diff --git a/gpu_mem_track.py b/gpu_mem_track.py new file mode 100644 index 0000000..8d7a51d --- /dev/null +++ b/gpu_mem_track.py @@ -0,0 +1,113 @@ +import gc +import datetime +import inspect + +import torch +import numpy as np + +dtype_memory_size_dict = { + torch.float64: 64/8, + torch.double: 64/8, + torch.float32: 32/8, + torch.float: 32/8, + torch.float16: 16/8, + torch.half: 16/8, + torch.int64: 64/8, + torch.long: 64/8, + torch.int32: 32/8, + torch.int: 32/8, + torch.int16: 16/8, + torch.short: 16/6, + torch.uint8: 8/8, + torch.int8: 8/8, +} +# compatibility of torch1.0 +if getattr(torch, "bfloat16", None) is not None: + dtype_memory_size_dict[torch.bfloat16] = 16/8 +if getattr(torch, "bool", None) is not None: + dtype_memory_size_dict[torch.bool] = 8/8 # pytorch use 1 byte for a bool, see https://github.com/pytorch/pytorch/issues/41571 + +def get_mem_space(x): + try: + ret = dtype_memory_size_dict[x] + except KeyError: + print(f"dtype {x} is not supported!") + return ret + +class MemTracker(object): + """ + Class used to track pytorch memory usage + Arguments: + detail(bool, default True): whether the function shows the detail gpu memory usage + path(str): where to save log file + verbose(bool, default False): whether show the trivial exception + device(int): GPU number, default is 0 + """ + def __init__(self, detail=True, path='', verbose=False, device=0): + self.print_detail = detail + self.last_tensor_sizes = set() + self.gpu_profile_fn = path + f'{datetime.datetime.now():%d-%b-%y-%H:%M:%S}-gpu_mem_track.txt' + self.verbose = verbose + self.begin = True + self.device = device + + def get_tensors(self): + for obj in gc.get_objects(): + try: + if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): + tensor = obj + else: + continue + if tensor.is_cuda: + yield tensor + except Exception as e: + if self.verbose: + print('A trivial exception occured: {}'.format(e)) + + def get_tensor_usage(self): + sizes = [np.prod(np.array(tensor.size())) * get_mem_space(tensor.dtype) for tensor in self.get_tensors()] + return np.sum(sizes) / 1024**2 + + def get_allocate_usage(self): + return torch.cuda.memory_allocated() / 1024**2 + + def clear_cache(self): + gc.collect() + torch.cuda.empty_cache() + + def print_all_gpu_tensor(self, file=None): + for x in self.get_tensors(): + print(x.size(), x.dtype, np.prod(np.array(x.size()))*get_mem_space(x.dtype)/1024**2, file=file) + + def track(self): + """ + Track the GPU memory usage + """ + frameinfo = inspect.stack()[1] + where_str = frameinfo.filename + ' line ' + str(frameinfo.lineno) + ': ' + frameinfo.function + + with open(self.gpu_profile_fn, 'a+') as f: + + if self.begin: + f.write(f"GPU Memory Track | {datetime.datetime.now():%d-%b-%y-%H:%M:%S} |" + f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb" + f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n") + self.begin = False + + if self.print_detail is True: + ts_list = [(tensor.size(), tensor.dtype) for tensor in self.get_tensors()] + new_tensor_sizes = {(type(x), + tuple(x.size()), + ts_list.count((x.size(), x.dtype)), + np.prod(np.array(x.size()))*get_mem_space(x.dtype)/1024**2, + x.dtype) for x in self.get_tensors()} + for t, s, n, m, data_type in new_tensor_sizes - self.last_tensor_sizes: + f.write(f'+ | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n') + for t, s, n, m, data_type in self.last_tensor_sizes - new_tensor_sizes: + f.write(f'- | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n') + + self.last_tensor_sizes = new_tensor_sizes + + f.write(f"\nAt {where_str:<50}" + f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb" + f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n") diff --git a/iccv_p2mpp.dat b/iccv_p2mpp.dat new file mode 100644 index 0000000..870d126 Binary files /dev/null and b/iccv_p2mpp.dat differ diff --git a/models/BASNet.py b/models/BASNet.py new file mode 100644 index 0000000..8b01477 --- /dev/null +++ b/models/BASNet.py @@ -0,0 +1,1508 @@ +import torch +import torch.nn as nn +from torchvision import models +import torch.nn.functional as F + +from .resnet_model import * + + +class RefUnet(nn.Module): + def __init__(self, in_ch, inc_ch): + super(RefUnet, self).__init__() + + self.conv0 = nn.Conv2d(in_ch, inc_ch, 3, padding=1) + + self.conv1 = nn.Conv2d(inc_ch, 64, 3, padding=1) + self.bn1 = nn.BatchNorm2d(64) + self.relu1 = nn.ReLU(inplace=True) + + self.pool1 = nn.MaxPool2d(2, 2, ceil_mode=True) + + self.conv2 = nn.Conv2d(64, 64, 3, padding=1) + self.bn2 = nn.BatchNorm2d(64) + self.relu2 = nn.ReLU(inplace=True) + + self.pool2 = nn.MaxPool2d(2, 2, ceil_mode=True) + + self.conv3 = nn.Conv2d(64, 64, 3, padding=1) + self.bn3 = nn.BatchNorm2d(64) + self.relu3 = nn.ReLU(inplace=True) + + self.pool3 = nn.MaxPool2d(2, 2, ceil_mode=True) + + self.conv4 = nn.Conv2d(64, 64, 3, padding=1) + self.bn4 = nn.BatchNorm2d(64) + self.relu4 = nn.ReLU(inplace=True) + + self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True) + + ##### + + self.conv5 = nn.Conv2d(64, 64, 3, padding=1) + self.bn5 = nn.BatchNorm2d(64) + self.relu5 = nn.ReLU(inplace=True) + + ##### + + self.conv_d4 = nn.Conv2d(128, 64, 3, padding=1) + self.bn_d4 = nn.BatchNorm2d(64) + self.relu_d4 = nn.ReLU(inplace=True) + + self.conv_d3 = nn.Conv2d(128, 64, 3, padding=1) + self.bn_d3 = nn.BatchNorm2d(64) + self.relu_d3 = nn.ReLU(inplace=True) + + self.conv_d2 = nn.Conv2d(128, 64, 3, padding=1) + self.bn_d2 = nn.BatchNorm2d(64) + self.relu_d2 = nn.ReLU(inplace=True) + + self.conv_d1 = nn.Conv2d(128, 64, 3, padding=1) + self.bn_d1 = nn.BatchNorm2d(64) + self.relu_d1 = nn.ReLU(inplace=True) + + self.conv_d0 = nn.Conv2d(64, 1, 3, padding=1) + + self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear') + + def forward(self, x): + hx = x + hx = self.conv0(hx) + + hx1 = self.relu1(self.bn1(self.conv1(hx))) + hx = self.pool1(hx1) + + hx2 = self.relu2(self.bn2(self.conv2(hx))) + hx = self.pool2(hx2) + + hx3 = self.relu3(self.bn3(self.conv3(hx))) + hx = self.pool3(hx3) + + hx4 = self.relu4(self.bn4(self.conv4(hx))) + hx = self.pool4(hx4) + + hx5 = self.relu5(self.bn5(self.conv5(hx))) + + hx = self.upscore2(hx5) + + d4 = self.relu_d4(self.bn_d4(self.conv_d4(torch.cat((hx, hx4), 1)))) + hx = self.upscore2(d4) + + d3 = self.relu_d3(self.bn_d3(self.conv_d3(torch.cat((hx, hx3), 1)))) + hx = self.upscore2(d3) + + d2 = self.relu_d2(self.bn_d2(self.conv_d2(torch.cat((hx, hx2), 1)))) + hx = self.upscore2(d2) + + d1 = self.relu_d1(self.bn_d1(self.conv_d1(torch.cat((hx, hx1), 1)))) + + residual = self.conv_d0(d1) + + return x + residual + + +class BASNet101(nn.Module): + def __init__(self, n_channels, n_classes, test_mode=False): + super(BASNet101, self).__init__() + self.test_mode = test_mode + resnet = models.resnet101(pretrained=True) + + ## -------------Encoder-------------- + + self.inconv = nn.Conv2d(n_channels, 64, 3, padding=1) + self.inbn = nn.BatchNorm2d(64) + self.inrelu = nn.ReLU(inplace=True) + # self.downsample = nn.MaxPool2d(2,2,ceil_mode=True) + + # stage 1 + self.encoder1 = resnet.layer1 # 224 + # stage 2 + self.encoder2 = resnet.layer2 # 112 + # stage 3 + self.encoder3 = resnet.layer3 # 56 + # stage 4 + self.encoder4 = resnet.layer4 # 28 + + self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True) + self.resb5_0 = torch.nn.Conv2d(2048, 512, 1, 1, padding=0) + self.resb4_0 = torch.nn.Conv2d(1024, 256, 1, 1, padding=0) + self.resb3_0 = torch.nn.Conv2d(512, 128, 1, 1, padding=0) + self.resb2_0 = torch.nn.Conv2d(256, 64, 1, 1, padding=0) + + # stage 5 + self.resb5_1 = BasicBlock(512, 512) + self.resb5_2 = BasicBlock(512, 512) + self.resb5_3 = BasicBlock(512, 512) # 14 + + self.pool5 = nn.MaxPool2d(2, 2, ceil_mode=True) + + # stage 6 + self.resb6_1 = BasicBlock(512, 512) + self.resb6_2 = BasicBlock(512, 512) + self.resb6_3 = BasicBlock(512, 512) # 7 + + ## -------------Bridge-------------- + + # stage Bridge + self.convbg_1 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) # 7 + self.bnbg_1 = nn.BatchNorm2d(512) + self.relubg_1 = nn.ReLU(inplace=True) + self.convbg_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bnbg_m = nn.BatchNorm2d(512) + self.relubg_m = nn.ReLU(inplace=True) + self.convbg_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bnbg_2 = nn.BatchNorm2d(512) + self.relubg_2 = nn.ReLU(inplace=True) + + ## -------------Decoder-------------- + + # stage 6d + self.conv6d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 16 + self.bn6d_1 = nn.BatchNorm2d(512) + self.relu6d_1 = nn.ReLU(inplace=True) + + self.conv6d_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2) ### + self.bn6d_m = nn.BatchNorm2d(512) + self.relu6d_m = nn.ReLU(inplace=True) + + self.conv6d_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bn6d_2 = nn.BatchNorm2d(512) + self.relu6d_2 = nn.ReLU(inplace=True) + + # stage 5d + self.conv5d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 16 + self.bn5d_1 = nn.BatchNorm2d(512) + self.relu5d_1 = nn.ReLU(inplace=True) + + self.conv5d_m = nn.Conv2d(512, 512, 3, padding=1) ### + self.bn5d_m = nn.BatchNorm2d(512) + self.relu5d_m = nn.ReLU(inplace=True) + + self.conv5d_2 = nn.Conv2d(512, 512, 3, padding=1) + self.bn5d_2 = nn.BatchNorm2d(512) + self.relu5d_2 = nn.ReLU(inplace=True) + + # stage 4d + self.conv4d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 32 + self.bn4d_1 = nn.BatchNorm2d(512) + self.relu4d_1 = nn.ReLU(inplace=True) + + self.conv4d_m = nn.Conv2d(512, 512, 3, padding=1) ### + self.bn4d_m = nn.BatchNorm2d(512) + self.relu4d_m = nn.ReLU(inplace=True) + + self.conv4d_2 = nn.Conv2d(512, 256, 3, padding=1) + self.bn4d_2 = nn.BatchNorm2d(256) + self.relu4d_2 = nn.ReLU(inplace=True) + + # stage 3d + self.conv3d_1 = nn.Conv2d(512, 256, 3, padding=1) # 64 + self.bn3d_1 = nn.BatchNorm2d(256) + self.relu3d_1 = nn.ReLU(inplace=True) + + self.conv3d_m = nn.Conv2d(256, 256, 3, padding=1) ### + self.bn3d_m = nn.BatchNorm2d(256) + self.relu3d_m = nn.ReLU(inplace=True) + + self.conv3d_2 = nn.Conv2d(256, 128, 3, padding=1) + self.bn3d_2 = nn.BatchNorm2d(128) + self.relu3d_2 = nn.ReLU(inplace=True) + + # stage 2d + + self.conv2d_1 = nn.Conv2d(256, 128, 3, padding=1) # 128 + self.bn2d_1 = nn.BatchNorm2d(128) + self.relu2d_1 = nn.ReLU(inplace=True) + + self.conv2d_m = nn.Conv2d(128, 128, 3, padding=1) ### + self.bn2d_m = nn.BatchNorm2d(128) + self.relu2d_m = nn.ReLU(inplace=True) + + self.conv2d_2 = nn.Conv2d(128, 64, 3, padding=1) + self.bn2d_2 = nn.BatchNorm2d(64) + self.relu2d_2 = nn.ReLU(inplace=True) + + # stage 1d + self.conv1d_1 = nn.Conv2d(128, 64, 3, padding=1) # 256 + self.bn1d_1 = nn.BatchNorm2d(64) + self.relu1d_1 = nn.ReLU(inplace=True) + + self.conv1d_m = nn.Conv2d(64, 64, 3, padding=1) ### + self.bn1d_m = nn.BatchNorm2d(64) + self.relu1d_m = nn.ReLU(inplace=True) + + self.conv1d_2 = nn.Conv2d(64, 64, 3, padding=1) + self.bn1d_2 = nn.BatchNorm2d(64) + self.relu1d_2 = nn.ReLU(inplace=True) + + ## -------------Bilinear Upsampling-------------- + self.upscore6 = nn.Upsample(scale_factor=32, mode='bilinear') ### + self.upscore5 = nn.Upsample(scale_factor=16, mode='bilinear') + self.upscore4 = nn.Upsample(scale_factor=8, mode='bilinear') + self.upscore3 = nn.Upsample(scale_factor=4, mode='bilinear') + self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear') + + ## -------------Side Output-------------- + self.outconvb = nn.Conv2d(512, n_classes, 3, padding=1) + self.outconv6 = nn.Conv2d(512, n_classes, 3, padding=1) + self.outconv5 = nn.Conv2d(512, n_classes, 3, padding=1) + self.outconv4 = nn.Conv2d(256, n_classes, 3, padding=1) + self.outconv3 = nn.Conv2d(128, n_classes, 3, padding=1) + self.outconv2 = nn.Conv2d(64, n_classes, 3, padding=1) + self.outconv1 = nn.Conv2d(64, n_classes, 3, padding=1) + + ## -------------Refine Module------------- + self.refunet = RefUnet(1,64) + + def forward(self, x): + + hx = x + + ## -------------Encoder------------- + hx = self.inconv(hx) + hx = self.inbn(hx) + hx = self.inrelu(hx) + # hx = self.downsample(hx) + + h1 = self.encoder1(hx) # 256 + h1x = self.resb2_0(h1) + h2 = self.encoder2(h1) # 128 + h2x = self.resb3_0(h2) + h3 = self.encoder3(h2) # 64 + h3x = self.resb4_0(h3) + h4 = self.encoder4(h3) # 32 + h4 = self.resb5_0(h4) + + hx = self.pool4(h4) # 16 + + hx = self.resb5_1(hx) + hx = self.resb5_2(hx) + h5 = self.resb5_3(hx) + + hx = self.pool5(h5) # 8 + + hx = self.resb6_1(hx) + hx = self.resb6_2(hx) + h6 = self.resb6_3(hx) + + ## -------------Bridge------------- + hx = self.relubg_1(self.bnbg_1(self.convbg_1(h6))) # 8 + hx = self.relubg_m(self.bnbg_m(self.convbg_m(hx))) + hbg = self.relubg_2(self.bnbg_2(self.convbg_2(hx))) + + ## -------------Decoder------------- + + hx = self.relu6d_1(self.bn6d_1(self.conv6d_1(torch.cat((hbg, h6), 1)))) + hx = self.relu6d_m(self.bn6d_m(self.conv6d_m(hx))) + hd6 = self.relu6d_2(self.bn6d_2(self.conv6d_2(hx))) + + hx = self.upscore2(hd6) # 8 -> 16 + + hx = self.relu5d_1(self.bn5d_1(self.conv5d_1(torch.cat((hx, h5), 1)))) + hx = self.relu5d_m(self.bn5d_m(self.conv5d_m(hx))) + hd5 = self.relu5d_2(self.bn5d_2(self.conv5d_2(hx))) + + hx = self.upscore2(hd5) # 16 -> 32 + + hx = self.relu4d_1(self.bn4d_1(self.conv4d_1(torch.cat((hx, h4), 1)))) + hx = self.relu4d_m(self.bn4d_m(self.conv4d_m(hx))) + hd4 = self.relu4d_2(self.bn4d_2(self.conv4d_2(hx))) + + hx = self.upscore2(hd4) # 32 -> 64 + + hx = self.relu3d_1(self.bn3d_1(self.conv3d_1(torch.cat((hx, h3x), 1)))) + hx = self.relu3d_m(self.bn3d_m(self.conv3d_m(hx))) + hd3 = self.relu3d_2(self.bn3d_2(self.conv3d_2(hx))) + + hx = self.upscore2(hd3) # 64 -> 128 + + hx = self.relu2d_1(self.bn2d_1(self.conv2d_1(torch.cat((hx, h2x), 1)))) + hx = self.relu2d_m(self.bn2d_m(self.conv2d_m(hx))) + hd2 = self.relu2d_2(self.bn2d_2(self.conv2d_2(hx))) + + hx = self.upscore2(hd2) # 128 -> 256 + + hx = self.relu1d_1(self.bn1d_1(self.conv1d_1(torch.cat((hx, h1x), 1)))) + hx = self.relu1d_m(self.bn1d_m(self.conv1d_m(hx))) + hd1 = self.relu1d_2(self.bn1d_2(self.conv1d_2(hx))) + + ## -------------Side Output------------- + if not self.test_mode: + db = self.outconvb(hbg) + db = self.upscore6(db) # 8->256 + + d6 = self.outconv6(hd6) + d6 = self.upscore6(d6) # 8->256 + + d5 = self.outconv5(hd5) + d5 = self.upscore5(d5) # 16->256 + + d4 = self.outconv4(hd4) + d4 = self.upscore4(d4) # 32->256 + + d3 = self.outconv3(hd3) + d3 = self.upscore3(d3) # 64->256 + + d2 = self.outconv2(hd2) + d2 = self.upscore2(d2) # 128->256 + + d1 = self.outconv1(hd1) # 256 + + ## -------------Refine Module------------- + dout = self.refunet(d1) # 256 + #dout = d1 + + return dout.sigmoid(), d1.sigmoid(), d2.sigmoid(), d3.sigmoid(), d4.sigmoid(), d5.sigmoid(), d6.sigmoid(), db.sigmoid() + else: + d1 = self.outconv1(hd1) # 256 + + ## -------------Refine Module------------- + dout = self.refunet(d1) # 256 + #dout = d1 + + return dout.sigmoid() + + +class BASNet50(nn.Module): + def __init__(self, n_channels, n_classes, test_mode=False): + super(BASNet50, self).