| import numpy as np | |
| import torch | |
| def print_arch(model, model_name='model'): | |
| print(f"| {model_name} Arch: ", model) | |
| num_params(model, model_name=model_name) | |
| def num_params(model, print_out=True, model_name="model"): | |
| parameters = filter(lambda p: p.requires_grad, model.parameters()) | |
| parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000 | |
| if print_out: | |
| print(f'| {model_name} Trainable Parameters: %.3fM' % parameters) | |
| return parameters | |
| def get_device_of_model(model): | |
| return model.parameters().__next__().device | |
| def requires_grad(model): | |
| if isinstance(model, torch.nn.Module): | |
| for p in model.parameters(): | |
| p.requires_grad = True | |
| else: | |
| model.requires_grad = True | |
| def not_requires_grad(model): | |
| if isinstance(model, torch.nn.Module): | |
| for p in model.parameters(): | |
| p.requires_grad = False | |
| else: | |
| model.requires_grad = False | |
Xet Storage Details
- Size:
- 946 Bytes
- Xet hash:
- 37f2bc340ee906b4180044443047e4b988c4780d10d774a96f3e00b611728cde
·
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