├── LICENSE ├── README.md ├── checkpoints └── CP_epoch381.pth ├── dice_loss.py ├── eval.py ├── metric.py ├── migrationnet ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── __init__.cpython-38.pyc │ ├── unet_model.cpython-37.pyc │ ├── unet_model.cpython-38.pyc │ ├── unet_parts.cpython-37.pyc │ └── unet_parts.cpython-38.pyc ├── migrationnet_model.py └── migrationnet_parts.py ├── predict_mat.py ├── requirements.txt ├── train_mat.py └── utils ├── __pycache__ ├── data_vis.cpython-38.pyc ├── dataset.cpython-37.pyc ├── dataset.cpython-38.pyc ├── dataset_img.cpython-38.pyc └── dataset_mat.cpython-38.pyc ├── data_vis.py └── dataset_mat.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MigrationNet: Underground Pipeline Interpretation with PyTorch 2 | 3 | [dataset](https://www.dropbox.com/s/tv0ne4bgiql7nco/tgrs_models.tar.gz?dl=0) 4 | 5 | 6 | Built based on the [U-Net](https://arxiv.org/abs/1505.04597) in PyTorch. 7 | 8 | 9 | ## Usage 10 | **Note : Use Python 3** 11 | ### Prediction 12 | 13 | You can easily test the output with our dataset: 14 | 15 | `python predict_mat.py -i path/to/test -o predict.png -m path/to/checkpoint` 16 | 17 | 18 | 19 | ### Training 20 | 21 | `python train_mat.py -f path/to/checkpoint -e 200 -b 1 -l 0.000005 -s 0.25 -x path/to/train -y path/to/gt` 22 | -------------------------------------------------------------------------------- /checkpoints/CP_epoch381.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jing-lun/MigrationNet/d5afb7079ed36fcfa2e3083fd98f3cd887f60a7e/checkpoints/CP_epoch381.pth -------------------------------------------------------------------------------- /dice_loss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.autograd import Function 3 | 4 | 5 | class DiceCoeff(Function): 6 | """Dice coeff for individual examples""" 7 | 8 | def forward(self, input, target): 9 | self.save_for_backward(input, target) 10 | eps = 0.0001 11 | self.inter = torch.dot(input.view(-1), target.view(-1)) 12 | self.union = torch.sum(input) + torch.sum(target) + eps 13 | 14 | t = (2 * self.inter.float() + eps) / self.union.float() 15 | return t 16 | 17 | # This function has only a single output, so it gets only one gradient 18 | def backward(self, grad_output): 19 | 20 | input, target = self.saved_variables 21 | grad_input = grad_target = None 22 | 23 | if self.needs_input_grad[0]: 24 | grad_input = grad_output * 2 * (target * self.union - self.inter) \ 25 | / (self.union * self.union) 26 | if self.needs_input_grad[1]: 27 | grad_target = None 28 | 29 | return grad_input, grad_target 30 | 31 | 32 | def dice_coeff(input, target): 33 | """Dice coeff for batches""" 34 | if input.is_cuda: 35 | s = torch.FloatTensor(1).cuda().zero_() 36 | else: 37 | s = torch.FloatTensor(1).zero_() 38 | 39 | for i, c in enumerate(zip(input, target)): 40 | s = s + DiceCoeff().forward(c[0], c[1]) 41 | 42 | return s / (i + 1) 43 | -------------------------------------------------------------------------------- /eval.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from tqdm import tqdm 4 | import sys 5 | sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages') 6 | 7 | from dice_loss import dice_coeff 8 | 9 | 10 | def eval_net(net, loader, device, n_val): 11 | """Evaluation without the densecrf with the dice coefficient""" 12 | net.eval() 13 | tot = 0 14 | 15 | with tqdm(total=n_val, desc='Validation round', unit='img', leave=False) as pbar: 16 | for batch in loader: 17 | imgs = batch['image'] 18 | true_masks = batch['mask'] 19 | 20 | imgs = imgs.to(device=device, dtype=torch.float32) 21 | mask_type = torch.float32 if net.n_classes == 1 else torch.long 22 | true_masks = true_masks.to(device=device, dtype=mask_type) 23 | 24 | mask_pred = net(imgs) 25 | 26 | for true_mask, pred in zip(true_masks, mask_pred): 27 | pred = (pred > 0.5).float() 28 | if net.n_classes > 1: 29 | tot += F.cross_entropy(pred.unsqueeze(dim=0), true_mask.unsqueeze(dim=0)).item() 30 | else: 31 | tot += dice_coeff(pred, true_mask.squeeze(dim=1)).item() 32 | pbar.update(imgs.shape[0]) 33 | 34 | return tot / n_val 35 | -------------------------------------------------------------------------------- /metric.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | 3 | import torch 4 | import torch.nn.functional as F 5 | from torch_scatter import scatter_add 6 | 7 | 8 | def accuracy(pred, target): 9 | r"""Computes the accuracy of predictions. 