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+/easytorch-3.7.6.tar.gz
diff --git a/python-easytorch.spec b/python-easytorch.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-easytorch
+Version:	3.7.6
+Release:	1
+Summary:	Easy Neural Network Experiments with pytorch
+License:	MIT
+URL:		https://github.com/sraashis/easytorch
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/89/21/59c36fb7e47db93bfd3966aa40c887a013e6f052a57f180006108a41ff9b/easytorch-3.7.6.tar.gz
+BuildArch:	noarch
+
+
+%description
+## A very lightweight framework on top of PyTorch with full functionality.
+
+#### **Just one way of doing things means no learning curve.**  ✅
+
+![Logo](assets/easytorchlogo.png)
+
+![PyPi version](https://img.shields.io/pypi/v/easytorch)
+[![YourActionName Actions Status](https://github.com/sraashis/easytorch/workflows/build/badge.svg)](https://github.com/sraashis/easytorch/actions)
+![Python versions](https://img.shields.io/pypi/pyversions/pybadges.svg)
+
+<hr />
+
+#### Installation
+
+1. `pip install --upgrade pip`
+2. `Install latest pytorch and torchvision from` [Pytorch](https://pytorch.org/)
+3. `pip install easytorch`
+
+* [MNIST](./examples/MNIST_easytorch_CNN.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github//sraashis/easytorch/blob/master/examples/MNIST_easytorch_CNN.ipynb)
+* [Retinal fundus image transfer learning tasks.](https://github.com/sraashis/retinal-fundus-transfer)
+* [Covid-19 chest x-ray classification.](https://github.com/sraashis/covidxfactory)
+* [DCGAN.](https://github.com/sraashis/gan-easytorch-celeb-faces)
+
+#### Let's start with something simple like MNIST digit classification:
+
+```python
+from easytorch import EasyTorch, ETRunner, ConfusionMatrix, ETMeter
+from torchvision import datasets, transforms
+import torch.nn.functional as F
+import torch
+from examples.models import MNISTNet
+
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize((0.1307,), (0.3081,))
+])
+
+
+class MNISTTrainer(ETRunner):
+    def _init_nn_model(self):
+        self.nn['model'] = MNISTNet()
+
+    def iteration(self, batch):
+        inputs, labels = batch[0].to(self.device['gpu']).float(), batch[1].to(self.device['gpu']).long()
+
+        out = self.nn['model'](inputs)
+        loss = F.nll_loss(out, labels)
+        _, pred = torch.max(out, 1)
+
+        meter = self.new_meter()
+        meter.averages.add(loss.item(), len(inputs))
+        meter.metrics['cfm'].add(pred, labels.float())
+
+        return {'loss': loss, 'meter': meter, 'predictions': pred}
+
+    def init_experiment_cache(self):
+        self.cache['log_header'] = 'Loss|Accuracy,F1,Precision,Recall'
+        self.cache.update(monitor_metric='f1', metric_direction='maximize')
+
+    def new_meter(self):
+        return ETMeter(
+            cfm=ConfusionMatrix(num_classes=10)
+        )
+
+
+if __name__ == "__main__":
+    train_dataset = datasets.MNIST('../data', train=True, download=True, transform=transform)
+    val_dataset = datasets.MNIST('../data', train=False, transform=transform)
+
+    dataloader_args = {'train': {'dataset': train_dataset}, 'validation': {'dataset': val_dataset}}
+    runner = EasyTorch(phase='train', batch_size=512,
+                       epochs=10, gpus=[0], dataloader_args=dataloader_args)
+    runner.run(MNISTTrainer)
+```
+
+### Run as:
+
+`python script.py -ph train -b 512 -e 10 -gpus 0`
+
+### ... with **20+** useful options. Check [here](./easytorch/config/) for full list.
+
+<hr />
+
+## General use case:
+
+#### 1. Define your trainer
+
+```python
+from easytorch import ETRunner, Prf1a, ETMeter, AUCROCMetrics
+
+
+class MyTrainer(ETRunner):
+
+    def _init_nn_model(self):
+        self.nn['model'] = NeuralNetModel(out_size=self.conf['num_class'])
+
+    def iteration(self, batch):
+        """Handle a single batch"""
+        """Must have loss and meter"""
+        meter = self.new_meter()
+        ...
+        return {'loss': ..., 'meter': ..., 'predictions': ...}
+
+    def new_meter(self):
+        return ETMeter(
+            num_averages=1,
+            prf1a=Prf1a(),
+            auc=AUCROCMetrics()
+        )
+
+    def init_cache(self):
+        """Will plot Loss in one plot, and Accuracy,F1_score in another."""
