Source code for monai.networks.utils

# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
Utilities and types for defining networks, these depend on PyTorch.
import re
import warnings
from collections import OrderedDict
from contextlib import contextmanager
from typing import Any, Callable, Mapping, Optional, Sequence, Union

import torch
import torch.nn as nn

from monai.utils.deprecated import deprecated_arg

__all__ = [

[docs]def one_hot(labels: torch.Tensor, num_classes: int, dtype: torch.dtype = torch.float, dim: int = 1) -> torch.Tensor: """ For every value v in `labels`, the value in the output will be either 1 or 0. Each vector along the `dim`-th dimension has the "one-hot" format, i.e., it has a total length of `num_classes`, with a one and `num_class-1` zeros. Note that this will include the background label, thus a binary mask should be treated as having two classes. Args: labels: input tensor of integers to be converted into the 'one-hot' format. Internally `labels` will be converted into integers `labels.long()`. num_classes: number of output channels, the corresponding length of `labels[dim]` will be converted to `num_classes` from `1`. dtype: the data type of the output one_hot label. dim: the dimension to be converted to `num_classes` channels from `1` channel, should be non-negative number. Example: For a tensor `labels` of dimensions [B]1[spatial_dims], return a tensor of dimensions `[B]N[spatial_dims]` when `num_classes=N` number of classes and `dim=1`. .. code-block:: python from monai.networks.utils import one_hot import torch a = torch.randint(0, 2, size=(1, 2, 2, 2)) out = one_hot(a, num_classes=2, dim=0) print(out.shape) # torch.Size([2, 2, 2, 2]) a = torch.randint(0, 2, size=(2, 1, 2, 2, 2)) out = one_hot(a, num_classes=2, dim=1) print(out.shape) # torch.Size([2, 2, 2, 2, 2]) """ # if `dim` is bigger, add singleton dim at the end if labels.ndim < dim + 1: shape = list(labels.shape) + [1] * (dim + 1 - len(labels.shape)) labels = torch.reshape(labels, shape) sh = list(labels.shape) if sh[dim] != 1: raise AssertionError("labels should have a channel with length equal to one.") sh[dim] = num_classes o = torch.zeros(size=sh, dtype=dtype, device=labels.device) labels = o.scatter_(dim=dim, index=labels.long(), value=1) return labels
def slice_channels(tensor: torch.Tensor, *slicevals: Optional[int]) -> torch.Tensor: slices = [slice(None)] * len(tensor.shape) slices[1] = slice(*slicevals) return tensor[slices]
[docs]def predict_segmentation(logits: torch.Tensor, mutually_exclusive: bool = False, threshold: float = 0.0) -> Any: """ Given the logits from a network, computing the segmentation by thresholding all values above 0 if multi-labels task, computing the `argmax` along the channel axis if multi-classes task, logits has shape `BCHW[D]`. Args: logits: raw data of model output. mutually_exclusive: if True, `logits` will be converted into a binary matrix using a combination of argmax, which is suitable for multi-classes task. Defaults to False. threshold: thresholding the prediction values if multi-labels task. """ if not mutually_exclusive: return (logits >= threshold).int() if logits.shape[1] == 1: warnings.warn("single channel prediction, `mutually_exclusive=True` ignored, use threshold instead.") return (logits >= threshold).int() return logits.argmax(1, keepdim=True)
[docs]def normalize_transform( shape: Sequence[int], device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, align_corners: bool = False, ) -> torch.Tensor: """ Compute an affine matrix according to the input shape. The transform normalizes the homogeneous image coordinates to the range of `[-1, 1]`. Args: shape: input spatial shape device: device on which the returned affine will be allocated. dtype: data type of the returned affine align_corners: if True, consider -1 and 1 to refer to the centers of the corner pixels rather than the image corners. See also: """ norm = torch.tensor(shape, dtype=torch.float64, device=device) # no in-place change if align_corners: norm[norm <= 1.0] = 2.0 norm = 2.0 / (norm - 1.0) norm = torch.diag(, torch.ones((1,), dtype=torch.float64, device=device)))) norm[:-1, -1] = -1.0 else: norm[norm <= 0.0] = 2.0 norm = 2.0 / norm norm = torch.diag(, torch.ones((1,), dtype=torch.float64, device=device)))) norm[:-1, -1] = 1.0 / torch.tensor(shape, dtype=torch.float64, device=device) - 1.0 norm = norm.unsqueeze(0).to(dtype=dtype) norm.requires_grad = False return norm