__init__() + self.test_mode = test_mode + resnet = models.resnet50(pretrained=True) + + ## -------------Encoder-------------- + + self.inconv = nn.Conv2d(n_channels, 64, 3, padding=1) + self.inbn = nn.BatchNorm2d(64) + self.inrelu = nn.ReLU(inplace=True) + # self.downsample = nn.MaxPool2d(2,2,ceil_mode=True) + + # stage 1 + self.encoder1 = resnet.layer1 # 224 + # stage 2 + self.encoder2 = resnet.layer2 # 112 + # stage 3 + self.encoder3 = resnet.layer3 # 56 + # stage 4 + self.encoder4 = resnet.layer4 # 28 + + self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True) + self.resb5_0 = torch.nn.Conv2d(2048, 512, 1, 1, padding=0) + self.resb4_0 = torch.nn.Conv2d(1024, 256, 1, 1, padding=0) + self.resb3_0 = torch.nn.Conv2d(512, 128, 1, 1, padding=0) + self.resb2_0 = torch.nn.Conv2d(256, 64, 1, 1, padding=0) + + # stage 5 + self.resb5_1 = BasicBlock(512, 512) + self.resb5_2 = BasicBlock(512, 512) + self.resb5_3 = BasicBlock(512, 512) # 14 + + self.pool5 = nn.MaxPool2d(2, 2, ceil_mode=True) + + # stage 6 + self.resb6_1 = BasicBlock(512, 512) + self.resb6_2 = BasicBlock(512, 512) + self.resb6_3 = BasicBlock(512, 512) # 7 + + ## -------------Bridge-------------- + + # stage Bridge + self.convbg_1 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) # 7 + self.bnbg_1 = nn.BatchNorm2d(512) + self.relubg_1 = nn.ReLU(inplace=True) + self.convbg_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bnbg_m = nn.BatchNorm2d(512) + self.relubg_m = nn.ReLU(inplace=True) + self.convbg_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bnbg_2 = nn.BatchNorm2d(512) + self.relubg_2 = nn.ReLU(inplace=True) + + ## -------------Decoder-------------- + + # stage 6d + self.conv6d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 16 + self.bn6d_1 = nn.BatchNorm2d(512) + self.relu6d_1 = nn.ReLU(inplace=True) + + self.conv6d_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2) ### + self.bn6d_m = nn.BatchNorm2d(512) + self.relu6d_m = nn.ReLU(inplace=True) + + self.conv6d_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bn6d_2 = nn.BatchNorm2d(512) + self.relu6d_2 = nn.ReLU(inplace=True) + + # stage 5d + self.conv5d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 16 + self.bn5d_1 = nn.BatchNorm2d(512) + self.relu5d_1 = nn.ReLU(inplace=True) + + self.conv5d_m = nn.Conv2d(512, 512, 3, padding=1) ### + self.bn5d_m = nn.BatchNorm2d(512) + self.relu5d_m = nn.ReLU(inplace=True) + + self.conv5d_2 = nn.Conv2d(512, 512, 3, padding=1) + self.bn5d_2 = nn.BatchNorm2d(512) + self.relu5d_2 = nn.ReLU(inplace=True) + + # stage 4d + self.conv4d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 32 + self.bn4d_1 = nn.BatchNorm2d(512) + self.relu4d_1 = nn.ReLU(inplace=True) + + self.conv4d_m = nn.Conv2d(512, 512, 3, padding=1) ### + self.bn4d_m = nn.BatchNorm2d(512) + self.relu4d_m = nn.ReLU(inplace=True) + + self.conv4d_2 = nn.Conv2d(512, 256, 3, padding=1) + self.bn4d_2 = nn.BatchNorm2d(256) + self.relu4d_2 = nn.ReLU(inplace=True) + + # stage 3d + self.conv3d_1 = nn.Conv2d(512, 256, 3, padding=1) # 64 + self.bn3d_1 = nn.BatchNorm2d(256) + self.relu3d_1 = nn.ReLU(inplace=True) + + self.conv3d_m = nn.Conv2d(256, 256, 3, padding=1) ### + self.bn3d_m = nn.BatchNorm2d(256) + self.relu3d_m = nn.ReLU(inplace=True) + + self.conv3d_2 = nn.Conv2d(256, 128, 3, padding=1) + self.bn3d_2 = nn.BatchNorm2d(128) + self.relu3d_2 = nn.ReLU(inplace=True) + + # stage 2d + + self.conv2d_1 = nn.Conv2d(256, 128, 3, padding=1) # 128 + self.bn2d_1 = nn.BatchNorm2d(128) + self.relu2d_1 = nn.ReLU(inplace=True) + + self.conv2d_m = nn.Conv2d(128, 128, 3, padding=1) ### + self.bn2d_m = nn.BatchNorm2d(128) + self.relu2d_m = nn.ReLU(inplace=True) + + self.conv2d_2 = nn.Conv2d(128, 64, 3, padding=1) + self.bn2d_2 = nn.BatchNorm2d(64) + self.relu2d_2 = nn.ReLU(inplace=True) + + # stage 1d + self.conv1d_1 = nn.Conv2d(128, 64, 3, padding=1) # 256 + self.bn1d_1 = nn.BatchNorm2d(64) + self.relu1d_1 = nn.ReLU(inplace=True) + + self.conv1d_m = nn.Conv2d(64, 64, 3, padding=1) ### + self.bn1d_m = nn.BatchNorm2d(64) + self.relu1d_m = nn.ReLU(inplace=True) + + self.conv1d_2 = nn.Conv2d(64, 64, 3, padding=1) + self.bn1d_2 = nn.BatchNorm2d(64) + self.relu1d_2 = nn.ReLU(inplace=True) + + ## -------------Bilinear Upsampling-------------- + self.upscore6 = nn.Upsample(scale_factor=32, mode='bilinear') ### + self.upscore5 = nn.Upsample(scale_factor=16, mode='bilinear') + self.upscore4 = nn.Upsample(scale_factor=8, mode='bilinear') + self.upscore3 = nn.Upsample(scale_factor=4, mode='bilinear') + self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear') + + ## -------------Side Output-------------- + self.outconvb = nn.Conv2d(512, n_classes, 3, padding=1) + self.outconv6 = nn.Conv2d(512, n_classes, 3, padding=1) + self.outconv5 = nn.Conv2d(512, n_classes, 3, padding=1) + self.outconv4 = nn.Conv2d(256, n_classes, 3, padding=1) + self.outconv3 = nn.Conv2d(128, n_classes, 3, padding=1) + self.outconv2 = nn.Conv2d(64, n_classes, 3, padding=1) + self.outconv1 = nn.Conv2d(64, n_classes, 3, padding=1) + + ## -------------Refine Module------------- + self.refunet = RefUnet(1,64) + + def forward(self, x): + + hx = x + + ## -------------Encoder------------- + hx = self.inconv(hx) + hx = self.inbn(hx) + hx = self.inrelu(hx) + # hx = self.downsample(hx) + + h1 = self.encoder1(hx) # 256 + h1x = self.resb2_0(h1) + h2 = self.encoder2(h1) # 128 + h2x = self.resb3_0(h2) + h3 = self.encoder3(h2) # 64 + h3x = self.resb4_0(h3) + h4 = self.encoder4(h3) # 32 + h4 = self.resb5_0(h4) + + hx = self.pool4(h4) # 16 + + hx = self.resb5_1(hx) + hx = self.resb5_2(hx) + h5 = self.resb5_3(hx) + + hx = self.pool5(h5) # 8 + + hx = self.resb6_1(hx) + hx = self.resb6_2(hx) + h6 = self.resb6_3(hx) + + ## -------------Bridge------------- + hx = self.relubg_1(self.bnbg_1(self.convbg_1(h6))) # 8 + hx = self.relubg_m(self.bnbg_m(self.convbg_m(hx))) + hbg = self.relubg_2(self.bnbg_2(self.convbg_2(hx))) + + ## -------------Decoder------------- + + hx = self.relu6d_1(self.bn6d_1(self.conv6d_1(torch.cat((hbg, h6), 1)))) + hx = self.relu6d_m(self.bn6d_m(self.conv6d_m(hx))) + hd6 = self.relu6d_2(self.bn6d_2(self.conv6d_2(hx))) + + hx = self.upscore2(hd6) # 8 -> 16 + + hx = self.relu5d_1(self.bn5d_1(self.conv5d_1(torch.cat((hx, h5), 1)))) + hx = self.relu5d_m(self.bn5d_m(self.conv5d_m(hx))) + hd5 = self.relu5d_2(self.bn5d_2(self.conv5d_2(hx))) + + hx = self.upscore2(hd5) # 16 -> 32 + + hx = self.relu4d_1(self.bn4d_1(self.conv4d_1(torch.cat((hx, h4), 1)))) + hx = self.relu4d_m(self.bn4d_m(self.conv4d_m(hx))) + hd4 = self.relu4d_2(self.bn4d_2(self.conv4d_2(hx))) + + hx = self.upscore2(hd4) # 32 -> 64 + + hx = self.relu3d_1(self.bn3d_1(self.conv3d_1(torch.cat((hx, h3x), 1)))) + hx = self.relu3d_m(self.bn3d_m(self.conv3d_m(hx))) + hd3 = self.relu3d_2(self.bn3d_2(self.conv3d_2(hx))) + + hx = self.upscore2(hd3) # 64 -> 128 + + hx = self.relu2d_1(self.bn2d_1(self.conv2d_1(torch.cat((hx, h2x), 1)))) + hx = self.relu2d_m(self.bn2d_m(self.conv2d_m(hx))) + hd2 = self.relu2d_2(self.bn2d_2(self.conv2d_2(hx))) + + hx = self.upscore2(hd2) # 128 -> 256 + + hx = self.relu1d_1(self.bn1d_1(self.conv1d_1(torch.cat((hx, h1x), 1)))) + hx = self.relu1d_m(self.bn1d_m(self.conv1d_m(hx))) + hd1 = self.relu1d_2(self.bn1d_2(self.conv1d_2(hx))) + + ## -------------Side Output------------- + if not self.test_mode: + db = self.outconvb(hbg) + db = self.upscore6(db) # 8->256 + + d6 = self.outconv6(hd6) + d6 = self.upscore6(d6) # 8->256 + + d5 = self.outconv5(hd5) + d5 = self.upscore5(d5) # 16->256 + + d4 = self.outconv4(hd4) + d4 = self.upscore4(d4) # 32->256 + + d3 = self.outconv3(hd3) + d3 = self.upscore3(d3) # 64->256 + + d2 = self.outconv2(hd2) + d2 = self.upscore2(d2) # 128->256 + + d1 = self.outconv1(hd1) # 256 + + ## -------------Refine Module------------- + dout = self.refunet(d1) # 256 + #dout = d1 + + return dout.sigmoid(), d1.sigmoid(), d2.sigmoid(), d3.sigmoid(), d4.sigmoid(), d5.sigmoid(), d6.sigmoid(), db.sigmoid() + else: + d1 = self.outconv1(hd1) # 256 + + ## -------------Refine Module------------- + dout = self.refunet(d1) # 256 + #dout = d1 + + return dout.sigmoid() + + + +class Bone34(nn.Module): + def __init__(self, n_channels, test_mode=False): + super(Bone34, self).__init__() + self.test_mode = test_mode + resnet = models.resnet34(pretrained=True) + + ## -------------Encoder-------------- + + self.inconv = nn.Conv2d(n_channels, 64, 3, padding=1) + self.inbn = nn.BatchNorm2d(64) + self.inrelu = nn.ReLU(inplace=True) + # self.downsample = nn.MaxPool2d(2,2,ceil_mode=True) + + # stage 1 + self.encoder1 = resnet.layer1 # 224 + # stage 2 + self.encoder2 = resnet.layer2 # 112 + # stage 3 + self.encoder3 = resnet.layer3 # 56 + # stage 4 + self.encoder4 = resnet.layer4 # 28 + + + + def forward(self, x): + + hx = x + + ## -------------Encoder------------- + hx = self.inconv(hx) + hx = self.inbn(hx) + hx = self.inrelu(hx) + # hx = self.downsample(hx) + + h1 = self.encoder1(hx) # 256 + h2 = self.encoder2(h1) # 128 + h3 = self.encoder3(h2) # 64 + h4 = self.encoder4(h3) # 32 + + + return [h1, h2, h3, h4] + + + + + + +class BASNet(nn.Module): + def __init__(self, n_channels, n_classes, test_mode=False): + super(BASNet, self).__init__() + self.test_mode = test_mode + resnet = models.resnet34(pretrained=True) + + ## -------------Encoder-------------- + + self.inconv = nn.Conv2d(n_channels, 64, 3, padding=1) + self.inbn = nn.BatchNorm2d(64) + self.inrelu = nn.ReLU(inplace=True) + # self.downsample = nn.MaxPool2d(2,2,ceil_mode=True) + + # stage 1 + self.encoder1 = resnet.layer1 # 224 + # stage 2 + self.encoder2 = resnet.layer2 # 112 + # stage 3 + self.encoder3 = resnet.layer3 # 56 + # stage 4 + self.encoder4 = resnet.layer4 # 28 + + self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True) + + # stage 5 + self.resb5_1 = BasicBlock(512, 512) + self.resb5_2 = BasicBlock(512, 512) + self.resb5_3 = BasicBlock(512, 512) # 14 + + self.pool5 = nn.MaxPool2d(2, 2, ceil_mode=True) + + # stage 6 + self.resb6_1 = BasicBlock(512, 512) + self.resb6_2 = BasicBlock(512, 512) + self.resb6_3 = BasicBlock(512, 512) # 7 + + ## -------------Bridge-------------- + + # stage Bridge + self.convbg_1 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) # 7 + self.bnbg_1 = nn.BatchNorm2d(512) + self.relubg_1 = nn.ReLU(inplace=True) + self.convbg_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bnbg_m = nn.BatchNorm2d(512) + self.relubg_m = nn.ReLU(inplace=True) + self.convbg_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bnbg_2 = nn.BatchNorm2d(512) + self.relubg_2 = nn.ReLU(inplace=True) + + ## -------------Decoder-------------- + + # stage 6d + self.conv6d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 16 + self.bn6d_1 = nn.BatchNorm2d(512) + self.relu6d_1 = nn.ReLU(inplace=True) + + self.conv6d_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2) ### + self.bn6d_m = nn.BatchNorm2d(512) + self.relu6d_m = nn.ReLU(inplace=True) + + self.conv6d_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bn6d_2 = nn.BatchNorm2d(512) + self.relu6d_2 = nn.ReLU(inplace=True) + + # stage 5d + self.conv5d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 16 + self.bn5d_1 = nn.BatchNorm2d(512) + self.relu5d_1 = nn.ReLU(inplace=True) + + self.conv5d_m = nn.Conv2d(512, 512, 3, padding=1) ### + self.bn5d_m = nn.BatchNorm2d(512) + self.relu5d_m = nn.ReLU(inplace=True) + + self.conv5d_2 = nn.Conv2d(512, 512, 3, padding=1) + self.bn5d_2 = nn.BatchNorm2d(512) + self.relu5d_2 = nn.ReLU(inplace=True) + + # stage 4d + self.conv4d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 32 + self.bn4d_1 = nn.BatchNorm2d(512) + self.relu4d_1 = nn.ReLU(inplace=True) + + self.conv4d_m = nn.Conv2d(512, 512, 3, padding=1) ### + self.bn4d_m = nn.BatchNorm2d(512) + self.relu4d_m = nn.ReLU(inplace=True) + + self.conv4d_2 = nn.Conv2d(512, 256, 3, padding=1) + self.bn4d_2 = nn.BatchNorm2d(256) + self.relu4d_2 = nn.ReLU(inplace=True) + + # stage 3d + self.conv3d_1 = nn.Conv2d(512, 256, 3, padding=1) # 64 + self.bn3d_1 = nn.BatchNorm2d(256) + self.relu3d_1 = nn.ReLU(inplace=True) + + self.conv3d_m = nn.Conv2d(256, 256, 3, padding=1) ### + self.bn3d_m = nn.BatchNorm2d(256) + self.relu3d_m = nn.ReLU(inplace=True) + + self.conv3d_2 = nn.Conv2d(256, 128, 3, padding=1) + self.bn3d_2 = nn.BatchNorm2d(128) + self.relu3d_2 = nn.ReLU(inplace=True) + + # stage 2d + + self.conv2d_1 = nn.Conv2d(256, 128, 3, padding=1) # 128 + self.bn2d_1 = nn.BatchNorm2d(128) + self.relu2d_1 = nn.ReLU(inplace=True) + + self.conv2d_m = nn.Conv2d(128, 128, 3, padding=1) ### + self.bn2d_m = nn.BatchNorm2d(128) + self.relu2d_m = nn.ReLU(inplace=True) + + self.conv2d_2 = nn.Conv2d(128, 64, 3, padding=1) + self.bn2d_2 = nn.BatchNorm2d(64) + self.relu2d_2 = nn.ReLU(inplace=True) + + # stage 1d + self.conv1d_1 = nn.Conv2d(128, 64, 3, padding=1) # 256 + self.bn1d_1 = nn.BatchNorm2d(64) + self.relu1d_1 = nn.ReLU(inplace=True) + + self.conv1d_m = nn.Conv2d(64, 64, 3, padding=1) ### + self.bn1d_m = nn.BatchNorm2d(64) + self.relu1d_m = nn.ReLU(inplace=True) + + self.conv1d_2 = nn.Conv2d(64, 64, 3, padding=1) + self.bn1d_2 = nn.BatchNorm2d(64) + self.relu1d_2 = nn.ReLU(inplace=True) + + ## -------------Bilinear Upsampling-------------- + self.upscore6 = nn.Upsample(scale_factor=32, mode='bilinear') ### + self.upscore5 = nn.Upsample(scale_factor=16, mode='bilinear') + self.upscore4 = nn.Upsample(scale_factor=8, mode='bilinear') + self.upscore3 = nn.Upsample(scale_factor=4, mode='bilinear') + self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear') + + ## -------------Side Output-------------- + self.outconvb = nn.Conv2d(512, n_classes, 3, padding=1) + self.outconv6 = nn.Conv2d(512, n_classes, 3, padding=1) + self.outconv5 = nn.Conv2d(512, n_classes, 3, padding=1) + self.outconv4 = nn.Conv2d(256, n_classes, 3, padding=1) + self.outconv3 = nn.Conv2d(128, n_classes, 3, padding=1) + self.outconv2 = nn.Conv2d(64, n_classes, 3, padding=1) + self.outconv1 = nn.Conv2d(64, n_classes, 3, padding=1) + + ## -------------Refine Module------------- + self.refunet = RefUnet(1,64) + + def forward(self, x): + + hx = x + + ## -------------Encoder------------- + hx = self.inconv(hx) + hx = self.inbn(hx) + hx = self.inrelu(hx) + # hx = self.downsample(hx) + + h1 = self.encoder1(hx) # 256 + h2 = self.encoder2(h1) # 128 + h3 = self.encoder3(h2) # 64 + h4 = self.encoder4(h3) # 32 + + hx = self.pool4(h4) # 16 + + hx = self.resb5_1(hx) + hx = self.resb5_2(hx) + h5 = self.resb5_3(hx) + + hx = self.pool5(h5) # 8 + + hx = self.resb6_1(hx) + hx = self.resb6_2(hx) + h6 = self.resb6_3(hx) + + ## -------------Bridge------------- + hx = self.relubg_1(self.bnbg_1(self.convbg_1(h6))) # 8 + hx = self.relubg_m(self.bnbg_m(self.convbg_m(hx))) + hbg = self.relubg_2(self.bnbg_2(self.convbg_2(hx))) + + ## -------------Decoder------------- + + hx = self.relu6d_1(self.bn6d_1(self.conv6d_1(torch.cat((hbg, h6), 1)))) + hx = self.relu6d_m(self.bn6d_m(self.conv6d_m(hx))) + hd6 = self.relu6d_2(self.bn6d_2(self.conv6d_2(hx))) + + hx = self.upscore2(hd6) # 8 -> 16 + + hx = self.relu5d_1(self.bn5d_1(self.conv5d_1(torch.cat((hx, h5), 1)))) + hx = self.relu5d_m(self.bn5d_m(self.conv5d_m(hx))) + hd5 = self.relu5d_2(self.bn5d_2(self.conv5d_2(hx))) + + hx = self.upscore2(hd5) # 16 -> 32 + + hx = self.relu4d_1(self.bn4d_1(self.conv4d_1(torch.cat((hx, h4), 1)))) + hx = self.relu4d_m(self.bn4d_m(self.conv4d_m(hx))) + hd4 = self.relu4d_2(self.bn4d_2(self.conv4d_2(hx))) + + hx = self.upscore2(hd4) # 32 -> 64 + + hx = self.relu3d_1(self.bn3d_1(self.conv3d_1(torch.cat((hx, h3), 1)))) + hx = self.relu3d_m(self.bn3d_m(self.conv3d_m(hx))) + hd3 = self.relu3d_2(self.bn3d_2(self.conv3d_2(hx))) + + hx = self.upscore2(hd3) # 64 -> 128 + + hx = self.relu2d_1(self.bn2d_1(self.conv2d_1(torch.cat((hx, h2), 1)))) + hx = self.relu2d_m(self.bn2d_m(self.conv2d_m(hx))) + hd2 = self.relu2d_2(self.bn2d_2(self.conv2d_2(hx))) + + hx = self.upscore2(hd2) # 128 -> 256 + + hx = self.relu1d_1(self.bn1d_1(self.conv1d_1(torch.cat((hx, h1), 1)))) + hx = self.relu1d_m(self.bn1d_m(self.conv1d_m(hx))) + hd1 = self.relu1d_2(self.bn1d_2(self.conv1d_2(hx))) + + ## -------------Side Output------------- + if not self.test_mode: + db = self.outconvb(hbg) + db = self.upscore6(db) # 8->256 + + d6 = self.outconv6(hd6) + d6 = self.upscore6(d6) # 8->256 + + d5 = self.outconv5(hd5) + d5 = self.upscore5(d5) # 16->256 + + d4 = self.outconv4(hd4) + d4 = self.upscore4(d4) # 32->256 + + d3 = self.outconv3(hd3) + d3 = self.upscore3(d3) # 64->256 + + d2 = self.outconv2(hd2) + d2 = self.upscore2(d2) # 128->256 + + d1 = self.outconv1(hd1) # 256 + + ## -------------Refine Module------------- + dout = self.refunet(d1) # 256 + #dout = d1 + + return dout.sigmoid(), d1.sigmoid(), d2.sigmoid(), d3.sigmoid(), d4.sigmoid(), d5.sigmoid(), d6.sigmoid(), db.sigmoid() + else: + d1 = self.outconv1(hd1) # 256 + + ## -------------Refine Module------------- + dout = self.refunet(d1) # 256 + #dout = d1 + + return dout.sigmoid() + + +class BASNet18(nn.Module): + def __init__(self, n_channels, n_classes, test_mode=False): + super(BASNet18, self).