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trainer_ape.py
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trainer_ape.py
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import os
import math
from decimal import Decimal
import numpy as np
import time
import utility
import warnings
warnings.filterwarnings("ignore")
import torch
import torch.nn as nn
import torch.nn.utils as utils
from tqdm import tqdm
from model.patchnet import PatchNet
# import lpips
from torch.nn import functional as F
from pytorch_msssim import ssim
from data.utils_image import calculate_ssim
class Trainer():
def __init__(self, args, loader, my_model, my_loss, ckp):
self.args = args
self.scale = args.scale
self.ssim = args.ssim
self.ckp = ckp
self.loader_train = loader.loader_train
self.loader_test = loader.loader_test
self.model = my_model
self.loss = my_loss
self.optimizer = utility.make_optimizer(args, self.model)
self.device = torch.device('cpu' if args.cpu else 'cuda')
self.de_loss = nn.MSELoss()
if self.args.load != '':
self.optimizer.load(ckp.dir, epoch=len(ckp.log))
self.error_last = 1e8
if self.args.patchnet:
self.patchnet = PatchNet(args).to(self.device)
def train(self):
self.loss.step()
epoch = self.optimizer.get_last_epoch() + 1
lr = self.optimizer.get_lr()
self.ckp.write_log(
'[Epoch {}]\tLearning rate: {:.2e}'.format(epoch, Decimal(lr))
)
self.loss.start_log()
self.model.train()
timer_data, timer_model = utility.timer(), utility.timer()
# TEMP
self.loader_train.dataset.set_scale(0)
for batch, (lr, hr, _,) in enumerate(self.loader_train):
lr, hr = self.prepare(lr, hr)
timer_data.hold()
timer_model.tic()
self.optimizer.zero_grad()
sr, ic = self.model(lr, 0)
loss = 0
pre_psnr = 0
for sr_i, ic_i in zip(sr, ic):
now_psnr = utility.calc_psnr(sr_i, hr, self.scale[0], self.args.rgb_range)
ic_i_gt = 1 - torch.tanh(torch.tensor(now_psnr - pre_psnr))
pre_psnr = now_psnr
loss = loss + self.loss(sr_i, hr) + self.de_loss(ic_i.T.squeeze(), ic_i_gt)
loss.backward()
if self.args.gclip > 0:
utils.clip_grad_value_(
self.model.parameters(),
self.args.gclip
)
self.optimizer.step()
timer_model.hold()
if (batch + 1) % self.args.print_every == 0:
self.ckp.write_log('[{}/{}]\t{}\t{:.1f}+{:.1f}s'.format(
(batch + 1) * self.args.batch_size,
len(self.loader_train.dataset),
self.loss.display_loss(batch),
timer_model.release(),
timer_data.release()))
timer_data.tic()
self.loss.end_log(len(self.loader_train))
self.error_last = self.loss.log[-1, -1]
self.optimizer.schedule()
torch.cuda.empty_cache()
def test(self):
torch.set_grad_enabled(False)
epoch = self.optimizer.get_last_epoch()
self.ckp.write_log('\nEvaluation:')
if self.args.model.find("RCAN") >= 0:
exit_len = int(self.args.n_resgroups/self.args.exit_interval)
elif self.args.model.find("EDSR") >= 0:
exit_len = int(self.args.n_resblocks/self.args.exit_interval)
elif self.args.model.find("VDSR") >= 0:
exit_len = int((self.args.n_resblocks-2)/self.args.exit_interval)
elif self.args.model.find("ECBSR") >= 0:
exit_len = int((self.args.m_ecbsr)/self.args.exit_interval)
elif self.args.model.find("FSRCNN") >= 0:
exit_len = int(4/self.args.exit_interval)
elif self.args.model.find("RRDB") >= 0:
exit_len = int(self.args.n_resblocks/self.args.exit_interval)
elif self.args.model.find("SWINIR") >= 0:
exit_len = int(6/self.args.exit_interval)
self.ckp.add_log(
torch.zeros(1, len(self.loader_test), exit_len)
)
