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Migrate video classification example to keras 3.0 #1674

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merged 6 commits into from
Dec 8, 2023
Merged

Migrate video classification example to keras 3.0 #1674

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ef4c53f
migrate video classification example to keras 3
GuichardVictor Dec 7, 2023
0f2fece
update ckpt path
GuichardVictor Dec 7, 2023
872ee72
update last updated date
GuichardVictor Dec 7, 2023
02aec1b
unfixed the backend used in the example
GuichardVictor Dec 8, 2023
41fd86d
update last updated date
GuichardVictor Dec 8, 2023
2643cdd
runned autogen for md and ipynb files
GuichardVictor Dec 8, 2023
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39 changes: 26 additions & 13 deletions examples/vision/ipynb/video_classification.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
"\n",
"**Author:** [Sayak Paul](https://twitter.com/RisingSayak)<br>\n",
"**Date created:** 2021/05/28<br>\n",
"**Last modified:** 2023/08/28<br>\n",
"**Last modified:** 2023/12/08<br>\n",
"**Description:** Training a video classifier with transfer learning and a recurrent model on the UCF101 dataset."
]
},
Expand Down Expand Up @@ -72,7 +72,7 @@
},
"outputs": [],
"source": [
"!wget -q https://github.com/sayakpaul/Action-Recognition-in-TensorFlow/releases/download/v1.0.0/ucf101_top5.tar.gz\n",
"!!wget -q https://github.com/sayakpaul/Action-Recognition-in-TensorFlow/releases/download/v1.0.0/ucf101_top5.tar.gz\n",
"!tar xf ucf101_top5.tar.gz"
]
},
Expand All @@ -93,17 +93,17 @@
},
"outputs": [],
"source": [
"from tensorflow_docs.vis import embed\n",
"from tensorflow import keras\n",
"import os\n",
"\n",
"import keras\n",
"from imutils import paths\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"import pandas as pd\n",
"import numpy as np\n",
"import imageio\n",
"import cv2\n",
"import os"
"from IPython.display import Image"
]
},
{
Expand Down Expand Up @@ -314,7 +314,7 @@
" num_samples = len(df)\n",
" video_paths = df[\"video_name\"].values.tolist()\n",
" labels = df[\"tag\"].values\n",
" labels = label_processor(labels[..., None]).numpy()\n",
" labels = keras.ops.convert_to_numpy(label_processor(labels[..., None]))\n",
"\n",
" # `frame_masks` and `frame_features` are what we will feed to our sequence model.\n",
" # `frame_masks` will contain a bunch of booleans denoting if a timestep is\n",
Expand All @@ -331,7 +331,13 @@
" frames = frames[None, ...]\n",
"\n",
" # Initialize placeholders to store the masks and features of the current video.\n",
" temp_frame_mask = np.zeros(shape=(1, MAX_SEQ_LENGTH,), dtype=\"bool\")\n",
" temp_frame_mask = np.zeros(\n",
" shape=(\n",
" 1,\n",
" MAX_SEQ_LENGTH,\n",
" ),\n",
" dtype=\"bool\",\n",
" )\n",
" temp_frame_features = np.zeros(\n",
" shape=(1, MAX_SEQ_LENGTH, NUM_FEATURES), dtype=\"float32\"\n",
" )\n",
Expand Down Expand Up @@ -388,6 +394,7 @@
},
"outputs": [],
"source": [
"\n",
"# Utility for our sequence model.\n",
"def get_sequence_model():\n",
" class_vocab = label_processor.get_vocabulary()\n",
Expand Down Expand Up @@ -415,7 +422,7 @@
"\n",
"# Utility for running experiments.\n",
"def run_experiment():\n",
" filepath = \"/tmp/video_classifier\"\n",
" filepath = \"/tmp/video_classifier/ckpt.weights.h5\"\n",
" checkpoint = keras.callbacks.ModelCheckpoint(\n",
" filepath, save_weights_only=True, save_best_only=True, verbose=1\n",
" )\n",
Expand Down Expand Up @@ -471,7 +478,13 @@
"\n",
"def prepare_single_video(frames):\n",
" frames = frames[None, ...]\n",
" frame_mask = np.zeros(shape=(1, MAX_SEQ_LENGTH,), dtype=\"bool\")\n",
" frame_mask = np.zeros(\n",
" shape=(\n",
" 1,\n",
" MAX_SEQ_LENGTH,\n",
" ),\n",
" dtype=\"bool\",\n",
" )\n",
" frame_features = np.zeros(shape=(1, MAX_SEQ_LENGTH, NUM_FEATURES), dtype=\"float32\")\n",
"\n",
" for i, batch in enumerate(frames):\n",
Expand Down Expand Up @@ -502,7 +515,7 @@
"def to_gif(images):\n",
" converted_images = images.astype(np.uint8)\n",
" imageio.mimsave(\"animation.gif\", converted_images, duration=100)\n",
" return embed.embed_file(\"animation.gif\")\n",
" return Image(\"animation.gif\")\n",
"\n",
"\n",
"test_video = np.random.choice(test_df[\"video_name\"].values.tolist())\n",
Expand All @@ -523,7 +536,7 @@
"from video frames. You could also fine-tune the pre-trained network to notice how that\n",
"affects the end results.\n",
"* For speed-accuracy trade-offs, you can try out other models present inside\n",
"`tf.keras.applications`.\n",
"`keras.applications`.\n",
"* Try different combinations of `MAX_SEQ_LENGTH` to observe how that affects the\n",
"performance.\n",
"* Train on a higher number of classes and see if you are able to get good performance.\n",
Expand Down Expand Up @@ -571,4 +584,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}
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