[github], tutorials [1] and [2]

Writing a better code with pytorch and einops

Rewriting building blocks of deep learning

Now let's get to examples from real world. These code fragments taken from official tutorials and popular repositories.

Learn how to improve code and how einops can help you.

Left: as it was, Right: improved version

# start from importing some stuff
import torch
import torch.nn as nn 
import torch.nn.functional as F
import numpy as np
import math

from einops import rearrange, reduce, asnumpy, parse_shape
from einops.layers.torch import Rearrange, Reduce

Simple ConvNet

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

conv_net_old = Net()

conv_net_new = nn.Sequential(
    nn.Conv2d(1, 10, kernel_size=5),
    nn.MaxPool2d(kernel_size=2),
    nn.ReLU(),
    nn.Conv2d(10, 20, kernel_size=5),
    nn.MaxPool2d(kernel_size=2),
    nn.ReLU(),
    nn.Dropout2d(),
    Rearrange('b c h w -> b (c h w)'),
    nn.Linear(320, 50),
    nn.ReLU(),
    nn.Dropout(),
    nn.Linear(50, 10),
    nn.LogSoftmax(dim=1)
)

Reasons to prefer new implementation:

in the original code (to the left) if input size is changed and batch size is divisible by 16 (that's usually so), we'll get something senseless after reshaping
- new code will explicitly raise an error in this case
we won't forget to use dropout with flag self.training with new version
code is straightforward to read and analyze
sequential makes printing / saving / passing trivial. And there is no need in your code to load a model (which also has a number of benefits)
don't need logsoftmax? Now you can use conv_net_new[:-1]. One more reason to prefer nn.Sequential
... and we could also add inplace for ReLU

Super-resolution

class SuperResolutionNetOld(nn.Module):
    def __init__(self, upscale_factor):
        super(SuperResolutionNetOld, self).__init__()

        self.relu = nn.ReLU()
        self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2))
        self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
        self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1))
        self.conv4 = nn.Conv2d(32, upscale_factor ** 2, (3, 3), (1, 1), (1, 1))
        self.pixel_shuffle = nn.PixelShuffle(upscale_factor)

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.relu(self.conv3(x))
        x = self.pixel_shuffle(self.conv4(x))
        return x

def SuperResolutionNetNew(upscale_factor):
    return nn.Sequential(
        nn.Conv2d(1, 64, kernel_size=5, padding=2),
        nn.ReLU(inplace=True),
        nn.Conv2d(64, 64, kernel_size=3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(64, 32, kernel_size=3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(32, upscale_factor ** 2, kernel_size=3, padding=1),
        Rearrange('b (h2 w2) h w -> b (h h2) (w w2)', h2=upscale_factor, w2=upscale_factor),
    )

Here is the difference:

no need in special instruction pixel_shuffle (and result is transferrable between frameworks)
output doesn't contain a fake axis (and we could do the same for the input)
inplace ReLU used now, for high resolution pictures that becomes critical and saves us much memory
and all the benefits of nn.Sequential again

Restyling Gram matrix for style transfer

Original code is already good - first line shows what kind of input is expected

einsum operation should be read like:
for each batch and for each pair of channels, we sum over h and w.
I've also changed normalization, because that's how Gram matrix is defined, otherwise we should call it normalized Gram matrix or alike

def gram_matrix_old(y):
    (b, ch, h, w) = y.size()
    features = y.view(b, ch, w * h)
    features_t = features.transpose(1, 2)
    gram = features.bmm(features_t) / (ch * h * w)
    return gram

def gram_matrix_new(y):
    b, ch, h, w = y.shape
    return torch.einsum('bchw,bdhw->bcd', [y, y]) / (h * w)

It would be great to use just 'b c1 h w,b c2 h w->b c1 c2', but einsum supports only one-letter axes

Recurrent model

All we did here is just made information about shapes explicit to skip deciphering

class RNNModelOld(nn.Module):
    """Container module with an encoder, a recurrent module, and a decoder."""
    def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5):
        super(RNNModel, self).__init__()
        self.drop = nn.Dropout(dropout)
        self.encoder = nn.Embedding(ntoken, ninp)
        self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout)
        self.decoder = nn.Linear(nhid, ntoken)

    def forward(self, input, hidden):
        emb = self.drop(self.encoder(input))
        output, hidden = self.rnn(emb, hidden)
        output = self.drop(output)
        decoded = self.decoder(output.view(output.size(0)*output.size(1), output.size(2)))
        return decoded.view(output.size(0), output.size(1), decoded.size(1)), hidden

class RNNModelNew(nn.Module):
    """Container module with an encoder, a recurrent module, and a decoder."""
    def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5):
        super(RNNModel, self).__init__()
        self.drop = nn.Dropout(p=dropout)
        self.encoder = nn.Embedding(ntoken, ninp)
        self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout)
        self.decoder = nn.Linear(nhid, ntoken)

    def forward(self, input, hidden):
        t, b = input.shape
        emb = self.drop(self.encoder(input))
        output, hidden = self.rnn(emb, hidden)
        output = rearrange(self.drop(output), 't b nhid -> (t b) nhid')
        decoded = rearrange(self.decoder(output), '(t b) token -> t b token', t=t, b=b)
        return decoded, hidden

