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Add coefficients for model
Browse files- model_coefficients/README.md +21 -0
- model_coefficients/best_coefficients/stg_0.pth +3 -0
- model_coefficients/best_coefficients/stg_1.pth +3 -0
- model_coefficients/best_coefficients/stg_2.pth +3 -0
- model_coefficients/best_coefficients/stg_3.pth +3 -0
- model_coefficients/best_coefficients/stg_4.pth +3 -0
- model_coefficients/last_coefficients/stg_0.pth +3 -0
- model_coefficients/last_coefficients/stg_1.pth +3 -0
- model_coefficients/last_coefficients/stg_2.pth +3 -0
- model_coefficients/last_coefficients/stg_3.pth +3 -0
- model_coefficients/last_coefficients/stg_4.pth +3 -0
model_coefficients/README.md
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# Model coeficients
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Here in this folder you can find the last and best coeficients obtained during training for each stage. To load these coeficients in the following way
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```python
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>>> import MSECNN
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>>> import train_model_utils
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>>>
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>>> # Initialize parameters
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>>> path_to_folder_with_model_params = "model_coefficients/best_coefficients"
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>>> device = "cuda:0"
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>>> qp = 32 # Quantisation Parameter
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>>>
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>>> # Initialize Model
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>>> stg1_2 = msecnn.MseCnnStg1(device=device, QP=qp).to(device)
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>>> stg3 = msecnn.MseCnnStgX(device=device, QP=qp).to(device)
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>>> stg4 = msecnn.MseCnnStgX(device=device, QP=qp).to(device)
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>>> stg5 = msecnn.MseCnnStgX(device=device, QP=qp).to(device)
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>>> stg6 = msecnn.MseCnnStgX(device=device, QP=qp).to(device)
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>>> model = (stg1_2, stg3, stg4, stg5, stg6)
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>>>
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>>> model = train_model_utils.load_model_parameters_eval(model, path_to_folder_with_model_params, device) # Load for all of the stages
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```
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model_coefficients/best_coefficients/stg_0.pth
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model_coefficients/best_coefficients/stg_1.pth
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model_coefficients/best_coefficients/stg_2.pth
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model_coefficients/best_coefficients/stg_3.pth
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model_coefficients/best_coefficients/stg_4.pth
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model_coefficients/last_coefficients/stg_0.pth
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model_coefficients/last_coefficients/stg_1.pth
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model_coefficients/last_coefficients/stg_2.pth
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model_coefficients/last_coefficients/stg_3.pth
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model_coefficients/last_coefficients/stg_4.pth
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