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app.py
CHANGED
@@ -19,8 +19,8 @@ import torchvision
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from huggingface_hub import HfApi, login, snapshot_download
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from PIL import Image
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session_token = os.environ.get("SessionToken")
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login(token=session_token)
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csv.field_size_limit(sys.maxsize)
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@@ -35,7 +35,7 @@ with open("imagenet-labels.json") as f:
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with open("id_to_label.json", "r") as f:
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id_to_labels = json.load(f)
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imagenet_training_samples_path = "
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bad_items = open("./ex2.txt", "r").read().split("\n")
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bad_items = [x.split(".")[0] for x in bad_items]
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@@ -48,12 +48,12 @@ gdown.cached_download(
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url="https://huggingface.co/datasets/taesiri/imagenet_hard_review_samples/resolve/main/data.zip",
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path="./data.zip",
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quiet=False,
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md5="
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)
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# EXTRACT if needed
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if not os.path.exists("./
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"./knn_cache_for_imagenet_hard"
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):
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torchvision.datasets.utils.extract_archive(
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@@ -106,18 +106,11 @@ def generate_dataset(username):
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if NUMBER_OF_IMAGES == 0:
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return []
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# random_indices = remaining
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# random_images = [imagenet_hard[int(i)]["image"] for i in random_indices]
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# random_gt_ids = [imagenet_hard[int(i)]["label"] for i in random_indices]
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# random_gt_labels = [imagenet_hard[int(x)]["english_label"] for x in random_indices]
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data = []
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for i, image in enumerate(remaining):
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data.append(
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{
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"id": remaining[i],
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# "correct_label": random_gt_labels[i],
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# "original_id": int(random_indices[i]),
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}
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)
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return data
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@@ -141,7 +134,7 @@ def string_to_image(text):
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return fig
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all_samples = glob("./
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qid_to_sample = {
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int(x.split("/")[-1].split(".")[0].split("_")[0]): x for x in all_samples
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}
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@@ -367,13 +360,15 @@ with gr.Blocks(css=newcss, theme=gr.themes.Soft()) as demo:
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reject_btn = gr.Button(value="Reject")
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with gr.Row():
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query_image = gr.Image(type="pil", label="Query", elem_id="query_image")
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with gr.
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accept_btn.click(
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update_app,
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from huggingface_hub import HfApi, login, snapshot_download
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from PIL import Image
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# session_token = os.environ.get("SessionToken")
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# login(token=session_token)
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csv.field_size_limit(sys.maxsize)
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with open("id_to_label.json", "r") as f:
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id_to_labels = json.load(f)
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imagenet_training_samples_path = "imagenet_samples"
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bad_items = open("./ex2.txt", "r").read().split("\n")
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bad_items = [x.split(".")[0] for x in bad_items]
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url="https://huggingface.co/datasets/taesiri/imagenet_hard_review_samples/resolve/main/data.zip",
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path="./data.zip",
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quiet=False,
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md5="ece2720fed664e71799f316a881d4324",
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)
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# EXTRACT if needed
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if not os.path.exists("./imagenet_samples") or not os.path.exists(
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"./knn_cache_for_imagenet_hard"
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):
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torchvision.datasets.utils.extract_archive(
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if NUMBER_OF_IMAGES == 0:
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return []
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data = []
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for i, image in enumerate(remaining):
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data.append(
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{
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"id": remaining[i],
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}
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)
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return data
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return fig
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all_samples = glob("./imagenet_samples/*.JPEG")
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qid_to_sample = {
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int(x.split("/")[-1].split(".")[0].split("_")[0]): x for x in all_samples
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}
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reject_btn = gr.Button(value="Reject")
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with gr.Row():
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query_image = gr.Image(type="pil", label="Query", elem_id="query_image")
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with gr.Row():
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with gr.Column(scale=1):
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label_plot = gr.Plot(
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label="Is this a correct label for this image?", type="fig"
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)
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with gr.Column(scale=3):
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training_samples = gr.Gallery(
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type="pil", label="Training samples", elem_id="sample_gallery"
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)
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accept_btn.click(
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update_app,
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