Channel shuffle (from shufflenet)

def channel_shuffle_old(x, groups):
    batchsize, num_channels, height, width = x.data.size()

    channels_per_group = num_channels // groups
    
    # reshape
    x = x.view(batchsize, groups, 
        channels_per_group, height, width)

    # transpose
    # - contiguous() required if transpose() is used before view().
    #   See https://github.com/pytorch/pytorch/issues/764
    x = torch.transpose(x, 1, 2).contiguous()

    # flatten
    x = x.view(batchsize, -1, height, width)

    return x

def channel_shuffle_new(x, groups):
    return rearrange(x, 'b (c1 c2) h w -> b (c2 c1) h w', c1=groups)

While progress is obvious, this is not the limit. As you'll see below, we don't even need to write these couple of lines.

Shufflenet

from collections import OrderedDict

def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()

    channels_per_group = num_channels // groups
    
    # reshape
    x = x.view(batchsize, groups, 
        channels_per_group, height, width)

    # transpose
    # - contiguous() required if transpose() is used before view().
    #   See https://github.com/pytorch/pytorch/issues/764
    x = torch.transpose(x, 1, 2).contiguous()

    # flatten
    x = x.view(batchsize, -1, height, width)

    return x

class ShuffleUnitOld(nn.Module):
    def __init__(self, in_channels, out_channels, groups=3,
                 grouped_conv=True, combine='add'):
        
        super(ShuffleUnitOld, self).__init__()

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.grouped_conv = grouped_conv
        self.combine = combine
        self.groups = groups
        self.bottleneck_channels = self.out_channels // 4

        # define the type of ShuffleUnit
        if self.combine == 'add':
            # ShuffleUnit Figure 2b
            self.depthwise_stride = 1
            self._combine_func = self._add
        elif self.combine == 'concat':
            # ShuffleUnit Figure 2c
            self.depthwise_stride = 2
            self._combine_func = self._concat
            
            # ensure output of concat has the same channels as 
            # original output channels.
            self.out_channels -= self.in_channels
        else:
            raise ValueError("Cannot combine tensors with \"{}\"" \
                             "Only \"add\" and \"concat\" are" \
                             "supported".format(self.combine))

        # Use a 1x1 grouped or non-grouped convolution to reduce input channels
        # to bottleneck channels, as in a ResNet bottleneck module.
        # NOTE: Do not use group convolution for the first conv1x1 in Stage 2.
        self.first_1x1_groups = self.groups if grouped_conv else 1

        self.g_conv_1x1_compress = self._make_grouped_conv1x1(
            self.in_channels,
            self.bottleneck_channels,
            self.first_1x1_groups,
            batch_norm=True,
            relu=True
            )

        # 3x3 depthwise convolution followed by batch normalization
        self.depthwise_conv3x3 = conv3x3(
            self.bottleneck_channels, self.bottleneck_channels,
            stride=self.depthwise_stride, groups=self.bottleneck_channels)
        self.bn_after_depthwise = nn.BatchNorm2d(self.bottleneck_channels)

        # Use 1x1 grouped convolution to expand from 
        # bottleneck_channels to out_channels
        self.g_conv_1x1_expand = self._make_grouped_conv1x1(
            self.bottleneck_channels,
            self.out_channels,
            self.groups,
            batch_norm=True,
            relu=False
            )


    @staticmethod
    def _add(x, out):
        # residual connection
        return x + out


    @staticmethod
    def _concat(x, out):
        # concatenate along channel axis
        return torch.cat((x, out), 1)


    def _make_grouped_conv1x1(self, in_channels, out_channels, groups,
        batch_norm=True, relu=False):

        modules = OrderedDict()
        conv = conv1x1(in_channels, out_channels, groups=groups)
        modules['conv1x1'] = conv

        if batch_norm:
            modules['batch_norm'] = nn.BatchNorm2d(out_channels)
        if relu:
            modules['relu'] = nn.ReLU()
        if len(modules) > 1:
            return nn.Sequential(modules)
        else:
            return conv


    def forward(self, x):
        # save for combining later with output
        residual = x
        if self.combine == 'concat':
            residual = F.avg_pool2d(residual, kernel_size=3, 
                stride=2, padding=1)

        out = self.g_conv_1x1_compress(x)
        out = channel_shuffle(out, self.groups)
        out = self.depthwise_conv3x3(out)
        out = self.bn_after_depthwise(out)
        out = self.g_conv_1x1_expand(out)
        
        out = self._combine_func(residual, out)
        return F.relu(out)

class ShuffleUnitNew(nn.Module):
    def __init__(self, in_channels, out_channels, groups=3, 
                 grouped_conv=True, combine='add'):
        super().__init__()
        first_1x1_groups = groups if grouped_conv else 1
        bottleneck_channels = out_channels // 4
        self.combine = combine
        if combine == 'add':
            # ShuffleUnit Figure 2b
            self.left = Rearrange('...->...') # identity
            depthwise_stride = 1
        else:
            # ShuffleUnit Figure 2c
            self.left = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
            depthwise_stride = 2
            # ensure output of concat has the same channels as original output channels.
            out_channels -= in_channels
            assert out_channels > 0

