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diff --git a/official/cv/unet2d/2dunet-pytorch/README.md b/official/cv/unet2d/2dunet-pytorch/README.md
new file mode 100644
index 0000000..cb4d4c9
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/README.md
@@ -0,0 +1,189 @@
+# U-Net: Semantic segmentation with PyTorch
+
+
+
+
+
+![input and output for a random image in the test dataset](https://i.imgur.com/GD8FcB7.png)
+
+
+Customized implementation of the [U-Net](https://arxiv.org/abs/1505.04597) in PyTorch for Kaggle's [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge) from high definition images.
+
+- [Quick start](#quick-start)
+ - [Without Docker](#without-docker)
+ - [With Docker](#with-docker)
+- [Description](#description)
+- [Usage](#usage)
+ - [Docker](#docker)
+ - [Training](#training)
+ - [Prediction](#prediction)
+- [Weights & Biases](#weights--biases)
+- [Pretrained model](#pretrained-model)
+- [Data](#data)
+
+## Quick start
+
+### Without Docker
+
+1. [Install CUDA](https://developer.nvidia.com/cuda-downloads)
+
+2. [Install PyTorch 1.13 or later](https://pytorch.org/get-started/locally/)
+
+3. Install dependencies
+```bash
+pip install -r requirements.txt
+```
+
+4. Download the data and run training:
+```bash
+bash scripts/download_data.sh
+python train.py --amp
+```
+
+### With Docker
+
+1. [Install Docker 19.03 or later:](https://docs.docker.com/get-docker/)
+```bash
+curl https://get.docker.com | sh && sudo systemctl --now enable docker
+```
+2. [Install the NVIDIA container toolkit:](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
+```bash
+distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
+ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
+ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
+sudo apt-get update
+sudo apt-get install -y nvidia-docker2
+sudo systemctl restart docker
+```
+3. [Download and run the image:](https://hub.docker.com/repository/docker/milesial/unet)
+```bash
+sudo docker run --rm --shm-size=8g --ulimit memlock=-1 --gpus all -it milesial/unet
+```
+
+4. Download the data and run training:
+```bash
+bash scripts/download_data.sh
+python train.py --amp
+```
+
+## Description
+This model was trained from scratch with 5k images and scored a [Dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) of 0.988423 on over 100k test images.
+
+It can be easily used for multiclass segmentation, portrait segmentation, medical segmentation, ...
+
+
+## Usage
+**Note : Use Python 3.6 or newer**
+
+### Docker
+
+A docker image containing the code and the dependencies is available on [DockerHub](https://hub.docker.com/repository/docker/milesial/unet).
+You can download and jump in the container with ([docker >=19.03](https://docs.docker.com/get-docker/)):
+
+```console
+docker run -it --rm --shm-size=8g --ulimit memlock=-1 --gpus all milesial/unet
+```
+
+
+### Training
+
+```console
+> python train.py -h
+usage: train.py [-h] [--epochs E] [--batch-size B] [--learning-rate LR]
+ [--load LOAD] [--scale SCALE] [--validation VAL] [--amp]
+
+Train the UNet on images and target masks
+
+optional arguments:
+ -h, --help show this help message and exit
+ --epochs E, -e E Number of epochs
+ --batch-size B, -b B Batch size
+ --learning-rate LR, -l LR
+ Learning rate
+ --load LOAD, -f LOAD Load model from a .pth file
+ --scale SCALE, -s SCALE
+ Downscaling factor of the images
+ --validation VAL, -v VAL
+ Percent of the data that is used as validation (0-100)
+ --amp Use mixed precision
+```
+
+By default, the `scale` is 0.5, so if you wish to obtain better results (but use more memory), set it to 1.
+
+Automatic mixed precision is also available with the `--amp` flag. [Mixed precision](https://arxiv.org/abs/1710.03740) allows the model to use less memory and to be faster on recent GPUs by using FP16 arithmetic. Enabling AMP is recommended.
+
+
+### Prediction
+
+After training your model and saving it to `MODEL.pth`, you can easily test the output masks on your images via the CLI.
+
+To predict a single image and save it:
+
+`python predict.py -i image.jpg -o output.jpg`
+
+To predict a multiple images and show them without saving them:
+
+`python predict.py -i image1.jpg image2.jpg --viz --no-save`
+
+```console
+> python predict.py -h
+usage: predict.py [-h] [--model FILE] --input INPUT [INPUT ...]
+ [--output INPUT [INPUT ...]] [--viz] [--no-save]
+ [--mask-threshold MASK_THRESHOLD] [--scale SCALE]
+
+Predict masks from input images
+
+optional arguments:
+ -h, --help show this help message and exit
+ --model FILE, -m FILE
+ Specify the file in which the model is stored
+ --input INPUT [INPUT ...], -i INPUT [INPUT ...]
+ Filenames of input images
+ --output INPUT [INPUT ...], -o INPUT [INPUT ...]
+ Filenames of output images
+ --viz, -v Visualize the images as they are processed
+ --no-save, -n Do not save the output masks
+ --mask-threshold MASK_THRESHOLD, -t MASK_THRESHOLD
+ Minimum probability value to consider a mask pixel white
+ --scale SCALE, -s SCALE
+ Scale factor for the input images
+```
+You can specify which model file to use with `--model MODEL.pth`.
+
+## Weights & Biases
+
+The training progress can be visualized in real-time using [Weights & Biases](https://wandb.ai/). Loss curves, validation curves, weights and gradient histograms, as well as predicted masks are logged to the platform.
+
+When launching a training, a link will be printed in the console. Click on it to go to your dashboard. If you have an existing W&B account, you can link it
+ by setting the `WANDB_API_KEY` environment variable. If not, it will create an anonymous run which is automatically deleted after 7 days.
+
+
+## Pretrained model
+A [pretrained model](https://github.com/milesial/Pytorch-UNet/releases/tag/v3.0) is available for the Carvana dataset. It can also be loaded from torch.hub:
+
+```python
+net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana', pretrained=True, scale=0.5)
+```
+Available scales are 0.5 and 1.0.
+
+## Data
+The Carvana data is available on the [Kaggle website](https://www.kaggle.com/c/carvana-image-masking-challenge/data).
+
+You can also download it using the helper script:
+
+```
+bash scripts/download_data.sh
+```
+
+The input images and target masks should be in the `data/imgs` and `data/masks` folders respectively (note that the `imgs` and `masks` folder should not contain any sub-folder or any other files, due to the greedy data-loader). For Carvana, images are RGB and masks are black and white.
+
+You can use your own dataset as long as you make sure it is loaded properly in `utils/data_loading.py`.
