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| import warnings | |
| import torch | |
| from torch import Tensor | |
| def torch_linspace(start: Tensor, stop: Tensor, num: int) -> torch.Tensor: | |
| """ | |
| Copy-pasted from https://github.com/pytorch/pytorch/issues/61292 | |
| Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive. | |
| Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch. | |
| """ | |
| # create a tensor of 'num' steps from 0 to 1 | |
| steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1) | |
| # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings | |
| # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript | |
| # "cannot statically infer the expected size of a list in this contex", hence the code below | |
| for i in range(start.ndim): | |
| steps = steps.unsqueeze(-1) | |
| # the output starts at 'start' and increments until 'stop' in each dimension | |
| out = start[None] + steps * (stop - start)[None] | |
| return out | |
| def load_weights( | |
| model: torch.nn.Module, checkpoint: dict, strict=True | |
| ) -> torch.nn.Module: | |
| """This function is used instead of the one provided by pytorch lightning | |
| because for unexplained reasons, the pytorch lightning load function did | |
| not behave as intended: loading several times from the same checkpoint | |
| resulted in different loaded weight values... | |
| Args: | |
| model: a model in which new weights should be set | |
| checkpoint: a loaded pytorch checkpoint (probably resulting from torch.load(filename)) | |
| strict: Default to True, wether to fail if | |
| """ | |
| if not strict: | |
| model_dict = model.state_dict() | |
| pretrained_dict = { | |
| k: v for k, v in checkpoint["state_dict"].items() if k in model_dict | |
| } | |
| diff1 = checkpoint["state_dict"].keys() - model_dict.keys() | |
| if diff1: | |
| warnings.warn( | |
| f"Found keys {diff1} in checkpoint without any match in the model, ignoring corresponding values." | |
| ) | |
| diff2 = model_dict.keys() - checkpoint["state_dict"].keys() | |
| if diff2: | |
| warnings.warn( | |
| f"Missing keys {diff2} from the checkpoint, the corresponding weights will keep their initial values." | |
| ) | |
| pretrained_dict = { | |
| k: v for k, v in checkpoint["state_dict"].items() if k in model_dict | |
| } | |
| model_dict.update(pretrained_dict) | |
| else: | |
| model_dict = checkpoint["state_dict"] | |
| model.load_state_dict(model_dict, strict=strict) | |
| return model | |