        self.right = nn.Sequential(
            # Use a 1x1 grouped or non-grouped convolution to reduce input channels
            # to bottleneck channels, as in a ResNet bottleneck module.
            conv1x1(in_channels, bottleneck_channels, groups=first_1x1_groups),
            nn.BatchNorm2d(bottleneck_channels),
            nn.ReLU(inplace=True),
            # channel shuffle
            Rearrange('b (c1 c2) h w -> b (c2 c1) h w', c1=groups),
            # 3x3 depthwise convolution followed by batch 
            conv3x3(bottleneck_channels, bottleneck_channels,
                    stride=depthwise_stride, groups=bottleneck_channels),
            nn.BatchNorm2d(bottleneck_channels),
            # Use 1x1 grouped convolution to expand from 
            # bottleneck_channels to out_channels
            conv1x1(bottleneck_channels, out_channels, groups=groups),
            nn.BatchNorm2d(out_channels),
        )        
        
    def forward(self, x):
        if self.combine == 'add':
            combined = self.left(x) + self.right(x)
        else:
            combined = torch.cat([self.left(x), self.right(x)], dim=1)
        return F.relu(combined, inplace=True)

Rewriting the code helped to identify:

There is no sense in doing reshuffling and not using groups in the first convolution (indeed, in the paper it is not so). However, result is an equivalent model.
It is also strange that the first convolution may be not grouped, while the last convolution is always grouped (and that is different from the paper)

Other comments:

There is an identity layer for pytorch introduced here
The last thing left is get rid of conv1x1 and conv3x3 in the code - those are not better than standard

Simplifying ResNet

class ResNetOld(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNetOld, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)

        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

def make_layer(inplanes, planes, block, n_blocks, stride=1):
    downsample = None
    if stride != 1 or inplanes != planes * block.expansion:
        # output size won't match input, so adjust residual
        downsample = nn.Sequential(
            nn.Conv2d(inplanes, planes * block.expansion,
                      kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(planes * block.expansion),
        )
    return nn.Sequential(
        block(inplanes, planes, stride, downsample),
        *[block(planes * block.expansion, planes) for _ in range(1, n_blocks)]
    )


def ResNetNew(block, layers, num_classes=1000):    
    e = block.expansion
    
    resnet = nn.Sequential(
        Rearrange('b c h w -> b c h w', c=3, h=224, w=224),
        nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
        nn.BatchNorm2d(64),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
        make_layer(64,      64,  block, layers[0], stride=1),
        make_layer(64 * e,  128, block, layers[1], stride=2),
        make_layer(128 * e, 256, block, layers[2], stride=2),
        make_layer(256 * e, 512, block, layers[3], stride=2),
        # combined AvgPool and view in one averaging operation
        Reduce('b c h w -> b c', 'mean'),
        nn.Linear(512 * e, num_classes),
    )
    
    # initialization
    for m in resnet.modules():
        if isinstance(m, nn.Conv2d):
            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            m.weight.data.normal_(0, math.sqrt(2. / n))
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
    return resnet

Changes:

explicit check for input shape
no views and simple sequential structure, output is just nn.Sequential, so can always be saved/passed/etc
no need in AvgPool and additional views, this place is much clearer now
make_layer doesn't use internal state (that's quite faulty place)

Improving RNN language modelling

class RNNOld(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout):
        super().__init__()
        
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.LSTM(embedding_dim, hidden_dim, num_layers=n_layers, 
                           bidirectional=bidirectional, dropout=dropout)
        self.fc = nn.Linear(hidden_dim*2, output_dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x):
        #x = [sent len, batch size]
        
        embedded = self.dropout(self.embedding(x))
        
        #embedded = [sent len, batch size, emb dim]
        
        output, (hidden, cell) = self.rnn(embedded)
        
        #output = [sent len, batch size, hid dim * num directions]
        #hidden = [num layers * num directions, batch size, hid dim]
        #cell = [num layers * num directions, batch size, hid dim]
        
        #concat the final forward (hidden[-2,:,:]) and backward (hidden[-1,:,:]) hidden layers
        #and apply dropout
        
        hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1))
                
        #hidden = [batch size, hid dim * num directions]
            
        return self.fc(hidden.squeeze(0))

class RNNNew(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout):
        super().__init__()
        
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.LSTM(embedding_dim, hidden_dim, num_layers=n_layers, 
                           bidirectional=bidirectional, dropout=dropout)
        self.dropout = nn.Dropout(dropout)
        self.directions = 2 if bidirectional else 1
        self.fc = nn.Linear(hidden_dim * self.directions, output_dim)
        
    def forward(self, x):
        #x = [sent len, batch size]        
        embedded = self.dropout(self.embedding(x))
        
        #embedded = [sent len, batch size, emb dim]
        output, (hidden, cell) = self.rnn(embedded)
        
        hidden = rearrange(hidden, '(layer dir) b c -> layer b (dir c)', 
                           dir=self.directions)
        # take the final layer's hidden
        return self.fc(self.dropout(hidden[-1]))

original code misbehaves for non-bidirectional models
... and fails when bidirectional = False, and there is only one layer
modification of the code shows both how hidden is structured and how it is modified

