Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10M<n<100M
ArXiv:
License:
mariosasko
commited on
Commit
•
83b2798
1
Parent(s):
059cb70
Make script parallelizable (#3)
Browse files- Make script parallelizable (b89324d4754d89c8332d8b2999b22353946d5809)
- quickdraw.py +6 -4
quickdraw.py
CHANGED
@@ -246,6 +246,7 @@ class Quickdraw(datasets.GeneratorBasedBuilder):
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files = dl_manager.download(
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{name: url for name, url in zip(_NAMES, [base_url.format(name) for name in _NAMES])}
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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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@@ -259,6 +260,7 @@ class Quickdraw(datasets.GeneratorBasedBuilder):
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files = dl_manager.download_and_extract(
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{name: url for name, url in zip(_NAMES, [base_url.format(name) for name in _NAMES])}
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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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@@ -286,7 +288,7 @@ class Quickdraw(datasets.GeneratorBasedBuilder):
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def _generate_examples(self, files, split):
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if self.config.name == "raw":
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idx = 0
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-
for _, file in files
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with open(file, encoding="utf-8") as f:
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for line in f:
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example = json.loads(line)
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@@ -296,7 +298,7 @@ class Quickdraw(datasets.GeneratorBasedBuilder):
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idx += 1
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elif self.config.name == "preprocessed_simplified_drawings":
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idx = 0
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-
for label, file in files
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with open(file, "rb") as f:
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while True:
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try:
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@@ -308,7 +310,7 @@ class Quickdraw(datasets.GeneratorBasedBuilder):
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idx += 1
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elif self.config.name == "preprocessed_bitmaps":
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idx = 0
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-
for label, file in files
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with open(file, "rb") as f:
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images = np.load(f)
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for image in images:
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@@ -319,7 +321,7 @@ class Quickdraw(datasets.GeneratorBasedBuilder):
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idx += 1
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else: # sketch_rnn, sketch_rnn_full
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idx = 0
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-
for label, file in files
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with open(os.path.join(file, f"{split}.npy"), "rb") as f:
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# read entire file since f.seek is not supported in the streaming mode
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drawings = np.load(io.BytesIO(f.read()), encoding="latin1", allow_pickle=True)
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files = dl_manager.download(
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{name: url for name, url in zip(_NAMES, [base_url.format(name) for name in _NAMES])}
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)
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+
files = [(name, file) for name, file in files.items()]
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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files = dl_manager.download_and_extract(
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{name: url for name, url in zip(_NAMES, [base_url.format(name) for name in _NAMES])}
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)
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+
files = [(name, file) for name, file in files.items()]
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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, files, split):
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if self.config.name == "raw":
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idx = 0
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+
for _, file in files:
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with open(file, encoding="utf-8") as f:
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for line in f:
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example = json.loads(line)
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idx += 1
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elif self.config.name == "preprocessed_simplified_drawings":
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idx = 0
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+
for label, file in files:
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with open(file, "rb") as f:
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while True:
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try:
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idx += 1
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elif self.config.name == "preprocessed_bitmaps":
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idx = 0
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+
for label, file in files:
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with open(file, "rb") as f:
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images = np.load(f)
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for image in images:
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idx += 1
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else: # sketch_rnn, sketch_rnn_full
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idx = 0
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+
for label, file in files:
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with open(os.path.join(file, f"{split}.npy"), "rb") as f:
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# read entire file since f.seek is not supported in the streaming mode
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drawings = np.load(io.BytesIO(f.read()), encoding="latin1", allow_pickle=True)
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