Upload 2 files
Browse files- image_feature.py +6 -3
image_feature.py
CHANGED
@@ -55,8 +55,8 @@ DEVICE = torch.device('cpu')
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# model = AutoModel.from_pretrained("google/vit-base-patch16-224-in21k").to(DEVICE)
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# processor = AutoImageProcessor.from_pretrained("chanhua/autotrain-izefx-v3qh0")
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# model = AutoModel.from_pretrained("chanhua/autotrain-izefx-v3qh0").to(DEVICE)
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processor = ViTImageProcessor.from_pretrained('google/vit-large-patch16-224-in21k')
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model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k')
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# processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')
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# model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
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@@ -66,12 +66,13 @@ model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k')
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# pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-384", device=DEVICE, pool=True)
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# pipe = pipeline(task="image-feature-extraction", model_name="chanhua/autotrain-izefx-v3qh0", device=DEVICE, pool=True)
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# pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-224", device=DEVICE, pool=True, revision="29e7a1e183")
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pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-224-in21k", device=DEVICE, pool=True)
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# ζ¨η
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def infer4(url1, url2):
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try:
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print("θΏε
₯ζ¨η")
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print("ζεΌεΎη1")
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# image_real = Image.open(requests.get(url1, stream=True).raw).convert("RGB")
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@@ -105,6 +106,8 @@ def infer4(url1, url2):
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# ζ¨η
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def infer2(url):
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# image_real = Image.open(requests.get(img_urls[0], stream=True).raw).convert("RGB")
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# image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
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image = Image.open(url).convert('RGB')
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# model = AutoModel.from_pretrained("google/vit-base-patch16-224-in21k").to(DEVICE)
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# processor = AutoImageProcessor.from_pretrained("chanhua/autotrain-izefx-v3qh0")
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# model = AutoModel.from_pretrained("chanhua/autotrain-izefx-v3qh0").to(DEVICE)
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# processor = ViTImageProcessor.from_pretrained('google/vit-large-patch16-224-in21k')
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# model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k')
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# processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')
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# model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
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# pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-384", device=DEVICE, pool=True)
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# pipe = pipeline(task="image-feature-extraction", model_name="chanhua/autotrain-izefx-v3qh0", device=DEVICE, pool=True)
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# pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-224", device=DEVICE, pool=True, revision="29e7a1e183")
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# ζ¨η
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def infer4(url1, url2):
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try:
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pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-224-in21k", device=DEVICE, pool=True)
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print("θΏε
₯ζ¨η")
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print("ζεΌεΎη1")
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# image_real = Image.open(requests.get(url1, stream=True).raw).convert("RGB")
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# ζ¨η
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def infer2(url):
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processor = AutoImageProcessor.from_pretrained('google/vit-large-patch16-224-in21k')
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model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k')
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# image_real = Image.open(requests.get(img_urls[0], stream=True).raw).convert("RGB")
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# image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
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image = Image.open(url).convert('RGB')
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