yonatanbitton
commited on
Commit
•
d42979f
1
Parent(s):
363b4b4
Update vasr.py
Browse files
vasr.py
CHANGED
@@ -32,7 +32,11 @@ _HOMEPAGE = "https://vasr-dataset.github.io/"
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_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
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_URL = "https://huggingface.co/datasets/nlphuji/vasr/blob/main"
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class Vasr(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.1.0")
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@@ -42,20 +46,13 @@ class Vasr(datasets.GeneratorBasedBuilder):
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# BUILDER_CONFIG_CLASS = MyBuilderConfig
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="
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datasets.BuilderConfig(name="dev", version=VERSION, description="vasr gold dev dataset (labeled)"),
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datasets.BuilderConfig(name="train", version=VERSION, description="vasr gold train dataset (labeled)"),
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]
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KEYS_UNLABELED = ["A", "A'", "B", 'candidates_images', 'A_str', "A'_str", "B"]
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print("*** in Vasr class ***")
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def _info(self):
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print(f"Curr config name: {self.config.name}")
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feats_dict = {
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"A": datasets.Image(),
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"A'": datasets.Image(),
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"B": datasets.Image(),
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@@ -63,23 +60,12 @@ class Vasr(datasets.GeneratorBasedBuilder):
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"candidates_images": [datasets.Image()],
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"label": datasets.Value("int64"),
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"candidates": [datasets.Value("string")],
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"A_verb": datasets.Value("string"),
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"A'_verb": datasets.Value("string"),
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"B_verb": datasets.Value("string"),
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"B'_verb": datasets.Value("string"),
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"diff_item_A": datasets.Value("string"),
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"diff_item_A_str_first": datasets.Value("string"),
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"diff_item_A'": datasets.Value("string"),
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"diff_item_A'_str_first": datasets.Value("string"),
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"A_str": datasets.Value("string"),
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"A'_str": datasets.Value("string"),
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"B_str": datasets.Value("string"),
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"B'_str": datasets.Value("string"),
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}
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print("Filtering feats dict")
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feats_dict = {k:v for k,v in feats_dict.items() if k in self.KEYS_UNLABELED}
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features = datasets.Features(feats_dict)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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@@ -105,13 +91,15 @@ class Vasr(datasets.GeneratorBasedBuilder):
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data_dir = dl_manager.download_and_extract({
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"images_dir": hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="vasr_images.zip")
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})
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return [
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, examples_csv, images_dir):
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@@ -119,15 +107,6 @@ class Vasr(datasets.GeneratorBasedBuilder):
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df = pd.read_csv(examples_csv)
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has_label = 'D_img' in df.columns
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d_keys = self.KEYS_LABELED if has_label else self.KEYS_UNLABELED
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dataset = self.config.name
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print(f'Curr config: {dataset}')
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print(f'curr keys')
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print(self.info.features)
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print(type(self.info.features))
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for r_idx, r in df.iterrows():
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r_dict = r.to_dict()
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r_dict['candidates'] = json.loads(r_dict['candidates'])
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@@ -137,22 +116,12 @@ class Vasr(datasets.GeneratorBasedBuilder):
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r_dict["A_str"] = r_dict['A_img']
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r_dict["A'_str"] = r_dict['B_img']
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r_dict["B_str"] = r_dict['C_img']
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imgs_list = ['A_img', 'B_img', 'C_img', 'D_img']
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else:
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imgs_list = ['A_img', 'B_img', 'C_img']
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for img in imgs_list:
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r_dict[img] = os.path.join(images_dir, "vasr_images", r_dict[img])
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r_dict["A"] = r_dict['A_img']
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r_dict["A'"] = r_dict['B_img']
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r_dict["B"] = r_dict['C_img']
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r_dict["A'_verb"] = r_dict['B_verb']
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r_dict["B_verb"] = r_dict['C_verb']
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r_dict["B'_verb"] = r_dict['D_verb']
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r_dict["diff_item_A'"] = r_dict['diff_item_B']
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r_dict["diff_item_A'_str_first"] = r_dict['diff_item_B_str_first']
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relevant_r_dict = {k:v for k,v in r_dict.items() if k in d_keys or k == 'candidates_images'}
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yield r_idx, relevant_r_dict
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_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
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_URL = "https://huggingface.co/datasets/nlphuji/vasr/blob/main"
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_URLS = {
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"train": os.path.join(_URL, "train_gold_unlabeled.csv"),
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"dev": os.path.join(_URL, "dev_gold.csv"),
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"test": os.path.join(_URL, "test_gold.csv"),
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}
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class Vasr(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.1.0")
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# BUILDER_CONFIG_CLASS = MyBuilderConfig
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="v1", version=VERSION, description="vasr gold test dataset"),
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]
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DATASET_KEYS = ["A", "A'", "B", "B'", 'candidates', 'label', "A_str", "A'_str", "B_str", "B'_str"]
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def _info(self):
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features = datasets.Features(
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{
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"A": datasets.Image(),
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"A'": datasets.Image(),
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"B": datasets.Image(),
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"candidates_images": [datasets.Image()],
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"label": datasets.Value("int64"),
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"candidates": [datasets.Value("string")],
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"A_str": datasets.Value("string"),
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"A'_str": datasets.Value("string"),
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"B_str": datasets.Value("string"),
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"B'_str": datasets.Value("string"),
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}
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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data_dir = dl_manager.download_and_extract({
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"images_dir": hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="vasr_images.zip")
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})
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test_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="test_gold.csv")
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dev_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="dev_gold.csv")
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train_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="train_gold.csv")
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train_gen = datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={**data_dir, **{'examples_csv': train_examples}})
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dev_gen = datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={**data_dir, **{'examples_csv': dev_examples}})
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test_gen = datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={**data_dir, **{'examples_csv': test_examples}})
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return [train_gen, dev_gen, test_gen]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, examples_csv, images_dir):
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df = pd.read_csv(examples_csv)
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for r_idx, r in df.iterrows():
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r_dict = r.to_dict()
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r_dict['candidates'] = json.loads(r_dict['candidates'])
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r_dict["A_str"] = r_dict['A_img']
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r_dict["A'_str"] = r_dict['B_img']
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r_dict["B_str"] = r_dict['C_img']
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r_dict["B'_str"] = r_dict['D_img']
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for img in ['A_img', 'B_img', 'C_img', 'D_img']:
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r_dict[img] = os.path.join(images_dir, "vasr_images", r_dict[img])
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r_dict["A"] = r_dict['A_img']
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r_dict["A'"] = r_dict['B_img']
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r_dict["B"] = r_dict['C_img']
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r_dict["B'"] = r_dict['D_img']
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relevant_r_dict = {k:v for k,v in r_dict.items() if k in self.DATASET_KEYS or k == 'candidates_images'}
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yield r_idx, relevant_r_dict
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