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+/pytorch-pretrained-vit-0.0.7.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-pytorch-pretrained-vit
+Version:	0.0.7
+Release:	1
+Summary:	Visual Transformers (ViT) in PyTorch.
+License:	Apache
+URL:		https://github.com/lukemelas/ViT-PyTorch
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/02/8d/b404fe410a984ce2bc95a8ce02d397e3b8b12d6dd3118db6ac9b8edaa370/pytorch-pretrained-vit-0.0.7.tar.gz
+BuildArch:	noarch
+
+
+%description
+# ViT PyTorch
+
+### Quickstart
+
+Install with `pip install pytorch_pretrained_vit` and load a pretrained ViT with:
+```python
+from pytorch_pretrained_vit import ViT
+model = ViT('B_16_imagenet1k', pretrained=True)
+```
+
+Or find a Google Colab example [here](https://colab.research.google.com/drive/1muZ4QFgVfwALgqmrfOkp7trAvqDemckO?usp=sharing).  
+
+### Overview
+This repository contains an op-for-op PyTorch reimplementation of the [Visual Transformer](https://openreview.net/forum?id=YicbFdNTTy) architecture from [Google](https://github.com/google-research/vision_transformer), along with pre-trained models and examples.
+
+The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. 
+
+At the moment, you can easily:
+ * Load pretrained ViT models
+ * Evaluate on ImageNet or your own data
+ * Finetune ViT on your own dataset
+
+_(Upcoming features)_ Coming soon: 
+ * Train ViT from scratch on ImageNet (1K)
+ * Export to ONNX for efficient inference
+
+### Table of contents
+1. [About ViT](#about-vit)
+2. [About ViT-PyTorch](#about-vit-pytorch)
+3. [Installation](#installation)
+4. [Usage](#usage)
+    * [Load pretrained models](#loading-pretrained-models)
+    * [Example: Classify](#example-classification)
+    <!-- * [Example: Extract features](#example-feature-extraction) -->
+    <!-- * [Example: Export to ONNX](#example-export) -->
+6. [Contributing](#contributing)
+
+### About ViT
+
+Visual Transformers (ViT) are a straightforward application of the [transformer architecture](https://arxiv.org/abs/1706.03762) to image classification. Even in computer vision, it seems, attention is all you need. 
+
+The ViT architecture works as follows: (1) it considers an image as a 1-dimensional sequence of patches, (2) it prepends a classification token to the sequence, (3) it passes these patches through a transformer encoder (like [BERT](https://arxiv.org/abs/1810.04805)), (4) it passes the first token of the output of the transformer through a small MLP to obtain the classification logits. 
+ViT is trained on a large-scale dataset (ImageNet-21k) with a huge amount of compute. 
+
+<div style="text-align: center; padding: 10px">
+    <img src="https://raw.githubusercontent.com/google-research/vision_transformer/master/figure1.png" width="100%" style="max-width: 300px; margin: auto"/>
+</div>
+
+
+### About ViT-PyTorch
+
+ViT-PyTorch is a PyTorch re-implementation of ViT. It is consistent with the [original Jax implementation](https://github.com/google-research/vision_transformer), so that it's easy to load Jax-pretrained weights.
+
+At the same time, we aim to make our PyTorch implementation as simple, flexible, and extensible as possible.
+
+### Installation
+
+Install with pip:
+```bash
+pip install pytorch_pretrained_vit
+```
+
+Or from source:
+```bash
+git clone https://github.com/lukemelas/ViT-PyTorch
+cd ViT-Pytorch
+pip install -e .
+```
+
+### Usage
+
+#### Loading pretrained models
+
+Loading a pretrained model is easy:
+```python
+from pytorch_pretrained_vit import ViT
+model = ViT('B_16_imagenet1k', pretrained=True)
+```
+
+Details about the models are below:
+
+|    *Name*         |* Pretrained on *|*Finetuned on*|*Available? *|
+|:-----------------:|:---------------:|:------------:|:-----------:|
+| `B_16`            |  ImageNet-21k   | -            |      ✓      |
+| `B_32`            |  ImageNet-21k   | -            |      ✓      |
+| `L_16`            |  ImageNet-21k   | -            |      -      |
+| `L_32`            |  ImageNet-21k   | -            |      ✓      |
+| `B_16_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+| `B_32_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+| `L_16_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+| `L_32_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+
+#### Custom ViT
+
+Loading custom configurations is just as easy: 
+```python
+from pytorch_pretrained_vit import ViT
+# The following is equivalent to ViT('B_16')
+config = dict(hidden_size=512, num_heads=8, num_layers=6)
+model = ViT.from_config(config)
+```
+
+#### Example: Classification
+
+Below is a simple, complete example. It may also be found as a Jupyter notebook in `examples/simple` or as a [Colab Notebook]().  
