__init__.py
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import os
from importlib import import_module
import torch
import torch.nn as nn
import torch.nn.parallel as P
import torch.utils.model_zoo
class Model(nn.Module):
def __init__(self, args, ckp):
super(Model, self).__init__()
print('Making model...')
self.scale = args.scale
self.idx_scale = 0
self.input_large = (args.model == 'VDSR')
self.self_ensemble = args.self_ensemble
self.chop = args.chop
self.precision = args.precision
self.cpu = args.cpu
self.device = torch.device('cpu' if args.cpu else 'cuda')
self.n_GPUs = args.n_GPUs
self.save_models = args.save_models
module = import_module('model.' + args.model.lower())
self.model = module.make_model(args).to(self.device)
if args.precision == 'half':
self.model.half()
self.load(
ckp.get_path('model'),
pre_train=args.pre_train,
resume=args.resume,
cpu=args.cpu
)
print(self.model, file=ckp.log_file)
def forward(self, x, idx_scale):
self.idx_scale = idx_scale
if hasattr(self.model, 'set_scale'):
self.model.set_scale(idx_scale)
if self.training:
if self.n_GPUs > 1:
return P.data_parallel(self.model, x, range(self.n_GPUs))
else:
return self.model(x)
else:
if self.chop:
forward_function = self.forward_chop
else:
forward_function = self.model.forward
if self.self_ensemble:
return self.forward_x8(x, forward_function=forward_function)
else:
return forward_function(x)
def save(self, apath, epoch, is_best=False):
save_dirs = [os.path.join(apath, 'model_latest.pt')]
if is_best:
save_dirs.append(os.path.join(apath, 'model_best.pt'))
if self.save_models:
save_dirs.append(
os.path.join(apath, 'model_{}.pt'.format(epoch))
)
for s in save_dirs:
torch.save(self.model.state_dict(), s)
def load(self, apath, pre_train='', resume=-1, cpu=False):
load_from = None
kwargs = {}
if cpu:
kwargs = {'map_location': lambda storage, loc: storage}
if resume == -1:
load_from = torch.load(
os.path.join(apath, 'model_latest.pt'),
**kwargs
)
elif resume == 0:
if pre_train == 'download':
print('Download the model')
dir_model = os.path.join('..', 'models')
os.makedirs(dir_model, exist_ok=True)
load_from = torch.utils.model_zoo.load_url(
self.model.url,
model_dir=dir_model,
**kwargs
)
elif pre_train:
print('Load the model from {}'.format(pre_train))
load_from = torch.load(pre_train, **kwargs)
else:
load_from = torch.load(
os.path.join(apath, 'model_{}.pt'.format(resume)),
**kwargs
)
if load_from:
self.model.load_state_dict(load_from, strict=False)
def forward_chop(self, *args, shave=10, min_size=160000):
scale = 1 if self.input_large else self.scale[self.idx_scale]
n_GPUs = min(self.n_GPUs, 4)
# height, width
h, w = args[0].size()[-2:]
top = slice(0, h//2 + shave)
bottom = slice(h - h//2 - shave, h)
left = slice(0, w//2 + shave)
right = slice(w - w//2 - shave, w)
x_chops = [torch.cat([
a[..., top, left],
a[..., top, right],
a[..., bottom, left],
a[..., bottom, right]
]) for a in args]
y_chops = []
if h * w < 4 * min_size:
for i in range(0, 4, n_GPUs):
x = [x_chop[i:(i + n_GPUs)] for x_chop in x_chops]
y = P.data_parallel(self.model, *x, range(n_GPUs))
if not isinstance(y, list): y = [y]
if not y_chops:
y_chops = [[c for c in _y.chunk(n_GPUs, dim=0)] for _y in y]
else:
for y_chop, _y in zip(y_chops, y):
y_chop.extend(_y.chunk(n_GPUs, dim=0))
else:
for p in zip(*x_chops):
p1 = [p[0].unsqueeze(0)]
y = self.forward_chop(*p1, shave=shave, min_size=min_size)
if not isinstance(y, list): y = [y]
if not y_chops:
y_chops = [[_y] for _y in y]
else:
for y_chop, _y in zip(y_chops, y): y_chop.append(_y)
h *= scale
w *= scale
top = slice(0, h//2)
bottom = slice(h - h//2, h)
bottom_r = slice(h//2 - h, None)
left = slice(0, w//2)
right = slice(w - w//2, w)
right_r = slice(w//2 - w, None)
# batch size, number of color channels
b, c = y_chops[0][0].size()[:-2]
y = [y_chop[0].new(b, c, h, w) for y_chop in y_chops]
for y_chop, _y in zip(y_chops, y):
_y[..., top, left] = y_chop[0][..., top, left]
_y[..., top, right] = y_chop[1][..., top, right_r]
_y[..., bottom, left] = y_chop[2][..., bottom_r, left]
_y[..., bottom, right] = y_chop[3][..., bottom_r, right_r]
if len(y) == 1: y = y[0]
return y
def forward_x8(self, *args, forward_function=None):
def _transform(v, op):
if self.precision != 'single': v = v.float()
v2np = v.data.cpu().numpy()
if op == 'v':
tfnp = v2np[:, :, :, ::-1].copy()
elif op == 'h':
tfnp = v2np[:, :, ::-1, :].copy()
elif op == 't':
tfnp = v2np.transpose((0, 1, 3, 2)).copy()
ret = torch.Tensor(tfnp).to(self.device)
if self.precision == 'half': ret = ret.half()
return ret
list_x = []
for a in args:
x = [a]
for tf in 'v', 'h', 't': x.extend([_transform(_x, tf) for _x in x])
list_x.append(x)
list_y = []
for x in zip(*list_x):
y = forward_function(*x)
if not isinstance(y, list): y = [y]
if not list_y:
list_y = [[_y] for _y in y]
else:
for _list_y, _y in zip(list_y, y): _list_y.append(_y)
for _list_y in list_y:
for i in range(len(_list_y)):
if i > 3:
_list_y[i] = _transform(_list_y[i], 't')
if i % 4 > 1:
_list_y[i] = _transform(_list_y[i], 'h')
if (i % 4) % 2 == 1:
_list_y[i] = _transform(_list_y[i], 'v')
y = [torch.cat(_y, dim=0).mean(dim=0, keepdim=True) for _y in list_y]
if len(y) == 1: y = y[0]
return y