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cyclegan.py
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cyclegan.py
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import torch.nn as nn
import torch.nn.functional as F
import torch
import pandas as pd
import sys
import random
import ttorch_datamodule
class ReplayBuffer:
def __init__(self, max_size=50):
assert max_size > 0, "Empty buffer or trying to create a black hole. Be careful."
self.max_size = max_size
self.data = []
def push_and_pop(self, data):
to_return = []
for element in data.data:
element = torch.unsqueeze(element, 0)
if len(self.data) < self.max_size:
self.data.append(element)
to_return.append(element)
else:
if random.uniform(0, 1) > 0.5:
i = random.randint(0, self.max_size - 1)
to_return.append(self.data[i].clone())
self.data[i] = element
else:
to_return.append(element)
return Variable(torch.cat(to_return))
class LambdaLR:
def __init__(self, n_epochs, offset, decay_start_epoch):
assert (n_epochs - decay_start_epoch) > 0, "Decay must start before the training session ends!"
self.n_epochs = n_epochs
self.offset = offset
self.decay_start_epoch = decay_start_epoch
def step(self, epoch):
return 1.0 - max(0, epoch + self.offset - self.decay_start_epoch) / (self.n_epochs - self.decay_start_epoch)
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
if hasattr(m, "bias") and m.bias is not None:
torch.nn.init.constant_(m.bias.data, 0.0)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
##############################
# RESNET
##############################
class ResidualBlock(nn.Module):
def __init__(self, in_features):
super(ResidualBlock, self).__init__()
self.block = nn.Sequential(
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
nn.InstanceNorm2d(in_features),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
nn.InstanceNorm2d(in_features),
)
def forward(self, x):
return x + self.block(x)
class GeneratorResNet(nn.Module):
def __init__(self, input_shape, num_residual_blocks):
super(GeneratorResNet, self).__init__()
channels = input_shape[0]
# Initial convolution block
out_features = 64
model = [
nn.ReflectionPad2d(channels),
nn.Conv2d(channels, out_features, 7),
nn.InstanceNorm2d(out_features),
nn.ReLU(inplace=True),
]
in_features = out_features
# Downsampling
for _ in range(2):
out_features *= 2
model += [
nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
nn.InstanceNorm2d(out_features),
nn.ReLU(inplace=True),
]
in_features = out_features
# Residual blocks
for _ in range(num_residual_blocks):
model += [ResidualBlock(out_features)]
# Upsampling
for _ in range(2):
out_features //= 2
model += [
nn.Upsample(scale_factor=2),
nn.Conv2d(in_features, out_features, 3, stride=1, padding=1),
nn.InstanceNorm2d(out_features),
nn.ReLU(inplace=True),
]
in_features = out_features
# Output layer
model += [nn.ReflectionPad2d(channels), nn.Conv2d(out_features, channels, 7), nn.Tanh()]
self.model = nn.Sequential(*model)
def forward(self, x):
return self.model(x)
##############################
# Discriminator
##############################
class Discriminator(nn.Module):
def __init__(self, input_shape):
super(Discriminator, self).__init__()
channels, height, width = input_shape
# Calculate output shape of image discriminator (PatchGAN)
self.output_shape = (1, height // 2 ** 4, width // 2 ** 4)
def discriminator_block(in_filters, out_filters, normalize=True):
"""Returns downsampling layers of each discriminator block"""
layers = [nn.Conv2d(in_filters, out_filters, 4, stride=2, padding=1)]
if normalize:
layers.append(nn.InstanceNorm2d(out_filters))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.model = nn.Sequential(
*discriminator_block(channels, 64, normalize=False),
*discriminator_block(64, 128),
*discriminator_block(128, 256),
*discriminator_block(256, 512),
nn.ZeroPad2d((1, 0, 1, 0)),
nn.Conv2d(512, 1, 4, padding=1)
)
def forward(self, img):
return self.model(img)
#####
import argparse
import os
import numpy as np
import math
import itertools
import datetime
import time
import torchvision.transforms as transforms
from torchvision.utils import save_image, make_grid
