mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-01-12 02:45:16 +00:00
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
"""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)
|