|
|
|
|
| """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
|
| This file contains code that is adapted from
|
| https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
|
| """
|
| import torch
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| import torch.nn as nn
|
|
|
| from .base_model import BaseModel
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| from .blocks import FeatureFusionBlock_custom, Interpolate, _make_encoder
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|
|
|
|
| class MidasNet_small(BaseModel):
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| """Network for monocular depth estimation.
|
| """
|
| def __init__(self,
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| path=None,
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| features=64,
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| backbone='efficientnet_lite3',
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| non_negative=True,
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| exportable=True,
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| channels_last=False,
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| align_corners=True,
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| blocks={'expand': True}):
|
| """Init.
|
|
|
| Args:
|
| path (str, optional): Path to saved model. Defaults to None.
|
| features (int, optional): Number of features. Defaults to 256.
|
| backbone (str, optional): Backbone network for encoder. Defaults to resnet50
|
| """
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| print('Loading weights: ', path)
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|
|
| super(MidasNet_small, self).__init__()
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|
|
| use_pretrained = False if path else True
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|
|
| self.channels_last = channels_last
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| self.blocks = blocks
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| self.backbone = backbone
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|
|
| self.groups = 1
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|
|
| features1 = features
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| features2 = features
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| features3 = features
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| features4 = features
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| self.expand = False
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| if 'expand' in self.blocks and self.blocks['expand'] is True:
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| self.expand = True
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| features1 = features
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| features2 = features * 2
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| features3 = features * 4
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| features4 = features * 8
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|
|
| self.pretrained, self.scratch = _make_encoder(self.backbone,
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| features,
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| use_pretrained,
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| groups=self.groups,
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| expand=self.expand,
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| exportable=exportable)
|
|
|
| self.scratch.activation = nn.ReLU(False)
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|
|
| self.scratch.refinenet4 = FeatureFusionBlock_custom(
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| features4,
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| self.scratch.activation,
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| deconv=False,
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| bn=False,
|
| expand=self.expand,
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| align_corners=align_corners)
|
| self.scratch.refinenet3 = FeatureFusionBlock_custom(
|
| features3,
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| self.scratch.activation,
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| deconv=False,
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| bn=False,
|
| expand=self.expand,
|
| align_corners=align_corners)
|
| self.scratch.refinenet2 = FeatureFusionBlock_custom(
|
| features2,
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| self.scratch.activation,
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| deconv=False,
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| bn=False,
|
| expand=self.expand,
|
| align_corners=align_corners)
|
| self.scratch.refinenet1 = FeatureFusionBlock_custom(
|
| features1,
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| self.scratch.activation,
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| deconv=False,
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| bn=False,
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| align_corners=align_corners)
|
|
|
| self.scratch.output_conv = 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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| groups=self.groups),
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| Interpolate(scale_factor=2, mode='bilinear'),
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| nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
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| self.scratch.activation,
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| nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
|
| nn.ReLU(True) if non_negative else nn.Identity(),
|
| nn.Identity(),
|
| )
|
|
|
| if path:
|
| self.load(path)
|
|
|
| def forward(self, x):
|
| """Forward pass.
|
|
|
| Args:
|
| x (tensor): input data (image)
|
|
|
| Returns:
|
| tensor: depth
|
| """
|
| if self.channels_last is True:
|
| print('self.channels_last = ', self.channels_last)
|
| x.contiguous(memory_format=torch.channels_last)
|
|
|
| layer_1 = self.pretrained.layer1(x)
|
| layer_2 = self.pretrained.layer2(layer_1)
|
| layer_3 = self.pretrained.layer3(layer_2)
|
| layer_4 = self.pretrained.layer4(layer_3)
|
|
|
| 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 torch.squeeze(out, dim=1)
|
|
|
|
|
| def fuse_model(m):
|
| prev_previous_type = nn.Identity()
|
| prev_previous_name = ''
|
| previous_type = nn.Identity()
|
| previous_name = ''
|
| for name, module in m.named_modules():
|
| if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(
|
| module) == nn.ReLU:
|
|
|
| torch.quantization.fuse_modules(
|
| m, [prev_previous_name, previous_name, name], inplace=True)
|
| elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
|
|
|
| torch.quantization.fuse_modules(
|
| m, [prev_previous_name, previous_name], inplace=True)
|
|
|
|
|
|
|
|
|
| prev_previous_type = previous_type
|
| prev_previous_name = previous_name
|
| previous_type = type(module)
|
| previous_name = name
|
|
|