Writing FastText faster

class FastTextOld(nn.Module):
    def __init__(self, vocab_size, embedding_dim, output_dim):
        super().__init__()
        
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.fc = nn.Linear(embedding_dim, output_dim)
        
    def forward(self, x):
        
        #x = [sent len, batch size]
        
        embedded = self.embedding(x)
                
        #embedded = [sent len, batch size, emb dim]
        
        embedded = embedded.permute(1, 0, 2)
        
        #embedded = [batch size, sent len, emb dim]
        
        pooled = F.avg_pool2d(embedded, (embedded.shape[1], 1)).squeeze(1) 
        
        #pooled = [batch size, embedding_dim]
                
        return self.fc(pooled)

def FastTextNew(vocab_size, embedding_dim, output_dim):
    return nn.Sequential(
        Rearrange('t b -> t b'),
        nn.Embedding(vocab_size, embedding_dim),
        Reduce('t b c -> b c', 'mean'),
        nn.Linear(embedding_dim, output_dim),
        Rearrange('b c -> b c'),
    )

Some comments on new code:

first and last operations do nothing and can be removed
- but were added to explicitly show expected input and output
this also gives you a flexibility of changing interface by editing a single line. Should you need to accept inputs as (batch, time), you just change first line to Rearrange('b t -> t b'),

CNNs for text classification

class CNNOld(nn.Module):
    def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, dropout):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.conv_0 = nn.Conv2d(in_channels=1, out_channels=n_filters, kernel_size=(filter_sizes[0],embedding_dim))
        self.conv_1 = nn.Conv2d(in_channels=1, out_channels=n_filters, kernel_size=(filter_sizes[1],embedding_dim))
        self.conv_2 = nn.Conv2d(in_channels=1, out_channels=n_filters, kernel_size=(filter_sizes[2],embedding_dim))
        self.fc = nn.Linear(len(filter_sizes)*n_filters, output_dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x):
        
        #x = [sent len, batch size]
        
        x = x.permute(1, 0)
                
        #x = [batch size, sent len]
        
        embedded = self.embedding(x)
                
        #embedded = [batch size, sent len, emb dim]
        
        embedded = embedded.unsqueeze(1)
        
        #embedded = [batch size, 1, sent len, emb dim]
        
        conved_0 = F.relu(self.conv_0(embedded).squeeze(3))
        conved_1 = F.relu(self.conv_1(embedded).squeeze(3))
        conved_2 = F.relu(self.conv_2(embedded).squeeze(3))
            
        #conv_n = [batch size, n_filters, sent len - filter_sizes[n]]
        
        pooled_0 = F.max_pool1d(conved_0, conved_0.shape[2]).squeeze(2)
        pooled_1 = F.max_pool1d(conved_1, conved_1.shape[2]).squeeze(2)
        pooled_2 = F.max_pool1d(conved_2, conved_2.shape[2]).squeeze(2)
        
        #pooled_n = [batch size, n_filters]
        
        cat = self.dropout(torch.cat((pooled_0, pooled_1, pooled_2), dim=1))

        #cat = [batch size, n_filters * len(filter_sizes)]
            
        return self.fc(cat)

class CNNNew(nn.Module):
    def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, dropout):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.convs = nn.ModuleList([
            nn.Conv1d(embedding_dim, n_filters, kernel_size=size) for size in filter_sizes
        ])
        self.fc = nn.Linear(len(filter_sizes) * n_filters, output_dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x):
        x = rearrange(x, 't b -> t b')
        emb = rearrange(self.embedding(x), 't b c -> b c t')
        pooled = [reduce(conv(emb), 'b c t -> b c', 'max') for conv in self.convs]
        concatenated = rearrange(pooled, 'filter b c -> b (filter c)')
        return self.fc(self.dropout(F.relu(concatenated)))

Original code misuses Conv2d, while Conv1d is the right choice
Fixed code can work with any number of filter_sizes (and won't fail)
First line in new code does nothing, but was added for simplicity

Highway convolutions

Highway convolutions are common in TTS systems. Code below makes splitting a bit more explicit.
Splitting policy may eventually turn out to be important if input had previously groups over channel axes (group convolutions or bidirectional LSTMs/GRUs)
Same applies to GLU and gated units in general

class HighwayConv1dOld(nn.Conv1d):
    def forward(self, inputs):
        L = super(HighwayConv1dOld, self).forward(inputs)
        H1, H2 = torch.chunk(L, 2, 1)  # chunk at the feature dim
        torch.sigmoid_(H1)
        return H1 * H2 + (1.0 - H1) * inputs

class HighwayConv1dNew(nn.Conv1d):
    def forward(self, inputs):
        L = super().forward(inputs)
        H1, H2 = rearrange(L, 'b (split c) t -> split b c t', split=2)
        torch.sigmoid_(H1)
        return H1 * H2 + (1.0 - H1) * inputs

