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| import torch.nn as nn | |
| from .hrnetv2.hrnet_ocr import HighResolutionNet | |
| from .hrnetv2.modifiers import LRMult | |
| from .base.basic_blocks import MaxPoolDownSize | |
| from .base.ih_model import IHModelWithBackbone, DeepImageHarmonization | |
| def build_backbone(name, opt): | |
| return eval(name)(opt) | |
| class baseline(IHModelWithBackbone): | |
| def __init__(self, opt, ocr=64): | |
| base_config = {'model': DeepImageHarmonization, | |
| 'params': {'depth': 7, 'batchnorm_from': 2, 'image_fusion': True, 'opt': opt}} | |
| params = base_config['params'] | |
| backbone = HRNetV2(opt, ocr=ocr) | |
| params.update(dict( | |
| backbone_from=2, | |
| backbone_channels=backbone.output_channels, | |
| backbone_mode='cat', | |
| opt=opt | |
| )) | |
| base_model = base_config['model'](**params) | |
| super(baseline, self).__init__(base_model, backbone, False, 'sum', opt=opt) | |
| class HRNetV2(nn.Module): | |
| def __init__( | |
| self, opt, | |
| cat_outputs=True, | |
| pyramid_channels=-1, pyramid_depth=4, | |
| width=18, ocr=128, small=False, | |
| lr_mult=0.1, pretained=True | |
| ): | |
| super(HRNetV2, self).__init__() | |
| self.opt = opt | |
| self.cat_outputs = cat_outputs | |
| self.ocr_on = ocr > 0 and cat_outputs | |
| self.pyramid_on = pyramid_channels > 0 and cat_outputs | |
| self.hrnet = HighResolutionNet(width, 2, ocr_width=ocr, small=small, opt=opt) | |
| self.hrnet.apply(LRMult(lr_mult)) | |
| if self.ocr_on: | |
| self.hrnet.ocr_distri_head.apply(LRMult(1.0)) | |
| self.hrnet.ocr_gather_head.apply(LRMult(1.0)) | |
| self.hrnet.conv3x3_ocr.apply(LRMult(1.0)) | |
| hrnet_cat_channels = [width * 2 ** i for i in range(4)] | |
| if self.pyramid_on: | |
| self.output_channels = [pyramid_channels] * 4 | |
| elif self.ocr_on: | |
| self.output_channels = [ocr * 2] | |
| elif self.cat_outputs: | |
| self.output_channels = [sum(hrnet_cat_channels)] | |
| else: | |
| self.output_channels = hrnet_cat_channels | |
| if self.pyramid_on: | |
| downsize_in_channels = ocr * 2 if self.ocr_on else sum(hrnet_cat_channels) | |
| self.downsize = MaxPoolDownSize(downsize_in_channels, pyramid_channels, pyramid_channels, pyramid_depth) | |
| if pretained: | |
| self.load_pretrained_weights( | |
| r".\pretrained_models/hrnetv2_w18_imagenet_pretrained.pth") | |
| self.output_resolution = (opt.input_size // 8) ** 2 | |
| def forward(self, image, mask, mask_features=None): | |
| outputs = list(self.hrnet(image, mask, mask_features)) | |
| return outputs | |
| def load_pretrained_weights(self, pretrained_path): | |
| self.hrnet.load_pretrained_weights(pretrained_path) | |