+        self.cache['log_header'] = 'Loss|Accuracy,F1_score'
+
+        """Model selection using validation set if present"""
+        self.cache.update(monitor_metric='f1', metric_direction='maximize')
+
+````
+
+* Method new_meter() returns ETMeter that takes any implementation of easytorch.meter.ETMetrics. Provided ones:
+    * easytorch.metrics.Prf1a() for binary classification that computes accuracy,f1,precision,recall, overlap/IOU.
+    * easytorch.metrics.ConfusionMatrix(num_classes=...) for multiclass classification that also computes global
+      accuracy,f1,precision,recall.
+    * easytorch.metrics.AUCROCMetrics for binary ROC-AUC score.
+
+#### 2. Define specification for your datasets:
+
+* EasyTorch automatically splits the training data in _**data_source**_ as specified by
+  _**split_ratio(-spl or --split-ratio 0.7, 0.15, 0.15, for train validation and test portion)**_ OR Custom splits in
+    1. Text files:
+        * data_source = "/some/path/*.txt", where it looks for 'train.txt', 'validation.txt', and 'test.txt' if phase is
+          _train_, and only 'test.txt' if phase is _test_
+    2. Json files:
+        * data_source = "some/path/split.json", where each split key has list of files as {'train': [], '
+          validation' :[], 'test':[]}
+    3. Just glob as data_source = "some/path/**/*.txt", must also provide split_ratio if phase = _train_
+
+```python
+from easytorch import ETDataset
+
+
+class MyDataset(ETDataset):
+    def load_index(self, file):
+        """(Optional) Load/Process something and add to diskcache as:
+                self.diskcahe.add(file, value)"""
+        """This method runs in multiple processes by default"""
+
+        self.indices.append([file, 'something_extra'])
+
+    def __getitem__(self, index):
+        file = self.indices[index]
+        """(Optional) Retrieve from diskcache as self.diskcache.get(file)"""
+
+        image =  # Todo # Load file/Image. 
+        label =  # Todo # Load corresponding label.
+
+        # Extra preprocessing, if needed.
+        # Apply transforms, if needed.
+
+        return image, label
+```
+
+#### 3. Entry point (say main.py)
+### Run as:
+
+`python main.py -ph train -b 512 -e 10 -gpus 0`
+
+One can also directly pass arguments as below which overrides all.
+```python
+from easytorch import EasyTorch
+
+runner = EasyTorch(phase="train", batch_size=4, epochs=10,
+                   gpus=[0], num_channel=1, 
+                   num_class=2, data_source="<some_data>/data_split.json")
+runner.run(MyTrainer, MyDataset)
+```
+
+### All the best! Cheers! 🎉
+
+#### Cite the following papers if you use this library:
+
+```
+@article{deepdyn_10.3389/fcomp.2020.00035,
+	title        = {Dynamic Deep Networks for Retinal Vessel Segmentation},
+	author       = {Khanal, Aashis and Estrada, Rolando},
+	year         = 2020,
+	journal      = {Frontiers in Computer Science},
+	volume       = 2,
+	pages        = 35,
+	doi          = {10.3389/fcomp.2020.00035},
+	issn         = {2624-9898}
+}
+
+@misc{2202.02382,
+        Author       = {Aashis Khanal and Saeid Motevali and Rolando Estrada},
+        Title        = {Fully Automated Tree Topology Estimation and Artery-Vein Classification},
+        Year         = {2022},
+        Eprint       = {arXiv:2202.02382},
+}
+```
+
+### Feature Higlights:
+
+* DataHandle that is always available, and decoupled from other modules enabling easy
+  customization ([ETDataHandle](easytorch/data/data.py)).
+    * Use custom & complex data handling mechanism.
+* Simple lightweight logger/plotter.
+    * **Plot:** set log_header = 'Loss,F1,Accuracy' to plot in same plot or set log_header = 'Loss|F1,Accuracy' to plot
+      Loss in one plot, and F1,Accuracy in another plot.
+    * **Logs:** all arguments/generated data will be saved in logs.json file after the experiment finishes.
+* Gradient accumulation, automatic logging/plotting, model checkpointing, save everything.
