| |
| |
| |
|
|
| import math |
| import torch |
| from pathlib import Path |
| from copy import deepcopy |
| 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): |
| module_flag = list(ckpt.keys())[0].startswith('module.') |
| compile_flag = '_orig_mod' in list(ckpt.keys())[0] |
|
|
| for source_key, source_value in model.state_dict().items(): |
| target_key = source_key |
| if compile_flag and (not '_orig_mod.' in source_key): |
| target_key = '_orig_mod.' + target_key |
| if module_flag and (not source_key.startswith('module')): |
| target_key = 'module.' + target_key |
|
|
| assert target_key in ckpt |
| source_value.copy_(ckpt[target_key]) |
|
|