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
#from models_file import *
#from datasets import *
#from utils import *
import models.asos
from tlib import tlearn, ttorch, tutils
from tqdm import tqdm as tqdm_dataloader
import torch.nn as nn
import torch.nn.functional as F
import torch
parser = argparse.ArgumentParser()
parser.add_argument("--epoch", type=int, default=0, help="epoch to start training from")
parser.add_argument("--n_epochs", type=int, default=1000, help="number of epochs of training")
parser.add_argument("--dataset_name", type=str, default="horse2zebra", help="name of the dataset")
parser.add_argument("--batch_size", type=int, default=8, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--decay_epoch", type=int, default=5, help="epoch from which to start lr decay")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--img_height", type=int, default=256, help="size of image height")
parser.add_argument("--img_width", type=int, default=256, help="size of image width")
parser.add_argument("--channels", type=int, default=3, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=100, help="interval between saving generator outputs")
parser.add_argument("--checkpoint_interval", type=int, default=-1, help="interval between saving model checkpoints")
parser.add_argument("--n_residual_blocks", type=int, default=9, help="number of residual blocks in generator")
parser.add_argument("--lambda_cyc", type=float, default=5.0, help="cycle loss weight")
parser.add_argument("--lambda_id", type=float, default=13.0, help="identity loss weight")
parser.add_argument("--lambda_act_max", type=float, default=5.0, help="identity loss weight")
opt = parser.parse_args()
print(opt)
import warnings
#import asos_model
from tlib import tlearn, ttorch, tutils
from tqdm import tqdm as tqdm_dataloader
channels = list(range(3))
#asos_data_path = os.path.expanduser('~/datasets/mapinwild')
asos_data_path = '/data/home/aemam/datasets/mapinwild'
csv_file = os.path.join(asos_data_path, 'tile_infos/file_infos.csv')
data_folder_tiles = os.path.join(asos_data_path, 'tiles')
def layer_hook(act_dict, layer_name):
def hook(module, input, output):
act_dict[layer_name] = output
return hook
hook_dict = dict()
experiment = 'mapinwild'
if experiment == 'horse2zebra':
# Using pretrained weights: we use resnett 50 pretrained classifier trained on imagenet1k dataset
resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)
resnet50(weights="IMAGENET1K_V1")
model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V1).to('cuda')
channels = list(range(3))
elif experiment in ['anthroprotect', 'mapinwild']:
channels = list(range(3)) # specify accoring to model: if rgb: list(range(3)), if all: list(range(10))
model = ttorch.model.load_model('./models/asos_mapinwild_rgb-channels_cutmix.pt', Class=models.asos.Model)
model.cuda()
else:
warnings.warn('Unvalid string for model!')
if experiment == 'horse2zebra':
model.fc.register_forward_hook(layer_hook(hook_dict, 'fc'))
elif experiment in ['anthroprotect', 'mapinwild']:
model.classifier[13].register_forward_hook(layer_hook(hook_dict, 13))
model.eval()
if experiment == 'anthroprotect':
datamodule = ttorch_datamodule.AnthroProtectDataModule(
csv_file=csv_file,
folder=data_folder_tiles,
channels=channels,
batch_size=opt.batch_size,
num_workers=opt.n_cpu,
)
elif experiment == 'mapinwild':
datamodule = ttorch_datamodule.MapInWildDataModule(
csv_file=csv_file,
folder=data_folder_tiles,
channels=channels,
batch_size=opt.batch_size,
num_workers=opt.n_cpu,
)
######
# Create sample and checkpoint directories
os.makedirs("images_mapinwild_act_max/%s" % opt.dataset_name, exist_ok=True)
os.makedirs("saved_models_mapinwild_act_max/%s" % opt.dataset_name, exist_ok=True)
# Losses
criterion_GAN = torch.nn.MSELoss()
criterion_cycle = torch.nn.L1Loss()
criterion_identity = torch.nn.L1Loss()
cuda = torch.cuda.is_available()
input_shape = (opt.channels, opt.img_height, opt.img_width)
# Initialize generator and discriminator
G_AB = GeneratorResNet(input_shape, opt.n_residual_blocks)
G_BA = GeneratorResNet(input_shape, opt.n_residual_blocks)
D_A = Discriminator(input_shape)
D_B = Discriminator(input_shape)
