├── requirements.txt ├── README.md ├── src ├── img_utils.py ├── datasets.py └── model.py ├── .gitignore └── train.ipynb /requirements.txt: -------------------------------------------------------------------------------- 1 | -f https://download.pytorch.org/whl/torch_stable.html 2 | torch==1.3.1 3 | torchvision==0.4.2 4 | Cython==0.29.14 5 | tqdm==4.41.1 6 | ipywidgets==7.5.1 7 | notebook==6.4.1 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Siamese Neural Network for Object Co-Segmentation 2 | 3 | This project aims to generate predictive background/foreground masks from image pairs with similar objects. [CoSegNet's architecture](https://www.ijcai.org/proceedings/2019/0095.pdf) was used as the basis for this project. 4 | 5 | ## Getting Started 6 | 7 | First, create the "icoseg_data" directory at this project's root and then download/extract the [iCoseg dataset](http://chenlab.ece.cornell.edu/projects/touch-coseg/) (what I used to train, feel free to use another alternative dataset) into the folder. 8 | 9 | Next, install all the python requirements: 10 | 11 | ```bash 12 | pip install -r requirements.txt 13 | ``` 14 | 15 | To enable the tqdm progress bar in the Jupyter Notebook, please run the following command after dependencies are installed: 16 | 17 | ```bash 18 | jupyter nbextension enable --py widgetsnbextension --sys-prefix 19 | ``` 20 | 21 | If you wish to train the model and evaluate it, run: 22 | 23 | ```bash 24 | jupyter notebook 25 | ``` 26 | and open up the "train.ipynb" file. 27 | 28 | The model, datasets, and other utilities can be found in the src folder. -------------------------------------------------------------------------------- /src/img_utils.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import torchvision.transforms.functional as F 3 | import numpy as np 4 | 5 | 6 | class ResizePad(object): 7 | def __init__(self, size): 8 | self.size = (size, size) 9 | 10 | def __call__(self, image, target): 11 | # Padding/resizing image 12 | resize_image = square_pad(image).resize(self.size, Image.BILINEAR) 13 | resize_image = F.to_tensor(resize_image) 14 | 15 | # Padding/resizing masks 16 | masks = target["masks"] 17 | new_masks = [] 18 | for i in range(len(masks)): 19 | mask = target["masks"][i] 20 | pil_mask = Image.fromarray(mask.numpy(), "L") 21 | resize_mask = square_pad(pil_mask, image_type="L").resize(self.size, Image.NEAREST) 22 | resize_mask = np.array(resize_mask, dtype=np.uint8) 23 | resize_mask = np.where(resize_mask > 0, 1, 0) 24 | new_masks.append(F.to_tensor(resize_mask).squeeze()) 25 | 26 | target["masks"] = new_masks 27 | 28 | return resize_image, target 29 | 30 | 31 | def square_pad(img, image_type="RGB"): 32 | width, height = img.size 33 | size = max(width, height) 34 | square_img = Image.new(image_type, (size, size)) 35 | 36 | width_center = int((size - width) / 2) 37 | height_center = int((size - height) / 2) 38 | square_img.paste(img, (width_center, height_center)) 39 | 40 | return square_img 41 | -------------------------------------------------------------------------------- /src/datasets.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | import torch 4 | from torchvision.datasets import ImageFolder, DatasetFolder 5 | from torchvision.transforms import ToTensor, Normalize, Compose 6 | from torch.utils.data import Dataset 7 | 8 | from PIL import Image 9 | import numpy as np 10 | 11 | 12 | class iCoSegDataset(Dataset): 13 | def __init__(self, images_path, masks_path, image_size=224): 14 | self.images_path = images_path 15 | self.masks_path = masks_path 16 | self.image_size = (image_size, image_size) 17 | 18 | preprocess = Compose([ 19 | ToTensor() 20 | ]) 21 | 22 | self.images = DatasetFolder( 23 | root=images_path, 24 | loader=self.iCoSegImageLoader, 25 | extensions=("jpg"), 26 | transform=preprocess 27 | ) 28 | self.masks = DatasetFolder( 29 | root=masks_path, 30 | loader=self.iCoSegMaskLoader, 31 | extensions=("png"), 32 | ) 33 | 34 | self.length = len(self.images) 35 | 36 | def iCoSegImageLoader(self, path): 37 | image = Image.open(path) 38 | image = image.resize(self.image_size) 39 | #image = np.array(image, dtype=np.float32) 40 | 41 | return image 42 | 43 | def iCoSegMaskLoader(self, path): 44 | mask = Image.open(path) 45 | mask = mask.resize(self.image_size) 46 | mask = np.array(mask, dtype=np.uint8) 47 | 48 | return mask 49 | 50 | def __getitem__(self, index): 51 | 52 | if type(index) == torch.Tensor: 53 | index = index.item() 54 | 55 | image, image_label = self.images[index] 56 | mask, mask_label = self.masks[index] 57 | 58 | sample = { 59 | "image": image, 60 | "mask": mask, 61 | "label": image_label 62 | } 63 | 64 | return sample 65 | 66 | def __len__(self): 67 | return self.length 68 | 69 | 70 | 71 | 72 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 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 | 131 | # Project specific 132 | .vscode/ 133 | *_data/ 134 | pycocotools 135 | weights/ -------------------------------------------------------------------------------- /src/model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torchvision.models as models 4 | import torch.nn.functional as F 5 | 6 | 7 | class CoSegNet(nn.Module): 8 | def __init__(self, input_channels=3, output_channels=2): 9 | super(CoSegNet, self).__init__() 10 | 11 | self.input_channels = input_channels # 3 = RGB 12 | self.output_channels = output_channels # 2 = Foreground + Background 13 | 14 | # Encoder 15 | # Using pretrained VGG16 as the backbone 16 | self.encoder = models.vgg16_bn(pretrained=True).features 17 | 18 | # Decoder 19 | self.decoder3 = nn.Sequential( 20 | nn.Upsample(scale_factor=2, mode="nearest"), 21 | nn.ConvTranspose2d(512, 512, kernel_size=3, padding=1), 22 | nn.ConvTranspose2d(512, 512, kernel_size=3, padding=1), 23 | nn.ConvTranspose2d(512, 512, kernel_size=3, padding=1), 24 | nn.BatchNorm2d(512), 25 | nn.ReLU() 26 | ) 27 | self.decoder6 = nn.Sequential( 28 | nn.Upsample(scale_factor=2, mode="nearest"), 29 | nn.ConvTranspose2d(512, 512, kernel_size=3, padding=1), 30 | nn.ConvTranspose2d(512, 512, kernel_size=3, padding=1), 31 | nn.ConvTranspose2d(512, 512, kernel_size=3, padding=1), 32 | nn.BatchNorm2d(512), 33 | nn.ReLU() 34 | ) 35 | self.decoder9 = nn.Sequential( 36 | nn.Upsample(scale_factor=2, mode="nearest"), 37 | nn.ConvTranspose2d(512, 256, kernel_size=3, padding=1), 38 | nn.ConvTranspose2d(256, 256, kernel_size=3, padding=1), 39 | nn.ConvTranspose2d(256, 256, kernel_size=3, padding=1), 40 | nn.BatchNorm2d(256), 41 | nn.ReLU() 42 | ) 43 | self.decoder11 = nn.Sequential( 44 | nn.Upsample(scale_factor=2, mode="nearest"), 45 | nn.ConvTranspose2d(256, 128, kernel_size=3, padding=1), 46 | nn.ConvTranspose2d(128, 128, kernel_size=3, padding=1), 47 | nn.BatchNorm2d(128), 48 | nn.ReLU() 49 | ) 50 | self.decoder13 = nn.Sequential( 51 | nn.Upsample(scale_factor=2, mode="nearest"), 52 | nn.ConvTranspose2d(128, 64, kernel_size=3, padding=1), 53 | nn.ConvTranspose2d(64, 1, kernel_size=3, padding=1), 54 | nn.BatchNorm2d(1), 55 | nn.Sigmoid() 56 | ) 57 | 58 | # Metric Net 59 | self.metricnet = SiameseMetricNet() 60 | 61 | # Decision Net 62 | self.decisionnet = SiameseDecisionNet() 63 | 64 | 65 | def forward(self, imageA, imageB): 66 | # Getting VGG features for the first stage of the encoding 67 | featuresA = self.encoder(imageA) 68 | featuresB = self.encoder(imageB) 69 | 70 | # Decoding 71 | # 3 layers 72 | featuresA = self.decoder3(featuresA) 73 | featuresB = self.decoder3(featuresB) 74 | # 6 layers 75 | featuresA = self.decoder6(featuresA) 76 | featuresB = self.decoder6(featuresB) 77 | # 9 layers 78 | featuresA = self.decoder9(featuresA) 79 | featuresB = self.decoder9(featuresB) 80 | 81 | # Siamese Metric Net 82 | metric_featureA = self.metricnet(featuresA) 83 | metric_featureB = self.metricnet(featuresB) 84 | 85 | # Siamese Decision Net 86 | # Concatenating the two vectors to make a single prediction vector 87 | decision_vector = torch.cat((metric_featureA, metric_featureB), dim=0) 88 | decision = self.decisionnet(decision_vector) 89 | 90 | # 11 layers 91 | featuresA = self.decoder11(featuresA) 92 | featuresB = self.decoder11(featuresB) 93 | # 13 layers 94 | featuresA = self.decoder13(featuresA) 95 | featuresB = self.decoder13(featuresB) 96 | 97 | return featuresA, featuresB, metric_featureA, metric_featureB, decision 98 | 99 | 100 | class SiameseMetricNet(nn.Module): 101 | def __init__(self): 102 | super(SiameseMetricNet, self).