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| # modified from https://github.com/isl-org/DPT | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from .base_model import BaseModel | |
| from .blocks import ( | |
| FeatureFusionBlock, | |
| FeatureFusionBlock_custom, | |
| Interpolate, | |
| _make_encoder, | |
| forward_vit, | |
| ) | |
| def _make_fusion_block(features, use_bn): | |
| return FeatureFusionBlock_custom( | |
| features, | |
| nn.ReLU(False), | |
| deconv=False, | |
| bn=use_bn, | |
| expand=False, | |
| align_corners=True, | |
| ) | |
| class DPT(BaseModel): | |
| def __init__( | |
| self, | |
| head, | |
| features=256, | |
| backbone="vitb_rn50_384", | |
| readout="project", | |
| channels_last=False, | |
| use_bn=False, | |
| ): | |
| super(DPT, self).__init__() | |
| self.channels_last = channels_last | |
| hooks = { | |
| "vitb_rn50_384": [0, 1, 8, 11], | |
| "vitb16_384": [2, 5, 8, 11], | |
| "vitl16_384": [5, 11, 17, 23], | |
| } | |
| # Instantiate backbone and reassemble blocks | |
| self.pretrained, self.scratch = _make_encoder( | |
| backbone, | |
| features, | |
| True, # Set to true of you want to train from scratch, uses ImageNet weights | |
| groups=1, | |
| expand=False, | |
| exportable=False, | |
| hooks=hooks[backbone], | |
| use_readout=readout, | |
| ) | |
| self.scratch.refinenet1 = _make_fusion_block(features, use_bn) | |
| self.scratch.refinenet2 = _make_fusion_block(features, use_bn) | |
| self.scratch.refinenet3 = _make_fusion_block(features, use_bn) | |
| self.scratch.refinenet4 = _make_fusion_block(features, use_bn) | |
| self.scratch.output_conv = head | |
| def forward(self, x, get_feat=False): | |
| if self.channels_last == True: | |
| x.contiguous(memory_format=torch.channels_last) | |
| layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x) | |
| # res 8x -> 4x -> 2x -> 1x base_size | |
| # base_size = H / 32 | |
| # all n_channels same (256 by default) after these | |
| layer_1_rn = self.scratch.layer1_rn(layer_1) | |
| layer_2_rn = self.scratch.layer2_rn(layer_2) | |
| layer_3_rn = self.scratch.layer3_rn(layer_3) | |
| layer_4_rn = self.scratch.layer4_rn(layer_4) | |
| # upsample by two with out changing n_channels each time, conv-sum for fusing | |
| path_4 = self.scratch.refinenet4(layer_4_rn) | |
| path_3 = self.scratch.refinenet3(path_4, layer_3_rn) | |
| path_2 = self.scratch.refinenet2(path_3, layer_2_rn) | |
| path_1 = self.scratch.refinenet1(path_2, layer_1_rn) | |
| out = self.scratch.output_conv(path_1) | |
| # save the feat if required | |
| if get_feat: | |
| return out, layer_4 | |
| return out | |
| class DPTDepthModel(DPT): | |
| def __init__(self, path=None, non_negative=True, num_channels=1, **kwargs): | |
| features = kwargs["features"] if "features" in kwargs else 256 | |
| head = nn.Sequential( | |
| nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), | |
| Interpolate(scale_factor=2, mode="bilinear", align_corners=True), | |
| nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), | |
| nn.ReLU(True), | |
| nn.Conv2d(32, num_channels, kernel_size=1, stride=1, padding=0), | |
| nn.ReLU(True) if non_negative else nn.Identity(), | |
| nn.Identity(), | |
| ) | |
| nn.init.constant_(head[-3].bias, 0.05) | |
| super().__init__(head, **kwargs) | |
| if path is not None: | |
| self.load(path) | |
| def forward(self, image, get_feat=False): | |
| x = image * 2 - 1 | |
| if get_feat: | |
| output, feat = super().forward(x, get_feat=get_feat) | |
| output = output.clamp(min=0, max=1) | |
| return output, feat | |
| else: | |
| output = super().forward(x, get_feat=get_feat).clamp(min=0, max=1) | |
| return output |