10 | 11 | Args: 12 | pred (Tensor): The predictions. 13 | target (Tensor): The targets. 14 | 15 | :rtype: int 16 | """ 17 | return (pred == target).sum().item() / target.numel() 18 | 19 | 20 | 21 | def true_positive(pred, target, num_classes): 22 | r"""Computes the number of true positive predictions. 23 | 24 | Args: 25 | pred (Tensor): The predictions. 26 | target (Tensor): The targets. 27 | num_classes (int): The number of classes. 28 | 29 | :rtype: :class:`LongTensor` 30 | """ 31 | out = [] 32 | for i in range(num_classes): 33 | out.append(((pred == i) & (target == i)).sum()) 34 | 35 | return torch.tensor(out) 36 | 37 | 38 | 39 | def true_negative(pred, target, num_classes): 40 | r"""Computes the number of true negative predictions. 41 | 42 | Args: 43 | pred (Tensor): The predictions. 44 | target (Tensor): The targets. 45 | num_classes (int): The number of classes. 46 | 47 | :rtype: :class:`LongTensor` 48 | """ 49 | out = [] 50 | for i in range(num_classes): 51 | out.append(((pred != i) & (target != i)).sum()) 52 | 53 | return torch.tensor(out) 54 | 55 | 56 | 57 | def false_positive(pred, target, num_classes): 58 | r"""Computes the number of false positive predictions. 59 | 60 | Args: 61 | pred (Tensor): The predictions. 62 | target (Tensor): The targets. 63 | num_classes (int): The number of classes. 64 | 65 | :rtype: :class:`LongTensor` 66 | """ 67 | out = [] 68 | for i in range(num_classes): 69 | out.append(((pred == i) & (target != i)).sum()) 70 | 71 | return torch.tensor(out) 72 | 73 | 74 | 75 | def false_negative(pred, target, num_classes): 76 | r"""Computes the number of false negative predictions. 77 | 78 | Args: 79 | pred (Tensor): The predictions. 80 | target (Tensor): The targets. 81 | num_classes (int): The number of classes. 82 | 83 | :rtype: :class:`LongTensor` 84 | """ 85 | out = [] 86 | for i in range(num_classes): 87 | out.append(((pred != i) & (target == i)).sum()) 88 | 89 | return torch.tensor(out) 90 | 91 | 92 | 93 | def precision(pred, target, num_classes): 94 | r"""Computes the precision 95 | :math:`\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}` of predictions. 96 | 97 | Args: 98 | pred (Tensor): The predictions. 99 | target (Tensor): The targets. 100 | num_classes (int): The number of classes. 101 | 102 | :rtype: :class:`Tensor` 103 | """ 104 | tp = true_positive(pred, target, num_classes).to(torch.float) 105 | fp = false_positive(pred, target, num_classes).to(torch.float) 106 | 107 | out = tp / (tp + fp) 108 | out[torch.isnan(out)] = 0 109 | 110 | return out 111 | 112 | 113 | 114 | def recall(pred, target, num_classes): 115 | r"""Computes the recall 116 | :math:`\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}` of predictions. 117 | 118 | Args: 119 | pred (Tensor): The predictions. 120 | target (Tensor): The targets. 121 | num_classes (int): The number of classes. 122 | 123 | :rtype: :class:`Tensor` 124 | """ 125 | tp = true_positive(pred, target, num_classes).to(torch.float) 126 | fn = false_negative(pred, target, num_classes).to(torch.float) 127 | 128 | out = tp / (tp + fn) 129 | out[torch.isnan(out)] = 0 130 | 131 | return out 132 | 133 | 134 | 135 | def f1_score(pred, target, num_classes): 136 | r"""Computes the :math:`F_1` score 137 | :math:`2 \cdot \frac{\mathrm{precision} \cdot \mathrm{recall}} 138 | {\mathrm{precision}+\mathrm{recall}}` of predictions. 139 | 140 | Args: 141 | pred (Tensor): The predictions. 142 | target (Tensor): The targets. 143 | num_classes (int): The number of classes. 144 | 145 | :rtype: :class:`Tensor` 146 | """ 147 | prec = precision(pred, target, num_classes) 148 | rec = recall(pred, target, num_classes) 149 | 150 | score = 2 * (prec * rec) / (prec + rec) 151 | score[torch.isnan(score)] = 0 152 | 153 | return score 154 | 155 | 156 | 157 | def intersection_and_union(pred, target, num_classes, batch=None): 158 | r"""Computes intersection and union of predictions. 159 | 160 | Args: 161 | pred (LongTensor): The predictions. 162 | target (LongTensor): The targets. 163 | num_classes (int): The number of classes. 164 | batch (LongTensor): The assignment vector which maps each pred-target 165 | pair to an example. 