if cuda:
G_AB = G_AB.cuda()
G_BA = G_BA.cuda()
D_A = D_A.cuda()
D_B = D_B.cuda()
criterion_GAN.cuda()
criterion_cycle.cuda()
criterion_identity.cuda()
if opt.epoch != 0:
# Load pretrained models
G_AB.load_state_dict(torch.load("saved_models/%s/G_AB_%d.pth" % (opt.dataset_name, opt.epoch)))
G_BA.load_state_dict(torch.load("saved_models/%s/G_BA_%d.pth" % (opt.dataset_name, opt.epoch)))
D_A.load_state_dict(torch.load("saved_models/%s/D_A_%d.pth" % (opt.dataset_name, opt.epoch)))
D_B.load_state_dict(torch.load("saved_models/%s/D_B_%d.pth" % (opt.dataset_name, opt.epoch)))
else:
# Initialize weights
G_AB.apply(weights_init_normal)
G_BA.apply(weights_init_normal)
D_A.apply(weights_init_normal)
D_B.apply(weights_init_normal)
# Optimizers
#optimizer_G = torch.optim.Adam(
# itertools.chain(G_AB.parameters(), G_BA.parameters()), lr=opt.lr, betas=(opt.b1, opt.b2)
#)
optimizer_G_AB = torch.optim.Adam(
G_AB.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)
)
optimizer_G_BA = torch.optim.Adam(
G_BA.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)
)
optimizer_D_A = torch.optim.Adam(D_A.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D_B = torch.optim.Adam(D_B.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
# Learning rate update schedulers
'''lr_scheduler_G = torch.optim.lr_scheduler.LambdaLR(
optimizer_G, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step
)'''
lr_scheduler_G_AB = torch.optim.lr_scheduler.LambdaLR(
optimizer_G_AB, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step
)
lr_scheduler_G_BA = torch.optim.lr_scheduler.LambdaLR(
optimizer_G_BA, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step
)
lr_scheduler_D_A = torch.optim.lr_scheduler.LambdaLR(
optimizer_D_A, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step
)
lr_scheduler_D_B = torch.optim.lr_scheduler.LambdaLR(
optimizer_D_B, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step
)
Tensor = torch.cuda.FloatTensor if cuda else torch.Tensor
# Buffers of previously generated samples
fake_A_buffer = ReplayBuffer()
fake_B_buffer = ReplayBuffer()
# Image transformations
'''transforms_ = [
transforms.Resize(int(opt.img_height * 1.12), Image.Resampling.BICUBIC),
transforms.RandomCrop((opt.img_height, opt.img_width)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]'''
# Training data loader
train_dataset = datamodule.train_dataset
dataloader = DataLoader(
train_dataset,
#ImageDataset('/home/ahmedemam576/A/*.*', transforms_=transforms_, unaligned=True) ,
batch_size=3,
shuffle=True,
num_workers=opt.n_cpu,
)
# Test data loader
val_dataloader = DataLoader(
#ImageDataset('/home/ahmedemam576/A/*.*', transforms_=transforms_, unaligned=True),
train_dataset,
batch_size=5,
shuffle=False,
num_workers=1,
)
def sample_images(batches_done):
"""Saves a generated sample from the test set"""
imgs = next(iter(val_dataloader))
G_AB.eval()
G_BA.eval()
real_A = Variable(imgs["x"].type(Tensor))
#real_A = Variable(imgs["A"].type(Tensor))
fake_B = G_AB(real_A)
real_B = Variable(imgs["x"].type(Tensor))
#real_B = Variable(imgs["A"].type(Tensor))
fake_A = G_BA(real_B)
difference = fake_B - fake_A
# Arange images along x-axis
real_A = make_grid(real_A, nrow=5, normalize=True)
#real_B = make_grid(real_B, nrow=5, normalize=True)
difference = make_grid(difference, nrow=5, normalize=True)
fake_A = make_grid(fake_A, nrow=5, normalize=False)
fake_B = make_grid(fake_B, nrow=5, normalize=False)
# Arange images along y-axis
image_grid = torch.cat((real_A, fake_A, fake_B, difference), 1)
save_image(image_grid, "images_mapinwild_act_max/%s/%s.png" % (opt.dataset_name, batches_done), normalize=False)
# ----------
# Training
# ----------
def train_loop():
prev_time = time.time()
#print('start___________>',prev_time)
for epoch in range(opt.epoch, opt.n_epochs):
for i, batch in enumerate(dataloader):
#print('start in batch loop___________>',prev_time)
# Set model input
#real_A = Variable(batch["A"].type(Tensor))
#real_B = Variable(batch["A"].type(Tensor))
real_A = Variable(batch["x"].type(Tensor))
real_B = Variable(batch["x"].type(Tensor))
model(real_A).retain_graph=True #forward path of the image to the input