__init__() 103 | self.metricnet = nn.Sequential( 104 | nn.Linear(256, 128), 105 | nn.ReLU(), 106 | nn.Linear(128, 64) 107 | ) 108 | 109 | 110 | def forward(self, feature): 111 | # Global Average Pooling (GAP) 112 | gap_feature_vector = F.avg_pool2d(feature, kernel_size=feature.size()[2:]) 113 | gap_feature_vector = gap_feature_vector.squeeze() 114 | 115 | return self.metricnet(gap_feature_vector) 116 | 117 | 118 | class SiameseDecisionNet(nn.Module): 119 | def __init__(self): 120 | super(SiameseDecisionNet, self).__init__() 121 | self.decisionnet = nn.Sequential( 122 | nn.Linear(128, 32), 123 | nn.ReLU(), 124 | nn.Linear(32, 1), 125 | nn.Sigmoid() 126 | ) 127 | 128 | def forward(self, vector): 129 | return self.decisionnet(vector) 130 | -------------------------------------------------------------------------------- /train.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Siamese Segmentation Training" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Imports" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "# Jupyter Notebook utils\n", 24 | "%load_ext autoreload\n", 25 | "%matplotlib inline" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 19, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "import time\n", 35 | "import os\n", 36 | "\n", 37 | "from tqdm.notebook import tqdm\n", 38 | "import numpy as np\n", 39 | "\n", 40 | "import torch\n", 41 | "import torch.nn as nn\n", 42 | "from torch.utils.data import DataLoader\n", 43 | "import torchvision.transforms.functional as F\n", 44 | "\n", 45 | "import matplotlib.pyplot as plt\n", 46 | "\n", 47 | "import src.img_utils as utils\n", 48 | "import src.model as siam_models\n", 49 | "from src.datasets import iCoSegDataset\n", 50 | "%autoreload 2" 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "metadata": {}, 56 | "source": [ 57 | "## Dataset Loading" 58 | ] 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "metadata": {}, 63 | "source": [ 64 | "### Constants" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 51, 70 | "metadata": {}, 71 | "outputs": [], 72 | "source": [ 73 | "BATCH_SIZE = 2 # TODO: Allow changing\n", 74 | "NUM_WORKERS = 1\n", 75 | "\n", 76 | "IMAGES_PATH = \"./icoseg_data/images_subset\"\n", 77 | "MASKS_PATH = \"./icoseg_data/ground_truth_subset\"\n", 78 | "\n", 79 | "VALIDATION_SPLIT = 0.2 # What % the validation set should be\n", 80 | "SHUFFLE = True" 81 | ] 82 | }, 83 | { 84 | "cell_type": "markdown", 85 | "metadata": {}, 86 | "source": [ 87 | "### Instantiation" 88 | ] 89 | }, 90 | { 91 | "cell_type": "code", 92 | "execution_count": 55, 93 | "metadata": {}, 94 | "outputs": [], 95 | "source": [ 96 | "dataset = iCoSegDataset(IMAGES_PATH, MASKS_PATH)\n", 97 | "\n", 98 | "# Test/train dataset split\n", 99 | "# Default is 80% training, 20% testing\n", 100 | "dataset_length = len(dataset)\n", 101 | "train_size = int((1.0 - VALIDATION_SPLIT) * dataset_length)\n", 102 | "test_size = dataset_length - train_size\n", 103 | "train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])\n", 104 | "\n", 105 | "train_loader = DataLoader(\n", 106 | " train_dataset,\n", 107 | " batch_size=BATCH_SIZE,\n", 108 | " num_workers=NUM_WORKERS,\n", 109 | " shuffle=SHUFFLE\n", 110 | ")\n", 111 | "test_loader = DataLoader(\n", 112 | " test_dataset,\n", 113 | " batch_size=BATCH_SIZE,\n", 114 | " num_workers=NUM_WORKERS,\n", 115 | " shuffle=SHUFFLE,\n", 116 | " drop_last=True\n", 117 | ")" 