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train.py
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train.py
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import random
import time
import numpy as np
from mxnet import image, ndarray, autograd
from mxnet.gluon import data as gdata
from mxnet.gluon.data.vision import transforms
from mxnet.base import numeric_types
from mxnet.gluon.data import DataLoader
import mxnet.ndarray as nd
import mxnet as mx
from mxboard import SummaryWriter
from mxnet.gluon.model_zoo import vision
from model import *
from dataset import *
from monitor import *
def weights_init(params):
for param_name in params:
param = params[param_name]
if param_name.find('conv') != -1:
if param_name.find('weight') != -1:
param.set_data(nd.random.normal(0.0,0.02,shape=param.data().shape))
elif param_name.find('bias') != -1:
param.set_data(nd.zeros(param.data().shape))
elif param_name.find('batchnorm') != -1:
if param_name.find('gamma') != -1:
param.set_data(nd.random.normal(1.0, 0.02,shape=param.data().shape))
elif param_name.find('beta') != -1:
param.set_data(nd.zeros(param.data().shape))
def mse_loss(output, target):
e = ((output - target) ** 2).mean(axis=0, exclude=True)
return e
def load_vgg_model_features(ctx_list, last_layer):
vgg19 = vision.vgg19(pretrained=True, root='/root/.mxnet/models/', ctx=ctx_list)
features = vgg19.features[:last_layer]
return features
def vgg_feature(input, features):
return features(input)
class loss_dict:
def __init__(self):
self.losses = {}
def __getitem__(self, item):
return self.losses[item]
def add(self, **kwargs):
for key, value in kwargs.items():
if key not in self.losses:
self.losses[key] = [value]
else:
self.losses[key].append(value)
def reset(self):
self.losses = {}
def train(opt):
sw = SummaryWriter(logdir='./logs', flush_secs=5)
decay_every = int(opt.n_epoch / 2)
if opt.experiment is None:
opt.experiment = 'samples'
os.system('mkdir {}'.format(opt.experiment))
if opt.gpu_ids == '-1':
context = [mx.cpu()]
else:
#context = mx.gpu(7)
context = [mx.gpu(int(i)) for i in opt.gpu_ids.split(',') if i.strip()]
print("context: {}".format(context))
features = load_vgg_model_features(ctx_list=context, last_layer=28)
##### Prapare data for training or validation #####
dataset = DataSet(opt.dataroot, RandomCrop(opt.fineSize), transforms.Resize(int(opt.fineSize / 4), interpolation=3),
transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
dataloader = DataLoader(dataset, batch_size=opt.batchSize,
shuffle=True, num_workers=int(opt.workers), last_batch='rollover')
##### Build Network #####
netG = SRGenerator()
netG.initialize(ctx=context[0])
netD = SRDiscriminator()
netD.initialize(ctx=context[0])
# Enforce non-deferred initialization by one forward pass computation
dummy_in = nd.random.uniform(0, 1, (1, 3, int(opt.fineSize / 4), int(opt.fineSize / 4)), ctx=context[0])
netD(netG(dummy_in))
# Our own re-setting on parameters
weights_init(netG.collect_params())
netG.collect_params().reset_ctx(context)
weights_init(netD.collect_params())
netD.collect_params().reset_ctx(context)
optimizer_G = gluon.Trainer(params=netG.collect_params(),
optimizer='adam',
optimizer_params={'learning_rate': opt.lr_init, 'beta1': opt.beta1},
kvstore='local')
optimizer_D = gluon.Trainer(params=netD.collect_params(),
optimizer='adam',
optimizer_params={'learning_rate': opt.lr_init, 'beta1': opt.beta1},
kvstore='local')
##### Stage 1/2 of Training Process #####
# Pre-train Generator G to avoid undesired local optima when training SRGAN.
print("Start pre-train Generator ...")
param_file = os.path.join(opt.experiment, 'netG_init_epoch.param')
if os.path.exists(param_file):
print("Load existed parameter file pre-trained: {}, skip the pre-train process.".format(param_file))
netG.load_parameters(param_file, ctx=context)
else:
print("No existed parameter file, keep going to pre-train.")
for epoch in range(opt.n_epoch_init):
start = time.time()
batch = 0
for hr_img_iter, lr_img_iter in dataloader:
#hr_img = hr_img.as_in_context(context)
#lr_img = lr_img.as_in_context(context)
hr_imgs = gluon.utils.split_and_load(hr_img_iter, ctx_list=context)
lr_imgs = gluon.utils.split_and_load(lr_img_iter, ctx_list=context)
with autograd.record():
ls = [mse_loss(hr_img, netG(lr_img)) for hr_img, lr_img in zip(hr_imgs, lr_imgs)]
for l in ls:
l.backward()
# with autograd.record():
# hr_img_pred = netG(mx.nd.array(lr_img))
# loss = mse_loss(mx.nd.array(hr_img), hr_img_predit)
# autograd.backward(loss)
optimizer_G.step(opt.batchSize)
print("Epoch %d: Batch %d: mse: %.8f" % (epoch, batch, ls[-1].mean().asscalar()))
batch += opt.batchSize
nd.waitall()
train_time = time.time() - start
print("Epoch %d: mse: %.8f trainning time:%.1f sec" % (epoch, ls[-1].mean().asscalar(), train_time))
if epoch % 20 == 0:
netG.save_parameters('{0}/netG_init_epoch_{1}.param'.format(opt.experiment, epoch))
if epoch == opt.n_epoch_init - 1:
netG.save_parameters('{0}/netG_init_epoch.param'.format(opt.experiment))
print("Pre-train Generator finished ...")
