnet_rand = pnet_rand
self.spatial = spatial
self.lpips = lpips
self.version = version
self.scaling_layer = ScalingLayer()
if (self.pnet_type in ['vgg', 'vgg16']):
net_type = vgg16
self.chns = [64, 128, 256, 512, 512]
elif (self.pnet_type == 'alex'):
net_type = alexnet
self.chns = [64, 192, 384, 256, 256]
elif (self.pnet_type == 'squeeze'):
net_type = squeezenet
self.chns = [64, 128, 256, 384, 384, 512, 512]
self.L = len(self.chns)
self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune)
if (lpips):
self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
self.lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
if (self.pnet_type == 'squeeze'): # 7 layers for squeezenet
self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)
self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)
self.lins += [self.lin5, self.lin6]
def forward(self, in0, in1, retPerLayer=False):
# v0.0 - original release had a bug, where input was not scaled
in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version == '0.1' else (
in0, in1)
outs0, outs1 = self.net(in0_input), self.net(in1_input)
feats0, feats1, diffs = {}, {}, {}
for kk in range(self.L):
feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
if (self.lpips):
if (self.spatial):
res = [upsample(self.lins[kk].model(diffs[kk]), out_H=in0.shape[2]) for kk in range(self.L)]
else:
res = [spatial_average(self.lins[kk].model(diffs[kk]), keepdim=True) for kk in range(self.L)]
else:
if (self.spatial):
res = [upsample(diffs[kk].sum(dim=1, keepdim=True), out_H=in0.shape[2]) for kk in range(self.L)]
else:
res = [spatial_average(diffs[kk].sum(dim=1, keepdim=True), keepdim=True) for kk in range(self.L)]
val = res[0]
for l in range(1, self.L):
val += res[l]
if (retPerLayer):
return (val, res)
else:
return val
class ScalingLayer(nn.Module):
def __init__(self):
super(ScalingLayer, self).__init__()
self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])
def forward(self, inp):
return (inp - self.shift) / self.scale
class NetLinLayer(nn.Module):
''' A single linear layer which does a 1x1 conv '''
def __init__(self, chn_in, chn_out=1, use_dropout=False):
super(NetLinLayer, self).__init__()
layers = [nn.Dropout(), ] if (use_dropout) else []
layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
self.model = nn.Sequential(*layers)
class Dist2LogitLayer(nn.Module):
''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) '''
def __init__(self, chn_mid=32, use_sigmoid=True):
super(Dist2LogitLayer, self).__init__()
layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True), ]
layers += [nn.LeakyReLU(0.2, True), ]
layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True), ]
layers += [nn.LeakyReLU(0.2, True), ]
layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True), ]
if (use_sigmoid):
layers += [nn.Sigmoid(), ]
self.model = nn.Sequential(*layers)
def forward(self, d0, d1, eps=0.1):
return self.model(torch.cat((d0, d1, d0 - d1, d0 / (d1 + eps), d1 / (d0 + eps)), dim=1))
class BCERankingLoss(nn.Module):
def __init__(self, chn_mid=32):
super(BCERankingLoss, self).__init__()
self.net = Dist2LogitLayer(chn_mid=chn_mid)
# self.parameters = list(self.net.parameters())
self.loss = torch.nn.BCELoss()
def forward(self, d0, d1, judge):
per = (judge + 1.) / 2.
