I’ve been implementing CNN for regression and classification.
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from einops import rearrange
from typing import List, Union, Optional, Tuple
try:
from collections.abc import Sequence as SequenceCollection
except:
from collections import Sequence as SequenceCollection
class TorchCNN(nn.Module):
"""A 1, 2, or 3 dimensional convolutional network for either regression or classification.
The network consists of the following sequence of layers:
- A configurable number of convolutional layers
- A global pooling layer (either max pool or average pool)
- A final dense layer to compute the output
It optionally can compose the model from pre-activation residual blocks, as
described in https://arxiv.org/abs/1603.05027, rather than a simple stack of
convolution layers. This often leads to easier training, especially when using a
large number of layers. Note that residual blocks can only be used when
successive layers have the same output shape. Wherever the output shape changes, a
simple convolution layer will be used even if residual=True.
Examples
--------
>>> model = CNN(n_tasks=5,n_features=8,dims=2,layer_filters=[3,8,8,16],kernel_size=3, n_classes = 5,mode='classification',uncertainty=False)
>>> x = torch.ones(2,8,224,224)
>>> y = model(x)
>>> y.shape
torch.Size([2,5,5])
"""
def __init__(self,
n_tasks: int,
n_features: int,
dims: int,
layer_filters: List[int] = [100],
kernel_size: Optional[Union[int, List[int]]] = 5,
strides: Optional[Union[int, List[int]]] = 1,
dropouts: Optional[Union[int, List[int]]] = 0.5,
activation_fns: Optional[Union[nn.Module, List[nn.Module]]] = nn.ReLU,
pool_type: Optional[str] = 'max',
mode: Optional[str] = 'classification',
n_classes: Optional[int] = 2,
uncertainty: Optional[bool] = False,
residual: Optional[bool] = False,
padding: Optional[Union[str, Union[int, Tuple[int]]]] = 'valid') -> None:
"""Create a CNN.
Parameters
----------
n_tasks: int
number of tasks
n_features: int
number of features
dims: int
the number of dimensions to apply convolutions over (1, 2, or 3)
layer_filters: list
the number of output filters for each convolutional layer in the network.
The length of this list determines the number of layers.
kernel_size: int, tuple, or list
a list giving the shape of the convolutional kernel for each layer. Each
element may be either an int (use the same kernel width for every dimension)
or a tuple (the kernel width along each dimension). Alternatively this may
be a single int or tuple instead of a list, in which case the same kernel
shape is used for every layer.
strides: int, tuple, or list
a list giving the stride between applications of the kernel for each layer.
Each element may be either an int (use the same stride for every dimension)
or a tuple (the stride along each dimension). Alternatively this may be a
single int or tuple instead of a list, in which case the same stride is
used for every layer.
dropouts: list or float
the dropout probablity to use for each layer. The length of this list should equal len(layer_filters).
Alternatively this may be a single value instead of a list, in which case the same value is used for every layer.
activation_fns: list or object
the activation function to apply to each layer. The length of this list should equal
len(layer_filters). Alternatively this may be a single value instead of a list, in which case the
same value is used for every layer.
pool_type: str
the type of pooling layer to use, either 'max' or 'average'
mode: str
Either 'classification' or 'regression'
n_classes: int
the number of classes to predict (only used in classification mode)
uncertainty: bool
if True, include extra outputs and loss terms to enable the uncertainty
in outputs to be predicted
residual: bool
if True, the model will be composed of pre-activation residual blocks instead
of a simple stack of convolutional layers.
