File size: 4,827 Bytes
2147e2e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 |
import os
from importlib import import_module
from loss import L0Loss
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Loss(nn.modules.loss._Loss):
def __init__(self, args, ckp):
super(Loss, self).__init__()
print('Preparing loss function:')
self.n_GPUs = args.n_GPUs
self.loss = []
self.loss_module = nn.ModuleList()
for loss in args.loss.split('+'):
weight, loss_type = loss.split('*')
if loss_type == 'MSE':
loss_function = nn.MSELoss()
elif loss_type == 'L1':
loss_function = nn.L1Loss()
elif loss_type.find('VGG') >= 0:
module = import_module('loss.vgg')
loss_function = getattr(module, 'VGG')(
loss_type[3:],
rgb_range=args.rgb_range
)
elif loss_type.find('GAN') >= 0:
module = import_module('loss.adversarial')
loss_function = getattr(module, 'Adversarial')(
args,
loss_type
)
self.loss.append({
'type': loss_type,
'weight': float(weight),
'function': loss_function}
)
if loss_type.find('GAN') >= 0:
self.loss.append({'type': 'DIS', 'weight': 1, 'function': None})
if len(self.loss) > 1:
self.loss.append({'type': 'Total', 'weight': 0, 'function': None})
for l in self.loss:
if l['function'] is not None:
print('{:.3f} * {}'.format(l['weight'], l['type']))
self.loss_module.append(l['function'])
self.log = torch.Tensor()
device = torch.device('cpu' if args.cpu else 'cuda')
self.loss_module.to(device)
if args.precision == 'half': self.loss_module.half()
if not args.cpu and args.n_GPUs > 1:
self.loss_module = nn.DataParallel(
self.loss_module, range(args.n_GPUs)
)
if args.load != '': self.load(ckp.dir, cpu=args.cpu)
def forward(self, sr, hr):
losses = []
for i, l in enumerate(self.loss):
if l['function'] is not None:
loss = l['function'](sr, hr)
effective_loss = l['weight'] * loss
losses.append(effective_loss)
self.log[-1, i] += effective_loss.item()
elif l['type'] == 'DIS':
self.log[-1, i] += self.loss[i - 1]['function'].loss
loss_sum = sum(losses)
if len(self.loss) > 1:
self.log[-1, -1] += loss_sum.item()
return loss_sum
def step(self):
for l in self.get_loss_module():
if hasattr(l, 'scheduler'):
l.scheduler.step()
def start_log(self):
self.log = torch.cat((self.log, torch.zeros(1, len(self.loss))))
def end_log(self, n_batches):
self.log[-1].div_(n_batches)
def display_loss(self, batch):
n_samples = batch + 1
log = []
for l, c in zip(self.loss, self.log[-1]):
log.append('[{}: {:.4f}]'.format(l['type'], c / n_samples))
return ''.join(log)
def plot_loss(self, apath, epoch):
axis = np.linspace(1, epoch, epoch)
for i, l in enumerate(self.loss):
label = '{} Loss'.format(l['type'])
fig = plt.figure()
plt.title(label)
plt.plot(axis, self.log[:, i].numpy(), label=label)
plt.legend()
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.grid(True)
plt.savefig(os.path.join(apath, 'loss_{}.pdf'.format(l['type'])))
plt.close(fig)
def get_loss_module(self):
if self.n_GPUs == 1:
return self.loss_module
else:
return self.loss_module.module
def save(self, apath):
torch.save(self.state_dict(), os.path.join(apath, 'loss.pt'))
torch.save(self.log, os.path.join(apath, 'loss_log.pt'))
def load(self, apath, cpu=False):
if cpu:
kwargs = {'map_location': lambda storage, loc: storage}
else:
kwargs = {}
self.load_state_dict(torch.load(
os.path.join(apath, 'loss.pt'),
**kwargs
))
self.log = torch.load(os.path.join(apath, 'loss_log.pt'))
for l in self.get_loss_module():
if hasattr(l, 'scheduler'):
for _ in range(len(self.log)): l.scheduler.step()
|