├── inference.py ├── README.md ├── .gitignore ├── losses.py ├── dataloader.py ├── model.py ├── train.py ├── tutorial.ipynb └── LICENSE /inference.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # U-Net for Semantic Segmentation on Unbalanced Aerial Imagery 2 | #### Note: this repository is still developing but the colab notebook is complete for training and evaluations. 3 | 4 | Read the article at [Towards data science](https://towardsdatascience.com/u-net-for-semantic-segmentation-on-unbalanced-aerial-imagery-3474fa1d3e56) 5 | 6 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1vYZYXDMfs9hK6KvXY3v1iTPiln9OeJZE?usp=sharing) 7 | 8 | [Kaggle Dataset](https://www.kaggle.com/humansintheloop/semantic-segmentation-of-aerial-imagery) 9 | 10 | ``` 11 | UNet-AerialSegmentation 12 | ├── dataloader.py 13 | ├── losses.py 14 | ├── model.py 15 | ├── train.py 16 | └── inference.py 17 | 18 | ``` 19 | 20 | ![dataset_sample1](https://user-images.githubusercontent.com/56114938/133141953-46df55be-4dfb-4084-b8d0-a63a56712ab0.png) 21 | 22 | ## Training 23 | ``` 24 | !python train.py --num_epochs 2 --batch 2 --loss focalloss 25 | ``` -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | *.pt 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /losses.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from torch.autograd import Variable 5 | 6 | class FocalLoss(nn.Module): 7 | def __init__(self, gamma=0, alpha=None, size_average=True): 8 | super(FocalLoss, self).__init__() 9 | self.gamma = gamma 10 | self.alpha = alpha 11 | if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha]) 12 | if isinstance(alpha,list): self.alpha = torch.Tensor(alpha) 13 | self.size_average = size_average 14 | 15 | def forward(self, input, target): 16 | if input.dim()>2: 17 | input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W 18 | input = input.transpose(1,2) # N,C,H*W => N,H*W,C 19 | input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C 20 | target = target.view(-1,1) 21 | 22 | logpt = F.log_softmax(input, dim=-1) 23 | logpt = logpt.gather(1,target) 24 | logpt = logpt.view(-1) 25 | pt = Variable(logpt.data.exp()) 26 | 27 | if self.alpha is not None: 28 | if self.alpha.type()!=input.data.type(): 29 | self.alpha = self.alpha.type_as(input.data) 30 | at = self.alpha.gather(0,target.data.view(-1)) 31 | logpt = logpt * Variable(at) 32 | 33 | loss = -1 * (1-pt)**self.gamma * logpt 34 | if self.size_average: return loss.mean() 35 | else: return loss.sum() 36 | 37 | 38 | class mIoULoss(nn.Module): 39 | def __init__(self, weight=None, size_average=True, n_classes=2): 40 | super(mIoULoss, self).__init__() 41 | self.classes = n_classes 42 | 43 | def to_one_hot(self, tensor): 44 | n,h,w = tensor.size() 45 | one_hot = torch.zeros(n,self.classes,h,w).to(tensor.device).scatter_(1,tensor.view(n,1,h,w),1) 46 | return one_hot 47 | 48 | def forward(self, inputs, target): 49 | # inputs => N x Classes x H x W 50 | # target_oneHot => N x Classes x H x W 51 | 52 | N = inputs.size()[0] 53 | 54 | # predicted probabilities for each pixel along channel 55 | inputs = F.softmax(inputs,dim=1) 56 | 57 | # Numerator Product 58 | target_oneHot = self.to_one_hot(target) 59 | inter = inputs * target_oneHot 60 | ## Sum over all pixels N x C x H x W => N x C 61 | inter = inter.view(N,self.classes,-1).sum(2) 62 | 63 | #Denominator 64 | union= inputs + target_oneHot - (inputs*target_oneHot) 65 | ## Sum over all pixels N x C x H x W => N x C 66 | union = union.view(N,self.classes,-1).sum(2) 67 | 68 | loss = inter/union 69 | 70 | ## Return average loss over classes and batch 71 | return 1-loss.mean() -------------------------------------------------------------------------------- /dataloader.