__init__() + self.test_mode = test_mode + resnet = models.resnet18(pretrained=True) + + ## -------------Encoder-------------- + + self.inconv = nn.Conv2d(n_channels, 64, 3, padding=1) + self.inbn = nn.BatchNorm2d(64) + self.inrelu = nn.ReLU(inplace=True) + + # stage 1 + self.encoder1 = resnet.layer1 # 224 + # stage 2 + self.encoder2 = resnet.layer2 # 112 + # stage 3 + self.encoder3 = resnet.layer3 # 56 + + # stage 4 + self.encoder4 = resnet.layer4 # 28 + + self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True) + + # stage 5 + self.resb5_1 = BasicBlock(512, 512) + self.resb5_2 = BasicBlock(512, 512) + self.resb5_3 = BasicBlock(512, 512) # 14 + self.rfb5_4 = RFB(512, 512) + + self.pool5 = nn.MaxPool2d(2, 2, ceil_mode=True) + + # stage 6 + self.resb6_1 = BasicBlock(512, 512) + self.resb6_2 = BasicBlock(512, 512) + self.resb6_3 = BasicBlock(512, 512) # 7 + self.rfb6_4 = RFB(512, 512) + + ## -------------Bridge-------------- + + # stage Bridge + self.convbg_1 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) # 7 + self.bnbg_1 = nn.BatchNorm2d(512) + self.relubg_1 = nn.ReLU(inplace=True) + self.convbg_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bnbg_m = nn.BatchNorm2d(512) + self.relubg_m = nn.ReLU(inplace=True) + self.convbg_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bnbg_2 = nn.BatchNorm2d(512) + self.relubg_2 = nn.ReLU(inplace=True) + + ## -------------Decoder-------------- + + # stage 6d + self.conv6d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 16 + self.bn6d_1 = nn.BatchNorm2d(512) + self.relu6d_1 = nn.ReLU(inplace=True) + + self.conv6d_m = nn.Conv2d(512, 512, 3, dilation=2, padding=2) ### + self.bn6d_m = nn.BatchNorm2d(512) + self.relu6d_m = nn.ReLU(inplace=True) + + self.conv6d_2 = nn.Conv2d(512, 512, 3, dilation=2, padding=2) + self.bn6d_2 = nn.BatchNorm2d(512) + self.relu6d_2 = nn.ReLU(inplace=True) + + # stage 5d + self.conv5d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 16 + self.bn5d_1 = nn.BatchNorm2d(512) + self.relu5d_1 = nn.ReLU(inplace=True) + + self.conv5d_m = nn.Conv2d(512, 512, 3, padding=1) ### + self.bn5d_m = nn.BatchNorm2d(512) + self.relu5d_m = nn.ReLU(inplace=True) + + self.conv5d_2 = nn.Conv2d(512, 512, 3, padding=1) + self.bn5d_2 = nn.BatchNorm2d(512) + self.relu5d_2 = nn.ReLU(inplace=True) + + # stage 4d + self.conv4d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 32 + self.bn4d_1 = nn.BatchNorm2d(512) + self.relu4d_1 = nn.ReLU(inplace=True) + + self.conv4d_m = nn.Conv2d(512, 512, 3, padding=1) ### + self.bn4d_m = nn.BatchNorm2d(512) + self.relu4d_m = nn.ReLU(inplace=True) + + self.conv4d_2 = nn.Conv2d(512, 256, 3, padding=1) + self.bn4d_2 = nn.BatchNorm2d(256) + self.relu4d_2 = nn.ReLU(inplace=True) + + # stage 3d + self.conv3d_1 = nn.Conv2d(512, 256, 3, padding=1) # 64 + self.bn3d_1 = nn.BatchNorm2d(256) + self.relu3d_1 = nn.ReLU(inplace=True) + + self.conv3d_m = nn.Conv2d(256, 256, 3, padding=1) ### + self.bn3d_m = nn.BatchNorm2d(256) + self.relu3d_m = nn.ReLU(inplace=True) + + self.conv3d_2 = nn.Conv2d(256, 128, 3, padding=1) + self.bn3d_2 = nn.BatchNorm2d(128) + self.relu3d_2 = nn.ReLU(inplace=True) + + # stage 2d + + self.conv2d_1 = nn.Conv2d(256, 128, 3, padding=1) # 128 + self.bn2d_1 = nn.BatchNorm2d(128) + self.relu2d_1 = nn.ReLU(inplace=True) + + self.conv2d_m = nn.Conv2d(128, 128, 3, padding=1) ### + self.bn2d_m = nn.BatchNorm2d(128) + self.relu2d_m = nn.ReLU(inplace=True) + + self.conv2d_2 = nn.Conv2d(128, 64, 3, padding=1) + self.bn2d_2 = nn.BatchNorm2d(64) + self.relu2d_2 = nn.ReLU(inplace=True) + + # stage 1d + self.conv1d_1 = nn.Conv2d(128, 64, 3, padding=1) # 256 + self.bn1d_1 = nn.BatchNorm2d(64) + self.relu1d_1 = nn.ReLU(inplace=True) + + self.conv1d_m = nn.Conv2d(64, 64, 3, padding=1) ### + self.bn1d_m = nn.BatchNorm2d(64) + self.relu1d_m = nn.ReLU(inplace=True) + + self.conv1d_2 = nn.Conv2d(64, 64, 3, padding=1) + self.bn1d_2 = nn.BatchNorm2d(64) + self.relu1d_2 = nn.ReLU(inplace=True) + + ## -------------Bilinear Upsampling-------------- + self.upscore6 = nn.Upsample(scale_factor=32, mode='bilinear') ### + self.upscore5 = nn.Upsample(scale_factor=16, mode='bilinear') + self.upscore4 = nn.Upsample(scale_factor=8, mode='bilinear') + self.upscore3 = nn.Upsample(scale_factor=4, mode='bilinear') + self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear') + + ## -------------Side Output-------------- + self.outconvb = nn.Conv2d(512, 1, 3, padding=1) + self.outconv6 = nn.Conv2d(512, 1, 3, padding=1) + self.outconv5 = nn.Conv2d(512, 1, 3, padding=1) + self.outconv4 = nn.Conv2d(256, 1, 3, padding=1) + self.outconv3 = nn.Conv2d(128, 1, 3, padding=1) + self.outconv2 = nn.Conv2d(64, 1, 3, padding=1) + self.outconv1 = nn.Conv2d(64, 1, 3, padding=1) + + ## -------------Refine Module------------- + self.refunet = RefUnet(1, 64) + + def forward(self, x): + + hx = x + + ## -------------Encoder------------- + hx = self.inconv(hx) + hx = self.inbn(hx) + hx = self.inrelu(hx) + + h1 = self.encoder1(hx) # 256 + h2 = self.encoder2(h1) # 128 + h3 = self.encoder3(h2) # 64 + h4 = self.encoder4(h3) # 32 + + hx = self.pool4(h4) # 16 + + hx = self.resb5_1(hx) + hx = self.resb5_2(hx) + h5 = self.resb5_3(hx) + h5 = self.rfb5_4(h5) + + hx = self.pool5(h5) # 8 + + hx = self.resb6_1(hx) + hx = self.resb6_2(hx) + h6 = self.resb6_3(hx) + h6 = self.rfb6_4(h6) + + ## -------------Bridge------------- + hx = self.relubg_1(self.bnbg_1(self.convbg_1(h6))) # 8 + hx = self.relubg_m(self.bnbg_m(self.convbg_m(hx))) + hbg = self.relubg_2(self.bnbg_2(self.convbg_2(hx))) + + ## -------------Decoder------------- + + hx = self.relu6d_1(self.bn6d_1(self.conv6d_1(torch.cat((hbg, h6), 1)))) + hx = self.relu6d_m(self.bn6d_m(self.conv6d_m(hx))) + hd6 = self.relu6d_2(self.bn6d_2(self.conv6d_2(hx))) + + hx = self.upscore2(hd6) # 8 -> 16 + + hx = self.relu5d_1(self.bn5d_1(self.conv5d_1(torch.cat((hx, h5), 1)))) + hx = self.relu5d_m(self.bn5d_m(self.conv5d_m(hx))) + hd5 = self.relu5d_2(self.bn5d_2(self.conv5d_2(hx))) + + hx = self.upscore2(hd5) # 16 -> 32 + + hx = self.relu4d_1(self.bn4d_1(self.conv4d_1(torch.cat((hx, h4), 1)))) + hx = self.relu4d_m(self.bn4d_m(self.conv4d_m(hx))) + hd4 = self.relu4d_2(self.bn4d_2(self.conv4d_2(hx))) + + hx = self.upscore2(hd4) # 32 -> 64 + + hx = self.relu3d_1(self.bn3d_1(self.conv3d_1(torch.cat((hx, h3), 1)))) + hx = self.relu3d_m(self.bn3d_m(self.conv3d_m(hx))) + hd3 = self.relu3d_2(self.bn3d_2(self.conv3d_2(hx))) + + hx = self.upscore2(hd3) # 64 -> 128 + + hx = self.relu2d_1(self.bn2d_1(self.conv2d_1(torch.cat((hx, h2), 1)))) + hx = self.relu2d_m(self.bn2d_m(self.conv2d_m(hx))) + hd2 = self.relu2d_2(self.bn2d_2(self.conv2d_2(hx))) + + hx = self.upscore2(hd2) # 128 -> 256 + + hx = self.relu1d_1(self.bn1d_1(self.conv1d_1(torch.cat((hx, h1), 1)))) + hx = self.relu1d_m(self.bn1d_m(self.conv1d_m(hx))) + hd1 = self.relu1d_2(self.bn1d_2(self.conv1d_2(hx))) + + ## -------------Side Output------------- + if not self.test_mode: + db = self.outconvb(hbg) + db = self.upscore6(db) # 8->256 + + d6 = self.outconv6(hd6) + d6 = self.upscore6(d6) # 8->256 + + d5 = self.outconv5(hd5) + d5 = self.upscore5(d5) # 16->256 + + d4 = self.outconv4(hd4) + d4 = self.upscore4(d4) # 32->256 + + d3 = self.outconv3(hd3) + d3 = self.upscore3(d3) # 64->256 + + d2 = self.outconv2(hd2) + d2 = self.upscore2(d2) # 128->256 + + d1 = self.outconv1(hd1) # 256 + + ## -------------Refine Module------------- + dout = self.refunet(d1) # 256 + + return dout.sigmoid(), d1.sigmoid(), d2.sigmoid(), d3.sigmoid(), d4.sigmoid(), d5.sigmoid(), d6.sigmoid(), db.sigmoid() + else: + d1 = self.outconv1(hd1) # 256 + + ## -------------Refine Module------------- + dout = self.refunet(d1) # 256 + + return dout.sigmoid() + + +class BASNet34_RFB(nn.Module): + def __init__(self, n_channels, n_classes, test_mode=False): + super(BASNet34_RFB, self).__init__() + self.test_mode = test_mode + resnet = models.resnet34(pretrained=True) + + ## -------------Encoder-------------- + + self.inconv = nn.Conv2d(n_channels, 64, 3, padding=1) + self.inbn = nn.BatchNorm2d(64) + self.inrelu = nn.ReLU(inplace=True) + + ##self.resb1_1 = BasicBlock(64, 64) + #self.resb1_2 = BasicBlock(64, 64) + #self.pool1 = nn.MaxPool2d(2, 2, ceil_mode=True) + + # stage 1 + self.encoder1 = resnet.layer1 # 256 + # stage 2 + self.encoder2 = resnet.layer2 # 128 + # stage 3 + self.encoder3 = resnet.layer3 # 64 + # stage 4 + self.encoder4 = resnet.layer4 # 32 + + #self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True) + + # stage 5 + # self.resb5_1 = BasicBlock(512,512) + # self.resb5_2 = BasicBlock(512,512) + # self.resb5_3 = BasicBlock(512,512) #16 + self.resb = RFB(512, 512) + # self.resb5_2 = RFB(512, 512) + # self.resb5_3 = RFB(512, 512) # 16 + # self.resb5_4 = RFB(512, 512) + + # self.pool5 = nn.MaxPool2d(2,2,ceil_mode=True) + + # stage 6 + # self.resb6_1 = RFB(256, 256) + # self.resb6_2 = RFB(256, 256) + # self.resb6_3 = RFB(256, 256) #8 + + ## -------------Bridge-------------- + + # stage Bridge + bridge_size = 512 + self.convbg_1 = nn.Conv2d(bridge_size, bridge_size, 3, dilation=2, padding=2) # 7 + self.bnbg_1 = nn.BatchNorm2d(bridge_size) + self.relubg_1 = nn.ReLU(inplace=True) + self.convbg_m = nn.Conv2d(bridge_size, bridge_size, 3, dilation=2, padding=2) + self.bnbg_m = nn.BatchNorm2d(bridge_size) + self.relubg_m = nn.ReLU(inplace=True) + self.convbg_2 = nn.Conv2d(bridge_size, bridge_size, 3, dilation=2, padding=2) + self.bnbg_2 = nn.BatchNorm2d(bridge_size) + self.relubg_2 = nn.ReLU(inplace=True) + + ## -------------Decoder-------------- + + # stage 6d + # self.conv6d_1 = nn.Conv2d(512,256,3,padding=1) # 16 + # self.bn6d_1 = nn.BatchNorm2d(256) + # self.relu6d_1 = nn.ReLU(inplace=True) + # + # self.conv6d_m = nn.Conv2d(256,256,3,dilation=2, padding=2)### + # self.bn6d_m = nn.BatchNorm2d(256) + # self.relu6d_m = nn.ReLU(inplace=True) + # + # self.conv6d_2 = nn.Conv2d(256,256,3,dilation=2, padding=2) + # self.bn6d_2 = nn.BatchNorm2d(256) + # self.relu6d_2 = nn.ReLU(inplace=True) + + # stage 5d + # self.conv5d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 16 + # self.bn5d_1 = nn.BatchNorm2d(512) + # self.relu5d_1 = nn.ReLU(inplace=True) + # + # self.conv5d_m = nn.Conv2d(512, 512, 3, padding=1) ### + # self.bn5d_m = nn.BatchNorm2d(512) + # self.relu5d_m = nn.ReLU(inplace=True) + # + # self.conv5d_2 = nn.Conv2d(512, 512, 3, padding=1) + # self.bn5d_2 = nn.BatchNorm2d(512) + # self.relu5d_2 = nn.ReLU(inplace=True) + + # stage 4d + self.conv4d_1 = nn.Conv2d(1024, 512, 3, padding=1) # 32 + self.bn4d_1 = nn.BatchNorm2d(512) + self.relu4d_1 = nn.ReLU(inplace=True) + + self.conv4d_m = nn.Conv2d(512, 512, 3, padding=1) ### + self.bn4d_m = nn.BatchNorm2d(512) + self.relu4d_m = nn.ReLU(inplace=True) + + self.conv4d_2 = nn.Conv2d(512, 256, 3, padding=1) + self.bn4d_2 = nn.BatchNorm2d(256) + self.relu4d_2 = nn.ReLU(inplace=True) + + # stage 3d + self.conv3d_1 = nn.Conv2d(512, 256, 3, padding=1) # 64 + self.bn3d_1 = nn.BatchNorm2d(256) + self.relu3d_1 = nn.ReLU(inplace=True) + + self.conv3d_m = nn.Conv2d(256, 256, 3, padding=1) ### + self.bn3d_m = nn.BatchNorm2d(256) + self.relu3d_m = nn.ReLU(inplace=True) + + self.conv3d_2 = nn.Conv2d(256, 128, 3, padding=1) + self.bn3d_2 = nn.BatchNorm2d(128) + self.relu3d_2 = nn.ReLU(inplace=True) + + # stage 2d + + self.conv2d_1 = nn.Conv2d(256, 128, 3, padding=1) # 128 + self.bn2d_1 = nn.BatchNorm2d(128) + self.relu2d_1 = nn.ReLU(inplace=True) + + self.conv2d_m = nn.Conv2d(128, 128, 3, padding=1) ### + self.bn2d_m = nn.BatchNorm2d(128) + self.relu2d_m = nn.ReLU(inplace=True) + + self.conv2d_2 = nn.Conv2d(128, 64, 3, padding=1) + self.bn2d_2 = nn.BatchNorm2d(64) + self.relu2d_2 = nn.ReLU(inplace=True) + + # stage 1d + self.conv1d_1 = nn.Conv2d(128, 64, 3, padding=1) # 256 + self.bn1d_1 = nn.BatchNorm2d(64) + self.relu1d_1 = nn.ReLU(inplace=True) + + self.conv1d_m = nn.Conv2d(64, 64, 3, padding=1) ### + self.bn1d_m = nn.BatchNorm2d(64) + self.relu1d_m = nn.ReLU(inplace=True) + + self.conv1d_2 = nn.Conv2d(64, 64, 3, padding=1) + self.bn1d_2 = nn.BatchNorm2d(64) + self.relu1d_2 = nn.ReLU(inplace=True) + + ## -------------Bilinear Upsampling-------------- + self.upscore6 = nn.Upsample(scale_factor=32, mode='bilinear') ### + #self.upscore5 = nn.Upsample(scale_factor=16, mode='bilinear') + self.upscore4 = nn.Upsample(scale_factor=8, mode='bilinear') + self.upscore3 = nn.Upsample(scale_factor=4, mode='bilinear') + self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear') + + ## -------------Side Output-------------- + self.outconvb = nn.Conv2d(512, 1, 3, padding=1) + #self.outconv6 = nn.Conv2d(512, 1, 3, padding=1) + #self.outconv5 = nn.Conv2d(512, 1, 3, padding=1) + self.outconv4 = nn.Conv2d(256, 1, 3, padding=1) + self.outconv3 = nn.Conv2d(128, 1, 3, padding=1) + self.outconv2 = nn.Conv2d(64, 1, 3, padding=1) + self.outconv1 = nn.Conv2d(64, 1, 3, padding=1) + + ## -------------Refine Module------------- + #self.refunet = RefUnet(1, 64) + self.convfilter = ConvGuidedFilter(views=1) + + def forward(self, x_orig): + x = F.interpolate(x_orig, size=(256, 256), mode='bilinear', align_corners=True) + hx = x + + ## -------------Encoder------------- + hx = self.inconv(hx) + hx = self.inbn(hx) + hx = self.inrelu(hx) + + #h0 = self.resb1_1(hx) + # h0 = self.resb1_2(h0) + #hx = self.pool1(h0) + + h1 = self.encoder1(hx) # 128 + h2 = self.encoder2(h1) # 64 + h3 = self.encoder3(h2) # 32 + h4 = self.encoder4(h3) # 16 + + #hx = self.pool4(h4) # 16 + + #hx = self.resb5_1(hx) + #hx = self.resb5_2(hx) + h5 = self.resb(h4) + # h5 = self.resb5_4(hx) + + # hx = self.pool5(h5) # 8 + + # hx = self.resb6_1(hx) + # h6 = self.resb6_2(hx) + # h6 = self.resb6_3(hx) + + ## -------------Bridge------------- + hx = self.relubg_1(self.bnbg_1(self.convbg_1(h5))) # 8 + #hx = self.relubg_m(self.bnbg_m(self.convbg_m(hx))) + hbg = self.relubg_2(self.bnbg_2(self.convbg_2(hx))) + + ## -------------Decoder------------- + + # hx = self.relu6d_1(self.bn6d_1(self.conv6d_1(torch.cat((hbg,h5),1)))) + # hx = self.relu6d_m(self.bn6d_m(self.conv6d_m(hx))) + # hd6 = self.relu6d_2(self.bn6d_2(self.conv6d_2(hx))) + + # hx = self.upscore2(hd6) # 8 -> 16 + + # hx = self.relu5d_1(self.bn5d_1(self.conv5d_1(torch.cat((hx, h5), 1)))) + # hx = self.relu5d_m(self.bn5d_m(self.conv5d_m(hx))) + # hd5 = self.relu5d_2(self.bn5d_2(self.conv5d_2(hx))) + # + # hx = self.upscore2(hd5) # 16 -> 32 + + hx = self.relu4d_1(self.bn4d_1(self.conv4d_1(torch.cat((hbg, h4), 1)))) + hx = self.relu4d_m(self.bn4d_m(self.conv4d_m(hx))) + hd4 = self.relu4d_2(self.bn4d_2(self.conv4d_2(hx))) + + hx = self.upscore2(hd4) # 32 -> 64 + + hx = self.relu3d_1(self.bn3d_1(self.conv3d_1(torch.cat((hx, h3), 1)))) + hx = self.relu3d_m(self.bn3d_m(self.conv3d_m(hx))) + hd3 = self.relu3d_2(self.bn3d_2(self.conv3d_2(hx))) + + hx = self.upscore2(hd3) # 64 -> 128 + + hx = self.relu2d_1(self.bn2d_1(self.conv2d_1(torch.cat((hx, h2), 1)))) + hx = self.relu2d_m(self.bn2d_m(self.conv2d_m(hx))) + hd2 = self.relu2d_2(self.bn2d_2(self.conv2d_2(hx))) + + hx = self.upscore2(hd2) # 128 -> 256 + + hx = self.relu1d_1(self.bn1d_1(self.conv1d_1(torch.cat((hx, h1), 1)))) + hx = self.relu1d_m(self.bn1d_m(self.conv1d_m(hx))) + hd1 = self.relu1d_2(self.bn1d_2(self.conv1d_2(hx))) + + ## -------------Side Output------------- + if not self.test_mode: + #db = self.outconvb(h5) + #db = self.upscore4(db) # 8->256 + + # d6 = self.outconv6(hd6) + # d6 = self.upscore6(d6) # 8->256 + + #d5 = self.outconv5(hd5) + #d5 = self.upscore5(d5) # 16->256 + + #d4 = self.outconv4(hd4) + #d4 = self.upscore4(d4) # 32->256 + + #d3 = self.outconv3(hd3) + #d3 = self.upscore3(d3) # 64->256 + + #d2 = self.outconv2(hd2) + #d2 = self.upscore2(d2) # 128->256 + + d1 = self.outconv1(hd1) # 256 + + ## -------------Refine Module------------- + #dout = self.refunet(d1) # 256 + dout = self.convfilter(x, d1, x_orig) + return dout.sigmoid(), d1.sigmoid()#, d2.sigmoid()#, d3.sigmoid()#, d4.sigmoid(), db.sigmoid()#, d5.sigmoid() + else: + d1 = self.outconv1(hd1) # 256 + + ## -------------Refine Module------------- + #dout = self.refunet(d1) # 256 + dout = self.convfilter(x, d1, x_orig) + + return dout.sigmoid() + + +class ConvGuidedFilter(nn.Module): + def __init__(self, views, radius=1, norm=nn.BatchNorm2d): + super(ConvGuidedFilter, self).