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Build error
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33f3505
1
Parent(s):
88e5141
Turn on half precision.
Browse files- app.py +3 -3
- app_local.py +3 -3
app.py
CHANGED
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@@ -87,7 +87,7 @@ usage_to_weights_file = {
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birefnet = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file['General'])), trust_remote_code=True)
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birefnet.to(device)
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birefnet.eval()
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@spaces.GPU
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@@ -100,7 +100,7 @@ def predict(images, resolution, weights_file):
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print('Using weights: {}.'.format(_weights_file))
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birefnet = AutoModelForImageSegmentation.from_pretrained(_weights_file, trust_remote_code=True)
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birefnet.to(device)
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birefnet.eval()
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try:
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resolution = [int(int(reso)//32*32) for reso in resolution.strip().split('x')]
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@@ -143,7 +143,7 @@ def predict(images, resolution, weights_file):
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# Prediction
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with torch.no_grad():
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preds = birefnet(image_proc.to(device))[-1].sigmoid().cpu()
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pred = preds[0].squeeze()
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# Show Results
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birefnet = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file['General'])), trust_remote_code=True)
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birefnet.to(device)
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birefnet.eval(); birefnet.half()
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@spaces.GPU
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print('Using weights: {}.'.format(_weights_file))
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birefnet = AutoModelForImageSegmentation.from_pretrained(_weights_file, trust_remote_code=True)
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birefnet.to(device)
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birefnet.eval(); birefnet.half()
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try:
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resolution = [int(int(reso)//32*32) for reso in resolution.strip().split('x')]
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# Prediction
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with torch.no_grad():
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preds = birefnet(image_proc.to(device).half())[-1].sigmoid().cpu()
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pred = preds[0].squeeze()
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# Show Results
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app_local.py
CHANGED
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@@ -87,7 +87,7 @@ usage_to_weights_file = {
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birefnet = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file['General'])), trust_remote_code=True)
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birefnet.to(device)
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-
birefnet.eval()
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# @spaces.GPU
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@@ -100,7 +100,7 @@ def predict(images, resolution, weights_file):
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print('Using weights: {}.'.format(_weights_file))
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birefnet = AutoModelForImageSegmentation.from_pretrained(_weights_file, trust_remote_code=True)
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birefnet.to(device)
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-
birefnet.eval()
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try:
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resolution = [int(int(reso)//32*32) for reso in resolution.strip().split('x')]
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@@ -143,7 +143,7 @@ def predict(images, resolution, weights_file):
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# Prediction
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with torch.no_grad():
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preds = birefnet(image_proc.to(device))[-1].sigmoid().cpu()
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pred = preds[0].squeeze()
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# Show Results
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birefnet = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file['General'])), trust_remote_code=True)
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birefnet.to(device)
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+
birefnet.eval(); birefnet.half()
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# @spaces.GPU
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print('Using weights: {}.'.format(_weights_file))
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birefnet = AutoModelForImageSegmentation.from_pretrained(_weights_file, trust_remote_code=True)
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birefnet.to(device)
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+
birefnet.eval(); birefnet.half()
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try:
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resolution = [int(int(reso)//32*32) for reso in resolution.strip().split('x')]
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# Prediction
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with torch.no_grad():
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preds = birefnet(image_proc.to(device).half())[-1].sigmoid().cpu()
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pred = preds[0].squeeze()
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# Show Results
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