118 | ] 119 | }, 120 | { 121 | "cell_type": "markdown", 122 | "metadata": {}, 123 | "source": [ 124 | "## Training" 125 | ] 126 | }, 127 | { 128 | "cell_type": "markdown", 129 | "metadata": {}, 130 | "source": [ 131 | "### Constants" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": 95, 137 | "metadata": {}, 138 | "outputs": [], 139 | "source": [ 140 | "LEARNING_RATE = 0.00001\n", 141 | "MOMENTUM = 0.9\n", 142 | "WEIGHT_DECAY = 0.0004\n", 143 | "\n", 144 | "SAVE_WEIGHTS = True\n", 145 | "WEIGHTS_PATH = \"./weights\"\n", 146 | "\n", 147 | "MARGIN_ALPHA = 0.25 # Parameter used in loss calculation\n", 148 | "\n", 149 | "EPOCHS = 100" 150 | ] 151 | }, 152 | { 153 | "cell_type": "markdown", 154 | "metadata": {}, 155 | "source": [ 156 | "### Preparation" 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": 102, 162 | "metadata": {}, 163 | "outputs": [ 164 | { 165 | "name": "stdout", 166 | "output_type": "stream", 167 | "text": [ 168 | "Using CUDA 0 as device\n" 169 | ] 170 | } 171 | ], 172 | "source": [ 173 | "# Instantiation\n", 174 | "device = None\n", 175 | "if torch.cuda.is_available():\n", 176 | " print(\"Using CUDA 0 as device\")\n", 177 | " device = torch.device(\"cuda:0\")\n", 178 | "else:\n", 179 | " print(\"Using CPU as device\")\n", 180 | " device = torch.device(\"cpu\")\n", 181 | "\n", 182 | "prev_loss = float(\"inf\")\n", 183 | "model = siam_models.CoSegNet();\n", 184 | "\n", 185 | "optimizer = torch.optim.SGD(\n", 186 | " model.parameters(),\n", 187 | " lr=LEARNING_RATE,\n", 188 | " momentum=MOMENTUM,\n", 189 | " weight_decay=WEIGHT_DECAY\n", 190 | ")\n", 191 | "model.to(device);\n", 192 | "\n", 193 | "# Citerions\n", 194 | "criterion_bce = nn.BCELoss()\n", 195 | "# criterion_triplet = nn.TripletMarginLoss(margin=MARGIN_ALPHA)\n", 196 | "# Using Cosine Embedding Loss as a similar loss to triplet\n", 197 | "# due to prescence of pairs\n", 198 | "criterion_cel = nn.CosineEmbeddingLoss(margin=MARGIN_ALPHA)" 199 | ] 200 | }, 201 | { 202 | "cell_type": "markdown", 203 | "metadata": {}, 204 | "source": [ 205 | "### Training Loop" 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": 105, 211 | "metadata": {}, 212 | "outputs": [ 213 | { 214 | "data": { 215 | "application/vnd.jupyter.widget-view+json": { 216 | "model_id": "01f29908e30a465985ee9756b26c0852", 217 | "version_major": 2, 218 | "version_minor": 0 219 | }, 220 | "text/plain": [ 221 | "HBox(children=(FloatProgress(value=0.0, description='Epoch Progress: ', max=50.0, style=ProgressStyle(descript…" 222 | ] 223 | }, 224 | "metadata": {}, 225 | "output_type": "display_data" 226 | }, 227 | { 228 | "data": { 229 | "application/vnd.jupyter.widget-view+json": { 230 | "model_id": "860e261978874329946c379e79dfeb87", 231 | "version_major": 2, 232 | "version_minor": 0 233 | }, 234 | "text/plain": [ 235 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 236 | ] 237 | }, 238 | "metadata": {}, 239 | "output_type": "display_data" 240 | }, 241 | { 242 | "name": "stdout", 243 | "output_type": "stream", 244 | "text": [ 245 | "\n", 246 | "Total Loss: 1.6927924305200577\n", 247 | "Saving Model\n" 248 | ] 249 | }, 250 | { 251 | "data": { 252 | "application/vnd.jupyter.widget-view+json": { 253 | "model_id": "4da0402a2dd043adad9c0e9104d5ac1a", 254 | "version_major": 2, 255 | "version_minor": 0 256 | }, 257 | "text/plain": [ 258 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 259 | ] 260 | }, 261 | "metadata": {}, 262 | "output_type": "display_data" 263 | }, 264 | { 265 | "name": "stdout", 266 | "output_type": "stream", 267 | "text": [ 268 | "\n", 269 | "Total Loss: 1.6602893471717834\n", 270 | "Saving Model\n" 271 | ] 272 | }, 273 | { 274 | "data": { 275 | "application/vnd.jupyter.widget-view+json": { 276 | "model_id": "78ebe88ebeff4f83804dd2e6168c525b", 277 | "version_major": 2, 278 | "version_minor": 0 279 | }, 280 | "text/plain": [ 281 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 282 | ] 283 | }, 284 | "metadata": {}, 