# Copyright 2020 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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch.nn as nn
from monai.networks.layers.factories import Norm, Act
from monai.networks.blocks import Convolution, ResidualUnit
from monai.networks.layers.simplelayers import Reshape
from monai.networks.layers.convutils import same_padding, calculate_out_shape
[docs]class Regressor(nn.Module):
"""
This defines a network for relating large-sized input tensors to small output tensors, ie. regressing large
values to a prediction. An output of a single dimension can be used as value regression or multi-label
classification prediction, an output of a single value can be used as a discriminator or critic prediction.
"""
def __init__(
self,
in_shape,
out_shape,
channels,
strides,
kernel_size=3,
num_res_units=2,
act=Act.PRELU,
norm=Norm.INSTANCE,
dropout=None,
bias=True,
):
"""
Construct the regressor network with the number of layers defined by `channels` and `strides`. Inputs are
first passed through the convolutional layers in the forward pass, the output from this is then pass
through a fully connected layer to relate them to the final output tensor.
Args:
in_shape: tuple of integers stating the dimension of the input tensor (minus batch dimension)
out_shape: tuple of integers stating the dimension of the final output tensor
channels: tuple of integers stating the output channels of each convolutional layer
strides: tuple of integers stating the stride (downscale factor) of each convolutional layer
kernel_size: integer or tuple of integers stating size of convolutional kernels
num_res_units: integer stating number of convolutions in residual units, 0 means no residual units
act: name or type defining activation layers
norm: name or type defining normalization layers
dropout: optional float value in range [0, 1] stating dropout probability for layers, None for no dropout
bias: boolean stating if convolution layers should have a bias component
"""
super().__init__()
self.in_channels, *self.in_shape = in_shape
self.dimensions = len(self.in_shape)
self.channels = channels
self.strides = strides
self.out_shape = out_shape
self.kernel_size = kernel_size
self.num_res_units = num_res_units
self.act = act
self.norm = norm
self.dropout = dropout
self.bias = bias
self.net = nn.Sequential()
echannel = self.in_channels
padding = same_padding(kernel_size)
self.final_size = np.asarray(self.in_shape, np.int)
self.reshape = Reshape(*self.out_shape)
# encode stage
for i, (c, s) in enumerate(zip(self.channels, self.strides)):
layer = self._get_layer(echannel, c, s, i == len(channels) - 1)
echannel = c # use the output channel number as the input for the next loop
self.net.add_module("layer_%i" % i, layer)
self.final_size = calculate_out_shape(self.final_size, kernel_size, s, padding)
self.final = self._get_final_layer((echannel,) + self.final_size)
def _get_layer(self, in_channels, out_channels, strides, is_last):
"""
Returns a layer accepting inputs with `in_channels` number of channels and producing outputs of `out_channels`
number of channels. The `strides` indicates downsampling factor, ie. convolutional stride. If `is_last`
is True this is the final layer and is not expected to include activation and normalization layers.
"""
common_kwargs = dict(
dimensions=self.dimensions,
in_channels=in_channels,
out_channels=out_channels,
strides=strides,
kernel_size=self.kernel_size,
act=self.act,
norm=self.norm,
dropout=self.dropout,
bias=self.bias,
)
if self.num_res_units > 0:
layer = ResidualUnit(subunits=self.num_res_units, last_conv_only=is_last, **common_kwargs)
else:
layer = Convolution(conv_only=is_last, **common_kwargs)
return layer
def _get_final_layer(self, in_shape):
linear = nn.Linear(int(np.product(in_shape)), int(np.product(self.out_shape)))
return nn.Sequential(nn.Flatten(), linear)
[docs] def forward(self, x):
x = self.net(x)
x = self.final(x)
x = self.reshape(x)
return x