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| import torch
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| import torch.nn as nn
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|
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| from .base_model import BaseModel
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| from .blocks import FeatureFusionBlock_custom, Interpolate, _make_encoder
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| from .vit import forward_vit
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| def _make_fusion_block(features, use_bn):
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| return FeatureFusionBlock_custom(
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| features,
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| nn.ReLU(False),
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| deconv=False,
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| bn=use_bn,
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| expand=False,
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| align_corners=True,
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| )
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| class DPT(BaseModel):
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| def __init__(
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| self,
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| head,
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| features=256,
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| backbone='vitb_rn50_384',
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| readout='project',
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| channels_last=False,
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| use_bn=False,
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| ):
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|
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| super(DPT, self).__init__()
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|
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| self.channels_last = channels_last
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|
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| hooks = {
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| 'vitb_rn50_384': [0, 1, 8, 11],
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| 'vitb16_384': [2, 5, 8, 11],
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| 'vitl16_384': [5, 11, 17, 23],
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| }
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| self.pretrained, self.scratch = _make_encoder(
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| backbone,
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| features,
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| False,
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| groups=1,
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| expand=False,
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| exportable=False,
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| hooks=hooks[backbone],
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| use_readout=readout,
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| )
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| self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
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| self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
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| self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
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| self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
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|
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| self.scratch.output_conv = head
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|
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| def forward(self, x):
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| if self.channels_last is True:
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| x.contiguous(memory_format=torch.channels_last)
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|
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| layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
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| layer_1_rn = self.scratch.layer1_rn(layer_1)
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| layer_2_rn = self.scratch.layer2_rn(layer_2)
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| layer_3_rn = self.scratch.layer3_rn(layer_3)
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| layer_4_rn = self.scratch.layer4_rn(layer_4)
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| path_4 = self.scratch.refinenet4(layer_4_rn)
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| path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
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| path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
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| path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
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| out = self.scratch.output_conv(path_1)
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|
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| return out
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|
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| class DPTDepthModel(DPT):
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| def __init__(self, path=None, non_negative=True, **kwargs):
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| features = kwargs['features'] if 'features' in kwargs else 256
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|
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| head = nn.Sequential(
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| nn.Conv2d(features,
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| features // 2,
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| kernel_size=3,
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| stride=1,
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| padding=1),
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| Interpolate(scale_factor=2, mode='bilinear', align_corners=True),
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| nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
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| nn.ReLU(True),
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| nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
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| nn.ReLU(True) if non_negative else nn.Identity(),
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| nn.Identity(),
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| )
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| super().__init__(head, **kwargs)
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| if path is not None:
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| self.load(path)
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|
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| def forward(self, x):
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| return super().forward(x).squeeze(dim=1)
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|