+
+
+---
+
+Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox:
+
+[U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
+
+![network architecture](https://i.imgur.com/jeDVpqF.png)
diff --git a/official/cv/unet2d/2dunet-pytorch/evaluate.py b/official/cv/unet2d/2dunet-pytorch/evaluate.py
new file mode 100644
index 0000000..9a4e3ba
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/evaluate.py
@@ -0,0 +1,40 @@
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+
+from utils.dice_score import multiclass_dice_coeff, dice_coeff
+
+
+@torch.inference_mode()
+def evaluate(net, dataloader, device, amp):
+ net.eval()
+ num_val_batches = len(dataloader)
+ dice_score = 0
+
+ # iterate over the validation set
+ with torch.autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp):
+ for batch in tqdm(dataloader, total=num_val_batches, desc='Validation round', unit='batch', leave=False):
+ image, mask_true = batch['image'], batch['mask']
+
+ # move images and labels to correct device and type
+ image = image.to(device=device, dtype=torch.float32, memory_format=torch.channels_last)
+ mask_true = mask_true.to(device=device, dtype=torch.long)
+
+ # predict the mask
+ mask_pred = net(image)
+
+ if net.n_classes == 1:
+ assert mask_true.min() >= 0 and mask_true.max() <= 1, 'True mask indices should be in [0, 1]'
+ mask_pred = (F.sigmoid(mask_pred) > 0.5).float()
+ # compute the Dice score
+ dice_score += dice_coeff(mask_pred, mask_true, reduce_batch_first=False)
+ else:
+ assert mask_true.min() >= 0 and mask_true.max() < net.n_classes, 'True mask indices should be in [0, n_classes['
+ # convert to one-hot format
+ mask_true = F.one_hot(mask_true, net.n_classes).permute(0, 3, 1, 2).float()
+ mask_pred = F.one_hot(mask_pred.argmax(dim=1), net.n_classes).permute(0, 3, 1, 2).float()
+ # compute the Dice score, ignoring background
+ dice_score += multiclass_dice_coeff(mask_pred[:, 1:], mask_true[:, 1:], reduce_batch_first=False)
+
+ net.train()
+ return dice_score / max(num_val_batches, 1)
diff --git a/official/cv/unet2d/2dunet-pytorch/hubconf.py b/official/cv/unet2d/2dunet-pytorch/hubconf.py
new file mode 100644
index 0000000..46433eb
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/hubconf.py
@@ -0,0 +1,23 @@
+import torch
+from unet import UNet as _UNet
+
+def unet_carvana(pretrained=False, scale=0.5):
+ """
+ UNet model trained on the Carvana dataset ( https://www.kaggle.com/c/carvana-image-masking-challenge/data ).
+ Set the scale to 0.5 (50%) when predicting.
+ """
+ net = _UNet(n_channels=3, n_classes=2, bilinear=False)
+ if pretrained:
+ if scale == 0.5:
+ checkpoint = 'https://github.com/milesial/Pytorch-UNet/releases/download/v3.0/unet_carvana_scale0.5_epoch2.pth'
+ elif scale == 1.0:
+ checkpoint = 'https://github.com/milesial/Pytorch-UNet/releases/download/v3.0/unet_carvana_scale1.0_epoch2.pth'
+ else:
+ raise RuntimeError('Only 0.5 and 1.0 scales are available')
+ state_dict = torch.hub.load_state_dict_from_url(checkpoint, progress=True)
+ if 'mask_values' in state_dict:
+ state_dict.pop('mask_values')
+ net.load_state_dict(state_dict)
+
+ return net
+
diff --git a/official/cv/unet2d/2dunet-pytorch/predict.py b/official/cv/unet2d/2dunet-pytorch/predict.py
new file mode 100644
index 0000000..b74c460
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/predict.py
@@ -0,0 +1,117 @@
+import argparse
+import logging
+import os
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from torchvision import transforms
+
+from utils.data_loading import BasicDataset
+from unet import UNet
+from utils.utils import plot_img_and_mask
+
+def predict_img(net,
+ full_img,
+ device,
+ scale_factor=1,
+ out_threshold=0.5):
+ net.eval()
+ img = torch.from_numpy(BasicDataset.preprocess(None, full_img, scale_factor, is_mask=False))
+ img = img.unsqueeze(0)
+ img = img.to(device=device, dtype=torch.float32)
+
+ with torch.no_grad():
+ output = net(img).cpu()
+ output = F.interpolate(output, (full_img.size[1], full_img.size[0]), mode='bilinear')
+ if net.n_classes > 1:
+ mask = output.argmax(dim=1)
+ else:
+ mask = torch.sigmoid(output) > out_threshold
+
+ return mask[0].long().squeeze().numpy()
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description='Predict masks from input images')
+ parser.add_argument('--model', '-m', default='MODEL.pth', metavar='FILE',
+ help='Specify the file in which the model is stored')
+ parser.add_argument('--input', '-i', metavar='INPUT', nargs='+', help='Filenames of input images', required=True)
+ parser.add_argument('--output', '-o', metavar='OUTPUT', nargs='+', help='Filenames of output images')
+ parser.add_argument('--viz', '-v', action='store_true',
+ help='Visualize the images as they are processed')
+ parser.add_argument('--no-save', '-n', action='store_true', help='Do not save the output masks')
+ parser.add_argument('--mask-threshold', '-t', type=float, default=0.5,
+ help='Minimum probability value to consider a mask pixel white')
+ parser.add_argument('--scale', '-s', type=float, default=0.5,
+ help='Scale factor for the input images')
+ parser.add_argument('--bilinear', action='store_true', default=False, help='Use bilinear upsampling')
+ parser.add_argument('--classes', '-c', type=int, default=2, help='Number of classes')
+
+ return parser.parse_args()
+
+
+def get_output_filenames(args):
+ def _generate_name(fn):
+ return f'{os.path.splitext(fn)[0]}_OUT.png'
+
+ return args.output or list(map(_generate_name, args.input))
+
+
+def mask_to_image(mask: np.ndarray, mask_values):
+ if isinstance(mask_values[0], list):
+ out = np.zeros((mask.shape[-2], mask.shape[-1], len(mask_values[0])), dtype=np.uint8)
+ elif mask_values == [0, 1]:
+ out = np.zeros((mask.shape[-2], mask.shape[-1]), dtype=bool)
+ else:
+ out = np.zeros((mask.shape[-2], mask.shape[-1]), dtype=np.uint8)
+
+ if mask.ndim == 3:
+ mask = np.argmax(mask, axis=0)
+
+ for i, v in enumerate(mask_values):
+ out[mask == i] = v
+
+ return Image.fromarray(out)
+
+
+if __name__ == '__main__':
+ args = get_args()
+ logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+
+ in_files = args.input
+ out_files = get_output_filenames(args)
+
+ net = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear)
+
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ logging.info(f'Loading model {args.model}')
+ logging.info(f'Using device {device}')
+
+ net.to(device=device)
+ state_dict = torch.load(args.model, map_location=device)
+ mask_values = state_dict.pop('mask_values', [0, 1])
+ net.load_state_dict(state_dict)
+
+ logging.info('Model loaded!')