+* Multiple metrics implementation at easytorch.metrics: Precision, Recall, Accuracy, Overlap, F1, ROC-AUC, Confusion
+  matrix
+* **For advanced training with multiple networks, and complex training steps,
+  click [here](assets/AdvancedTraining.md):**
+* **Implement custom metrics as [here](assets/CustomMetrics.md).**
+
+
+
+%package -n python3-easytorch
+Summary:	Easy Neural Network Experiments with pytorch
+Provides:	python-easytorch
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-easytorch
+## A very lightweight framework on top of PyTorch with full functionality.
+
+#### **Just one way of doing things means no learning curve.**  ✅
+
+![Logo](assets/easytorchlogo.png)
+
+![PyPi version](https://img.shields.io/pypi/v/easytorch)
+[![YourActionName Actions Status](https://github.com/sraashis/easytorch/workflows/build/badge.svg)](https://github.com/sraashis/easytorch/actions)
+![Python versions](https://img.shields.io/pypi/pyversions/pybadges.svg)
+
+<hr />
+
+#### Installation
+
+1. `pip install --upgrade pip`
+2. `Install latest pytorch and torchvision from` [Pytorch](https://pytorch.org/)
+3. `pip install easytorch`
+
+* [MNIST](./examples/MNIST_easytorch_CNN.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github//sraashis/easytorch/blob/master/examples/MNIST_easytorch_CNN.ipynb)
+* [Retinal fundus image transfer learning tasks.](https://github.com/sraashis/retinal-fundus-transfer)
+* [Covid-19 chest x-ray classification.](https://github.com/sraashis/covidxfactory)
+* [DCGAN.](https://github.com/sraashis/gan-easytorch-celeb-faces)
+
+#### Let's start with something simple like MNIST digit classification:
+
+```python
+from easytorch import EasyTorch, ETRunner, ConfusionMatrix, ETMeter
+from torchvision import datasets, transforms
+import torch.nn.functional as F
+import torch
+from examples.models import MNISTNet
+
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize((0.1307,), (0.3081,))
+])
+
+
+class MNISTTrainer(ETRunner):
+    def _init_nn_model(self):
+        self.nn['model'] = MNISTNet()
+
+    def iteration(self, batch):
+        inputs, labels = batch[0].to(self.device['gpu']).float(), batch[1].to(self.device['gpu']).long()
+
+        out = self.nn['model'](inputs)
+        loss = F.nll_loss(out, labels)
+        _, pred = torch.max(out, 1)
+
+        meter = self.new_meter()
+        meter.averages.add(loss.item(), len(inputs))
+        meter.metrics['cfm'].add(pred, labels.float())
+
+        return {'loss': loss, 'meter': meter, 'predictions': pred}
+
+    def init_experiment_cache(self):
+        self.cache['log_header'] = 'Loss|Accuracy,F1,Precision,Recall'
+        self.cache.update(monitor_metric='f1', metric_direction='maximize')
+
+    def new_meter(self):
+        return ETMeter(
+            cfm=ConfusionMatrix(num_classes=10)
+        )
+
+
+if __name__ == "__main__":
+    train_dataset = datasets.MNIST('../data', train=True, download=True, transform=transform)
+    val_dataset = datasets.MNIST('../data', train=False, transform=transform)
+
+    dataloader_args = {'train': {'dataset': train_dataset}, 'validation': {'dataset': val_dataset}}
+    runner = EasyTorch(phase='train', batch_size=512,
+                       epochs=10, gpus=[0], dataloader_args=dataloader_args)
+    runner.run(MNISTTrainer)
+```
+
+### Run as:
+
+`python script.py -ph train -b 512 -e 10 -gpus 0`
+
+### ... with **20+** useful options. Check [here](./easytorch/config/) for full list.
+
+<hr />
+
+## General use case:
+
+#### 1. Define your trainer
+
+```python
+from easytorch import ETRunner, Prf1a, ETMeter, AUCROCMetrics
+
+
+class MyTrainer(ETRunner):
+
+    def _init_nn_model(self):
+        self.nn['model'] = NeuralNetModel(out_size=self.conf['num_class'])
+
+    def iteration(self, batch):
+        """Handle a single batch"""
+        """Must have loss and meter"""
+        meter = self.new_meter()
+        ...
+        return {'loss': ..., 'meter': ..., 'predictions': ...}
+
+    def new_meter(self):
+        return ETMeter(
+            num_averages=1,
+            prf1a=Prf1a(),
+            auc=AUCROCMetrics()
+        )
+
+    def init_cache(self):
+        """Will plot Loss in one plot, and Accuracy,F1_score in another."""
