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train.py
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train.py
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import argparse
import logging
import os
import time
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch.utils.data import DataLoader
from nvae.dataset import ImageFolderDataset
from nvae.utils import add_sn
from nvae.vae_celeba import NVAE
class WarmupKLLoss:
def __init__(self, init_weights, steps,
M_N=0.005,
eta_M_N=1e-5,
M_N_decay_step=3000):
"""
预热KL损失，先对各级别的KL损失进行预热，预热完成后，对M_N的值进行衰减,所有衰减策略采用线性衰减
:param init_weights: 各级别 KL 损失的初始权重
:param steps: 各级别KL损失从初始权重增加到1所需的步数
:param M_N: 初始M_N值
:param eta_M_N: 最小M_N值
:param M_N_decay_step: 从初始M_N值到最小M_N值所需的衰减步数
"""
self.init_weights = init_weights
self.M_N = M_N
self.eta_M_N = eta_M_N
self.M_N_decay_step = M_N_decay_step
self.speeds = [(1. - w) / s for w, s in zip(init_weights, steps)]
self.steps = np.cumsum(steps)
self.stage = 0
self._ready_start_step = 0
self._ready_for_M_N = False
self._M_N_decay_speed = (self.M_N - self.eta_M_N) / self.M_N_decay_step
def _get_stage(self, step):
while True:
if self.stage > len(self.steps) - 1:
break
if step <= self.steps[self.stage]:
return self.stage
else:
self.stage += 1
return self.stage
def get_loss(self, step, losses):
loss = 0.
stage = self._get_stage(step)
for i, l in enumerate(losses):
# Update weights
if i == stage:
speed = self.speeds[stage]
t = step if stage == 0 else step - self.steps[stage - 1]
w = min(self.init_weights[i] + speed * t, 1.)
elif i < stage:
w = 1.
else:
w = self.init_weights[i]
# 如果所有级别的KL损失的预热都已完成
if self._ready_for_M_N == False and i == len(losses) - 1 and w == 1.:
# 准备M_N的衰减
self._ready_for_M_N = True
self._ready_start_step = step
l = losses[i] * w
loss += l
if self._ready_for_M_N:
M_N = max(self.M_N - self._M_N_decay_speed *
(step - self._ready_start_step), self.eta_M_N)
else:
M_N = self.M_N
return M_N * loss
if __name__ == '__main__':
LOG_FORMAT = "%(asctime)s - %(levelname)s - %(message)s"
logging.basicConfig(level=logging.INFO, format=LOG_FORMAT)
parser = argparse.ArgumentParser(description="Trainer for state AutoEncoder model.")
parser.add_argument("--epochs", type=int, default=500, help="number of epochs.")
parser.add_argument("--batch_size", type=int, default=128, help="size of each sample batch")
parser.add_argument("--dataset_path", type=str, required=True, help="dataset path")
parser.add_argument("--pretrained_weights", type=str, help="if specified starts from checkpoint model")
parser.add_argument("--n_cpu", type=int, default=16, help="number of cpu threads to use during batch generation")
opt = parser.parse_args()
epochs = opt.epochs
batch_size = opt.batch_size
dataset_path = opt.dataset_path
train_ds = ImageFolderDataset(dataset_path, img_dim=64)
train_dataloader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=opt.n_cpu)
os.makedirs("checkpoints", exist_ok=True)
os.makedirs("output", exist_ok=True)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = NVAE(z_dim=512, img_dim=(64, 64))
# apply Spectral Normalization
model.apply(add_sn)
model.to(device)
if opt.pretrained_weights:
model.load_state_dict(torch.load(opt.pretrained_weights, map_location=device), strict=False)
warmup_kl = WarmupKLLoss(init_weights=[1., 1. / 2, 1. / 8],
steps=[4500, 3000, 1500],
M_N=opt.batch_size / len(train_ds),
eta_M_N=5e-6,
M_N_decay_step=36000)
print('M_N=', warmup_kl.M_N, 'ETA_M_N=', warmup_kl.eta_M_N)
optimizer = torch.optim.Adamax(model.parameters(), lr=0.01)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=15, eta_min=1e-4)
step = 0
for epoch in range(epochs):
model.train()
n_true = 0
total_size = 0
total_loss = 0
for i, image in enumerate(train_dataloader):
optimizer.zero_grad()
image = image.to(device)
image_recon, recon_loss, kl_losses = model(image)
kl_loss = warmup_kl.get_loss(step, kl_losses)
loss = recon_loss + kl_loss
log_str = "\r---- [Epoch %d/%d, Step %d/%d] loss: %.6f----" % (
epoch, epochs, i, len(train_dataloader), loss.item())
logging.info(log_str)
loss.backward()
optimizer.step()
step += 1
if step != 0 and step % 100 == 0:
with torch.no_grad():
z = torch.randn((1, 512, 2, 2)).to(device)
gen_img, _ = model.decoder(z)
gen_img = gen_img.permute(0, 2, 3, 1)
gen_img = gen_img[0].cpu().numpy() * 255
gen_img = gen_img.astype(np.uint8)
plt.imshow(gen_img)
# plt.savefig(f"output/ae_ckpt_%d_%.6f.png" % (epoch, total_loss))
plt.show()
scheduler.step()
torch.save(model.state_dict(), f"checkpoints/ae_ckpt_%d_%.6f.pth" % (epoch, loss.item()))
model.eval()
with torch.no_grad():
z = torch.randn((1, 512, 2, 2)).to(device)
gen_img, _ = model.decoder(z)
gen_img = gen_img.permute(0, 2, 3, 1)
gen_img = gen_img[0].cpu().numpy() * 255
gen_img = gen_img.astype(np.uint8)
plt.imshow(gen_img)
# plt.savefig(f"output/ae_ckpt_%d_%.6f.png" % (epoch, total_loss))
plt.show()