+
+ for i, filename in enumerate(in_files):
+ logging.info(f'Predicting image {filename} ...')
+ img = Image.open(filename)
+
+ mask = predict_img(net=net,
+ full_img=img,
+ scale_factor=args.scale,
+ out_threshold=args.mask_threshold,
+ device=device)
+
+ if not args.no_save:
+ out_filename = out_files[i]
+ result = mask_to_image(mask, mask_values)
+ result.save(out_filename)
+ logging.info(f'Mask saved to {out_filename}')
+
+ if args.viz:
+ logging.info(f'Visualizing results for image {filename}, close to continue...')
+ plot_img_and_mask(img, mask)
diff --git a/official/cv/unet2d/2dunet-pytorch/requirements.txt b/official/cv/unet2d/2dunet-pytorch/requirements.txt
new file mode 100644
index 0000000..256fb59
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/requirements.txt
@@ -0,0 +1,5 @@
+matplotlib==3.6.2
+numpy==1.23.5
+Pillow==9.3.0
+tqdm==4.64.1
+wandb==0.13.5
diff --git a/official/cv/unet2d/2dunet-pytorch/scripts/download_data.sh b/official/cv/unet2d/2dunet-pytorch/scripts/download_data.sh
new file mode 100644
index 0000000..a151ede
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/scripts/download_data.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+
+if [[ ! -f ~/.kaggle/kaggle.json ]]; then
+ echo -n "Kaggle username: "
+ read USERNAME
+ echo
+ echo -n "Kaggle API key: "
+ read APIKEY
+
+ mkdir -p ~/.kaggle
+ echo "{\"username\":\"$USERNAME\",\"key\":\"$APIKEY\"}" > ~/.kaggle/kaggle.json
+ chmod 600 ~/.kaggle/kaggle.json
+fi
+
+pip install kaggle --upgrade
+
+kaggle competitions download -c carvana-image-masking-challenge -f train_hq.zip
+unzip train_hq.zip
+mv train_hq/* data/imgs/
+rm -d train_hq
+rm train_hq.zip
+
+kaggle competitions download -c carvana-image-masking-challenge -f train_masks.zip
+unzip train_masks.zip
+mv train_masks/* data/masks/
+rm -d train_masks
+rm train_masks.zip
diff --git a/official/cv/unet2d/2dunet-pytorch/train.py b/official/cv/unet2d/2dunet-pytorch/train.py
new file mode 100644
index 0000000..8748b23
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/train.py
@@ -0,0 +1,238 @@
+import argparse
+import logging
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '1'
+import random
+import sys
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+import torchvision.transforms.functional as TF
+from pathlib import Path
+from torch import optim
+from torch.utils.data import DataLoader, random_split
+from tqdm import tqdm
+
+import wandb
+from evaluate import evaluate
+from unet import UNet
+from utils.data_loading import BasicDataset, CarvanaDataset
+from utils.dice_score import dice_loss
+
+dir_img = Path('./data/imgs/')
+dir_mask = Path('./data/masks/')
+dir_checkpoint = Path('./checkpoints/')
+
+
+def train_model(
+ model,
+ device,
+ epochs: int = 5,
+ batch_size: int = 1,
+ learning_rate: float = 1e-5,
+ val_percent: float = 0.1,
+ save_checkpoint: bool = True,
+ img_scale: float = 0.5,
+ amp: bool = False,
+ weight_decay: float = 1e-8,
+ momentum: float = 0.999,
+ gradient_clipping: float = 1.0,
+):
+ # 1. Create dataset
+ try:
+ dataset = CarvanaDataset(dir_img, dir_mask, img_scale)
+ except (AssertionError, RuntimeError, IndexError):
+ dataset = BasicDataset(dir_img, dir_mask, img_scale)
+
+ # 2. Split into train / validation partitions
+ n_val = int(len(dataset) * val_percent)
+ n_train = len(dataset) - n_val
+ train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0))
+
+ # 3. Create data loaders
+ loader_args = dict(batch_size=batch_size, num_workers=os.cpu_count(), pin_memory=True)
+ train_loader = DataLoader(train_set, shuffle=True, **loader_args)
+ val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args)
+
+ # (Initialize logging)
+ experiment = wandb.init(project='U-Net', resume='allow', anonymous='must')
+ experiment.config.update(
+ dict(epochs=epochs, batch_size=batch_size, learning_rate=learning_rate,
+ val_percent=val_percent, save_checkpoint=save_checkpoint, img_scale=img_scale, amp=amp)
+ )
+
+ logging.info(f'''Starting training:
+ Epochs: {epochs}
+ Batch size: {batch_size}
+ Learning rate: {learning_rate}
+ Training size: {n_train}
+ Validation size: {n_val}
+ Checkpoints: {save_checkpoint}
+ Device: {device.type}
+ Images scaling: {img_scale}
+ Mixed Precision: {amp}
+ ''')
+
+ # 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for AMP
+ optimizer = optim.RMSprop(model.parameters(),
+ lr=learning_rate, weight_decay=weight_decay, momentum=momentum)
+ scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'max', patience=5) # goal: maximize Dice score
+ grad_scaler = torch.cuda.amp.GradScaler(enabled=amp)
+ criterion = nn.CrossEntropyLoss() if model.n_classes > 1 else nn.BCEWithLogitsLoss()
+ global_step = 0
+
+ # 5. Begin training
+ for epoch in range(1, epochs + 1):
+ model.train()
+ epoch_loss = 0
+ with tqdm(total=n_train, desc=f'Epoch {epoch}/{epochs}', unit='img') as pbar:
+ for batch in train_loader:
+ images, true_masks = batch['image'], batch['mask']
+
+ assert images.shape[1] == model.n_channels, \
+ f'Network has been defined with {model.n_channels} input channels, ' \
+ f'but loaded images have {images.shape[1]} channels. Please check that ' \
+ 'the images are loaded correctly.'