# Adversarial ground truths
valid = Variable(Tensor(np.ones((real_A.size(0), *D_A.output_shape))), requires_grad=False)
fake = Variable(Tensor(np.zeros((real_A.size(0), *D_A.output_shape))), requires_grad=False)
# ------------------
# Train Generators
# ------------------
G_AB.train()
G_BA.train()
#optimizer_G.zero_grad()
optimizer_G_AB.zero_grad()
optimizer_G_BA.zero_grad()
# Identity loss
loss_id_A = criterion_identity(G_BA(real_A), real_A)
loss_id_B = criterion_identity(G_AB(real_B), real_B)
#loss_identity = (loss_id_A + loss_id_B) / 2
# GAN loss
fake_B = G_AB(real_A)
loss_GAN_AB = criterion_GAN(D_B(fake_B), valid)
fake_A = G_BA(real_B)#.retain_graph=True
loss_GAN_BA = criterion_GAN(D_A(fake_A), valid)
#loss_GAN = (loss_GAN_AB + loss_GAN_BA) / 2
# Cycle loss
recov_A = G_BA(fake_B)
loss_cycle_A = criterion_cycle(recov_A, real_A)
recov_B = G_AB(fake_A)
loss_cycle_B = criterion_cycle(recov_B, real_B)
#loss_cycle = (loss_cycle_A + loss_cycle_B) / 2
# activation_maximization_loss
activation= hook_dict[13][0][0]
print('activation --------------------->', activation.data)
loss_AM_AB = criterion_identity (activation, torch.ones_like(activation)) # maximzing the wilderness class minimize(output -1)
loss_AM_BA = criterion_identity (activation, torch.zeros_like(activation)) # maximzing the anthropogenic class minimize(output -0)
# Total loss
#loss_G = loss_GAN + opt.lambda_cyc * loss_cycle + opt.lambda_id * loss_identity
loss_G_AB = loss_GAN_AB + opt.lambda_cyc * loss_cycle_B + opt.lambda_id* loss_id_B + opt.lambda_act_max * loss_AM_AB
loss_G_BA = loss_GAN_BA + opt.lambda_cyc * loss_cycle_A + opt.lambda_id* loss_id_A + opt.lambda_act_max * loss_AM_BA
loss_G_AB.backward(retain_graph=True)
loss_G_BA.backward()
optimizer_G_AB.step()
optimizer_G_BA.step()
#loss_G.backward()
#optimizer_G.step()
# -----------------------
# Train Discriminator A
# -----------------------
optimizer_D_A.zero_grad()
# Real loss
loss_real = criterion_GAN(D_A(real_A.cuda()), valid) # added a gaussian noise
# Fake loss (on batch of previously generated samples)
fake_A_ = fake_A_buffer.push_and_pop(fake_A)
loss_fake = criterion_GAN(D_A(fake_A_.detach().cuda()), fake)
# Total loss
loss_D_A = (loss_real + loss_fake) / 2
loss_D_A.backward()
optimizer_D_A.step()
# -----------------------
# Train Discriminator B
# -----------------------
optimizer_D_B.zero_grad()
# Real loss
loss_real = criterion_GAN(D_B(real_B), valid)
# Fake loss (on batch of previously generated samples)
fake_B_ = fake_B_buffer.push_and_pop(fake_B)
loss_fake = criterion_GAN(D_B(fake_B_.detach()), fake)
# Total loss
loss_D_B = (loss_real + loss_fake) / 2
loss_D_B.backward()
optimizer_D_B.step()
loss_D = (loss_D_A + loss_D_B) / 2
#print(loss_D)
#print('end batchloop___________>',prev_time)
# --------------
# Log Progress
# --------------
# Determine approximate time left
batches_done = epoch * len(dataloader) + i
batches_left = opt.n_epochs * len(dataloader) - batches_done
time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))
prev_time = time.time()
# Print log
sys.stdout.write(
"\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G_AB loss: %f, [G_BA loss: %f cycleA: %f, cycleB: %f, identity_a: %f, identity_b: %f, loss_AM_Ab: %f] ETA: %s"
% (
epoch,
opt.n_epochs,
i,
len(dataloader),
loss_D.item(),
loss_G_AB.item(),
loss_G_BA.item(),
loss_cycle_A.item(),
loss_cycle_B.item(),
loss_id_A.item(),
loss_id_B.item(),
loss_AM_AB.item(),
time_left,
)
)
# If at sample interval save image
if batches_done % opt.sample_interval == 0:
sample_images(batches_done)
# Update learning rates
lr_scheduler_G_AB.step()
lr_scheduler_G_BA.step()
lr_scheduler_D_A.step()
lr_scheduler_D_B.step()
if opt.checkpoint_interval != -1 and epoch % opt.checkpoint_interval == 0:
# Save model checkpoints
torch.save(G_AB.state_dict(), "saved_models/%s/G_AB_%d.pth" % (opt.dataset_name, epoch))
torch.save(G_BA.state_dict(), "saved_models/%s/G_BA_%d.pth" % (opt.dataset_name, epoch))
torch.save(D_A.state_dict(), "saved_models/%s/D_A_%d.pth" % (opt.dataset_name, epoch))
torch.save(D_B.state_dict(), "saved_models/%s/D_B_%d.pth" % (opt.dataset_name, epoch))
if __name__ == "__main__" :
train_loop()