376 lines
14 KiB
Python
376 lines
14 KiB
Python
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# -*- coding: utf-8 -*-
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import os
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import torch
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import numpy as np
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from torch import nn
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import warnings
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warnings.filterwarnings("ignore")
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"""
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This code is mainly the deformation process of our DSConv
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"""
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class DSConv(nn.Module):
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def __init__(self, in_ch, out_ch, kernel_size, extend_scope, morph,
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if_offset, device):
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"""
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The Dynamic Snake Convolution
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:param in_ch: input channel
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:param out_ch: output channel
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:param kernel_size: the size of kernel
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:param extend_scope: the range to expand (default 1 for this method)
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:param morph: the morphology of the convolution kernel is mainly divided into two types
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along the x-axis (0) and the y-axis (1) (see the paper for details)
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:param if_offset: whether deformation is required, if it is False, it is the standard convolution kernel
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:param device: set on gpu
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"""
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super(DSConv, self).__init__()
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# use the <offset_conv> to learn the deformable offset
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self.offset_conv = nn.Conv2d(in_ch, 2 * kernel_size, 3, padding=1)
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self.bn = nn.BatchNorm2d(2 * kernel_size)
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self.kernel_size = kernel_size
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# two types of the DSConv (along x-axis and y-axis)
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self.dsc_conv_x = nn.Conv2d(
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in_ch,
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out_ch,
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kernel_size=(kernel_size, 1),
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stride=(kernel_size, 1),
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padding=0,
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)
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self.dsc_conv_y = nn.Conv2d(
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in_ch,
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out_ch,
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kernel_size=(1, kernel_size),
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stride=(1, kernel_size),
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padding=0,
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)
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self.gn = nn.GroupNorm(out_ch // 4, out_ch)
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self.relu = nn.ReLU(inplace=True)
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self.extend_scope = extend_scope
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self.morph = morph
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self.if_offset = if_offset
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self.device = device
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def forward(self, f):
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offset = self.offset_conv(f)
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offset = self.bn(offset)
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# We need a range of deformation between -1 and 1 to mimic the snake's swing
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offset = torch.tanh(offset)
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input_shape = f.shape
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dsc = DSC(input_shape, self.kernel_size, self.extend_scope, self.morph,
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self.device)
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deformed_feature = dsc.deform_conv(f, offset, self.if_offset)
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if self.morph == 0:
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x = self.dsc_conv_x(deformed_feature)
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x = self.gn(x)
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x = self.relu(x)
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return x
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else:
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x = self.dsc_conv_y(deformed_feature)
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x = self.gn(x)
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x = self.relu(x)
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return x
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# Core code, for ease of understanding, we mark the dimensions of input and output next to the code
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class DSC(object):
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def __init__(self, input_shape, kernel_size, extend_scope, morph, device):
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self.num_points = kernel_size
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self.width = input_shape[2]
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self.height = input_shape[3]
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self.morph = morph
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self.device = device
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self.extend_scope = extend_scope # offset (-1 ~ 1) * extend_scope
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# define feature map shape
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"""
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B: Batch size C: Channel W: Width H: Height
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"""
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self.num_batch = input_shape[0]
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self.num_channels = input_shape[1]
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"""
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input: offset [B,2*K,W,H] K: Kernel size (2*K: 2D image, deformation contains <x_offset> and <y_offset>)
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output_x: [B,1,W,K*H] coordinate map
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output_y: [B,1,K*W,H] coordinate map
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"""
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def _coordinate_map_3D(self, offset, if_offset):
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# offset
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y_offset, x_offset = torch.split(offset, self.num_points, dim=1)
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y_center = torch.arange(0, self.width).repeat([self.height])
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y_center = y_center.reshape(self.height, self.width)
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y_center = y_center.permute(1, 0)
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y_center = y_center.reshape([-1, self.width, self.height])
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y_center = y_center.repeat([self.num_points, 1, 1]).float()
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y_center = y_center.unsqueeze(0)
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x_center = torch.arange(0, self.height).repeat([self.width])