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import torch 4 | import torchvision.transforms as transforms 5 | from scipy import ndimage 6 | from glob import glob 7 | 8 | 9 | class segDataset(torch.utils.data.Dataset): 10 | def __init__(self, root, training, transform=None): 11 | super(segDataset, self).__init__() 12 | self.root = root 13 | self.training = training 14 | self.transform = transform 15 | self.IMG_NAMES = sorted(glob(self.root + '/*/images/*.jpg')) 16 | self.BGR_classes = {'Water' : [ 41, 169, 226], 17 | 'Land' : [246, 41, 132], 18 | 'Road' : [228, 193, 110], 19 | 'Building' : [152, 16, 60], 20 | 'Vegetation' : [ 58, 221, 254], 21 | 'Unlabeled' : [155, 155, 155]} # in BGR 22 | 23 | self.bin_classes = ['Water', 'Land', 'Road', 'Building', 'Vegetation', 'Unlabeled'] 24 | 25 | 26 | def __getitem__(self, idx): 27 | img_path = self.IMG_NAMES[idx] 28 | mask_path = img_path.replace('images', 'masks').replace('.jpg', '.png') 29 | 30 | image = cv2.imread(img_path) 31 | mask = cv2.imread(mask_path) 32 | cls_mask = np.zeros(mask.shape) 33 | cls_mask[mask == self.BGR_classes['Water']] = self.bin_classes.index('Water') 34 | cls_mask[mask == self.BGR_classes['Land']] = self.bin_classes.index('Land') 35 | cls_mask[mask == self.BGR_classes['Road']] = self.bin_classes.index('Road') 36 | cls_mask[mask == self.BGR_classes['Building']] = self.bin_classes.index('Building') 37 | cls_mask[mask == self.BGR_classes['Vegetation']] = self.bin_classes.index('Vegetation') 38 | cls_mask[mask == self.BGR_classes['Unlabeled']] = self.bin_classes.index('Unlabeled') 39 | cls_mask = cls_mask[:,:,0] 40 | 41 | if self.training==True: 42 | if self.transform: 43 | image = transforms.functional.to_pil_image(image) 44 | image = self.transform(image) 45 | image = np.array(image) 46 | 47 | # 90 degree rotation 48 | if np.random.rand()<0.5: 49 | angle = np.random.randint(4) * 90 50 | image = ndimage.rotate(image,angle,reshape=True) 51 | cls_mask = ndimage.rotate(cls_mask,angle,reshape=True) 52 | 53 | # vertical flip 54 | if np.random.rand()<0.5: 55 | image = np.flip(image, 0) 56 | cls_mask = np.flip(cls_mask, 0) 57 | 58 | # horizonal flip 59 | if np.random.rand()<0.5: 60 | image = np.flip(image, 1) 61 | cls_mask = np.flip(cls_mask, 1) 62 | 63 | image = cv2.resize(image, (512,512))/255.0 64 | cls_mask = cv2.resize(cls_mask, (512,512)) 65 | image = np.moveaxis(image, -1, 0) 66 | 67 | return torch.tensor(image).float(), torch.tensor(cls_mask, dtype=torch.int64) 68 | 69 | 70 | def __len__(self): 71 | return len(self.IMG_NAMES) -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | # Unet model derived from https://github.com/milesial/Pytorch-UNet 6 | 7 | class DoubleConv(nn.Module): 8 | """(convolution => [BN] => ReLU) * 2""" 9 | 10 | def __init__(self, in_channels, out_channels, mid_channels=None): 11 | super().