285 | "output_type": "display_data" 286 | }, 287 | { 288 | "name": "stdout", 289 | "output_type": "stream", 290 | "text": [ 291 | "\n", 292 | "Total Loss: 1.6928275525569916\n" 293 | ] 294 | }, 295 | { 296 | "data": { 297 | "application/vnd.jupyter.widget-view+json": { 298 | "model_id": "01cce4422d334d9483c0cb30a9687be1", 299 | "version_major": 2, 300 | "version_minor": 0 301 | }, 302 | "text/plain": [ 303 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 304 | ] 305 | }, 306 | "metadata": {}, 307 | "output_type": "display_data" 308 | }, 309 | { 310 | "name": "stdout", 311 | "output_type": "stream", 312 | "text": [ 313 | "\n", 314 | "Total Loss: 1.7734732776880264\n" 315 | ] 316 | }, 317 | { 318 | "data": { 319 | "application/vnd.jupyter.widget-view+json": { 320 | "model_id": "190de91f86434c4fa6b3bcab60c76aec", 321 | "version_major": 2, 322 | "version_minor": 0 323 | }, 324 | "text/plain": [ 325 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 326 | ] 327 | }, 328 | "metadata": {}, 329 | "output_type": "display_data" 330 | }, 331 | { 332 | "name": "stdout", 333 | "output_type": "stream", 334 | "text": [ 335 | "\n", 336 | "Total Loss: 1.870010793209076\n" 337 | ] 338 | }, 339 | { 340 | "data": { 341 | "application/vnd.jupyter.widget-view+json": { 342 | "model_id": "097adfc34a5246a49f59365d4ff5a8b6", 343 | "version_major": 2, 344 | "version_minor": 0 345 | }, 346 | "text/plain": [ 347 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 348 | ] 349 | }, 350 | "metadata": {}, 351 | "output_type": "display_data" 352 | }, 353 | { 354 | "name": "stdout", 355 | "output_type": "stream", 356 | "text": [ 357 | "\n", 358 | "Total Loss: 1.945670872926712\n" 359 | ] 360 | }, 361 | { 362 | "data": { 363 | "application/vnd.jupyter.widget-view+json": { 364 | "model_id": "036de6c71d8840af89e0eaa5b3390dfd", 365 | "version_major": 2, 366 | "version_minor": 0 367 | }, 368 | "text/plain": [ 369 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 370 | ] 371 | }, 372 | "metadata": {}, 373 | "output_type": "display_data" 374 | }, 375 | { 376 | "name": "stdout", 377 | "output_type": "stream", 378 | "text": [ 379 | "\n", 380 | "Total Loss: 1.951790526509285\n" 381 | ] 382 | }, 383 | { 384 | "data": { 385 | "application/vnd.jupyter.widget-view+json": { 386 | "model_id": "2cc60928dd554736976eeea3f6a23d1d", 387 | "version_major": 2, 388 | "version_minor": 0 389 | }, 390 | "text/plain": [ 391 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 392 | ] 393 | }, 394 | "metadata": {}, 395 | "output_type": "display_data" 396 | }, 397 | { 398 | "name": "stdout", 399 | "output_type": "stream", 400 | "text": [ 401 | "\n", 402 | "Total Loss: 1.8777571022510529\n" 403 | ] 404 | }, 405 | { 406 | "data": { 407 | "application/vnd.jupyter.widget-view+json": { 408 | "model_id": "fa34d29435dd4cdaa9cc0496bdc445e8", 409 | "version_major": 2, 410 | "version_minor": 0 411 | }, 412 | "text/plain": [ 413 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 414 | ] 415 | }, 416 | "metadata": {}, 417 | "output_type": "display_data" 418 | }, 419 | { 420 | "name": "stdout", 421 | "output_type": "stream", 422 | "text": [ 423 | "\n", 424 | "Total Loss: 1.7375290542840958\n" 425 | ] 426 | }, 427 | { 428 | "data": { 429 | "application/vnd.jupyter.widget-view+json": { 430 | "model_id": "110ed119eb7e493c9766c0fa4adc1bcc", 431 | "version_major": 2, 432 | "version_minor": 0 433 | }, 434 | "text/plain": [ 435 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 436 | ] 437 | }, 438 | "metadata": {}, 439 | "output_type": "display_data" 440 | }, 441 | { 442 | "name": "stdout", 443 | "output_type": "stream", 444 | "text": [ 445 | "\n", 446 | "Total Loss: 1.566610112786293\n", 447 | "Saving Model\n" 448 | ] 449 | }, 450 | { 451 | "data": { 452 | "application/vnd.jupyter.widget-view+json": { 453 | "model_id": "6306f0446eb944149c52fbc3fb92b70a", 454 | 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[ 1110 | "\n", 1111 | "Total Loss: 1.5289373993873596\n" 1112 | ] 1113 | }, 1114 | { 1115 | "data": { 1116 | "application/vnd.jupyter.widget-view+json": { 1117 | "model_id": "6b538dc687994c11b2f4ebaa498326a4", 1118 | "version_major": 2, 1119 | "version_minor": 0 1120 | }, 1121 | "text/plain": [ 1122 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 1123 | ] 1124 | }, 1125 | "metadata": {}, 1126 | "output_type": "display_data" 1127 | }, 1128 | { 1129 | "name": "stdout", 1130 | "output_type": "stream", 1131 | "text": [ 1132 | "\n", 1133 | "Total Loss: 1.5576649606227875\n" 1134 | ] 1135 | }, 1136 | { 1137 | "data": { 1138 | "application/vnd.jupyter.widget-view+json": { 1139 | "model_id": "2427645116d741c0915657f91fdb3ec5", 1140 | "version_major": 2, 1141 | "version_minor": 0 1142 | }, 1143 | "text/plain": [ 1144 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 1145 | ] 1146 | }, 1147 | "metadata": {}, 1148 | "output_type": "display_data" 1149 | }, 1150 | { 1151 | "name": "stdout", 1152 | "output_type": "stream", 1153 | "text": [ 1154 | "\n", 1155 | "Total Loss: 1.5467615649104118\n" 1156 | ] 1157 | }, 1158 | { 1159 | "data": { 1160 | "application/vnd.jupyter.widget-view+json": { 1161 | "model_id": "e5f3a75fc8ec4f2797bb0a8d8e132dda", 1162 | "version_major": 2, 1163 | "version_minor": 0 1164 | }, 1165 | "text/plain": [ 1166 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 1167 | ] 1168 | }, 1169 | "metadata": {}, 1170 | "output_type": "display_data" 1171 | }, 1172 | { 1173 | "name": "stdout", 1174 | "output_type": "stream", 1175 | "text": [ 1176 | "\n", 1177 | "Total Loss: 1.495627447962761\n" 1178 | ] 1179 | }, 1180 | { 1181 | "data": { 1182 | "application/vnd.jupyter.widget-view+json": { 1183 | "model_id": "a177cfcbf9ae497fbd8206e0a6c86c9c", 1184 | "version_major": 2, 1185 | "version_minor": 0 1186 | }, 1187 | "text/plain": [ 1188 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 1189 | ] 1190 | }, 1191 | "metadata": {}, 1192 | "output_type": "display_data" 1193 | }, 1194 | { 1195 | "name": "stdout", 1196 | "output_type": "stream", 1197 | "text": [ 1198 | "\n", 1199 | "Total Loss: 1.4099234193563461\n" 1200 | ] 1201 | }, 1202 | { 1203 | "data": { 1204 | "application/vnd.jupyter.widget-view+json": { 1205 | "model_id": "c3d6dd4755ac4bfb9dc2e5b27665d834", 1206 | "version_major": 2, 1207 | "version_minor": 0 1208 | }, 1209 | "text/plain": [ 1210 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 1211 | ] 1212 | }, 1213 | "metadata": {}, 1214 | "output_type": "display_data" 1215 | }, 1216 | { 1217 | "name": "stdout", 1218 | "output_type": "stream", 1219 | "text": [ 1220 | "\n", 1221 | "Total Loss: 1.2934547439217567\n" 1222 | ] 1223 | }, 1224 | { 1225 | "data": { 1226 | "application/vnd.jupyter.widget-view+json": { 1227 | "model_id": "f38a092f8721408fa6ff68f7b93d1a53", 1228 | "version_major": 2, 1229 | "version_minor": 0 1230 | }, 1231 | "text/plain": [ 1232 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 1233 | ] 1234 | }, 1235 | "metadata": {}, 1236 | "output_type": "display_data" 1237 | }, 1238 | { 1239 | "name": "stdout", 1240 | "output_type": "stream", 1241 | "text": [ 1242 | "\n", 1243 | "Total Loss: 1.1789302676916122\n", 1244 | "Saving Model\n" 1245 | ] 1246 | }, 1247 | { 1248 | "data": { 1249 | "application/vnd.jupyter.widget-view+json": { 1250 | "model_id": "bae58ab36ebb41ad95f416bc614f9e34", 1251 | "version_major": 2, 1252 | "version_minor": 0 1253 | }, 1254 | "text/plain": [ 1255 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 1256 | ] 1257 | }, 1258 | "metadata": {}, 1259 | "output_type": "display_data" 1260 | }, 1261 | { 1262 | "name": "stdout", 1263 | "output_type": "stream", 1264 | "text": [ 1265 | "\n", 1266 | "Total Loss: 1.0810576602816582\n", 1267 | "Saving Model\n" 1268 | ] 1269 | }, 1270 | { 1271 | "data": { 1272 | "application/vnd.jupyter.widget-view+json": { 1273 | "model_id": "5d31379849a342579b345e37c880344c", 1274 | "version_major": 2, 1275 | "version_minor": 0 1276 | }, 1277 | "text/plain": [ 1278 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 1279 | ] 1280 | }, 1281 | "metadata": {}, 1282 | "output_type": "display_data" 1283 | }, 1284 | { 1285 | "name": "stdout", 1286 | "output_type": "stream", 1287 | "text": [ 1288 | "\n", 1289 | "Total Loss: 1.0233119428157806\n", 1290 | "Saving Model\n" 1291 | ] 1292 | }, 1293 | { 1294 | "data": { 1295 | "application/vnd.jupyter.widget-view+json": { 1296 | "model_id": "a49518b0289245f6b7e26079a50bbe70", 1297 | "version_major": 2, 1298 | "version_minor": 0 1299 | }, 1300 | "text/plain": [ 1301 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 1302 | ] 1303 | }, 1304 | "metadata": {}, 1305 | "output_type": "display_data" 1306 | }, 1307 | { 1308 | "name": "stdout", 1309 | "output_type": "stream", 1310 | "text": [ 1311 | "\n", 1312 | "Total Loss: 1.0005386620759964\n", 1313 | "Saving Model\n" 1314 | ] 1315 | }, 1316 | { 1317 | "data": { 1318 | "application/vnd.jupyter.widget-view+json": { 1319 | "model_id": "f2ce1ca6019845aaae5d46b3ee4455ba", 1320 | "version_major": 2, 1321 | "version_minor": 0 1322 | }, 1323 | "text/plain": [ 1324 | "HBox(children=(FloatProgress(value=0.0, description='Batch Progress: ', max=10.0, style=ProgressStyle(descript…" 1325 | ] 1326 | }, 1327 | "metadata": {}, 1328 | "output_type": "display_data" 1329 | }, 1330 | { 1331 | "name": "stdout", 1332 | "output_type": "stream", 1333 | "text": [ 1334 | "\n", 1335 | "Total Loss: 1.0272977575659752\n", 1336 | "\n" 1337 | ] 1338 | } 1339 | ], 1340 | "source": [ 1341 | "for epoch in tqdm(range(EPOCHS), desc=\"Epoch Progress: \"):\n", 1342 | " # Losses and Loss Weights\n", 1343 | " # Loss_final = W1*L1 + W2*L2 + W3*L3\n", 1344 | " total_loss = 0\n", 1345 | " loss_final = 0\n", 1346 | " loss1A = 0 # Pixel-wise binary cross entropy\n", 1347 | " loss1B = 0\n", 1348 | " weight1 = 0\n", 1349 | " loss2 = 0 # Triplet loss\n", 1350 | " weight2 = 0\n", 1351 | " loss3 = 0 # Cross Entropy\n", 1352 | " weight3 = 0\n", 1353 | " \n", 1354 | " #Statistics\n", 1355 | " predictions_correct = 0\n", 1356 | " predictions_total = 0\n", 1357 | " background_percent_correct = 0\n", 1358 | " \n", 1359 | " loader = tqdm(enumerate(train_loader), desc=\"Batch Progress: \", total=(train_size/BATCH_SIZE))\n", 1360 | " \n", 1361 | " model.train()\n", 1362 | " \n", 1363 | " time_start = time.time()\n", 1364 | " \n", 1365 | " for i, sample in loader:\n", 1366 | " # Image Tensors\n", 1367 | " imageA = sample[\"image\"][0]\n", 1368 | " imageB = sample[\"image\"][1]\n", 1369 | " # Mask Tensors\n", 1370 | " maskA = sample[\"mask\"][0].float().to(device)\n", 1371 | " maskB = sample[\"mask\"][1].float().to(device)\n", 1372 | " # Labels\n", 1373 | " labelA = sample[\"label\"][0]\n", 1374 | " labelB = sample[\"label\"][1]\n", 1375 | " \n", 1376 | " # Normalizing images before sending to the model\n", 1377 | " # Forming into batch-shape for processing\n", 1378 | " norm_imageA = F.normalize(imageA,\n", 1379 | " mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]).unsqueeze(0)\n", 1380 | " norm_imageB = F.normalize(imageB, \n", 1381 | " mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]).unsqueeze(0)\n", 1382 | " \n", 1383 | " norm_imageA = norm_imageA.to(device)\n", 1384 | " norm_imageB = norm_imageB.to(device)\n", 1385 | " \n", 1386 | " # Network Run\n", 1387 | " pmapA, pmapB, vectorA, vectorB, decision = model(norm_imageA, norm_imageB)\n", 1388 | " \n", 1389 | " # Obtaining co-segmentation masks\n", 1390 | " # based off of the decision by the decision net\n", 1391 | " # During testing, the decision net's value\n", 1392 | " # would be thresheld before multiplying\n", 1393 | " pred_maskA = pmapA*decision\n", 1394 | " pred_maskB = pmapB*decision\n", 1395 | " \n", 1396 | " # Loss Calculations/Evaluations\n", 1397 | " # Configuring loss weights depending on sample\n", 1398 | " # Also deciding if we produce a groundtruth mask\n", 1399 | " truth = None\n", 1400 | " pairwise = None\n", 1401 | " if labelA == labelB: # Positive Sample found\n", 1402 | " # Weighting loss evenly\n", 1403 | " w1=w2=w3 = 0.33\n", 1404 | " \n", 1405 | " truth = 1\n", 1406 | " pairwise = 1\n", 1407 | " else: # Negative sample\n", 1408 | " # Prevent Loss1 from backpropogating\n", 1409 | " w1 = 0\n", 1410 | " w2=w3 = 0.5\n", 1411 | " \n", 1412 | " # Create a null mask from the groundtruths\n", 1413 | " maskA = maskA * 0\n", 1414 | " maskB = maskB * 0\n", 1415 | " \n", 1416 | " truth = 0\n", 1417 | " pairwise = -1\n", 1418 | " \n", 1419 | " # Loss 1\n", 1420 | " # Pixel-wise Binary Cross Entropy Loss\n", 1421 | " loss1A = criterion_bce(pred_maskA, maskA.unsqueeze(0).unsqueeze(0))\n", 1422 | " loss1B = criterion_bce(pred_maskB, maskB.unsqueeze(0).unsqueeze(0))\n", 1423 | " \n", 1424 | " # Loss 2\n", 1425 | " # Standard Triplet Loss with Margin\n", 1426 | " pairwise = torch.tensor(pairwise).to(device)\n", 1427 | " loss2 = criterion_cel(vectorA.unsqueeze(0), vectorB.unsqueeze(0), pairwise)\n", 1428 | " \n", 1429 | " # Loss 3\n", 1430 | " # Binary Cross Entropy Loss\n", 1431 | " truth = torch.tensor(truth).float().unsqueeze(0).to(device)\n", 1432 | " loss3 = criterion_bce(decision, truth)\n", 1433 | " \n", 1434 | " loss_final = w1*(loss1A + loss1B) + w2*loss2 + w3*loss3\n", 1435 | " \n", 1436 | " loss_final.backward()\n", 1437 | " optimizer.step()\n", 1438 | " \n", 1439 | " total_loss = total_loss + loss_final.item()\n", 1440 | " \n", 1441 | " print(\"Total Loss: \" + str(total_loss))\n", 1442 | " \n", 1443 | " if total_loss < prev_loss and SAVE_WEIGHTS:\n", 1444 | " # Check for dir, create if it doesn't exist\n", 1445 | " if not os.path.exists(WEIGHTS_PATH):\n", 1446 | " os.makedirs(WEIGHTS_PATH)\n", 1447 | " \n", 1448 | " prev_loss = total_loss\n", 1449 | " print(\"Saving Model\")\n", 1450 | " torch.save(model.state_dict(), os.path.join(WEIGHTS_PATH, \"CoSegNet_VGG16.path\"))\n", 1451 | " \n", 1452 | " \n", 1453 | " time_total = round(time.time() - time_start, 2)" 1454 | ] 1455 | }, 1456 | { 1457 | "cell_type": "markdown", 1458 | "metadata": {}, 1459 | "source": [ 1460 | "## Testing" 1461 | ] 1462 | }, 1463 | { 1464 | "cell_type": "code", 1465 | "execution_count": 112, 1466 | "metadata": {}, 1467 | "outputs": [ 1468 | { 1469 | "name": "stdout", 1470 | "output_type": "stream", 1471 | "text": [ 1472 | "tensor([1.], device='cuda:0', grad_fn=)\n" 1473 | ] 1474 | }, 1475 | { 1476 | 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\n", 1478 | "text/plain": [ 1479 | "
" 1480 | ] 1481 | }, 1482 | "metadata": { 1483 | "needs_background": "light" 1484 | }, 1485 | "output_type": "display_data" 1486 | } 1487 | ], 1488 | "source": [ 1489 | "sample = next(iter(test_loader))\n", 1490 | "\n", 1491 | "imageA = sample[\"image\"][0].unsqueeze(0).to(device)\n", 1492 | "imageB = sample[\"image\"][1].unsqueeze(0).to(device)\n", 1493 | "\n", 1494 | "model.eval()\n", 1495 | "pmapA, pmapB, vectorA, vectorB, decision = model(imageA, imageB)\n", 1496 | "\n", 1497 | "mapA = F.to_pil_image(pmapA.detach().cpu().squeeze())\n", 1498 | "\n", 1499 | "imageA_cpu = F.to_pil_image(imageA.cpu().squeeze())\n", 1500 | "plt.imshow(imageA_cpu)\n", 1501 | "plt.imshow(mapA, cmap=\"jet\", alpha=0.4)\n", 1502 | "plt.plot()\n", 1503 | "\n", 1504 | "print(decision)\n" 1505 | ] 1506 | } 1507 | ], 1508 | "metadata": { 1509 | "kernelspec": { 1510 | "display_name": "Python 3", 1511 | "language": "python", 1512 | "name": "python3" 1513 | }, 1514 | "language_info": { 1515 | "codemirror_mode": { 1516 | "name": "ipython", 1517 | "version": 3 1518 | }, 1519 | "file_extension": ".py", 1520 | "mimetype": "text/x-python", 1521 | "name": "python", 1522 | "nbconvert_exporter": "python", 1523 | "pygments_lexer": "ipython3", 1524 | "version": "3.7.4" 1525 | } 1526 | }, 1527 | "nbformat": 4, 1528 | "nbformat_minor": 2 1529 | } 1530 | --------------------------------------------------------------------------------