update
Browse files- .ipynb_checkpoints/AVA-checkpoint.py +5 -3
- AVA.py +5 -3
- notebooks/Test.ipynb +24 -28
.ipynb_checkpoints/AVA-checkpoint.py
CHANGED
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@@ -37,7 +37,7 @@ class AVA(datasets.GeneratorBasedBuilder):
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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archives = dl_manager.download(_DATA_URL)
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-
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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@@ -50,17 +50,19 @@ class AVA(datasets.GeneratorBasedBuilder):
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def _generate_examples(self, archives, split):
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"""Yields examples."""
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-
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idx = 0
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for archive in archives:
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for path, file in archive:
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if path.endswith(".jpg"):
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# image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
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_id = int(os.path.splitext(b[0])[0].split('/')[-1])
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-
_metadata = DICT_METADATA[_id]
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ex = {"image": {"path": path, "bytes": file.read()},
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"rating_counts": _metadata[0],
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"text_tag0":_metadata[1],
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"text_tag1": _metadata[2]}
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yield idx, ex
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idx += 1
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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archives = dl_manager.download(_DATA_URL)
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+
self.DICT_METADATA = Path(self.dl_manager.download_and_extract(_BASE_HF_URL)) / "metadata.pkl"
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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def _generate_examples(self, archives, split):
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"""Yields examples."""
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+
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idx = 0
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for archive in archives:
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for path, file in archive:
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if path.endswith(".jpg"):
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# image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
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_id = int(os.path.splitext(b[0])[0].split('/')[-1])
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_metadata = self.DICT_METADATA[_id]
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ex = {"image": {"path": path, "bytes": file.read()},
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"rating_counts": _metadata[0],
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"text_tag0":_metadata[1],
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"text_tag1": _metadata[2]}
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yield idx, ex
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idx += 1
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+
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+
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AVA.py
CHANGED
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@@ -37,7 +37,7 @@ class AVA(datasets.GeneratorBasedBuilder):
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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archives = dl_manager.download(_DATA_URL)
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-
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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@@ -50,17 +50,19 @@ class AVA(datasets.GeneratorBasedBuilder):
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def _generate_examples(self, archives, split):
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"""Yields examples."""
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-
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idx = 0
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for archive in archives:
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for path, file in archive:
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if path.endswith(".jpg"):
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# image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
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_id = int(os.path.splitext(b[0])[0].split('/')[-1])
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-
_metadata = DICT_METADATA[_id]
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ex = {"image": {"path": path, "bytes": file.read()},
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"rating_counts": _metadata[0],
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"text_tag0":_metadata[1],
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"text_tag1": _metadata[2]}
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yield idx, ex
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idx += 1
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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archives = dl_manager.download(_DATA_URL)
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+
self.DICT_METADATA = Path(self.dl_manager.download_and_extract(_BASE_HF_URL)) / "metadata.pkl"
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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def _generate_examples(self, archives, split):
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"""Yields examples."""
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+
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idx = 0
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for archive in archives:
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for path, file in archive:
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if path.endswith(".jpg"):
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# image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
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_id = int(os.path.splitext(b[0])[0].split('/')[-1])
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_metadata = self.DICT_METADATA[_id]
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ex = {"image": {"path": path, "bytes": file.read()},
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"rating_counts": _metadata[0],
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"text_tag0":_metadata[1],
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"text_tag1": _metadata[2]}
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yield idx, ex
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idx += 1
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+
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+
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notebooks/Test.ipynb
CHANGED
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@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count":
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"id": "aef315bf",
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"metadata": {},
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"outputs": [],
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@@ -12,35 +12,21 @@
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},
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{
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"cell_type": "code",
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"execution_count":
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"id": "c0ed6498",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2dc0ecb6d071440fbb63d9eb37239d51",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Downloading builder script: 0%| | 0.00/2.12k [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading and preparing dataset ava/default to /home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "
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"version_major": 2,
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"version_minor": 0
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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]
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},
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"metadata": {},
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"output_type": "display_data"
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{
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"\
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}
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],
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"source": [
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"%%time\n",
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"ds = load_dataset(\"will33am/AVA\"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "
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"metadata": {},
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"outputs": [],