+
+ images = images.to(device=device, dtype=torch.float32, memory_format=torch.channels_last)
+ true_masks = true_masks.to(device=device, dtype=torch.long)
+
+ with torch.autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp):
+ masks_pred = model(images)
+ if model.n_classes == 1:
+ loss = criterion(masks_pred.squeeze(1), true_masks.float())
+ loss += dice_loss(F.sigmoid(masks_pred.squeeze(1)), true_masks.float(), multiclass=False)
+ else:
+ loss = criterion(masks_pred, true_masks)
+ loss += dice_loss(
+ F.softmax(masks_pred, dim=1).float(),
+ F.one_hot(true_masks, model.n_classes).permute(0, 3, 1, 2).float(),
+ multiclass=True
+ )
+
+ optimizer.zero_grad(set_to_none=True)
+ grad_scaler.scale(loss).backward()
+ torch.nn.utils.clip_grad_norm_(model.parameters(), gradient_clipping)
+ grad_scaler.step(optimizer)
+ grad_scaler.update()
+
+ pbar.update(images.shape[0])
+ global_step += 1
+ epoch_loss += loss.item()
+ experiment.log({
+ 'train loss': loss.item(),
+ 'step': global_step,
+ 'epoch': epoch
+ })
+ pbar.set_postfix(**{'loss (batch)': loss.item()})
+
+ # Evaluation round
+ division_step = (n_train // (5 * batch_size))
+ if division_step > 0:
+ if global_step % division_step == 0:
+ histograms = {}
+ for tag, value in model.named_parameters():
+ tag = tag.replace('/', '.')
+ if not (torch.isinf(value) | torch.isnan(value)).any():
+ histograms['Weights/' + tag] = wandb.Histogram(value.data.cpu())
+ if not (torch.isinf(value.grad) | torch.isnan(value.grad)).any():
+ histograms['Gradients/' + tag] = wandb.Histogram(value.grad.data.cpu())
+
+ val_score = evaluate(model, val_loader, device, amp)
+ scheduler.step(val_score)
+
+ logging.info('Validation Dice score: {}'.format(val_score))
+ try:
+ experiment.log({
+ 'learning rate': optimizer.param_groups[0]['lr'],
+ 'validation Dice': val_score,
+ 'images': wandb.Image(images[0].cpu()),
+ 'masks': {
+ 'true': wandb.Image(true_masks[0].float().cpu()),
+ 'pred': wandb.Image(masks_pred.argmax(dim=1)[0].float().cpu()),
+ },
+ 'step': global_step,
+ 'epoch': epoch,
+ **histograms
+ })
+ except:
+ pass
+
+ if save_checkpoint:
+ Path(dir_checkpoint).mkdir(parents=True, exist_ok=True)
+ state_dict = model.state_dict()
+ state_dict['mask_values'] = dataset.mask_values
+ torch.save(state_dict, str(dir_checkpoint / 'checkpoint_epoch{}.pth'.format(epoch)))
+ logging.info(f'Checkpoint {epoch} saved!')
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description='Train the UNet on images and target masks')
+ parser.add_argument('--epochs', '-e', metavar='E', type=int, default=5, help='Number of epochs')
+ parser.add_argument('--batch-size', '-b', dest='batch_size', metavar='B', type=int, default=1, help='Batch size')
+ parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=1e-5,
+ help='Learning rate', dest='lr')
+ parser.add_argument('--load', '-f', type=str, default=False, help='Load model from a .pth file')
+ parser.add_argument('--scale', '-s', type=float, default=0.5, help='Downscaling factor of the images')
+ parser.add_argument('--validation', '-v', dest='val', type=float, default=10.0,
+ help='Percent of the data that is used as validation (0-100)')
+ parser.add_argument('--amp', action='store_true', default=False, help='Use mixed precision')
+ parser.add_argument('--bilinear', action='store_true', default=False, help='Use bilinear upsampling')
+ parser.add_argument('--classes', '-c', type=int, default=2, help='Number of classes')
+
+ return parser.parse_args()
+
+
+if __name__ == '__main__':
+ args = get_args()
+
+ logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ logging.info(f'Using device {device}')
+
+ # Change here to adapt to your data
+ # n_channels=3 for RGB images
+ # n_classes is the number of probabilities you want to get per pixel
+ model = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear)
+ model = model.to(memory_format=torch.channels_last)
+
+ logging.info(f'Network:\n'
+ f'\t{model.n_channels} input channels\n'
+ f'\t{model.n_classes} output channels (classes)\n'
+ f'\t{"Bilinear" if model.bilinear else "Transposed conv"} upscaling')
+
+ if args.load:
+ state_dict = torch.load(args.load, map_location=device)
+ del state_dict['mask_values']
+ model.load_state_dict(state_dict)
+ logging.info(f'Model loaded from {args.load}')
+
+ model.to(device=device)
+ try:
+ train_model(
+ model=model,
+ epochs=args.epochs,
+ batch_size=args.batch_size,
+ learning_rate=args.lr,
+ device=device,
+ img_scale=args.scale,
+ val_percent=args.val / 100,
+ amp=args.amp
+ )
+ except :
+ logging.error('Detected OutOfMemoryError! '
+ 'Enabling checkpointing to reduce memory usage, but this slows down training. '
+ 'Consider enabling AMP (--amp) for fast and memory efficient training')
+ torch.cuda.empty_cache()
+ #model.use_checkpointing()
+ train_model(
+ model=model,
+ epochs=args.epochs,
+ batch_size=args.batch_size,
+ learning_rate=args.lr,
+ device=device,
+ img_scale=args.scale,
+ val_percent=args.val / 100,
+ amp=args.amp
+ )
diff --git a/official/cv/unet2d/2dunet-pytorch/unet/__init__.py b/official/cv/unet2d/2dunet-pytorch/unet/__init__.py
new file mode 100644
index 0000000..2e9b63b
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/unet/__init__.py
@@ -0,0 +1 @@
+from .unet_model import UNet
diff --git a/official/cv/unet2d/2dunet-pytorch/unet/unet_model.py b/official/cv/unet2d/2dunet-pytorch/unet/unet_model.py
new file mode 100644
index 0000000..caf79f4
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/unet/unet_model.py
@@ -0,0 +1,48 @@
+""" Full assembly of the parts to form the complete network """
+
+from .unet_parts import *
+
+
+class UNet(nn.Module):
+ def __init__(self, n_channels, n_classes, bilinear=False):
+ super(UNet, self).__init__()
+ self.n_channels = n_channels
+ self.n_classes = n_classes
+ self.bilinear = bilinear
+
+ self.inc = (DoubleConv(n_channels, 64))
+ self.down1 = (Down(64, 128))
+ self.down2 = (Down(128, 256))
+ self.down3 = (Down(256, 512))
+ factor = 2 if bilinear else 1
+ self.down4 = (Down(512, 1024 // factor))
+ self.up1 = (Up(1024, 512 // factor, bilinear))
+ self.up2 = (Up(512, 256 // factor, bilinear))
+ self.up3 = (Up(256, 128 // factor, bilinear))
+ self.up4 = (Up(128, 64, bilinear))
