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generator.py
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import os
import numpy as np
import torch
import torch.nn as nn
from torchvision.models import vgg19
from scipy.stats import ortho_group
from utils import activation_value_hook, sliced_transport, image_preprocessing, vgg_normalization
class Generator:
"""Texture generator model.
"""
def __init__(self, source_image, observed_layers, n_bins=128):
"""
:param source_image: source image
:type image: PIL Image object
:param observed_layers: dictionary containing layer-specific information ; see bottom of file decoders.py
:type observed_layers: dictionary
:param n_bins: number of transportation histogram bins, defaults to 128 [TO BE IMPLEMENTED]
:type n_bins: int, optional
"""
# source image
self.source_tensor = image_preprocessing(source_image)
self.normalized_source_batch = vgg_normalization(
self.source_tensor).unsqueeze(0)
self.source_batch = self.source_tensor.unsqueeze(0)
# set encoder
self.encoder = vgg19(pretrained=True).float()
self.encoder.eval()
for param in self.encoder.features.parameters():
param.requires_grad = False
self.encoder_layers = {}
self.set_encoder_hooks(observed_layers)
self.n_bins = n_bins
def set_encoder_hooks(self, observed_layers):
"""Set up hooks to observe the acivation value of the encoder layers.
:param observed_layers: layer information
:type observed_layers: dictionary
"""
self.observed_layers = observed_layers
self.error_values = {layer_name: []
for layer_name in observed_layers.keys()}
self.decoder_loss_values = {}
for layer_name, layer_information in observed_layers.items():
# set a hook in the encoder
layer_index = layer_information['index']
layer = self.encoder.features[layer_index]
layer.register_forward_hook(
activation_value_hook(layer_name, self.encoder_layers))
def set_layer_decoders(self, train=False, state_dir_path='decoder_states'):
"""Load decoder weights or train decoders.
:param train: train the decoders on the provided source image, defaults to False
:type train: bool, optional
:param state_dir_path: relative path to the directory containing .pth decoder weight files, defaults to 'decoder_states'
:type state_dir_path: str, optional
"""
# train
if train:
for layer_name, layer_information in self.observed_layers.items():
decoder = layer_information['decoder']
print(f'training decoder for layer {layer_name}')
loss_values = self.train_decoder(layer_name)
self.decoder_loss_values[layer_name] = loss_values
# load or save weights
for layer_name, layer_information in self.observed_layers.items():
decoder = layer_information['decoder']
decoder.eval()
decoder_state_path = os.path.join(
state_dir_path, f'{layer_name}_decoder_state.pth')
# save the tained weights
if train:
torch.save(decoder.state_dict(), decoder_state_path)
print(f'saved decoder weights for layer {layer_name}')
else:
decoder.load_state_dict(torch.load(decoder_state_path))
print(f'loaded decoder weights for layer {layer_name}')
decoder = decoder.float()
def generate(self, n_passes=5):
"""Image generation process.
:param n_passes: number of global passes, defaults to 5
:type n_passes: int, optional
:return: generated images layer by layer, step by step
:rtype: list
"""
self.n_passes = n_passes
pass_generated_images = []
# initialize with noise
self.target_tensor = torch.randn_like(self.source_tensor)
for global_pass in range(n_passes):
print(f'global pass {global_pass}')
for layer_name, layer_information in self.observed_layers.items():
print(f'layer {layer_name}')
# forward pass on source image
self.encoder(self.normalized_source_batch)
source_layer = self.encoder_layers[layer_name]
# forward pass on target image
target_batch = vgg_normalization(
self.target_tensor).unsqueeze(0)
self.encoder(target_batch)
target_layer = self.encoder_layers[layer_name]
# transport
target_layer = self.optimal_transport(layer_name,
source_layer.squeeze(), target_layer.squeeze())
target_layer = target_layer.view_as(source_layer)
# decode
decoder = layer_information['decoder']
self.target_tensor = decoder(target_layer).squeeze()
generated_image = np.transpose(
self.target_tensor.numpy(), (1, 2, 0)).copy()
pass_generated_images.append(generated_image)
return pass_generated_images
def optimal_transport(self, layer_name, source_layer, target_layer):
"""Sliced optimal transportation of the activation values source_layer towards target_layer,
seen as pointclouds of an Eucliean space of dimension n_channels.
:param layer_name: layer name, as stored in 'observed_layers' dictionary
:type layer_name: string
:param source_layer: source activation tensor of shape (n_channels, width, height)
:type source_layer: tensor
:param target_layer: target activation tensor of shape (n_channels, width, height)
:type target_layer: tensor
:return: transported tensor
:rtype: tensor
"""
n_channels = source_layer.shape[0]
assert n_channels == target_layer.shape[0]
default_n_slices = n_channels // self.n_passes
n_slices = self.observed_layers[layer_name].get(
'n_slices', default_n_slices)
for slice in range(n_slices):
# random orthonormal basis
basis = torch.from_numpy(ortho_group.rvs(n_channels)).float()
# project on the basis
source_rotated_layer = basis @ source_layer.view(
n_channels, -1)
target_rotated_layer = basis @ target_layer.view(
n_channels, -1)
# sliced transport
target_rotated_layer = sliced_transport(
source_rotated_layer, target_rotated_layer)
target_layer = basis.t() @ target_rotated_layer
return target_layer
def reconstruct(self):
"""Encode and decode the source image through the different observed layers.
:return: generated images, list of size n_layers
:rtype: list
"""
reconstructed_images = []
for layer_name, layer_information in self.observed_layers.items():
print(f'layer {layer_name}')
self.encoder(self.normalized_source_batch)
source_layer = self.encoder_layers[layer_name]
decoder = layer_information['decoder']
input_reconstruction = np.transpose(
decoder(source_layer), (1, 2, 0)).copy()
reconstructed_images.append(input_reconstruction)
return reconstructed_images
def train_decoder(self, layer_name):
"""Train the decoder corresponding to layer layer_name
:return: epoch loss values
:rtype: list
"""
decoder = self.observed_layers[layer_name]['decoder']
n_epochs = self.observed_layers[layer_name]['n_epochs']
learning_rate = self.observed_layers[layer_name].get(
'learning_rate', 1e-3)
training_loss_values = []
image_loss = nn.MSELoss()
optimizer = torch.optim.Adam(decoder.parameters(), lr=learning_rate)
for epoch_index in range(n_epochs):
#print(f'Epoch {epoch_index}')
epoch_loss = 0
# reconstruct
self.encoder(vgg_normalization(self.source_tensor).unsqueeze(0))
embedding = self.encoder_layers[layer_name]
generated_tensor = decoder(embedding).squeeze()
# re-embed
self.encoder(vgg_normalization(generated_tensor).unsqueeze(0))
generated_embedding = self.encoder_layers[layer_name]
loss = image_loss(self.source_tensor, generated_tensor) + \
torch.norm(embedding - generated_embedding)
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss += loss
training_loss_values.append(epoch_loss)
return training_loss_values