LIU, Zichen
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"""
Author: Zhuo Su, Wenzhe Liu
Date: Feb 18, 2021
"""
import math
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def img2tensor(imgs, bgr2rgb=True, float32=True):
"""Numpy array to tensor.
Args:
imgs (list[ndarray] | ndarray): Input images.
bgr2rgb (bool): Whether to change bgr to rgb.
float32 (bool): Whether to change to float32.
Returns:
list[tensor] | tensor: Tensor images. If returned results only have
one element, just return tensor.
"""
def _totensor(img, bgr2rgb, float32):
if img.shape[2] == 3 and bgr2rgb:
if img.dtype == 'float64':
img = img.astype('float32')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = torch.from_numpy(img.transpose(2, 0, 1))
if float32:
img = img.float()
return img
if isinstance(imgs, list):
return [_totensor(img, bgr2rgb, float32) for img in imgs]
else:
return _totensor(imgs, bgr2rgb, float32)
nets = {
'baseline': {
'layer0': 'cv',
'layer1': 'cv',
'layer2': 'cv',
'layer3': 'cv',
'layer4': 'cv',
'layer5': 'cv',
'layer6': 'cv',
'layer7': 'cv',
'layer8': 'cv',
'layer9': 'cv',
'layer10': 'cv',
'layer11': 'cv',
'layer12': 'cv',
'layer13': 'cv',
'layer14': 'cv',
'layer15': 'cv',
},
'c-v15': {
'layer0': 'cd',
'layer1': 'cv',
'layer2': 'cv',
'layer3': 'cv',
'layer4': 'cv',
'layer5': 'cv',
'layer6': 'cv',
'layer7': 'cv',
'layer8': 'cv',
'layer9': 'cv',
'layer10': 'cv',
'layer11': 'cv',
'layer12': 'cv',
'layer13': 'cv',
'layer14': 'cv',
'layer15': 'cv',
},
'a-v15': {
'layer0': 'ad',
'layer1': 'cv',
'layer2': 'cv',
'layer3': 'cv',
'layer4': 'cv',
'layer5': 'cv',
'layer6': 'cv',
'layer7': 'cv',
'layer8': 'cv',
'layer9': 'cv',
'layer10': 'cv',
'layer11': 'cv',
'layer12': 'cv',
'layer13': 'cv',
'layer14': 'cv',
'layer15': 'cv',
},
'r-v15': {
'layer0': 'rd',
'layer1': 'cv',
'layer2': 'cv',
'layer3': 'cv',
'layer4': 'cv',
'layer5': 'cv',
'layer6': 'cv',
'layer7': 'cv',
'layer8': 'cv',
'layer9': 'cv',
'layer10': 'cv',
'layer11': 'cv',
'layer12': 'cv',
'layer13': 'cv',
'layer14': 'cv',
'layer15': 'cv',
},
'cvvv4': {
'layer0': 'cd',
'layer1': 'cv',
'layer2': 'cv',
'layer3': 'cv',
'layer4': 'cd',
'layer5': 'cv',
'layer6': 'cv',
'layer7': 'cv',
'layer8': 'cd',
'layer9': 'cv',
'layer10': 'cv',
'layer11': 'cv',
'layer12': 'cd',
'layer13': 'cv',
'layer14': 'cv',
'layer15': 'cv',
},
'avvv4': {
'layer0': 'ad',
'layer1': 'cv',
'layer2': 'cv',
'layer3': 'cv',
'layer4': 'ad',
'layer5': 'cv',
'layer6': 'cv',
'layer7': 'cv',
'layer8': 'ad',
'layer9': 'cv',
'layer10': 'cv',
'layer11': 'cv',
'layer12': 'ad',
'layer13': 'cv',
'layer14': 'cv',
'layer15': 'cv',
},
'rvvv4': {
'layer0': 'rd',
'layer1': 'cv',
'layer2': 'cv',
'layer3': 'cv',
'layer4': 'rd',
'layer5': 'cv',
'layer6': 'cv',
'layer7': 'cv',
'layer8': 'rd',
'layer9': 'cv',
'layer10': 'cv',
'layer11': 'cv',
'layer12': 'rd',
'layer13': 'cv',
'layer14': 'cv',
'layer15': 'cv',
},
'cccv4': {
'layer0': 'cd',
'layer1': 'cd',
'layer2': 'cd',
'layer3': 'cv',
'layer4': 'cd',
'layer5': 'cd',
'layer6': 'cd',
'layer7': 'cv',
'layer8': 'cd',
'layer9': 'cd',
'layer10': 'cd',
'layer11': 'cv',
'layer12': 'cd',
'layer13': 'cd',
'layer14': 'cd',
'layer15': 'cv',
},
'aaav4': {
'layer0': 'ad',
'layer1': 'ad',
'layer2': 'ad',
'layer3': 'cv',
'layer4': 'ad',
'layer5': 'ad',
'layer6': 'ad',
'layer7': 'cv',
'layer8': 'ad',
'layer9': 'ad',
'layer10': 'ad',
'layer11': 'cv',
'layer12': 'ad',
'layer13': 'ad',
'layer14': 'ad',
'layer15': 'cv',
},
'rrrv4': {
'layer0': 'rd',
'layer1': 'rd',
'layer2': 'rd',
'layer3': 'cv',
'layer4': 'rd',
'layer5': 'rd',
'layer6': 'rd',
'layer7': 'cv',
'layer8': 'rd',
'layer9': 'rd',
'layer10': 'rd',
'layer11': 'cv',
'layer12': 'rd',
'layer13': 'rd',
'layer14': 'rd',
'layer15': 'cv',
},
'c16': {
'layer0': 'cd',
'layer1': 'cd',
'layer2': 'cd',
'layer3': 'cd',
'layer4': 'cd',
'layer5': 'cd',
'layer6': 'cd',
'layer7': 'cd',
'layer8': 'cd',