+        self.cache['log_header'] = 'Loss|Accuracy,F1_score'
+
+        """Model selection using validation set if present"""
+        self.cache.update(monitor_metric='f1', metric_direction='maximize')
+
+````
+
+* Method new_meter() returns ETMeter that takes any implementation of easytorch.meter.ETMetrics. Provided ones:
+    * easytorch.metrics.Prf1a() for binary classification that computes accuracy,f1,precision,recall, overlap/IOU.
+    * easytorch.metrics.ConfusionMatrix(num_classes=...) for multiclass classification that also computes global
+      accuracy,f1,precision,recall.
+    * easytorch.metrics.AUCROCMetrics for binary ROC-AUC score.
+
+#### 2. Define specification for your datasets:
+
+* EasyTorch automatically splits the training data in _**data_source**_ as specified by
+  _**split_ratio(-spl or --split-ratio 0.7, 0.15, 0.15, for train validation and test portion)**_ OR Custom splits in
+    1. Text files:
+        * data_source = "/some/path/*.txt", where it looks for 'train.txt', 'validation.txt', and 'test.txt' if phase is
+          _train_, and only 'test.txt' if phase is _test_
+    2. Json files:
+        * data_source = "some/path/split.json", where each split key has list of files as {'train': [], '
+          validation' :[], 'test':[]}
+    3. Just glob as data_source = "some/path/**/*.txt", must also provide split_ratio if phase = _train_
+
+```python
+from easytorch import ETDataset
+
+
+class MyDataset(ETDataset):
+    def load_index(self, file):
+        """(Optional) Load/Process something and add to diskcache as:
+                self.diskcahe.add(file, value)"""
+        """This method runs in multiple processes by default"""
+
+        self.indices.append([file, 'something_extra'])
+
+    def __getitem__(self, index):
+        file = self.indices[index]
+        """(Optional) Retrieve from diskcache as self.diskcache.get(file)"""
+
+        image =  # Todo # Load file/Image. 
+        label =  # Todo # Load corresponding label.
+
+        # Extra preprocessing, if needed.
+        # Apply transforms, if needed.
+
+        return image, label
+```
+
+#### 3. Entry point (say main.py)
+### Run as:
+
+`python main.py -ph train -b 512 -e 10 -gpus 0`
+
+One can also directly pass arguments as below which overrides all.
+```python
+from easytorch import EasyTorch
+
+runner = EasyTorch(phase="train", batch_size=4, epochs=10,
+                   gpus=[0], num_channel=1, 
+                   num_class=2, data_source="<some_data>/data_split.json")
+runner.run(MyTrainer, MyDataset)
+```
+
+### All the best! Cheers! 🎉
+
+#### Cite the following papers if you use this library:
+
+```
+@article{deepdyn_10.3389/fcomp.2020.00035,
+	title        = {Dynamic Deep Networks for Retinal Vessel Segmentation},
+	author       = {Khanal, Aashis and Estrada, Rolando},
+	year         = 2020,
+	journal      = {Frontiers in Computer Science},
+	volume       = 2,
+	pages        = 35,
+	doi          = {10.3389/fcomp.2020.00035},
+	issn         = {2624-9898}
+}
+
+@misc{2202.02382,
+        Author       = {Aashis Khanal and Saeid Motevali and Rolando Estrada},
+        Title        = {Fully Automated Tree Topology Estimation and Artery-Vein Classification},
+        Year         = {2022},
+        Eprint       = {arXiv:2202.02382},
+}
+```
+
+### Feature Higlights:
+
+* DataHandle that is always available, and decoupled from other modules enabling easy
+  customization ([ETDataHandle](easytorch/data/data.py)).
+    * Use custom & complex data handling mechanism.
+* Simple lightweight logger/plotter.
+    * **Plot:** set log_header = 'Loss,F1,Accuracy' to plot in same plot or set log_header = 'Loss|F1,Accuracy' to plot
+      Loss in one plot, and F1,Accuracy in another plot.
+    * **Logs:** all arguments/generated data will be saved in logs.json file after the experiment finishes.
+* Gradient accumulation, automatic logging/plotting, model checkpointing, save everything.
+* Multiple metrics implementation at easytorch.metrics: Precision, Recall, Accuracy, Overlap, F1, ROC-AUC, Confusion
+  matrix
+* **For advanced training with multiple networks, and complex training steps,
+  click [here](assets/AdvancedTraining.md):**
+* **Implement custom metrics as [here](assets/CustomMetrics.md).**
+
+
+
+%package help
+Summary:	Development documents and examples for easytorch
+Provides:	python3-easytorch-doc
+%description help
+## A very lightweight framework on top of PyTorch with full functionality.