Tacotron's CBHG module

class CBHG_Old(nn.Module):
    """CBHG module: a recurrent neural network composed of:
        - 1-d convolution banks
        - Highway networks + residual connections
        - Bidirectional gated recurrent units
    """

    def __init__(self, in_dim, K=16, projections=[128, 128]):
        super(CBHG, self).__init__()
        self.in_dim = in_dim
        self.relu = nn.ReLU()
        self.conv1d_banks = nn.ModuleList(
            [BatchNormConv1d(in_dim, in_dim, kernel_size=k, stride=1,
                             padding=k // 2, activation=self.relu)
             for k in range(1, K + 1)])
        self.max_pool1d = nn.MaxPool1d(kernel_size=2, stride=1, padding=1)

        in_sizes = [K * in_dim] + projections[:-1]
        activations = [self.relu] * (len(projections) - 1) + [None]
        self.conv1d_projections = nn.ModuleList(
            [BatchNormConv1d(in_size, out_size, kernel_size=3, stride=1,
                             padding=1, activation=ac)
             for (in_size, out_size, ac) in zip(
                 in_sizes, projections, activations)])

        self.pre_highway = nn.Linear(projections[-1], in_dim, bias=False)
        self.highways = nn.ModuleList(
            [Highway(in_dim, in_dim) for _ in range(4)])

        self.gru = nn.GRU(
            in_dim, in_dim, 1, batch_first=True, bidirectional=True)

def forward_old(self, inputs):
    # (B, T_in, in_dim)
    x = inputs

    # Needed to perform conv1d on time-axis
    # (B, in_dim, T_in)
    if x.size(-1) == self.in_dim:
        x = x.transpose(1, 2)

    T = x.size(-1)

    # (B, in_dim*K, T_in)
    # Concat conv1d bank outputs
    x = torch.cat([conv1d(x)[:, :, :T] for conv1d in self.conv1d_banks], dim=1)
    assert x.size(1) == self.in_dim * len(self.conv1d_banks)
    x = self.max_pool1d(x)[:, :, :T]

    for conv1d in self.conv1d_projections:
        x = conv1d(x)

    # (B, T_in, in_dim)
    # Back to the original shape
    x = x.transpose(1, 2)

    if x.size(-1) != self.in_dim:
        x = self.pre_highway(x)

    # Residual connection
    x += inputs
    for highway in self.highways:
        x = highway(x)

    # (B, T_in, in_dim*2)
    outputs, _ = self.gru(x)

    return outputs

def forward_new(self, inputs, input_lengths=None):
    x = rearrange(inputs, 'b t c -> b c t')
    _, _, T = x.shape
    # Concat conv1d bank outputs
    x = rearrange([conv1d(x)[:, :, :T] for conv1d in self.conv1d_banks], 
                 'bank b c t -> b (bank c) t', c=self.in_dim)
    x = self.max_pool1d(x)[:, :, :T]

    for conv1d in self.conv1d_projections:
        x = conv1d(x)
    x = rearrange(x, 'b c t -> b t c')
    if x.size(-1) != self.in_dim:
        x = self.pre_highway(x)

    # Residual connection
    x += inputs
    for highway in self.highways:
        x = highway(x)

    # (B, T_in, in_dim*2)
    outputs, _ = self.gru(self.highways(x))

    return outputs

There is still a large room for improvements, but in this example only forward function was changed

Simple attention

Good news: there is no more need to guess order of dimensions. Neither for inputs nor for outputs

class Attention(nn.Module):
    def __init__(self):
        super(Attention, self).__init__()
    
    def forward(self, K, V, Q):
        A = torch.bmm(K.transpose(1,2), Q) / np.sqrt(Q.shape[1])
        A = F.softmax(A, 1)
        R = torch.bmm(V, A)
        return torch.cat((R, Q), dim=1)

def attention(K, V, Q):
    _, n_channels, _ = K.shape
    A = torch.einsum('bct,bcl->btl', [K, Q])
    A = F.softmax(A * n_channels ** (-0.5), 1)
    R = torch.einsum('bct,btl->bcl', [V, A])
    return torch.cat((R, Q), dim=1)

Transformer's attention needs more attention

class ScaledDotProductAttention(nn.Module):
    ''' Scaled Dot-Product Attention '''

    def __init__(self, temperature, attn_dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = nn.Dropout(attn_dropout)
        self.softmax = nn.Softmax(dim=2)

    def forward(self, q, k, v, mask=None):

        attn = torch.bmm(q, k.transpose(1, 2))
        attn = attn / self.temperature

        if mask is not None:
            attn = attn.masked_fill(mask, -np.inf)

        attn = self.softmax(attn)
        attn = self.dropout(attn)
        output = torch.bmm(attn, v)

        return output, attn



class MultiHeadAttentionOld(nn.Module):
    ''' Multi-Head Attention module '''

    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
        super().__init__()

        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v

        self.w_qs = nn.Linear(d_model, n_head * d_k)
        self.w_ks = nn.Linear(d_model, n_head * d_k)
        self.w_vs = nn.Linear(d_model, n_head * d_v)
        nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v)))

        self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
        self.layer_norm = nn.LayerNorm(d_model)

        self.fc = nn.Linear(n_head * d_v, d_model)
        nn.init.xavier_normal_(self.fc.weight)

        self.dropout = nn.Dropout(dropout)


    def forward(self, q, k, v, mask=None):
        