'layer9': 'cd',
'layer10': 'cd',
'layer11': 'cd',
'layer12': 'cd',
'layer13': 'cd',
'layer14': 'cd',
'layer15': 'cd',
},
'a16': {
'layer0': 'ad',
'layer1': 'ad',
'layer2': 'ad',
'layer3': 'ad',
'layer4': 'ad',
'layer5': 'ad',
'layer6': 'ad',
'layer7': 'ad',
'layer8': 'ad',
'layer9': 'ad',
'layer10': 'ad',
'layer11': 'ad',
'layer12': 'ad',
'layer13': 'ad',
'layer14': 'ad',
'layer15': 'ad',
},
'r16': {
'layer0': 'rd',
'layer1': 'rd',
'layer2': 'rd',
'layer3': 'rd',
'layer4': 'rd',
'layer5': 'rd',
'layer6': 'rd',
'layer7': 'rd',
'layer8': 'rd',
'layer9': 'rd',
'layer10': 'rd',
'layer11': 'rd',
'layer12': 'rd',
'layer13': 'rd',
'layer14': 'rd',
'layer15': 'rd',
},
'carv4': {
'layer0': 'cd',
'layer1': 'ad',
'layer2': 'rd',
'layer3': 'cv',
'layer4': 'cd',
'layer5': 'ad',
'layer6': 'rd',
'layer7': 'cv',
'layer8': 'cd',
'layer9': 'ad',
'layer10': 'rd',
'layer11': 'cv',
'layer12': 'cd',
'layer13': 'ad',
'layer14': 'rd',
'layer15': 'cv',
},
}
def createConvFunc(op_type):
assert op_type in ['cv', 'cd', 'ad', 'rd'], 'unknown op type: %s' % str(op_type)
if op_type == 'cv':
return F.conv2d
if op_type == 'cd':
def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
assert dilation in [1, 2], 'dilation for cd_conv should be in 1 or 2'
assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for cd_conv should be 3x3'
assert padding == dilation, 'padding for cd_conv set wrong'
weights_c = weights.sum(dim=[2, 3], keepdim=True)
yc = F.conv2d(x, weights_c, stride=stride, padding=0, groups=groups)
y = F.conv2d(x, weights, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
return y - yc
return func
elif op_type == 'ad':
def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
assert dilation in [1, 2], 'dilation for ad_conv should be in 1 or 2'
assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for ad_conv should be 3x3'
assert padding == dilation, 'padding for ad_conv set wrong'
shape = weights.shape
weights = weights.view(shape[0], shape[1], -1)
weights_conv = (weights - weights[:, :, [3, 0, 1, 6, 4, 2, 7, 8, 5]]).view(shape) # clock-wise
y = F.conv2d(x, weights_conv, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
return y
return func
elif op_type == 'rd':
def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
assert dilation in [1, 2], 'dilation for rd_conv should be in 1 or 2'
assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for rd_conv should be 3x3'
padding = 2 * dilation
shape = weights.shape
if weights.is_cuda:
buffer = torch.cuda.FloatTensor(shape[0], shape[1], 5 * 5).fill_(0)
else:
buffer = torch.zeros(shape[0], shape[1], 5 * 5).to(weights.device)
weights = weights.view(shape[0], shape[1], -1)
buffer[:, :, [0, 2, 4, 10, 14, 20, 22, 24]] = weights[:, :, 1:]
buffer[:, :, [6, 7, 8, 11, 13, 16, 17, 18]] = -weights[:, :, 1:]
buffer[:, :, 12] = 0
buffer = buffer.view(shape[0], shape[1], 5, 5)
y = F.conv2d(x, buffer, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
return y
return func
else:
print('impossible to be here unless you force that')
return None
class Conv2d(nn.Module):
def __init__(self, pdc, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=False):
super(Conv2d, self).__init__()
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.groups = groups
self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size))
if bias:
self.bias = nn.Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
self.pdc = pdc
def reset_parameters(self):
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
nn.init.uniform_(self.bias, -bound, bound)
def forward(self, input):
return self.pdc(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
class CSAM(nn.Module):
"""
Compact Spatial Attention Module
"""
def __init__(self, channels):
super(CSAM, self).__init__()
mid_channels = 4
self.relu1 = nn.ReLU()
self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0)
