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trainer.py
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import datetime
import time
import cv2
import mindspore
import mindspore.nn as nn
import mindspore.dataset as ds
import numpy as np
import os
from models import commen
from utils.generator import *
import dataset
def save_sample_png(sample_folder, sample_name, img_list, name_list, pixel_max_cnt = 255, height = -1, width = -1):
# Save image one-by-one
for i in range(len(img_list)):
img = img_list[i]
# Recover normalization
img = img * 255.0
# Process img_copy and do not destroy the data of img
#print(img.size())
img_copy = img.clone().data.permute(0, 2, 3, 1).cpu().numpy()
img_copy = np.clip(img_copy, 0, pixel_max_cnt)
img_copy = img_copy.astype(np.uint8)[0, :, :, :]
img_copy = cv2.cvtColor(img_copy, cv2.COLOR_BGR2RGB)
if (height != -1) and (width != -1):
img_copy = cv2.resize(img_copy, (width, height))
# Save to certain path
save_img_name = sample_name + '_' + name_list[i] + '.png'
save_img_path = os.path.join(sample_folder, save_img_name)
cv2.imwrite(save_img_path, img_copy)
def Pre_train(opt):
# ----------------------------------------
# Network training parameters
# ----------------------------------------
save_folder = opt.save_path
sample_folder = opt.sample_path
# utils.check_path(save_folder)
# utils.check_path(sample_folder)
criterion_L1 = nn.L1Loss()
criterion_L2 = nn.MSELoss()
#criterion_rainypred = mindspore.nn.L1Loss().cuda()
criterion_ssim = commen.SSIM()
generator = create_generator(opt)
optimizer_G = nn.Adam(params=generator.trainable_params(), learning_rate=opt.lr_g, beta1=opt.b1, beta2=opt.b2, weight_decay=opt.weight_decay)
print("pretrained models loaded")
def adjust_learning_rate(opt, epoch, optimizer):
target_epoch = opt.epochs - opt.lr_decrease_epoch
remain_epoch = opt.epochs - epoch
if epoch >= opt.lr_decrease_epoch:
lr = opt.lr_g * remain_epoch / target_epoch
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def save_model(opt, epoch, iteration, len_dataset, generator):
"""Save the model at "checkpoint_interval" and its multiple"""
# Define the name of trained model
"""
if opt.save_mode == 'epoch':
model_name = 'KPN_single_image_epoch%d_bs%d_mu%d_sigma%d.pth' % (epoch, opt.train_batch_size, opt.mu, opt.sigma)
if opt.save_mode == 'iter':
model_name = 'KPN_single_image_iter%d_bs%d_mu%d_sigma%d.pth' % (iteration, opt.train_batch_size, opt.mu, opt.sigma)
"""
if opt.save_mode == 'epoch':
model_name = 'KPN_rainy_image_epoch%d_bs%d.pth' % (epoch, opt.train_batch_size)
if opt.save_mode == 'iter':
model_name = 'KPN_rainy_image_iter%d_bs%d.pth' % (iteration, opt.train_batch_size)
save_model_path = os.path.join(opt.save_path, model_name)
if opt.multi_gpu == True:
if opt.save_mode == 'epoch':
if (epoch % opt.save_by_epoch == 0) and (iteration % len_dataset == 0):
mindspore.save_checkpoint(generator.module.state_dict(), save_model_path)
print('The trained model is successfully saved at epoch %d' % (epoch))
if opt.save_mode == 'iter':
if iteration % opt.save_by_iter == 0:
mindspore.save_checkpoint(generator.module.state_dict(), save_model_path)
print('The trained model is successfully saved at iteration %d' % (iteration))
else:
if opt.save_mode == 'epoch':
if (epoch % opt.save_by_epoch == 0) and (iteration % len_dataset == 0):
mindspore.save_checkpoint(generator.state_dict(), save_model_path)
print('The trained model is successfully saved at epoch %d' % (epoch))
if opt.save_mode == 'iter':
if iteration % opt.save_by_iter == 0:
mindspore.save_checkpoint(generator.state_dict(), save_model_path)
print('The trained model is successfully saved at iteration %d' % (iteration))
trainset = dataset.DenoisingDataset(opt)
print('The overall number of training images:', len(trainset))
train_loader = ds.GeneratorDataset(trainset,shuffle = True, num_parallel_workers = opt.num_workers)
train_loader.batch(opt.train_batch_size)
# ----------------------------------------
# Training
# ----------------------------------------
prev_time = time.time()
# For loop training
for epoch in range(opt.epochs):
for i, (true_input, true_target) in enumerate(train_loader):