        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
        
        sz_b, len_q, _ = q.size()
        sz_b, len_k, _ = k.size()
        sz_b, len_v, _ = v.size()
        
        residual = q
        
        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
        
        q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk
        k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk
        v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv
        
        mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x ..
        output, attn = self.attention(q, k, v, mask=mask)
        
        output = output.view(n_head, sz_b, len_q, d_v)
        output = output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1) # b x lq x (n*dv)
        
        output = self.dropout(self.fc(output))
        output = self.layer_norm(output + residual)
        
        return output, attn

class MultiHeadAttentionNew(nn.Module):
    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
        super().__init__()
        self.n_head = n_head
        
        self.w_qs = nn.Linear(d_model, n_head * d_k)
        self.w_ks = nn.Linear(d_model, n_head * d_k)
        self.w_vs = nn.Linear(d_model, n_head * d_v)
        
        nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v)))
        
        self.fc = nn.Linear(n_head * d_v, d_model)
        nn.init.xavier_normal_(self.fc.weight)
        self.dropout = nn.Dropout(p=dropout)
        self.layer_norm = nn.LayerNorm(d_model)

    def forward(self, q, k, v, mask=None):
        residual = q
        q = rearrange(self.w_qs(q), 'b l (head k) -> head b l k', head=self.n_head)
        k = rearrange(self.w_ks(k), 'b t (head k) -> head b t k', head=self.n_head)
        v = rearrange(self.w_vs(v), 'b t (head v) -> head b t v', head=self.n_head)
        attn = torch.einsum('hblk,hbtk->hblt', [q, k]) / np.sqrt(q.shape[-1])
        if mask is not None:
            attn = attn.masked_fill(mask[None], -np.inf)
        attn = torch.softmax(attn, dim=3)
        output = torch.einsum('hblt,hbtv->hblv', [attn, v])
        output = rearrange(output, 'head b l v -> b l (head v)')
        output = self.dropout(self.fc(output))
        output = self.layer_norm(output + residual)
        return output, attn

Benefits of new implementation

we have one module, not two
now code does not fail for None mask
the amount of caveats in the original code that we removed is huge. Try erasing comments and deciphering what happens there

Self-attention GANs

SAGANs are currently SotA for image generation, and can be simplified using same tricks.

class Self_Attn_Old(nn.Module):
    """ Self attention Layer"""
    def __init__(self,in_dim,activation):
        super(Self_Attn_Old,self).__init__()
        self.chanel_in = in_dim
        self.activation = activation
        
        self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)
        self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)
        self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1)
        self.gamma = nn.Parameter(torch.zeros(1))
        
        self.softmax  = nn.Softmax(dim=-1) #

    def forward(self, x):
        """
            inputs :
                x : input feature maps( B X C X W X H)
            returns :
                out : self attention value + input feature 
                attention: B X N X N (N is Width*Height)
        """
        
        m_batchsize,C,width ,height = x.size()
        proj_query  = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N)
        proj_key =  self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H)
        energy =  torch.bmm(proj_query,proj_key) # transpose check
        attention = self.softmax(energy) # BX (N) X (N) 
        proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N

        out = torch.bmm(proj_value,attention.permute(0,2,1) )
        out = out.view(m_batchsize,C,width,height)
        
        out = self.gamma*out + x
        return out,attention

class Self_Attn_New(nn.Module):
    """ Self attention Layer"""
    def __init__(self, in_dim):
        super().__init__()
        self.query_conv = nn.Conv2d(in_dim, out_channels=in_dim//8, kernel_size=1)
        self.key_conv = nn.Conv2d(in_dim, out_channels=in_dim//8, kernel_size=1)
        self.value_conv = nn.Conv2d(in_dim, out_channels=in_dim, kernel_size=1)
        self.gamma = nn.Parameter(torch.zeros([1]))

    def forward(self, x):
        proj_query = rearrange(self.query_conv(x), 'b c h w -> b (h w) c')
        proj_key = rearrange(self.key_conv(x), 'b c h w -> b c (h w)')
        proj_value = rearrange(self.value_conv(x), 'b c h w -> b (h w) c')
        energy = torch.bmm(proj_query, proj_key)
        attention = F.softmax(energy, dim=2)
        out = torch.bmm(attention, proj_value)
        out = x + self.gamma * rearrange(out, 'b (h w) c -> b c h w',
                                         **parse_shape(x, 'b c h w'))
        return out, attention

Improving time sequence prediction

While this example was considered to be simplistic, I had to analyze surrounding code to understand what kind of input was expected. You can try yourself.

