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This commit is contained in:
Lihe Yang
148
metric_depth/depth_anything_v2/util/blocks.py
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148
metric_depth/depth_anything_v2/util/blocks.py
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import torch.nn as nn
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def _make_scratch(in_shape, out_shape, groups=1, expand=False):
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scratch = nn.Module()
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out_shape1 = out_shape
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out_shape2 = out_shape
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out_shape3 = out_shape
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if len(in_shape) >= 4:
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out_shape4 = out_shape
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if expand:
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out_shape1 = out_shape
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out_shape2 = out_shape * 2
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out_shape3 = out_shape * 4
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if len(in_shape) >= 4:
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out_shape4 = out_shape * 8
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scratch.layer1_rn = nn.Conv2d(in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
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scratch.layer2_rn = nn.Conv2d(in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
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scratch.layer3_rn = nn.Conv2d(in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
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if len(in_shape) >= 4:
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scratch.layer4_rn = nn.Conv2d(in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
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return scratch
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class ResidualConvUnit(nn.Module):
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"""Residual convolution module.
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"""
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def __init__(self, features, activation, bn):
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"""Init.
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Args:
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features (int): number of features
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"""
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super().__init__()
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self.bn = bn
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self.groups=1
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self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
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self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
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if self.bn == True:
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self.bn1 = nn.BatchNorm2d(features)
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self.bn2 = nn.BatchNorm2d(features)
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self.activation = activation
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self.skip_add = nn.quantized.FloatFunctional()
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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
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Returns:
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tensor: output
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"""
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out = self.activation(x)
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out = self.conv1(out)
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if self.bn == True:
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out = self.bn1(out)
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out = self.activation(out)
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out = self.conv2(out)
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if self.bn == True:
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out = self.bn2(out)
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if self.groups > 1:
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out = self.conv_merge(out)
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return self.skip_add.add(out, x)
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class FeatureFusionBlock(nn.Module):
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"""Feature fusion block.
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"""
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def __init__(
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self,
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features,
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activation,
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deconv=False,
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bn=False,
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expand=False,
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align_corners=True,
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size=None
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):
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"""Init.
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Args:
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features (int): number of features
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"""
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super(FeatureFusionBlock, self).__init__()
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self.deconv = deconv
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self.align_corners = align_corners
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self.groups=1
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self.expand = expand
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out_features = features
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if self.expand == True:
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out_features = features // 2
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self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
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self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
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self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
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self.skip_add = nn.quantized.FloatFunctional()
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self.size=size
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def forward(self, *xs, size=None):
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"""Forward pass.
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Returns:
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tensor: output
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"""
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output = xs[0]
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if len(xs) == 2:
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res = self.resConfUnit1(xs[1])
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output = self.skip_add.add(output, res)
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output = self.resConfUnit2(output)
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if (size is None) and (self.size is None):
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modifier = {"scale_factor": 2}
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elif size is None:
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modifier = {"size": self.size}
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else:
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modifier = {"size": size}
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output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners)
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output = self.out_conv(output)
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return output
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158
metric_depth/depth_anything_v2/util/transform.py
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158
metric_depth/depth_anything_v2/util/transform.py
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import numpy as np
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import cv2
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class Resize(object):
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"""Resize sample to given size (width, height).
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"""
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def __init__(
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self,
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width,
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height,
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resize_target=True,
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keep_aspect_ratio=False,
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ensure_multiple_of=1,
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resize_method="lower_bound",
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image_interpolation_method=cv2.INTER_AREA,
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):
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"""Init.
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Args:
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width (int): desired output width
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height (int): desired output height
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resize_target (bool, optional):
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True: Resize the full sample (image, mask, target).
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False: Resize image only.
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Defaults to True.
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keep_aspect_ratio (bool, optional):
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True: Keep the aspect ratio of the input sample.
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Output sample might not have the given width and height, and
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resize behaviour depends on the parameter 'resize_method'.
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Defaults to False.
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ensure_multiple_of (int, optional):
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Output width and height is constrained to be multiple of this parameter.
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Defaults to 1.
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resize_method (str, optional):
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"lower_bound": Output will be at least as large as the given size.
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"upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
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"minimal": Scale as least as possible. (Output size might be smaller than given size.)
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Defaults to "lower_bound".
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"""
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self.__width = width
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self.__height = height
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self.__resize_target = resize_target
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self.__keep_aspect_ratio = keep_aspect_ratio
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self.__multiple_of = ensure_multiple_of
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self.__resize_method = resize_method
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self.__image_interpolation_method = image_interpolation_method
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def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
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y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
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if max_val is not None and y > max_val:
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y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
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if y < min_val:
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y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
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return y
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def get_size(self, width, height):
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# determine new height and width
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scale_height = self.__height / height
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scale_width = self.__width / width
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if self.__keep_aspect_ratio:
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if self.__resize_method == "lower_bound":
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# scale such that output size is lower bound
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if scale_width > scale_height:
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# fit width
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scale_height = scale_width
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else:
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# fit height
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scale_width = scale_height
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elif self.__resize_method == "upper_bound":
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# scale such that output size is upper bound
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if scale_width < scale_height:
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# fit width
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scale_height = scale_width
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else:
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# fit height
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scale_width = scale_height
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elif self.__resize_method == "minimal":
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# scale as least as possbile
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if abs(1 - scale_width) < abs(1 - scale_height):
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# fit width
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scale_height = scale_width
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else:
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# fit height
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scale_width = scale_height
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else:
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raise ValueError(f"resize_method {self.__resize_method} not implemented")
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if self.__resize_method == "lower_bound":
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new_height = self.constrain_to_multiple_of(scale_height * height, min_val=self.__height)
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new_width = self.constrain_to_multiple_of(scale_width * width, min_val=self.__width)
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elif self.__resize_method == "upper_bound":
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new_height = self.constrain_to_multiple_of(scale_height * height, max_val=self.__height)
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new_width = self.constrain_to_multiple_of(scale_width * width, max_val=self.__width)
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elif self.__resize_method == "minimal":
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new_height = self.constrain_to_multiple_of(scale_height * height)
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new_width = self.constrain_to_multiple_of(scale_width * width)
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else:
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raise ValueError(f"resize_method {self.__resize_method} not implemented")
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return (new_width, new_height)
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def __call__(self, sample):
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width, height = self.get_size(sample["image"].shape[1], sample["image"].shape[0])
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# resize sample
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sample["image"] = cv2.resize(sample["image"], (width, height), interpolation=self.__image_interpolation_method)
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if self.__resize_target:
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if "depth" in sample:
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sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST)
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if "mask" in sample:
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sample["mask"] = cv2.resize(sample["mask"].astype(np.float32), (width, height), interpolation=cv2.INTER_NEAREST)
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return sample
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class NormalizeImage(object):
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"""Normlize image by given mean and std.
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"""
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def __init__(self, mean, std):
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self.__mean = mean
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self.__std = std
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def __call__(self, sample):
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sample["image"] = (sample["image"] - self.__mean) / self.__std
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return sample
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class PrepareForNet(object):
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"""Prepare sample for usage as network input.
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"""
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def __init__(self):
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pass
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def __call__(self, sample):
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image = np.transpose(sample["image"], (2, 0, 1))
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sample["image"] = np.ascontiguousarray(image).astype(np.float32)
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if "depth" in sample:
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depth = sample["depth"].astype(np.float32)
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sample["depth"] = np.ascontiguousarray(depth)
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if "mask" in sample:
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sample["mask"] = sample["mask"].astype(np.float32)
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sample["mask"] = np.ascontiguousarray(sample["mask"])
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return sample
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