+<!-- TODO: new Colab -->
+
+```python
+import json
+from PIL import Image
+import torch
+from torchvision import transforms
+
+# Load ViT
+from pytorch_pretrained_vit import ViT
+model = ViT('B_16_imagenet1k', pretrained=True)
+model.eval()
+
+# Load image
+# NOTE: Assumes an image `img.jpg` exists in the current directory
+img = transforms.Compose([
+    transforms.Resize((384, 384)), 
+    transforms.ToTensor(),
+    transforms.Normalize(0.5, 0.5),
+])(Image.open('img.jpg')).unsqueeze(0)
+print(img.shape) # torch.Size([1, 3, 384, 384])
+
+# Classify
+with torch.no_grad():
+    outputs = model(img)
+print(outputs.shape)  # (1, 1000)
+```
+
+<!-- #### Example: Feature Extraction
+
+You can easily extract features with `model.extract_features`:
+```python
+from efficientnet_pytorch import EfficientNet
+model = EfficientNet.from_pretrained('efficientnet-b0')
+
+# ... image preprocessing as in the classification example ...
+print(img.shape) # torch.Size([1, 3, 384, 384])
+
+features = model.extract_features(img)
+print(features.shape) # torch.Size([1, 1280, 7, 7])
+``` -->
+
+<!-- #### Example: Export to ONNX
+
+Exporting to ONNX for deploying to production is now simple:
+```python
+import torch
+from efficientnet_pytorch import EfficientNet
+
+model = EfficientNet.from_pretrained('efficientnet-b1')
+dummy_input = torch.randn(10, 3, 240, 240)
+
+model.set_swish(memory_efficient=False)
+torch.onnx.export(model, dummy_input, "test-b1.onnx", verbose=True)
+```
+
+[Here](https://colab.research.google.com/drive/1rOAEXeXHaA8uo3aG2YcFDHItlRJMV0VP) is a Colab example. -->
+
+
+#### ImageNet
+
+See `examples/imagenet` for details about evaluating on ImageNet.
+
+#### Credit
+
+Other great repositories with this model include: 
+ - [Ross Wightman's repo](https://github.com/rwightman/pytorch-image-models)
+ - [Phil Wang's repo](https://github.com/lucidrains/vit-pytorch)
+
+### Contributing
+
+If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.
+
+I look forward to seeing what the community does with these models!
+
+%package -n python3-pytorch-pretrained-vit
+Summary:	Visual Transformers (ViT) in PyTorch.
+Provides:	python-pytorch-pretrained-vit
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pytorch-pretrained-vit
+# ViT PyTorch
+
+### Quickstart
+
+Install with `pip install pytorch_pretrained_vit` and load a pretrained ViT with:
+```python
+from pytorch_pretrained_vit import ViT
+model = ViT('B_16_imagenet1k', pretrained=True)
+```
+
+Or find a Google Colab example [here](https://colab.research.google.com/drive/1muZ4QFgVfwALgqmrfOkp7trAvqDemckO?usp=sharing).  
+
+### Overview
+This repository contains an op-for-op PyTorch reimplementation of the [Visual Transformer](https://openreview.net/forum?id=YicbFdNTTy) architecture from [Google](https://github.com/google-research/vision_transformer), along with pre-trained models and examples.
+
+The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. 