166 | 167 | :rtype: (:class:`LongTensor`, :class:`LongTensor`) 168 | """ 169 | pred, target = F.one_hot(pred, num_classes), F.one_hot(target, num_classes) 170 | 171 | if batch is None: 172 | i = (pred & target).sum(dim=0) 173 | u = (pred | target).sum(dim=0) 174 | else: 175 | i = scatter_add(pred & target, batch, dim=0) 176 | u = scatter_add(pred | target, batch, dim=0) 177 | 178 | return i, u 179 | 180 | 181 | 182 | def mean_iou(pred, target, num_classes, batch=None): 183 | r"""Computes the mean intersection over union score of predictions. 184 | 185 | Args: 186 | pred (LongTensor): The predictions. 187 | target (LongTensor): The targets. 188 | num_classes (int): The number of classes. 189 | batch (LongTensor): The assignment vector which maps each pred-target 190 | pair to an example. 191 | 192 | :rtype: :class:`Tensor` 193 | """ 194 | i, u = intersection_and_union(pred, target, num_classes, batch) 195 | iou = i.to(torch.float) / u.to(torch.float) 196 | iou[torch.isnan(iou)] = 1 197 | iou = iou.mean(dim=-1) 198 | return iou 199 | -------------------------------------------------------------------------------- /migrationnet/__init__.py: -------------------------------------------------------------------------------- 1 | from .unet_model import UNet, UNet_mat 2 | -------------------------------------------------------------------------------- /migrationnet/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/Jing-lun/MigrationNet/d5afb7079ed36fcfa2e3083fd98f3cd887f60a7e/migrationnet/__pycache__/unet_parts.cpython-38.pyc -------------------------------------------------------------------------------- /migrationnet/migrationnet_model.py: -------------------------------------------------------------------------------- 1 | import torch.nn.functional as F 2 | import torch 3 | from .migrationnet_parts import * 4 | 5 | class MigrationNet(nn.Module): 6 | def __init__(self, n_channels, n_classes, bilinear=True): 7 | super(MigrationNet, self).__init__() 8 | self.n_channels = n_channels 9 | self.n_classes = n_classes 10 | self.bilinear = bilinear 11 | 12 | self.inc1 = DoubleConv(64, 64) 13 | self.down1_1 = Down1(64, 128) 14 | self.down2_1 = Down1(128, 256) 15 | self.down3_1 = Down1(256, 512) 16 | self.down4_1 = Down1(512, 512) 17 | 18 | self.inc2 = DoubleConv(128, 128) 19 | self.down1_2 = Down2(128, 256) 20 | self.down2_2 = Down1(256, 512) 21 | self.down3_2 = Down1(512, 512) 22 | 23 | self.inc3 = DoubleConv(n_channels, 256) 24 | self.down1_3 = Down3(256, 512) 25 | self.down2_3 = Down1(512, 512) 26 | 27 | self.outc_global1 = OutConv(1536, 1024) 28 | self.outc_global2 = OutConv(1024, 512) 29 | self.outc = OutConv(64, n_classes) 30 | 31 | self.up1 = Up(1024, 256, bilinear) 32 | self.up2 = Up(512, 128, bilinear) 33 | self.up3 = Up(256, 64, bilinear) 34 | self.up4 = Up(128, 64, bilinear) 35 | 36 | def forward(self, x): 37 | #64 38 | x_256 = x 39 | [a,b,c,d] = x.shape 40 | x_128 = x[:,0:b:2,:,:] 41 | x_64 = x[:,0:b:4,:,:] 42 | print('256',x_256.shape) 43 | print('128',x_128.shape) 44 | print('64',x_64.shape) 45 | 46 | #64-128-256 47 | x1_1 = self.inc1(x_64) 48 | x2_1 = self.down1_1(x1_1) 49 | x3_1 = self.down2_1(x2_1) 50 | x4_1 = self.down3_1(x3_1) 51 | x5_1 = self.down4_1(x4_1) 52 | 53 | x1_2 = self.inc2(x_128) 54 | x2_2 = self.down1_2(x1_2) 55 | x3_2 = self.down2_2(x2_2) 56 | x4_2 = self.down3_2(x3_2) 57 | 58 | x1_3 = self.inc3(x_256) 59 | x2_3 = self.down1_3(x1_3) 60 | x3_3 = self.down2_3(x2_3) 61 | 62 | 63 | x_sum_512 = (x4_1 + x3_2 + x2_3) 64 | x_sum_256 = (x3_1 + x2_2) 65 | x_sum_128 = x2_1 66 | 67 | L = [x5_1,x4_2,x3_3] 68 | x = torch.cat(L,1) 69 | x_global1 = self.outc_global1(x) 70 | x_global2 = self.outc_global2(x_global1) 71 | 72 | x = self.up1(x_sum_512, x_global2) 73 | x = self.up2(x, x_sum_256) 74 | x = self.up3(x, x_sum_128) 75 | x = self.up4(x, x1_1) 76 | logits = self.outc(x) 77 | 78 | return logits 79 | 80 | 81 | 82 | if __name__ == '__main__': 83 | input = torch.randn(1,3,256,700) 84 | net = MigrationNet(n_channels=3, n_classes=1) 85 | output = net(input) 86 | -------------------------------------------------------------------------------- /migrationnet/migrationnet_parts.py: -------------------------------------------------------------------------------- 1 | """ Parts of the MigrationNet model """ 2 | 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | 7 | 8 | class DoubleConv(nn.Module): 9 | """(convolution => [BN] => ReLU) * 2""" 10 | 11 | def __init__(self, in_channels, out_channels): 12 | super().