self.conv2 = nn.Conv2d(mid_channels, 1, kernel_size=3, padding=1, bias=False)
self.sigmoid = nn.Sigmoid()
nn.init.constant_(self.conv1.bias, 0)
def forward(self, x):
y = self.relu1(x)
y = self.conv1(y)
y = self.conv2(y)
y = self.sigmoid(y)
return x * y
class CDCM(nn.Module):
"""
Compact Dilation Convolution based Module
"""
def __init__(self, in_channels, out_channels):
super(CDCM, self).__init__()
self.relu1 = nn.ReLU()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0)
self.conv2_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=5, padding=5, bias=False)
self.conv2_2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=7, padding=7, bias=False)
self.conv2_3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=9, padding=9, bias=False)
self.conv2_4 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=11, padding=11, bias=False)
nn.init.constant_(self.conv1.bias, 0)
def forward(self, x):
x = self.relu1(x)
x = self.conv1(x)
x1 = self.conv2_1(x)
x2 = self.conv2_2(x)
x3 = self.conv2_3(x)
x4 = self.conv2_4(x)
return x1 + x2 + x3 + x4
class MapReduce(nn.Module):
"""
Reduce feature maps into a single edge map
"""
def __init__(self, channels):
super(MapReduce, self).__init__()
self.conv = nn.Conv2d(channels, 1, kernel_size=1, padding=0)
nn.init.constant_(self.conv.bias, 0)
def forward(self, x):
return self.conv(x)
class PDCBlock(nn.Module):
def __init__(self, pdc, inplane, ouplane, stride=1):
super(PDCBlock, self).__init__()
self.stride=stride
self.stride=stride
if self.stride > 1:
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0)
self.conv1 = Conv2d(pdc, inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False)
self.relu2 = nn.ReLU()
self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False)
def forward(self, x):
if self.stride > 1:
x = self.pool(x)
y = self.conv1(x)
y = self.relu2(y)
y = self.conv2(y)
if self.stride > 1:
x = self.shortcut(x)
y = y + x
return y
class PDCBlock_converted(nn.Module):
"""
CPDC, APDC can be converted to vanilla 3x3 convolution
RPDC can be converted to vanilla 5x5 convolution
"""
def __init__(self, pdc, inplane, ouplane, stride=1):
super(PDCBlock_converted, self).__init__()
self.stride=stride
if self.stride > 1:
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0)
if pdc == 'rd':
self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=5, padding=2, groups=inplane, bias=False)
else:
self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False)
self.relu2 = nn.ReLU()
self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False)
def forward(self, x):
if self.stride > 1:
x = self.pool(x)
y = self.conv1(x)
y = self.relu2(y)
y = self.conv2(y)
if self.stride > 1:
x = self.shortcut(x)
y = y + x
return y
class PiDiNet(nn.Module):
def __init__(self, inplane, pdcs, dil=None, sa=False, convert=False):
super(PiDiNet, self).__init__()
self.sa = sa
if dil is not None:
assert isinstance(dil, int), 'dil should be an int'
self.dil = dil
self.fuseplanes = []
self.inplane = inplane
if convert:
if pdcs[0] == 'rd':
init_kernel_size = 5
init_padding = 2
else:
init_kernel_size = 3
init_padding = 1
self.init_block = nn.Conv2d(3, self.inplane,
kernel_size=init_kernel_size, padding=init_padding, bias=False)
block_class = PDCBlock_converted
else:
self.init_block = Conv2d(pdcs[0], 3, self.inplane, kernel_size=3, padding=1)
block_class = PDCBlock
self.block1_1 = block_class(pdcs[1], self.inplane, self.inplane)
self.block1_2 = block_class(pdcs[2], self.inplane, self.inplane)
self.block1_3 = block_class(pdcs[3], self.inplane, self.inplane)
self.fuseplanes.append(self.inplane) # C
inplane = self.inplane
self.inplane = self.inplane * 2
self.block2_1 = block_class(pdcs[4], inplane, self.inplane, stride=2)
self.block2_2 = block_class(pdcs[5], self.inplane, self.inplane)
self.block2_3 = block_class(pdcs[6], self.inplane, self.inplane)