self.model.eval()
timer_test = utility.timer()
if self.args.save_results: self.ckp.begin_background()
for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale)
ssim_total = 0
save_dict = {}
for lr, hr, filename in tqdm(d, ncols=80):
lr, hr = self.prepare(lr, hr)
if self.args.model.find("SWINIR") >= 0:
window_size = 8
scale = self.args.scale[0]
mod_pad_h, mod_pad_w = 0, 0
_, _, h, w = lr.size()
if h % window_size != 0:
mod_pad_h = window_size - h % window_size
if w % window_size != 0:
mod_pad_w = window_size - w % window_size
lr = F.pad(lr, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
sr, ic = self.model(lr, idx_scale)
for i in range(len(sr)):
_, _, h, w = sr[i].size()
sr[i] = sr[i][:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]
else:
sr, ic = self.model(lr, idx_scale)
# sr, ics = self.model(lr, idx_scale)
for i, sr_i in enumerate(sr):
sr_i = utility.quantize(sr_i, self.args.rgb_range)
save_dict['SR-{}'.format(i)] = sr_i
item_psnr = utility.calc_psnr(sr_i, hr, scale, self.args.rgb_range, dataset=d)
self.ckp.log[-1, idx_data, i] += item_psnr.cpu()
if self.ssim:
sr_i_np = sr_i.squeeze().cpu().permute(1,2,0).numpy()
hr_np = hr.squeeze().cpu().permute(1,2,0).numpy()
ssim = calculate_ssim(sr_i_np, hr_np)
ssim_total += ssim
if self.args.save_gt:
save_dict['LR'] = lr
save_dict['HR'] = hr
if self.args.save_results:
self.ckp.save_results_dynamic(d, filename[0], save_dict, scale)
torch.cuda.empty_cache()
self.ckp.log[-1, idx_data, :] /= len(d)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, -1],
best[0][idx_data, -1],
best[1][idx_data, -1] + 1
)
)
if self.ssim:
self.ckp.write_log(
'[{} x{}]\tSSIM: {:.4f}'.format(
d.dataset.name,
scale,
ssim_total/len(d)
)
)
self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
self.ckp.write_log('Saving...')
if self.args.save_results:
self.ckp.end_background()
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
def test_dynamic(self):
torch.set_grad_enabled(False)
if self.args.model.find("RCAN") >= 0:
exit_len = int(self.args.n_resgroups/self.args.exit_interval)
elif self.args.model.find("EDSR") >= 0:
exit_len = int(self.args.n_resblocks/self.args.exit_interval)
elif self.args.model.find("VDSR") >= 0:
exit_len = int((self.args.n_resblocks-2)/self.args.exit_interval)
elif self.args.model.find("ECBSR") >= 0:
exit_len = int((self.args.m_ecbsr)/self.args.exit_interval)
elif self.args.model.find("FSRCNN") >= 0:
exit_len = int(4/self.args.exit_interval)
elif self.args.model.find("RRDB") >= 0:
exit_len = int(self.args.n_resblocks/self.args.exit_interval)
elif self.args.model.find("SWINIR") >= 0:
exit_len = int(6/self.args.exit_interval)
self.ckp.add_log(
torch.zeros(1, len(self.loader_test), len(self.scale))
)
self.model.eval()
# self.warm_up()
self.ckp.write_log('\nEvaluation:')
timer_test = utility.timer()
timer_pre = utility.timer()
timer_model = utility.timer()
timer_post = utility.timer()
if self.args.save_results: self.ckp.begin_background()
for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale)
ssim_total = 0
save_dict = {}
exit_list = torch.zeros((1,exit_len))
AVG_exit = 0
pass_time_list = []
total_exit_index_list = torch.Tensor()
for lr, hr, filename in d:
pass_start = time.time()
timer_pre.tic()
lr, hr = self.prepare(lr, hr) # (B,C,H,W)
lr_list, num_h, num_w, new_h, new_w = utility.crop_parallel(lr, self.patch_size//scale, self.step//scale)
timer_pre.hold()
