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| #!/usr/bin/env python | |
| # -*- encoding: utf-8 -*- | |
| """ | |
| @Author : Peike Li | |
| @Contact : peike.li@yahoo.com | |
| @File : schp.py | |
| @Time : 4/8/19 2:11 PM | |
| @Desc : | |
| @License : This source code is licensed under the license found in the | |
| LICENSE file in the root directory of this source tree. | |
| """ | |
| import os | |
| import torch | |
| import modules | |
| def moving_average(net1, net2, alpha=1): | |
| for param1, param2 in zip(net1.parameters(), net2.parameters()): | |
| param1.data *= (1.0 - alpha) | |
| param1.data += param2.data * alpha | |
| def _check_bn(module, flag): | |
| if issubclass(module.__class__, modules.bn.InPlaceABNSync): | |
| flag[0] = True | |
| def check_bn(model): | |
| flag = [False] | |
| model.apply(lambda module: _check_bn(module, flag)) | |
| return flag[0] | |
| def reset_bn(module): | |
| if issubclass(module.__class__, modules.bn.InPlaceABNSync): | |
| module.running_mean = torch.zeros_like(module.running_mean) | |
| module.running_var = torch.ones_like(module.running_var) | |
| def _get_momenta(module, momenta): | |
| if issubclass(module.__class__, modules.bn.InPlaceABNSync): | |
| momenta[module] = module.momentum | |
| def _set_momenta(module, momenta): | |
| if issubclass(module.__class__, modules.bn.InPlaceABNSync): | |
| module.momentum = momenta[module] | |
| def bn_re_estimate(loader, model): | |
| if not check_bn(model): | |
| print('No batch norm layer detected') | |
| return | |
| model.train() | |
| momenta = {} | |
| model.apply(reset_bn) | |
| model.apply(lambda module: _get_momenta(module, momenta)) | |
| n = 0 | |
| for i_iter, batch in enumerate(loader): | |
| images, labels, _ = batch | |
| b = images.data.size(0) | |
| momentum = b / (n + b) | |
| for module in momenta.keys(): | |
| module.momentum = momentum | |
| model(images) | |
| n += b | |
| model.apply(lambda module: _set_momenta(module, momenta)) | |
| def save_schp_checkpoint(states, is_best_parsing, output_dir, filename='schp_checkpoint.pth.tar'): | |
| save_path = os.path.join(output_dir, filename) | |
| if os.path.exists(save_path): | |
| os.remove(save_path) | |
| torch.save(states, save_path) | |
| if is_best_parsing and 'state_dict' in states: | |
| best_save_path = os.path.join(output_dir, 'model_parsing_best.pth.tar') | |
| if os.path.exists(best_save_path): | |
| os.remove(best_save_path) | |
| torch.save(states, best_save_path) | |