autoconvo 0.0.6
A package to automatically set up simple convolutional neural networks in PyTorch.
Requires Python: >=3.6

# pytorch-convo
 A package to automatically set up simple convolutional neural networks in pytorch.
 
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## `convo.make_convolutions()`

This is the only function exported by this one-module package, intended to make designing 'quick and dirty'
convolutional sub-networks easy and quick!

Give it the shapes and activations you want, it'll give you a network taking your `in_shape` to your `out_shape`
in `n_layers` steps.

The solver is currently a little shaky, and may fail or be slow for larger input sizes and numbers of layers.

### Signature:
```python
convo.make_convolutions(
		    in_shape, out_shape, n_layers, kernel_size=None, stride=None,
            	    padding_mode="zeros", dilation=1, bias=True, 
            	    activation=nn.ReLU, pool_type="max", norm_type=None, 
		    module_list=False, cache=True
		    ) -> torch.nn.Sequential or torch.nn.ModuleList
```

### Args:

- `in_shape`: A tuple of ints specifying the shape of the input to the convolutional
system. `(C, X, [Y], [Z])`

- `out_shape`: A tuple with the same shape as in_shape, specifying the desired
output shape. 

- `n_layers`: int, the number of convolutional layers in the system.

- `kernel_size`: None, int, tuple of int (one int per layer) or tuple of tuples of int
 (shape \[n_layers, n_spatial_dims], one int per dimension per layer). The kernel size
 for each convolutional filter. See PyTorch docs for more detail. None means the solver will
 find an appropriate value itself.
 
- `stride`: None, int, tuple of int (one int per layer) or tuple of tuples of int
 (shape \[n_layers, n_spatial_dims], one int per dimension per layer). The stride
 of each convolutional filter. See PyTorch docs for more detail. None means the solver will
 find an appropriate value itself.
 
- `padding_mode`: one of 'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'.
	The type of padding used where neccessary. See PyTorch docs for more detail.
	
- `dilation`: None, int, tuple of int (one int per layer) or tuple of tuples of int
 (shape \[n_layers, n_spatial_dims], one int per dimension per layer). The dilation for each 
 convolutional filter. See PyTorch docs for more detail. None means the solver will
 find an appropriate value itself.
 
- `bias`: Bool or array of Bool. Whether the convolutional layers will use a bias tensor or not.

- `activation`: None or class inheriting torch.nn.Module. One instance of this type will be 
added after each convolutional layer. n.b. this needs to be a class object, NOT an instance
that class (`activation=nn.ReLU` and not `activation=nn.ReLU()`).

- `pool_type`: 'max' or 'avg'. Default 'max'. Indicates whether MaxPool or AvgPool layers will be used.

- `norm_type`: None or 'batch'. Default None. Indicates whether BatchNorm layers will be added after
each pooling layer. In the future other norm types will be implemented.

- `module_list`: Bool. Whether the returned object will be an instance of `torch.nn.Sequential`
or `torch.nn.ModuleList`.

- `cache`: Bool. Whether or not to use the caching system to check whether a solution has already
been generated for the given parameters.

### Returns:

A system of (`n_layers` of) convolutional, activation, pooling and (optionally) norm layers 
taking an input of shape `(batch_size, *in_shape)` and returning a result of shape `(batch_size, *out_shape)`.
These layers are contained in a `torch.nn.Sequential` object or a `torch.nn.ModuleList` if specified by the
`module_list` argument.

## Using convo

Currently, convo is in the testing phase and has been released in alpha on PyPI. You will find bugs so please make PRs or post issues
here so I can get them squashed.

*Also check out my other baby, QutiePy - a python quantum computing library.*

### Installing pytorch-convo

The package can be installed with `pip`:
```
pip install pytorch-convo
```
and imported with:
```python
from convo import make_convolutions
```

### Example

Below is an example of how to use `make_convolutions` to automate convolution parameter calculations and
keep network definitions tidy. Remember, if you want to check the parameters `make_convolutions` has suggested,
just `print` the resulting `nn.Sequential` or `nn.ModuleList` for a nice representation of the subnetwork in the console.

```python
import torch.nn as nn
from torch import flatten
import convo

class MyCNN(nn.Module):
	def __init__(self):
		super(MyCNN, self).__init__()
		self.conv_subnet = convo.make_convolutions([3, 256, 256], [100, 1, 1], 3)
		## Uncomment the following to inspect the subnet params:
		# print(self.conv_subnet)
		self.flatten = nn.Flatten()
		self.full_conn = nn.Linear(100, 1)
		self.activation = nn.Sigmoid()
	
	def forward(self, batch):
		# batch has shape [BATCH_SIZE, 3, 256, 256]
		flat = self.flattten(self.conv_subnet(batch))
		output = self.activation(self.full_conn(flat))
```

### Tips and Tricks

- The argument `activation` can be any `nn.Module` subtype and therefore can be an `nn.Sequential` of several layers, so long as these layers do not change the shape of the
tensor passed to `forward` as this will mess up the shape solver (in its current state at least :) ).

## TODO:
- Make proper docs.
- Test, test, test...
- Automate testing.
- Add more norm layer types.