diff options
Diffstat (limited to 'text_recognizer')
| -rw-r--r-- | text_recognizer/data/emnist_essentials.json | 2 | ||||
| -rw-r--r-- | text_recognizer/networks/encoders/efficient_net/__init__.py | 0 | ||||
| -rw-r--r-- | text_recognizer/networks/encoders/efficient_net/block.py | 143 |
3 files changed, 144 insertions, 1 deletions
diff --git a/text_recognizer/data/emnist_essentials.json b/text_recognizer/data/emnist_essentials.json index 956c28d..c412425 100644 --- a/text_recognizer/data/emnist_essentials.json +++ b/text_recognizer/data/emnist_essentials.json @@ -1 +1 @@ -{"characters": ["<b>", "<s>", "<e>", "<p>", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", " ", "!", "\"", "#", "&", "'", "(", ")", "*", "+", ",", "-", ".", "/", ":", ";", "?"], "input_shape": [28, 28]} +{"characters": ["<b>", "<s>", "<e>", "<p>", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", " ", "!", "\"", "#", "&", "'", "(", ")", "*", "+", ",", "-", ".", "/", ":", ";", "?"], "input_shape": [28, 28]}
\ No newline at end of file diff --git a/text_recognizer/networks/encoders/efficient_net/__init__.py b/text_recognizer/networks/encoders/efficient_net/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/text_recognizer/networks/encoders/efficient_net/__init__.py diff --git a/text_recognizer/networks/encoders/efficient_net/block.py b/text_recognizer/networks/encoders/efficient_net/block.py new file mode 100644 index 0000000..4302bbd --- /dev/null +++ b/text_recognizer/networks/encoders/efficient_net/block.py @@ -0,0 +1,143 @@ +"""Mobile inverted residual block.""" +from typing import Tuple + +import torch +from torch import nn, Tensor + +from .utils import get_same_padding_conv2d + + +class MBConvBlock(nn.Module): + """Mobile Inverted Residual Bottleneck block.""" + + def __init__( + self, + in_channels: int, + kernel_size: int, + stride: int, + bn_momentum: float, + bn_eps: float, + se_ratio: float, + id_skip: bool, + expand_ratio: int, + image_size: Tuple[int, int], + ) -> None: + super().__init__() + self.kernel_size = kernel_size + self.bn_momentum = bn_momentum + self.bn_eps = bn_eps + self.has_se = se_ratio is not None and 0.0 < se_ratio < 1.0 + + out_channels = in_channels * expand_ratio + self._inverted_bottleneck = ( + self._configure_inverted_bottleneck( + image_size=image_size, + in_channels=in_channels, + out_channels=out_channels, + ) + if expand_ratio != 1 + else None + ) + + self._depthwise = self._configure_depthwise( + image_size=image_size, + in_channels=in_channels, + out_channels=out_channels, + groups=out_channels, + kernel_size=kernel_size, + stride=stride, + ) + + image_size = calculate_output_image_size(image_size, stride) + self._squeeze_excite = ( + self._configure_squeeze_excite( + in_channels=out_channels, out_channels=out_channels, se_ratio=se_ratio + ) + if self.has_se + else None + ) + + self._pointwise = self._configure_pointwise( + image_size=image_size, in_channels=out_channels, out_channels=out_channels + ) + + def _configure_inverted_bottleneck( + self, + image_size: Tuple[int, int], + in_channels: int, + out_channels: int, + ) -> nn.Sequential: + """Expansion phase.""" + Conv2d = get_same_padding_conv2d(image_size=image_size) + return nn.Sequential( + Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + bias=False, + ), + nn.BatchNorm2d( + num_features=out_channels, momentum=self.bn_momentum, eps=self.bn_eps + ), + nn.SiLU(inplace=True), + ) + + def _configure_depthwise( + self, + image_size: Tuple[int, int], + in_channels: int, + out_channels: int, + groups: int, + kernel_size: int, + stride: int, + ) -> nn.Sequential: + Conv2d = get_same_padding_conv2d(image_size=image_size) + return nn.Sequential( + Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + groups=groups, + bias=False, + ), + nn.BatchNorm2d( + num_features=out_channels, momentum=self.bn_momentum, eps=self.bn_eps + ), + nn.SiLU(inplace=True), + ) + + def _configure_squeeze_excite( + self, in_channels: int, out_channels: int, se_ratio: float + ) -> nn.Sequential: + Conv2d = get_same_padding_conv2d(image_size=(1, 1)) + num_squeezed_channels = max(1, int(in_channels * se_ratio)) + return nn.Sequential( + Conv2d( + in_channels=in_channels, + out_channels=num_squeezed_channels, + kernel_size=1, + ), + nn.SiLU(inplace=True), + Conv2d( + in_channels=num_squeezed_channels, + out_channels=out_channels, + kernel_size=1, + ), + ) + + def _configure_pointwise( + self, image_size: Tuple[int, int], in_channels: int, out_channels: int + ) -> nn.Sequential: + Conv2d = get_same_padding_conv2d(image_size=image_size) + return nn.Sequential( + Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + bias=False, + ), + nn.BatchNorm2d( + num_features=out_channels, momentum=self.bn_momentum, eps=self.bn_eps + ), + ) |