Add MobileViTv2 (#22820)

* generated code from add-new-model-like * Add code for modeling, config, and weight conversion * add tests for image-classification, update modeling and config * add code, tests for semantic-segmentation * make style, make quality, make fix-copies * make fix-copies * Update modeling_mobilevitv2.py fix bugs * Update _toctree.yml * update modeling, config fix bugs * Edit docs - fix bug MobileViTv2v2 -> MobileViTv2 * Update mobilevitv2.mdx * update docstrings * Update configuration_mobilevitv2.py make style * Update convert_mlcvnets_to_pytorch.py remove unused options * Update convert_mlcvnets_to_pytorch.py make style * Add suggestions from code review Co-Authored-By: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * make style, make quality * Add suggestions from code review Co-Authored-By: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Add suggestions from code review Remove MobileViTv2ImageProcessor Co-Authored-By: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * make style * Add suggestions from code review Rename MobileViTv2 -> MobileViTV2 Co-Authored-By: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Add suggestions from code review Co-Authored-By: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update modeling_mobilevitv2.py make style * Update serialization.mdx * Update modeling_mobilevitv2.py --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
2023-06-02 15:07:02 +05:30
parent 5dfd407b37
commit 07c54413ac
24 changed files with 2116 additions and 3 deletions
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -494,6 +494,8 @@
        title: MobileNetV2
      - local: model_doc/mobilevit
        title: MobileViT
+      - local: model_doc/mobilevitv2
+        title: MobileViTV2
      - local: model_doc/nat
        title: NAT
      - local: model_doc/poolformer
--- a/docs/source/en/index.mdx
+++ b/docs/source/en/index.mdx
@@ -167,6 +167,7 @@ The documentation is organized into five sections:
 1. **[MobileNetV1](model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
 1. **[MobileNetV2](model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
 1. **[MobileViT](model_doc/mobilevit)** (from Apple) released with the paper [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari.
+1. **[MobileViTV2](model_doc/mobilevitv2)** (from Apple) released with the paper [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari.
 1. **[MPNet](model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
 1. **[MT5](model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
 1. **[MVP](model_doc/mvp)** (from RUC AI Box) released with the paper [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
@@ -369,6 +370,7 @@ Flax), PyTorch, and/or TensorFlow.
 |          MobileNetV1          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          MobileNetV2          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           MobileViT           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|          MobileViTV2          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             MPNet             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |              MT5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |              MVP              |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
--- a/docs/source/en/model_doc/mobilevitv2.mdx
+++ b/docs/source/en/model_doc/mobilevitv2.mdx
@@ -0,0 +1,53 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# MobileViTV2
+
+## Overview
+
+The MobileViTV2 model was proposed in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari.
+
+MobileViTV2 is the second version of MobileViT, constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.
+
+The abstract from the paper is the following:
+
+*Mobile vision transformers (MobileViT) can achieve state-of-the-art performance across several mobile vision tasks, including classification and detection. Though these models have fewer parameters, they have high latency as compared to convolutional neural network-based models. The main efficiency bottleneck in MobileViT is the multi-headed self-attention (MHA) in transformers, which requires O(k2) time complexity with respect to the number of tokens (or patches) k. Moreover, MHA requires costly operations (e.g., batch-wise matrix multiplication) for computing self-attention, impacting latency on resource-constrained devices. This paper introduces a separable self-attention method with linear complexity, i.e. O(k). A simple yet effective characteristic of the proposed method is that it uses element-wise operations for computing self-attention, making it a good choice for resource-constrained devices. The improved model, MobileViTV2, is state-of-the-art on several mobile vision tasks, including ImageNet object classification and MS-COCO object detection. With about three million parameters, MobileViTV2 achieves a top-1 accuracy of 75.6% on the ImageNet dataset, outperforming MobileViT by about 1% while running 3.2× faster on a mobile device.*
+
+Tips:
+
+- MobileViTV2 is more like a CNN than a Transformer model. It does not work on sequence data but on batches of images. Unlike ViT, there are no embeddings. The backbone model outputs a feature map.
+- One can use [`MobileViTImageProcessor`] to prepare images for the model. Note that if you do your own preprocessing, the pretrained checkpoints expect images to be in BGR pixel order (not RGB).
+- The available image classification checkpoints are pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k) (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes).
+- The segmentation model uses a [DeepLabV3](https://arxiv.org/abs/1706.05587) head. The available semantic segmentation checkpoints are pre-trained on [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/).
+
+This model was contributed by [shehan97](https://huggingface.co/shehan97).
+The original code can be found [here](https://github.com/apple/ml-cvnets).
+
+
+## MobileViTV2Config
+
+[[autodoc]] MobileViTV2Config
+
+## MobileViTV2Model
+
+[[autodoc]] MobileViTV2Model
+    - forward
+
+## MobileViTV2ForImageClassification
+
+[[autodoc]] MobileViTV2ForImageClassification
+    - forward
+
+## MobileViTV2ForSemanticSegmentation
+
+[[autodoc]] MobileViTV2ForSemanticSegmentation
+    - forward
--- a/docs/source/en/tasks/image_classification.mdx
+++ b/docs/source/en/tasks/image_classification.mdx
@@ -30,7 +30,7 @@ The task illustrated in this tutorial is supported by the following model archit

 <!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->

-[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [ConvNeXT](../model_doc/convnext), [ConvNeXTV2](../model_doc/convnextv2), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [EfficientFormer](../model_doc/efficientformer), [EfficientNet](../model_doc/efficientnet), [FocalNet](../model_doc/focalnet), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [SwiftFormer](../model_doc/swiftformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
+[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [ConvNeXT](../model_doc/convnext), [ConvNeXTV2](../model_doc/convnextv2), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [EfficientFormer](../model_doc/efficientformer), [EfficientNet](../model_doc/efficientnet), [FocalNet](../model_doc/focalnet), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [SwiftFormer](../model_doc/swiftformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
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 </Tip>
--- a/docs/source/en/tasks/semantic_segmentation.mdx
+++ b/docs/source/en/tasks/semantic_segmentation.mdx
@@ -28,7 +28,7 @@ The task illustrated in this tutorial is supported by the following model archit

 <!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->

-[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)
+[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)

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