self.logit = self.net(d0, d1)
return self.loss(self.logit, per)
# L2, DSSIM metrics
class FakeNet(nn.Module):
def __init__(self, use_gpu=True, colorspace='Lab'):
super(FakeNet, self).__init__()
self.use_gpu = use_gpu
self.colorspace = colorspace
class L2(FakeNet):
def forward(self, in0, in1, retPerLayer=None):
assert (in0.size()[0] == 1) # currently only supports batchSize 1
if (self.colorspace == 'RGB'):
(N, C, X, Y) = in0.size()
value = torch.mean(torch.mean(torch.mean((in0 - in1) ** 2, dim=1).view(N, 1, X, Y), dim=2).view(N, 1, 1, Y),
dim=3).view(N)
return value
elif (self.colorspace == 'Lab'):
value = l2(tensor2np(tensor2tensorlab(in0.data, to_norm=False)),
tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float')
ret_var = Variable(torch.Tensor((value,)))
# if (self.use_gpu):
# ret_var = ret_var.cuda()
return ret_var
class DSSIM(FakeNet):
def forward(self, in0, in1, retPerLayer=None):
assert (in0.size()[0] == 1) # currently only supports batchSize 1
if (self.colorspace == 'RGB'):
value = dssim(1. * tensor2im(in0.data), 1. * tensor2im(in1.data), range=255.).astype('float')
elif (self.colorspace == 'Lab'):
value = dssim(tensor2np(tensor2tensorlab(in0.data, to_norm=False)),
tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float')
ret_var = Variable(torch.Tensor((value,)))
# if (self.use_gpu):
# ret_var = ret_var.cuda()
return ret_var
def print_network(net):
num_params = 0
for param in net.parameters():
num_params += param.numel()
print('Network', net)
print('Total number of parameters: %d' % num_params)
############################################################
# pretrained_networks.py #
############################################################
from collections import namedtuple
import torch
from torchvision import models as tv
class squeezenet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(squeezenet, self).__init__()
pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
self.slice5 = torch.nn.Sequential()
self.slice6 = torch.nn.Sequential()
self.slice7 = torch.nn.Sequential()
self.N_slices = 7
for x in range(2):
self.slice1.add_module(str(x), pretrained_features[x])
for x in range(2, 5):
self.slice2.add_module(str(x), pretrained_features[x])
for x in range(5, 8):
self.slice3.add_module(str(x), pretrained_features[x])
for x in range(8, 10):
self.slice4.add_module(str(x), pretrained_features[x])
for x in range(10, 11):
self.slice5.add_module(str(x), pretrained_features[x])
for x in range(11, 12):
self.slice6.add_module(str(x), pretrained_features[x])
for x in range(12, 13):
self.slice7.add_module(str(x), pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, X):
h = self.slice1(X)
h_relu1 = h
h = self.slice2(h)
h_relu2 = h
h = self.slice3(h)
h_relu3 = h
h = self.slice4(h)
h_relu4 = h
h = self.slice5(h)
h_relu5 = h
h = self.slice6(h)
h_relu6 = h
h = self.slice7(h)
h_relu7 = h
vgg_outputs = namedtuple("SqueezeOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5', 'relu6', 'relu7'])
out = vgg_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5, h_relu6, h_relu7)
return out
class alexnet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(alexnet, self).__init__()
alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
self.slice5 = torch.nn.Sequential()
self.N_slices = 5
for x in range(2):
self.slice1.add_module(str(x), alexnet_pretrained_features[x])
for x in range(2, 5):
self.slice2.add_module(str(x), alexnet_pretrained_features[x])
for x in range(5, 8):
self.slice3.add_module(str(x), alexnet_pretrained_features[x])
for x in range(8, 10):
self.slice4.add_module(str(x), alexnet_pretrained_features[x])
for x in range(10, 12):
self.slice5.add_module(str(x), alexnet_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, X):
h = self.slice1(X)
h_relu1 = h
h = self.slice2(h)
h_relu2 = h
h = self.slice3(h)
h_relu3 = h
h = self.slice4(h)
h_relu4 = h
h = self.slice5(h)
h_relu5 = h
alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
return out
class vgg16(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(vgg16, self).__init__()
vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
self.slice5 = torch.nn.Sequential()
self.N_slices = 5
for x in range(4):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(4, 9):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(9, 16):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(16, 23):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
for x in range(23, 30):
self.slice5.add_module(str(x), vgg_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, X):
h = self.slice1(X)
h_relu1_2 = h
h = self.slice2(h)
h_relu2_2 = h
h = self.slice3(h)
h_relu3_3 = h
h = self.slice4(h)
h_relu4_3 = h
h = self.slice5(h)
h_relu5_3 = h
vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
return out
class resnet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True, num=18):
super(resnet, self).__init__()
if (num == 18):
self.net = tv.resnet18(pretrained=pretrained)
elif (num == 34):
self.net = tv.resnet34(pretrained=pretrained)
elif (num == 50):
self.net = tv.resnet50(pretrained=pretrained)
elif (num == 101):
self.net = tv.resnet101(pretrained=pretrained)
elif (num == 152):
self.net = tv.resnet152(pretrained=pretrained)
self.N_slices = 5
self.conv1 = self.net.conv1
self.bn1 = self.net.bn1
self.relu = self.net.relu
self.maxpool = self.net.maxpool
self.layer1 = self.net.layer1
self.layer2 = self.net.layer2
self.layer3 = self.net.layer3
self.layer4 = self.net.layer4
def forward(self, X):
h = self.conv1(X)
h = self.bn1(h)
h = self.relu(h)
h_relu1 = h
h = self.maxpool(h)
h = self.layer1(h)
h_conv2 = h
h = self.layer2(h)
h_conv3 = h
h = self.layer3(h)
h_conv4 = h
h = self.layer4(h)
h_conv5 = h
outputs = namedtuple("Outputs", ['relu1', 'conv2', 'conv3', 'conv4', 'conv5'])
out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
return out