__init__() 13 | self.double_conv = nn.Sequential( 14 | nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), 15 | nn.BatchNorm2d(out_channels), 16 | nn.ReLU(inplace=True), 17 | nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), 18 | nn.BatchNorm2d(out_channels), 19 | nn.ReLU(inplace=True) 20 | ) 21 | 22 | def forward(self, x): 23 | return self.double_conv(x) 24 | 25 | 26 | class Down1(nn.Module): 27 | """Downscaling with maxpool then double conv""" 28 | 29 | def __init__(self, in_channels, out_channels): 30 | super().__init__() 31 | self.maxpool_conv = nn.Sequential( 32 | nn.MaxPool2d(2), 33 | DoubleConv(in_channels, out_channels) 34 | ) 35 | 36 | def forward(self, x): 37 | return self.maxpool_conv(x) 38 | 39 | class Down2(nn.Module): 40 | """Downscaling with maxpool then double conv""" 41 | 42 | def __init__(self, in_channels, out_channels): 43 | super().__init__() 44 | self.maxpool_conv = nn.Sequential( 45 | nn.MaxPool2d(4), 46 | # nn.MaxPool2d(2, stride = 4), 47 | DoubleConv(in_channels, out_channels) 48 | ) 49 | 50 | def forward(self, x): 51 | return self.maxpool_conv(x) 52 | 53 | class Down3(nn.Module): 54 | """Downscaling with maxpool then double conv""" 55 | 56 | def __init__(self, in_channels, out_channels): 57 | super().__init__() 58 | self.maxpool_conv = nn.Sequential( 59 | nn.MaxPool2d(8), 60 | # nn.MaxPool2d(2, stride = 8), 61 | DoubleConv(in_channels, out_channels) 62 | ) 63 | 64 | def forward(self, x): 65 | return self.maxpool_conv(x) 66 | 67 | 68 | class Up(nn.Module): 69 | """Upscaling then double conv""" 70 | 71 | def __init__(self, in_channels, out_channels, bilinear=True): 72 | super().__init__() 73 | 74 | # if bilinear, use the normal convolutions to reduce the number of channels 75 | if bilinear: 76 | self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) 77 | else: 78 | self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2) 79 | 80 | self.conv = DoubleConv(in_channels, out_channels) 81 | 82 | def forward(self, x1, x2): 83 | x1 = self.up(x1) 84 | # input is CHW 85 | diffY = torch.tensor([x2.size()[2] - x1.size()[2]]) 86 | diffX = torch.tensor([x2.size()[3] - x1.size()[3]]) 87 | 88 | x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, 89 | diffY // 2, diffY - diffY // 2]) 90 | x = torch.cat([x2, x1], dim=1) 91 | return self.conv(x) 92 | 93 | 94 | class OutConv(nn.Module): 95 | def __init__(self, in_channels, out_channels): 96 | super(OutConv, self).__init__() 97 | self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) 98 | 99 | def forward(self, x): 100 | return self.conv(x) 101 | -------------------------------------------------------------------------------- /predict_mat.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import logging 3 | import os 4 | 5 | import numpy as np 6 | import torch 7 | import torch.nn.functional as F 8 | from PIL import Image 9 | from torchvision import transforms 10 | 11 | from migrationnet import MigrationNet 12 | from utils.data_vis import plot_img_and_mask 13 | from utils.dataset_mat import BasicDataset 14 | import h5py 15 | from torchvision.utils import save_image 16 | 17 | step = 0 18 | def predict_img(net, 19 | full_img, 20 | device, 21 | scale_factor=1, 22 | out_threshold=0.5 23 | ): 24 | net.eval() 25 | img = torch.from_numpy(BasicDataset.preprocess_mat(full_img, scale_factor)) 26 | 27 | img = img.unsqueeze(0) 28 | img = img.to(device=device, dtype=torch.float32) 29 | 30 | 31 | global step 32 | step = step+1 33 | with torch.no_grad(): 34 | output = net(img) 35 | 36 | if net.n_classes > 1: 37 | probs = F.softmax(output, dim=1) 38 | else: 39 | probs = torch.sigmoid(output) 40 | 41 | probs = probs.squeeze(0) 42 | probs = probs.cpu() 43 | probs = probs.to(torch.float) 44 | out_fn = '/home/jinglun/Data/migration/iros2020/predict_train_test/' + str(step) + '.png' 45 | save_image(probs, out_fn) 46 | 47 | return #predict_img 48 | 49 | 50 | def get_args(): 51 | parser = argparse.ArgumentParser(description='Predict masks from input images', 52 | formatter_class=argparse.ArgumentDefaultsHelpFormatter) 53 | parser.add_argument('--model', '-m', default='MODEL.pth', 54 | metavar='FILE', 55 | help="Specify the file in which the model is stored") 56 | parser.add_argument('--input', '-i', metavar='INPUT', nargs='+', 57 | help='filenames of input images', required=True) 58 | 59 | parser.add_argument('--output', '-o', metavar='INPUT', nargs='+', 60 | help='Filenames of ouput images') 61 | parser.add_argument('--viz', '-v', action='store_true', 