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Browse files- app.py +3 -3
- modeling/architectures/seem_model_demo.py +3 -2
- requirements.txt +0 -2
app.py
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@@ -53,7 +53,6 @@ def init_huggingface():
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)
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return pretrained_path
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@torch.no_grad()
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@spaces.GPU
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def init_distributed(opt):
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opt['CUDA'] = opt.get('CUDA', True) and torch.cuda.is_available()
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@@ -88,10 +87,10 @@ def init_distributed(opt):
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def setup_model():
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"""Initialize the model on CPU without CUDA initialization."""
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opt = load_opt_from_config_files(["configs/biomedparse_inference.yaml"])
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opt['CUDA'] = opt.get('CUDA', True) and torch.cuda.is_available() # Vérifier la disponibilité de CUDA
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opt = init_distributed(opt)
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pretrained_path = init_huggingface()
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model = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_path)
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return model
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@torch.no_grad()
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@@ -146,6 +145,7 @@ def predict_image(model, image, prompts):
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mode='bilinear')[0,:,:data['height'],:data['width']].sigmoid().cpu().detach().numpy()
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return pred_mask_prob
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def process_image(image, prompts, model):
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"""Process image with proper error handling."""
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try:
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)
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return pretrained_path
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@spaces.GPU
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def init_distributed(opt):
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opt['CUDA'] = opt.get('CUDA', True) and torch.cuda.is_available()
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def setup_model():
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"""Initialize the model on CPU without CUDA initialization."""
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opt = load_opt_from_config_files(["configs/biomedparse_inference.yaml"])
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opt = init_distributed(opt)
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opt['device'] = 'cuda' if torch.cuda.is_available() else 'cpu'
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pretrained_path = init_huggingface()
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model = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_path).eval()
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return model
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@torch.no_grad()
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mode='bilinear')[0,:,:data['height'],:data['width']].sigmoid().cpu().detach().numpy()
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return pred_mask_prob
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def process_image(image, prompts, model):
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"""Process image with proper error handling."""
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try:
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modeling/architectures/seem_model_demo.py
CHANGED
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@@ -134,8 +134,9 @@ class GeneralizedSEEM(nn.Module):
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interactive_mode = 'best'
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interactive_iter = 20
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dilation = 3
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return {
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"backbone": backbone,
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"sem_seg_head": sem_seg_head,
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interactive_mode = 'best'
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interactive_iter = 20
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dilation = 3
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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dilation_kernel = torch.ones((1, 1, dilation, dilation), device=device)
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return {
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"backbone": backbone,
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"sem_seg_head": sem_seg_head,
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requirements.txt
CHANGED
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@@ -1,5 +1,4 @@
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pillow==9.4.0
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accelerate
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opencv-python==4.8.1.78
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pyyaml==6.0.1
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json_tricks==3.17.3
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@@ -15,7 +14,6 @@ sentencepiece==0.1.99
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ftfy==6.1.1
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regex==2023.10.3
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nltk==3.8.1
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#mpi4py
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vision-datasets==0.2.2
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cython==3.0.2
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pycocotools==2.0.7
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pillow==9.4.0
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opencv-python==4.8.1.78
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pyyaml==6.0.1
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json_tricks==3.17.3
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ftfy==6.1.1
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regex==2023.10.3
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nltk==3.8.1
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vision-datasets==0.2.2
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cython==3.0.2
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pycocotools==2.0.7
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