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neural_style_transfer.py
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import utils.utils as utils
from utils.video_utils import create_video_from_intermediate_results
import torch
from torch.optim import Adam
from torch.autograd import Variable
import numpy as np
import os
import argparse
def build_loss(neural_net, optimizing_img, target_representations, content_feature_maps_index, style_feature_maps_indices, config):
target_content_representation = target_representations[0]
target_style_representation = target_representations[1]
current_set_of_feature_maps = neural_net(optimizing_img)
current_content_representation = current_set_of_feature_maps[content_feature_maps_index].squeeze(
axis=0)
content_loss = torch.nn.MSELoss(reduction='mean')(
target_content_representation, current_content_representation)
style_loss = 0.0
current_style_representation = [utils.gram_matrix(x) for cnt, x in enumerate(
current_set_of_feature_maps) if cnt in style_feature_maps_indices]
for gram_gt, gram_hat in zip(target_style_representation, current_style_representation):
style_loss += torch.nn.MSELoss(reduction='sum')(gram_gt[0], gram_hat[0])
style_loss /= len(target_style_representation)
tv_loss = utils.total_variation(optimizing_img)
total_loss = config['content_weight'] * content_loss + \
config['style_weight'] * style_loss + config['tv_weight'] * tv_loss
return total_loss, content_loss, style_loss, tv_loss
def make_tuning_step(neural_net, optimizer, target_representations, content_feature_maps_index, style_feature_maps_indices, config):
# Builds function that performs a step in the tuning loop
def tuning_step(optimizing_img):
total_loss, content_loss, style_loss, tv_loss = build_loss(
neural_net, optimizing_img, target_representations, content_feature_maps_index, style_feature_maps_indices, config)
# Computes gradients
total_loss.backward()
# Updates parameters and zeroes gradients
optimizer.step()
optimizer.zero_grad()
return total_loss, content_loss, style_loss, tv_loss
# Returns the function that will be called inside the tuning loop
return tuning_step
def neural_style_transfer(config):
content_img_path = os.path.join(config['content_images_dir'], config['content_img_name'])
style_img_path = os.path.join(config['style_images_dir'], config['style_img_name'])
out_dir_name = 'combined_' + \
os.path.split(content_img_path)[1].split('.')[0] + '_' + \
os.path.split(style_img_path)[1].split('.')[0]
dump_path = os.path.join(config['output_img_dir'], out_dir_name)
os.makedirs(dump_path, exist_ok=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
content_img = utils.prepare_img(content_img_path, config['height'], config['width'], device)
style_img = utils.prepare_img(style_img_path, config['height'], config['width'], device)
if config['init_method'] == 'random':
# white_noise_img = np.random.uniform(-90., 90., content_img.shape).astype(np.float32)
gaussian_noise_img = np.random.normal(
loc=0, scale=90., size=content_img.shape).astype(np.float32)
init_img = torch.from_numpy(gaussian_noise_img).float().to(device)
elif config['init_method'] == 'content':
init_img = content_img
else:
# init image has same dimension as content image - this is a hard constraint
# feature maps need to be of same size for content image and init image
style_img_resized = utils.prepare_img(
style_img_path, np.asarray(content_img.shape[2:]), device)
init_img = style_img_resized
# we are tuning optimizing_img's pixels! (that's why requires_grad=True)
optimizing_img = Variable(init_img, requires_grad=True)
neural_net, content_feature_maps_index_name, style_feature_maps_indices_names = utils.prepare_model(
config['model'], device)
print(f'Using {config["model"]} in the optimization procedure.')
