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Running
on
Zero
| # -*- coding: utf-8 -*- | |
| """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, Interpolate, _make_encoder | |
| class MidasNet(BaseModel): | |
| """Network for monocular depth estimation. | |
| """ | |
| def __init__(self, path=None, features=256, non_negative=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, self).__init__() | |
| use_pretrained = False if path is None else True | |
| self.pretrained, self.scratch = _make_encoder( | |
| backbone='resnext101_wsl', | |
| features=features, | |
| use_pretrained=use_pretrained) | |
| self.scratch.refinenet4 = FeatureFusionBlock(features) | |
| self.scratch.refinenet3 = FeatureFusionBlock(features) | |
| self.scratch.refinenet2 = FeatureFusionBlock(features) | |
| self.scratch.refinenet1 = FeatureFusionBlock(features) | |
| self.scratch.output_conv = nn.Sequential( | |
| nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1), | |
| Interpolate(scale_factor=2, mode='bilinear'), | |
| nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1), | |
| nn.ReLU(True), | |
| nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), | |
| nn.ReLU(True) if non_negative else nn.Identity(), | |
| ) | |
| if path: | |
| self.load(path) | |
| def forward(self, x): | |
| """Forward pass. | |
| Args: | |
| x (tensor): input data (image) | |
| Returns: | |
| tensor: depth | |
| """ | |
| 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) | |