Still working on efficientnet

1 files changed, 40 insertions, 0 deletions

diff --git a/text_recognizer/networks/encoders/efficientnet/utils.py b/text_recognizer/networks/encoders/efficientnet/utils.py
index 4b4a787..ff52485 100644
--- a/text_recognizer/networks/encoders/efficientnet/utils.py
+++ b/text_recognizer/networks/encoders/efficientnet/utils.py
@@ -3,6 +3,7 @@ from functools import partial
 import math
 from typing import Any, Optional, Tuple, Type
 
+from omegaconf import OmegaConf
 import torch
 from torch import nn, Tensor
 import torch.functional as F
@@ -139,3 +140,42 @@ class Conv2dStaticSamePadding(nn.Conv2d):
             self.groups,
         )
         return x
+
+
+def round_filters(filters: int, arch: Tuple[float, float, float]) -> int:
+    multiplier = arch[0]
+    divisor = 8
+    filters *= multiplier
+    new_filters = max(divisor, (filters + divisor // 2) // divisor * divisor)
+    if new_filters < 0.9 * filters:
+        new_filters += divisor
+    return int(new_filters)
+
+
+def round_repeats(repeats: int, arch: Tuple[float, float, float]) -> int:
+    return int(math.ceil(arch[1] * repeats))
+
+
+def block_args():
+    keys = [
+        "num_repeats",
+        "kernel_size",
+        "strides",
+        "expand_ratio",
+        "in_channels",
+        "out_channels",
+        "se_ratio",
+    ]
+    args = [
+        [1, 3, (1, 1), 1, 32, 16, 0.25],
+        [2, 3, (2, 2), 6, 16, 24, 0.25],
+        [2, 5, (2, 2), 6, 24, 40, 0.25],
+        [3, 3, (2, 2), 6, 40, 80, 0.25],
+        [3, 5, (1, 1), 6, 80, 112, 0.25],
+        [4, 5, (2, 2), 6, 112, 192, 0.25],
+        [1, 3, (1, 1), 6, 192, 320, 0.25],
+    ]
+    block_args_ = []
+    for row in args:
+        block_args_.append(OmegaConf.create(dict(zip(keys, row))))
+    return block_args_