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app.py ADDED
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+ import torch
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+ from transformers import pipeline
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+ import torch
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+
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+ import gradio as gr
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+
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+ with gr.Blocks() as demo:
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+ def submit(image):
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+ torch.backends.cuda.matmul.allow_tf32 = True
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+ torch.backends.cudnn.allow_tf32 = True
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+ pipe = pipeline("image-classification", model=".\\checkpoint-600")
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+ output = pipe(images=[image])
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+
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+ result = {}
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+
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+ for index, item in enumerate(output[0]):
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+ result[item["label"]] = item["score"]
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+
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+ return result
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+
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+ with gr.Row():
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+ with gr.Column():
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+ image_input = gr.Image(label="Input image", type="filepath")
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+
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+ with gr.Column():
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+ label = gr.Label()
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+ submit_button = gr.Button(value="Submit", variant="primary")
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+ submit_button.click(submit, inputs=[image_input], outputs=label)
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+
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+ demo.launch()
checkpoint-600/config.json ADDED
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+ {
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+ "_name_or_path": "google/vit-base-patch16-224-in21k",
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+ "architectures": [
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+ "ViTForImageClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "encoder_stride": 16,
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+ "finetuning_task": "image-classification",
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+ "hidden_act": "gelu",
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "blured_man",
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+ "1": "deanon_modov",
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+ "10": "surprised_face",
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+ "11": "ua_flag",
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+ "2": "money",
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+ "3": "nsfw",
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+ "4": "ok",
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+ "5": "shinobu",
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+ "6": "soldatik_opt1",
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+ "7": "soldatik_opt2",
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+ "8": "soldatik_opt3",
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+ "9": "sosok"
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+ "qkv_bias": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.39.0.dev0"
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+ }
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