diff --git a/chapters/en/unit0/welcome/TableOfContents.mdx b/chapters/en/unit0/welcome/TableOfContents.mdx index 111dedeee..6fb3ac593 100644 --- a/chapters/en/unit0/welcome/TableOfContents.mdx +++ b/chapters/en/unit0/welcome/TableOfContents.mdx @@ -26,7 +26,7 @@ Feel free to browse them at your own speed and interest. | | [Image Similarity](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%204%20-%20Multimodal%20Models/CLIP%20and%20relatives/Image_similarity.ipynb) | [Image Similarity](https://colab.research.google.com/github/fariddinar/computer-vision-course/blob/main/notebooks/Unit%204%20-%20Multimodal%20Models/CLIP%20and%20relatives/Image_similarity.ipynb) | | Unit 5 - Generative Models | No Notebook | No Colab | | Unit 6 - Basic CV Tasks | [Fine-tune SAM on Custom Dataset]() | [Fine-tune SAM on Custom Dataset]() | -| Unit 7 - Video and Video Processing | No Notebook | No Colab | +| Unit 7 - Video and Video Processing | [Fine-tune ViViT for Video Classification](https://github.com/DiwakarBasnet/computer-vision-course/blob/unit-7_Video_and_VideoProcessing/notebooks/Unit%207%20-%20Video%20and%20Video%20Processing/Vivit_Fine_tuned_Video_Classification.ipynb) | [Fine-tune ViViT for Video Classification](https://github.com/DiwakarBasnet/computervisioncourse/blob/unit7_Video_and_VideoProcessing/notebooks/Unit%207%20%20Video%20and%20Video%20Processing/Vivit_Fine_tuned_Video_Classification.ipynb) | | Unit 8 - 3D Vision, Scene Rendering, and Reconstruction | No Notebook | No Colab | | Unit 9 - Model Optimization | [Edge TPU](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/edge_tpu.ipynb) | [Edge TPU](https://colab.research.google.com/github/fariddinar/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/edge_tpu.ipynb) | | | [ONNX](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/onnx.ipynb) | [ONNX](https://colab.research.google.com/github/fariddinar/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/onnx.ipynb) | diff --git a/notebooks/Unit 7 - Video and Video Processing/Vivit_Fine_tuned_Video_Classification.ipynb b/notebooks/Unit 7 - Video and Video Processing/Vivit_Fine_tuned_Video_Classification.ipynb new file mode 100644 index 000000000..ee36aa336 --- /dev/null +++ b/notebooks/Unit 7 - Video and Video Processing/Vivit_Fine_tuned_Video_Classification.ipynb @@ -0,0 +1,6390 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "4fe1a498f90e4b37b1b3b4ad83cf91b2": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a885db853b01424bae352ef9003d12e2", + "IPY_MODEL_cefb31705ae34a6d9053d0dcd909fdfb", + "IPY_MODEL_c811c761a0cb4f37882233eacdae6a13", + "IPY_MODEL_d9380b3e2802443ca7c15c54448de1af" + ], + "layout": "IPY_MODEL_306b38aa31044fe2a9e37f0b8997a833" + } + }, + "d190cb16248641bd92b8fbe0820c98b4": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_74532c0fb8f642da870af9adccdb1e53", + "placeholder": "​", + "style": "IPY_MODEL_299e031dbc564e269e9be5ea3bc1be18", + "value": "

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}, + { + "cell_type": "markdown", + "source": [ + "## Introduction" + ], + "metadata": { + "id": "4r9-V7wUAucR" + } + }, + { + "cell_type": "markdown", + "source": [ + "Video classification is a task in machine learning where algorithms are trained to recognize and categorize the content of videos into predefined classes or categories by analyzing the spatial and temporal information present in the video frames. The field of video classification has been dominated by deep learning models that leverage convolutional neural networks (CNNs) for a long now. However, the emergence of transformer based models has sparked a revolution across various domains, including computer vision.\n", + "\n", + "Video classification has various applications including action recognition, surveillance, content filtering, and recommendation systems. One of the transformers based algorithms developed for video classification is ViViT, which is also one of the initial successful pure transformer architecture model for video processing and understanding.