Additionally now the code works with any dtype, not only double; and new code supports using GPU.

class SequencePredictionOld(nn.Module):
    def __init__(self):
        super(SequencePredictionOld, self).__init__()
        self.lstm1 = nn.LSTMCell(1, 51)
        self.lstm2 = nn.LSTMCell(51, 51)
        self.linear = nn.Linear(51, 1)

    def forward(self, input, future = 0):
        outputs = []
        h_t = torch.zeros(input.size(0), 51, dtype=torch.double)
        c_t = torch.zeros(input.size(0), 51, dtype=torch.double)
        h_t2 = torch.zeros(input.size(0), 51, dtype=torch.double)
        c_t2 = torch.zeros(input.size(0), 51, dtype=torch.double)

        for i, input_t in enumerate(input.chunk(input.size(1), dim=1)):
            h_t, c_t = self.lstm1(input_t, (h_t, c_t))
            h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
            output = self.linear(h_t2)
            outputs += [output]
            
        for i in range(future):# if we should predict the future
            h_t, c_t = self.lstm1(output, (h_t, c_t))
            h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
            output = self.linear(h_t2)
            outputs += [output]
        outputs = torch.stack(outputs, 1).squeeze(2)
        return outputs

class SequencePredictionNew(nn.Module):
    def __init__(self):
        super(SequencePredictionNew, self).__init__()
        self.lstm1 = nn.LSTMCell(1, 51)
        self.lstm2 = nn.LSTMCell(51, 51)
        self.linear = nn.Linear(51, 1)

    def forward(self, input, future=0):
        b, t = input.shape
        h_t, c_t, h_t2, c_t2 = torch.zeros(4, b, 51, dtype=self.linear.weight.dtype, 
                                           device=self.linear.weight.device)

        outputs = []
        for input_t in rearrange(input, 'b t -> t b ()'):
            h_t, c_t = self.lstm1(input_t, (h_t, c_t))
            h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
            output = self.linear(h_t2)
            outputs += [output]
            
        for i in range(future): # if we should predict the future
            h_t, c_t = self.lstm1(output, (h_t, c_t))
            h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
            output = self.linear(h_t2)
            outputs += [output]
        return rearrange(outputs, 't b () -> b t')

Transforming spacial transformer network (STN)

class SpacialTransformOld(nn.Module):
    def __init__(self):
        super(Net, self).__init__()

        # Spatial transformer localization-network
        self.localization = nn.Sequential(
            nn.Conv2d(1, 8, kernel_size=7),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True),
            nn.Conv2d(8, 10, kernel_size=5),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True)
        )

        # Regressor for the 3 * 2 affine matrix
        self.fc_loc = nn.Sequential(
            nn.Linear(10 * 3 * 3, 32),
            nn.ReLU(True),
            nn.Linear(32, 3 * 2)
        )

        # Initialize the weights/bias with identity transformation
        self.fc_loc[2].weight.data.zero_()
        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))

    # Spatial transformer network forward function
    def stn(self, x):
        xs = self.localization(x)
        xs = xs.view(-1, 10 * 3 * 3)
        theta = self.fc_loc(xs)
        theta = theta.view(-1, 2, 3)

        grid = F.affine_grid(theta, x.size())
        x = F.grid_sample(x, grid)

        return x

class SpacialTransformNew(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # Spatial transformer localization-network
        linear = nn.Linear(32, 3 * 2)
        # Initialize the weights/bias with identity transformation
        linear.weight.data.zero_()
        linear.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
        
        self.compute_theta = nn.Sequential(
            nn.Conv2d(1, 8, kernel_size=7),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True),
            nn.Conv2d(8, 10, kernel_size=5),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True),
            Rearrange('b c h w -> b (c h w)', h=3, w=3),
            nn.Linear(10 * 3 * 3, 32),
            nn.ReLU(True),
            linear,
            Rearrange('b (row col) -> b row col', row=2, col=3),
        )

    # Spatial transformer network forward function
    def stn(self, x):
        grid = F.affine_grid(self.compute_theta(x), x.size())
        return F.grid_sample(x, grid)

new code will give reasonable errors when passed image size is different from expected
if batch size is divisible by 18, whatever you input in the old code, it'll fail no sooner than affine_grid.

Improving GLOW

That's a good old depth-to-space written manually!

Since GLOW is revertible, it will frequently rely on rearrange-like operations.

def unsqueeze2d_old(input, factor=2):
    assert factor >= 1 and isinstance(factor, int)
    factor2 = factor ** 2
    if factor == 1:
        return input
    size = input.size()
    B = size[0]
    C = size[1]
    H = size[2]
    W = size[3]
    assert C % (factor2) == 0, "{}".format(C)
    x = input.view(B, C // factor2, factor, factor, H, W)
    x = x.permute(0, 1, 4, 2, 5, 3).contiguous()
    x = x.view(B, C // (factor2), H * factor, W * factor)
    return x

def squeeze2d_old(input, factor=2):
    assert factor >= 1 and isinstance(factor, int)
    if factor == 1:
        return input
    size = input.size()
    B = size[0]
    C = size[1]
    H = size[2]
    W = size[3]
    assert H % factor == 0 and W % factor == 0, "{}".format((H, W))
    x = input.view(B, C, H // factor, factor, W // factor, factor)
    x = x.permute(0, 1, 3, 5, 2, 4).contiguous()
    x = x.view(B, C * factor * factor, H // factor, W // factor)
    return x

def unsqueeze2d_new(input, factor=2):
    return rearrange(input, 'b (c h2 w2) h w -> b c (h h2) (w w2)', h2=factor, w2=factor)

def squeeze2d_new(input, factor=2):
    return rearrange(input, 'b c (h h2) (w w2) -> b (c h2 w2) h w', h2=factor, w2=factor)

term squeeze isn't very helpful: which dimension is squeezed? There is torch.squeeze, but it's very different.
in fact, we could skip creating functions completely - it is a single call to einops anyway

