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103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
"""Classes for depth estimation from images"""
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import torch
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from torch import nn
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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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super().__init__()
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self.channels_last = channels_last
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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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# Instantiate backbone and reassemble blocks
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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, # Set to true of you want to train from scratch, uses ImageNet weights
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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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self.scratch.output_conv = head
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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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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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return out
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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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head = nn.Sequential(
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nn.Conv2d(features, features // 2, kernel_size=3, stride=1, 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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def forward(self, x):
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return super().forward(x).squeeze(dim=1)
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