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Browse files- inference_2.py +3 -3
inference_2.py
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@@ -10,7 +10,7 @@ from models import image
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from onnx2pytorch import ConvertModel
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onnx_model = onnx.load('checkpoints
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pytorch_model = ConvertModel(onnx_model)
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#Set random seed for reproducibility.
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@@ -75,7 +75,7 @@ def model_summary(args):
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def load_multimodal_model(args):
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'''Load multimodal model'''
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model = ETMC(args)
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ckpt = torch.load('checkpoints
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model.load_state_dict(ckpt, strict = True)
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model.eval()
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return model
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@@ -84,7 +84,7 @@ def load_img_modality_model(args):
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'''Loads image modality model.'''
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rgb_encoder = pytorch_model
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ckpt = torch.load('checkpoints
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rgb_encoder.load_state_dict(ckpt['rgb_encoder'], strict = True)
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rgb_encoder.eval()
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return rgb_encoder
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from onnx2pytorch import ConvertModel
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onnx_model = onnx.load('checkpoints/efficientnet.onnx')
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pytorch_model = ConvertModel(onnx_model)
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#Set random seed for reproducibility.
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def load_multimodal_model(args):
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'''Load multimodal model'''
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model = ETMC(args)
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ckpt = torch.load('checkpoints/model.pth', map_location = torch.device('cpu'))
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model.load_state_dict(ckpt, strict = True)
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model.eval()
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return model
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'''Loads image modality model.'''
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rgb_encoder = pytorch_model
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ckpt = torch.load('checkpoints/model.pth', map_location = torch.device('cpu'))
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rgb_encoder.load_state_dict(ckpt['rgb_encoder'], strict = True)
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rgb_encoder.eval()
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return rgb_encoder
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