+
+At the moment, you can easily:
+ * Load pretrained ViT models
+ * Evaluate on ImageNet or your own data
+ * Finetune ViT on your own dataset
+
+_(Upcoming features)_ Coming soon: 
+ * Train ViT from scratch on ImageNet (1K)
+ * Export to ONNX for efficient inference
+
+### Table of contents
+1. [About ViT](#about-vit)
+2. [About ViT-PyTorch](#about-vit-pytorch)
+3. [Installation](#installation)
+4. [Usage](#usage)
+    * [Load pretrained models](#loading-pretrained-models)
+    * [Example: Classify](#example-classification)
+    <!-- * [Example: Extract features](#example-feature-extraction) -->
+    <!-- * [Example: Export to ONNX](#example-export) -->
+6. [Contributing](#contributing)
+
+### About ViT
+
+Visual Transformers (ViT) are a straightforward application of the [transformer architecture](https://arxiv.org/abs/1706.03762) to image classification. Even in computer vision, it seems, attention is all you need. 
+
+The ViT architecture works as follows: (1) it considers an image as a 1-dimensional sequence of patches, (2) it prepends a classification token to the sequence, (3) it passes these patches through a transformer encoder (like [BERT](https://arxiv.org/abs/1810.04805)), (4) it passes the first token of the output of the transformer through a small MLP to obtain the classification logits. 
+ViT is trained on a large-scale dataset (ImageNet-21k) with a huge amount of compute. 
+
+<div style="text-align: center; padding: 10px">
+    <img src="https://raw.githubusercontent.com/google-research/vision_transformer/master/figure1.png" width="100%" style="max-width: 300px; margin: auto"/>
+</div>
+
+
+### About ViT-PyTorch
+
+ViT-PyTorch is a PyTorch re-implementation of ViT. It is consistent with the [original Jax implementation](https://github.com/google-research/vision_transformer), so that it's easy to load Jax-pretrained weights.
+
+At the same time, we aim to make our PyTorch implementation as simple, flexible, and extensible as possible.
+
+### Installation
+
+Install with pip:
+```bash
+pip install pytorch_pretrained_vit
+```
+
+Or from source:
+```bash
+git clone https://github.com/lukemelas/ViT-PyTorch
+cd ViT-Pytorch
+pip install -e .
+```
+
+### Usage
+
+#### Loading pretrained models
+
+Loading a pretrained model is easy:
+```python
+from pytorch_pretrained_vit import ViT
+model = ViT('B_16_imagenet1k', pretrained=True)
+```
+
+Details about the models are below:
+
+|    *Name*         |* Pretrained on *|*Finetuned on*|*Available? *|
+|:-----------------:|:---------------:|:------------:|:-----------:|
+| `B_16`            |  ImageNet-21k   | -            |      ✓      |
+| `B_32`            |  ImageNet-21k   | -            |      ✓      |
+| `L_16`            |  ImageNet-21k   | -            |      -      |
+| `L_32`            |  ImageNet-21k   | -            |      ✓      |
+| `B_16_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+| `B_32_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+| `L_16_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+| `L_32_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+
+#### Custom ViT
+
+Loading custom configurations is just as easy: 
+```python
+from pytorch_pretrained_vit import ViT
+# The following is equivalent to ViT('B_16')
+config = dict(hidden_size=512, num_heads=8, num_layers=6)
+model = ViT.from_config(config)
+```
+
+#### Example: Classification
+
+Below is a simple, complete example. It may also be found as a Jupyter notebook in `examples/simple` or as a [Colab Notebook]().  
+<!-- TODO: new Colab -->
+
+```python
+import json
+from PIL import Image
+import torch
+from torchvision import transforms
+
+# Load ViT
+from pytorch_pretrained_vit import ViT
+model = ViT('B_16_imagenet1k', pretrained=True)
+model.eval()
+
+# Load image
+# NOTE: Assumes an image `img.jpg` exists in the current directory
+img = transforms.Compose([
+    transforms.Resize((384, 384)), 
+    transforms.ToTensor(),
+    transforms.Normalize(0.5, 0.5),
+])(Image.open('img.jpg')).unsqueeze(0)
+print(img.shape) # torch.Size([1, 3, 384, 384])
+
+# Classify
+with torch.no_grad():
+    outputs = model(img)
+print(outputs.shape)  # (1, 1000)
+```
+
+<!-- #### Example: Feature Extraction
+
+You can easily extract features with `model.extract_features`:
+```python
+from efficientnet_pytorch import EfficientNet
+model = EfficientNet.from_pretrained('efficientnet-b0')
+
+# ... image preprocessing as in the classification example ...
