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-rw-r--r--text_recognizer/networks/encoders/__init__.py2
-rw-r--r--text_recognizer/networks/encoders/efficientnet.py145
-rw-r--r--text_recognizer/networks/encoders/efficientnet/efficientnet.py9
-rw-r--r--text_recognizer/networks/encoders/efficientnet/mbconv.py (renamed from text_recognizer/networks/encoders/efficientnet/mbconv_block.py)56
-rw-r--r--text_recognizer/networks/encoders/efficientnet/utils.py141
5 files changed, 186 insertions, 167 deletions
diff --git a/text_recognizer/networks/encoders/__init__.py b/text_recognizer/networks/encoders/__init__.py
index 25aed0e..b526b0c 100644
--- a/text_recognizer/networks/encoders/__init__.py
+++ b/text_recognizer/networks/encoders/__init__.py
@@ -1,2 +1,2 @@
"""Vision backbones."""
-from .efficientnet import EfficientNet
+# from .efficientnet import EfficientNet
diff --git a/text_recognizer/networks/encoders/efficientnet.py b/text_recognizer/networks/encoders/efficientnet.py
deleted file mode 100644
index 61dea77..0000000
--- a/text_recognizer/networks/encoders/efficientnet.py
+++ /dev/null
@@ -1,145 +0,0 @@
-"""Efficient net b0 implementation."""
-import torch
-from torch import nn
-from torch import Tensor
-
-
-class ConvNorm(nn.Module):
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: int,
- stride: int,
- padding: int,
- groups: int = 1,
- ) -> None:
- super().__init__()
- self.block = nn.Sequential(
- nn.Conv2d(
- in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=kernel_size,
- stride=stride,
- padding=padding,
- groups=groups,
- bias=False,
- ),
- nn.BatchNorm2d(num_features=out_channels),
- nn.SiLU(inplace=True),
- )
-
- def forward(self, x: Tensor) -> Tensor:
- return self.block(x)
-
-
-class SqueezeExcite(nn.Module):
- def __init__(self, in_channels: int, reduce_dim: int) -> None:
- super().__init__()
- self.se = nn.Sequential(
- nn.AdaptiveAvgPool2d(1), # [C, H, W] -> [C, 1, 1]
- nn.Conv2d(in_channels=in_channels, out_channels=reduce_dim, kernel_size=1),
- nn.SiLU(),
- nn.Conv2d(in_channels=reduce_dim, out_channels=in_channels, kernel_size=1),
- nn.Sigmoid(),
- )
-
- def forward(self, x: Tensor) -> Tensor:
- return x * self.se(x)
-
-
-class InvertedResidulaBlock(nn.Module):
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: int,
- stride: int,
- padding: int,
- expand_ratio: float,
- reduction: int = 4,
- survival_prob: float = 0.8,
- ) -> None:
- super().__init__()
- self.survival_prob = survival_prob
- self.use_residual = in_channels == out_channels and stride == 1
- hidden_dim = in_channels * expand_ratio
- self.expand = in_channels != hidden_dim
- reduce_dim = in_channels // reduction
-
- if self.expand:
- self.expand_conv = ConvNorm(
- in_channels, hidden_dim, kernel_size=3, stride=1, padding=1
- )
-
- self.conv = nn.Sequential(
- ConvNorm(
- hidden_dim, hidden_dim, kernel_size, stride, padding, groups=hidden_dim
- ),
- SqueezeExcite(hidden_dim, reduce_dim),
- nn.Conv2d(
- in_channels=hidden_dim,
- out_channels=out_channels,
- kernel_size=1,
- bias=False,
- ),
- nn.BatchNorm2d(num_features=out_channels),
- )
-
- def stochastic_depth(self, x: Tensor) -> Tensor:
- if not self.training:
- return x
-
- binary_tensor = (
- torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.survival_prob
- )
- return torch.div(x, self.survival_prob) * binary_tensor
-
- def forward(self, x: Tensor) -> Tensor:
- out = self.expand_conv(x) if self.expand else x
- if self.use_residual:
- return self.stochastic_depth(self.conv(out)) + x
- return self.conv(out)
-
-
-class EfficientNet(nn.Module):
- """Efficient net b0 backbone."""
-
- def __init__(self) -> None:
- super().__init__()
- self.base_model = [
- # expand_ratio, channels, repeats, stride, kernel_size
- [1, 16, 1, 1, 3],
- [6, 24, 2, 2, 3],
- [6, 40, 2, 2, 5],
- [6, 80, 3, 2, 3],
- [6, 112, 3, 1, 5],
- [6, 192, 4, 2, 5],
- [6, 320, 1, 1, 3],
- ]
-
- self.backbone = self._build_b0()
-
- def _build_b0(self) -> nn.Sequential:
- in_channels = 32
- layers = [ConvNorm(1, in_channels, 3, stride=2, padding=1)]
-
- for expand_ratio, out_channels, repeats, stride, kernel_size in self.base_model:
- for i in range(repeats):
- layers.append(
- InvertedResidulaBlock(
- in_channels,
- out_channels,
- expand_ratio=expand_ratio,
- stride=stride if i == 0 else 1,
- kernel_size=kernel_size,
- padding=kernel_size // 2,
- )
- )
- in_channels = out_channels
- layers.append(ConvNorm(in_channels, 256, kernel_size=1, stride=1, padding=0))
-
- return nn.Sequential(*layers)
-
- def forward(self, x: Tensor) -> Tensor:
- return self.backbone(x)
diff --git a/text_recognizer/networks/encoders/efficientnet/efficientnet.py b/text_recognizer/networks/encoders/efficientnet/efficientnet.py
new file mode 100644
index 0000000..d953c10
--- /dev/null
+++ b/text_recognizer/networks/encoders/efficientnet/efficientnet.py
@@ -0,0 +1,9 @@
+"""Efficient net."""