+ self.outc = (OutConv(64, n_classes))
+
+ def forward(self, x):
+ x1 = self.inc(x)
+ x2 = self.down1(x1)
+ x3 = self.down2(x2)
+ x4 = self.down3(x3)
+ x5 = self.down4(x4)
+ x = self.up1(x5, x4)
+ x = self.up2(x, x3)
+ x = self.up3(x, x2)
+ x = self.up4(x, x1)
+ logits = self.outc(x)
+ return logits
+
+ def use_checkpointing(self):
+ self.inc = torch.utils.checkpoint(self.inc)
+ self.down1 = torch.utils.checkpoint(self.down1)
+ self.down2 = torch.utils.checkpoint(self.down2)
+ self.down3 = torch.utils.checkpoint(self.down3)
+ self.down4 = torch.utils.checkpoint(self.down4)
+ self.up1 = torch.utils.checkpoint(self.up1)
+ self.up2 = torch.utils.checkpoint(self.up2)
+ self.up3 = torch.utils.checkpoint(self.up3)
+ self.up4 = torch.utils.checkpoint(self.up4)
+ self.outc = torch.utils.checkpoint(self.outc)
\ No newline at end of file
diff --git a/official/cv/unet2d/2dunet-pytorch/unet/unet_parts.py b/official/cv/unet2d/2dunet-pytorch/unet/unet_parts.py
new file mode 100644
index 0000000..986ba25
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/unet/unet_parts.py
@@ -0,0 +1,77 @@
+""" Parts of the U-Net model """
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DoubleConv(nn.Module):
+ """(convolution => [BN] => ReLU) * 2"""
+
+ def __init__(self, in_channels, out_channels, mid_channels=None):
+ super().__init__()
+ if not mid_channels:
+ mid_channels = out_channels
+ self.double_conv = nn.Sequential(
+ nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
+ nn.BatchNorm2d(mid_channels),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
+ nn.BatchNorm2d(out_channels),
+ nn.ReLU(inplace=True)
+ )
+
+ def forward(self, x):
+ return self.double_conv(x)
+
+
+class Down(nn.Module):
+ """Downscaling with maxpool then double conv"""
+
+ def __init__(self, in_channels, out_channels):
+ super().__init__()
+ self.maxpool_conv = nn.Sequential(
+ nn.MaxPool2d(2),
+ DoubleConv(in_channels, out_channels)
+ )
+
+ def forward(self, x):
+ return self.maxpool_conv(x)
+
+
+class Up(nn.Module):
+ """Upscaling then double conv"""
+
+ def __init__(self, in_channels, out_channels, bilinear=True):
+ super().__init__()
+
+ # if bilinear, use the normal convolutions to reduce the number of channels
+ if bilinear:
+ self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
+ self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
+ else:
+ self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
+ self.conv = DoubleConv(in_channels, out_channels)
+
+ def forward(self, x1, x2):
+ x1 = self.up(x1)
+ # input is CHW
+ diffY = x2.size()[2] - x1.size()[2]
+ diffX = x2.size()[3] - x1.size()[3]
+
+ x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
+ diffY // 2, diffY - diffY // 2])
+ # if you have padding issues, see
+ # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
+ # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
+ x = torch.cat([x2, x1], dim=1)
+ return self.conv(x)
+
+
+class OutConv(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super(OutConv, self).__init__()
+ self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
+
+ def forward(self, x):
+ return self.conv(x)
diff --git a/official/cv/unet2d/2dunet-pytorch/utils/__init__.py b/official/cv/unet2d/2dunet-pytorch/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/official/cv/unet2d/2dunet-pytorch/utils/data_loading.py b/official/cv/unet2d/2dunet-pytorch/utils/data_loading.py
new file mode 100644
index 0000000..11296e7
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/utils/data_loading.py
@@ -0,0 +1,117 @@
+import logging
+import numpy as np
+import torch
+from PIL import Image
+from functools import lru_cache
+from functools import partial
+from itertools import repeat
+from multiprocessing import Pool
+from os import listdir
+from os.path import splitext, isfile, join
+from pathlib import Path
+from torch.utils.data import Dataset
+from tqdm import tqdm
+
+
+def load_image(filename):
+ ext = splitext(filename)[1]
+ if ext == '.npy':
+ return Image.fromarray(np.load(filename))
+ elif ext in ['.pt', '.pth']:
+ return Image.fromarray(torch.load(filename).numpy())
+ else:
+ return Image.open(filename)
+
+
+def unique_mask_values(idx, mask_dir, mask_suffix):
+ mask_file = list(mask_dir.glob(idx + mask_suffix + '.*'))[0]
+ mask = np.asarray(load_image(mask_file))
+ if mask.ndim == 2:
+ return np.unique(mask)
+ elif mask.ndim == 3:
+ mask = mask.reshape(-1, mask.shape[-1])
+ return np.unique(mask, axis=0)
+ else:
+ raise ValueError(f'Loaded masks should have 2 or 3 dimensions, found {mask.ndim}')
+
+
+class BasicDataset(Dataset):
+ def __init__(self, images_dir: str, mask_dir: str, scale: float = 1.0, mask_suffix: str = ''):
+ self.images_dir = Path(images_dir)
+ self.mask_dir = Path(mask_dir)
+ assert 0 < scale <= 1, 'Scale must be between 0 and 1'
+ self.scale = scale
+ self.mask_suffix = mask_suffix
+
+ self.ids = [splitext(file)[0] for file in listdir(images_dir) if isfile(join(images_dir, file)) and not file.startswith('.')]
+ if not self.ids:
+ raise RuntimeError(f'No input file found in {images_dir}, make sure you put your images there')
+
+ logging.info(f'Creating dataset with {len(self.ids)} examples')
+ logging.info('Scanning mask files to determine unique values')
+ with Pool() as p:
+ unique = list(tqdm(
+ p.imap(partial(unique_mask_values, mask_dir=self.mask_dir, mask_suffix=self.mask_suffix), self.ids),
+ total=len(self.ids)
+ ))
+
+ self.mask_values = list(sorted(np.unique(np.concatenate(unique), axis=0).tolist()))
+ logging.info(f'Unique mask values: {self.mask_values}')
+
+ def __len__(self):
+ return len(self.ids)
+
+ @staticmethod
+ def preprocess(mask_values, pil_img, scale, is_mask):
+ w, h = pil_img.size
+ newW, newH = int(scale * w), int(scale * h)
+ assert newW > 0 and newH > 0, 'Scale is too small, resized images would have no pixel'
+ pil_img = pil_img.resize((newW, newH), resample=Image.NEAREST if is_mask else Image.BICUBIC)
+ img = np.asarray(pil_img)
+
+ if is_mask:
+ mask = np.zeros((newH, newW), dtype=np.int64)
+ for i, v in enumerate(mask_values):
+ if img.ndim == 2:
+ mask[img == v] = i
+ else:
+ mask[(img == v).all(-1)] = i
+
+ return mask
+
+ else:
+ if img.ndim == 2:
+ img = img[np.newaxis, ...]
