Upload NewDataset.py with huggingface_hub
Browse files- NewDataset.py +11 -8
NewDataset.py
CHANGED
@@ -6,7 +6,7 @@ import zipfile
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class NewDataset(datasets.GeneratorBasedBuilder):
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def _info(self):
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return datasets.DatasetInfo(features=datasets.Features({'image':datasets.Image(),'label':datasets.
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def extract_all(self, dir):
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zip_files = glob(dir+'/**/**.zip', recursive=True)
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@@ -25,9 +25,14 @@ class NewDataset(datasets.GeneratorBasedBuilder):
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def _split_generators(self, dl_manager):
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url = [os.path.abspath(os.path.expanduser(dl_manager.manual_dir))]
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downloaded_files = dl_manager.download_and_extract(url)
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepaths':{'inputs':sorted(glob(downloaded_files[0]+'/data
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def read_image(self, filepath):
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if filepath.endswith('.jpg') or filepath.endswith('.png'):
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raw_data = {'bytes':[filepath]}
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@@ -39,13 +44,11 @@ class NewDataset(datasets.GeneratorBasedBuilder):
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_id = 0
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for i,filepath in enumerate(filepaths['inputs']):
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df = self.read_image(filepath)
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dfs.append(self.read_image(filepaths['targets1'][i]))
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df = pd.concat(dfs, axis = 1)
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if len(df.columns) != 2:
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continue
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df.columns = ['image'
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for _, record in df.iterrows():
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yield str(_id), {'image':record['image'],'label':
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_id += 1
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class NewDataset(datasets.GeneratorBasedBuilder):
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def _info(self):
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return datasets.DatasetInfo(features=datasets.Features({'image':datasets.Image(),'label': datasets.features.ClassLabel(names=['dogs', 'cats'])}))
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def extract_all(self, dir):
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zip_files = glob(dir+'/**/**.zip', recursive=True)
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def _split_generators(self, dl_manager):
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url = [os.path.abspath(os.path.expanduser(dl_manager.manual_dir))]
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downloaded_files = dl_manager.download_and_extract(url)
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepaths':{'inputs':sorted(glob(downloaded_files[0]+'/data/**/**.png')),} })]
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def get_label_from_path(self, labels, label):
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for l in labels:
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if l == label:
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return label
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def read_image(self, filepath):
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if filepath.endswith('.jpg') or filepath.endswith('.png'):
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raw_data = {'bytes':[filepath]}
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_id = 0
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for i,filepath in enumerate(filepaths['inputs']):
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df = self.read_image(filepath)
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if len(df.columns) != 1:
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continue
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df.columns = ['image']
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label = self.get_label_from_path(['dogs', 'cats'], filepath.split('/')[-2])
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for _, record in df.iterrows():
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yield str(_id), {'image':record['image'],'label':str(label)}
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_id += 1
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