62 | help="Visualize the images as they are processed", 63 | default=False) 64 | parser.add_argument('--no-save', '-n', action='store_true', 65 | help="Do not save the output masks", 66 | default=False) 67 | parser.add_argument('--mask-threshold', '-t', type=float, 68 | help="Minimum probability value to consider a mask pixel white", 69 | default=0.5) 70 | parser.add_argument('--scale', '-s', type=float, 71 | help="Scale factor for the input images", 72 | default=0.5) 73 | parser.add_argument('--step', '-ss', type=int, 74 | help="Scale factor for the input images", 75 | default=0) 76 | 77 | return parser.parse_args() 78 | 79 | 80 | def get_output_filenames(args): 81 | in_files = args.input 82 | out_files = [] 83 | 84 | if not args.output: 85 | for f in in_files: 86 | pathsplit = os.path.splitext(f) 87 | out_files.append("{}_OUT{}".format(pathsplit[0], pathsplit[1])) 88 | elif len(in_files) != len(args.output): 89 | logging.error("Input files and output files are not of the same length") 90 | raise SystemExit() 91 | else: 92 | out_files = args.output 93 | 94 | return out_files 95 | 96 | 97 | def mask_to_image(mask): 98 | return Image.fromarray((mask * 255).astype(np.uint8)) 99 | # global global_step 100 | if __name__ == "__main__": 101 | args = get_args() 102 | in_files = args.input 103 | out_files = get_output_filenames(args) 104 | 105 | net = MigrationNet(n_channels=128, n_classes=1) 106 | 107 | logging.info("Loading model {}".format(args.model)) 108 | 109 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 110 | logging.info(f'Using device {device}') 111 | net.to(device=device) 112 | net.load_state_dict(torch.load(args.model, map_location=device)) 113 | 114 | logging.info("Model loaded !") 115 | 116 | 117 | 118 | 119 | for i, fn in enumerate(in_files): 120 | logging.info("\nPredicting image {} ...".format(fn)) 121 | with h5py.File(fn, 'r') as f: 122 | dset = f['input_matrix'] 123 | img = dset[:] 124 | mask = predict_img(net=net, 125 | full_img=img, 126 | scale_factor=args.scale, 127 | out_threshold=args.mask_threshold, 128 | device=device) 129 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | numpy 3 | Pillow 4 | torch 5 | torchvision 6 | tensorboard -------------------------------------------------------------------------------- /train_mat.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import logging 3 | import os 4 | import sys 5 | 6 | import numpy as np 7 | import torch 8 | import torch.nn as nn 9 | from torch import optim 10 | from tqdm import tqdm 11 | import pytorch_msssim 12 | from metric import mean_iou, intersection_and_union, accuracy 13 | 14 | from eval import eval_net 15 | from migrationnet import MigrationNet 16 | from PIL import Image 17 | 18 | from torch.utils.tensorboard import SummaryWriter 19 | from utils.dataset_mat import BasicDataset,BasicDataset_mat 20 | from torch.utils.data import DataLoader, random_split 21 | from torchvision.utils import save_image 22 | 23 | 24 | dir_img = 'data/imgs/' 25 | dir_mask = 'data/masks/' 26 | dir_checkpoint = 'checkpoints/' 27 | 28 | def mask_to_image(mask): 29 | return Image.fromarray((mask * 255).astype(np.uint8)) 30 | 31 | def train_net(net, 32 | device, 33 | epochs=5, 34 | batch_size=1, 35 | lr=0.1, 36 | val_percent=0.1, 37 | save_cp=True, 38 | img_scale=0.5): 39 | 40 | dataset = BasicDataset(args.img, args.mask, img_scale) 41 | n_val = int(len(dataset) * val_percent) 42 | n_train = len(dataset) - n_val 43 | train, val = random_split(dataset, [n_train, n_val]) 44 | train_loader = DataLoader(train, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True) 45 | val_loader = DataLoader(val, batch_size=batch_size, shuffle=False, num_workers=8, pin_memory=True) 46 | 47 | writer = SummaryWriter(comment=f'LR_{lr}_BS_{batch_size}_SCALE_{img_scale}') 48 | global_step = 0 49 | 50 | logging.info(f'''Starting training: 51 | Epochs: {epochs} 52 | Batch size: {batch_size} 53 | Learning rate: {lr} 54 | Training size: {n_train} 55 | Validation size: {n_val} 56 | Checkpoints: {save_cp} 57 | Device: {device.type} 58 | Images scaling: {img_scale} 59 | ''') 60 | 61 | optimizer = optim.RMSprop(net.parameters(), lr=lr, weight_decay=1e-8) 62 | if net.n_classes > 1: 63 | criterion = nn.CrossEntropyLoss() 64 | else: 65 | criterion = nn.BCEWithLogitsLoss() 66 | 67 | for epoch in range(epochs): 68 | net.train() 69 | 70 | epoch_loss = 0 71 | with tqdm(total=n_train, desc=f'Epoch {epoch + 1}/{epochs}', unit='img') as pbar: 72 | for batch in train_loader: 73 | imgs = batch['image'] 74 | true_masks = batch['mask'] 75 | assert imgs.shape[1] == net.n_channels, \ 76 | f'Network has been defined with {net.n_channels} input channels, ' \ 77 | f'but loaded images have {imgs.shape[1]} channels. Please check that ' \ 78 | 'the images are loaded correctly.' 