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x_center = x_center.reshape(self.width, self.height)
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x_center = x_center.permute(0, 1)
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x_center = x_center.reshape([-1, self.width, self.height])
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x_center = x_center.repeat([self.num_points, 1, 1]).float()
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x_center = x_center.unsqueeze(0)
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if self.morph == 0:
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"""
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Initialize the kernel and flatten the kernel
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y: only need 0
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x: -num_points//2 ~ num_points//2 (Determined by the kernel size)
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!!! The related PPT will be submitted later, and the PPT will contain the whole changes of each step
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"""
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y = torch.linspace(0, 0, 1)
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x = torch.linspace(
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-int(self.num_points // 2),
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int(self.num_points // 2),
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int(self.num_points),
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)
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y, x = torch.meshgrid(y, x)
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y_spread = y.reshape(-1, 1)
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x_spread = x.reshape(-1, 1)
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y_grid = y_spread.repeat([1, self.width * self.height])
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y_grid = y_grid.reshape([self.num_points, self.width, self.height])
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y_grid = y_grid.unsqueeze(0) # [B*K*K, W,H]
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x_grid = x_spread.repeat([1, self.width * self.height])
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x_grid = x_grid.reshape([self.num_points, self.width, self.height])
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x_grid = x_grid.unsqueeze(0) # [B*K*K, W,H]
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y_new = y_center + y_grid
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x_new = x_center + x_grid
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y_new = y_new.repeat(self.num_batch, 1, 1, 1).to(self.device)
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x_new = x_new.repeat(self.num_batch, 1, 1, 1).to(self.device)
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y_offset_new = y_offset.detach().clone()
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if if_offset:
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y_offset = y_offset.permute(1, 0, 2, 3)
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y_offset_new = y_offset_new.permute(1, 0, 2, 3)
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center = int(self.num_points // 2)
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# The center position remains unchanged and the rest of the positions begin to swing
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# This part is quite simple. The main idea is that "offset is an iterative process"
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y_offset_new[center] = 0
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for index in range(1, center):
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y_offset_new[center + index] = (y_offset_new[center + index - 1] + y_offset[center + index])
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y_offset_new[center - index] = (y_offset_new[center - index + 1] + y_offset[center - index])
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y_offset_new = y_offset_new.permute(1, 0, 2, 3).to(self.device)
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y_new = y_new.add(y_offset_new.mul(self.extend_scope))
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y_new = y_new.reshape(
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[self.num_batch, self.num_points, 1, self.width, self.height])
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y_new = y_new.permute(0, 3, 1, 4, 2)
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y_new = y_new.reshape([
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self.num_batch, self.num_points * self.width, 1 * self.height
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])
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x_new = x_new.reshape(
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[self.num_batch, self.num_points, 1, self.width, self.height])
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x_new = x_new.permute(0, 3, 1, 4, 2)
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x_new = x_new.reshape([
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self.num_batch, self.num_points * self.width, 1 * self.height
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])
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return y_new, x_new
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else:
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"""
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Initialize the kernel and flatten the kernel
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y: -num_points//2 ~ num_points//2 (Determined by the kernel size)
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x: only need 0
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"""
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y = torch.linspace(
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-int(self.num_points // 2),
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int(self.num_points // 2),
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int(self.num_points),
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)
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x = torch.linspace(0, 0, 1)
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y, x = torch.meshgrid(y, x)
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y_spread = y.reshape(-1, 1)
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x_spread = x.reshape(-1, 1)
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y_grid = y_spread.repeat([1, self.width * self.height])
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y_grid = y_grid.reshape([self.num_points, self.width, self.height])
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y_grid = y_grid.unsqueeze(0)
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x_grid = x_spread.repeat([1, self.width * self.height])
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x_grid = x_grid.reshape([self.num_points, self.width, self.height])
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x_grid = x_grid.unsqueeze(0)
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y_new = y_center + y_grid
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x_new = x_center + x_grid
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y_new = y_new.repeat(self.num_batch, 1, 1, 1)
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x_new = x_new.repeat(self.num_batch, 1, 1, 1)
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y_new = y_new.to(self.device)
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x_new = x_new.to(self.device)
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x_offset_new = x_offset.detach().clone()
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if if_offset:
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x_offset = x_offset.permute(1, 0, 2, 3)
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x_offset_new = x_offset_new.permute(1, 0, 2, 3)
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center = int(self.num_points // 2)
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x_offset_new[center] = 0
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for index in range(1, center):
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x_offset_new[center + index] = (x_offset_new[center + index - 1] + x_offset[center + index])
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x_offset_new[center - index] = (x_offset_new[center - index + 1] + x_offset[center - index])