__init__() 12 | if not mid_channels: 13 | mid_channels = out_channels 14 | self.double_conv = nn.Sequential( 15 | nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), 16 | nn.BatchNorm2d(mid_channels), 17 | nn.ReLU(inplace=True), 18 | nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), 19 | nn.BatchNorm2d(out_channels), 20 | nn.ReLU(inplace=True) 21 | ) 22 | 23 | def forward(self, x): 24 | return self.double_conv(x) 25 | 26 | 27 | class Down(nn.Module): 28 | """Downscaling with maxpool then double conv""" 29 | 30 | def __init__(self, in_channels, out_channels): 31 | super().__init__() 32 | self.maxpool_conv = nn.Sequential( 33 | nn.MaxPool2d(2), 34 | DoubleConv(in_channels, out_channels) 35 | ) 36 | 37 | def forward(self, x): 38 | return self.maxpool_conv(x) 39 | 40 | 41 | class Up(nn.Module): 42 | """Upscaling then double conv""" 43 | 44 | def __init__(self, in_channels, out_channels, bilinear=True): 45 | super().__init__() 46 | 47 | # if bilinear, use the normal convolutions to reduce the number of channels 48 | if bilinear: 49 | self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) 50 | self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) 51 | else: 52 | self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2) 53 | self.conv = DoubleConv(in_channels, out_channels) 54 | 55 | 56 | def forward(self, x1, x2): 57 | x1 = self.up(x1) 58 | # input is CHW 59 | diffY = x2.size()[2] - x1.size()[2] 60 | diffX = x2.size()[3] - x1.size()[3] 61 | 62 | x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, 63 | diffY // 2, diffY - diffY // 2]) 64 | # if you have padding issues, see 65 | # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a 66 | # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd 67 | x = torch.cat([x2, x1], dim=1) 68 | return self.conv(x) 69 | 70 | 71 | class OutConv(nn.Module): 72 | def __init__(self, in_channels, out_channels): 73 | super(OutConv, self).__init__() 74 | self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) 75 | 76 | def forward(self, x): 77 | return self.conv(x) 78 | 79 | 80 | class UNet(nn.Module): 81 | def __init__(self, n_channels, n_classes, bilinear=True): 82 | super(UNet, self).__init__() 83 | self.n_channels = n_channels 84 | self.n_classes = n_classes 85 | self.bilinear = bilinear 86 | 87 | self.inc = DoubleConv(n_channels, 64) 88 | self.down1 = Down(64, 128) 89 | self.down2 = Down(128, 256) 90 | self.down3 = Down(256, 512) 91 | factor = 2 if bilinear else 1 92 | self.down4 = Down(512, 1024 // factor) 93 | self.up1 = Up(1024, 512 // factor, bilinear) 94 | self.up2 = Up(512, 256 // factor, bilinear) 95 | self.up3 = Up(256, 128 // factor, bilinear) 96 | self.up4 = Up(128, 64, bilinear) 97 | self.outc = OutConv(64, n_classes) 98 | 99 | def forward(self, x): 100 | x1 = self.inc(x) 101 | x2 = self.down1(x1) 102 | x3 = self.down2(x2) 103 | x4 = self.down3(x3) 104 | x5 = self.down4(x4) 105 | x = self.up1(x5, x4) 106 | x = self.up2(x, x3) 107 | x = self.up3(x, x2) 108 | x = self.up4(x, x1) 109 | logits = self.outc(x) 110 | return logits -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import sys 4 | import argparse 5 | import matplotlib.pyplot as plt 6 | import torch 7 | from torch import nn 8 | import torchvision.transforms as transforms 9 | 10 | from losses import FocalLoss, mIoULoss 11 | from model import