padding: str, int or tuple
the padding to use for convolutional layers, either 'valid' or 'same'
"""
super(TorchCNN, self).__init__()
if dims not in (1, 2, 3):
raise ValueError('Number of dimensions must be 1, 2 or 3')
if mode not in ['classification', 'regression']:
raise ValueError("mode must be either 'classification' or 'regression'")
self.n_tasks = n_tasks
self.n_features = n_features
self.dims = dims
self.mode = mode
self.n_classes = n_classes
self.uncertainty = uncertainty
self.mode = mode
self.layer_filters = layer_filters
self.residual = residual
n_layers = len(layer_filters)
# PyTorch layers require input and output channels as parameter
# if only one layer to make the model creating loop below work, multiply layer_filters wutg 2
if len(layer_filters) == 1:
layer_filters = layer_filters * 2
if not isinstance(kernel_size, list):
kernel_size = [kernel_size] * n_layers
if not isinstance(strides, SequenceCollection):
strides = [strides] * n_layers
if not isinstance(dropouts, SequenceCollection):
dropouts = [dropouts] * n_layers
if not isinstance(activation_fns, SequenceCollection):
activation_fns = [activation_fns] * n_layers
if uncertainty:
if mode != 'regression':
raise ValueError("Uncertainty is only supported in regression mode")
if any(d == 0.0 for d in dropouts):
raise ValueError('Dropout must be included in every layer to predict uncertainty')
# Python tuples use 0 based indexing, dims defines number of dimension for convolutional operation
ConvLayer = (nn.Conv1d, nn.Conv2d, nn.Conv3d)[self.dims - 1]
if pool_type == 'average':
PoolLayer = (nn.AvgPool1d, nn.AvgPool2d, nn.AvgPool3d)[self.dims - 1]
elif pool_type == 'max':
PoolLayer = (nn.MaxPool1d, nn.MaxPool2d, nn.MaxPool3d)[self.dims - 1]
else:
raise ValueError("pool_type must be either 'average' or 'max'")
self.layers = nn.ModuleList()
in_shape = n_features
for out_shape, size, stride, dropout, activation_fn in zip(
layer_filters, kernel_size, strides, dropouts,
activation_fns):
self.layers.append(
ConvLayer(in_channels=in_shape,
out_channels=out_shape,
kernel_size=size,
stride=stride,
padding=padding,
dilation=1,
groups=1,
bias=True))
if dropout > 0.0:
self.layers.append(nn.Dropout(dropout))
if activation_fn is not None:
self.layers.append(activation_fn())
self.layers.append(PoolLayer(size))
in_shape = out_shape
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Parameters
----------
x: torch.Tensor
Input Tensor
Returns
-------
torch.Tensor
The tensor to be fed into ODEBlock,
the length of this tensor is equal to ODEBlock Input Dimension
"""
prev_layer = x
for layer in self.layers:
x = layer(x)
# residual blocks can only be used when successive layers have the same output shape
if self.residual and layer.in_channels == layer.out_channels:
x = x + prev_layer
prev_layer = x
outputs, output_types = x, None
batch_size = x.shape[0]
pattern = ("b c h -> b (c h)", "b c h w -> b (c h w)", "b c h w a -> b (c h w a)")[self.dims - 1]
x = rearrange(x, pattern)
if self.mode is "classification":
logits = nn.Linear(x.shape[1], self.n_tasks * self.n_classes)(x)
logits = logits.view(batch_size, self.n_tasks, self.n_classes)
output = F.softmax(logits, dim=1)
outputs = [output, logits]
assert logits.shape == (batch_size, self.n_tasks, self.n_classes)
else:
output = nn.Linear(x.shape[1], self.n_tasks)(x)
print(f"Output Regression {output.shape}")
output = output.view(batch_size, self.n_tasks)
if self.uncertainty:
log_var = (nn.Linear(x.shape[1], self.n_tasks)(x))
log_var = log_var.view(batch_size, self.n_tasks, 1)
var = torch.exp(log_var)
outputs = [output, var, output, log_var]
else:
outputs = [output]
return outputs
My use case only allows me to pass numpy arrays as inputs to model. An example is as following
def test_cnn2d_torch():
torch.manual_seed(0)
n_tasks = 5
n_features = 8
n_classes = 7
batch_size = 2
mode = 'regression'
model = TorchCNN(n_tasks=n_tasks,
n_features=n_features,
dims=2,
dropouts=[0.5, 0.2, 0.4, 0.7],
layer_filters=[3, 8, 8, 16],
kernel_size=3,
n_classes=n_classes,
mode=mode,
uncertainty=True)
x = np.ones((batch_size, 8, 196, 196))
y = model(x)
test_cnn2d_torch()
But when I do so I face following error :
TypeError: conv2d() received an invalid combination of arguments - got (numpy.ndarray, Parameter, Parameter, tuple, str, tuple, int), but expected one of:
* (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, tuple of ints padding, tuple of ints dilation, int groups)
didn't match because some of the arguments have invalid types: (!numpy.ndarray!, !Parameter!, !Parameter!, !tuple!, !str!, !tuple!, int)
* (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, str padding, tuple of ints dilation, int groups)
didn't match because some of the arguments have invalid types: (!numpy.ndarray!, !Parameter!, !Parameter!, !tuple!, str, !tuple!, int)