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trainer.py
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trainer.py
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import os
import math
from decimal import Decimal
import utility
import pdb
import torch
from torch.autograd import Variable
from tqdm import tqdm
class Trainer():
def __init__(self, args, loader, my_model, my_loss, ckp):
self.args = args
self.scale = args.scale
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.scheduler = utility.make_scheduler(args, self.optimizer)
if self.args.load != '.':
self.optimizer.load_state_dict(
torch.load(os.path.join(ckp.dir, 'optimizer.pt'))
)
for _ in range(len(ckp.log)): self.scheduler.step()
self.error_last = 1e8
######by given the scale and the size of input image
######we caculate the input matrix for the weight prediction network
###### input matrix for weight prediction network
def input_matrix_wpn(self,inH, inW, scale, add_scale=True):
'''
inH, inW: the size of the feature maps
scale: is the upsampling times
'''
outH, outW = int(scale*inH), int(scale*inW)
#### mask records which pixel is invalid, 1 valid or o invalid
#### h_offset and w_offset caculate the offset to generate the input matrix
scale_int = int(math.ceil(scale))
h_offset = torch.ones(inH, scale_int, 1)
mask_h = torch.zeros(inH, scale_int, 1)
w_offset = torch.ones(1, inW, scale_int)
mask_w = torch.zeros(1, inW, scale_int)
if add_scale:
scale_mat = torch.zeros(1,1)
scale_mat[0,0] = 1.0/scale
#res_scale = scale_int - scale
#scale_mat[0,scale_int-1]=1-res_scale
#scale_mat[0,scale_int-2]= res_scale
scale_mat = torch.cat([scale_mat]*(inH*inW*(scale_int**2)),0) ###(inH*inW*scale_int**2, 4)
####projection coordinate and caculate the offset
h_project_coord = torch.arange(0,outH, 1).float().mul(1.0/scale)
int_h_project_coord = torch.floor(h_project_coord)
offset_h_coord = h_project_coord - int_h_project_coord
int_h_project_coord = int_h_project_coord.int()
w_project_coord = torch.arange(0, outW, 1).float().mul(1.0/scale)
int_w_project_coord = torch.floor(w_project_coord)
offset_w_coord = w_project_coord - int_w_project_coord
int_w_project_coord = int_w_project_coord.int()
####flag for number for current coordinate LR image
flag = 0
number = 0
for i in range(outH):
if int_h_project_coord[i] == number:
h_offset[int_h_project_coord[i], flag, 0] = offset_h_coord[i]
mask_h[int_h_project_coord[i], flag, 0] = 1
flag += 1
else:
h_offset[int_h_project_coord[i], 0, 0] = offset_h_coord[i]
mask_h[int_h_project_coord[i], 0, 0] = 1
number += 1
flag = 1
flag = 0
number = 0
for i in range(outW):
if int_w_project_coord[i] == number:
w_offset[0, int_w_project_coord[i], flag] = offset_w_coord[i]
mask_w[0, int_w_project_coord[i], flag] = 1
flag += 1
else:
w_offset[0, int_w_project_coord[i], 0] = offset_w_coord[i]
mask_w[0, int_w_project_coord[i], 0] = 1
number += 1
flag = 1
## the size is scale_int* inH* (scal_int*inW)
h_offset_coord = torch.cat([h_offset] * (scale_int * inW), 2).view(-1, scale_int * inW, 1)
w_offset_coord = torch.cat([w_offset] * (scale_int * inH), 0).view(-1, scale_int * inW, 1)
####
mask_h = torch.cat([mask_h] * (scale_int * inW), 2).view(-1, scale_int * inW, 1)
mask_w = torch.cat([mask_w] * (scale_int * inH), 0).view(-1, scale_int * inW, 1)
pos_mat = torch.cat((h_offset_coord, w_offset_coord), 2)
mask_mat = torch.sum(torch.cat((mask_h,mask_w),2),2).view(scale_int*inH,scale_int*inW)
mask_mat = mask_mat.eq(2)
pos_mat = pos_mat.contiguous().view(1, -1,2)
if add_scale:
pos_mat = torch.cat((scale_mat.view(1,-1,1), pos_mat),2)
return pos_mat,mask_mat ##outH*outW*2 outH=scale_int*inH , outW = scale_int *inW
def train(self):
self.scheduler.step()