79 | 80 | imgs = imgs.to(device=device, dtype=torch.float32) 81 | mask_type = torch.float32 if net.n_classes == 1 else torch.long 82 | true_masks = true_masks.to(device=device, dtype=mask_type) 83 | 84 | masks_pred = net(imgs) 85 | loss = criterion(masks_pred, true_masks) 86 | epoch_loss += loss.item() 87 | writer.add_scalar('Loss/train', loss.item(), global_step) 88 | 89 | pbar.set_postfix(**{'loss (batch)': loss.item()}) 90 | 91 | optimizer.zero_grad() 92 | loss.backward() 93 | optimizer.step() 94 | 95 | pbar.update(imgs.shape[0]) 96 | global_step += 1 97 | # if global_step % (len(dataset) // (1 * batch_size)) == 0: 98 | val_score = eval_net(net, val_loader, device, n_val) 99 | if net.n_classes > 1: 100 | logging.info('Validation cross entropy: {}'.format(val_score)) 101 | writer.add_scalar('Loss/test', val_score, global_step) 102 | 103 | else: 104 | logging.info('Validation Dice Coeff: {}'.format(val_score)) 105 | writer.add_scalar('Dice/test', val_score, global_step) 106 | 107 | # writer.add_images('images', imgs, global_step) 108 | if net.n_classes == 1: 109 | writer.add_images('masks/true', true_masks, global_step) 110 | writer.add_images('masks/pred', torch.sigmoid(masks_pred) > 0.5, global_step) 111 | temp_img = (torch.sigmoid(masks_pred) > 0.5) 112 | temp_img = temp_img.squeeze(0) 113 | temp_img = temp_img.cpu() 114 | temp_img = temp_img.to(torch.float) 115 | out_fn = '/home/jinglun/Data/migration/iros2020/predict_train_test/' + str(global_step) + '.png' 116 | save_image(temp_img, out_fn) 117 | 118 | if epoch % 20 == 0: 119 | if save_cp: 120 | try: 121 | os.mkdir(dir_checkpoint) 122 | logging.info('Created checkpoint directory') 123 | except OSError: 124 | pass 125 | torch.save(net.state_dict(), 126 | dir_checkpoint + f'CP_epoch{epoch + 1}.pth') 127 | logging.info(f'Checkpoint {epoch + 1} saved !') 128 | 129 | writer.close() 130 | 131 | 132 | def get_args(): 133 | parser = argparse.ArgumentParser(description='Train the MigrationNet on images and target masks', 134 | formatter_class=argparse.ArgumentDefaultsHelpFormatter) 135 | parser.add_argument('-e', '--epochs', metavar='E', type=int, default=5, 136 | help='Number of epochs', dest='epochs') 137 | parser.add_argument('-b', '--batch-size', metavar='B', type=int, nargs='?', default=1, 138 | help='Batch size', dest='batchsize') 139 | parser.add_argument('-l', '--learning-rate', metavar='LR', type=float, nargs='?', default=0.1, 140 | help='Learning rate', dest='lr') 141 | parser.add_argument('-f', '--load', dest='load', type=str, default=False, 142 | help='Load model from a .pth file') 143 | parser.add_argument('-s', '--scale', dest='scale', type=float, default=0.5, 144 | help='Downscaling factor of the images') 145 | parser.add_argument('-v', '--validation', dest='val', type=float, default=10.0, 146 | help='Percent of the data that is used as validation (0-100)') 147 | parser.add_argument('-x', '--img', default='/home/jinglun/Data/DATA/crackData/training/image/', 148 | type=str, metavar='PATH', help='path to img dataset') 149 | parser.add_argument('-x1', '--img1', default='/home/jinglun/Data/DATA/crackData/training/image/', 150 | type=str, metavar='PATH', help='path to img dataset') 151 | parser.add_argument('-x2', '--img2', default='/home/jinglun/Data/DATA/crackData/training/image/', 152 | type=str, metavar='PATH', help='path to img dataset') 153 | parser.add_argument('-x3', '--img3', default='/home/jinglun/Data/DATA/crackData/training/image/', 154 | type=str, metavar='PATH', help='path to img dataset') 155 | parser.add_argument('-y', '--mask', default='/home/jinglun/Data/DATA/crackData/training/bw_image/', 156 | type=str, metavar='PATH', help='path to mask dataset') 157 | parser.add_argument('-c', '--channel', metavar='C', type=int, default=3, 158 | help='Number of channels', dest='channels') 159 | 160 | return parser.parse_args() 161 | 162 | 163 | if __name__ == '__main__': 164 | logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s') 