UNet 12 | from dataloader import segDataset 13 | 14 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 15 | 16 | def get_args(): 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('--data', type=str, default='./Semantic segmentation dataset', help='path to your dataset') 19 | parser.add_argument('--num_epochs', type=int, default=100, help='dnumber of epochs') 20 | parser.add_argument('--batch', type=int, default=4, help='batch size') 21 | parser.add_argument('--loss', type=str, default='focalloss', help='focalloss | iouloss | crossentropy') 22 | return parser.parse_args() 23 | 24 | def acc(label, predicted): 25 | seg_acc = (y.cpu() == torch.argmax(pred_mask, axis=1).cpu()).sum() / torch.numel(y.cpu()) 26 | return seg_acc 27 | 28 | if __name__ == '__main__': 29 | args = get_args() 30 | N_EPOCHS = args.num_epochs 31 | BACH_SIZE = args.batch 32 | 33 | color_shift = transforms.ColorJitter(.1,.1,.1,.1) 34 | blurriness = transforms.GaussianBlur(3, sigma=(0.1, 2.0)) 35 | 36 | t = transforms.Compose([color_shift, blurriness]) 37 | dataset = segDataset(args.data, training = True, transform= t) 38 | 39 | print('Number of data : '+ str(len(dataset))) 40 | 41 | test_num = int(0.1 * len(dataset)) 42 | print(f'test data : {test_num}') 43 | train_dataset, test_dataset = torch.utils.data.random_split(dataset, [len(dataset)-test_num, test_num], generator=torch.Generator().manual_seed(101)) 44 | N_DATA, N_TEST = len(train_dataset), len(test_dataset) 45 | 46 | train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=BACH_SIZE, shuffle=True, num_workers=2) 47 | test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=BACH_SIZE, shuffle=False, num_workers=1) 48 | 49 | if args.loss == 'focalloss': 50 | criterion = FocalLoss(gamma=3/4).to(device) 51 | elif args.loss == 'iouloss': 52 | criterion = mIoULoss(n_classes=6).to(device) 53 | elif args.loss == 'crossentropy': 54 | criterion = nn.CrossEntropyLoss().to(device) 55 | else: 56 | print('Loss function not found!') 57 | 58 | 59 | model = UNet(n_channels=3, n_classes=6, bilinear=True).to(device) 60 | optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) 61 | lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.5) 62 | min_loss = torch.tensor(float('inf')) 63 | 64 | os.makedirs('./saved_models', exist_ok=True) 65 | 66 | plot_losses = [] 67 | scheduler_counter = 0 68 | 69 | for epoch in range(N_EPOCHS): 70 | # training 71 | model.train() 72 | loss_list = [] 73 | acc_list = [] 74 | for batch_i, (x, y) in enumerate(train_dataloader): 75 | 76 | pred_mask = model(x.to(device)) 77 | loss = criterion(pred_mask, y.to(device)) 78 | 79 | optimizer.zero_grad() 80 | loss.backward() 81 | optimizer.step() 82 | loss_list.append(loss.cpu().detach().numpy()) 83 | acc_list.append(acc(y,pred_mask).numpy()) 84 | 85 | sys.stdout.write( 86 | "\r[Epoch %d/%d] [Batch %d/%d] [Loss: %f (%f)]" 87 | % ( 88 | epoch, 89 | N_EPOCHS, 90 | batch_i, 91 | len(train_dataloader), 92 | loss.cpu().detach().numpy(), 93 | np.mean(loss_list), 94 | ) 95 | ) 96 | scheduler_counter += 1 97 | # testing 98 | model.eval() 99 | val_loss_list = [] 100 | val_acc_list = [] 101 | for batch_i, (x, y) in enumerate(test_dataloader): 102 | with torch.no_grad(): 103 | pred_mask = model(x.to(device)) 104 | val_loss = criterion(pred_mask, y.to(device)) 