+print(img.shape) # torch.Size([1, 3, 384, 384])
+
+features = model.extract_features(img)
+print(features.shape) # torch.Size([1, 1280, 7, 7])
+``` -->
+
+<!-- #### Example: Export to ONNX
+
+Exporting to ONNX for deploying to production is now simple:
+```python
+import torch
+from efficientnet_pytorch import EfficientNet
+
+model = EfficientNet.from_pretrained('efficientnet-b1')
+dummy_input = torch.randn(10, 3, 240, 240)
+
+model.set_swish(memory_efficient=False)
+torch.onnx.export(model, dummy_input, "test-b1.onnx", verbose=True)
+```
+
+[Here](https://colab.research.google.com/drive/1rOAEXeXHaA8uo3aG2YcFDHItlRJMV0VP) is a Colab example. -->
+
+
+#### ImageNet
+
+See `examples/imagenet` for details about evaluating on ImageNet.
+
+#### Credit
+
+Other great repositories with this model include: 
+ - [Ross Wightman's repo](https://github.com/rwightman/pytorch-image-models)
+ - [Phil Wang's repo](https://github.com/lucidrains/vit-pytorch)
+
+### Contributing
+
+If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.
+
+I look forward to seeing what the community does with these models!
+
+%package help
+Summary:	Development documents and examples for pytorch-pretrained-vit
+Provides:	python3-pytorch-pretrained-vit-doc
+%description help
+# ViT PyTorch
+
+### Quickstart
+
+Install with `pip install pytorch_pretrained_vit` and load a pretrained ViT with:
+```python
+from pytorch_pretrained_vit import ViT
+model = ViT('B_16_imagenet1k', pretrained=True)
+```
+
+Or find a Google Colab example [here](https://colab.research.google.com/drive/1muZ4QFgVfwALgqmrfOkp7trAvqDemckO?usp=sharing).  
+
+### Overview
+This repository contains an op-for-op PyTorch reimplementation of the [Visual Transformer](https://openreview.net/forum?id=YicbFdNTTy) architecture from [Google](https://github.com/google-research/vision_transformer), along with pre-trained models and examples.
+
+The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. 
+
+At the moment, you can easily:
+ * Load pretrained ViT models
+ * Evaluate on ImageNet or your own data
+ * Finetune ViT on your own dataset
+
+_(Upcoming features)_ Coming soon: 
+ * Train ViT from scratch on ImageNet (1K)
+ * Export to ONNX for efficient inference
+
+### Table of contents
+1. [About ViT](#about-vit)
+2. [About ViT-PyTorch](#about-vit-pytorch)
+3. [Installation](#installation)
+4. [Usage](#usage)
+    * [Load pretrained models](#loading-pretrained-models)
+    * [Example: Classify](#example-classification)
+    <!-- * [Example: Extract features](#example-feature-extraction) -->
+    <!-- * [Example: Export to ONNX](#example-export) -->
+6. [Contributing](#contributing)
+
+### About ViT
+
+Visual Transformers (ViT) are a straightforward application of the [transformer architecture](https://arxiv.org/abs/1706.03762) to image classification. Even in computer vision, it seems, attention is all you need. 
+
+The ViT architecture works as follows: (1) it considers an image as a 1-dimensional sequence of patches, (2) it prepends a classification token to the sequence, (3) it passes these patches through a transformer encoder (like [BERT](https://arxiv.org/abs/1810.04805)), (4) it passes the first token of the output of the transformer through a small MLP to obtain the classification logits. 
+ViT is trained on a large-scale dataset (ImageNet-21k) with a huge amount of compute. 
+
+<div style="text-align: center; padding: 10px">
+    <img src="https://raw.githubusercontent.com/google-research/vision_transformer/master/figure1.png" width="100%" style="max-width: 300px; margin: auto"/>
+</div>
+
+
+### About ViT-PyTorch
+
+ViT-PyTorch is a PyTorch re-implementation of ViT. It is consistent with the [original Jax implementation](https://github.com/google-research/vision_transformer), so that it's easy to load Jax-pretrained weights.
+
+At the same time, we aim to make our PyTorch implementation as simple, flexible, and extensible as possible.