print("in epoch %d" % i)
# Train Generator
optimizer_G.zero_grad()
fake_target = generator(true_input, true_input)
ssim_loss = -criterion_ssim(true_target, fake_target)
'''
#trans for enc_net
enc_trans = transforms.Compose([transforms.Normalize([.485, .456, .406], [.229, .224, .225])])
fake_target_norm = torch.from_numpy(np.zeros(fake_target.size())).cuda()
true_target_norm = torch.from_numpy(np.zeros(true_target.size())).cuda()
for j in range(fake_target.size()[0]):
fake_target_norm[j] = enc_trans(fake_target[j])
true_target_norm[j] = enc_trans(true_target[j])
'''
#print(fake_target_norm.size())
#enc_pred = encnet.evaluate(fake_target_norm.type(torch.FloatTensor).cuda())
#enc_pred = encnet(fake_target_norm.type(torch.FloatTensor).cuda())[0]
#enc_gt = encnet(true_target_norm.type(torch.FloatTensor).cuda())[0]
'''
enc_feat_pred = encnet_feat(fake_target_norm.type(torch.FloatTensor).cuda())[0]
enc_feat_gt = encnet_feat(true_target_norm.type(torch.FloatTensor).cuda())[0]
'''
#rain_layer_gt = true_input - true_target
#rain_layer_pred = true_input - fake_target
#rainy_pred = true_input - (fake_target * rain_layer_pred)
#print(type(true_input))
#print(type(fake_target))
# L1 Loss
Pixellevel_L1_Loss = criterion_L1(fake_target, true_target)
#enc_loss = criterion_L1(enc_pred, enc_gt)
#enc_feat_loss = criterion_L1(enc_feat_pred, enc_feat_gt)
#Pixellevel_L2_Loss = criterion_L2(fake_target, true_target)
#Pixellevel_L2_Loss = criterion_L2(rain_layer_pred, rain_layer_gt)
#Loss_rainypred = criterion_rainypred(rainy_pred, true_input)
# Overall Loss and optimize
loss = Pixellevel_L1_Loss + 0.2*ssim_loss
#loss = Pixellevel_L1_Loss
#loss = Pixellevel_L1_Loss + Pixellevel_L2_Loss + Loss_rainypred
loss.backward()
optimizer_G.step()
#check
'''
for j in encnet.named_parameters():
print(j)
break
'''
# Determine approximate time left
iters_done = epoch * len(train_loader) + i
iters_left = opt.epochs * len(train_loader) - iters_done
time_left = datetime.timedelta(seconds = iters_left * (time.time() - prev_time))
prev_time = time.time()
# Print log
print("\r[Epoch %d/%d] [Batch %d/%d] [Loss: %.4f %.4f] Time_left: %s" %
((epoch + 1), opt.epochs, i, len(train_loader), Pixellevel_L1_Loss.item(), ssim_loss.item(), time_left))
# Save model at certain epochs or iterations
save_model(opt, (epoch + 1), (iters_done + 1), len(train_loader), generator)
# Learning rate decrease at certain epochs
adjust_learning_rate(opt, (epoch + 1), optimizer_G)
### Sample data every epoch
if (epoch + 1) % 1 == 0:
img_list = [true_input, fake_target, true_target]
name_list = ['in', 'pred', 'gt']
save_sample_png(sample_folder = sample_folder, sample_name = 'train_epoch%d' % (epoch + 1), img_list = img_list, name_list = name_list, pixel_max_cnt = 255)
"""### Validation
val_PSNR = 0
num_of_val_image = 0
for j, (true_input, true_target) in enumerate(val_loader):
# To device
# A is for input image, B is for target image
true_input = true_input.cuda()
true_target = true_target.cuda()
# Forward propagation
with torch.no_grad():
fake_target = generator(true_input)
# Accumulate num of image and val_PSNR
num_of_val_image += true_input.shape[0]
val_PSNR += utils.psnr(fake_target, true_target, 1) * true_input.shape[0]
val_PSNR = val_PSNR / num_of_val_image
### Sample data every epoch
if (epoch + 1) % 1 == 0:
img_list = [true_input, fake_target, true_target]
name_list = ['in', 'pred', 'gt']
utils.save_sample_png(sample_folder = sample_folder, sample_name = 'val_epoch%d' % (epoch + 1), img_list = img_list, name_list = name_list, pixel_max_cnt = 255)
# Record average PSNR
print('PSNR at epoch %d: %.4f' % ((epoch + 1), val_PSNR))"""
# # 设计随机种子
# from mindspore.common.initializer import *
# mindspore.set_seed(1)
# net = nn.Conv1d(120, 240, 4, has_bias=False)
# print(net.__class__.__name__.find('Conv'))
# print(hasattr(net, 'weight'))
# net.weight.set_data(initializer(One(), net.weight.shape,net.weight.dtype))
# x = mindspore.Tensor(np.random.randn(1, 120, 32), mindspore.float32)
# y = net(x)
# optimizer_G = nn.Adam(params=net.trainable_params(), learning_rate=0.01, beta1=0.5, beta2=0.99, weight_decay=0)