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@sayakpaul
Last active August 25, 2022 05:10
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Demonstrates the compatibility between Feather and TensorFlow with a real dataset.
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{
"cells": [
{
"cell_type": "markdown",
"id": "04dd71c8",
"metadata": {},
"source": [
"## Initial setup"
]
},
{
"cell_type": "markdown",
"id": "69e522d3",
"metadata": {},
"source": [
"Install dependencies."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01097ef1",
"metadata": {},
"outputs": [],
"source": [
"!pip install -q transformers datasets tensorflow pyarrow matplotlib tensorflow-io-nightly -q "
]
},
{
"cell_type": "markdown",
"id": "bd8c993f",
"metadata": {},
"source": [
"Gather some data."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "cdd2b176",
"metadata": {},
"outputs": [],
"source": [
"!wget -q https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz -O flower_photos.tgz\n",
"!tar xf flower_photos.tgz"
]
},
{
"cell_type": "markdown",
"id": "bc342911",
"metadata": {},
"source": [
"## Initial imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "252a0988",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"import glob\n",
"import os\n",
"\n",
"from pyarrow.feather import write_feather\n",
"import pyarrow as pa\n",
"import tqdm\n",
"import math"
]
},
{
"cell_type": "markdown",
"id": "9a230672",
"metadata": {},
"source": [
"## Collect all image paths and gather labels"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "74f5bc6f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(['flower_photos/roses/16209331331_343c899d38.jpg',\n",
" 'flower_photos/roses/5777669976_a205f61e5b.jpg',\n",
" 'flower_photos/roses/4860145119_b1c3cbaa4e_n.jpg',\n",
" 'flower_photos/roses/15011625580_7974c44bce.jpg',\n",
" 'flower_photos/roses/17953368844_be3d18cf30_m.jpg'],\n",
" 3670)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image_paths = glob.glob(\"flower_photos/*/*.jpg\")\n",
"image_paths[:5], len(image_paths)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d9d885b5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['roses', 'roses', 'roses', 'roses', 'roses']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_labels = list(map(lambda x: x.split(\"/\")[1], image_paths))\n",
"all_labels[:5]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "46ea492d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_labels = sorted(set(all_labels))\n",
"label2_id = {label: idx for idx, label in enumerate(unique_labels)}\n",
"label2_id"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5face38d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 2, 2, 2, 2]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_integer_labels = list(map(lambda x: label2_id.get(x), all_labels))\n",
"all_integer_labels[:5]"
]
},
{
"cell_type": "markdown",
"id": "19775d1c",
"metadata": {},
"source": [
"## Write feather files"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e1537a28",
"metadata": {},
"outputs": [],
"source": [
"batch_size = 1000\n",
"chunk_size = 1000"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "032795c8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"path: string\n",
"image_bytes: binary\n",
"label: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"schema = pa.schema(\n",
" pa.struct({\"path\": pa.string(), \"image_bytes\": pa.binary(), \"label\": pa.int64()})\n",
")\n",
"schema"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "be22173e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/z_/d29z43w90kz6f4kbzv5c9m9r0000gn/T/ipykernel_36130/1787992456.py:8: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`\n",
" for step in tqdm.tnrange(int(math.ceil(len(image_paths) / batch_size))):\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4adbd0e064be4dc6a3551984c98d7345",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/4 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total images written: 1000.\n",
"Total images written: 2000.\n",
"Total images written: 3000.\n",
"Total images written: 3670.\n"
]
}
],
"source": [
"def read_image(path):\n",
" with open(path, \"rb\") as f:\n",
" return f.read()\n",
"\n",
"\n",
"total_images_written = 0\n",
"\n",