+ else:
+ img = img.transpose((2, 0, 1))
+
+ if (img > 1).any():
+ img = img / 255.0
+
+ return img
+
+ def __getitem__(self, idx):
+ name = self.ids[idx]
+ mask_file = list(self.mask_dir.glob(name + self.mask_suffix + '.*'))
+ img_file = list(self.images_dir.glob(name + '.*'))
+
+ assert len(img_file) == 1, f'Either no image or multiple images found for the ID {name}: {img_file}'
+ assert len(mask_file) == 1, f'Either no mask or multiple masks found for the ID {name}: {mask_file}'
+ mask = load_image(mask_file[0])
+ img = load_image(img_file[0])
+
+ assert img.size == mask.size, \
+ f'Image and mask {name} should be the same size, but are {img.size} and {mask.size}'
+
+ img = self.preprocess(self.mask_values, img, self.scale, is_mask=False)
+ mask = self.preprocess(self.mask_values, mask, self.scale, is_mask=True)
+
+ return {
+ 'image': torch.as_tensor(img.copy()).float().contiguous(),
+ 'mask': torch.as_tensor(mask.copy()).long().contiguous()
+ }
+
+
+class CarvanaDataset(BasicDataset):
+ def __init__(self, images_dir, mask_dir, scale=1):
+ super().__init__(images_dir, mask_dir, scale, mask_suffix='_mask')
diff --git a/official/cv/unet2d/2dunet-pytorch/utils/dice_score.py b/official/cv/unet2d/2dunet-pytorch/utils/dice_score.py
new file mode 100644
index 0000000..c89eebe
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/utils/dice_score.py
@@ -0,0 +1,28 @@
+import torch
+from torch import Tensor
+
+
+def dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon: float = 1e-6):
+ # Average of Dice coefficient for all batches, or for a single mask
+ assert input.size() == target.size()
+ assert input.dim() == 3 or not reduce_batch_first
+
+ sum_dim = (-1, -2) if input.dim() == 2 or not reduce_batch_first else (-1, -2, -3)
+
+ inter = 2 * (input * target).sum(dim=sum_dim)
+ sets_sum = input.sum(dim=sum_dim) + target.sum(dim=sum_dim)
+ sets_sum = torch.where(sets_sum == 0, inter, sets_sum)
+
+ dice = (inter + epsilon) / (sets_sum + epsilon)
+ return dice.mean()
+
+
+def multiclass_dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon: float = 1e-6):
+ # Average of Dice coefficient for all classes
+ return dice_coeff(input.flatten(0, 1), target.flatten(0, 1), reduce_batch_first, epsilon)
+
+
+def dice_loss(input: Tensor, target: Tensor, multiclass: bool = False):
+ # Dice loss (objective to minimize) between 0 and 1
+ fn = multiclass_dice_coeff if multiclass else dice_coeff
+ return 1 - fn(input, target, reduce_batch_first=True)
diff --git a/official/cv/unet2d/2dunet-pytorch/utils/utils.py b/official/cv/unet2d/2dunet-pytorch/utils/utils.py
new file mode 100644
index 0000000..6ed8d8d
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/utils/utils.py
@@ -0,0 +1,13 @@
+import matplotlib.pyplot as plt
+
+
+def plot_img_and_mask(img, mask):
+ classes = mask.max() + 1
+ fig, ax = plt.subplots(1, classes + 1)
+ ax[0].set_title('Input image')
+ ax[0].imshow(img)
+ for i in range(classes):
+ ax[i + 1].set_title(f'Mask (class {i + 1})')
+ ax[i + 1].imshow(mask == i)
+ plt.xticks([]), plt.yticks([])
+ plt.show()
diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/debug-cli.root.log b/official/cv/unet2d/2dunet-pytorch/wandb/debug-cli.root.log
new file mode 100644
index 0000000..e69de29
diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/debug-internal.log b/official/cv/unet2d/2dunet-pytorch/wandb/debug-internal.log
new file mode 100644
index 0000000..4c52d3a
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/wandb/debug-internal.log
@@ -0,0 +1,778 @@
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diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_230452-uhtrz7dz/files/conda-environment.yaml b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_230452-uhtrz7dz/files/conda-environment.yaml
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diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_230452-uhtrz7dz/files/config.yaml b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_230452-uhtrz7dz/files/config.yaml
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+ value: 1
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+ value: 5
+img_scale:
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+ value: 0.5
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+ value: 1.0e-05
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+ value: true
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diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_230452-uhtrz7dz/files/output.log b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_230452-uhtrz7dz/files/output.log
new file mode 100644
index 0000000..fbeee45
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+++ b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_230452-uhtrz7dz/files/output.log
@@ -0,0 +1,21 @@
+INFO: Starting training:
+ Epochs: 5
+ Batch size: 1
+ Learning rate: 1e-05
+ Training size: 4580
+ Validation size: 508
+ Checkpoints: True
+ Device: cuda
+ Images scaling: 0.5
+ Mixed Precision: False
+Traceback (most recent call last):
+ File "train.py", line 220, in
+ amp=args.amp
+ File "train.py", line 78, in train_model
+ lr=learning_rate, weight_decay=weight_decay, momentum=momentum, foreach=True)
+TypeError: __init__() got an unexpected keyword argument 'foreach'
+During handling of the above exception, another exception occurred:
+Traceback (most recent call last):
+ File "train.py", line 222, in
+ except torch.cuda.OutOfMemoryError:
+AttributeError: module 'torch.cuda' has no attribute 'OutOfMemoryError'
\ No newline at end of file
diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_230452-uhtrz7dz/files/requirements.txt b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_230452-uhtrz7dz/files/requirements.txt
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+ amp=args.amp
+ File "train.py", line 78, in train_model
+ lr=learning_rate, weight_decay=weight_decay, momentum=momentum, foreach=True)
+TypeError: __init__() got an unexpected keyword argument 'foreach'
+During handling of the above exception, another exception occurred:
+Traceback (most recent call last):
+ File "train.py", line 224, in
+ except torch.cuda.OutOfMemoryError:
+AttributeError: module 'torch.cuda' has no attribute 'OutOfMemoryError'
\ No newline at end of file
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+ amp=args.amp
