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Gram.py
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from matplotlib import artist
import torch
import numpy as np
import matplotlib.pyplot as plt
from torchvision import transforms
import torchvision.models as models
from torch import optim
from model import *
from PIL import Image
def load_image(img_path, max_size=400, shape=None):
''' Load in and transform an image, making sure the image
is <= 400 pixels in the x-y dims.'''
image = Image.open(img_path).convert('RGB')
# large images will slow down processing
if max(image.size) > max_size:
size = max_size
else:
size = max(image.size)
if shape is not None:
size = shape
in_transform = transforms.Compose([
transforms.Resize(size),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
# discard the transparent, alpha channel (that's the :3) and add the batch dimension
image = in_transform(image)[:3,:,:].unsqueeze(0)
return image
# helper function for un-normalizing an image
# and converting it from a Tensor image to a NumPy image for display
def im_convert(tensor):
image = tensor.to("cpu").clone().detach()
image = image.numpy().squeeze()
image = image.transpose(1,2,0)
image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))
image = image.clip(0, 1)
return image
def get_features(image, model, layers=None):
""" Run an image forward through a model and get the features for
a set of layers. Default layers are for VGGNet matching Gatys et al (2016)
"""
'''
vgg16: features, avg_pool, classifier
'''
## Need the layers for the content and style representations of an image
if layers is None:
layers = {'0': 'conv1_1',
'5': 'conv2_1',
'10': 'conv3_1',
'19': 'conv4_1',
'21': 'conv4_2', ## content representation is output of this layer
'28': 'conv5_1'
}
features = {}
x = image
# model._modules is a dictionary holding each module in the model
# 31 layers, 19 Conv2D
for name, layer in model._modules.items(): # TODO: 少了个flatten层？
if name == 'features':
extract_ls = [0, 5, 10, 19, 21, 28]
for i in range(31):
x = layer[i](x)
if i in extract_ls:
features[layers[str(i)]] = x
elif name == 'classifier':
x = torch.flatten(x)
x = layer(x)
else:
x = layer(x)
return features
# get content and style features only once before training
def gram_matrix(tensor):
""" Calculate the Gram Matrix of a given tensor
Gram Matrix: https://en.wikipedia.org/wiki/Gramian_matrix
"""
# get the batch_size, depth, height, and width of the Tensor
_, d, h, w = tensor.size()
# reshape so we're multiplying the features for each channel
tensor = tensor.view(d, h * w)
# calculate the gram matrix
gram = torch.mm(tensor, tensor.t())
return gram
if __name__ == "__main__":
device = 'cpu'
vgg16 = models.vgg16(pretrained=False)
vgg16.to(device)
layers = {'0': 'conv1_1',
'5': 'conv2_1',
'10': 'conv3_1',
'19': 'conv4_1',
'21': 'conv4_2', ## content representation is output of this layer
'28': 'conv5_1'
}
style_weights = {
'conv1_1': 1,
'conv2_1': 1,
'conv3_1': 1,
'conv4_1': 1,
'conv4_2': 1,
'conv5_1': 1,
}
# loss weight hyperparams
content_weight = 1
style_weight = 1
content_dic = '/home/tongtong/python_project/CV/StyleTransfer/images/content/'
style_dic = '/home/tongtong/python_project/CV/StyleTransfer/images/reference/'
result_dic = '/home/tongtong/python_project/CV/StyleTransfer/images/result/'
content_name = '1'
artist_name = 'van-gogh'
index = '1'
# load in content and style image, using shape parameter to make both content and style of same shape to make processing easier
content = load_image(content_dic + content_name + ".jpg", shape=[400,400]).to(device)
style = load_image(style_dic + artist_name + '/' + index + ".jpg", shape=[400,400]).to(device)
target = load_image(content_dic + content_name + ".jpg", shape=[400,400]).to(device)
target.requires_grad = True
content_features = get_features(content, vgg16)
style_features = get_features(style, vgg16)
# for displaying the target image, intermittently
show_every = 100
# iteration hyperparameters
optimizer = optim.Adam([target], lr=0.003)
steps = 2000 # decide how many iterations to update your image (5000)
for ii in range(1, steps+1):
# get the features from your target image
target_features = get_features(target, vgg16)
# the content loss
content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2'])**2) # TODO: choice here
# the style loss
# initialize the style loss to 0
style_loss = 0
# then add to it for each layer's gram matrix loss
for layer in layers.values():
# get the "target" style representation for the layer
target_feature = target_features[layer]
style_feature = style_features[layer]
target_gram = gram_matrix(target_feature)
style_gram = gram_matrix(style_feature)
_, d, h, w = target_feature.shape
# the style loss for one layer, weighted appropriately
layer_style_loss = style_weights[layer] * torch.mean((target_gram - style_gram)**2)
# add to the style loss
style_loss += layer_style_loss / (d * h * w)
# calculate the *total* loss
total_loss = content_weight * content_loss + style_weight * style_loss
# update your target image
optimizer.zero_grad()
total_loss.backward(retain_graph=True)
optimizer.step()
# display intermediate images and print the loss
if ii % 10 == 0:
print('Total loss: ', total_loss.item())
if ii % show_every == 0:
plt.imshow(im_convert(target))
plt.savefig(result_dic + content_name + '-' + artist_name + '-' + str(ii) + '.jpg')
print(1)