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206 lines
6.5 KiB
206 lines
6.5 KiB
import sys
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import numpy
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import torch
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import torch.nn as nn
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from torch.autograd import Function
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from torch.optim.lr_scheduler import _LRScheduler
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import torchvision
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import torchvision.transforms as transforms
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import torchvision.utils as vutils
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from torch.utils.data import DataLoader
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from dataset import Dataset_FullImg, Dataset_DiscRegion
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import math
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import PIL
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import matplotlib.pyplot as plt
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import seaborn as sns
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import collections
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import logging
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import math
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import os
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import time
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from datetime import datetime
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import dateutil.tz
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from typing import Union, Optional, List, Tuple, Text, BinaryIO
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import pathlib
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import warnings
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont, ImageColor
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from lucent.optvis.param.spatial import pixel_image, fft_image, init_image
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from lucent.optvis.param.color import to_valid_rgb
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from torchvision.models import vgg19
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import torch.nn.functional as F
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import cfg
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import warnings
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from collections import OrderedDict
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import numpy as np
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from tqdm import tqdm
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from PIL import Image
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import torch
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args = cfg.parse_args()
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device = torch.device('cuda', args.gpu_device)
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cnn = vgg19(pretrained=True).features.to(device).eval()
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content_layers_default = ['conv_4']
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style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
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cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
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cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
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class ContentLoss(nn.Module):
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def __init__(self, target,):
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super(ContentLoss, self).__init__()
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# we 'detach' the target content from the tree used
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# to dynamically compute the gradient: this is a stated value,
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# not a variable. Otherwise the forward method of the criterion
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# will throw an error.
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self.target = target.detach()
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def forward(self, input):
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self.loss = F.mse_loss(input, self.target)
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return input
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def gram_matrix(input):
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a, b, c, d = input.size() # a=batch size(=1)
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# b=number of feature maps
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# (c,d)=dimensions of a f. map (N=c*d)
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features = input.view(a * b, c * d) # resise F_XL into \hat F_XL
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G = torch.mm(features, features.t()) # compute the gram product
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# we 'normalize' the values of the gram matrix
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# by dividing by the number of element in each feature maps.
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return G.div(a * b * c * d)
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class StyleLoss(nn.Module):
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def __init__(self, target_feature):
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super(StyleLoss, self).__init__()
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self.target = gram_matrix(target_feature).detach()
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def forward(self, input):
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G = gram_matrix(input)
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self.loss = F.mse_loss(G, self.target)
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return input
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# create a module to normalize input image so we can easily put it in a
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# nn.Sequential
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class Normalization(nn.Module):
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def __init__(self, mean, std):
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super(Normalization, self).__init__()
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# .view the mean and std to make them [C x 1 x 1] so that they can
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# directly work with image Tensor of shape [B x C x H x W].
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# B is batch size. C is number of channels. H is height and W is width.
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self.mean = torch.tensor(mean).view(-1, 1, 1)
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self.std = torch.tensor(std).view(-1, 1, 1)
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def forward(self, img):
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# normalize img
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return (img - self.mean) / self.std
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def run_precpt(cnn, normalization_mean, normalization_std,
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content_img, style_img, input_img,
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style_weight=1000000, content_weight=1):
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model, style_losses, content_losses = precpt_loss(cnn,
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normalization_mean, normalization_std, style_img, content_img)
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# We want to optimize the input and not the model parameters so we
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# update all the requires_grad fields accordingly
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model.requires_grad_(False)
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input_img.requires_grad_(True)
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model(input_img)
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style_score = 0
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content_score = 0
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for sl in style_losses:
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style_score += sl.loss
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for cl in content_losses:
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content_score += cl.loss
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content_weight = 100
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style_weight = 100000
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style_score *= style_weight
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content_score *= content_weight
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loss = style_score + content_score
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# loss = content_score
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return loss
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def precpt_loss(cnn, normalization_mean, normalization_std,
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style_img, content_img,
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content_layers=content_layers_default,
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style_layers=style_layers_default):
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# normalization module
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normalization = Normalization(normalization_mean, normalization_std).to(device)
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# just in order to have an iterable access to or list of content/syle
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# losses
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content_losses = []
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style_losses = []
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# assuming that cnn is a nn.Sequential, so we make a new nn.Sequential
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# to put in modules that are supposed to be activated sequentially
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model = nn.Sequential(normalization)
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i = 0 # increment every time we see a conv
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for layer in cnn.children():
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if isinstance(layer, nn.Conv2d):
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i += 1
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name = 'conv_{}'.format(i)
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elif isinstance(layer, nn.ReLU):
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name = 'relu_{}'.format(i)
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# The in-place version doesn't play very nicely with the ContentLoss
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# and StyleLoss we insert below. So we replace with out-of-place
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# ones here.
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layer = nn.ReLU(inplace=False)
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elif isinstance(layer, nn.MaxPool2d):
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name = 'pool_{}'.format(i)
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elif isinstance(layer, nn.BatchNorm2d):
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name = 'bn_{}'.format(i)
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else:
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raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
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model.add_module(name, layer)
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if name in content_layers:
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# add content loss:
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target = model(content_img).detach()
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content_loss = ContentLoss(target)
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model.add_module("content_loss_{}".format(i), content_loss)
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content_losses.append(content_loss)
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if name in style_layers:
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# add style loss:
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if style_img.size(1) == 1:
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style_img = style_img.expand(style_img.size(0),3, style_img.size(2),style_img.size(3))
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target_feature = model(style_img).detach()
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style_loss = StyleLoss(target_feature)
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model.add_module("style_loss_{}".format(i), style_loss)
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style_losses.append(style_loss)
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# now we trim off the layers after the last content and style losses
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for i in range(len(model) - 1, -1, -1):
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if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
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break
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model = model[:(i + 1)]
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return model, style_losses, content_losses
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