+from torch import nn, Tensor
+
+
+class EfficientNet(nn.Module):
+ def __init__(
+ self,
+ ) -> None:
+ super().__init__()
diff --git a/text_recognizer/networks/encoders/efficientnet/mbconv_block.py b/text_recognizer/networks/encoders/efficientnet/mbconv.py
index c501777..602aeb7 100644
--- a/text_recognizer/networks/encoders/efficientnet/mbconv_block.py
+++ b/text_recognizer/networks/encoders/efficientnet/mbconv.py
@@ -5,7 +5,7 @@ import torch
from torch import nn, Tensor
from torch.nn import functional as F
-from .utils import get_same_padding_conv2d
+from .utils import calculate_output_image_size, drop_connection, get_same_padding_conv2d
class MBConvBlock(nn.Module):
@@ -22,19 +22,23 @@ class MBConvBlock(nn.Module):
se_ratio: float,
id_skip: bool,
expand_ratio: int,
- image_size: Tuple[int, int],
+ image_size: Optional[Tuple[int, int]],
) -> None:
super().__init__()
self.kernel_size = kernel_size
self.bn_momentum = bn_momentum
self.bn_eps = bn_eps
self.id_skip = id_skip
- (
- self._inverted_bottleneck,
- self._depthwise,
- self._squeeze_excite,
- self._pointwise,
- ) = self._build(
+ self.in_channels = self.in_channels
+ self.out_channels = out_channels
+
+ # Placeholders for layers.
+ self._inverted_bottleneck: nn.Sequential = None
+ self._depthwise: nn.Sequential = None
+ self._squeeze_excite: nn.Sequential = None
+ self._pointwise: nn.Sequential = None
+
+ self._build(
image_size=image_size,
in_channels=in_channels,
out_channels=out_channels,
@@ -46,19 +50,17 @@ class MBConvBlock(nn.Module):
def _build(
self,
- image_size: Tuple[int, int],
+ image_size: Optional[Tuple[int, int]],
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
expand_ratio: int,
se_ratio: float,
- ) -> Tuple[
- Optional[nn.Sequential], nn.Sequential, Optional[nn.Sequential], nn.Sequential
- ]:
+ ) -> None:
has_se = se_ratio is not None and 0.0 < se_ratio < 1.0
inner_channels = in_channels * expand_ratio
- inverted_bottleneck = (
+ self._inverted_bottleneck = (
self._configure_inverted_bottleneck(
image_size=image_size,
in_channels=in_channels,
@@ -68,7 +70,7 @@ class MBConvBlock(nn.Module):
else None
)
- depthwise = self._configure_depthwise(
+ self._depthwise = self._configure_depthwise(
image_size=image_size,
in_channels=inner_channels,
out_channels=inner_channels,
@@ -78,7 +80,7 @@ class MBConvBlock(nn.Module):
)
image_size = calculate_output_image_size(image_size, stride)
- squeeze_excite = (
+ self._squeeze_excite = (
self._configure_squeeze_excite(
in_channels=inner_channels,
out_channels=inner_channels,
@@ -88,14 +90,13 @@ class MBConvBlock(nn.Module):
else None
)
- pointwise = self._configure_pointwise(
+ self._pointwise = self._configure_pointwise(
image_size=image_size, in_channels=inner_channels, out_channels=out_channels
)
- return inverted_bottleneck, depthwise, squeeze_excite, pointwise
def _configure_inverted_bottleneck(
self,
- image_size: Tuple[int, int],
+ image_size: Optional[Tuple[int, int]],
in_channels: int,
out_channels: int,
) -> nn.Sequential:
@@ -116,7 +117,7 @@ class MBConvBlock(nn.Module):
def _configure_depthwise(
self,
- image_size: Tuple[int, int],
+ image_size: Optional[Tuple[int, int]],
in_channels: int,
out_channels: int,
groups: int,
@@ -159,7 +160,7 @@ class MBConvBlock(nn.Module):
)
def _configure_pointwise(
- self, image_size: Tuple[int, int], in_channels: int, out_channels: int
+ self, image_size: Optional[Tuple[int, int]], in_channels: int, out_channels: int
) -> nn.Sequential:
Conv2d = get_same_padding_conv2d(image_size=image_size)
return nn.Sequential(
@@ -174,7 +175,18 @@ class MBConvBlock(nn.Module):
),
)
- def forward(self, x: Tensor, drop_connection_rate: Optional[float]) -> Tensor:
+ def _stochastic_depth(
+ self, x: Tensor, residual: Tensor, drop_connection_rate: Optional[float]
+ ) -> Tensor:
+ if self.id_skip and self.stride == 1 and self.in_channels == self.out_channels:
+ if drop_connection_rate:
+ x = drop_connection(x, p=drop_connection_rate, training=self.training)
+ x += residual
+ return x
+
+ def forward(
+ self, x: Tensor, drop_connection_rate: Optional[float] = None
+ ) -> Tensor:
residual = x
if self._inverted_bottleneck is not None:
x = self._inverted_bottleneck(x)
@@ -189,3 +201,5 @@ class MBConvBlock(nn.Module):
x = self._pointwise(x)
# Stochastic depth
+ x = self._stochastic_depth(x, residual, drop_connection_rate)
+ return x
diff --git a/text_recognizer/networks/encoders/efficientnet/utils.py b/text_recognizer/networks/encoders/efficientnet/utils.py
new file mode 100644
index 0000000..4b4a787
--- /dev/null
+++ b/text_recognizer/networks/encoders/efficientnet/utils.py
@@ -0,0 +1,141 @@
+"""Util functions for efficient net."""