+
+#### **Just one way of doing things means no learning curve.**  ✅
+
+![Logo](assets/easytorchlogo.png)
+
+![PyPi version](https://img.shields.io/pypi/v/easytorch)
+[![YourActionName Actions Status](https://github.com/sraashis/easytorch/workflows/build/badge.svg)](https://github.com/sraashis/easytorch/actions)
+![Python versions](https://img.shields.io/pypi/pyversions/pybadges.svg)
+
+<hr />
+
+#### Installation
+
+1. `pip install --upgrade pip`
+2. `Install latest pytorch and torchvision from` [Pytorch](https://pytorch.org/)
+3. `pip install easytorch`
+
+* [MNIST](./examples/MNIST_easytorch_CNN.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github//sraashis/easytorch/blob/master/examples/MNIST_easytorch_CNN.ipynb)
+* [Retinal fundus image transfer learning tasks.](https://github.com/sraashis/retinal-fundus-transfer)
+* [Covid-19 chest x-ray classification.](https://github.com/sraashis/covidxfactory)
+* [DCGAN.](https://github.com/sraashis/gan-easytorch-celeb-faces)
+
+#### Let's start with something simple like MNIST digit classification:
+
+```python
+from easytorch import EasyTorch, ETRunner, ConfusionMatrix, ETMeter
+from torchvision import datasets, transforms
+import torch.nn.functional as F
+import torch
+from examples.models import MNISTNet
+
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize((0.1307,), (0.3081,))
+])
+
+
+class MNISTTrainer(ETRunner):
+    def _init_nn_model(self):
+        self.nn['model'] = MNISTNet()
+
+    def iteration(self, batch):
+        inputs, labels = batch[0].to(self.device['gpu']).float(), batch[1].to(self.device['gpu']).long()
+
+        out = self.nn['model'](inputs)
+        loss = F.nll_loss(out, labels)
+        _, pred = torch.max(out, 1)
+
+        meter = self.new_meter()
+        meter.averages.add(loss.item(), len(inputs))
+        meter.metrics['cfm'].add(pred, labels.float())
+
+        return {'loss': loss, 'meter': meter, 'predictions': pred}
+
+    def init_experiment_cache(self):
+        self.cache['log_header'] = 'Loss|Accuracy,F1,Precision,Recall'
+        self.cache.update(monitor_metric='f1', metric_direction='maximize')
+
+    def new_meter(self):
+        return ETMeter(
+            cfm=ConfusionMatrix(num_classes=10)
+        )
+
+
+if __name__ == "__main__":
+    train_dataset = datasets.MNIST('../data', train=True, download=True, transform=transform)
+    val_dataset = datasets.MNIST('../data', train=False, transform=transform)
+
+    dataloader_args = {'train': {'dataset': train_dataset}, 'validation': {'dataset': val_dataset}}
+    runner = EasyTorch(phase='train', batch_size=512,
+                       epochs=10, gpus=[0], dataloader_args=dataloader_args)
+    runner.run(MNISTTrainer)
+```
+
+### Run as:
+
+`python script.py -ph train -b 512 -e 10 -gpus 0`
+
+### ... with **20+** useful options. Check [here](./easytorch/config/) for full list.
+
+<hr />
+
+## General use case:
+
+#### 1. Define your trainer
+
+```python
+from easytorch import ETRunner, Prf1a, ETMeter, AUCROCMetrics
+
+
+class MyTrainer(ETRunner):
+
+    def _init_nn_model(self):
+        self.nn['model'] = NeuralNetModel(out_size=self.conf['num_class'])
+
+    def iteration(self, batch):
+        """Handle a single batch"""
+        """Must have loss and meter"""
+        meter = self.new_meter()
+        ...
+        return {'loss': ..., 'meter': ..., 'predictions': ...}
+
+    def new_meter(self):
+        return ETMeter(
+            num_averages=1,
+            prf1a=Prf1a(),
+            auc=AUCROCMetrics()
+        )
+
+    def init_cache(self):
+        """Will plot Loss in one plot, and Accuracy,F1_score in another."""
+        self.cache['log_header'] = 'Loss|Accuracy,F1_score'
+
+        """Model selection using validation set if present"""
+        self.cache.update(monitor_metric='f1', metric_direction='maximize')
+
+````
+
+* Method new_meter() returns ETMeter that takes any implementation of easytorch.meter.ETMetrics. Provided ones:
+    * easytorch.metrics.Prf1a() for binary classification that computes accuracy,f1,precision,recall, overlap/IOU.