"for step in tqdm.tnrange(int(math.ceil(len(image_paths) / batch_size))):\n",
" batch_image_paths = image_paths[step * batch_size : (step + 1) * batch_size]\n",
" batch_image_labels = all_integer_labels[step * batch_size : (step + 1) * batch_size]\n",
"\n",
" data = [read_image(path) for path in batch_image_paths]\n",
" # table = pa.Table.from_arrays(\n",
" # [batch_image_paths, data, batch_image_labels], schema=schema\n",
" # )\n",
" table = pa.table(\n",
" {\"path\": batch_image_paths, \"image_bytes\": data, \"label\": batch_image_labels}\n",
" )\n",
" write_feather(table, f\"/tmp/flowers_feather_{step}.feather\", chunksize=chunk_size)\n",
" total_images_written += len(batch_image_paths)\n",
" print(f\"Total images written: {total_images_written}.\")\n",
"\n",
" del data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4a2f1b5a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 sayakpaul wheel 64M Aug 25 10:36 /tmp/flowers_feather_0.feather\r\n",
"-rw-r--r-- 1 sayakpaul wheel 59M Aug 25 10:36 /tmp/flowers_feather_1.feather\r\n",
"-rw-r--r-- 1 sayakpaul wheel 51M Aug 25 10:36 /tmp/flowers_feather_2.feather\r\n",
"-rw-r--r-- 1 sayakpaul wheel 45M Aug 25 10:36 /tmp/flowers_feather_3.feather\r\n"
]
}
],
"source": [
"ls -lh /tmp/*.feather"
]
},
{
"cell_type": "markdown",
"id": "3a816c02",
"metadata": {},
"source": [
"## Decoding the feather files"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "16478d1d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.9.1\n",
"0.26.0\n"
]
}
],
"source": [
"import tensorflow_io.arrow as arrow_io\n",
"import tensorflow_io as tfio\n",
"\n",
"print(tf.__version__)\n",
"print(tfio.__version__)"
]
},
{
"cell_type": "markdown",
"id": "af16dee2",
"metadata": {},
"source": [
"### Prepare an `ArrowFeatherDataset` for individual samples"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "449ae824",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-08-25 10:37:01.391456: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2022-08-25 10:37:01.468065: I tensorflow_io/core/kernels/cpu_check.cc:128] Your CPU supports instructions that this TensorFlow IO binary was not compiled to use: AVX2 FMA\n",
"2022-08-25 10:37:01.599849: E tensorflow/core/framework/dataset.cc:580] UNIMPLEMENTED: Cannot compute input sources for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n",
"2022-08-25 10:37:01.599885: E tensorflow/core/framework/dataset.cc:584] UNIMPLEMENTED: Cannot merge options for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n"
]
}
],
"source": [
"dataset = arrow_io.ArrowFeatherDataset(\n",
" [\"/tmp/flowers_feather_0.feather\"],\n",
" columns=(0, 1, 2),\n",
" output_types=(tf.string, tf.string, tf.int64),\n",
" output_shapes=([], [], []),\n",
" batch_size=32,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "155f4331",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))\n"
]
}
],
"source": [
"print(dataset.element_spec)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "652c2e85",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-08-25 10:37:13.277447: E tensorflow/core/framework/dataset.cc:580] UNIMPLEMENTED: Cannot compute input sources for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n",
"2022-08-25 10:37:13.277470: E tensorflow/core/framework/dataset.cc:584] UNIMPLEMENTED: Cannot merge options for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n",
"2022-08-25 10:37:13.279257: E tensorflow/core/framework/dataset.cc:580] FAILED_PRECONDITION: Cannot compute input sources for dataset of type RootDataset, because sources could not be computed for input dataset of type IO>ArrowFeatherDataset\n"
]
}
],
"source": [
"sample = next(iter(dataset))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "42a58bb3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(TensorShape([32]), TensorShape([32]), TensorShape([32]))"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# path, image data, and labels\n",
"sample[0].shape, sample[1].shape, sample[2].shape"
]
},
{
"cell_type": "markdown",
"id": "12527944",
"metadata": {},
"source": [
"## Prepare an end-to-end dataset (`tf.data.Dataset`)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "75fddd4f",
"metadata": {},
"outputs": [],
"source": [
"# https://towardsdatascience.com/e55179eeeb72\n",
"\n",
"\n",
"def decode_image(path, encoded_string, label):\n",