+from functools import partial
+import math
+from typing import Any, Optional, Tuple, Type
+
+import torch
+from torch import nn, Tensor
+import torch.functional as F
+
+
+def calculate_output_image_size(
+ image_size: Optional[Tuple[int, int]], stride: int
+) -> Optional[Tuple[int, int]]:
+ """Calculates the output image size when using conv2d with same padding."""
+ if image_size is None:
+ return None
+ height = int(math.ceil(image_size[0] / stride))
+ width = int(math.ceil(image_size[1] / stride))
+ return height, width
+
+
+def drop_connection(x: Tensor, p: float, training: bool) -> Tensor:
+ """Drop connection.
+
+ Drops the entire convolution with a given survival probability.
+
+ Args:
+ x (Tensor): Input tensor.
+ p (float): Survival probability between 0.0 and 1.0.
+ training (bool): The running mode.
+
+ Shapes:
+ - x: :math: `(B, C, W, H)`.
+ - out: :math: `(B, C, W, H)`.
+
+ where B is the batch size, C is the number of channels, W is the width, and H
+ is the height.
+
+ Returns:
+ out (Tensor): Output after drop connection.
+ """
+ assert 0.0 <= p <= 1.0, "p must be in range of [0, 1]"
+
+ if not training:
+ return x
+
+ bsz = x.shape[0]
+ survival_prob = 1 - p
+
+ # Generate a binary tensor mask according to probability (p for 0, 1-p for 1)
+ random_tensor = survival_prob
+ random_tensor += torch.rand([bsz, 1, 1, 1]).type_as(x)
+ binary_tensor = torch.floor(random_tensor)
+
+ out = x / survival_prob * binary_tensor
+ return out
+
+
+def get_same_padding_conv2d(image_size: Optional[Tuple[int, int]]) -> Type[nn.Conv2d]:
+ if image_size is None:
+ return Conv2dDynamicSamePadding
+ return partial(Conv2dStaticSamePadding, image_size=image_size)
+
+
+class Conv2dDynamicSamePadding(nn.Conv2d):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride: int = 1,
+ dilation: int = 1,
+ groups: int = 1,
+ bias: bool = True,
+ ) -> None:
+ super().__init__(
+ in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias
+ )
+ self.stride = [self.stride] * 2
+
+ def forward(self, x: Tensor) -> Tensor:
+ ih, iw = x.shape[-2:]
+ kh, kw = self.weight.shape[-2:]
+ sh, sw = self.stride
+ oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
+ pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
+ pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
+ if pad_h > 0 or pad_w > 0:
+ x = F.pad(
+ x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]
+ )
+ return F.conv2d(
+ x,
+ self.weight,
+ self.bias,
+ self.stride,
+ self.padding,
+ self.dilation,
+ self.groups,
+ )
+
+
+class Conv2dStaticSamePadding(nn.Conv2d):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ image_size: Tuple[int, int],
+ stride: int = 1,
+ **kwargs: Any
+ ):
+ super().__init__(in_channels, out_channels, kernel_size, stride, **kwargs)
+ self.stride = [self.stride] * 2
+
+ # Calculate padding based on image size and save it.
+ ih, iw = image_size
+ kh, kw = self.weight.shape[-2:]
+ sh, sw = self.stride
+ oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
+ pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
+ pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
+ if pad_h > 0 or pad_w > 0:
+ self.static_padding = nn.ZeroPad2d(
+ (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2)
+ )
+ else:
+ self.static_padding = nn.Identity()
+
+ def forward(self, x: Tensor) -> Tensor:
+ x = self.static_padding(x)
+ x = F.pad(
+ x,
+ self.weight,
+ self.bias,
+ self.stride,
+ self.padding,
+ self.dilation,
+ self.groups,
+ )
+ return x
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-11-10 11:54:30 +0000