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"""Defines a Densely Connected Convolutional Networks in PyTorch.
Sources:
https://arxiv.org/abs/1608.06993
https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py
"""
from typing import List, Optional, Union
from einops.layers.torch import Rearrange
import torch
from torch import nn
from torch import Tensor
from text_recognizer.networks.util import activation_function
class _DenseLayer(nn.Module):
"""A dense layer with pre-batch norm -> activation function -> Conv-layer x 2."""
def __init__(
self,
in_channels: int,
growth_rate: int,
bn_size: int,
dropout_rate: float,
activation: str = "relu",
) -> None:
super().__init__()
activation_fn = activation_function(activation)
self.dense_layer = [
nn.BatchNorm2d(in_channels),
activation_fn,
nn.Conv2d(
in_channels=in_channels,
out_channels=bn_size * growth_rate,
kernel_size=1,
stride=1,
bias=False,
),
nn.BatchNorm2d(bn_size * growth_rate),
activation_fn,
nn.Conv2d(
in_channels=bn_size * growth_rate,
out_channels=growth_rate,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
]
if dropout_rate:
self.dense_layer.append(nn.Dropout(p=dropout_rate))
self.dense_layer = nn.Sequential(*self.dense_layer)
def forward(self, x: Union[Tensor, List[Tensor]]) -> Tensor:
if isinstance(x, list):
x = torch.cat(x, 1)
return self.dense_layer(x)
class _DenseBlock(nn.Module):
def __init__(
self,
num_layers: int,
in_channels: int,
bn_size: int,
growth_rate: int,
dropout_rate: float,
activation: str = "relu",
) -> None:
super().__init__()
self.dense_block = self._build_dense_blocks(
num_layers, in_channels, bn_size, growth_rate, dropout_rate, activation,
)
def _build_dense_blocks(
self,
num_layers: int,
in_channels: int,
bn_size: int,
growth_rate: int,
dropout_rate: float,
activation: str = "relu",
) -> nn.ModuleList:
dense_block = []
for i in range(num_layers):
dense_block.append(
_DenseLayer(
in_channels=in_channels + i * growth_rate,
growth_rate=growth_rate,
bn_size=bn_size,
dropout_rate=dropout_rate,
activation=activation,
)
)
return nn.ModuleList(dense_block)
def forward(self, x: Tensor) -> Tensor:
feature_maps = [x]
for layer in self.dense_block:
x = layer(feature_maps)
feature_maps.append(x)
return torch.cat(feature_maps, 1)
class _Transition(nn.Module):
def __init__(
self, in_channels: int, out_channels: int, activation: str = "relu",
) -> None:
super().__init__()
activation_fn = activation_function(activation)
self.transition = nn.Sequential(
nn.BatchNorm2d(in_channels),
activation_fn,
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
bias=False,
),
nn.AvgPool2d(kernel_size=2, stride=2),
)
def forward(self, x: Tensor) -> Tensor:
return self.transition(x)
class DenseNet(nn.Module):
"""Implementation of Densenet, a network archtecture that concats previous layers for maximum infomation flow."""
def __init__(
self,
growth_rate: int = 32,
block_config: List[int] = (6, 12, 24, 16),
in_channels: int = 1,
base_channels: int = 64,
num_classes: int = 80,
bn_size: int = 4,
dropout_rate: float = 0,
classifier: bool = True,
activation: str = "relu",
) -> None:
super().__init__()
self.densenet = self._configure_densenet(
in_channels,
base_channels,
num_classes,
growth_rate,
block_config,
bn_size,
dropout_rate,
classifier,
activation,
)
def _configure_densenet(
self,
in_channels: int,
base_channels: int,
num_classes: int,
growth_rate: int,
block_config: List[int],
bn_size: int,
dropout_rate: float,
classifier: bool,
activation: str,
) -> nn.Sequential:
activation_fn = activation_function(activation)
densenet = [
nn.Conv2d(
in_channels=in_channels,
out_channels=base_channels,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
nn.BatchNorm2d(base_channels),
activation_fn,
]
num_features = base_channels
for i, num_layers in enumerate(block_config):
densenet.append(
_DenseBlock(
num_layers=num_layers,
in_channels=num_features,
bn_size=bn_size,
growth_rate=growth_rate,
dropout_rate=dropout_rate,
activation=activation,
)
)
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
densenet.append(
_Transition(
in_channels=num_features,
out_channels=num_features // 2,
activation=activation,
)
)
num_features = num_features // 2
densenet.append(activation_fn)
if classifier:
densenet.append(nn.AdaptiveAvgPool2d((1, 1)))
densenet.append(Rearrange("b c h w -> b (c h w)"))
densenet.append(
nn.Linear(in_features=num_features, out_features=num_classes)
)
return nn.Sequential(*densenet)
def forward(self, x: Tensor) -> Tensor:
"""Forward pass of Densenet."""
# If batch dimenstion is missing, it will be added.
if len(x.shape) < 4:
x = x[(None,) * (4 - len(x.shape))]
return self.densenet(x)
|