[docs]def to_norm_affine( affine: torch.Tensor, src_size: Sequence[int], dst_size: Sequence[int], align_corners: bool = False ) -> torch.Tensor: """ Given ``affine`` defined for coordinates in the pixel space, compute the corresponding affine for the normalized coordinates. Args: affine: Nxdxd batched square matrix src_size: source image spatial shape dst_size: target image spatial shape align_corners: if True, consider -1 and 1 to refer to the centers of the corner pixels rather than the image corners. See also: Raises: TypeError: When ``affine`` is not a ``torch.Tensor``. ValueError: When ``affine`` is not Nxdxd. ValueError: When ``src_size`` or ``dst_size`` dimensions differ from ``affine``. """ if not isinstance(affine, torch.Tensor): raise TypeError(f"affine must be a torch.Tensor but is {type(affine).__name__}.") if affine.ndimension() != 3 or affine.shape[1] != affine.shape[2]: raise ValueError(f"affine must be Nxdxd, got {tuple(affine.shape)}.") sr = affine.shape[1] - 1 if sr != len(src_size) or sr != len(dst_size): raise ValueError(f"affine suggests {sr}D, got src={len(src_size)}D, dst={len(dst_size)}D.") src_xform = normalize_transform(src_size, affine.device, affine.dtype, align_corners) dst_xform = normalize_transform(dst_size, affine.device, affine.dtype, align_corners) return src_xform @ affine @ torch.inverse(dst_xform)
[docs]def normal_init( m, std: float = 0.02, normal_func: Callable[[torch.Tensor, float, float], Any] = torch.nn.init.normal_ ) -> None: """ Initialize the weight and bias tensors of `m' and its submodules to values from a normal distribution with a stddev of `std'. Weight tensors of convolution and linear modules are initialized with a mean of 0, batch norm modules with a mean of 1. The callable `normal_func', used to assign values, should have the same arguments as its default normal_(). This can be used with `nn.Module.apply` to visit submodules of a network. """ cname = m.__class__.__name__ if getattr(m, "weight", None) is not None and (cname.find("Conv") != -1 or cname.find("Linear") != -1): normal_func(, 0.0, std) if getattr(m, "bias", None) is not None: nn.init.constant_(, 0.0) elif cname.find("BatchNorm") != -1: normal_func(, 1.0, std) nn.init.constant_(, 0)
[docs]def icnr_init(conv, upsample_factor, init=nn.init.kaiming_normal_): """ ICNR initialization for 2D/3D kernels adapted from Aitken et al.,2017 , "Checkerboard artifact free sub-pixel convolution". """ out_channels, in_channels, *dims = conv.weight.shape scale_factor = upsample_factor ** len(dims) oc2 = int(out_channels / scale_factor) kernel = torch.zeros([oc2, in_channels] + dims) kernel = init(kernel) kernel = kernel.transpose(0, 1) kernel = kernel.reshape(oc2, in_channels, -1) kernel = kernel.repeat(1, 1, scale_factor) kernel = kernel.reshape([in_channels, out_channels] + dims) kernel = kernel.transpose(0, 1)
[docs]@deprecated_arg( name="dimensions", new_name="spatial_dims", since="0.6", msg_suffix="Please use `spatial_dims` instead." ) def pixelshuffle( x: torch.Tensor, spatial_dims: int, scale_factor: int, dimensions: Optional[int] = None ) -> torch.Tensor: """ Apply pixel shuffle to the tensor `x` with spatial dimensions `spatial_dims` and scaling factor `scale_factor`. See: Shi et al., 2016, "Real-Time Single Image and Video Super-Resolution Using a nEfficient Sub-Pixel Convolutional Neural Network." See: Aitken et al., 2017, "Checkerboard artifact free sub-pixel convolution". Args: x: Input tensor spatial_dims: number of spatial dimensions, typically 2 or 3 for 2D or 3D scale_factor: factor to rescale the spatial dimensions by, must be >=1 .. deprecated:: 0.6.0 ``dimensions`` is deprecated, use ``spatial_dims`` instead. Returns: Reshuffled version of `x`. Raises: ValueError: When input channels of `x` are not divisible by (scale_factor ** spatial_dims) """ if dimensions is not None: spatial_dims = dimensions dim, factor = spatial_dims, scale_factor input_size = list(x.size()) batch_size, channels = input_size[:2] scale_divisor = factor ** dim if channels % scale_divisor != 0: raise ValueError( f"Number of input channels ({channels}) must be evenly " f"divisible by scale_factor ** dimensions ({factor}**{dim}={scale_divisor})." ) org_channels = channels // scale_divisor output_size = [batch_size, org_channels] + [d * factor for d in input_size[2:]] indices = tuple(range(2, 2 + 2 * dim)) indices_factor, indices_dim = indices[:dim], indices[dim:] permute_indices = (0, 1) + sum(zip(indices_dim, indices_factor), ()) x = x.reshape(batch_size, org_channels, *([factor] * dim + input_size[2:])) x = x.permute(permute_indices).reshape(output_size) return x