\n", + "\n", + "The Video Vision Transformer (ViViT) is like an upgraded version of Vision Transformer (ViT), but for videos instead of just images. The reason we need a separate algorithm for videos is because ViT, the one for images, only looks at each frame individually without considering how they relate to each other over time. But understanding how frames connect in a video is crucial for truly understanding its content, so we need a special algorithm, like ViViT, that can handle that temporal aspect. It has set new benchmarks for video classification accuracy. The model outperforms previous methodologies based on deep 3D convolutional networks across a variety of video classification benchmarks." + ], + "metadata": { + "id": "fh5OfYw9AxKE" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Installation" + ], + "metadata": { + "id": "JDQFEgcHdGEW" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "REjcTBIYrpOn" + }, + "outputs": [], + "source": [ + "# Install required libraries\n", + "!pip install pytorchvideo evaluate accelerate transformers > /dev/null 2>&1" + ] + }, + { + "cell_type": "markdown", + "source": [ + "We will now uninstall the latest version of torchvision and depricate it to version 0.14.1 since we are going to use packages and functions in pytorchvideo that are no longer supported in new versions." + ], + "metadata": { + "id": "0hFJR3RjryTY" + } + }, + { + "cell_type": "code", + "source": [ + "!pip uninstall -y torchvision > /dev/null 2>&1\n", + "!pip install torchvision==0.14.1 > /dev/null 2>&1" + ], + "metadata": { + "id": "qjHUi8uLYkzM" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Authentication" + ], + "metadata": { + "id": "Ahz4UqE5vj_R" + } + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "4fe1a498f90e4b37b1b3b4ad83cf91b2", + "d190cb16248641bd92b8fbe0820c98b4", + "20d93cfe8dcf43cfa0d2e7f7a9ea2afc", + "6f2a4f367bcd42c6a500ab61a79e705c", + "7e9f00ae7acf4e28932669e044627c49", + "9b1c4e5b3d1145c59e4f80e21d5e1815", + "306b38aa31044fe2a9e37f0b8997a833", + "74532c0fb8f642da870af9adccdb1e53", + "299e031dbc564e269e9be5ea3bc1be18", + "e5082f35ff3a4ea984d856abb2516c8e", + "bcb81a9e63f04c7fb5b8f7ca53e6b2d1", + "4e4204bc814a4185b95aed66c8c07a3c", + "5625e6e3bab04879860b6421012051d6", + "f3f9da4cff924e85aab8886c8712952f", + "bbe7798a1cc6491688e2260bcf1600eb", + "11a99a42ad2c4602995715c7944b4fb9", + "c357a680046c49529bb8c2c35758d135", + "eb51a4be61ee4e939929f8627a416cd2", + "f006d7d59678475ca190801e67fa05e3", + "d9ab970258764074adff455c6262f527", + "a885db853b01424bae352ef9003d12e2", + "cefb31705ae34a6d9053d0dcd909fdfb", + "c811c761a0cb4f37882233eacdae6a13", + "d9380b3e2802443ca7c15c54448de1af", + "7f2937adfa3644fdaa7b712bb13d8ae6", + "73ec36b0fc2e47c0bbdbf67bb26de2b2", + "14e56564858d448485594597305e10b5", + "ce823b9584d6413493e10580e56d0c6c", + "a059a901f5c54557bdd7e27f5ef08156", + "431f4b2941ff414a90bc45c8ba485ae0", + "43ad112b85fc4a658d588106bbc376ba", + "2f6e013db6074780afacfe3f3bd0dbab" + ] + }, + "id": "TGi9tQbNvssf", + "outputId": "1c100b33-3aab-40d6-e930-f41b18961fda" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Train the model" + ], + "metadata": { + "id": "Ez6OM0aRxqcD" + } + }, + { + "cell_type": "markdown", + "source": [ + "We'll leverage Trainer from 🤗 Transformers for training the model. To instantiate a Trainer, we will need to define the training configuration and an evaluation metric. We will also use the TrainingArguments class, which contains all the attributes to configure the training. It requires an output folder name, which will be used to save the checkpoints of the model.