165 | args = get_args() 166 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 167 | logging.info(f'Using device {device}') 168 | 169 | net = MigrationNet(n_channels=256, n_classes=1) 170 | logging.info(f'Network:\n' 171 | f'\t{net.n_channels} input channels\n' 172 | f'\t{net.n_classes} output channels (classes)\n' 173 | f'\t{"Bilinear" if net.bilinear else "Dilated conv"} upscaling') 174 | 175 | if args.load: 176 | net.load_state_dict( 177 | torch.load(args.load, map_location=device) 178 | ) 179 | logging.info(f'Model loaded from {args.load}') 180 | 181 | net.to(device=device) 182 | 183 | try: 184 | train_net(net=net, 185 | epochs=args.epochs, 186 | batch_size=args.batchsize, 187 | lr=args.lr, 188 | device=device, 189 | img_scale=args.scale, 190 | val_percent=args.val / 100) 191 | except KeyboardInterrupt: 192 | torch.save(net.state_dict(), 'INTERRUPTED.pth') 193 | logging.info('Saved interrupt') 194 | try: 195 | sys.exit(0) 196 | except SystemExit: 197 | os._exit(0) 198 | -------------------------------------------------------------------------------- /utils/__pycache__/data_vis.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jing-lun/MigrationNet/d5afb7079ed36fcfa2e3083fd98f3cd887f60a7e/utils/__pycache__/data_vis.cpython-38.pyc -------------------------------------------------------------------------------- /utils/__pycache__/dataset.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jing-lun/MigrationNet/d5afb7079ed36fcfa2e3083fd98f3cd887f60a7e/utils/__pycache__/dataset.cpython-37.pyc -------------------------------------------------------------------------------- /utils/__pycache__/dataset.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jing-lun/MigrationNet/d5afb7079ed36fcfa2e3083fd98f3cd887f60a7e/utils/__pycache__/dataset.cpython-38.pyc -------------------------------------------------------------------------------- /utils/__pycache__/dataset_img.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jing-lun/MigrationNet/d5afb7079ed36fcfa2e3083fd98f3cd887f60a7e/utils/__pycache__/dataset_img.cpython-38.pyc -------------------------------------------------------------------------------- /utils/__pycache__/dataset_mat.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jing-lun/MigrationNet/d5afb7079ed36fcfa2e3083fd98f3cd887f60a7e/utils/__pycache__/dataset_mat.cpython-38.pyc -------------------------------------------------------------------------------- /utils/data_vis.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | 3 | 4 | def plot_img_and_mask(img, mask): 5 | classes = mask.shape[2] if len(mask.shape) > 2 else 1 6 | fig, ax = plt.subplots(1, classes + 1) 7 | ax[0].set_title('Input image') 8 | ax[0].imshow(img) 9 | if classes > 1: 10 | for i in range(classes): 11 | ax[i+1].set_title(f'Output mask (class {i+1})') 12 | ax[i+1].imshow(mask[:, :, i]) 13 | else: 14 | ax[1].set_title(f'Output mask') 15 | ax[1].imshow(mask) 16 | plt.xticks([]), plt.yticks([]) 17 | plt.show() 18 | -------------------------------------------------------------------------------- /utils/dataset_mat.py: -------------------------------------------------------------------------------- 1 | from os.path import splitext 2 | from os import listdir 3 | import numpy as np 4 | from glob import glob 5 | import torch 6 | from torch.utils.data import Dataset 7 | import logging 8 | from PIL import Image 9 | import h5py 10 | 11 | class BasicDataset(Dataset): 12 | def __init__(self, imgs_dir, masks_dir, scale=1): 13 | self.imgs_dir = imgs_dir 14 | self.masks_dir = masks_dir 15 | self.scale = scale 16 | assert 0 < scale <= 1, 'Scale must be between 0 and 1' 17 | 18 | self.ids = [splitext(file)[0] for file in listdir(imgs_dir) 19 | if not file.startswith('.')] 20 | logging.info(f'Creating dataset with {len(self.ids)} examples') 21 | 22 | def __len__(self): 23 | return len(self.ids) 24 | 25 | @classmethod 26 | def preprocess(cls, pil_img, scale): 27 | w, h = pil_img.size 28 | newW, newH = int(scale * w), int(scale * h) 29 | assert newW > 0 and newH > 0, 'Scale is too small' 30 | pil_img = pil_img.resize((newW, newH)) 31 | 32 | img_nd = np.array(pil_img) 33 | 34 | if len(img_nd.shape) == 2: 35 | img_nd = np.expand_dims(img_nd, axis=2) 36 | 37 | # HWC to CHW 38 | img_trans = img_nd.transpose((2, 0, 1)) 39 | if img_trans.max() > 1: 40 | img_trans = img_trans / 255 41 | 42 | return img_trans 43 | 44 | @classmethod 45 | def