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train.py
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train.py
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import argparse
import os
import numpy as np
import math
import itertools
from datetime import date
import torchvision.transforms as transforms
from torchvision.utils import save_image
import torchvision
from config import *
from utils import *
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from data import Fashion_attr_prediction
from aae import * # import the model
import torch.nn as nn
import torch.nn.functional as F
import torch
# ----------
# Training
# ----------
cuda = True if torch.cuda.is_available() else False
today = date.today().strftime("%Y%m%d")
def train(b1, b2):
# Use binary cross-entropy loss
adversarial_loss = torch.nn.BCELoss()
pixelwise_loss = torch.nn.L1Loss()
device = torch.device("cuda" if cuda else "cpu")
# Initialize generator and discriminator
encoder = Encoder().to(device)
decoder = Decoder().to(device)
discriminator = Discriminator().to(device)
if cuda:
encoder.cuda()
decoder.cuda()
discriminator.cuda()
adversarial_loss.cuda()
pixelwise_loss.cuda()
# Configure data loader
# os.makedirs("../../data/deepfashion", exist_ok=True)
dataloader = torch.utils.data.DataLoader(
Fashion_attr_prediction(
categories=CATEGORIES,
type="train",
transform=TRANSFORM_FN,
crop=True
),
batch_size=TRAIN_BATCH_SIZE,
num_workers=NUM_WORKERS,
shuffle=True,
)
#for testing input images
#dataiter = iter(dataloader)
#images, labels = dataiter.next()
#save_image(images, "test.png", normalize=True)
#img = torchvision.utils.make_grid(images, normalize=True)
#npimg = img.numpy()
#plt.imshow(np.transpose(npimg, (1, 2, 0)))
#plt.show()
#return
# Optimizers
optimizer_G = torch.optim.Adam(
itertools.chain(encoder.parameters(), decoder.parameters()), lr=LR, betas=(b1, b2)
)
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=LR, betas=(b1, b2))
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
# generate fixed noise vector
n_row = 10
fixed_noise = Variable(Tensor(np.random.normal(0, 1, (n_row ** 2, LATENT_DIM))))
# make directory for saving images
path = "/".join([str(c) for c in [GENERATED_BASE, "aae", CONFIG_AS_STR, "train"]])
os.makedirs(path, exist_ok=True)
# save losses across all
G_losses = []
D_losses = []
# training loop
for epoch in range(N_EPOCHS):
for i, (imgs, paths) in enumerate(dataloader):
# Configure input
real_imgs = Variable(imgs.type(Tensor))
# Adversarial ground truths
valid = Variable(Tensor(imgs.shape[0], 1).fill_(1.0), requires_grad=False)
fake = Variable(Tensor(imgs.shape[0], 1).fill_(0.0), requires_grad=False)
# ---------------------
# Train Discriminator
# ---------------------
optimizer_D.zero_grad()
# Sample noise as discriminator ground truth
z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], LATENT_DIM))))
encoded_imgs = encoder(real_imgs)
# Measure discriminator's ability to classify real from generated samples
real_loss = adversarial_loss(discriminator(z), valid)
fake_loss = adversarial_loss(discriminator(encoded_imgs.detach()), fake)
d_loss = 0.5 * (real_loss + fake_loss)
d_loss.backward()
optimizer_D.step()
if i % N_CRITIC == 0:
# -----------------
# Train Generator
# -----------------
optimizer_G.zero_grad()
encoded_imgs = encoder(real_imgs)
decoded_imgs = decoder(encoded_imgs)
# Loss measures generator's ability to fool the discriminator
g_loss = 0.001 * adversarial_loss(discriminator(encoded_imgs), valid) + 0.999 * pixelwise_loss(
decoded_imgs, real_imgs
)
g_loss.backward()
optimizer_G.step()
batches_done = epoch * len(dataloader) + i
if batches_done % 50 == 0:
print(
"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
% (epoch, N_EPOCHS, i, len(dataloader), d_loss.item(), g_loss.item())
)
if batches_done % SAMPLE_INTERVAL == 0:
name = gen_name(today, batches_done)
if FIXED_NOISE:
sample_image(decoder=decoder, n_row=n_row, path=path, name=name, fixed_noise=fixed_noise)
else:
sample_image(decoder=decoder, n_row=n_row, path=path, name=name)
# save losses
G_losses.append(g_loss.item())
D_losses.append(d_loss.item())
#save_model(encoder, epoch, "encoder")
#save_model(decoder, epoch, "decoder")
#save_model(discriminator, epoch, "discriminator")
plot_losses("aae", G_losses, D_losses, CONFIG_AS_STR, today)
return encoder, decoder, discriminator
if __name__=="__main__":
os.makedirs("images", exist_ok=True)
parser = argparse.ArgumentParser()
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("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
opt = parser.parse_args()
print(opt)
encoder, decoder, discriminator = train(opt.b1, opt.b2)
# ----------
# Save Model and create Training Log
# ----------
# TODO: save this to a folder logs
print(opt)
print("Saved Encoder to {}".format(save_model(encoder, "aae_encoder", CONFIG_AS_STR, today)))
print("Saved Decoder to {}".format(save_model(decoder, "aae_decoder", CONFIG_AS_STR, today)))
print("Saved Discriminator to {}".format(save_model(discriminator, "aae_discriminator", CONFIG_AS_STR, today)))