__init__() + + self.box_filter = nn.Conv2d(3, 3, kernel_size=3, padding=radius, dilation=radius, bias=False) # , groups=3) + self.conv_a = nn.Sequential(nn.Conv2d(6, 32, kernel_size=1, bias=False), + norm(32), + nn.ReLU(), + nn.Conv2d(32, 32, kernel_size=1, bias=False), + norm(32), + nn.ReLU(), + nn.Conv2d(32, 3, kernel_size=1, bias=False)) + self.box_filter.weight.data.fill_(1.0) + self.compress = nn.Conv2d(3, 1, 1, padding=0) + self.views = views + #self.upper = nn.Upsample(size=(1280, 720), mode='bilinear', align_corners=True) + # self.upscore = nn.Upsample(size=8, mode='bilinear') + + def forward(self, x_lr, y_lr, x_hr): + chs = self.views + ots = [] + for i in range(chs): + ot = self.forward_single(x_lr[:, (3 * i):(3 * i + 3), :, :], y_lr[:, i:i + 1, :, :], + x_hr[:, (3 * i):(3 * i + 3), :, :]) + ots.append(ot) + ot = torch.cat(ots, dim=1) + return ot + + def forward_single(self, x_lr, y_lr, x_hr): + # _, _, h_lrx, w_lrx = x_lr.size() + # _, _, h_hrx, w_hrx = x_hr.size() + + y_lr = torch.cat([y_lr, y_lr, y_lr], dim=1) + + out_size = (x_hr.shape[2], x_hr.shape[3]) + + # N = self.box_filter(x_lr.data.new().resize_((1, 3, h_lrx, w_lrx)).fill_(1.0)) + N = self.box_filter( + torch.ones((x_lr.size(0), x_lr.size(1), x_lr.size(2), x_lr.size(3)), dtype=x_lr.dtype, device=x_lr.device)) + ## mean_x + mean_x = self.box_filter(x_lr) / N + ## mean_y + mean_y = self.box_filter(y_lr) / N + ## cov_xy + cov_xy = self.box_filter(x_lr * y_lr) / N - mean_x * mean_y + ## var_x + var_x = self.box_filter(x_lr * x_lr) / N - mean_x * mean_x + + ## A + A = self.conv_a(torch.cat([cov_xy, var_x], dim=1)) + ## b + b = mean_y - A * mean_x + + ## mean_A; mean_b + mean_A = F.interpolate(A, size=out_size, mode='bilinear', align_corners=True) + mean_b = F.interpolate(b, size=out_size, mode='bilinear', align_corners=True) + #print(mean_A.shape, mean_b.shape, x_hr.shape) + + return self.compress(mean_A * x_hr + mean_b) diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/modules/__init__.py b/models/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/modules/chamfer.py b/models/modules/chamfer.py new file mode 100644 index 0000000..551f4cf --- /dev/null +++ b/models/modules/chamfer.py @@ -0,0 +1,84 @@ +import tensorflow as tf +from tensorflow.python.framework import ops + +nn_distance_module = tf.load_op_library('./external/tf_nndistance_so.so') + + +def nn_distance(xyz1, xyz2): + """ + Computes the distance of nearest neighbors for a pair of point clouds + input: xyz1: (batch_size,#points_1,3) the first point cloud + input: xyz2: (batch_size,#points_2,3) the second point cloud + output: dist1: (batch_size,#point_1) distance from first to second + output: idx1: (batch_size,#point_1) nearest neighbor from first to second + output: dist2: (batch_size,#point_2) distance from second to first + output: idx2: (batch_size,#point_2) nearest neighbor from second to first + """ + xyz1 = tf.expand_dims(xyz1, 0) + xyz2 = tf.expand_dims(xyz2, 0) + return nn_distance_module.nn_distance(xyz1, xyz2) + + +# @tf.RegisterShape('NnDistance') +# def _nn_distance_shape(op): +# shape1=op.inputs[0].get_shape().with_rank(3) +# shape2=op.inputs[1].get_shape().with_rank(3) +# return [tf.TensorShape([shape1.dims[0],shape1.dims[1]]),tf.TensorShape([shape1.dims[0],shape1.dims[1]]), +# tf.TensorShape([shape2.dims[0],shape2.dims[1]]),tf.TensorShape([shape2.dims[0],shape2.dims[1]])] +@ops.RegisterGradient('NnDistance') +def _nn_distance_grad(op, grad_dist1, grad_idx1, grad_dist2, grad_idx2): + xyz1 = op.inputs[0] + xyz2 = op.inputs[1] + idx1 = op.outputs[1] + idx2 = op.outputs[3] + return nn_distance_module.nn_distance_grad(xyz1, xyz2, grad_dist1, idx1, grad_dist2, idx2) + + +if __name__ == '__main__': + import numpy as np + import random + import time + from tensorflow.python.ops.gradient_checker import compute_gradient + + random.seed(100) + np.random.seed(100) + with tf.Session('') as sess: + xyz1 = np.random.randn(32, 16384, 3).astype('float32') + xyz2 = np.random.randn(32, 1024, 3).astype('float32') + # with tf.device('/gpu:0'): + if True: + inp1 = tf.Variable(xyz1) + inp2 = tf.constant(xyz2) + reta, retb, retc, retd = nn_distance(inp1, inp2) + loss = tf.reduce_sum(reta) + tf.reduce_sum(retc) + train = tf.train.GradientDescentOptimizer(learning_rate=0.05).minimize(loss) + sess.run(tf.initialize_all_variables()) + t0 = time.time() + t1 = t0 + best = 1e100 + for i in xrange(100): + trainloss, _ = sess.run([loss, train]) + newt = time.time() + best = min(best, newt - t1) + print + i, trainloss, (newt - t0) / (i + 1), best + t1 = newt +# print sess.run([inp1,retb,inp2,retd]) +# grads=compute_gradient([inp1,inp2],[(16,32,3),(16,32,3)],loss,(1,),[xyz1,xyz2]) +# for i,j in grads: +# print i.shape,j.shape,np.mean(np.abs(i-j)),np.mean(np.abs(i)),np.mean(np.abs(j)) +# for i in xrange(10): +# t0=time.time() +# a,b,c,d=sess.run([reta,retb,retc,retd],feed_dict={inp1:xyz1,inp2:xyz2}) +# print 'time',time.time()-t0 +# print a.shape,b.shape,c.shape,d.shape +# print a.dtype,b.dtype,c.dtype,d.dtype +# samples=np.array(random.sample(range(xyz2.shape[1]),100),dtype='int32') +# dist1=((xyz1[:,samples,None,:]-xyz2[:,None,:,:])**2).sum(axis=-1).min(axis=-1) +# idx1=((xyz1[:,samples,None,:]-xyz2[:,None,:,:])**2).sum(axis=-1).argmin(axis=-1) +# print np.abs(dist1-a[:,samples]).max() +# print np.abs(idx1-b[:,samples]).max() +# dist2=((xyz2[:,samples,None,:]-xyz1[:,None,:,:])**2).sum(axis=-1).min(axis=-1) +# idx2=((xyz2[:,samples,None,:]-xyz1[:,None,:,:])**2).sum(axis=-1).argmin(axis=-1) +# print np.abs(dist2-c[:,samples]).max() +# print np.abs(idx2-d[:,samples]).max() diff --git a/models/modules/config.py b/models/modules/config.py new file mode 100644 index 0000000..f2a003c --- /dev/null +++ b/models/modules/config.py @@ -0,0 +1,89 @@ +# Copyright (C) 2019 Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu +# All rights reserved. +# This code is licensed under BSD 3-Clause License. +import argparse +import yaml +import re + + +def str2bool(v): + if v.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif v.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Unsupported value encountered.') + + +def execute(): + return parse_args(create_parser()) + + +def create_parser(): + parser = argparse.ArgumentParser() + + # g = parser.add_argument_group('Device Targets') + parser.add_argument('-f', '--config_file', dest='config_file', type=argparse.FileType(mode='r')) + + parser.add_argument('--train_file_path', help='train file path') + parser.add_argument('--train_data_path', help='train data path') + parser.add_argument('--train_image_path', help='train image path') + + parser.add_argument('--coarse_result_file_path', help='coarse result file path') + parser.add_argument('--coarse_result_data_path', help='coarse result data path') + parser.add_argument('--coarse_result_image_path', help='coarse result image path') + + parser.add_argument('--test_file_path', help='test file path') + parser.add_argument('--test_data_path', help='test data path') + parser.add_argument('--test_image_path', help='test image path') + + parser.add_argument('--train_mesh_root', help='init mesh root path') + parser.add_argument('--test_mesh_root', help='init mesh root path') + parser.add_argument('--batch_size', type=int, default=1, help='batch size') + parser.add_argument('--lr', type=float, default=3e-5, help='learning rate') + parser.add_argument('--seed', type=int, default=123, help='random seed') + parser.add_argument('--init_epoch', type=int, default=0, help='init epoch') + parser.add_argument('--test_epoch', type=int, default=0, help='test epoch') + parser.add_argument('--hidden_dim', type=int, default=192, help='hidden dim') + parser.add_argument('--feat_dim', type=int, default=2883, help='feat dim') + parser.add_argument('--stage2_feat_dim', type=int, default=339, help='stage2 feat dim') + + parser.add_argument('--coord_dim', type=int, default=3, help='coord dim') + parser.add_argument('-e', '--epochs', type=int, default=20, help='number of epochs') + parser.add_argument('-g', '--gpu_id', help='devices ids') + parser.add_argument('-s', '--save_path', help='save root') + parser.add_argument('-n', '--name', help='exprtiments name') + parser.add_argument('--restore', type=str2bool, default=False) + parser.add_argument('--is_debug', type=str2bool, default='no', help='is debug') + parser.add_argument('--is_voxel_input', type=str2bool, default='no', help='is debug') + # pretrained cnn + parser.add_argument('--load_cnn', type=str2bool, default=False) + parser.add_argument('--pre_trained_cnn_path', help='pre-trained cnn path') + parser.add_argument('--cnn_step', type=int, help='cnn pre-trained step') + return parser + + +def parse_args(parser): + args = parser.parse_args() + if args.config_file: + loader = yaml.SafeLoader + loader.add_implicit_resolver( + u'tag:yaml.org,2002:float', + re.compile(u'''^(?: + [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)? + |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+) + |\\.[0-9_]+(?:[eE][-+][0-9]+)? + |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]* + |[-+]?\\.(?:inf|Inf|INF) + |\\.(?:nan|NaN|NAN))$''', re.X), + list(u'-+0123456789.')) + data = yaml.load(args.config_file, Loader=loader) + delattr(args, 'config_file') + arg_dict = args.__dict__ + # print(len(list(arg_dict.keys()))) + # print(len(list(data.keys()))) + for key, value in arg_dict.items(): + default_arg = parser.get_default(key) + if arg_dict[key] == default_arg and key in data: + arg_dict[key] = data[key] + return args diff --git a/models/modules/inits.py b/models/modules/inits.py new file mode 100644 index 0000000..1d0d7bb --- /dev/null +++ b/models/modules/inits.py @@ -0,0 +1,30 @@ +# Copyright (C) 2019 Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu +# All rights reserved. +# This code is licensed under BSD 3-Clause License. +#import tensorflow as tf +import numpy as np +import torch + +def uniform(shape, scale=0.05, name=None): + """Uniform init.""" + initial = 2*scale * torch.rand(shape) - scale + return torch.autograd.Variable(initial, name=name) + + +def glorot(shape, name=None): + """Glorot & Bengio (AISTATS 2010) init.""" + scale = np.sqrt(6.0 / (shape[0] + shape[1])) + initial = 2*scale * torch.rand(shape) - scale + return torch.autograd.Variable(initial, name=name) + + +def zeros(shape, name=None): + """All zeros.""" + initial = torch.zeros(shape, dtype=torch.float32) + return torch.autograd.Variable(initial, name=name) + + +def ones(shape, name=None): + """All ones.""" + initial = torch.ones(shape, dtype=torch.float32) + return torch.autograd.Variable(initial, name=name) diff --git a/models/modules/layers.py b/models/modules/layers.py new file mode 100644 index 0000000..c0511ab --- /dev/null +++ b/models/modules/layers.py @@ -0,0 +1,439 @@ +# Copyright (C) 2019 Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu +# All rights reserved. +# This code is licensed under BSD 3-Clause License. +#import tensorflow as tf +#import tflearn + +from inits import * +from tools import camera_trans, camera_trans_inv, reduce_var, reduce_std + +_LAYER_UIDS = {} + + +def get_layer_uid(layer_name=''): + """Helper function, assigns unique layer IDs.""" + if layer_name not in _LAYER_UIDS: + _LAYER_UIDS[layer_name] = 1 + return 1 + else: + _LAYER_UIDS[layer_name] += 1 + return _LAYER_UIDS[layer_name] + + +# def sparse_dropout(x, keep_prob, noise_shape): +# """Dropout for sparse tensors.""" +# random_tensor = keep_prob +# random_tensor += torch.rand(noise_shape) +# dropout_mask = (torch.floor(random_tensor)).type(torch.BoolTensor) +# pre_out = tf.sparse_retain(x, dropout_mask) +# return pre_out * (1. / keep_prob) + + +def dot(x, y, sparse=False): + """Wrapper for tf.matmul (sparse vs dense).""" + if sparse: + res = tf.sparse_tensor_dense_matmul(x, y) + else: + res = tf.matmul(x, y) + return res + + +class Layer(object): + """Base layer class. Defines basic API for all layer objects. + Implementation inspired by keras (http://keras.io). + + # Properties + name: String, defines the variable scope of the layer. + logging: Boolean, switches Tensorflow histogram logging on/off + + # Methods + _call(inputs): Defines computation graph of layer + (i.e. takes input, returns output) + __call__(inputs): Wrapper for _call() + _log_vars(): Log all variables + """ + + def __init__(self, **kwargs): + allowed_kwargs = {'name', 'logging'} + for kwarg in kwargs.keys(): + assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg + name = kwargs.get('name') + if not name: + layer = self.__class__.__name__.lower() + name = layer + '_' + str(get_layer_uid(layer)) + self.name = name + self.vars = {} + logging = kwargs.get('logging', False) + self.logging = logging + self.sparse_inputs = False + + def _call(self, inputs): + return inputs + + def __call__(self, inputs): + with tf.name_scope(self.name): + if self.logging and not self.sparse_inputs: + tf.summary.histogram(self.name + '/inputs', inputs) + outputs = self._call(inputs) + if self.logging: + tf.summary.histogram(self.name + '/outputs', outputs) + return outputs + + def _log_vars(self): + for var in self.vars: + tf.summary.histogram(self.name + '/vars/' + var, self.vars[var]) + + +class Dense(Layer): + def __init__(self, input_dim, output_dim, placeholders, dropout=0., sparse_inputs=False, + act=tf.nn.relu, bias=False, featureless=False, **kwargs): + super(Dense, self).__init__(**kwargs) + + if dropout: + self.dropout = placeholders['dropout'] + else: + self.dropout = 0. + + self.act = act + self.sparse_inputs = sparse_inputs + self.featureless = featureless + self.bias = bias + + # helper variable for sparse dropout + self.num_features_nonzero = placeholders['num_features_nonzero'] + + with tf.variable_scope(self.name + '_vars'): + self.vars['weights'] = glorot([input_dim, output_dim], name='weights') + if self.bias: + self.vars['bias'] = zeros([output_dim], name='bias') + + if self.logging: + self._log_vars() + + def _call(self, inputs): + x = inputs + + # dropout + if self.sparse_inputs: + x = sparse_dropout(x, 1 - self.dropout, self.num_features_nonzero) + else: + x = tf.nn.dropout(x, 1 - self.dropout) + + # transform + output = dot(x, self.vars['weights'], sparse=self.sparse_inputs) + + # bias + if self.bias: + output += self.vars['bias'] + + return self.act(output) + + +class GraphConvolution(Layer): + def __init__(self, input_dim, output_dim, placeholders, dropout=False, + sparse_inputs=False, act=tf.nn.relu, bias=True, gcn_block_id=1, + featureless=False, **kwargs): + super(GraphConvolution, self).__init__(**kwargs) + + if dropout: + self.dropout = placeholders['dropout'] + else: + self.dropout = 0. + + self.act = act + if gcn_block_id == 1: + self.support = placeholders['support1'] + elif gcn_block_id == 2: + self.support = placeholders['support2'] + elif gcn_block_id == 3: + self.support = placeholders['support3'] + + self.sparse_inputs = sparse_inputs + self.featureless = featureless + self.bias = bias + + # helper variable for sparse dropout + self.num_features_nonzero = placeholders['num_features_nonzero'] + + with tf.variable_scope(self.name + '_vars'): + for i in range(len(self.support)): + self.vars['weights_' + str(i)] = glorot([input_dim, output_dim], name='weights_' + str(i)) + if self.bias: + self.vars['bias'] = zeros([output_dim], name='bias') + if self.logging: + self._log_vars() + + def _call(self, inputs): + x = inputs + # dropout + if self.sparse_inputs: + x = sparse_dropout(x, 1 - self.dropout, self.num_features_nonzero) + else: + x = tf.nn.dropout(x, 1 - self.dropout) + # convolve + supports = list() + for i in range(len(self.support)): + if not self.featureless: + pre_sup = dot(x, self.vars['weights_' + str(i)], + sparse=self.sparse_inputs) + else: + pre_sup = self.vars['weights_' + str(i)] + support = dot(self.support[i], pre_sup, sparse=True) + supports.append(support) + output = tf.add_n(supports) + # bias + if self.bias: + output += self.vars['bias'] + return self.act(output) + + +class GraphPooling(Layer): + def __init__(self, placeholders, pool_id=1, **kwargs): + super(GraphPooling, self).