105 | val_loss_list.append(val_loss.cpu().detach().numpy()) 106 | val_acc_list.append(acc(y,pred_mask).numpy()) 107 | 108 | print(' epoch {} - loss : {:.5f} - acc : {:.2f} - val loss : {:.5f} - val acc : {:.2f}'.format(epoch, 109 | np.mean(loss_list), 110 | np.mean(acc_list), 111 | np.mean(val_loss_list), 112 | np.mean(val_acc_list))) 113 | plot_losses.append([epoch, np.mean(loss_list), np.mean(val_loss_list)]) 114 | 115 | compare_loss = np.mean(val_loss_list) 116 | is_best = compare_loss < min_loss 117 | if is_best == True: 118 | scheduler_counter = 0 119 | min_loss = min(compare_loss, min_loss) 120 | torch.save(model.state_dict(), './saved_models/unet_epoch_{}_{:.5f}.pt'.format(epoch,np.mean(val_loss_list))) 121 | 122 | if scheduler_counter > 5: 123 | lr_scheduler.step() 124 | print(f"lowering learning rate to {optimizer.param_groups[0]['lr']}") 125 | scheduler_counter = 0 126 | 127 | 128 | # plot loss 129 | plot_losses = np.array(plot_losses) 130 | plt.figure(figsize=(12,8)) 131 | plt.plot(plot_losses[:,0], plot_losses[:,1], color='b', linewidth=4) 132 | plt.plot(plot_losses[:,0], plot_losses[:,2], color='r', linewidth=4) 133 | plt.title(args.loss, fontsize=20) 134 | plt.xlabel('epoch',fontsize=20) 135 | plt.ylabel('loss',fontsize=20) 136 | plt.grid() 137 | plt.legend(['training', 'validation']) # using a named size 138 | plt.savefig('loss_plots.png') 139 | 140 | -------------------------------------------------------------------------------- /tutorial.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# U-Net for Semantic Segmentation on Unbalanced Aerial Imagery\n" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "Read the article at [Towards data science](https://towardsdatascience.com/u-net-for-semantic-segmentation-on-unbalanced-aerial-imagery-3474fa1d3e56)" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "## Unzip dataset" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 10, 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "# download dataset from \"https://drive.google.com/u/0/uc?id=1uJDMDv0bRqu30_b6kEGZjW0S4Sr6TBOi&export=download\"\n", 31 | "\n", 32 | "import shutil \n", 33 | "filename = \"./Semantic segmentation of aerial imagery.zip\"\n", 34 | "extract_dir = \"./\"\n", 35 | "archive_format = \"zip\"\n", 36 | "\n", 37 | "shutil.unpack_archive(filename, extract_dir, archive_format) " 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "## Training" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 11, 50 | "metadata": {}, 51 | "outputs": [ 52 | { 53 | "name": "stdout", 54 | "output_type": "stream", 55 | "text": [ 56 | "Number of data : 72\n", 57 | "test data : 7\n", 58 | "\n", 59 | "[Epoch 0/2] [Batch 0/33] [Loss: 1.570523 (1.570523)]\n", 60 | "[Epoch 0/2] [Batch 1/33] [Loss: 1.419828 (1.495176)]\n", 61 | "[Epoch 0/2] [Batch 2/33] [Loss: 1.635502 (1.541951)]\n", 62 | "[Epoch 0/2] [Batch 3/33] [Loss: 1.216439 (1.460573)]\n", 63 | "[Epoch 0/2] [Batch 4/33] [Loss: 1.115803 (1.391619)]\n", 64 | "[Epoch 0/2] [Batch 5/33] [Loss: 1.545698 (1.417299)]\n", 65 | "[Epoch 0/2] [Batch 6/33] [Loss: 1.281903 (1.397957)]\n", 66 | "[Epoch 0/2] [Batch 7/33] [Loss: 1.265271 (1.381371)]\n", 67 | "[Epoch 0/2] [Batch 8/33] [Loss: 0.919163 (1.330014)]\n", 68 | "[Epoch 0/2] [Batch 9/33] [Loss: 1.406972 (1.337710)]\n", 