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"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
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This file contains code that is adapted from
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https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
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"""
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import torch
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import torch.nn as 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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class MidasNet_small(BaseModel):
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"""Network for monocular depth estimation.
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"""
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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}):
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"""Init.
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Args:
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path (str, optional): Path to saved model. Defaults to None.
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features (int, optional): Number of features. Defaults to 256.
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backbone (str, optional): Backbone network for encoder. Defaults to resnet50
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"""
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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)
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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,
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expand=self.expand,
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align_corners=align_corners)
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self.scratch.refinenet3 = FeatureFusionBlock_custom(
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features3,
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self.scratch.activation,
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deconv=False,
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bn=False,
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expand=self.expand,
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align_corners=align_corners)
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self.scratch.refinenet2 = FeatureFusionBlock_custom(
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features2,
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self.scratch.activation,
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deconv=False,
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bn=False,
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expand=self.expand,
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align_corners=align_corners)
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self.scratch.refinenet1 = FeatureFusionBlock_custom(
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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)
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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),
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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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if path:
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self.load(path)
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def forward(self, x):
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"""Forward pass.
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Args:
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x (tensor): input data (image)
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Returns:
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tensor: depth
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"""
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if self.channels_last is True:
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print('self.channels_last = ', self.channels_last)
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x.contiguous(memory_format=torch.channels_last)
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layer_1 = self.pretrained.layer1(x)
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layer_2 = self.pretrained.layer2(layer_1)
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layer_3 = self.pretrained.layer3(layer_2)
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layer_4 = self.pretrained.layer4(layer_3)
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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 torch.squeeze(out, dim=1)
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def fuse_model(m):
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prev_previous_type = nn.Identity()
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prev_previous_name = ''
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previous_type = nn.Identity()
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previous_name = ''
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for name, module in m.named_modules():
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if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(
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module) == nn.ReLU:
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torch.quantization.fuse_modules(
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m, [prev_previous_name, previous_name, name], inplace=True)
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elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
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torch.quantization.fuse_modules(
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m, [prev_previous_name, previous_name], inplace=True)
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prev_previous_type = previous_type
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prev_previous_name = previous_name
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previous_type = type(module)
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previous_name = name
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