<filename>pytorch_toolbelt/utils/torch_utils.py
"""Common functions to marshal data to/from PyTorch

"""
import collections
from typing import Optional, Sequence, Union, Dict

import numpy as np
import torch
from torch import nn

__all__ = [
    "rgb_image_from_tensor",
    "tensor_from_mask_image",
    "tensor_from_rgb_image",
    "count_parameters",
    "transfer_weights",
    "maybe_cuda",
    "mask_from_tensor",
    "logit",
    "to_numpy",
    "to_tensor",
]


def logit(x: torch.Tensor, eps=1e-5) -> torch.Tensor:
    """
    Compute inverse of sigmoid of the input.
    Note: This function has not been tested for numerical stability.
    :param x:
    :param eps:
    :return:
    """
    x = torch.clamp(x, eps, 1.0 - eps)
    return torch.log(x / (1.0 - x))


def count_parameters(model: nn.Module, keys: Optional[Sequence[str]] = None) -> Dict[str, int]:
    """
    Count number of total and trainable parameters of a model
    :param model: A model
    :param keys: Optional list of top-level blocks
    :return: Tuple (total, trainable)
    """
    if keys is None:
        keys = ["encoder", "decoder", "logits", "head", "final"]
    total = int(sum(p.numel() for p in model.parameters()))
    trainable = int(sum(p.numel() for p in model.parameters() if p.requires_grad))
    parameters = {"total": total, "trainable": trainable}

    for key in keys:
        if hasattr(model, key) and model.__getattr__(key) is not None:
            parameters[key] = int(sum(p.numel() for p in model.__getattr__(key).parameters()))

    return parameters


def to_numpy(x) -> np.ndarray:
    """
    Convert whatever to numpy array
    :param x: List, tuple, PyTorch tensor or numpy array
    :return: Numpy array
    """
    if isinstance(x, np.ndarray):
        return x
    elif isinstance(x, torch.Tensor):
        return x.detach().cpu().numpy()
    elif isinstance(x, (list, tuple, int, float)):
        return np.array(x)
    else:
        raise ValueError("Unsupported type")


def to_tensor(x, dtype=None) -> torch.Tensor:
    if isinstance(x, torch.Tensor):
        if dtype is not None:
            x = x.type(dtype)
        return x
    if isinstance(x, np.ndarray):
        x = torch.from_numpy(x)
        if dtype is not None:
            x = x.type(dtype)
        return x
    if isinstance(x, (list, tuple)):
        x = np.ndarray(x)
        x = torch.from_numpy(x)
        if dtype is not None:
            x = x.type(dtype)
        return x

    raise ValueError("Unsupported input type" + str(type(x)))


def tensor_from_rgb_image(image: np.ndarray) -> torch.Tensor:
    image = np.moveaxis(image, -1, 0)
    image = np.ascontiguousarray(image)
    image = torch.from_numpy(image)
    return image


def tensor_from_mask_image(mask: np.ndarray) -> torch.Tensor:
    if len(mask.shape) == 2:
        mask = np.expand_dims(mask, -1)
    return tensor_from_rgb_image(mask)


def rgb_image_from_tensor(image: torch.Tensor, mean, std, max_pixel_value=255.0, dtype=np.uint8) -> np.ndarray:
    image = np.moveaxis(to_numpy(image), 0, -1)
    mean = to_numpy(mean)
    std = to_numpy(std)
    rgb_image = (max_pixel_value * (image * std + mean)).astype(dtype)
    return rgb_image


def mask_from_tensor(mask: torch.Tensor, squeeze_single_channel=False, dtype=None) -> np.ndarray:
    mask = np.moveaxis(to_numpy(mask), 0, -1)
    if squeeze_single_channel and mask.shape[-1] == 1:
        mask = np.squeeze(mask, -1)

    if dtype is not None:
        mask = mask.astype(dtype)
    return mask


def maybe_cuda(x: Union[torch.Tensor, nn.Module]) -> Union[torch.Tensor, nn.Module]:
    """
    Move input Tensor or Module to CUDA device if CUDA is available.
    :param x:
    :return:
    """
    if torch.cuda.is_available():
        return x.cuda()
    return x


def transfer_weights(model: nn.Module, model_state_dict: collections.OrderedDict):
    """
    Copy weights from state dict to model, skipping layers that are incompatible.
    This method is helpful if you are doing some model surgery and want to load
    part of the model weights into different model.
    :param model: Model to load weights into
    :param model_state_dict: Model state dict to load weights from
    :return: None
    """
    for name, value in model_state_dict.items():
        try:
            model.load_state_dict(collections.OrderedDict([(name, value)]), strict=False)
        except Exception as e:
            print(e)

<filename>pytorch_toolbelt/utils/torch_utils.py
"""Common functions to marshal data to/from PyTorch