69 | "[Epoch 0/2] [Batch 10/33] [Loss: 0.984000 (1.305555)]\n", 70 | "[Epoch 0/2] [Batch 11/33] [Loss: 0.884090 (1.270433)]\n", 71 | "[Epoch 0/2] [Batch 12/33] [Loss: 0.918141 (1.243333)]\n", 72 | "[Epoch 0/2] [Batch 13/33] [Loss: 0.880535 (1.217419)]\n", 73 | "[Epoch 0/2] [Batch 14/33] [Loss: 1.285458 (1.221955)]\n", 74 | "[Epoch 0/2] [Batch 15/33] [Loss: 0.791899 (1.195077)]\n", 75 | "[Epoch 0/2] [Batch 16/33] [Loss: 0.720790 (1.167177)]\n", 76 | "[Epoch 0/2] [Batch 17/33] [Loss: 1.047363 (1.160521)]\n", 77 | "[Epoch 0/2] [Batch 18/33] [Loss: 0.921020 (1.147916)]\n", 78 | "[Epoch 0/2] [Batch 19/33] [Loss: 1.327467 (1.156893)]\n", 79 | "[Epoch 0/2] [Batch 20/33] [Loss: 1.251421 (1.161395)]\n", 80 | "[Epoch 0/2] [Batch 21/33] [Loss: 0.901546 (1.149583)]\n", 81 | "[Epoch 0/2] [Batch 22/33] [Loss: 0.813873 (1.134987)]\n", 82 | "[Epoch 0/2] [Batch 23/33] [Loss: 1.168082 (1.136366)]\n", 83 | "[Epoch 0/2] [Batch 24/33] [Loss: 0.847167 (1.124798)]\n", 84 | "[Epoch 0/2] [Batch 25/33] [Loss: 0.992579 (1.119713)]\n", 85 | "[Epoch 0/2] [Batch 26/33] [Loss: 0.641205 (1.101990)]\n", 86 | "[Epoch 0/2] [Batch 27/33] [Loss: 0.915920 (1.095345)]\n", 87 | "[Epoch 0/2] [Batch 28/33] [Loss: 1.074774 (1.094636)]\n", 88 | "[Epoch 0/2] [Batch 29/33] [Loss: 0.732019 (1.082548)]\n", 89 | "[Epoch 0/2] [Batch 30/33] [Loss: 0.531569 (1.064775)]\n", 90 | "[Epoch 0/2] [Batch 31/33] [Loss: 0.926037 (1.060439)]\n", 91 | "[Epoch 0/2] [Batch 32/33] [Loss: 1.535817 (1.074845)] epoch 0 - loss : 1.07484 - val loss : 0.85285\n", 92 | "\n", 93 | "[Epoch 1/2] [Batch 0/33] [Loss: 1.176346 (1.176346)]\n", 94 | "[Epoch 1/2] [Batch 1/33] [Loss: 0.623049 (0.899698)]\n", 95 | "[Epoch 1/2] [Batch 2/33] [Loss: 0.824851 (0.874749)]\n", 96 | "[Epoch 1/2] [Batch 3/33] [Loss: 0.824296 (0.862136)]\n", 97 | "[Epoch 1/2] [Batch 4/33] [Loss: 0.625901 (0.814889)]\n", 98 | "[Epoch 1/2] [Batch 5/33] [Loss: 0.880468 (0.825819)]\n", 99 | "[Epoch 1/2] [Batch 6/33] [Loss: 1.043558 (0.856924)]\n", 100 | "[Epoch 1/2] [Batch 7/33] [Loss: 1.073645 (0.884014)]\n", 101 | "[Epoch 1/2] [Batch 8/33] [Loss: 0.870202 (0.882480)]\n", 102 | "[Epoch 1/2] [Batch 9/33] [Loss: 0.759421 (0.870174)]\n", 103 | "[Epoch 1/2] [Batch 10/33] [Loss: 1.290275 (0.908365)]\n", 104 | "[Epoch 1/2] [Batch 11/33] [Loss: 0.570421 (0.880203)]\n", 105 | "[Epoch 1/2] [Batch 12/33] [Loss: 1.300935 (0.912567)]\n", 106 | "[Epoch 1/2] [Batch 13/33] [Loss: 0.718843 (0.898729)]\n", 107 | "[Epoch 1/2] [Batch 14/33] [Loss: 1.168896 (0.916741)]\n", 108 | "[Epoch 1/2] [Batch 15/33] [Loss: 0.991722 (0.921427)]\n", 109 | "[Epoch 1/2] [Batch 16/33] [Loss: 0.657889 (0.905925)]\n", 110 | "[Epoch 1/2] [Batch 17/33] [Loss: 1.062768 (0.914638)]\n", 111 | "[Epoch 1/2] [Batch 18/33] [Loss: 0.786919 (0.907916)]\n", 112 | "[Epoch 1/2] [Batch 19/33] [Loss: 0.521236 (0.888582)]\n", 113 | "[Epoch 1/2] [Batch 20/33] [Loss: 1.239008 (0.905269)]\n", 114 | "[Epoch 1/2] [Batch 21/33] [Loss: 0.959929 (0.907754)]\n", 115 | "[Epoch 1/2] [Batch 22/33] [Loss: 0.521815 (0.890974)]\n", 116 | "[Epoch 1/2] [Batch 23/33] [Loss: 1.190181 (0.903441)]\n", 117 | "[Epoch 1/2] [Batch 24/33] [Loss: 0.955929 (0.905540)]\n", 118 | "[Epoch 1/2] [Batch 25/33] [Loss: 0.692246 (0.897337)]\n", 119 | "[Epoch 1/2] [Batch 26/33] [Loss: 0.705160 (0.890219)]\n", 120 | "[Epoch 