self.block2_4 = block_class(pdcs[7], self.inplane, self.inplane)
self.fuseplanes.append(self.inplane) # 2C
inplane = self.inplane
self.inplane = self.inplane * 2
self.block3_1 = block_class(pdcs[8], inplane, self.inplane, stride=2)
self.block3_2 = block_class(pdcs[9], self.inplane, self.inplane)
self.block3_3 = block_class(pdcs[10], self.inplane, self.inplane)
self.block3_4 = block_class(pdcs[11], self.inplane, self.inplane)
self.fuseplanes.append(self.inplane) # 4C
self.block4_1 = block_class(pdcs[12], self.inplane, self.inplane, stride=2)
self.block4_2 = block_class(pdcs[13], self.inplane, self.inplane)
self.block4_3 = block_class(pdcs[14], self.inplane, self.inplane)
self.block4_4 = block_class(pdcs[15], self.inplane, self.inplane)
self.fuseplanes.append(self.inplane) # 4C
self.conv_reduces = nn.ModuleList()
if self.sa and self.dil is not None:
self.attentions = nn.ModuleList()
self.dilations = nn.ModuleList()
for i in range(4):
self.dilations.append(CDCM(self.fuseplanes[i], self.dil))
self.attentions.append(CSAM(self.dil))
self.conv_reduces.append(MapReduce(self.dil))
elif self.sa:
self.attentions = nn.ModuleList()
for i in range(4):
self.attentions.append(CSAM(self.fuseplanes[i]))
self.conv_reduces.append(MapReduce(self.fuseplanes[i]))
elif self.dil is not None:
self.dilations = nn.ModuleList()
for i in range(4):
self.dilations.append(CDCM(self.fuseplanes[i], self.dil))
self.conv_reduces.append(MapReduce(self.dil))
else:
for i in range(4):
self.conv_reduces.append(MapReduce(self.fuseplanes[i]))
self.classifier = nn.Conv2d(4, 1, kernel_size=1) # has bias
nn.init.constant_(self.classifier.weight, 0.25)
nn.init.constant_(self.classifier.bias, 0)
# print('initialization done')
def get_weights(self):
conv_weights = []
bn_weights = []
relu_weights = []
for pname, p in self.named_parameters():
if 'bn' in pname:
bn_weights.append(p)
elif 'relu' in pname:
relu_weights.append(p)
else:
conv_weights.append(p)
return conv_weights, bn_weights, relu_weights
def forward(self, x):
H, W = x.size()[2:]
x = self.init_block(x)
x1 = self.block1_1(x)
x1 = self.block1_2(x1)
x1 = self.block1_3(x1)
x2 = self.block2_1(x1)
x2 = self.block2_2(x2)
x2 = self.block2_3(x2)
x2 = self.block2_4(x2)
x3 = self.block3_1(x2)
x3 = self.block3_2(x3)
x3 = self.block3_3(x3)
x3 = self.block3_4(x3)
x4 = self.block4_1(x3)
x4 = self.block4_2(x4)
x4 = self.block4_3(x4)
x4 = self.block4_4(x4)
x_fuses = []
if self.sa and self.dil is not None:
for i, xi in enumerate([x1, x2, x3, x4]):
x_fuses.append(self.attentions[i](self.dilations[i](xi)))
elif self.sa:
for i, xi in enumerate([x1, x2, x3, x4]):
x_fuses.append(self.attentions[i](xi))
elif self.dil is not None:
for i, xi in enumerate([x1, x2, x3, x4]):
x_fuses.append(self.dilations[i](xi))
else:
x_fuses = [x1, x2, x3, x4]
e1 = self.conv_reduces[0](x_fuses[0])
e1 = F.interpolate(e1, (H, W), mode="bilinear", align_corners=False)
e2 = self.conv_reduces[1](x_fuses[1])
e2 = F.interpolate(e2, (H, W), mode="bilinear", align_corners=False)
e3 = self.conv_reduces[2](x_fuses[2])
e3 = F.interpolate(e3, (H, W), mode="bilinear", align_corners=False)
e4 = self.conv_reduces[3](x_fuses[3])
e4 = F.interpolate(e4, (H, W), mode="bilinear", align_corners=False)
outputs = [e1, e2, e3, e4]
output = self.classifier(torch.cat(outputs, dim=1))
#if not self.training:
# return torch.sigmoid(output)
outputs.append(output)
outputs = [torch.sigmoid(r) for r in outputs]
return outputs
def config_model(model):
model_options = list(nets.keys())
assert model in model_options, \
'unrecognized model, please choose from %s' % str(model_options)
# print(str(nets[model]))
pdcs = []
for i in range(16):
layer_name = 'layer%d' % i
op = nets[model][layer_name]
pdcs.append(createConvFunc(op))
return pdcs
def pidinet():
pdcs = config_model('carv4')
dil = 24 #if args.dil else None
return PiDiNet(60, pdcs, dil=dil, sa=True)