__init__(**kwargs) + self.pool_idx = placeholders['pool_idx'][pool_id - 1] + + def _call(self, inputs): + X = inputs + add_feat = (1 / 2.0) * tf.reduce_sum(tf.gather(X, self.pool_idx), 1) + outputs = tf.concat([X, add_feat], 0) + return outputs + + +class GraphProjection(Layer): + def __init__(self, placeholders, **kwargs): + super(GraphProjection, self).__init__(**kwargs) + + self.img_feat = placeholders['img_feat'] + self.camera = placeholders['cameras'] + self.view_number = 3 + + def _call(self, inputs): + coord = inputs + out1_list = [] + out2_list = [] + out3_list = [] + out4_list = [] + + for i in range(self.view_number): + point_origin = camera_trans_inv(self.camera[0], inputs) + point_crrent = camera_trans(self.camera[i], point_origin) + X = point_crrent[:, 0] + Y = point_crrent[:, 1] + Z = point_crrent[:, 2] + h = 248.0 * tf.divide(-Y, -Z) + 112.0 + w = 248.0 * tf.divide(X, -Z) + 112.0 + + h = tf.minimum(tf.maximum(h, 0), 223) + w = tf.minimum(tf.maximum(w, 0), 223) + n = tf.cast(tf.fill(tf.shape(h), i), tf.float32) + + indeces = tf.stack([n, h, w], 1) + + idx = tf.cast(indeces / (224.0 / 56.0), tf.int32) + out1 = tf.gather_nd(self.img_feat[0], idx) + out1_list.append(out1) + idx = tf.cast(indeces / (224.0 / 28.0), tf.int32) + out2 = tf.gather_nd(self.img_feat[1], idx) + out2_list.append(out2) + idx = tf.cast(indeces / (224.0 / 14.0), tf.int32) + out3 = tf.gather_nd(self.img_feat[2], idx) + out3_list.append(out3) + idx = tf.cast(indeces / (224.0 / 7.00), tf.int32) + out4 = tf.gather_nd(self.img_feat[3], idx) + out4_list.append(out4) + # ---- + all_out1 = tf.stack(out1_list, 0) + all_out2 = tf.stack(out2_list, 0) + all_out3 = tf.stack(out3_list, 0) + all_out4 = tf.stack(out4_list, 0) + + # 3*N*[64+128+256+512] -> 3*N*F + image_feature = tf.concat([all_out1, all_out2, all_out3, all_out4], 2) + # 3*N*F -> N*F + # image_feature = tf.reshape(tf.transpose(image_feature, [1, 0, 2]), [-1, FLAGS.feat_dim * 3]) + + #image_feature = tf.reduce_max(image_feature, axis=0) + image_feature_max = tf.reduce_max(image_feature, axis=0) + image_feature_mean = tf.reduce_mean(image_feature, axis=0) + image_feature_std = reduce_std(image_feature, axis=0) + + outputs = tf.concat([coord, image_feature_max, image_feature_mean, image_feature_std], 1) + return outputs + + +class SampleHypothesis(Layer): + def __init__(self, placeholders, **kwargs): + super(SampleHypothesis, self).__init__(**kwargs) + self.sample_delta = placeholders['sample_coord'] + + def __call__(self, mesh_coords): + """ + Local Grid Sample for fast matching init mesh + :param mesh_coords: + [N,S,3] ->[NS,3] for projection + :return: sample_points_per_vertices: [NS, 3] + """ + with tf.name_scope(self.name): + center_points = tf.expand_dims(mesh_coords, axis=1) + center_points = tf.tile(center_points, [1, 43, 1]) + + delta = tf.expand_dims(self.sample_delta, 0) + + sample_points_per_vertices = tf.add(center_points, delta) + + outputs = tf.reshape(sample_points_per_vertices, [-1, 3]) + return outputs + + +class LocalGConv(Layer): + def __init__(self, input_dim, output_dim, placeholders, dropout=False, act=tf.nn.relu, bias=True, **kwargs): + super(LocalGConv, self).__init__(**kwargs) + + if dropout: + self.dropout = placeholders['dropout'] + else: + self.dropout = 0. + + self.act = act + self.support = placeholders['sample_adj'] + + self.bias = bias + self.local_graph_vert = 43 + + self.output_dim = output_dim + with tf.variable_scope(self.name + '_vars'): + for i in range(len(self.support)): + self.vars['weights_' + str(i)] = glorot([input_dim, output_dim], name='weights_' + str(i)) + if self.bias: + self.vars['bias'] = zeros([output_dim], name='bias') + + if self.logging: + self._log_vars() + + def _call(self, inputs): + x = inputs # N, S, VF + # dropout + x = tf.nn.dropout(x, 1 - self.dropout) + # convolve + supports = list() + for i in range(len(self.support)): + pre_sup = tf.einsum('ijk,kl->ijl', x, self.vars['weights_' + str(i)]) + support = tf.einsum('ij,kjl->kil', self.support[i], pre_sup) + supports.append(support) + output = tf.add_n(supports) + # bias + if self.bias: + output += self.vars['bias'] + + return self.act(output) + + +class DeformationReasoning(Layer): + def __init__(self, input_dim, output_dim, placeholders, gcn_block=-1, args=None, **kwargs): + super(DeformationReasoning, self).__init__(**kwargs) + self.delta_coord = placeholders['sample_coord'] + self.s = 43 + self.f = args.stage2_feat_dim + self.hidden_dim = 192 + with tf.variable_scope(self.name): + self.local_conv1 = LocalGConv(input_dim=input_dim, output_dim=self.hidden_dim, placeholders=placeholders) + self.local_conv2 = LocalGConv(input_dim=self.hidden_dim, output_dim=self.hidden_dim, placeholders=placeholders) + self.local_conv3 = LocalGConv(input_dim=self.hidden_dim, output_dim=self.hidden_dim, placeholders=placeholders) + self.local_conv4 = LocalGConv(input_dim=self.hidden_dim, output_dim=self.hidden_dim, placeholders=placeholders) + self.local_conv5 = LocalGConv(input_dim=self.hidden_dim, output_dim=self.hidden_dim, placeholders=placeholders) + self.local_conv6 = LocalGConv(input_dim=self.hidden_dim, output_dim=1, placeholders=placeholders) + + def _call(self, inputs): + proj_feat, prev_coord = inputs[0], inputs[1] + with tf.name_scope(self.name): + x = proj_feat # NS, F + x = tf.reshape(x, [-1, self.s, self.f]) # N,S,F + x1 = self.local_conv1(x) + x2 = self.local_conv2(x1) + x3 = tf.add(self.local_conv3(x2), x1) + x4 = self.local_conv4(x3) + x5 = tf.add(self.local_conv5(x4), x3) + x6 = self.local_conv6(x5) # N, S, 1 + score = tf.nn.softmax(x6, axis=1) # N, S, 1 + tf.summary.histogram('score', score) + delta_coord = score * self.delta_coord + next_coord = tf.reduce_sum(delta_coord, axis=1) + next_coord += prev_coord + return next_coord + + +class LocalGraphProjection(Layer): + def __init__(self, placeholders, **kwargs): + super(LocalGraphProjection, self).__init__(**kwargs) + + self.img_feat = placeholders['img_feat'] + self.camera = placeholders['cameras'] + self.view_number = 3 + + def _call(self, inputs): + coord = inputs + out1_list = [] + out2_list = [] + out3_list = [] + # out4_list = [] + + for i in range(self.view_number): + point_origin = camera_trans_inv(self.camera[0], inputs) + point_crrent = camera_trans(self.camera[i], point_origin) + X = point_crrent[:, 0] + Y = point_crrent[:, 1] + Z = point_crrent[:, 2] + h = 248.0 * tf.divide(-Y, -Z) + 112.0 + w = 248.0 * tf.divide(X, -Z) + 112.0 + + h = tf.minimum(tf.maximum(h, 0), 223) + w = tf.minimum(tf.maximum(w, 0), 223) + n = tf.cast(tf.fill(tf.shape(h), i), tf.int32) + + x = h / (224.0 / 224) + y = w / (224.0 / 224) + out1 = self.bi_linear_sample(self.img_feat[0], n, x, y) + out1_list.append(out1) + x = h / (224.0 / 112) + y = w / (224.0 / 112) + out2 = self.bi_linear_sample(self.img_feat[1], n, x, y) + out2_list.append(out2) + x = h / (224.0 / 56) + y = w / (224.0 / 56) + out3 = self.bi_linear_sample(self.img_feat[2], n, x, y) + out3_list.append(out3) + # ---- + all_out1 = tf.stack(out1_list, 0) + all_out2 = tf.stack(out2_list, 0) + all_out3 = tf.stack(out3_list, 0) + + # 3*N*[16+32+64] -> 3*N*F + image_feature = tf.concat([all_out1, all_out2, all_out3], 2) + + image_feature_max = tf.reduce_max(image_feature, axis=0) + image_feature_mean = tf.reduce_mean(image_feature, axis=0) + image_feature_std = reduce_std(image_feature, axis=0) + + outputs = tf.concat([coord, image_feature_max, image_feature_mean, image_feature_std], 1) + return outputs + + def bi_linear_sample(self, img_feat, n, x, y): + x1 = tf.floor(x) + x2 = tf.ceil(x) + y1 = tf.floor(y) + y2 = tf.ceil(y) + Q11 = tf.gather_nd(img_feat, tf.stack([n, tf.cast(x1, tf.int32), tf.cast(y1, tf.int32)], 1)) + Q12 = tf.gather_nd(img_feat, tf.stack([n, tf.cast(x1, tf.int32), tf.cast(y2, tf.int32)], 1)) + Q21 = tf.gather_nd(img_feat, tf.stack([n, tf.cast(x2, tf.int32), tf.cast(y1, tf.int32)], 1)) + Q22 = tf.gather_nd(img_feat, tf.stack([n, tf.cast(x2, tf.int32), tf.cast(y2, tf.int32)], 1)) + + weights = tf.multiply(tf.subtract(x2, x), tf.subtract(y2, y)) + Q11 = tf.multiply(tf.expand_dims(weights, 1), Q11) + weights = tf.multiply(tf.subtract(x, x1), tf.subtract(y2, y)) + Q21 = tf.multiply(tf.expand_dims(weights, 1), Q21) + weights = tf.multiply(tf.subtract(x2, x), tf.subtract(y, y1)) + Q12 = tf.multiply(tf.expand_dims(weights, 1), Q12) + weights = tf.multiply(tf.subtract(x, x1), tf.subtract(y, y1)) + Q22 = tf.multiply(tf.expand_dims(weights, 1), Q22) + outputs = tf.add_n([Q11, Q21, Q12, Q22]) + return outputs diff --git a/models/modules/losses.py b/models/modules/losses.py new file mode 100644 index 0000000..71bef5e --- /dev/null +++ b/models/modules/losses.py @@ -0,0 +1,131 @@ +# Copyright (C) 2019 Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu +# All rights reserved. +# This code is licensed under BSD 3-Clause License. +import numpy as np +import tensorflow as tf +from modules.chamfer import nn_distance +from scipy import stats + + +def laplace_coord(pred, placeholders, block_id): + vertex = tf.concat([pred, tf.zeros([1, 3])], 0) + indices = placeholders['lape_idx'][block_id - 1][:, :8] + weights = tf.cast(placeholders['lape_idx'][block_id - 1][:, -1], tf.float32) + + weights = tf.tile(tf.reshape(tf.reciprocal(weights), [-1, 1]), [1, 3]) + laplace = tf.reduce_sum(tf.gather(vertex, indices), 1) + laplace = tf.subtract(pred, tf.multiply(laplace, weights)) + return laplace + + +def laplace_loss(pred1, pred2, placeholders, block_id): + # laplace term + lap1 = laplace_coord(pred1, placeholders, block_id) + lap2 = laplace_coord(pred2, placeholders, block_id) + laplace_loss = tf.reduce_mean(tf.reduce_sum(tf.square(tf.subtract(lap1, lap2)), 1)) * 1500 + move_loss = tf.reduce_mean(tf.reduce_sum(tf.square(tf.subtract(pred1, pred2)), 1)) * 50 + return laplace_loss + move_loss + + +def unit(tensor): + return tf.nn.l2_normalize(tensor, axis=1) + + +def mesh_loss(pred, placeholders, block_id): + gt_pt = placeholders['labels'][:, :3] # gt points + gt_nm = placeholders['labels'][:, 3:] # gt normals + + # edge in graph + nod1 = tf.gather(pred, placeholders['edges'][block_id - 1][:, 0]) + nod2 = tf.gather(pred, placeholders['edges'][block_id - 1][:, 1]) + edge = tf.subtract(nod1, nod2) + + # edge length loss + edge_length = tf.reduce_sum(tf.square(edge), 1) + edge_loss = tf.reduce_mean(edge_length) * 350 + + # chamfer distance + sample_pt = sample(pred, placeholders, block_id) + sample_pred = tf.concat([pred, sample_pt], axis=0) + dist1, idx1, dist2, idx2 = nn_distance(gt_pt, sample_pred) + point_loss = (tf.reduce_mean(dist1) + 0.55 * tf.reduce_mean(dist2)) * 3000 + + # normal cosine loss + normal = tf.gather(gt_nm, tf.squeeze(idx2, 0)) + normal = tf.gather(normal, placeholders['edges'][block_id - 1][:, 0]) + cosine = tf.abs(tf.reduce_sum(tf.multiply(unit(normal), unit(edge)), 1)) + normal_loss = tf.reduce_mean(cosine) * 0.5 + + total_loss = point_loss + edge_loss + normal_loss + return total_loss + + +def sample(pred, placeholders, block_id): + uni = tf.distributions.Uniform(low=0.0, high=1.0) + faces = placeholders['faces_triangle'][block_id - 1] + tilefaces = tf.py_func(choice_faces, [pred, faces], tf.int32) + + num_of_tile_faces = tf.shape(tilefaces)[0] + + xs = tf.gather(pred, tilefaces[:, 0]) + ys = tf.gather(pred, tilefaces[:, 1]) + zs = tf.gather(pred, tilefaces[:, 2]) + + u = tf.sqrt(uni.sample([num_of_tile_faces, 1])) + v = uni.sample([num_of_tile_faces, 1]) + points = (1 - u) * xs + (u * (1 - v)) * ys + u * v * zs + return points + + +def choice_faces(verts, faces): + num = 4000 + u1, u2, u3 = np.split(verts[faces[:, 0]] - verts[faces[:, 1]], 3, axis=1) + v1, v2, v3 = np.split(verts[faces[:, 1]] - verts[faces[:, 2]], 3, axis=1) + a = (u2 * v3 - u3 * v2) ** 2 + b = (u3 * v1 - u1 * v3) ** 2 + c = (u1 * v2 - u2 * v1) ** 2 + Areas = np.sqrt(a + b + c) / 2 + Areas = Areas / np.sum(Areas) + choices = np.expand_dims(np.arange(Areas.shape[0]), 1) + dist = stats.rv_discrete(name='custm', values=(choices, Areas)) + choices = dist.rvs(size=num) + select_faces = faces[choices] + return select_faces + + +def laplace_loss_2(pred1, pred2, placeholders, block_id): + # laplace term + lap1 = laplace_coord(pred1, placeholders, block_id) + lap2 = laplace_coord(pred2, placeholders, block_id) + laplace_loss = tf.reduce_mean(tf.reduce_sum(tf.square(tf.subtract(lap1, lap2)), 1)) * 1500 + move_loss = tf.reduce_mean(tf.reduce_sum(tf.square(tf.subtract(pred1, pred2)), 1)) * 100 + return laplace_loss + move_loss + + +def mesh_loss_2(pred, placeholders, block_id): + gt_pt = placeholders['labels'][:, :3] # gt points + gt_nm = placeholders['labels'][:, 3:] # gt normals + + # edge in graph + nod1 = tf.gather(pred, placeholders['edges'][block_id - 1][:, 0]) + nod2 = tf.gather(pred, placeholders['edges'][block_id - 1][:, 1]) + edge = tf.subtract(nod1, nod2) + + # edge length loss + edge_length = tf.reduce_sum(tf.square(edge), 1) + edge_loss = tf.reduce_mean(edge_length) * 500 + + # chamfer distance + sample_pt = sample(pred, placeholders, block_id) + sample_pred = tf.concat([pred, sample_pt], axis=0) + dist1, idx1, dist2, idx2 = nn_distance(gt_pt, sample_pred) + point_loss = (tf.reduce_mean(dist1) + 0.55 * tf.reduce_mean(dist2)) * 3000 + + # normal cosine loss + normal = tf.gather(gt_nm, tf.squeeze(idx2, 0)) + normal = tf.gather(normal, placeholders['edges'][block_id - 1][:, 0]) + cosine = tf.abs(tf.reduce_sum(tf.multiply(unit(normal), unit(edge)), 1)) + normal_loss = tf.reduce_mean(cosine) * 0.5 + + total_loss = point_loss + edge_loss + normal_loss + return total_loss diff --git a/models/modules/models_mvp2m.py b/models/modules/models_mvp2m.py new file mode 100644 index 0000000..545f93c --- /dev/null +++ b/models/modules/models_mvp2m.py @@ -0,0 +1,255 @@ +# Copyright (C) 2019 Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu +# All rights reserved. +# This code is licensed under BSD 3-Clause License. +import tensorflow as tf +import tflearn +import os +import tensorflow.contrib.layers as tfcontriblayers + +from modules.losses import mesh_loss, laplace_loss +from modules.layers import GraphConvolution, GraphPooling, GraphProjection + + +class Model(object): + def __init__(self, **kwargs): + allowed_kwargs = {'name', 'logging', 'suffix'} + for kwarg in kwargs.keys(): + assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg + name = kwargs.get('name') + if not name: + name = self.__class__.