+ File "train.py", line 79, in train_model
+ lr=learning_rate, weight_decay=weight_decay, momentum=momentum, foreach=True)
+TypeError: __init__() got an unexpected keyword argument 'foreach'
+During handling of the above exception, another exception occurred:
+Traceback (most recent call last):
+ File "train.py", line 228, in
+ model.use_checkpointing()
+ File "/code/UNet-master/2dunet-pytorch/unet/unet_model.py", line 39, in use_checkpointing
+ self.inc = torch.utils.checkpoint(self.inc)
+TypeError: 'module' object is not callable
\ No newline at end of file
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diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/conda-environment.yaml b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/conda-environment.yaml
new file mode 100644
index 0000000..e69de29
diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/config.yaml b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/config.yaml
new file mode 100644
index 0000000..9c9a6a7
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+++ b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/config.yaml
@@ -0,0 +1,49 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.13.11
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: true
+ python_version: 3.7.11
+ start_time: 1678375513.665997
+ t:
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+ desc: null
+ value: false
+batch_size:
+ desc: null
+ value: 1
+epochs:
+ desc: null
+ value: 5
+img_scale:
+ desc: null
+ value: 0.5
+learning_rate:
+ desc: null
+ value: 1.0e-05
+save_checkpoint:
+ desc: null
+ value: true
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diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/output.log b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/output.log
new file mode 100644
index 0000000..f71d330
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/output.log
@@ -0,0 +1,50 @@
+INFO: Starting training:
+ Epochs: 5
+ Batch size: 1
+ Learning rate: 1e-05
+ Training size: 4580
+ Validation size: 508
+ Checkpoints: True
+ Device: cuda
+ Images scaling: 0.5
+ Mixed Precision: False
+ERROR: Detected OutOfMemoryError! Enabling checkpointing to reduce memory usage, but this slows down training. Consider enabling AMP (--amp) for fast and memory efficient training
+INFO: Creating dataset with 5088 examples
+INFO: Scanning mask files to determine unique values
+
+
+
+
+
+
+
+
+
+
+
+
+100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5088/5088 [00:25<00:00, 195.99it/s]
+INFO: Unique mask values: [0, 1]
+INFO: Starting training:
+ Epochs: 5
+ Batch size: 1
+ Learning rate: 1e-05
+ Training size: 4580
+ Validation size: 508
+ Checkpoints: True
+ Device: cuda
+ Images scaling: 0.5
+ Mixed Precision: False
+Traceback (most recent call last):
+ File "train.py", line 221, in
+ amp=args.amp
+ File "train.py", line 79, in train_model
+ lr=learning_rate, weight_decay=weight_decay, momentum=momentum, foreach=True)
+TypeError: __init__() got an unexpected keyword argument 'foreach'
+During handling of the above exception, another exception occurred:
+Traceback (most recent call last):
+ File "train.py", line 237, in
+ amp=args.amp
+ File "train.py", line 79, in train_model
+ lr=learning_rate, weight_decay=weight_decay, momentum=momentum, foreach=True)
+TypeError: __init__() got an unexpected keyword argument 'foreach'
\ No newline at end of file
diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/requirements.txt b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/requirements.txt
new file mode 100644
index 0000000..a70f071
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232513-ba99nlnr/files/requirements.txt
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diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232551-j3ne0zj4/logs/debug.log b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232551-j3ne0zj4/logs/debug.log
new file mode 100644
index 0000000..a1c74e2
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232551-j3ne0zj4/logs/debug.log
@@ -0,0 +1,15 @@
+2023-03-09 23:25:51,420 INFO MainThread:12858 [wandb_setup.py:_flush():76] Configure stats pid to 12858
+2023-03-09 23:25:51,420 INFO MainThread:12858 [wandb_setup.py:_flush():76] Loading settings from /root/.config/wandb/settings
+2023-03-09 23:25:51,420 INFO MainThread:12858 [wandb_setup.py:_flush():76] Loading settings from /code/UNet-master/2dunet-pytorch/wandb/settings
+2023-03-09 23:25:51,420 INFO MainThread:12858 [wandb_setup.py:_flush():76] Loading settings from environment variables: {}
+2023-03-09 23:25:51,420 INFO MainThread:12858 [wandb_setup.py:_flush():76] Applying setup settings: {'_disable_service': False}
+2023-03-09 23:25:51,420 INFO MainThread:12858 [wandb_setup.py:_flush():76] Inferring run settings from compute environment: {'program_relpath': 'train.py', 'program': 'train.py'}
+2023-03-09 23:25:51,420 INFO MainThread:12858 [wandb_setup.py:_flush():76] Applying login settings: {'anonymous': 'must'}
+2023-03-09 23:25:51,420 INFO MainThread:12858 [wandb_init.py:_log_setup():506] Logging user logs to /code/UNet-master/2dunet-pytorch/wandb/run-20230309_232551-j3ne0zj4/logs/debug.log
+2023-03-09 23:25:51,421 INFO MainThread:12858 [wandb_init.py:_log_setup():507] Logging internal logs to /code/UNet-master/2dunet-pytorch/wandb/run-20230309_232551-j3ne0zj4/logs/debug-internal.log
+2023-03-09 23:25:51,421 INFO MainThread:12858 [wandb_init.py:init():546] calling init triggers
+2023-03-09 23:25:51,421 INFO MainThread:12858 [wandb_init.py:init():553] wandb.init called with sweep_config: {}
+config: {}
+2023-03-09 23:25:51,421 INFO MainThread:12858 [wandb_init.py:init():597] wandb.init() called when a run is still active
+2023-03-09 23:25:51,422 INFO MainThread:12858 [wandb_run.py:_config_callback():1249] config_cb None None {'epochs': 5, 'batch_size': 1, 'learning_rate': 1e-05, 'val_percent': 0.1, 'save_checkpoint': True, 'img_scale': 0.5, 'amp': False}
+2023-03-09 23:25:58,846 WARNING MsgRouterThr:12858 [router.py:message_loop():77] message_loop has been closed
diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/conda-environment.yaml b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/conda-environment.yaml
new file mode 100644
index 0000000..e69de29
diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/config.yaml b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/config.yaml