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"source": []
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "aef315bf",
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"metadata": {},
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"outputs": [],
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "c0ed6498",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading and preparing dataset ava/default to /home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/dc18bb43c11395496a83e96a91fdb26162bab200a16d35297b4d6e6ceccb4864...\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d06e8600ce884a008bc0356e22aaaf97",
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"version_major": 2,
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"version_minor": 0
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},
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{
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"data": {
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"model_id": "f139bae410ec45eb81a034742f6ea059",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Computing checksums: 0%| | 0/1 [00:01<?, ?it/s]"
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]
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"metadata": {},
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"output_type": "display_data"
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"File \u001b[0;32m<timed exec>:1\u001b[0m\n",
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"File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/load.py:1757\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)\u001b[0m\n\u001b[1;32m 1754\u001b[0m try_from_hf_gcs \u001b[38;5;241m=\u001b[39m path \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[1;32m 1756\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 1757\u001b[0m \u001b[43mbuilder_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1758\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1759\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1760\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_verifications\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_verifications\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1761\u001b[0m \u001b[43m \u001b[49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1762\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1763\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1765\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m 1766\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1767\u001b[0m keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size)\n\u001b[1;32m 1768\u001b[0m )\n",
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"File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:860\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 859\u001b[0m prepare_split_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_proc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_proc\n\u001b[0;32m--> 860\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 864\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 866\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n",
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+
"File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1611\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m 1610\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_download_and_prepare\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager, verify_infos, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_splits_kwargs):\n\u001b[0;32m-> 1611\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1612\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_splits_kwargs\u001b[49m\n\u001b[1;32m 1613\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
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+
"File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:931\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m 929\u001b[0m split_dict \u001b[38;5;241m=\u001b[39m SplitDict(dataset_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 930\u001b[0m split_generators_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_split_generators_kwargs(prepare_split_kwargs)\n\u001b[0;32m--> 931\u001b[0m split_generators \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_split_generators\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msplit_generators_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 933\u001b[0m \u001b[38;5;66;03m# Checksums verification\u001b[39;00m\n\u001b[1;32m 934\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_infos \u001b[38;5;129;01mand\u001b[39;00m dl_manager\u001b[38;5;241m.\u001b[39mrecord_checksums:\n",
|
| 66 |
+
"File \u001b[0;32m~/.cache/huggingface/modules/datasets_modules/datasets/will33am--AVA/dc18bb43c11395496a83e96a91fdb26162bab200a16d35297b4d6e6ceccb4864/AVA.py:39\u001b[0m, in \u001b[0;36mAVA._split_generators\u001b[0;34m(self, dl_manager)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_split_generators\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Returns SplitGenerators.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 39\u001b[0m archives \u001b[38;5;241m=\u001b[39m \u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_DATA_URL\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[1;32m 42\u001b[0m datasets\u001b[38;5;241m.\u001b[39mSplitGenerator(\n\u001b[1;32m 43\u001b[0m name\u001b[38;5;241m=\u001b[39mdatasets\u001b[38;5;241m.\u001b[39mSplit\u001b[38;5;241m.\u001b[39mTRAIN,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 48\u001b[0m )\n\u001b[1;32m 49\u001b[0m ]\n",
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| 67 |
+
"File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/download/download_manager.py:346\u001b[0m, in \u001b[0;36mDownloadManager.download\u001b[0;34m(self, url_or_urls)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdownloaded_paths\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(url_or_urls\u001b[38;5;241m.\u001b[39mflatten(), downloaded_path_or_paths\u001b[38;5;241m.\u001b[39mflatten())))\n\u001b[1;32m 345\u001b[0m start_time \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow()\n\u001b[0;32m--> 346\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_record_sizes_checksums\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_urls\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdownloaded_path_or_paths\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 347\u001b[0m duration \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow() \u001b[38;5;241m-\u001b[39m start_time\n\u001b[1;32m 348\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mChecksum Computation took \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mduration\u001b[38;5;241m.\u001b[39mtotal_seconds()\u001b[38;5;250m \u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m60\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m min\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
|
| 68 |
+
"File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/download/download_manager.py:246\u001b[0m, in \u001b[0;36mDownloadManager._record_sizes_checksums\u001b[0;34m(self, url_or_urls, downloaded_path_or_paths)\u001b[0m\n\u001b[1;32m 238\u001b[0m delay \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m url, path \u001b[38;5;129;01min\u001b[39;00m tqdm(\n\u001b[1;32m 240\u001b[0m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(url_or_urls\u001b[38;5;241m.\u001b[39mflatten(), downloaded_path_or_paths\u001b[38;5;241m.\u001b[39mflatten())),\n\u001b[1;32m 241\u001b[0m delay\u001b[38;5;241m=\u001b[39mdelay,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 244\u001b[0m ):\n\u001b[1;32m 245\u001b[0m \u001b[38;5;66;03m# call str to support PathLike objects\u001b[39;00m\n\u001b[0;32m--> 246\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_recorded_sizes_checksums[\u001b[38;5;28mstr\u001b[39m(url)] \u001b[38;5;241m=\u001b[39m \u001b[43mget_size_checksum_dict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 247\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrecord_checksum\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecord_checksums\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m warn_about_checksums \u001b[38;5;129;01mand\u001b[39;00m _time \u001b[38;5;241m+\u001b[39m delay \u001b[38;5;241m<\u001b[39m time\u001b[38;5;241m.\u001b[39mtime():\n\u001b[1;32m 250\u001b[0m warn_about_checksums \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
|
| 69 |
+
"File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/utils/info_utils.py:84\u001b[0m, in \u001b[0;36mget_size_checksum_dict\u001b[0;34m(path, record_checksum)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28miter\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m: f\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m<<\u001b[39m \u001b[38;5;241m20\u001b[39m), \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m---> 84\u001b[0m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 85\u001b[0m checksum \u001b[38;5;241m=\u001b[39m m\u001b[38;5;241m.\u001b[39mhexdigest()\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
|
| 70 |
+
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
| 71 |
]
|
| 72 |
}
|
| 73 |
],
|
| 74 |
"source": [
|
| 75 |
"%%time\n",
|
| 76 |
+
"ds = load_dataset(\"will33am/AVA\")"
|
| 77 |
]
|
| 78 |
},
|
| 79 |
{
|
| 80 |
"cell_type": "code",
|
| 81 |
"execution_count": null,
|
| 82 |
+
"id": "2f025a25",
|
| 83 |
"metadata": {},
|
| 84 |
"outputs": [],
|
| 85 |
"source": []
|