self.loss.step()
epoch = self.scheduler.last_epoch + 1
lr = self.scheduler.get_lr()[0]
self.ckp.write_log(
'[Epoch {}]\tLearning rate: {:.2e}'.format(epoch, Decimal(lr))
)
self.loss.start_log()
self.model.train()
device = torch.device('cpu' if self.args.cpu else 'cuda')
timer_data, timer_model = utility.timer(), utility.timer()
for batch, (lr, hr, _, idx_scale) in enumerate(self.loader_train):
lr, hr = self.prepare(lr, hr)
timer_data.hold()
timer_model.tic()
N,C,H,W = lr.size()
_,_,outH,outW = hr.size()
scale_coord_map, mask = self.input_matrix_wpn(H,W,self.args.scale[idx_scale]) ### get the position matrix, mask
if self.args.n_GPUs>1 and not self.args.cpu:
scale_coord_map = torch.cat([scale_coord_map]*self.args.n_GPUs,0)
else:
scale_coord_map = scale_coord_map.to(device)
self.optimizer.zero_grad()
sr = self.model(lr, idx_scale, scale_coord_map)
re_sr = torch.masked_select(sr,mask.to(device))
re_sr = re_sr.contiguous().view(N,C,outH,outW)
loss = self.loss(re_sr, hr)
if loss.item() < self.args.skip_threshold * self.error_last:
loss.backward()
self.optimizer.step()
else:
print('Skip this batch {}! (Loss: {})'.format(
batch + 1, loss.item()
))
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]
if self.args.n_GPUs == 1:
target = self.model
else:
target = self.model #.module
torch.save(
target.state_dict(),
os.path.join(self.ckp.dir,'model', 'model_{}.pt'.format(epoch))
)
## save models
def test(self):
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(torch.zeros(1, len(self.scale)))
self.model.eval()
timer_test = utility.timer()
device = torch.device('cpu' if self.args.cpu else 'cuda')
with torch.no_grad():
for idx_scale, scale in enumerate(self.scale):
eval_acc = 0
eval_acc_ssim = 0
self.loader_test.dataset.set_scale(idx_scale)
#tqdm_test = tqdm(self.loader_test, ncols=80)
for idx_img, (lr, hr, filename, _) in enumerate(self.loader_test):
filename = filename[0]
no_eval = (hr.nelement() == 1)
if not no_eval:
lr, hr = self.prepare(lr, hr)
else:
lr, = self.prepare(lr)
N,C,H,W = lr.size()
scale = self.args.scale[idx_scale]
outH,outW = int(H*scale),int(W*scale)
#_,_,outH,outW = hr.size()
#timer_test.tic()
scale_coord_map, mask = self.input_matrix_wpn(H,W,self.args.scale[idx_scale])
#position, mask = self.pos_matrix(H,W,self.args.scale[idx_scale])
#print(timer_test.toc())
if self.args.n_GPUs>1 and not self.args.cpu:
scale_coord_map = torch.cat([scale_coord_map]*self.args.n_GPUs,0)
else:
scale_coord_map = scale_coord_map.to(device)
timer_test.tic()
sr = self.model(lr, idx_scale,scale_coord_map)
timer_test.hold()
re_sr = torch.masked_select(sr,mask.to(device))
sr = re_sr.contiguous().view(N,C,outH,outW)
sr = utility.quantize(sr, self.args.rgb_range)
#timer_test.hold()
save_list = [sr]
if not no_eval:
eval_acc += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range,
benchmark=self.loader_test.dataset.benchmark
)
eval_acc_ssim += utility.calc_ssim(
sr, hr, scale,
benchmark=self.loader_test.dataset.benchmark
)
save_list.extend([lr, hr])
if self.args.save_results:
a=1
self.ckp.save_results(filename, save_list, scale)
self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
best = self.ckp.log.max(0)
# print(timer_test.acc/100)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} SSIM: {:.4f} (Best: {:.3f} @epoch {})'.format(
self.args.data_test,
scale,
self.ckp.log[-1, idx_scale],
eval_acc_ssim / len(self.loader_test),
best[0][idx_scale],
best[1][idx_scale] + 1
)
)
self.ckp.write_log(
'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch))
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:
self.test()
return True
else:
epoch = self.scheduler.last_epoch + 1
return epoch >= self.args.epochs