"""
import collections
from typing import Optional, Sequence, Union, Dict

import numpy as np
import torch
from torch import nn

__all__ = [
    "rgb_image_from_tensor",
    "tensor_from_mask_image",
    "tensor_from_rgb_image",
    "count_parameters",
    "transfer_weights",
    "maybe_cuda",
    "mask_from_tensor",
    "logit",
    "to_numpy",
    "to_tensor",
]


def logit(x: torch.Tensor, eps=1e-5) -> torch.Tensor:
    """
    Compute inverse of sigmoid of the input.
    Note: This function has not been tested for numerical stability.
    :param x:
    :param eps:
    :return:
    """
    x = torch.clamp(x, eps, 1.0 - eps)
    return torch.log(x / (1.0 - x))


def count_parameters(model: nn.Module, keys: Optional[Sequence[str]] = None) -> Dict[str, int]:
    """
    Count number of total and trainable parameters of a model
    :param model: A model
    :param keys: Optional list of top-level blocks
    :return: Tuple (total, trainable)
    """
    if keys is None:
        keys = ["encoder", "decoder", "logits", "head", "final"]
    total = int(sum(p.numel() for p in model.parameters()))
    trainable = int(sum(p.numel() for p in model.parameters() if p.requires_grad))
    parameters = {"total": total, "trainable": trainable}

    for key in keys:
        if hasattr(model, key) and model.__getattr__(key) is not None:
            parameters[key] = int(sum(p.numel() for p in model.__getattr__(key).parameters()))

    return parameters


def to_numpy(x) -> np.ndarray:
    """
    Convert whatever to numpy array
    :param x: List, tuple, PyTorch tensor or numpy array
    :return: Numpy array
    """
    if isinstance(x, np.ndarray):
        return x
    elif isinstance(x, torch.Tensor):
        return x.detach().cpu().numpy()
    elif isinstance(x, (list, tuple, int, float)):
        return np.array(x)
    else:
        raise ValueError("Unsupported type")


def to_tensor(x, dtype=None) -> torch.Tensor:
    if isinstance(x, torch.Tensor):
        if dtype is not None:
            x = x.type(dtype)
        return x
    if isinstance(x, np.ndarray):
        x = torch.from_numpy(x)
        if dtype is not None:
            x = x.type(dtype)
        return x
    if isinstance(x, (list, tuple)):
        x = np.ndarray(x)
        x = torch.from_numpy(x)
        if dtype is not None:
            x = x.type(dtype)
        return x

    raise ValueError("Unsupported input type" + str(type(x)))


def tensor_from_rgb_image(image: np.ndarray) -> torch.Tensor:
    image = np.moveaxis(image, -1, 0)
    image = np.ascontiguousarray(image)
    image = torch.from_numpy(image)
    return image


def tensor_from_mask_image(mask: np.ndarray) -> torch.Tensor:
    if len(mask.shape) == 2:
        mask = np.expand_dims(mask, -1)
    return tensor_from_rgb_image(mask)


def rgb_image_from_tensor(image: torch.Tensor, mean, std, max_pixel_value=255.0, dtype=np.uint8) -> np.ndarray:
    image = np.moveaxis(to_numpy(image), 0, -1)
    mean = to_numpy(mean)
    std = to_numpy(std)
    rgb_image = (max_pixel_value * (image * std + mean)).astype(dtype)
    return rgb_image


def mask_from_tensor(mask: torch.Tensor, squeeze_single_channel=False, dtype=None) -> np.ndarray:
    mask = np.moveaxis(to_numpy(mask), 0, -1)
    if squeeze_single_channel and mask.shape[-1] == 1:
        mask = np.squeeze(mask, -1)

    if dtype is not None:
        mask = mask.astype(dtype)
    return mask


def maybe_cuda(x: Union[torch.Tensor, nn.Module]) -> Union[torch.Tensor, nn.Module]:
    """
    Move input Tensor or Module to CUDA device if CUDA is available.
    :param x:
    :return:
    """
    if torch.cuda.is_available():
        return x.cuda()
    return x


def transfer_weights(model: nn.Module, model_state_dict: collections.OrderedDict):
    """
    Copy weights from state dict to model, skipping layers that are incompatible.
    This method is helpful if you are doing some model surgery and want to load
    part of the model weights into different model.
    :param model: Model to load weights into
    :param model_state_dict: Model state dict to load weights from
    :return: None
    """
    for name, value in model_state_dict.items():
        try:
            model.load_state_dict(collections.OrderedDict([(name, value)]), strict=False)
        except Exception as e:
            print(e)

Common functions to marshal data to/from PyTorch Compute inverse of sigmoid of the input.     Note: This function has not been tested for numerical stability.     :param x:     :param eps:     :return: Count number of total and trainable parameters of a model     :param model: A model     :param keys: Optional list of top-level blocks     :return: Tuple (total, trainable) Convert whatever to numpy array     :param x: List, tuple, PyTorch tensor or numpy array     :return: Numpy array Move input Tensor or Module to CUDA device if CUDA is available.     :param x:     :return: Copy weights from state dict to model, skipping layers that are incompatible.     This method is helpful if you are doing some model surgery and want to load     part of the model weights into different model.     :param model: Model to load weights into     :param model_state_dict: Model state dict to load weights from     :return: None