sr_list = torch.Tensor()
exit_index_list = torch.Tensor()
avg_exit = 0
pbar = tqdm(range(len(lr_list)//self.args.n_parallel + 1), ncols=120)
for lr_patch_index in pbar:
timer_model.tic()
sr_patches, exit_index, ic = self.model(lr_list[lr_patch_index*self.args.n_parallel:(lr_patch_index+1)*self.args.n_parallel], idx_scale)
torch.cuda.synchronize()
timer_model.hold()
sr_list = torch.cat([sr_list, sr_patches.cpu()])
for index in exit_index:
exit_list[-1, int(index)] += 1
exit_index_list = torch.cat([exit_index_list,exit_index.cpu()])
total_exit_index_list = torch.cat([total_exit_index_list,exit_index_list])
timer_post.tic()
sr = utility.combine(sr_list, num_h, num_w, new_h*scale, new_w*scale, self.patch_size, self.step)
pass_end = time.time()
pass_time = pass_end-pass_start
pass_time_list.append(pass_time)
sr = utility.quantize(sr, self.args.rgb_range)
save_dict['SR'] = sr
timer_post.hold()
if self.args.add_mask:
sr_mask = utility.add_mask(sr, scale, num_h, num_w, new_h*scale, new_w*scale, self.patch_size, self.step, exit_index_list)
save_dict['SR_MASK'] = sr_mask
hr = hr[:, :, 0:new_h*scale, 0:new_w*scale].cpu()
item_psnr = utility.calc_psnr(sr, hr, scale, self.args.rgb_range, dataset=d)
self.ckp.log[-1, idx_data, idx_scale] += item_psnr.cpu()
avg_exit = utility.calc_avg_exit(exit_list[-1])
avg_flops, avg_flops_percent = utility.calc_flops(exit_list[-1], self.args.model, scale, self.args.exit_interval)
# self.ckp.write_log("{}\tPSNR:{:.3f}\taverage exit:[{:.2f}/{}]\tFlops:{:.2f}GFlops ({:.2f}%) pass time:{:.3f}s".format(filename, item_psnr, avg_exit, exit_len-1, avg_flops, avg_flops_percent, pass_time))
if self.ssim:
ssim_item = ssim(sr, hr, data_range=255, size_average=False).item()
ssim_total += ssim_item
self.ckp.write_log("{}\tPSNR:{:.3f}\tSSIM:{:.4f}\taverage exit:[{:.2f}/{}]\tFlops:{:.2f}GFlops ({:.2f}%) pass time:{:.3f}s".format(filename, item_psnr, ssim_item, avg_exit, exit_len-1, avg_flops, avg_flops_percent, pass_time))
else:
self.ckp.write_log("{}\tPSNR:{:.3f}\taverage exit:[{:.2f}/{}]\tFlops:{:.2f}GFlops ({:.2f}%) pass time:{:.3f}s".format(filename, item_psnr, avg_exit, exit_len-1, avg_flops, avg_flops_percent, pass_time))
if self.args.save_gt:
save_dict['LR'] = lr
save_dict['HR'] = hr
if self.args.save_results:
self.ckp.save_results_dynamic(d, filename[0], save_dict, scale)
# torch.cuda.empty_cache()
exit_list = torch.cat([exit_list,torch.zeros((1,exit_len))])
self.ckp.log[-1, idx_data, :] /= len(d)
AVG_exit = utility.calc_avg_exit(exit_list)
AVG_flops, AVG_flops_percent = utility.calc_flops(exit_list, self.args.model, scale, self.args.exit_interval)
AVG_pass_time = np.array(pass_time_list)[1:].mean()
if self.ssim:
self.ckp.write_log(
'[{} x{}] PSNR: {:.3f}\tSSIM: {:.4f}\tThreshold: {}\tAverage exits:[{:.2f}/{}]\tFlops:{:.2f}GFlops ({:.3f}%) avg pass time: {:.2f}s'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, -1],
ssim_total/len(d),
self.args.exit_threshold,
AVG_exit,
exit_len-1,
AVG_flops,
AVG_flops_percent,
AVG_pass_time
)
)
else:
self.ckp.write_log(
'[{} x{}] PSNR: {:.3f}\tThreshold: {}\tAverage exits:[{:.2f}/{}]\tFlops:{:.2f}GFlops ({:.3f}%) avg pass time: {:.2f}s'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, -1],
self.args.exit_threshold,
AVG_exit,
exit_len-1,
AVG_flops,
AVG_flops_percent,
AVG_pass_time
)
)
# self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
self.ckp.write_log('Saving...')
