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| #!/usr/bin/env python | |
| # -*- coding:utf-8 -*- | |
| # Power by Zongsheng Yue 2021-11-24 20:29:36 | |
| import math | |
| import torch | |
| from pathlib import Path | |
| from collections import OrderedDict | |
| import torch.nn.functional as F | |
| def calculate_parameters(net): | |
| out = 0 | |
| for param in net.parameters(): | |
| out += param.numel() | |
| return out | |
| def pad_input(x, mod): | |
| h, w = x.shape[-2:] | |
| bottom = int(math.ceil(h/mod)*mod -h) | |
| right = int(math.ceil(w/mod)*mod - w) | |
| x_pad = F.pad(x, pad=(0, right, 0, bottom), mode='reflect') | |
| return x_pad | |
| def forward_chop(net, x, net_kwargs=None, scale=1, shave=10, min_size=160000): | |
| n_GPUs = 1 | |
| b, c, h, w = x.size() | |
| h_half, w_half = h // 2, w // 2 | |
| h_size, w_size = h_half + shave, w_half + shave | |
| lr_list = [ | |
| x[:, :, 0:h_size, 0:w_size], | |
| x[:, :, 0:h_size, (w - w_size):w], | |
| x[:, :, (h - h_size):h, 0:w_size], | |
| x[:, :, (h - h_size):h, (w - w_size):w]] | |
| if w_size * h_size < min_size: | |
| sr_list = [] | |
| for i in range(0, 4, n_GPUs): | |
| lr_batch = torch.cat(lr_list[i:(i + n_GPUs)], dim=0) | |
| if net_kwargs is None: | |
| sr_batch = net(lr_batch) | |
| else: | |
| sr_batch = net(lr_batch, **net_kwargs) | |
| sr_list.extend(sr_batch.chunk(n_GPUs, dim=0)) | |
| else: | |
| sr_list = [ | |
| forward_chop(patch, shave=shave, min_size=min_size) \ | |
| for patch in lr_list | |
| ] | |
| h, w = scale * h, scale * w | |
| h_half, w_half = scale * h_half, scale * w_half | |
| h_size, w_size = scale * h_size, scale * w_size | |
| shave *= scale | |
| output = x.new(b, c, h, w) | |
| output[:, :, 0:h_half, 0:w_half] \ | |
| = sr_list[0][:, :, 0:h_half, 0:w_half] | |
| output[:, :, 0:h_half, w_half:w] \ | |
| = sr_list[1][:, :, 0:h_half, (w_size - w + w_half):w_size] | |
| output[:, :, h_half:h, 0:w_half] \ | |
| = sr_list[2][:, :, (h_size - h + h_half):h_size, 0:w_half] | |
| output[:, :, h_half:h, w_half:w] \ | |
| = sr_list[3][:, :, (h_size - h + h_half):h_size, (w_size - w + w_half):w_size] | |
| return output | |
| def measure_time(net, inputs, num_forward=100): | |
| ''' | |
| Measuring the average runing time (seconds) for pytorch. | |
| out = net(*inputs) | |
| ''' | |
| start = torch.cuda.Event(enable_timing=True) | |
| end = torch.cuda.Event(enable_timing=True) | |
| start.record() | |
| with torch.set_grad_enabled(False): | |
| for _ in range(num_forward): | |
| out = net(*inputs) | |
| end.record() | |
| torch.cuda.synchronize() | |
| return start.elapsed_time(end) / 1000 | |
| def reload_model(model, ckpt): | |
| if list(model.state_dict().keys())[0].startswith('module.'): | |
| if list(ckpt.keys())[0].startswith('module.'): | |
| ckpt = ckpt | |
| else: | |
| ckpt = OrderedDict({f'module.{key}':value for key, value in ckpt.items()}) | |
| else: | |
| if list(ckpt.keys())[0].startswith('module.'): | |
| ckpt = OrderedDict({key[7:]:value for key, value in ckpt.items()}) | |
| else: | |
| ckpt = ckpt | |
| model.load_state_dict(ckpt) | |