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x_offset_new = x_offset_new.permute(1, 0, 2, 3).to(self.device)
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x_new = x_new.add(x_offset_new.mul(self.extend_scope))
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y_new = y_new.reshape(
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[self.num_batch, 1, self.num_points, self.width, self.height])
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y_new = y_new.permute(0, 3, 1, 4, 2)
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y_new = y_new.reshape([
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self.num_batch, 1 * self.width, self.num_points * self.height
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])
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x_new = x_new.reshape(
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[self.num_batch, 1, self.num_points, self.width, self.height])
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x_new = x_new.permute(0, 3, 1, 4, 2)
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x_new = x_new.reshape([
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self.num_batch, 1 * self.width, self.num_points * self.height
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])
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return y_new, x_new
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"""
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input: input feature map [N,C,D,W,H]；coordinate map [N,K*D,K*W,K*H]
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output: [N,1,K*D,K*W,K*H] deformed feature map
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"""
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def _bilinear_interpolate_3D(self, input_feature, y, x):
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y = y.reshape([-1]).float()
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x = x.reshape([-1]).float()
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zero = torch.zeros([]).int()
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max_y = self.width - 1
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max_x = self.height - 1
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# find 8 grid locations
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y0 = torch.floor(y).int()
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y1 = y0 + 1
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x0 = torch.floor(x).int()
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x1 = x0 + 1
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# clip out coordinates exceeding feature map volume
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y0 = torch.clamp(y0, zero, max_y)
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y1 = torch.clamp(y1, zero, max_y)
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x0 = torch.clamp(x0, zero, max_x)
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x1 = torch.clamp(x1, zero, max_x)
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input_feature_flat = input_feature.flatten()
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input_feature_flat = input_feature_flat.reshape(
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self.num_batch, self.num_channels, self.width, self.height)
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input_feature_flat = input_feature_flat.permute(0, 2, 3, 1)
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input_feature_flat = input_feature_flat.reshape(-1, self.num_channels)
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dimension = self.height * self.width
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base = torch.arange(self.num_batch) * dimension
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base = base.reshape([-1, 1]).float()
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repeat = torch.ones([self.num_points * self.width * self.height
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]).unsqueeze(0)
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repeat = repeat.float()
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base = torch.matmul(base, repeat)
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base = base.reshape([-1])
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base = base.to(self.device)
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base_y0 = base + y0 * self.height
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base_y1 = base + y1 * self.height
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# top rectangle of the neighbourhood volume
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index_a0 = base_y0 - base + x0
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index_c0 = base_y0 - base + x1
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# bottom rectangle of the neighbourhood volume
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index_a1 = base_y1 - base + x0
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index_c1 = base_y1 - base + x1
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# get 8 grid values
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value_a0 = input_feature_flat[index_a0.type(torch.int64)].to(self.device)
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value_c0 = input_feature_flat[index_c0.type(torch.int64)].to(self.device)
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value_a1 = input_feature_flat[index_a1.type(torch.int64)].to(self.device)
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value_c1 = input_feature_flat[index_c1.type(torch.int64)].to(self.device)
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# find 8 grid locations
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y0 = torch.floor(y).int()
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y1 = y0 + 1
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x0 = torch.floor(x).int()
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x1 = x0 + 1
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# clip out coordinates exceeding feature map volume
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y0 = torch.clamp(y0, zero, max_y + 1)
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y1 = torch.clamp(y1, zero, max_y + 1)
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x0 = torch.clamp(x0, zero, max_x + 1)
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x1 = torch.clamp(x1, zero, max_x + 1)
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x0_float = x0.float()
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x1_float = x1.float()
|
|||
|
|
y0_float = y0.float()
|
|||
|
|
y1_float = y1.float()
|
|||
|
|
|
|||
|
|
vol_a0 = ((y1_float - y) * (x1_float - x)).unsqueeze(-1).to(self.device)
|
|||
|
|
vol_c0 = ((y1_float - y) * (x - x0_float)).unsqueeze(-1).to(self.device)
|
|||
|
|
vol_a1 = ((y - y0_float) * (x1_float - x)).unsqueeze(-1).to(self.device)
|
|||
|
|
vol_c1 = ((y - y0_float) * (x - x0_float)).unsqueeze(-1).to(self.device)
|
|||
|
|
|
|||
|
|
outputs = (value_a0 * vol_a0 + value_c0 * vol_c0 + value_a1 * vol_a1 +
|
|||
|
|
value_c1 * vol_c1)
|
|||
|
|
|
|||
|
|
if self.morph == 0:
|
|||
|
|
outputs = outputs.reshape([
|
|||
|
|
self.num_batch,
|
|||
|
|
self.num_points * self.width,
|
|||
|
|
1 * self.height,
|
|||
|
|
self.num_channels,
|
|||
|
|
])
|
|||
|
|
outputs = outputs.permute(0, 3, 1, 2)
|
|||
|
|
else:
|
|||
|
|
outputs = outputs.reshape([
|
|||
|
|
self.num_batch,
|
|||
|
|
1 * self.width,
|
|||
|
|
self.num_points * self.height,
|
|||
|
|
self.num_channels,
|
|||
|
|
])
|
|||
|
|
outputs = outputs.permute(0, 3, 1, 2)
|
|||
|
|
return outputs
|
|||
|
|
|
|||
|
|
def deform_conv(self, input, offset, if_offset):
|
|||
|
|
y, x = self._coordinate_map_3D(offset, if_offset)
|
|||
|
|
deformed_feature = self._bilinear_interpolate_3D(input, y, x)
|
|||
|
|
return deformed_feature
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
|
|||
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|||
|
|
A = np.random.rand(4, 5, 6, 7)
|
|||
|
|
# A = np.ones(shape=(3, 2, 2, 3), dtype=np.float32)
|
|||
|
|
# print(A)
|
|||
|
|
A = A.astype(dtype=np.float32)
|
|||
|
|
A = torch.from_numpy(A)
|
|||
|
|
# print(A.shape)
|
|||
|
|
conv0 = DSConv(
|
|||
|
|
in_ch=5,
|
|||
|
|
out_ch=10,
|
|||
|
|
kernel_size=15,
|
|||
|
|
extend_scope=1,
|
|||
|
|
morph=0,
|
|||
|
|
if_offset=True,
|
|||
|
|
device=device)
|
|||
|
|
if torch.cuda.is_available():
|
|||
|
|
A = A.to(device)
|
|||
|
|
conv0 = conv0.to(device)
|
|||
|
|
out = conv0(A)
|
|||
|
|
print(out.shape)
|