preprocess_mat(cls, mat, scale): 46 | c, w, h = mat.shape 47 | newW, newH = int(scale * w), int(scale * h) 48 | assert newW > 0 and newH > 0, 'Scale is too small' 49 | mat = np.resize(mat, (c, newH, newW)) 50 | if mat.max() > 1: 51 | mat = mat / 255 52 | 53 | return mat 54 | 55 | def __getitem__(self, i): 56 | idx = self.ids[i] 57 | mask_file = glob(self.masks_dir + idx + '.png') 58 | mat_file = glob(self.imgs_dir + idx + '.mat') 59 | # print(mat_file) 60 | mask = Image.open(mask_file[0]) 61 | ''' 62 | mat73 method 63 | ''' 64 | # mat = mat73.loadmat(mat_file[0]) 65 | # mat = mat['input_matrix'] 66 | ''' 67 | h5py method 68 | ''' 69 | with h5py.File(mat_file[0], 'r') as f: 70 | dset = f['input_matrix'] 71 | # dset = f['new_scan'] 72 | mat = dset[:] 73 | 74 | mat = self.preprocess_mat(mat, self.scale) 75 | mask = self.preprocess(mask, self.scale) 76 | 77 | return {'image': torch.from_numpy(mat), 'mask': torch.from_numpy(mask)} 78 | 79 | class BasicDataset_mat(Dataset): 80 | def __init__(self, imgs_dir1, imgs_dir2, imgs_dir3, masks_dir, scale=1): 81 | self.imgs_dir1 = imgs_dir1 82 | self.imgs_dir2 = imgs_dir2 83 | self.imgs_dir3 = imgs_dir3 84 | self.masks_dir = masks_dir 85 | self.scale = scale 86 | assert 0 < scale <= 1, 'Scale must be between 0 and 1' 87 | 88 | self.ids = [splitext(file)[0] for file in listdir(imgs_dir1) 89 | if not file.startswith('.')] 90 | logging.info(f'Creating dataset with {len(self.ids)} examples') 91 | 92 | def __len__(self): 93 | return len(self.ids) 94 | 95 | @classmethod 96 | def preprocess(cls, pil_img, scale): 97 | w, h = pil_img.size 98 | newW, newH = int(scale * w), int(scale * h) 99 | assert newW > 0 and newH > 0, 'Scale is too small' 100 | pil_img = pil_img.resize((newW, newH)) 101 | 102 | img_nd = np.array(pil_img) 103 | 104 | if len(img_nd.shape) == 2: 105 | img_nd = np.expand_dims(img_nd, axis=2) 106 | 107 | # HWC to CHW 108 | img_trans = img_nd.transpose((2, 0, 1)) 109 | if img_trans.max() > 1: 110 | img_trans = img_trans / 255 111 | 112 | return img_trans 113 | 114 | @classmethod 115 | def preprocess_mat(cls, mat, scale): 116 | c, w, h = mat.shape 117 | newW, newH = int(scale * w), int(scale * h) 118 | assert newW > 0 and newH > 0, 'Scale is too small' 119 | mat = np.resize(mat, (c, newH, newW)) 120 | if mat.max() > 1: 121 | mat = mat / 255 122 | 123 | return mat 124 | 125 | def __getitem__(self, i): 126 | idx = self.ids[i] 127 | mask_file = glob(self.masks_dir + idx + '.png') 128 | mat_file1 = glob(self.imgs_dir1 + idx + '.mat') 129 | mat_file2 = glob(self.imgs_dir2 + idx + '.mat') 130 | mat_file3 = glob(self.imgs_dir3 + idx + '.mat') 131 | # print(mat_file) 132 | mask = Image.open(mask_file[0]) 133 | ''' 134 | mat73 method 135 | ''' 136 | # mat = mat73.loadmat(mat_file[0]) 137 | # mat = mat['input_matrix'] 138 | ''' 139 | h5py method 140 | ''' 141 | with h5py.File(mat_file1[0], 'r') as f: 142 | dset = f['input_matrix'] 143 | # dset = f['new_scan'] 144 | mat1 = dset[:] 145 | mat1 = self.preprocess_mat(mat1, self.scale) 146 | 147 | with h5py.File(mat_file2[0], 'r') as f: 148 | dset = f['input_matrix'] 149 | # dset = f['new_scan'] 150 | mat2 = dset[:] 151 | mat2 = self.preprocess_mat(mat2, self.scale) 152 | 153 | with h5py.File(mat_file3[0], 'r') as f: 154 | dset = f['input_matrix'] 155 | # dset = f['new_scan'] 156 | mat3 = dset[:] 157 | mat3 = self.preprocess_mat(mat3, self.scale) 158 | 159 | mask = self.preprocess(mask, self.scale) 160 | 161 | return {'image1': torch.from_numpy(mat1),'image2': torch.from_numpy(mat2),'image3': torch.from_numpy(mat3),'mask': torch.from_numpy(mask)} 162 | 163 | if __name__ == '__main__': 164 | # dataset = BasicDataset('/home/jinglun/Data/migration/iros2020/bp_full/mat_128_layers', '/home/jinglun/Data/migration/iros2020/new_gt_imgs/', 0.5) 165 | # val_percent=0.1 166 | # n_val = int(len(dataset) * val_percent) 167 | # n_train = len(dataset) - n_val 168 | # from torch.utils.data import DataLoader, random_split 169 | # train, val = random_split(dataset, [n_train, n_val]) 170 | dataset = BasicDataset_mat('/home/jinglun/Data/migration/iros2020/bp_full/mat_64_layers','/home/jinglun/Data/migration/iros2020/bp_full/mat_128_layers','/home/jinglun/Data/migration/iros2020/bp_full/mat_256_layers', '/home/jinglun/Data/migration/iros2020/new_gt_imgs/', 0.5) 171 | --------------------------------------------------------------------------------