\n", + "\n", + "Most of the training arguments are pretty self-explanatory, but one that is quite important here is remove_unused_columns=False. This one will drop any features not used by the model's call function. By default it's True because usually it's ideal to drop unused feature columns, making it easier to unpack inputs into the model's call function. However, in our case, we need the unused features ('***video***' in particular) in order to create pixel_values which is expected by our model in its inputs." + ], + "metadata": { + "id": "dvSo7BYy_ARJ" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import TrainingArguments, Trainer\n", + "\n", + "model_name = model_checkpt.split(\"/\")[-1]\n", + "new_model_name = f\"{model_name}-finetuned-ucf101-subset\"\n", + "num_epochs = 5\n", + "\n", + "args = TrainingArguments(\n", + " new_model_name,\n", + " auto_find_batch_size=True,\n", + " remove_unused_columns=False,\n", + " evaluation_strategy=\"epoch\",\n", + " save_strategy=\"epoch\",\n", + " learning_rate=5e-5,\n", + " per_device_train_batch_size=batch_size,\n", + " per_device_eval_batch_size=batch_size,\n", + " warmup_ratio=0.1,\n", + " logging_steps=10,\n", + " load_best_model_at_end=True,\n", + " metric_for_best_model=\"accuracy\",\n", + " push_to_hub=False,\n", + " max_steps=(train_dataset.num_videos // batch_size) * num_epochs,\n", + " num_train_epochs=num_epochs,\n", + ")" + ], + "metadata": { + "id": "tsiOWwikxtRN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4d1f0264-1539-4117-ea49-97ac3223c05f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Next we'll need a function for evaluating the accuracy of our model for training purpose." + ], + "metadata": { + "id": "PDYJvt-UCXUs" + } + }, + { + "cell_type": "code", + "source": [ + "import evaluate\n", + "\n", + "metric = evaluate.load(\"accuracy\")\n", + "\n", + "def compute_metrics(eval_pred):\n", + " \"\"\"Computes accuracy on a batch of predictions.\"\"\"\n", + " predictions = np.argmax(eval_pred.predictions, axis=1)\n", + " return metric.compute(predictions=predictions, references=eval_pred.label_ids)" + ], + "metadata": { + "id": "_XsjL0RSxwtE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We'll also need to create a collate function for creating batches of data for training purpose. Each batch consists of 2 keys, pixel values and labels." + ], + "metadata": { + "id": "wIoE5kugCiUq" + } + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "\n", + "def collate_fn(examples):\n", + " \"\"\"The collation function to be used by `Trainer` to prepare data batches.\"\"\"\n", + " # permute to (num_frames, num_channels, height, width)\n", + " pixel_values = torch.stack(\n", + " [example[\"video\"].permute(1, 0, 2, 3) for example in examples]\n", + " )\n", + " labels = torch.tensor([example[\"label\"] for example in examples])\n", + " return {\"pixel_values\": pixel_values, \"labels\": labels}" + ], + "metadata": { + "id": "ieLEdVQQx3Tn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "trainer = Trainer(\n", + " model,\n", + " args,\n", + " train_dataset=train_dataset,\n", + " eval_dataset=val_dataset,\n", + " tokenizer=image_processor,\n", + " compute_metrics=compute_metrics,\n", + " data_collator=collate_fn,\n", + ")" + ], + "metadata": { + "id": "tjglLfCox5LK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ab5c11c4-1a9b-401f-fe77-da02ffb175db" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "max_steps is given, it will override any value given in num_train_epochs\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "train_results = trainer.train()" + ], + "metadata": { + "id": "VEs2qvXFx69U", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 138 + }, + "outputId": "eb17a3ea-553b-4e67-c968-65b798a0df06" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
