From 9995922ff957ce424dca0655a01d8338a519aa86 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sun, 20 Jun 2021 22:17:49 +0200
Subject: Working on new implementation of efficientnet

---
 .../networks/encoders/efficientnet/utils.py        | 141 +++++++++++++++++++++
 1 file changed, 141 insertions(+)
 create mode 100644 text_recognizer/networks/encoders/efficientnet/utils.py

(limited to 'text_recognizer/networks/encoders/efficientnet/utils.py')

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
-- 
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