1 files changed, 0 insertions, 145 deletions
diff --git a/text_recognizer/networks/backbones/efficientnet.py b/text_recognizer/networks/backbones/efficientnet.py
deleted file mode 100644
index 61dea77..0000000
--- a/text_recognizer/networks/backbones/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)