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" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "trainer.save_model()\n", + "test_results = trainer.evaluate(test_dataset)\n", + "trainer.log_metrics(\"test\", test_results)\n", + "trainer.save_metrics(\"test\", test_results)\n", + "trainer.save_state()" + ], + "metadata": { + "id": "m4np55a3x-VT", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "04429e1e-073d-4ebd-abc5-836a20a42f02" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "***** test metrics *****\n", + " epoch = 1.2\n", + " eval_accuracy = 1.0\n", + " eval_loss = 0.0122\n", + " eval_runtime = 0:01:00.68\n", + " eval_samples_per_second = 1.434\n", + " eval_steps_per_second = 0.363\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We can now upload our training result to the hub." + ], + "metadata": { + "id": "vx5y4mKZDpWi" + } + }, + { + "cell_type": "code", + "source": [ + "trainer.push_to_hub()" + ], + "metadata": { + "id": "OaYtbggjyAx8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 244, + "referenced_widgets": [ + "ba73a0b4b78342e195c8d56188870af0", + "c062c3b3f62f4ac8b2c50741ef14ccbe", + "d24fe51b6fc4405eb38d689f4547e62f", + "52cc999f539f43c8876b54c4644c1cec", + "e577cfe812e440a8a288f7b1a1c1520c", + "f39e1157966946608e08a799c8ba1776", + "e259a5c2421646a390baf8c5ea9a1b26", + "fe0d33c17e8b4016bd364c64d9e2255a", + "f1e8b3d5970241a58e6fb31f3268725a", + "f855f923f187460b84d512869a8683c7", + "92f7179d482b472cb1ab16ee83b0295a", + "c97dd9d0dcf44d68b7cc6def43b9516b", + "6e5e6fd19af34048a4ca80f4b6a2fe5b", + "8bb9c14a17044ffe87ce6b65a9295bc8", + "c39c477555be472d979ae246eedb9c5f", + "c6a45a8269c44af48f6ce851b5c5c9e0", + "1f1a524105664da3ac4d672dc81d0567", + "790beefea6a4491a9337b8295f8eb712", + "a1f91db399f147ffa151d894ef4d1000", + "aa1bedb37b5f4be7b41e16b389220a43", + "875b0b523f794c7f8f225fae23a21b6b", + "9df124317af3485181f1330e89ffbc31", + "9cd2ebbdd3274ed89fb438433c345f11", + "d244bc7e15914904ad7781c3eea0185e", + "4f61773e6d38431b8d1e74c36b885ac8", + "2d3dfcf4d2ac47ba875577ed03f2aee2", + "c964cf0d5fae4839957134ffb3d36946", + "144fcc757bfc463f860298bae09bc306", + "3799cbb9a1a94de59bec43c3a3a036de", + "5b99ce1a407b4e68b4af17172737db53", + "8f6bb4229fa7440c9c70bd5491d6a3bd", + "77ca9aac841d45bda3ac1f4ff3c0a48b", + "b306333e0e3c4b15b0ced069b0452605", + "6f72ea2f36374427b02af6bba1ae73e6", + "cb9e13d174624cfead5d52250300f27a", + "97e9468aeb514dfd907453f653ebfe05", + "07bc06213bfe4863bece098190dc9dbe", + "e4c3761e09a6480280f088b00a80cbd6", + "0e91b058199944c9a81fe86b79cbdd91", + "4d76a5e5cb684d56a112365539295f70", + "5c5d414f943a44ad82b0ef157d0053a1", + "3c8b442873ed4af1b4531cc80bfc37ab", + "6946e469524542c7ab6c86efcd31cbdb", + "faab4e2a2bb64a5cb868620e32b9f806", + "0bc2ee4841d845b28d18dbafadc99ca6", + "875c797ef0fa497192224eff4e0eb232", + "dc24ed8ff7b343adbd501e01ec2704bf", + "d94720c230a44b50b3fbcb2fe43edaac", + "6273631d95c64f92ab11233e58f29bf7", + "8ab7328f473b4c2ba5ebb19723c48aba", + "5149e59100dd4786b7d5c13d0c9ea664", + "63fac523b1e4447ebf2767269f280fc1", + "bc5b8c56b3ab4b8bb24def8c8168b6f4", + "c5b1a48f0fb44fa58f88dea4c4dbd5ac", + "512d64b1bf2d4751a60ed8e25cb84a1a", + "d23b159a719642f9b11d18fa0c576085", + "7874fc3181b349aaa7485d8ddf11f915", + "1ba39f95cd7443c5a25b54c3f587ec63", + "2ea426d50bd848f1808a954293f98537", + "78e5a033fbbf409486bac54250227b67", + "0be3f39168084bcb98d23716a0eb11ee", + "111fe69f57c546a39ef0e79e4c1a750c", + "8c032de4c9974dedab4f3ff49ede4757", + "60be9ae8b1b2442fb010577fdea0ac57", + "efecf6471ebb431c87687702cbc6e49b", + "37545942ed5546c59ad8d01cfa1ffb84" + ] + }, + "outputId": "5e9e3798-428e-4104-d512-25ded5f0692d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "events.out.tfevents.1723289519.c82016455c61.1590.3: 0%| | 0.00/24.9k [00:00" + ] + }, + "metadata": {}, + "execution_count": 50 + } + ] + }, + { + "cell_type": "code", + "source": [ + "predicted_class_idx = logits.argmax(-1).item()\n", + "print(\"Predicted class:\", model.config.id2label[predicted_class_idx])" + ], + "metadata": { + "id": "_5gBIfqtySkA", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ab91b1d8-9132-47ab-ac98-cef2224507a9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicted class: BenchPress\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "vIop5V4GqZMQ" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file