+    * easytorch.metrics.ConfusionMatrix(num_classes=...) for multiclass classification that also computes global
+      accuracy,f1,precision,recall.
+    * easytorch.metrics.AUCROCMetrics for binary ROC-AUC score.
+
+#### 2. Define specification for your datasets:
+
+* EasyTorch automatically splits the training data in _**data_source**_ as specified by
+  _**split_ratio(-spl or --split-ratio 0.7, 0.15, 0.15, for train validation and test portion)**_ OR Custom splits in
+    1. Text files:
+        * data_source = "/some/path/*.txt", where it looks for 'train.txt', 'validation.txt', and 'test.txt' if phase is
+          _train_, and only 'test.txt' if phase is _test_
+    2. Json files:
+        * data_source = "some/path/split.json", where each split key has list of files as {'train': [], '
+          validation' :[], 'test':[]}
+    3. Just glob as data_source = "some/path/**/*.txt", must also provide split_ratio if phase = _train_
+
+```python
+from easytorch import ETDataset
+
+
+class MyDataset(ETDataset):
+    def load_index(self, file):
+        """(Optional) Load/Process something and add to diskcache as:
+                self.diskcahe.add(file, value)"""
+        """This method runs in multiple processes by default"""
+
+        self.indices.append([file, 'something_extra'])
+
+    def __getitem__(self, index):
+        file = self.indices[index]
+        """(Optional) Retrieve from diskcache as self.diskcache.get(file)"""
+
+        image =  # Todo # Load file/Image. 
+        label =  # Todo # Load corresponding label.
+
+        # Extra preprocessing, if needed.
+        # Apply transforms, if needed.
+
+        return image, label
+```
+
+#### 3. Entry point (say main.py)
+### Run as:
+
+`python main.py -ph train -b 512 -e 10 -gpus 0`
+
+One can also directly pass arguments as below which overrides all.
+```python
+from easytorch import EasyTorch
+
+runner = EasyTorch(phase="train", batch_size=4, epochs=10,
+                   gpus=[0], num_channel=1, 
+                   num_class=2, data_source="<some_data>/data_split.json")
+runner.run(MyTrainer, MyDataset)
+```
+
+### All the best! Cheers! 🎉
+
+#### Cite the following papers if you use this library:
+
+```
+@article{deepdyn_10.3389/fcomp.2020.00035,
+	title        = {Dynamic Deep Networks for Retinal Vessel Segmentation},
+	author       = {Khanal, Aashis and Estrada, Rolando},
+	year         = 2020,
+	journal      = {Frontiers in Computer Science},
+	volume       = 2,
+	pages        = 35,
+	doi          = {10.3389/fcomp.2020.00035},
+	issn         = {2624-9898}
+}
+
+@misc{2202.02382,
+        Author       = {Aashis Khanal and Saeid Motevali and Rolando Estrada},
+        Title        = {Fully Automated Tree Topology Estimation and Artery-Vein Classification},
+        Year         = {2022},
+        Eprint       = {arXiv:2202.02382},
+}
+```
+
+### Feature Higlights:
+
+* DataHandle that is always available, and decoupled from other modules enabling easy
+  customization ([ETDataHandle](easytorch/data/data.py)).
+    * Use custom & complex data handling mechanism.
+* Simple lightweight logger/plotter.
+    * **Plot:** set log_header = 'Loss,F1,Accuracy' to plot in same plot or set log_header = 'Loss|F1,Accuracy' to plot
+      Loss in one plot, and F1,Accuracy in another plot.
+    * **Logs:** all arguments/generated data will be saved in logs.json file after the experiment finishes.
+* Gradient accumulation, automatic logging/plotting, model checkpointing, save everything.
+* Multiple metrics implementation at easytorch.metrics: Precision, Recall, Accuracy, Overlap, F1, ROC-AUC, Confusion
+  matrix
+* **For advanced training with multiple networks, and complex training steps,
+  click [here](assets/AdvancedTraining.md):**
+* **Implement custom metrics as [here](assets/CustomMetrics.md).**
+
+
+
+%prep
+%autosetup -n easytorch-3.7.6
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-easytorch -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 3.7.6-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..30aa765
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+1e796023ea5af780bb99e9b5d6250caa  easytorch-3.7.6.tar.gz