[docs]@contextmanager def eval_mode(*nets: nn.Module): """ Set network(s) to eval mode and then return to original state at the end. Args: nets: Input network(s) Examples .. code-block:: python t=torch.rand(1,1,16,16) p=torch.nn.Conv2d(1,1,3) print( # True with eval_mode(p): print( # False print(p(t).sum().backward()) # will correctly raise an exception as gradients are calculated """ # Get original state of network(s) training = [n for n in nets if] try: # set to eval mode with torch.no_grad(): yield [n.eval() for n in nets] finally: # Return required networks to training for n in training: n.train()
[docs]@contextmanager def train_mode(*nets: nn.Module): """ Set network(s) to train mode and then return to original state at the end. Args: nets: Input network(s) Examples .. code-block:: python t=torch.rand(1,1,16,16) p=torch.nn.Conv2d(1,1,3) p.eval() print( # False with train_mode(p): print( # True print(p(t).sum().backward()) # No exception """ # Get original state of network(s) eval_list = [n for n in nets if not] try: # set to train mode with torch.set_grad_enabled(True): yield [n.train() for n in nets] finally: # Return required networks to eval_list for n in eval_list: n.eval()
[docs]def copy_model_state( dst: Union[torch.nn.Module, Mapping], src: Union[torch.nn.Module, Mapping], dst_prefix="", mapping=None, exclude_vars=None, inplace=True, ): """ Compute a module state_dict, of which the keys are the same as `dst`. The values of `dst` are overwritten by the ones from `src` whenever their keys match. The method provides additional `dst_prefix` for the `dst` key when matching them. `mapping` can be a `{"src_key": "dst_key"}` dict, indicating `dst[dst_prefix + dst_key] = src[src_key]`. This function is mainly to return a model state dict for loading the `src` model state into the `dst` model, `src` and `dst` can have different dict keys, but their corresponding values normally have the same shape. Args: dst: a pytorch module or state dict to be updated. src: a pytorch module or state dist used to get the values used for the update. dst_prefix: `dst` key prefix, so that `dst[dst_prefix + src_key]` will be assigned to the value of `src[src_key]`. mapping: a `{"src_key": "dst_key"}` dict, indicating that `dst[dst_prefix + dst_key]` to be assigned to the value of `src[src_key]`. exclude_vars: a regular expression to match the `dst` variable names, so that their values are not overwritten by `src`. inplace: whether to set the `dst` module with the updated `state_dict` via `load_state_dict`. This option is only available when `dst` is a `torch.nn.Module`. Examples: .. code-block:: python from monai.networks.nets import BasicUNet from monai.networks.utils import copy_model_state model_a = BasicUNet(in_channels=1, out_channels=4) model_b = BasicUNet(in_channels=1, out_channels=2) model_a_b, changed, unchanged = copy_model_state( model_a, model_b, exclude_vars="conv_0.conv_0", inplace=False) # dst model updated: 76 of 82 variables. model_a.load_state_dict(model_a_b) # <All keys matched successfully> Returns: an OrderedDict of the updated `dst` state, the changed, and unchanged keys. """ if isinstance(src, (nn.DataParallel, nn.parallel.DistributedDataParallel)): src = src.module if isinstance(dst, (nn.DataParallel, nn.parallel.DistributedDataParallel)): dst = dst.module src_dict = src.state_dict() if isinstance(src, torch.nn.Module) else src dst_dict = dst.state_dict() if isinstance(dst, torch.nn.Module) else dst dst_dict = OrderedDict(dst_dict) to_skip = {s_key for s_key in src_dict if exclude_vars and re.compile(exclude_vars).search(s_key)} # update dst with items from src all_keys, updated_keys = list(dst_dict), list() for s, val in src_dict.items(): dst_key = f"{dst_prefix}{s}" if dst_key in dst_dict and dst_key not in to_skip and dst_dict[dst_key].shape == val.shape: dst_dict[dst_key] = val updated_keys.append(dst_key) for s in mapping if mapping else {}: dst_key = f"{dst_prefix}{mapping[s]}" if dst_key in dst_dict and dst_key not in to_skip: if dst_dict[dst_key].shape != src_dict[s].shape: warnings.warn(f"Param. shape changed from {dst_dict[dst_key].shape} to {src_dict[s].shape}.") dst_dict[dst_key] = src_dict[s] updated_keys.append(dst_key) updated_keys = sorted(set(updated_keys)) unchanged_keys = sorted(set(all_keys).difference(updated_keys)) print(f"'dst' model updated: {len(updated_keys)} of {len(dst_dict)} variables.") if inplace and isinstance(dst, torch.nn.Module): dst.load_state_dict(dst_dict) return dst_dict, updated_keys, unchanged_keys