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on
Zero
Running
on
Zero
| 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, | |
| enable_attention_hooks=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, | |
| False, # 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, | |
| enable_attention_hooks=enable_attention_hooks, | |
| ) | |
| 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): | |
| 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) | |
| 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) | |
| 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) | |
| return out | |
| class DPTDepthModel(DPT): | |
| def __init__( | |
| self, path=None, non_negative=True, scale=1.0, shift=0.0, invert=False, **kwargs | |
| ): | |
| features = kwargs["features"] if "features" in kwargs else 256 | |
| self.scale = scale | |
| self.shift = shift | |
| self.invert = invert | |
| 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, 1, kernel_size=1, stride=1, padding=0), | |
| nn.ReLU(True) if non_negative else nn.Identity(), | |
| nn.Identity(), | |
| ) | |
| super().__init__(head, **kwargs) | |
| if path is not None: | |
| self.load(path) | |
| def forward(self, x): | |
| inv_depth = super().forward(x).squeeze(dim=1) | |
| if self.invert: | |
| depth = self.scale * inv_depth + self.shift | |
| depth[depth < 1e-8] = 1e-8 | |
| depth = 1.0 / depth | |
| return depth | |
| else: | |
| return inv_depth | |
| class DPTSegmentationModel(DPT): | |
| def __init__(self, num_classes, path=None, **kwargs): | |
| features = kwargs["features"] if "features" in kwargs else 256 | |
| kwargs["use_bn"] = True | |
| head = nn.Sequential( | |
| nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False), | |
| nn.BatchNorm2d(features), | |
| nn.ReLU(True), | |
| nn.Dropout(0.1, False), | |
| nn.Conv2d(features, num_classes, kernel_size=1), | |
| Interpolate(scale_factor=2, mode="bilinear", align_corners=True), | |
| ) | |
| super().__init__(head, **kwargs) | |
| self.auxlayer = nn.Sequential( | |
| nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False), | |
| nn.BatchNorm2d(features), | |
| nn.ReLU(True), | |
| nn.Dropout(0.1, False), | |
| nn.Conv2d(features, num_classes, kernel_size=1), | |
| ) | |
| if path is not None: | |
| self.load(path) | |