if self.args.save_results:
self.ckp.end_background()
self.ckp.save_exit_list(total_exit_index_list)
self.ckp.write_log(
'[whole]\t {:.4f}s\n'.format(timer_test.toc()), refresh=True
)
self.ckp.write_log(
'[Total]\t pre:{:.4f}s\tmodel:{:.4f}s\tpost:{:.4f}s\n'.format(
timer_pre.release(reset=False),
timer_model.release(reset=False),
timer_post.release(reset=False)
),refresh=True
)
self.ckp.write_log(
'[Average]\t pre:{:.4f}s\tmodel:{:.4f}s\tpost:{:.4f}s\n'.format(
timer_pre.release(avg=True),
timer_model.release(avg=True),
timer_post.release(avg=True)
),refresh=True
)
torch.set_grad_enabled(True)
def test_static(self):
torch.set_grad_enabled(False)
epoch = self.optimizer.get_last_epoch()
self.ckp.add_log(
torch.zeros(1, len(self.loader_test), len(self.scale))
)
self.model.eval()
# self.warm_up()
self.ckp.write_log('\nEvaluation:')
timer_test = utility.timer()
timer_pre = utility.timer()
timer_model = utility.timer()
timer_post = utility.timer()
if self.args.save_results: self.ckp.begin_background()
for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale)
ssim_total = 0
save_dict = {}
for lr, hr, filename in d:
timer_pre.tic()
lr, hr = self.prepare(lr, hr) # (B,C,H,W)
lr_list, num_h, num_w, new_h, new_w = utility.crop_parallel(lr, self.patch_size//scale, self.step//scale)
timer_pre.hold()
sr_list = torch.Tensor()
pbar = tqdm(range(len(lr_list)//self.args.n_parallel + 1), ncols=120)
for lr_patch_index in pbar:
timer_model.tic()
sr_patches = self.model(lr_list[lr_patch_index*self.args.n_parallel:(lr_patch_index+1)*self.args.n_parallel], idx_scale)
torch.cuda.synchronize()
timer_model.hold()
sr_list = torch.cat([sr_list, sr_patches.cpu()])
timer_post.tic()
# sr = utility.combine(sr_list, num_h, num_w, new_h*scale, new_w*scale, self.patch_size, self.step)
sr = utility.seamless_combine(sr_list, num_h, num_w, new_h*scale, new_w*scale, self.patch_size, self.step)
sr = utility.quantize(sr, self.args.rgb_range)
save_dict['SR'] = sr
timer_post.hold()
hr = hr[:, :, 0:new_h*scale, 0:new_w*scale].cpu()
item_psnr = utility.calc_psnr(sr, hr, scale, self.args.rgb_range, dataset=d)
self.ckp.log[-1, idx_data, idx_scale] += item_psnr.cpu()
if self.ssim:
sr_np = sr.squeeze().cpu().permute(1,2,0).numpy()
hr_np = hr.squeeze().cpu().permute(1,2,0).numpy()
ssim = calculate_ssim(sr_np, hr_np)
ssim_total += ssim
self.ckp.write_log("{}\tPSNR:{:.3f}\tSSIM:{:.4f}".format(filename, item_psnr, ssim))
else:
self.ckp.write_log("{}\tPSNR:{:3f}".format(filename, item_psnr))
if self.args.save_gt:
save_dict['LR'] = lr
save_dict['HR'] = hr
if self.args.save_results:
self.ckp.save_results_dynamic(d, filename[0], save_dict, scale)
torch.cuda.empty_cache()
self.ckp.log[-1, idx_data, :] /= len(d)
best = self.ckp.log.max(0)
if self.ssim:
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f}\tSSIM: {:.4f} (Best: {:.3f} @epoch {})'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, idx_scale],
ssim_total/len(d),
best[0][idx_data, idx_scale],
best[1][idx_data, idx_scale] + 1
)
)
else:
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, idx_scale],
best[0][idx_data, idx_scale],
best[1][idx_data, idx_scale] + 1
)
)
# self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
self.ckp.write_log('Saving...')
if self.args.save_results:
self.ckp.end_background()
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))
self.ckp.write_log(
'[whole]\t {:.4f}s\n'.format(timer_test.toc()), refresh=True
)
self.ckp.write_log(
'[Total]\t pre:{:.4f}s\tmodel:{:.4f}s\tpost:{:.4f}s\n'.format(
timer_pre.release(reset=False),
timer_model.release(reset=False),
timer_post.release(reset=False)
),refresh=True
)
self.ckp.write_log(
'[Average]\t pre:{:.4f}s\tmodel:{:.4f}s\tpost:{:.4f}s\n'.format(
timer_pre.release(avg=True),
timer_model.release(avg=True),
timer_post.release(avg=True)
),refresh=True
)
torch.set_grad_enabled(True)
def warm_up(self):
self.ckp.write_log("warming up...\n")
for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale)
for i, (lr, hr, filename) in enumerate(d):
lr, hr = self.prepare(lr, hr)
sr = self.model(lr, idx_scale)
if i > 2:
break
# torch.cuda.empty_cache()
torch.cuda.synchronize()
self.ckp.write_log("warm up ended\n")
def prepare(self, *args):
device = torch.device('cpu' if self.args.cpu else 'cuda')
def _prepare(tensor):
if self.args.precision == 'half': tensor = tensor.half()
return tensor.to(device)
return [_prepare(a) for a in args]
def terminate(self):
if self.args.test_only:
if self.args.model.find("APE") >= 0:
self.test_dynamic()
else:
self.test_static()
return True
else:
epoch = self.optimizer.get_last_epoch() + 1
return epoch >= self.args.epochs