" return path, tf.image.decode_png(encoded_string, channels=3), label\n",
"\n",
"\n",
"def get_dataset(batch_size=32, image_size=224):\n",
" autotune = tf.data.AUTOTUNE\n",
" filenames = tf.data.Dataset.list_files(\"/tmp/*.feather\", shuffle=True)\n",
"\n",
" def make_ds(file):\n",
" ds = arrow_io.ArrowFeatherDataset(\n",
" [file],\n",
" [0, 1, 2],\n",
" output_types=(tf.string, tf.string, tf.int64),\n",
" output_shapes=([], [], []),\n",
" batch_mode=\"auto\",\n",
" )\n",
" return ds\n",
"\n",
" ds = filenames.interleave(make_ds, num_parallel_calls=autotune, deterministic=False)\n",
" ds = ds.unbatch()\n",
" ds = ds.map(decode_image, num_parallel_calls=autotune)\n",
" ds = ds.map(\n",
" lambda x, y, z: (x, tf.image.resize(y, (image_size, image_size)), z),\n",
" num_parallel_calls=autotune,\n",
" )\n",
" return ds.batch(batch_size).prefetch(autotune)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "fe2d4e59",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(TensorSpec(shape=(None,), dtype=tf.string, name=None),\n",
" TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None),\n",
" TensorSpec(shape=(None,), dtype=tf.int64, name=None))"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prepared_ds = get_dataset()\n",
"prepared_ds.element_spec"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "912a68c2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-08-25 10:37:27.502214: E tensorflow/core/framework/dataset.cc:580] UNIMPLEMENTED: Cannot compute input sources for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n",
"2022-08-25 10:37:27.502234: E tensorflow/core/framework/dataset.cc:584] UNIMPLEMENTED: Cannot merge options for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n",
"2022-08-25 10:37:27.502613: E tensorflow/core/framework/dataset.cc:580] UNIMPLEMENTED: Cannot compute input sources for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n",
"2022-08-25 10:37:27.502628: E tensorflow/core/framework/dataset.cc:584] UNIMPLEMENTED: Cannot merge options for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n",
"2022-08-25 10:37:27.502712: E tensorflow/core/framework/dataset.cc:580] UNIMPLEMENTED: Cannot compute input sources for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n",
"2022-08-25 10:37:27.502728: E tensorflow/core/framework/dataset.cc:584] UNIMPLEMENTED: Cannot merge options for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n",
"2022-08-25 10:37:27.502834: E tensorflow/core/framework/dataset.cc:580] UNIMPLEMENTED: Cannot compute input sources for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n",
"2022-08-25 10:37:27.502847: E tensorflow/core/framework/dataset.cc:584] UNIMPLEMENTED: Cannot merge options for dataset of type IO>ArrowFeatherDataset, because the dataset does not implement `InputDatasets`.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(32, 224, 224, 3) (32,)\n"
]
}
],
"source": [
"for batch in prepared_ds.take(1):\n",
" paths, image_batch, label_batch = batch[0], batch[1], batch[2]\n",
" print(image_batch.shape, label_batch.shape)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "185b0ec6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"index = tf.experimental.numpy.random.randint(0, len(image_batch))\n",
"\n",
"plt.imshow(image_batch[index].numpy().squeeze().astype(\"int\"))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "53bb55f3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(5,), dtype=string, numpy=\n",
"array([b'flower_photos/tulips/8713389178_66bceb71a8_n.jpg',\n",
" b'flower_photos/tulips/7166618384_850905fc63_n.jpg',\n",
" b'flower_photos/tulips/130684927_a05164ba13_m.jpg',\n",
" b'flower_photos/tulips/5674125303_953b0ecf38.jpg',\n",
" b'flower_photos/tulips/13999402743_f563f6b685_n.jpg'], dtype=object)>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"paths[:5]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "476a7fce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(5,), dtype=int64, numpy=array([4, 4, 4, 4, 4])>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label_batch[:5]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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tensorflow==2.9.1
tensorflow-io==0.26.0
pyarrow
tqdm
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