def init_weights()

in lib/models/pose_resnet.py [0:0]

• 42 lines of code
• 13 McCabe index (conditional complexity)

    def init_weights(self, pretrained=''):
        if os.path.isfile(pretrained):
            logger.info('=> init deconv weights from normal distribution')
            for name, m in self.deconv_layers.named_modules():
                if isinstance(m, nn.ConvTranspose2d):
                    logger.info('=> init {}.weight as normal(0, 0.001)'.format(name))
                    logger.info('=> init {}.bias as 0'.format(name))
                    nn.init.normal_(m.weight, std=0.001)
                    if self.deconv_with_bias:
                        nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.BatchNorm2d):
                    logger.info('=> init {}.weight as 1'.format(name))
                    logger.info('=> init {}.bias as 0'.format(name))
                    nn.init.constant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
            logger.info('=> init final conv weights from normal distribution')
            for m in self.final_layer.modules():
                if isinstance(m, nn.Conv2d):
                    # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                    logger.info('=> init {}.weight as normal(0, 0.001)'.format(name))
                    logger.info('=> init {}.bias as 0'.format(name))
                    nn.init.normal_(m.weight, std=0.001)
                    nn.init.constant_(m.bias, 0)

            # pretrained_state_dict = torch.load(pretrained)
            logger.info('=> loading pretrained model {}'.format(pretrained))
            # self.load_state_dict(pretrained_state_dict, strict=False)
            checkpoint = torch.load(pretrained)
            if isinstance(checkpoint, OrderedDict):
                state_dict = checkpoint
            elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
                state_dict_old = checkpoint['state_dict']
                state_dict = OrderedDict()
                # delete 'module.' because it is saved from DataParallel module
                for key in state_dict_old.keys():
                    if key.startswith('module.'):
                        # state_dict[key[7:]] = state_dict[key]
                        # state_dict.pop(key)
                        state_dict[key[7:]] = state_dict_old[key]
                    else:
                        state_dict[key] = state_dict_old[key]
            else:
                raise RuntimeError(
                    'No state_dict found in checkpoint file {}'.format(pretrained))
            self.load_state_dict(state_dict, strict=False)
        else:
            logger.error('=> imagenet pretrained model dose not exist')
            logger.error('=> please download it first')
            raise ValueError('imagenet pretrained model does not exist')