__name__.lower() + self.name = name + + logging = kwargs.get('logging', False) + self.logging = logging + + save_dir_suffix = kwargs.get('suffix', '') + self.save_dir_suffix = save_dir_suffix + + self.vars = {} + self.placeholders = {} + + self.layers = [] + self.activations = [] + + self.inputs = None + self.output1 = None + self.output2 = None + self.output3 = None + self.output1_2 = None + self.output2_2 = None + + self.loss = 0 + self.inc_loss = 0 + self.pose_loss = 0 + self.optimizer = None + self.optimizer_inc = None + self.opt_op_vp = None + self.opt_op_vi = None + self.opt_op = None + self.summary = None + + def _build(self): + raise NotImplementedError + + def build(self): + raise NotImplementedError + + def forward(self): + raise NotImplementedError + + def predict(self): + raise NotImplementedError + + def _loss(self): + raise NotImplementedError + + def save(self, sess=None, ckpt_path=None, step=None): + if not sess: + raise AttributeError('TensorFlow session not provided.') + saver = tf.train.Saver(self.vars, max_to_keep=0) + save_path = saver.save(sess, os.path.join(ckpt_path, '{}.ckpt'.format(self.name)), global_step=step) + print('Model saved in file: {}, epoch {}'.format(save_path, step)) + + def load(self, sess=None, ckpt_path=None, step=None): + if not sess: + raise AttributeError('TensorFlow session not provided.') + # print(self.vars) + saver = tf.train.Saver(self.vars) + save_path = os.path.join(ckpt_path, '{}.ckpt-{}'.format(self.name, step)) + saver.restore(sess, save_path) + print('Model restored from file: {}, epoch {}'.format(save_path, step)) + + + +class MeshNetMVP2M(Model): + def __init__(self, placeholders, args, **kwargs): + super(MeshNetMVP2M, self).__init__(**kwargs) + + self.inputs = placeholders['features'] + self.placeholders = placeholders + self.args = args + self.optimizer = tf.train.AdamOptimizer(learning_rate=self.args.lr) + self.summary_loss = None + self.merged_summary_op = None + self.build() + + def _loss(self): + # Pixel2mesh loss + self.loss += mesh_loss(self.output1, self.placeholders, 1) + self.loss += mesh_loss(self.output2, self.placeholders, 2) + self.loss += mesh_loss(self.output3, self.placeholders, 3) + self.loss += laplace_loss(self.inputs, self.output1, self.placeholders, 1) + self.loss += laplace_loss(self.output1_2, self.output2, self.placeholders, 2) + self.loss += laplace_loss(self.output2_2, self.output3, self.placeholders, 3) + + # Weight decay loss + conv_layers = list(range(1, 15)) + list(range(17, 31)) + list(range(33, 48)) + for layer_id in conv_layers: + for var in self.layers[layer_id].vars.values(): + self.loss += 5e-6 * tf.nn.l2_loss(var) + + def _build(self): + with tf.name_scope('pixel2mesh'): + self.build_cnn18() # update image feature + # first project block + self.layers.append(GraphProjection(placeholders=self.placeholders, name='graph_proj_1_layer_0')) + self.layers.append(GraphConvolution(input_dim=self.args.feat_dim, + output_dim=self.args.hidden_dim, + gcn_block_id=1, + placeholders=self.placeholders, + name='graph_conv_blk1_1_layer_1', logging=self.logging)) + for _ in range(12): + self.layers.append(GraphConvolution(input_dim=self.args.hidden_dim, + output_dim=self.args.hidden_dim, + gcn_block_id=1, + placeholders=self.placeholders, + name='graph_conv_blk1_{}_layer_{}'.format(2 + _, 2 + _), + logging=self.logging)) + # activation #15; layer #14; output 1 + self.layers.append(GraphConvolution(input_dim=self.args.hidden_dim, + output_dim=self.args.coord_dim, + act=lambda x: x, + gcn_block_id=1, + placeholders=self.placeholders, + name='graph_conv_blk1_14_layer_14', logging=self.logging)) + # second project block + self.layers.append(GraphProjection(placeholders=self.placeholders, name='graph_proj_2_layer_15')) + self.layers.append(GraphPooling(placeholders=self.placeholders, pool_id=1, name='graph_pool_1to2_layer_16')) + self.layers.append(GraphConvolution(input_dim=self.args.feat_dim + self.args.hidden_dim, + output_dim=self.args.hidden_dim, + gcn_block_id=2, + placeholders=self.placeholders, + name='graph_conv_blk2_1_layer_17', logging=self.logging)) + for _ in range(12): + self.layers.append(GraphConvolution(input_dim=self.args.hidden_dim, + output_dim=self.args.hidden_dim, + gcn_block_id=2, + placeholders=self.placeholders, + name='graph_conv_blk2_{}_layer_{}'.format(2 + _, 18 + _), + logging=self.logging)) + self.layers.append(GraphConvolution(input_dim=self.args.hidden_dim, + output_dim=self.args.coord_dim, + act=lambda x: x, + gcn_block_id=2, + placeholders=self.placeholders, + name='graph_conv_blk2_14_layer_30', logging=self.logging)) + # third project block + self.layers.append(GraphProjection(placeholders=self.placeholders, name='graph_proj_3_layer_31')) + self.layers.append(GraphPooling(placeholders=self.placeholders, pool_id=2, name='graph_pool_2to3_layer_32')) + self.layers.append(GraphConvolution(input_dim=self.args.feat_dim + self.args.hidden_dim, + output_dim=self.args.hidden_dim, + gcn_block_id=3, + placeholders=self.placeholders, + name='graph_conv_blk3_1_layer_33', logging=self.logging)) + for _ in range(13): + self.layers.append(GraphConvolution(input_dim=self.args.hidden_dim, + output_dim=self.args.hidden_dim, + gcn_block_id=3, + placeholders=self.placeholders, + name='graph_conv_blk3_{}_layer_{}'.format(2 + _, 34 + _), + logging=self.logging)) + self.layers.append(GraphConvolution(input_dim=self.args.hidden_dim, + output_dim=self.args.coord_dim, + act=lambda x: x, + gcn_block_id=3, + placeholders=self.placeholders, + name='graph_conv_blk3_15_layer_47', logging=self.logging)) + + def build_cnn18(self): + x = self.placeholders['img_inp'] + # x = tf.expand_dims(x, 0) +# 224 224 + x = tflearn.layers.conv.conv_2d(x, 16, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_1') + x = tflearn.layers.conv.conv_2d(x, 16, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_2') + x0 = x + x = tflearn.layers.conv.conv_2d(x, 32, (3, 3), strides=2, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_3') +# 112 112 + x = tflearn.layers.conv.conv_2d(x, 32, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_4') + x = tflearn.layers.conv.conv_2d(x, 32, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_5') + x1 = x + x = tflearn.layers.conv.conv_2d(x, 64, (3, 3), strides=2, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_6') +# 56 56 + x = tflearn.layers.conv.conv_2d(x, 64, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_7') + x = tflearn.layers.conv.conv_2d(x, 64, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_8') + x2 = x + x = tflearn.layers.conv.conv_2d(x, 128, (3, 3), strides=2, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_9') +# 28 28 + x = tflearn.layers.conv.conv_2d(x, 128, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_10') + x = tflearn.layers.conv.conv_2d(x, 128, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_11') + x3 = x + x = tflearn.layers.conv.conv_2d(x, 256, (5, 5), strides=2, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_12') +# 14 14 + x = tflearn.layers.conv.conv_2d(x, 256, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_13') + x = tflearn.layers.conv.conv_2d(x, 256, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_14') + x4 = x + x = tflearn.layers.conv.conv_2d(x, 512, (5, 5), strides=2, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_15') +# 7 7 + x = tflearn.layers.conv.conv_2d(x, 512, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_16') + x = tflearn.layers.conv.conv_2d(x, 512, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_17') + x = tflearn.layers.conv.conv_2d(x, 512, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_18') + x5 = x + # updata image feature + self.placeholders.update({'img_feat': [tf.squeeze(x2), tf.squeeze(x3), tf.squeeze(x4), tf.squeeze(x5)]}) + self.loss += tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) * 0.3 + + def build(self): + ''' Wrapper for _build() ''' + with tf.variable_scope(self.name): + self._build() + + # Build sequential resnet model + eltwise = [3, 5, 7, 9, 11, 13, + 19, 21, 23, 25, 27, 29, + 35, 37, 39, 41, 43, 45] + concat = [15, 31] + # proj = [0, 15, 31] + self.activations.append(self.inputs) + for idx, layer in enumerate(self.layers[:48]): + hidden = layer(self.activations[-1]) + if idx in eltwise: + hidden = tf.add(hidden, self.activations[-2]) * 0.5 + if idx in concat: + hidden = tf.concat([hidden, self.activations[-2]], 1) + self.activations.append(hidden) + + self.output1 = self.activations[15] + unpool_layer = GraphPooling(placeholders=self.placeholders, pool_id=1) + self.output1_2 = unpool_layer(self.output1) + + self.output2 = self.activations[31] + unpool_layer = GraphPooling(placeholders=self.placeholders, pool_id=2) + self.output2_2 = unpool_layer(self.output2) + + self.output3 = self.activations[48] + + # Store model variables for easy access + variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name+'/') + self.vars = {var.name: var for var in variables} + + # Build metrics + self._loss() + self.opt_op = self.optimizer.minimize(self.loss) + + self.summary_loss = tf.summary.scalar('loss', self.loss) + self.merged_summary_op = tf.summary.merge([self.summary_loss]) diff --git a/models/modules/models_p2mpp.py b/models/modules/models_p2mpp.py new file mode 100644 index 0000000..7cd9020 --- /dev/null +++ b/models/modules/models_p2mpp.py @@ -0,0 +1,214 @@ +# Copyright (C) 2019 Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu +# All rights reserved. +# This code is licensed under BSD 3-Clause License. +import tensorflow as tf +import tflearn +import os +import tensorflow.contrib.layers as tfcontriblayers + +from modules.losses import mesh_loss_2, laplace_loss_2 +from modules.layers import LocalGraphProjection, SampleHypothesis, DeformationReasoning + + +class Model(object): + def __init__(self, **kwargs): + allowed_kwargs = {'name', 'logging', 'suffix'} + for kwarg in kwargs.keys(): + assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg + name = kwargs.get('name') + if not name: + name = self.__class__.__name__.lower() + self.name = name + + logging = kwargs.get('logging', False) + self.logging = logging + + save_dir_suffix = kwargs.get('suffix', '') + self.save_dir_suffix = save_dir_suffix + + self.vars = {} + self.placeholders = {} + + self.layers = [] + self.activations = [] + + self.inputs = None + self.output1 = None + self.output2 = None + self.output3 = None + self.output1_2 = None + self.output2_2 = None + + self.loss = 0 + self.inc_loss = 0 + self.pose_loss = 0 + self.optimizer = None + self.optimizer_inc = None + self.opt_op_vp = None + self.opt_op_vi = None + self.opt_op = None + self.summary = None + + def _build(self): + raise NotImplementedError + + def build(self): + raise NotImplementedError + + def forward(self): + raise NotImplementedError + + def predict(self): + raise NotImplementedError + + def _loss(self): + raise NotImplementedError + + def save(self, sess=None, ckpt_path=None, step=None): + if not sess: + raise AttributeError('TensorFlow session not provided.') + saver = tf.train.Saver(self.vars, max_to_keep=0) + save_path = saver.save(sess, os.path.join(ckpt_path, '{}.ckpt'.format(self.name)), global_step=step) + print('Model saved in file: {}, epoch {}'.format(save_path, step)) + + def load(self, sess=None, ckpt_path=None, step=None): + if not sess: + raise AttributeError('TensorFlow session not provided.') + # print(self.vars) + saver = tf.train.Saver(self.vars) + save_path = os.path.join(ckpt_path, '{}.ckpt-{}'.format(self.name, step)) + saver.restore(sess, save_path) + print('Model restored from file: {}, epoch {}'.format(save_path, step)) + + +class MeshNet(Model): + def __init__(self, placeholders, args, **kwargs): + super(MeshNet, self).__init__(**kwargs) + + self.inputs = placeholders['features'] + self.placeholders = placeholders + self.args = args + self.optimizer = tf.train.AdamOptimizer(learning_rate=self.args.lr) + self.summary_loss = None + self.merged_summary_op = None + self.output1l = None + self.output2l = None + self.sample1 = None + self.sample2 = None + self.proj1 = None + self.proj2 = None + self.drb1 = None + self.drb2 = None + self.build() + + def loadcnn(self, sess=None, ckpt_path=None, step=None): + if not sess: + raise AttributeError('TensorFlow session not provided.') + variables_to_restore = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='meshnet/cnn/') + var_list = {var.name: var for var in variables_to_restore} + saver = tf.train.Saver(var_list) + save_path = os.path.join(ckpt_path, '{}.ckpt-{}'.format(self.name, step)) + saver.restore(sess, save_path) + print('=> !!CNN restored from file: {}, epoch {}'.format(save_path, step)) + + def _loss(self): + # Pixel2mesh loss + self.loss += mesh_loss_2(self.output1l, self.placeholders, 3) + self.loss += laplace_loss_2(self.inputs, self.output1l, self.placeholders, 3) + self.loss += mesh_loss_2(self.output2l, self.placeholders, 3) + self.loss += laplace_loss_2(self.output1l, self.output2l, self.placeholders, 3) + + conv_layers = [self.drb1, self.drb2] + for l in conv_layers: + for var in l.vars.values(): + self.loss += 5e-6 * tf.nn.l2_loss(var) + + def _build(self): + with tf.name_scope('pixel2mesh'): + self.build_cnn18() # update image feature + # sample hypothesis points + self.sample1 = SampleHypothesis(placeholders=self.placeholders, name='graph_sample_hypothesis_1_layer_0') + # 1st projection block + self.proj1 = LocalGraphProjection(placeholders=self.placeholders, name='graph_localproj_1_layer_1') + # 1st DRB + self.drb1 = DeformationReasoning(input_dim=self.args.stage2_feat_dim, + output_dim=3, + placeholders=self.placeholders, + gcn_block=3, + args=self.args, + name='graph_drb_blk1_layer_2') + # sample hypothesis points + self.sample2 = SampleHypothesis(placeholders=self.placeholders, name='graph_sample_hypothesis_2_layer_3') + # 2nd projection block + self.proj2 = LocalGraphProjection(placeholders=self.placeholders, name='graph_localproj_2_layer_4') + # 2nd DRB + self.drb2 = DeformationReasoning(input_dim=self.args.stage2_feat_dim, + output_dim=3, + placeholders=self.placeholders, + gcn_block=3, + args=self.args, + name='graph_drb_blk2_layer_5') + + def build_cnn18(self): + x = self.placeholders['img_inp'] + # x = tf.expand_dims(x, 0) +# 224 224 + x = tflearn.layers.conv.conv_2d(x, 16, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_1') + x = tflearn.layers.conv.conv_2d(x, 16, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_2') + x0 = x + x = tflearn.layers.conv.conv_2d(x, 32, (3, 3), strides=2, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_3') +# 112 112 + x = tflearn.layers.conv.conv_2d(x, 32, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_4') + x = tflearn.layers.conv.conv_2d(x, 32, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_5') + x1 = x + x = tflearn.layers.conv.conv_2d(x, 64, (3, 3), strides=2, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_6') +# 56 56 + x = tflearn.layers.conv.conv_2d(x, 64, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_7') + x = tflearn.layers.conv.conv_2d(x, 64, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_8') + x2 = x + x = tflearn.layers.conv.conv_2d(x, 128, (3, 3), strides=2, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_9') +# 28 28 + x = tflearn.layers.conv.conv_2d(x, 128, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_10') + x = tflearn.layers.conv.conv_2d(x, 128, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_11') + x3 = x + x = tflearn.layers.conv.conv_2d(x, 256, (5, 5), strides=2, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_12') +# 14 14 + x = tflearn.layers.conv.conv_2d(x, 256, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_13') + x = tflearn.layers.conv.conv_2d(x, 256, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_14') + x4 = x + x = tflearn.layers.conv.conv_2d(x, 512, (5, 5), strides=2, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_15') +# 7 7 + x = tflearn.layers.conv.conv_2d(x, 512, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_16') + x = tflearn.layers.conv.conv_2d(x, 512, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_17') + x = tflearn.layers.conv.conv_2d(x, 512, (3, 3), strides=1, activation='relu', weight_decay=1e-5, regularizer='L2', scope='cnn/conv2d_18') + x5 = x + # updata image feature + self.placeholders.update({'img_feat': [tf.squeeze(x0), tf.squeeze(x1), tf.squeeze(x2)]}) + self.loss += tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) * 0.3 + + def build(self): + ''' Wrapper for _build() ''' + with tf.variable_scope(self.name): + self._build() + + blk1_sample = self.sample1(self.inputs) + blk1_proj_feat = self.proj1(blk1_sample) + blk1_out = self.drb1((blk1_proj_feat, self.inputs)) + + blk2_sample = self.sample2(blk1_out) + blk2_proj_feat = self.proj2(blk2_sample) + blk2_out = self.drb2((blk2_proj_feat, blk1_out)) + + self.output1l = blk1_out + self.output2l = blk2_out + + # Store model variables for easy access + variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name+'/') + self.vars = {var.name: var for var in variables} + + # Build metrics + self._loss() + self.opt_op = self.optimizer.minimize(self.loss) + + self.summary_loss = tf.summary.scalar('loss', self.loss) + self.merged_summary_op = tf.summary.merge_all() diff --git a/models/modules/tools.py b/models/modules/tools.py new file mode 100644 index 0000000..dd3761b --- /dev/null +++ b/models/modules/tools.py @@ -0,0 +1,127 @@ +# Copyright (C) 2019 Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu +# All rights reserved. +# This code is licensed under BSD 3-Clause License. +import numpy as np +import torch +import cv2 + + +def construct_feed_dict(pkl, placeholders): + coord = pkl['coord'] + pool_idx = pkl['pool_idx'] + faces = pkl['faces'] + lape_idx = pkl['lape_idx'] + + edges = [] + for i in range(1, 4): + adj = pkl['stage{}'.format(i)][1] + edges.append(adj[0]) + + feed_dict = dict() + feed_dict.update({placeholders['features']: coord}) + feed_dict.update({placeholders['edges'][i]: edges[i] for i in list(range(len(edges)))}) + feed_dict.update({placeholders['faces'][i]: faces[i] for i in list(range(len(faces)))}) + feed_dict.update({placeholders['pool_idx'][i]: pool_idx[i] for i in list(range(len(pool_idx)))}) + feed_dict.update({placeholders['lape_idx'][i]: lape_idx[i] for i in list(range(len(lape_idx)))}) + feed_dict.update({placeholders['support1'][i]: pkl['stage1'][i] for i in list(range(len(pkl['stage1'])))}) + feed_dict.update({placeholders['support2'][i]: pkl['stage2'][i] for i in list(range(len(pkl['stage2'])))}) + feed_dict.update({placeholders['support3'][i]: pkl['stage3'][i] for i in list(range(len(pkl['stage3'])))}) + + for k in range(3): + feed_dict.update({placeholders['faces_triangle'][k]: pkl['faces_triangle'][k]}) + + feed_dict.update({placeholders['sample_coord']: pkl['sample_coord']}) + + feed_dict.update({placeholders['sample_adj'][i]: pkl['sample_cheb_dense'][i] for i in range(len(pkl['sample_cheb_dense']))}) + + return feed_dict + + +def reduce_var(x, axis=None, keepdims=False): + """Variance of a tensor, alongside the specified axis. + + # Arguments + x: A tensor or variable. + axis: An integer, the axis to compute the variance. + keepdims: A boolean, whether to keep the dimensions or not. + If `keepdims` is `False`, the rank of the tensor is reduced + by 1. If `keepdims` is `True`, + the reduced dimension is retained with length 1. + + # Returns + A tensor with the variance of elements of `x`. + """ + m = torch.mean(x, axis, True) + devs_squared = torch.square(x - m) + return torch.mean(devs_squared, axis, keepdims) + + +def reduce_std(x, axis=None, keepdims=False): + """Standard deviation of a tensor, alongside the specified axis. + + # Arguments + x: A tensor or variable. + axis: An integer, the axis to compute the standard deviation. + keepdims: A boolean, whether to keep the dimensions or not. + If `keepdims` is `False`, the rank of the tensor is reduced + by 1. If `keepdims` is `True`, + the reduced dimension is retained with length 1. + + # Returns + A tensor with the standard deviation of elements of `x`. + """ + return torch.sqrt(reduce_var(x, axis=axis, keepdims=keepdims) + 1e-6) + +# ------------------------------------------------------------------- +# cameras +# ------------------------------------------------------------------- + + +def normal(v): + norm = torch.norm(v) + if norm == 0: + return v + return torch.divide(v, norm) + + +def cameraMat(param): + theta = param[0] * np.pi / 180.0 + camy = param[3] * torch.sin(param[1] * np.pi / 180.0) + lens = param[3] * torch.cos(param[1] * np.pi / 180.0) + camx = lens * torch.cos(theta) + camz = lens * torch.sin(theta) + Z = torch.stack([camx, camy, camz]) + + x = camy * torch.cos(theta + np.pi) + z = camy * torch.sin(theta + np.pi) + Y = torch.stack([x, lens, z]) + X = torch.cross(Y, Z) + + cm_mat = torch.stack([normal(X), normal(Y), normal(Z)]) + return cm_mat, Z + + +def camera_trans(camera_metadata, xyz): + c, o = cameraMat(camera_metadata) + points = xyz[:, :3] + pt_trans = points - o + pt_trans = torch.matmul(pt_trans, torch.transpose(c, 0, 1)) + return pt_trans + + +def camera_trans_inv(camera_metadata, xyz): + c, o = cameraMat(camera_metadata) + inv_xyz = (torch.matmul(xyz, torch.linalg.inv(torch.transpose(c, 0, 1)))) + o + return inv_xyz + + +def load_demo_image(demo_image_list): + imgs = np.zeros((3, 224, 224, 3)) + for idx, demo_img_path in enumerate(demo_image_list): + img = cv2.imread(demo_img_path, cv2.IMREAD_UNCHANGED) + if img.shape[2] == 4: + img[np.where(img[:, :, 3] == 0)] = 255 + img = cv2.resize(img, (224, 224)) + img_inp = img.astype('float32') / 255.0 + imgs[idx] = img_inp[:, :, :3] + return imgs diff --git a/models/resnet_model.py b/models/resnet_model.py new file mode 100644 index 0000000..c92a972 --- /dev/null +++ b/models/resnet_model.py @@ -0,0 +1,229 @@ +## code from: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py +import torch.nn as nn +import math +import torch.utils.model_zoo as model_zoo +import torch +import torchvision +# +# __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', +# 'resnet152', 'ResNet34P','ResNet50S','ResNet50P','ResNet101P'] +# +# resnet18_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet18-5c106cde.pth' +# resnet34_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet34-333f7ec4.pth' +# resnet50_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet50-19c8e357.pth' +# resnet101_dir = '/local/sda4/yqian3/RoadNets/resnet_model/resnet101-5d3b4d8f.pth' +# +# model_urls = { +# 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', +# 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', +# 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', +# 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', +# 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', +# } + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + +class BasicBlockDe(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlockDe, self).__init__() + + self.convRes = conv3x3(inplanes,planes,stride) + self.bnRes = nn.BatchNorm2d(planes) + self.reluRes = nn.ReLU(inplace=True) + + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = self.convRes(x) + residual = self.bnRes(residual) + residual = self.reluRes(residual) + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class RFB(nn.Module): + # RFB-like multi-scale module + def __init__(self, in_channel, out_channel): + super(RFB, self).__init__() + self.relu = nn.ReLU(True) + self.branch0 = nn.Sequential( + BasicConv2d(in_channel, out_channel, 1), + ) + self.branch1 = nn.Sequential( + BasicConv2d(in_channel, out_channel, 1), + BasicConv2d(out_channel, out_channel, kernel_size=(1, 3), padding=(0, 1)), + BasicConv2d(out_channel, out_channel, kernel_size=(3, 1), padding=(1, 0)), + BasicConv2d(out_channel, out_channel, 3, padding=3, dilation=3) + ) + self.branch2 = nn.Sequential( + BasicConv2d(in_channel, out_channel, 1), + BasicConv2d(out_channel, out_channel, kernel_size=(1, 5), padding=(0, 2)), + BasicConv2d(out_channel, out_channel, kernel_size=(5, 1), padding=(2, 0)), + BasicConv2d(out_channel, out_channel, 3, padding=5, dilation=5) + ) + self.branch3 = nn.Sequential( + BasicConv2d(in_channel, out_channel, 1), + BasicConv2d(out_channel, out_channel, kernel_size=(1, 7), padding=(0, 3)), + BasicConv2d(out_channel, out_channel, kernel_size=(7, 1), padding=(3, 0)), + BasicConv2d(out_channel, out_channel, 3, padding=7, dilation=7) + ) + self.conv_cat = BasicConv2d(4*out_channel, out_channel, 3, padding=1) + self.conv_res = BasicConv2d(in_channel, out_channel, 1) + + def forward(self, x): + x0 = self.branch0(x) + x1 = self.branch1(x) + x2 = self.branch2(x) + x3 = self.branch3(x) + + x_cat = self.conv_cat(torch.cat((x0, x1, x2, x3), 1)) + + x = self.relu(x_cat + self.conv_res(x)) + return x + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class BasicConv2d(nn.Module): + def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1): + super(BasicConv2d, self).__init__() + self.conv = nn.Conv2d(in_planes, out_planes, + kernel_size=kernel_size, stride=stride, + padding=padding, dilation=dilation, bias=False) + self.bn = nn.BatchNorm2d(out_planes) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x \ No newline at end of file diff --git a/modelsize_estimate.py b/modelsize_estimate.py new file mode 100644 index 0000000..94b0656 --- /dev/null +++ b/modelsize_estimate.py @@ -0,0 +1,38 @@ +import torch +import torch.nn as nn +import numpy as np + + +def modelsize(model, input, type_size=4): + para = sum([np.prod(list(p.size())) for p in model.parameters()]) + # print('Model {} : Number of params: {}'.format(model._get_name(), para)) + print('Model {} : params: {:4f}M'.format(model._get_name(), para * type_size / 1000 / 1000)) + + input_ = input.clone() + input_.requires_grad_(requires_grad=False) + + mods = list(model.modules()) + out_sizes = [] + + for i in range(1, len(mods)): + m = mods[i] + if isinstance(m, nn.ReLU): + if m.inplace: + continue + out = m(input_) + out_sizes.append(np.array(out.size())) + input_ = out + + total_nums = 0 + for i in range(len(out_sizes)): + s = out_sizes[i] + nums = np.prod(np.array(s)) + total_nums += nums + + # print('Model {} : Number of intermedite variables without backward: {}'.format(model._get_name(), total_nums)) + # print('Model {} : Number of intermedite variables with backward: {}'.format(model._get_name(), total_nums*2)) + print('Model {} : intermedite variables: {:3f} M (without backward)' + .format(model._get_name(), total_nums * type_size / 1000 / 1000)) + print('Model {} : intermedite variables: {:3f} M (with backward)' + .format(model._get_name(), total_nums * type_size*2 / 1000 / 1000)) + diff --git a/multi_data.py b/multi_data.py new file mode 100644 index 0000000..6cd28bc --- /dev/null +++ b/multi_data.py @@ -0,0 +1,687 @@ +import os +from PIL import Image +import torch.utils.data as data +import torchvision.transforms as transforms +import numpy as np +import random +import time +import torch +import torchvision.transforms.functional as TF +import cv2 +from PIL import ImageFile +from mesh_load import * +import pywt + +ImageFile.LOAD_TRUNCATED_IMAGES = True + + +def cv_resize(image, dim): + image = np.array(image, dtype=np.uint8) + image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA) + return image + + +def cv_resize_pil(image, dim): + image = np.array(image, dtype=np.uint8) + image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA) + image = Image.fromarray(image) + return image + + +class SalObjDataset(data.Dataset): + def __init__(self, image_root, mesh_root, lap_root, trainsize, origsize, num_views, fake_back_rate=0, + back_dir=None, pb_rate=0, plate_dir=None, with_plate=False, examine_mode=False, + back_img=None, test_mode=False, trimap_dir=None, of_list=None, mask_ext='.jpg', max_view=8, + with_gray=0, sample_size=None): + self.sample_size = sample_size + self.n_view = num_views + self.lap_root = lap_root + self.view_list = list(range(max_view)) + self.max_view = max_view + self.trainsize = trainsize + self.origsize = origsize + self.with_plate = with_plate + self.plate_dir = plate_dir + self.trimap_dir = trimap_dir + self.test_mode = test_mode + self.back_img = back_img + self.pb_rate = pb_rate + self.with_gray = with_gray + self.cameras = camera_setting_loader(os.path.join(image_root, 'default_camera_setting.xml')) + + if num_views > 1: + self.images = [image_root + f for f in + os.listdir(os.path.join(image_root, str(self.cameras['camera_id'][0]))) if + f.endswith('.jpg') or f.endswith('.png')] + + else: + self.images = [image_root + f for f in os.listdir(image_root) if + f.endswith('.jpg') or f.endswith('.png')] + + self.mesh_root = mesh_root + self.mask_ext = mask_ext + self.image_root = image_root + self.images = sorted(self.images) + # self.gts = sorted(self.gts) + self.size = len(self.images) + self.totensor = transforms.Compose([ + # transforms.Resize((self.trainsize, self.trainsize)), + transforms.ToTensor(), + ]) + self.examine_mode = examine_mode + self.img_transform_after = transforms.Compose([ + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) + + self.fake_back_rate = fake_back_rate + if trimap_dir: + with_trimap = True + else: + with_trimap = False + self.fb = FakeBack(back_dir, trainsize=self.trainsize, test_mode=test_mode, back_img=back_img, + with_trimap=with_trimap, views=self.n_view) + + if of_list: + with open(of_list, "r") as f: + of_list = [i for line in f for i in line.split('\n')] + of_list = list(filter(None, of_list)) + self.of_list = of_list + else: + self.of_list = [] + + def check_size(self, image, label): + force_size = self.origsize + if not image.size == force_size: + image = cv_resize_pil(image, force_size) + label = cv_resize_pil(label, force_size) + return image, label + + def load_process(self, image_dir, mask_dir): + image = self.rgb_loader(image_dir) + if self.test_mode: + gt = self.binary_loader(image_dir) + else: + gt = self.binary_loader(mask_dir) + image, gt = self.check_size(image, gt) + + # if self.trimap_dir: + # trimap = self.binary_loader(self.trimap_dir + filename[:-4] + '.png') + + if self.fake_back_rate + self.pb_rate: + luck = random.random() + if image_dir.split('/')[-1] in self.of_list: + luck = self.fake_back_rate + if luck > self.fake_back_rate + self.pb_rate: + orig_image = np.array(image) + orig_label = np.array(gt) + image = cv_resize(image, self.trainsize) + gt = cv_resize(gt, self.trainsize) + # if self.trimap_dir: + # trimap = cv_resize(trimap, (self.trainsize, self.trainsize)) + # trimap = self.totensor(trimap) + orig_image = self.totensor(orig_image) + image = self.totensor(image) + gt = self.totensor(gt) + orig_label = self.totensor(orig_label) + else: + # if self.trimap_dir: + # sample = {'image': image, 'label': gt, 'trimap': trimap} + # else: + sample = {'image': image, 'label': gt} + if luck > self.fake_back_rate and (luck <= self.fake_back_rate + self.pb_rate): + sample = self.fb(sample, pb=True) + else: + sample = self.fb(sample, pb=False) + image = sample['image'] + gt = sample['label'] + orig_image = sample['orig_image'] + orig_label = sample['orig_label'] + if self.with_plate: + plate = sample['plate'] + # if self.trimap_dir: + # trimap = sample['trimap'] + + else: + orig_image = np.array(image) + orig_label = np.array(gt) + image = cv_resize(image, self.trainsize) + gt = cv_resize(gt, self.trainsize) + + gt = self.totensor(gt) + orig_image = self.totensor(orig_image) + image = self.totensor(image) + orig_label = self.totensor(orig_label) + + if not self.examine_mode: + image = self.img_transform_after(image) + orig_image = self.img_transform_after(orig_image) + + if self.with_plate: + if not self.examine_mode: + plate = self.img_transform_after(plate) + image = torch.cat((image, plate), dim=0) + + if random.random() < self.with_gray: + rgb_weights = [0.2989, 0.5870, 0.1140] + image_g = rgb_weights[0] * image[0:1, :, :] + rgb_weights[1] * image[1:2, :, :] + rgb_weights[2] * image[ + 2:3, :, + :] + image = torch.tile(image_g, (3, 1, 1)) + orig_image_g = rgb_weights[0] * orig_image[0:1, :, :] + rgb_weights[1] * orig_image[1:2, :, :] + \ + rgb_weights[2] * orig_image[2:3, :, :] + orig_image = torch.tile(orig_image_g, (3, 1, 1)) + + + return image, gt, orig_image, orig_label + + def p2m_load_img(self, image_dir): + image = self.rgb_loader(image_dir) + image = cv_resize(image, self.trainsize) + image = self.totensor(image) + if not self.examine_mode: + image = self.img_transform_after(image) + return image + + def p2m_load_depth(self, image_dir): + dmax = 3000 + image = cv2.imread(image_dir, cv2.IMREAD_UNCHANGED) + image = np.clip(image, 0, dmax) + image = cv2.resize(image, self.trainsize, interpolation=cv2.INTER_AREA) + image = np.expand_dims(image, axis=0) + image = image.astype('float32') + image = (image/float(dmax)).astype('float32') + image = torch.from_numpy(image).type(torch.float32) + return image + + + def decomp(self, image, level): + out_list = [] + coeff = image + out_list.append(coeff) + for l in range(level): + coeff = pywt.dwt2(coeff, 'db1', mode='zero') + stack = np.concatenate([np.concatenate(coeff[1], axis=1), coeff[0]], axis=1) + out_list.append(stack) + coeff = coeff[0] + return out_list + + def p2m_load_mask(self, image_dir): + mask = self.binary_loader(image_dir) + mask = cv_resize(mask, self.trainsize) + mask = self.totensor(mask) + return mask + + + def binary_loader(self, path): + with open(path, 'rb') as f: + img = Image.open(f) + # return img.convert('1') + return img.convert('L') + + + def get_multi(self, index): + step = 1 + images = [] + masks = [] + names = [] + depths = [] + #sample_views = random.sample(self.cameras['camera_id'], k=self.n_view) + sample_views = self.cameras['camera_id'] + thisfile = self.images[index].split('/')[-1] + file_index = int(thisfile[5:-4]) + index_views = [] + for v in sample_views: + index_views.append(self.cameras['camera_id'].index(v)) + this_mesh_name = 'mesh' + str(file_index).zfill(6) + '.ply' + next_mesh_name = 'mesh' + str(file_index+step).zfill(6) + '.ply' + filename = 'color'+str(file_index + step).zfill(6)+'.png' + + next_mesh = load_mesh(os.path.join(self.mesh_root, next_mesh_name), self.sample_size) + if next_mesh['vert'].shape[0] == 0: + file_index -= step + this_mesh_name = 'mesh' + str(file_index).zfill(6) + '.ply' + next_mesh_name = 'mesh' + str(file_index + step).zfill(6) + '.ply' + + next_mesh = load_mesh(os.path.join(self.mesh_root, next_mesh_name), self.sample_size) + filename = 'color'+str(file_index + step).zfill(6)+'.png' + next_mesh = load_lap(os.path.join(self.lap_root, 'laplacian'+str(file_index+step).zfill(6)+'.numpy'), next_mesh) + this_mesh = load_mesh(os.path.join(self.mesh_root, this_mesh_name), self.sample_size) + this_mesh = load_lap(os.path.join(self.lap_root, 'laplacian'+str(file_index).zfill(6)+'.numpy'), this_mesh) + + + for v in sample_views: + names.append(filename) + depth_dir = os.path.join(self.image_root, str(v)+'_depth', 'depth'+filename[5:-4]+'.png') + mask_dir = os.path.join(self.image_root, str(v) + '_mask', filename[:-4] + '.jpg') + image_dir = os.path.join(self.image_root, str(v), filename[:-4]+'.png') + image = self.p2m_load_img(image_dir) + depth = self.p2m_load_depth(depth_dir) + mask = self.p2m_load_mask(mask_dir) + + #image = image * mask + #depth = depth * mask + images.append(image) + masks.append(mask) + depths.append(depth) + + + image = torch.stack(images, dim=0) + image = image.float() + + mask = torch.stack(masks, dim=0) + mask = mask.float() + + depth = torch.stack(depths, dim=0) + depth = depth.float() + + # img_feats = self.decomp(image, 5) + # mask_feats = self.decom + # print(len(decomp_list)) + #image = torch.cat([image, mask], dim=1) + #image = self.decomp(image, 5) + return image, this_mesh, next_mesh, names, index_views, mask, depth + + # def get_single(self, index): + # + # filename = self.images[index].split('/')[-1] + # image_dir = os.path.join(self.image_root, filename) + # mask_dir = os.path.join(self.gt_root, filename[:-4]) + self.mask_ext + # + # image, gt, orig_image, orig_label = self.load_process(image_dir, mask_dir) + # + # image = image.float() + # gt = gt.float() + # orig_image = orig_image.float() + # orig_label = orig_label.float() + # + # return image, gt, orig_image, orig_label, filename + + def __getitem__(self, index): + num = 4 + max_ind = len(self.images) - num - 1 + sequence = [] + if index > max_ind: + index = random.randint(0, max_ind) + + for i in range(num): + image, this_mesh, next_mesh, names, sample_views, mask, depth = self.get_multi(index + i) + sequence.append({'image': image, 'this_mesh': this_mesh, 'next_mesh': next_mesh, 'names': names, 'sample_views': sample_views, 'mask': mask, 'depth': depth}) + + # if self.test_mode: + # return image, this_mesh, next_mesh, names, sample_views, mask, depth + # else: + # return image, this_mesh, next_mesh, names, sample_views, mask, depth + return sequence + + def filter_files(self): + # print(len(self.images), len(self.gts)) + assert len(self.images) == len(self.gts) + images = [] + gts = [] + for img_path, gt_path in zip(self.images, self.gts): + img = Image.open(img_path) + gt = Image.open(gt_path) + if img.size == gt.size: + images.append(img_path) + gts.append(gt_path) + self.images = images + self.gts = gts + + def rgb_loader(self, path): + with open(path, 'rb') as f: + img = Image.open(f) + return img.convert('RGB') + + + + def resize(self, img, gt): + assert img.size == gt.size + w, h = img.size + if h < self.trainsize or w < self.trainsize: + h = max(h, self.trainsize) + w = max(w, self.trainsize) + return img.resize((w, h), Image.BILINEAR), gt.resize((w, h), Image.NEAREST) + else: + return img, gt + + def __len__(self): + return self.size + + +def get_loader(data_root, mesh_root, lap_root, batchsize, trainsize, origsize, n_views, shuffle=True, num_workers=24, + pin_memory=True, sample_size=None, + fake_back_rate=0, back_dir=None, back_img=None, pure_back_rate=0, with_plate=False, + examine_mode=False, trimap_dir=None, of_list=None, with_gray=0, mask_ext='.jpg'): + if back_img: + test_mode = True + else: + test_mode = False + + dataset = SalObjDataset(data_root, mesh_root, trainsize=trainsize, origsize=origsize, num_views=n_views, + fake_back_rate=fake_back_rate, + back_dir=back_dir, pb_rate=pure_back_rate, plate_dir=None, with_plate=with_plate, + examine_mode=examine_mode, back_img=back_img, test_mode=test_mode, trimap_dir=trimap_dir, + of_list=of_list, mask_ext=mask_ext, with_gray=with_gray, sample_size=sample_size, lap_root=lap_root) + data_loader = data.DataLoader(dataset=dataset, + batch_size=batchsize, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=pin_memory, + worker_init_fn=worker_init_fn + ) + return data_loader + + +class test_dataset: + def __init__(self, image_root, gt_root, plate_root, testsize, orig=False, w_plate=False): + self.testsize = testsize + self.images = [image_root + f for f in os.listdir(image_root)] + plate = self.rgb_loader(plate_root) + self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg') + or f.endswith('.png')] + self.images = sorted(self.images) + self.gts = sorted(self.gts) + self.transform = transforms.Compose([ + transforms.Resize((self.testsize, self.testsize)), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + ]) + self.gt_transform = transforms.ToTensor() + self.size = len(self.images) + self.w_plate = w_plate + self.index = 0 + self.orig = orig + self.plate = self.transform(plate).unsqueeze(0) + + def load_data(self): + image = self.rgb_loader(self.images[self.index]) + + if self.orig: + image_orig = image.copy() + image = self.transform(image).unsqueeze(0) + print(image.shape) + gt = self.binary_loader(self.gts[self.index]) + name = self.images[self.index].split('/')[-1] + + self.index += 1 + if self.w_plate: + image = torch.cat((image, self.plate), dim=1) + if self.orig: + return np.array(image_orig), image, gt, name + else: + return image, gt, name + + def rgb_loader(self, path): + with open(path, 'rb') as f: + img = Image.open(f) + return img.convert('RGB') + + def binary_loader(self, path): + with open(path, 'rb') as f: + img = Image.open(f) + return img.convert('L') + + +class FakeBack(object): + + def __init__(self, back_dir, trainsize, views, back_img=None, test_mode=False, with_trimap=False): + import torchvision.transforms as T + self.back_dir = back_dir + self.test_mode = test_mode + self.back_img = back_img + self.with_trimap = with_trimap + self.n_view = views + self.trainsize = trainsize + if self.back_dir: + path, dirs, files = next(os.walk(back_dir)) + random.shuffle(files) + num = len(files) + self.selected_backs = files[:int(num * 1.)] + + path, dirs, files = next(os.walk('/home/hypevr/Desktop/datasets/background/studio/')) + random.shuffle(files) + self.studio = files + self.autocontrast = T.RandomAutocontrast(p=0.5) + self.sharpness_adjuster = T.RandomAdjustSharpness(sharpness_factor=2, p=0.5) + self.equalizer = T.RandomEqualize(p=0.5) + self.blurrer = T.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)) + self.perspective_transformer = T.RandomPerspective(distortion_scale=0.5, p=0.5) + # self.randomcrop = T.RandomCrop(, padding=None, pad_if_needed=False, fill=0, padding_mode='constant') + + def cv_resize(self, image): + image = cv2.resize(image, self.trainsize, interpolation=cv2.INTER_AREA) + return image + + def random_resize_crop(self, image, crop_pct, out_mode='same', out_size=None, p=0.8): + if crop_pct > 1: + raise Exception('crop percentage should be less than 1.0') + + w, h = image.size + new_w = int(w * crop_pct) + new_h = int(h * crop_pct) + + if out_mode == 'same': + out_size = (w, h) + elif out_mode == 'fixed': + if not out_size: + raise Exception('if fixed mode is chosen, out_size has to be specified') + else: + out_size = out_size + elif out_mode == 'no_resize': + out_size = (new_w, new_h) + else: + raise Exception('out_mode error: choose from "fixed", "same" and "no_resize"') + + prob = random.random() + if prob > p: + out_image = cv_resize_pil(image, out_size) + else: + pos_h = random.randint(0, h - new_h) + pos_w = random.randint(0, w - new_w) + out_image = TF.crop(image, top=pos_h, left=pos_w, height=new_h, width=new_w) + out_image = cv_resize_pil(out_image, out_size) + return out_image + + def totensor(self, image): + image = image / 255. + if len(image.shape) == 3: + image = np.transpose(image, (2, 0, 1)) + elif len(image.shape) == 2: + image = np.expand_dims(image, axis=0) + image = torch.tensor(image) + return image + + def back_transform(self, back, dim): + + # angle = random.randint(-10, 10) + # shear = random.uniform(-10, 10) + + # back = TF.affine(back, angle=0, translate=[0, 0], fill=0, shear=shear, scale=1) + + if random.random() > 0.5: + back = TF.hflip(back) + + if random.random() > 0.5: + back = TF.vflip(back) + + if random.random() > 0.2: + back = self.autocontrast(back) + back = self.sharpness_adjuster(back) + back = self.equalizer(back) + + rotate_p = random.random() + if rotate_p < 0.1: + back = back.rotate(90) + elif 0.1 <= rotate_p < 0.2: + back = back.rotate(270) + + back = self.random_resize_crop(back, crop_pct=random.uniform(0.4, 0.9), out_mode='fixed', out_size=dim, p=0.8) + + return back + + def __call__(self, sample, pb): + + image, label = sample['image'], sample['label'] + orig_w, orig_h = image.size + if self.with_trimap: + trimap = sample['trimap'] + + else: + trimap = label + + if not self.test_mode: + random.seed(time.time()) + trans_r_v = 0.05 + trans_r_h = 0.4 + rand_shift_v = int(orig_h * trans_r_v) + rand_shift_h = int(orig_w * trans_r_h) + + zoom = random.uniform(0.7, 1.2) + angle = random.randint(-25, 25) + translate = [random.randint(-rand_shift_h, rand_shift_h), + random.randint(-rand_shift_v, rand_shift_v)] + + zoom_size_w = int(orig_w * zoom) + if zoom_size_w % 2 == 1: + zoom_size_w += 1 + zoom_size_h = int(orig_h * zoom) + if zoom_size_h % 2 == 1: + zoom_size_h += 1 + zoom_size = (zoom_size_w, zoom_size_h) + + image = cv_resize_pil(image, zoom_size) + label = cv_resize_pil(label, zoom_size) + trimap = cv_resize_pil(trimap, zoom_size) + + if zoom > 1: + image = TF.center_crop(image, [orig_h, orig_w]) + label = TF.center_crop(label, [orig_h, orig_w]) + trimap = TF.center_crop(trimap, [orig_h, orig_w]) + else: + pad_w = int((orig_w - zoom_size[0]) / 2) + pad_h = int((orig_h - zoom_size[1]) / 2) + image = TF.pad(image, padding=[pad_w, pad_h, pad_w, pad_h]) + label = TF.pad(label, padding=[pad_w, pad_h, pad_w, pad_h]) + trimap = TF.pad(trimap, padding=[pad_w, pad_h, pad_w, pad_h]) + + image = TF.affine(image, angle=angle, translate=translate, fill=0, shear=[0, 0], scale=1) + label = TF.affine(label, angle=angle, translate=translate, fill=0, shear=[0, 0], scale=1) + trimap = TF.affine(trimap, angle=angle, translate=translate, fill=0, shear=[0, 0], scale=1) + + image = self.equalizer(image) + image = self.autocontrast(image) + image = self.sharpness_adjuster(image) + + if random.random() > 0.5: + image = TF.hflip(image) + label = TF.hflip(label) + trimap = TF.hflip(trimap) + + if random.random() > 0.5: + image = TF.vflip(image) + label = TF.vflip(label) + trimap = TF.vflip(trimap) + + if random.random() > 0.8: + back = Image.open(self.back_dir + random.choice(self.selected_backs)) + back = back.convert('RGB') + back = self.back_transform(back, (720, 1280)) + else: + # back = Image.open(self.back_dir + random.choice(self.selected_backs)) + back = Image.open('/home/hypevr/Desktop/datasets/background/studio/' + random.choice(self.studio)) + back = back.convert('RGB') + back = self.back_transform(back, (720, 1280)) + + else: + back = Image.open(self.back_img) + back = back.convert('RGB') + back = np.array(back) + + image = np.array(image) + label = np.array(label) + trimap = np.array(trimap) + if np.sum(label / 255.) / (orig_w * orig_h) < 0.01: + pb = True + # orig_trimap = np.array(trimap) + label_cp = label.copy() + + if not self.test_mode: + label_cp = 255. * (label_cp.astype(np.float32) / float(label_cp.max() + 1e-8)) + if not self.with_trimap: + label_cp[label_cp < 100] = 0 + # label_cp[np.logical_and(50 <= label, label < 128)] = 128 + label_cp[label_cp >= 100] = 255 + + mask = np.tile(np.expand_dims(label_cp, axis=-1), (1, 1, 3)) / 255. + image = image * mask # ).astype('uint8') + # kernel = np.ones((3, 3), np.uint8)#cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) + # image = cv2.erode(image, kernel, iterations=2) + # image = image * mask + olay = image.copy() + compare = np.all(image == (0, 0, 0), axis=-1) + + ##randomly dim the image to match the brightness of the background + random_dim = random.uniform(0.7, 1) + olay = olay * random_dim + + ##random perspective transform + # olay = torch.tensor(olay) + # olay = self.perspective_transformer(olay) + # olay = np.array(olay) + + ##Apply random gaussian noise to the image + noise = np.random.normal(0, 1, (orig_h, orig_w, 3)) + + ##apply the overlay + olay[compare] = back[compare] + + olay = cv2.bilateralFilter(olay, 3, 75, 75) + + olay = np.clip(olay + noise, 0, 255) + + else: + olay = image # / 255. + + plate = back + back = np.clip(back + noise, 0, 255) + orig_olay = olay + orig_label = label_cp + orig_back = back + + orig_olay = self.totensor(orig_olay) + orig_label = self.totensor(orig_label) + orig_back = self.totensor(orig_back) + + olay = self.cv_resize(olay) + olay = self.totensor(olay) + back = self.cv_resize(back) + back = self.totensor(back) + trimap = self.cv_resize(trimap) + trimap = self.totensor(trimap) + label_cp = self.cv_resize(label_cp) + label_cp = self.totensor(label_cp) + + trimap[trimap < 64] = 0 + trimap[np.logical_and(64 <= trimap, trimap < 192)] = 1 + trimap[trimap >= 192] = 2 + + trimap = np.expand_dims(trimap, axis=0) + # trimap = torch.tensor(trimap) + + plate = self.totensor(plate) + + if not pb: + return {'image': olay, 'label': label_cp, 'plate': plate, 'trimap': trimap, 'orig_image': orig_olay, + 'orig_label': orig_label} + else: + return {'image': back, 'label': torch.zeros((1, self.trainsize[0], self.trainsize[1])), + 'plate': plate, 'trimap': torch.zeros((1, self.trainsize[0], self.trainsize[1])), + 'orig_image': orig_back, 'orig_label': torch.zeros((1, orig_h, orig_w)) + } + + +def worker_init_fn(worker_id): + torch_seed = torch.initial_seed() + random.seed(torch_seed + worker_id) + if torch_seed >= 2 ** 30: # make sure torch_seed + workder_id < 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