content_img_set_of_feature_maps = neural_net(content_img)
style_img_set_of_feature_maps = neural_net(style_img)
target_content_representation = content_img_set_of_feature_maps[content_feature_maps_index_name[0]].squeeze(
axis=0)
target_style_representation = [utils.gram_matrix(x) for cnt, x in enumerate(
style_img_set_of_feature_maps) if cnt in style_feature_maps_indices_names[0]]
target_representations = [target_content_representation, target_style_representation]
# magic numbers in general are a big no no - some things in this code are left like this by design to avoid clutter
num_of_iterations = {
"adam": 3000,
}
#
# Start of optimization procedure
#
if config['optimizer'] == 'adam':
optimizer = Adam((optimizing_img,), lr=1e1)
tuning_step = make_tuning_step(neural_net, optimizer, target_representations,
content_feature_maps_index_name[0], style_feature_maps_indices_names[0], config)
for cnt in range(num_of_iterations[config['optimizer']]):
total_loss, content_loss, style_loss, tv_loss = tuning_step(optimizing_img)
with torch.no_grad():
print(
f'Adam | iteration: {cnt:03}, total loss={total_loss.item():12.4f}, content_loss={config["content_weight"] * content_loss.item():12.4f}, style loss={config["style_weight"] * style_loss.item():12.4f}, tv loss={config["tv_weight"] * tv_loss.item():12.4f}')
utils.save_and_maybe_display(
optimizing_img, dump_path, config, cnt, num_of_iterations[config['optimizer']], should_display=False)
return dump_path
if __name__ == "__main__":
#
# fixed args - don't change these unless you have a good reason
#
default_resource_dir = os.path.join(os.path.dirname(__file__), 'data')
content_images_dir = os.path.join(default_resource_dir, 'content-images')
style_images_dir = os.path.join(default_resource_dir, 'style-images')
output_img_dir = os.path.join(default_resource_dir, 'output-images')
img_format = (4, '.jpg') # saves images in the format: %04d.jpg
#
# modifiable args - feel free to play with these (only small subset is exposed by design to avoid cluttering)
# sorted so that the ones on the top are more likely to be changed than the ones on the bottom
#
parser = argparse.ArgumentParser()
parser.add_argument("--content_img_name", type=str,
help="content image name", default='lion.jpg')
parser.add_argument("--style_img_name", type=str, help="style image name",
default='mosaic.jpg')
parser.add_argument("--height", type=int,
help="height of content and style images", default=400)
parser.add_argument("--width", type=int, help="width of content and style images", default=600)
parser.add_argument("--content_weight", type=float,
help="weight factor for content loss", default=1e5)
parser.add_argument("--style_weight", type=float,
help="weight factor for style loss", default=3e4)
parser.add_argument("--tv_weight", type=float,
help="weight factor for total variation loss", default=1e0)
parser.add_argument("--optimizer", type=str, default='adam')
parser.add_argument("--model", type=str, default='vgg19')
parser.add_argument("--init_method", type=str,
choices=['random', 'content', 'style'], default='random')
parser.add_argument("--saving_freq", type=int,
help="saving frequency for intermediate images (-1 means only final)", default=100)
args = parser.parse_args()
# some values of weights that worked for figures.jpg, vg_starry_night.jpg (starting point for finding good images)
# once you understand what each one does it gets really easy -> also see README.md
# lbfgs, content init -> (cw, sw, tv) = (1e5, 3e4, 1e0)
# lbfgs, style init -> (cw, sw, tv) = (1e5, 1e1, 1e-1)
# lbfgs, random init -> (cw, sw, tv) = (1e5, 1e3, 1e0)
# adam, content init -> (cw, sw, tv, lr) = (1e5, 1e5, 1e-1, 1e1)
# adam, style init -> (cw, sw, tv, lr) = (1e5, 1e2, 1e-1, 1e1)
# adam, random init -> (cw, sw, tv, lr) = (1e5, 1e2, 1e-1, 1e1)
# just wrapping settings into a dictionary
optimization_config = dict()
for arg in vars(args):
optimization_config[arg] = getattr(args, arg)
optimization_config['content_images_dir'] = content_images_dir
optimization_config['style_images_dir'] = style_images_dir
optimization_config['output_img_dir'] = output_img_dir
optimization_config['img_format'] = img_format
# original NST (Neural Style Transfer) algorithm (Gatys et al.)
results_path = neural_style_transfer(optimization_config)
# uncomment this if you want to create a video from images dumped during the optimization procedure
create_video_from_intermediate_results(results_path, img_format)