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7704_inference.py

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+import json
+import time
+from PIL import Image
+from timm.data import resolve_data_config
+import torch
+from torchvision.transforms import transforms
+
+model = torch.load('model.pth').to("cuda")
+model.eval()
+config = resolve_data_config({}, model=model)
+transform = transforms.Compose([
+                transforms.Resize((384, 384)),
+                transforms.ToTensor(),
+                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+            ])
+
+with open("tags.json", "r") as file:
+    tags = json.load(file)
+allowed_tags = sorted(tags)
+allowed_tags.extend(["placeholder0", "placeholder1", "placeholder2"])
+tag_count = len(allowed_tags)
+
+
+image_path="path/to/your/image.png"
+start = time.time()
+img = Image.open(image_path).convert('RGB')
+tensor = transform(img).unsqueeze(0).to("cuda") # transform and add batch dimension
+
+with torch.no_grad():
+    out = model(tensor)
+probabilities = torch.nn.functional.sigmoid(out[0])
+
+top10_prob, top10_catid = torch.topk(probabilities, 100)
+for i in range(top10_prob.size(0)):
+    print(allowed_tags[top10_catid[i]], top10_prob[i].item())
+end = time.time()
+print(f'Executed in {end - start} seconds')
+print("\n\n", end="")

tags.json.7z

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5a1be541d0f5005f2fff6cb8c75dc7ea216ae337813aaecb5dc36a03b0266d76
+size 35480