""" 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 from basicsr.utils import img2tensor 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) 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) if __name__ == '__main__': model = pidinet() ckp = torch.load('table5_pidinet.pth')['state_dict'] model.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()}) im = cv2.imread('examples/test_my/cat_v4.png') im = img2tensor(im).unsqueeze(0)/255. res = model(im)[-1] res = res>0.5 res = res.float() res = (res[0,0].cpu().data.numpy()*255.).astype(np.uint8) print(res.shape) cv2.imwrite('edge.png', res)