new file mode 100644
index 0000000..2172dd0
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/config.yaml
@@ -0,0 +1,48 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.13.11
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: true
+ python_version: 3.7.11
+ start_time: 1678375634.838134
+ t:
+ 1:
+ - 1
+ - 41
+ - 55
+ 2:
+ - 1
+ - 41
+ - 55
+ 3:
+ - 23
+ 4: 3.7.11
+ 5: 0.13.11
+ 8:
+ - 2
+ - 5
+amp:
+ desc: null
+ value: false
+batch_size:
+ desc: null
+ value: 1
+epochs:
+ desc: null
+ value: 5
+img_scale:
+ desc: null
+ value: 0.5
+learning_rate:
+ desc: null
+ value: 1.0e-05
+save_checkpoint:
+ desc: null
+ value: true
+val_percent:
+ desc: null
+ value: 0.1
diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/output.log b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/output.log
new file mode 100644
index 0000000..8030533
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/output.log
@@ -0,0 +1,171 @@
+INFO: Starting training:
+ Epochs: 5
+ Batch size: 1
+ Learning rate: 1e-05
+ Training size: 4580
+ Validation size: 508
+ Checkpoints: True
+ Device: cuda
+ Images scaling: 0.5
+ Mixed Precision: False
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Epoch 1/5: 2%|██▌ | 81/4580 [03:29<3:07:14, 2.50s/img, loss (batch)=0.122]Exception ignored in:
+Traceback (most recent call last):
+ File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1328, in __del__
+ self._shutdown_workers()
+ File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1282, in _shutdown_workers
+ self._worker_result_queue.put((None, None))
+ File "/opt/conda/lib/python3.7/multiprocessing/queues.py", line 87, in put
+ self._start_thread()
+ File "/opt/conda/lib/python3.7/multiprocessing/queues.py", line 165, in _start_thread
+ name='QueueFeederThread'
+ File "/opt/conda/lib/python3.7/threading.py", line 799, in __init__
+ self._started = Event()
+ File "/opt/conda/lib/python3.7/threading.py", line 500, in __init__
+ self._cond = Condition(Lock())
+ File "/opt/conda/lib/python3.7/threading.py", line 228, in __init__
+ except AttributeError:
+KeyboardInterrupt:
+Epoch 1/5: 2%|██▌ | 81/4580 [03:32<3:16:25, 2.62s/img, loss (batch)=0.122]
+ERROR: Detected OutOfMemoryError! Enabling checkpointing to reduce memory usage, but this slows down training. Consider enabling AMP (--amp) for fast and memory efficient training
+INFO: Creating dataset with 5088 examples
+INFO: Scanning mask files to determine unique values
+
+
+
+
+
+
+
+
+
+
+
+ 91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 4634/5088 [00:24<00:02, 193.05it/s]
+Traceback (most recent call last):
+ File "train.py", line 221, in
+ amp=args.amp
+ File "train.py", line 122, in train_model
+ epoch_loss += loss.item()
+KeyboardInterrupt
+During handling of the above exception, another exception occurred:
+Traceback (most recent call last):
+ File "/opt/conda/lib/python3.7/multiprocessing/pool.py", line 733, in next
+ item = self._items.popleft()
+IndexError: pop from an empty deque
+During handling of the above exception, another exception occurred:
+Traceback (most recent call last):
+ File "/code/UNet-master/2dunet-pytorch/utils/data_loading.py", line 55, in __init__
+ total=len(self.ids)
+ File "/opt/conda/lib/python3.7/site-packages/tqdm/std.py", line 1185, in __iter__
+ for obj in iterable:
+ File "/opt/conda/lib/python3.7/multiprocessing/pool.py", line 737, in next
+ self._cond.wait(timeout)
+ File "/opt/conda/lib/python3.7/threading.py", line 296, in wait
+ waiter.acquire()
+KeyboardInterrupt
+During handling of the above exception, another exception occurred:
+Traceback (most recent call last):
+ File "train.py", line 237, in
+ amp=args.amp
+ File "train.py", line 44, in train_model
+ dataset = CarvanaDataset(dir_img, dir_mask, img_scale)
+ File "/code/UNet-master/2dunet-pytorch/utils/data_loading.py", line 117, in __init__
+ super().__init__(images_dir, mask_dir, scale, mask_suffix='_mask')
+ File "/code/UNet-master/2dunet-pytorch/utils/data_loading.py", line 55, in __init__
+ total=len(self.ids)
+ File "/opt/conda/lib/python3.7/multiprocessing/pool.py", line 623, in __exit__
+ self.terminate()
+ File "/opt/conda/lib/python3.7/multiprocessing/pool.py", line 548, in terminate
+ self._terminate()
+ File "/opt/conda/lib/python3.7/multiprocessing/util.py", line 224, in __call__
+ res = self._callback(*self._args, **self._kwargs)
+ File "/opt/conda/lib/python3.7/multiprocessing/pool.py", line 617, in _terminate_pool
+ p.join()
+ File "/opt/conda/lib/python3.7/multiprocessing/process.py", line 140, in join
+ res = self._popen.wait(timeout)
+ File "/opt/conda/lib/python3.7/multiprocessing/popen_fork.py", line 48, in wait
+ return self.poll(os.WNOHANG if timeout == 0.0 else 0)
+ File "/opt/conda/lib/python3.7/multiprocessing/popen_fork.py", line 28, in poll
+ pid, sts = os.waitpid(self.pid, flag)
+KeyboardInterrupt
\ No newline at end of file
diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/requirements.txt b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/requirements.txt
new file mode 100644
index 0000000..a70f071
--- /dev/null
+++ b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/files/requirements.txt
@@ -0,0 +1,136 @@
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+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+asttokens==2.0.8
+astunparse==1.6.3
+attrs==21.4.0
+backcall==0.2.0
+beautifulsoup4==4.10.0
+bleach==5.0.0
+brotlipy==0.7.0
+certifi==2021.10.8
+cffi==1.14.6
+chardet==4.0.0
+click==8.1.3
+conda-build==3.21.5
+conda-package-handling==1.7.3
+conda==4.10.3
+cryptography==35.0.0
+cycler==0.11.0
+debugpy==1.6.0
+decorator==5.1.0
+defusedxml==0.7.1
+dnspython==2.1.0
+docker-pycreds==0.4.0
+easydict==1.10
+entrypoints==0.4
+fastjsonschema==2.15.3
+filelock==3.3.1
+fonttools==4.38.0
+gitdb==4.0.10
+gitpython==3.1.31
+glob2==0.7
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+imageio==2.25.0
+importlib-metadata==4.11.3
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new file mode 100644
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diff --git a/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/run-76baujih.wandb b/official/cv/unet2d/2dunet-pytorch/wandb/run-20230309_232714-76baujih/run-76baujih.wandb
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