+
+### Installation
+
+Install with pip:
+```bash
+pip install pytorch_pretrained_vit
+```
+
+Or from source:
+```bash
+git clone https://github.com/lukemelas/ViT-PyTorch
+cd ViT-Pytorch
+pip install -e .
+```
+
+### Usage
+
+#### Loading pretrained models
+
+Loading a pretrained model is easy:
+```python
+from pytorch_pretrained_vit import ViT
+model = ViT('B_16_imagenet1k', pretrained=True)
+```
+
+Details about the models are below:
+
+|    *Name*         |* Pretrained on *|*Finetuned on*|*Available? *|
+|:-----------------:|:---------------:|:------------:|:-----------:|
+| `B_16`            |  ImageNet-21k   | -            |      ✓      |
+| `B_32`            |  ImageNet-21k   | -            |      ✓      |
+| `L_16`            |  ImageNet-21k   | -            |      -      |
+| `L_32`            |  ImageNet-21k   | -            |      ✓      |
+| `B_16_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+| `B_32_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+| `L_16_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+| `L_32_imagenet1k` |  ImageNet-21k   | ImageNet-1k  |      ✓      |
+
+#### Custom ViT
+
+Loading custom configurations is just as easy: 
+```python
+from pytorch_pretrained_vit import ViT
+# The following is equivalent to ViT('B_16')
+config = dict(hidden_size=512, num_heads=8, num_layers=6)
+model = ViT.from_config(config)
+```
+
+#### Example: Classification
+
+Below is a simple, complete example. It may also be found as a Jupyter notebook in `examples/simple` or as a [Colab Notebook]().  
+<!-- TODO: new Colab -->
+
+```python
+import json
+from PIL import Image
+import torch
+from torchvision import transforms
+
+# Load ViT
+from pytorch_pretrained_vit import ViT
+model = ViT('B_16_imagenet1k', pretrained=True)
+model.eval()
+
+# Load image
+# NOTE: Assumes an image `img.jpg` exists in the current directory
+img = transforms.Compose([
+    transforms.Resize((384, 384)), 
+    transforms.ToTensor(),
+    transforms.Normalize(0.5, 0.5),
+])(Image.open('img.jpg')).unsqueeze(0)
+print(img.shape) # torch.Size([1, 3, 384, 384])
+
+# Classify
+with torch.no_grad():
+    outputs = model(img)
+print(outputs.shape)  # (1, 1000)
+```
+
+<!-- #### Example: Feature Extraction
+
+You can easily extract features with `model.extract_features`:
+```python
+from efficientnet_pytorch import EfficientNet
+model = EfficientNet.from_pretrained('efficientnet-b0')
+
+# ... image preprocessing as in the classification example ...
+print(img.shape) # torch.Size([1, 3, 384, 384])
+
+features = model.extract_features(img)
+print(features.shape) # torch.Size([1, 1280, 7, 7])
+``` -->
+
+<!-- #### Example: Export to ONNX
+
+Exporting to ONNX for deploying to production is now simple:
+```python
+import torch
+from efficientnet_pytorch import EfficientNet
+
+model = EfficientNet.from_pretrained('efficientnet-b1')
+dummy_input = torch.randn(10, 3, 240, 240)
+
+model.set_swish(memory_efficient=False)
+torch.onnx.export(model, dummy_input, "test-b1.onnx", verbose=True)
+```
+
+[Here](https://colab.research.google.com/drive/1rOAEXeXHaA8uo3aG2YcFDHItlRJMV0VP) is a Colab example. -->
+
+
+#### ImageNet
+
+See `examples/imagenet` for details about evaluating on ImageNet.
+
+#### Credit
+
+Other great repositories with this model include: 
+ - [Ross Wightman's repo](https://github.com/rwightman/pytorch-image-models)
+ - [Phil Wang's repo](https://github.com/lucidrains/vit-pytorch)
+
+### Contributing
+
+If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.
+
+I look forward to seeing what the community does with these models!
+
+%prep
+%autosetup -n pytorch-pretrained-vit-0.0.7
+
+%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-pytorch-pretrained-vit -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.7-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..66af2a3
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f868742c7a9a78993493e1fdfaf128bf  pytorch-pretrained-vit-0.0.7.tar.gz