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import os |
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from inspect import signature |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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__all__ = [ |
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"is_parallel", |
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"get_device", |
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"get_same_padding", |
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"resize", |
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"build_kwargs_from_config", |
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"load_state_dict_from_file", |
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] |
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def is_parallel(model: nn.Module) -> bool: |
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return isinstance( |
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model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) |
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) |
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def get_device(model: nn.Module) -> torch.device: |
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return model.parameters().__next__().device |
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def get_same_padding(kernel_size: int or tuple[int, ...]) -> int or tuple[int, ...]: |
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if isinstance(kernel_size, tuple): |
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return tuple([get_same_padding(ks) for ks in kernel_size]) |
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else: |
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assert kernel_size % 2 > 0, "kernel size should be odd number" |
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return kernel_size // 2 |
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def resize( |
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x: torch.Tensor, |
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size: any or None = None, |
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scale_factor: list[float] or None = None, |
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mode: str = "bicubic", |
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align_corners: bool or None = False, |
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) -> torch.Tensor: |
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if mode in {"bilinear", "bicubic"}: |
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return F.interpolate( |
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x, |
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size=size, |
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scale_factor=scale_factor, |
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mode=mode, |
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align_corners=align_corners, |
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) |
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elif mode in {"nearest", "area"}: |
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return F.interpolate(x, size=size, scale_factor=scale_factor, mode=mode) |
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else: |
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raise NotImplementedError(f"resize(mode={mode}) not implemented.") |
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def build_kwargs_from_config(config: dict, target_func: callable) -> dict[str, any]: |
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valid_keys = list(signature(target_func).parameters) |
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kwargs = {} |
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for key in config: |
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if key in valid_keys: |
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kwargs[key] = config[key] |
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return kwargs |
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def load_state_dict_from_file( |
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file: str, only_state_dict=True |
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) -> dict[str, torch.Tensor]: |
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file = os.path.realpath(os.path.expanduser(file)) |
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checkpoint = torch.load(file, map_location="cpu") |
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if only_state_dict and "state_dict" in checkpoint: |
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checkpoint = checkpoint["state_dict"] |
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return checkpoint |
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