##### Stage 2/2 of Training Process #####
# Jointly optimize G and D, namely train SRGAN.
print("Start to train SRGAN ...")
mean_mask = nd.zeros((opt.batchSize, 3, opt.fineSize, opt.fineSize), ctx=context[0])
mean_mask[:, 0, :, :] = 0.485
mean_mask[:, 1, :, :] = 0.456
mean_mask[:, 2, :, :] = 0.406
std_mask = nd.zeros((opt.batchSize, 3, opt.fineSize, opt.fineSize), ctx=context[0])
std_mask[:, 0, :, :] = 0.229
std_mask[:, 1, :, :] = 0.224
std_mask[:, 2, :, :] = 0.225
real_label = nd.ones((opt.batchSize,), ctx=context[0])
fake_label = nd.zeros((opt.batchSize,), ctx=context[0])
mean_masks = mx.gluon.utils.split_and_load(mean_mask, ctx_list=context)
std_masks = mx.gluon.utils.split_and_load(std_mask, ctx_list=context)
real_labels = mx.gluon.utils.split_and_load(real_label, ctx_list=context)
fake_labels = mx.gluon.utils.split_and_load(fake_label, ctx_list=context)
loss_d = gluon.loss.SigmoidBinaryCrossEntropyLoss()
losses_log = loss_dict()
for epoch in range(0, opt.n_epoch):
start = time.time()
batch = 0
train_errD = 0
train_errG = 0
for hr_img_iter, lr_img_iter in dataloader:
losses_log.reset()
hr_imgs = gluon.utils.split_and_load(hr_img_iter, ctx_list=context)
lr_imgs = gluon.utils.split_and_load(lr_img_iter, ctx_list=context)
hr_fake_imgs = []
# Step1. Optimize D
# Step2. Optimize G
batch_errD = []
batch_errG = []
print("Optimize D in a Batch...")
with autograd.record():
for hr_img, lr_img, mean_mask, std_mask, real_label, fake_label in zip(hr_imgs, lr_imgs, mean_masks, std_masks, real_labels, fake_labels):
# errD computation
output = netD(hr_img).reshape((-1, 1))
errD_real = loss_d(output, real_label)
hr_img_fake = netG(lr_img)
hr_fake_imgs.append(hr_img_fake)
output = netD(hr_img_fake.detach()).reshape((-1, 1))
errD_fake = loss_d(output, fake_label)
errD = errD_real + errD_fake
batch_errD.append(errD)
losses_log.add(lr_img=lr_img, hr_img=hr_img, hr_img_fake=hr_img_fake)
# run backward on batch errD and update parameters
autograd.backward(batch_errD)
optimizer_D.step(opt.batchSize)
print("Optimize G in a Batch...")
with autograd.record():
for hr_img, lr_img, hr_img_fake, mean_mask, std_mask, real_label, fake_label in zip(hr_imgs, lr_imgs, hr_fake_imgs, mean_masks, std_masks, real_labels, fake_labels):
# errG computation
errM = mse_loss(hr_img_fake, hr_img)
input_fake = ((hr_img_fake + 1) / 2 - mean_mask) / std_mask
fake_emb = vgg_feature(input_fake, features)
input_real = ((hr_img + 1) / 2 - mean_mask) / std_mask
real_emb = vgg_feature(input_real, features)
errV = 6e-3 * mse_loss(fake_emb, real_emb)
output = netD(hr_img_fake).reshape((-1, 1))
errA = 1e-3 * loss_d(output, real_label)
errG = errM + errV + errA
batch_errG.append(errG)
# run backward on batch errG and update parameters
autograd.backward(batch_errG)
# for errG in batch_errG:
# errG.backward()
# losses_log.add(errG=errG, errM=errM, errV=errV, errA=errA)
optimizer_G.step(opt.batchSize)
# sum losses over all devices
train_errD += sum([errD.sum().asscalar() for errD in batch_errD])
train_errG += sum([errG.sum().asscalar() for errG in batch_errG])
print("Epoch:%d, Batch:%d ----- D-Loss = %.3f, G-Loss = %.3f (Time %.1f sec)"% (epoch, batch*opt.batchSize, train_errD, train_errG, time.time() - start))
batch += 1
plot_loss(sw, losses_log, epoch * len(dataloader) + batch, epoch, batch)
if epoch != 0 and (epoch % decay_every == 0):
optimizer_G.set_learning_rate(optimizer_G.learning_rate * opt.lr_decay)
optimizer_D.set_learning_rate(optimizer_D.learning_rate * opt.lr_decay)
if (epoch != 0) and (epoch % 10 == 0):
plot_img(sw, losses_log)
netG.save_parameters('{0}/netG_epoch_{1}.param'.format(opt.experiment, epoch))
netD.save_parameters('{0}/netD_epoch_{1}.param'.format(opt.experiment, epoch))
print("Train SRGAN finished ...")