1/2] [Batch 27/33] [Loss: 0.587765 (0.879417)]\n", 121 | "[Epoch 1/2] [Batch 28/33] [Loss: 0.875069 (0.879267)]\n", 122 | "[Epoch 1/2] [Batch 29/33] [Loss: 0.715596 (0.873811)]\n", 123 | "[Epoch 1/2] [Batch 30/33] [Loss: 0.776434 (0.870670)]\n", 124 | "[Epoch 1/2] [Batch 31/33] [Loss: 0.541104 (0.860371)]\n", 125 | "[Epoch 1/2] [Batch 32/33] [Loss: 1.301719 (0.873745)] epoch 1 - loss : 0.87375 - val loss : 0.78682\n" 126 | ] 127 | } 128 | ], 129 | "source": [ 130 | "!python train.py --num_epochs 2 --batch 2 --loss focalloss" 131 | ] 132 | }, 133 | { 134 | "cell_type": "markdown", 135 | "metadata": {}, 136 | "source": [ 137 | "## Validation" 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": 12, 143 | "metadata": {}, 144 | "outputs": [], 145 | "source": [ 146 | "# under development! (:D)" 147 | ] 148 | } 149 | ], 150 | "metadata": { 151 | "kernelspec": { 152 | "display_name": "Python 3", 153 | "language": "python", 154 | "name": "python3" 155 | }, 156 | "language_info": { 157 | "codemirror_mode": { 158 | "name": "ipython", 159 | "version": 3 160 | }, 161 | "file_extension": ".py", 162 | "mimetype": "text/x-python", 163 | "name": "python", 164 | "nbconvert_exporter": "python", 165 | "pygments_lexer": "ipython3", 166 | "version": "3.8.5" 167 | } 168 | }, 169 | "nbformat": 4, 170 | "nbformat_minor": 4 171 | } 172 | -------------------------------------------------------------------------------- /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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Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. For 483 | purposes of this definition, "control" includes the right to grant 484 | patent sublicenses in a manner consistent with the requirements of 485 | this License. 486 | 487 | Each contributor grants you a non-exclusive, worldwide, royalty-free 488 | patent license under the contributor's essential patent claims, to 489 | make, use, sell, offer for sale, import and otherwise run, modify and 490 | propagate the contents of its contributor version. 491 | 492 | In the following three paragraphs, a "patent license" is any express 493 | agreement or commitment, however denominated, not to enforce a patent 494 | (such as an express permission to practice a patent or covenant not to 495 | sue for patent infringement). To "grant" such a patent license to a 496 | party means to make such an agreement or commitment not to enforce a 497 | patent against the party. 498 | 499 | If you convey a covered work, knowingly relying on a patent license, 500 | and the Corresponding Source of the work is not available for anyone 501 | to copy, free of charge and under the terms of this License, through a 502 | publicly available network server or other readily accessible means, 503 | then you must either (1) cause the Corresponding Source to be so 504 | available, or (2) arrange to deprive yourself of the benefit of the 505 | patent license for this particular work, or (3) arrange, in a manner 506 | consistent with the requirements of this License, to extend the patent 507 | license to downstream recipients. "Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. 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 | --------------------------------------------------------------------------------