Detecting problems in YOLO detection

def YOLO_prediction_old(input, num_classes, num_anchors, anchors, stride_h, stride_w):
    bs = input.size(0)
    in_h = input.size(2)
    in_w = input.size(3)
    scaled_anchors = [(a_w / stride_w, a_h / stride_h) for a_w, a_h in anchors]

    prediction = input.view(bs, num_anchors,
                            5 + num_classes, in_h, in_w).permute(0, 1, 3, 4, 2).contiguous()
    # Get outputs
    x = torch.sigmoid(prediction[..., 0])  # Center x
    y = torch.sigmoid(prediction[..., 1])  # Center y
    w = prediction[..., 2]  # Width
    h = prediction[..., 3]  # Height
    conf = torch.sigmoid(prediction[..., 4])  # Conf
    pred_cls = torch.sigmoid(prediction[..., 5:])  # Cls pred.

    FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
    LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
    # Calculate offsets for each grid
    grid_x = torch.linspace(0, in_w - 1, in_w).repeat(in_w, 1).repeat(
        bs * num_anchors, 1, 1).view(x.shape).type(FloatTensor)
    grid_y = torch.linspace(0, in_h - 1, in_h).repeat(in_h, 1).t().repeat(
        bs * num_anchors, 1, 1).view(y.shape).type(FloatTensor)
    # Calculate anchor w, h
    anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0]))
    anchor_h = FloatTensor(scaled_anchors).index_select(1, LongTensor([1]))
    anchor_w = anchor_w.repeat(bs, 1).repeat(1, 1, in_h * in_w).view(w.shape)
    anchor_h = anchor_h.repeat(bs, 1).repeat(1, 1, in_h * in_w).view(h.shape)
    # Add offset and scale with anchors
    pred_boxes = FloatTensor(prediction[..., :4].shape)
    pred_boxes[..., 0] = x.data + grid_x
    pred_boxes[..., 1] = y.data + grid_y
    pred_boxes[..., 2] = torch.exp(w.data) * anchor_w
    pred_boxes[..., 3] = torch.exp(h.data) * anchor_h
    # Results
    _scale = torch.Tensor([stride_w, stride_h] * 2).type(FloatTensor)
    output = torch.cat((pred_boxes.view(bs, -1, 4) * _scale,
                        conf.view(bs, -1, 1), pred_cls.view(bs, -1, num_classes)), -1)
    return output

def YOLO_prediction_new(input, num_classes, num_anchors, anchors, stride_h, stride_w):
    raw_predictions = rearrange(input, 'b (anchor prediction) h w -> prediction b anchor h w', 
                                anchor=num_anchors, prediction=5 + num_classes)
    anchors = torch.FloatTensor(anchors).to(input.device)
    anchor_sizes = rearrange(anchors, 'anchor dim -> dim () anchor () ()')

    _, _, _, in_h, in_w = raw_predictions.shape
    grid_h = rearrange(torch.arange(in_h).float(), 'h -> () () h ()').to(input.device)
    grid_w = rearrange(torch.arange(in_w).float(), 'w -> () () () w').to(input.device)

    predicted_bboxes = torch.zeros_like(raw_predictions)
    predicted_bboxes[0] = (raw_predictions[0].sigmoid() + grid_w) * stride_w  # center x
    predicted_bboxes[1] = (raw_predictions[1].sigmoid() + grid_h) * stride_h  # center y
    predicted_bboxes[2:4] = (raw_predictions[2:4].exp()) * anchor_sizes  # bbox width and height
    predicted_bboxes[4] = raw_predictions[4].sigmoid()  # confidence
    predicted_bboxes[5:] = raw_predictions[5:].sigmoid()  # class predictions
    # merging all predicted bboxes for each image
    return rearrange(predicted_bboxes, 'prediction b anchor h w -> b (anchor h w) prediction')

We changed and fixed a lot:

new code won't fail if input is not on the first GPU
old code has wrong grid_x and grid_y for non-square images
new code doesn't use replication when broadcasting is sufficient
old code strangely sometimes takes .data, but this has no real effect, as some branches preserve gradient till the end
- if gradients not needed, torch.no_grad should be used, so it's redundant

Simpler output for a bunch of pictures

Next time you need to output drawings of you generative models, you can use this trick

device = 'cpu'
plt.imshow(np.transpose(vutils.make_grid(fake_batch.to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))

padded = F.pad(fake_batch[:64], [1, 1, 1, 1])
plt.imshow(rearrange(padded, '(b1 b2) c h w -> (b1 h) (b2 w) c', b1=8).cpu())

Instead of conclusion

Better code is a vague term; to be specific, code is expected to be:

reliable: does what expected and does not fail. Explicitly fails for wrong inputs
maintainable and modifiable
reusable: understanding and modifying code should be easier than writing from scratch
fast: in my measurements, proposed versions have speed similar to the original code
readability counts, as a mean to achieve previous goals

Provided examples show how to improve these criteria for deep learning code. And einops helps a lot.