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demo.py
CHANGED
@@ -1,5 +1,5 @@
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import os
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os.system("pip install datasets einops
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import os, cv2, time, math
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print("=> Loading libraries...")
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@@ -15,6 +15,8 @@ from pytorch_grad_cam import GradCAM
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from pytorch_grad_cam.utils.image import show_cam_on_image
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print(f"=> Libraries loaded in {time.time()- start:.2f} sec(s).")
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print("=> Loading model...")
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start = time.time()
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@@ -25,7 +27,7 @@ crop_pct = 0.9
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IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
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model = create_model(f"tpmlp_{size}").
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load_checkpoint(model, f"../tpmlp_{size}.pth.tar", True)
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model.eval()
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@@ -56,7 +58,7 @@ def transform(img):
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def predict(inp):
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img, inp = transform(inp)
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inp = inp.unsqueeze(0)
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with GradCAM(model=model, target_layers=[model.layers[3]], use_cuda=
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grayscale_cam, probs = cam(input_tensor=inp, aug_smooth=False, eigen_smooth=False, return_probs=True)
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# Here grayscale_cam has only one image in the batch
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import os
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os.system("pip install datasets einops tabulate opencv-python ttach -U")
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import os, cv2, time, math
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print("=> Loading libraries...")
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from pytorch_grad_cam.utils.image import show_cam_on_image
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"=> Libraries loaded in {time.time()- start:.2f} sec(s).")
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print("=> Loading model...")
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start = time.time()
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IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
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model = create_model(f"tpmlp_{size}").to(device)
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load_checkpoint(model, f"../tpmlp_{size}.pth.tar", True)
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model.eval()
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def predict(inp):
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img, inp = transform(inp)
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inp = inp.unsqueeze(0)
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with GradCAM(model=model, target_layers=[model.layers[3]], use_cuda=device=="cuda") as cam:
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grayscale_cam, probs = cam(input_tensor=inp, aug_smooth=False, eigen_smooth=False, return_probs=True)
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# Here grayscale_cam has only one image in the batch
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