| |
|
| |
|
| | """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
|
| | import torch.nn as nn
|
| |
|
| | from .base_model import BaseModel
|
| | from .blocks import FeatureFusionBlock_custom, Interpolate, _make_encoder
|
| |
|
| |
|
| | class MidasNet_small(BaseModel):
|
| | """Network for monocular depth estimation.
|
| | """
|
| | def __init__(self,
|
| | path=None,
|
| | features=64,
|
| | backbone='efficientnet_lite3',
|
| | non_negative=True,
|
| | exportable=True,
|
| | channels_last=False,
|
| | align_corners=True,
|
| | 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
|
| | """
|
| | print('Loading weights: ', path)
|
| |
|
| | super(MidasNet_small, self).__init__()
|
| |
|
| | use_pretrained = False if path else True
|
| |
|
| | self.channels_last = channels_last
|
| | self.blocks = blocks
|
| | self.backbone = backbone
|
| |
|
| | self.groups = 1
|
| |
|
| | features1 = features
|
| | features2 = features
|
| | features3 = features
|
| | features4 = features
|
| | self.expand = False
|
| | if 'expand' in self.blocks and self.blocks['expand'] is True:
|
| | self.expand = True
|
| | features1 = features
|
| | features2 = features * 2
|
| | features3 = features * 4
|
| | features4 = features * 8
|
| |
|
| | self.pretrained, self.scratch = _make_encoder(self.backbone,
|
| | features,
|
| | use_pretrained,
|
| | groups=self.groups,
|
| | expand=self.expand,
|
| | exportable=exportable)
|
| |
|
| | self.scratch.activation = nn.ReLU(False)
|
| |
|
| | self.scratch.refinenet4 = FeatureFusionBlock_custom(
|
| | features4,
|
| | self.scratch.activation,
|
| | deconv=False,
|
| | bn=False,
|
| | expand=self.expand,
|
| | align_corners=align_corners)
|
| | self.scratch.refinenet3 = FeatureFusionBlock_custom(
|
| | features3,
|
| | self.scratch.activation,
|
| | deconv=False,
|
| | bn=False,
|
| | expand=self.expand,
|
| | align_corners=align_corners)
|
| | self.scratch.refinenet2 = FeatureFusionBlock_custom(
|
| | features2,
|
| | self.scratch.activation,
|
| | deconv=False,
|
| | bn=False,
|
| | expand=self.expand,
|
| | align_corners=align_corners)
|
| | self.scratch.refinenet1 = FeatureFusionBlock_custom(
|
| | features1,
|
| | self.scratch.activation,
|
| | deconv=False,
|
| | bn=False,
|
| | align_corners=align_corners)
|
| |
|
| | self.scratch.output_conv = nn.Sequential(
|
| | nn.Conv2d(features,
|
| | features // 2,
|
| | kernel_size=3,
|
| | stride=1,
|
| | padding=1,
|
| | groups=self.groups),
|
| | Interpolate(scale_factor=2, mode='bilinear'),
|
| | nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
|
| | self.scratch.activation,
|
| | 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
|
| |
|
