Readme for model (ivy-llc#86)

* made readme file for bert * made readme file for unet * made changes as requested
HaiderSultanArc · Aug 29, 2023 · cb97624 · cb97624
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diff --git a/ivy_models/bert/README.rst b/ivy_models/bert/README.rst
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+.. image:: https://github.com/unifyai/unifyai.github.io/blob/main/img/externally_linked/logo.png?raw=true#gh-light-mode-only
+   :width: 100%
+   :class: only-light
+
+.. image:: https://github.com/unifyai/unifyai.github.io/blob/main/img/externally_linked/logo_dark.png?raw=true#gh-dark-mode-only
+   :width: 100%
+   :class: only-dark
+
+
+.. raw:: html
+
+    <br/>
+    <a href="https://pypi.org/project/ivy-models">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://badge.fury.io/py/ivy-models.svg">
+    </a>
+    <a href="https://github.com/unifyai/models/actions?query=workflow%3Adocs">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://github.com/unifyai/models/actions/workflows/docs.yml/badge.svg">
+    </a>
+    <a href="https://github.com/unifyai/models/actions?query=workflow%3Anightly-tests">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://github.com/unifyai/models/actions/workflows/nightly-tests.yml/badge.svg">
+    </a>
+    <a href="https://discord.gg/G4aR9Q7DTN">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://img.shields.io/discord/799879767196958751?color=blue&label=%20&logo=discord&logoColor=white">
+    </a>
+    <br clear="all" />
+
+BERT
+===========
+
+`BERT <https://arxiv.org/abs/1810.04805>`_ short for Bidirectional Encoder Representations from Transformers, differentiates itself from 
+recent language representation models by its focus on pretraining deep bidirectional representations from unannotated text. 
+This approach involves considering both left and right context in all layers.
+Consequently, the pretrained BERT model can be enhanced with just a single additional output layer to excel in various tasks, 
+such as question answering and language inference. This achievement is possible without extensive modifications to task-specific architecture.
+
+Getting started
+-----------------
+
+.. code-block:: python
+
+    import ivy
+    ivy.set_backend("torch")
+
+    # Instantiate Bert
+    ivy_bert = ivy_models.bert_base_uncased(pretrained=True)
+
+    # Convert the input data to Ivy tensors
+    ivy_inputs = {k: ivy.asarray(v.numpy()) for k, v in inputs.items()}
+
+    # Compile the Ivy BERT model with the Ivy input tensors
+    ivy_bert.compile(kwargs=ivy_inputs)
+
+    # Pass the Ivy input tensors through the Ivy BERT model and obtain the pooler output
+    ivy_output = ivy_bert(**ivy_inputs)['pooler_output']
+
+
+See `this demo <https://github.com/unifyai/demos/blob/main/examples_and_demos/bert_demo.ipynb>`_ for more usage example.
+
+Citation
+--------
+
+::
+
+    @article{
+      title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
+      author={Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova},
+      journal={arXiv preprint arXiv:1810.04805},
+      year={2019}
+    }
+
+
+    @article{lenton2021ivy,
+      title={Ivy: Templated deep learning for inter-framework portability},
+      author={Lenton, Daniel and Pardo, Fabio and Falck, Fabian and James, Stephen and Clark, Ronald},
+      journal={arXiv preprint arXiv:2102.02886},
+      year={2021}
+    }
diff --git a/ivy_models/unet/README.rst b/ivy_models/unet/README.rst
@@ -0,0 +1,93 @@
+.. image:: https://github.com/unifyai/unifyai.github.io/blob/main/img/externally_linked/logo.png?raw=true#gh-light-mode-only
+   :width: 100%
+   :class: only-light
+
+.. image:: https://github.com/unifyai/unifyai.github.io/blob/main/img/externally_linked/logo_dark.png?raw=true#gh-dark-mode-only
+   :width: 100%
+   :class: only-dark
+
+
+.. raw:: html
+
+    <br/>
+    <a href="https://pypi.org/project/ivy-models">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://badge.fury.io/py/ivy-models.svg">
+    </a>
+    <a href="https://github.com/unifyai/models/actions?query=workflow%3Adocs">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://github.com/unifyai/models/actions/workflows/docs.yml/badge.svg">
+    </a>
+    <a href="https://github.com/unifyai/models/actions?query=workflow%3Anightly-tests">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://github.com/unifyai/models/actions/workflows/nightly-tests.yml/badge.svg">
+    </a>
+    <a href="https://discord.gg/G4aR9Q7DTN">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://img.shields.io/discord/799879767196958751?color=blue&label=%20&logo=discord&logoColor=white">
+    </a>
+    <br clear="all" />
+
+U-Net
+===========
+
+`Unet <https://arxiv.org/abs/1505.04597>`_ The UNET architecture and training approach effectively leverage data augmentation to make the most of 
+available annotated samples, even with limited data. The design features a contracting path for context capture and a symmetric expanding path 
+for precise localization. Notably, this UNET network achieves superior performance with minimal images during end-to-end training. 
+It surpasses previous methods, including a sliding-window convolutional network, in the ISBI challenge for segmenting neuronal structures in 
+electron microscopic stacks. Additionally, the same UNET model excels in the ISBI cell tracking challenge for transmitted light microscopy images 
+(phase contrast and DIC), showcasing its versatility. Furthermore, the network demonstrates remarkable speed, capable of segmenting a 512x512 image 
+in under a second using a modern GPU.
+
+Getting started
+-----------------
+
+.. code-block:: python
+
+    import ivy
+    import ivy_models
+    ivy.set_backend("torch")
+
+    # load the unet model from ivy_models
+    ivy_unet = ivy_models.unet_carvana(n_channels=3, n_classes=2, pretrained=True)
+
+    # Preprocess image with preprocess function
+    from PIL import Image
+    !wget https://raw.githubusercontent.com/unifyai/models/master/images/car.jpg
+    filename = "car.jpg"
+    full_img = Image.open(filename)
+    torch_img = torch.from_numpy(preprocess(None, full_img, 0.5, False)).unsqueeze(0).to("cuda")
+
+    # Convert to ivy
+    ivy.set_backend("torch")
+    img = ivy.asarray(torch_img.permute((0, 2, 3, 1)), dtype="float32", device="gpu:0")
+    img_numpy = img.cpu().numpy()
+
+    # Compile the forward pass
+    ivy_unet.compile(args=(img,))
+
+    # Generating the mask 
+    output = ivy_unet(img)
+    output = ivy.interpolate(output.permute((0, 3, 1, 2)), (full_img.size[1], full_img.size[0]), mode="bilinear")
+    mask = output.argmax(axis=1)
+    mask = ivy.squeeze(mask[0], axis=None).to_numpy()
+    result = mask_to_image(mask, [0,1])
+
+
+See `this demo <https://github.com/unifyai/demos/blob/main/examples_and_demos/image_segmentation_with_ivy_unet.ipynb>`_ for more usage example.
+
+Citation
+--------
+
+::
+
+    @article{
+      title={U-Net: Convolutional Networks for Biomedical Image Segmentation},
+      author={Olaf Ronneberger, Philipp Fischer and Thomas Brox},
+      journal={arXiv preprint arXiv:1505.04597},
+      year={2015}
+    }
+
+
+    @article{lenton2021ivy,
+      title={Ivy: Templated deep learning for inter-framework portability},
+      author={Lenton, Daniel and Pardo, Fabio and Falck, Fabian and James, Stephen and Clark, Ronald},
+      journal={arXiv preprint arXiv:2102.02886},
+      year={2021}
+    }