From 53bfdaa0b4d3a04c5f2a274c5657ada81e9bf135 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Thu, 8 Jul 2021 22:25:04 +0200
Subject: Add comments

---
 text_recognizer/networks/encoders/efficientnet/efficientnet.py |  1 +
 text_recognizer/networks/encoders/efficientnet/utils.py        | 10 ++++++----
 2 files changed, 7 insertions(+), 4 deletions(-)

(limited to 'text_recognizer/networks/encoders/efficientnet')

diff --git a/text_recognizer/networks/encoders/efficientnet/efficientnet.py b/text_recognizer/networks/encoders/efficientnet/efficientnet.py
index fb4f002..59598b5 100644
--- a/text_recognizer/networks/encoders/efficientnet/efficientnet.py
+++ b/text_recognizer/networks/encoders/efficientnet/efficientnet.py
@@ -10,6 +10,7 @@ from .utils import (
 
 
 class EfficientNet(nn.Module):
+    # TODO: attr
     archs = {
         #     width,depth0res,dropout
         "b0": (1.0, 1.0, 0.2),
diff --git a/text_recognizer/networks/encoders/efficientnet/utils.py b/text_recognizer/networks/encoders/efficientnet/utils.py
index 6f293db..5234324 100644
--- a/text_recognizer/networks/encoders/efficientnet/utils.py
+++ b/text_recognizer/networks/encoders/efficientnet/utils.py
@@ -1,9 +1,8 @@
 """Util functions for efficient net."""
-from functools import partial
 import math
-from typing import Any, Optional, Union, Tuple, Type
+from typing import List, Tuple
 
-from omegaconf import OmegaConf
+from omegaconf import DictConfig, OmegaConf
 import torch
 from torch import Tensor
 
@@ -46,6 +45,7 @@ def stochastic_depth(x: Tensor, p: float, training: bool) -> Tensor:
 
 
 def round_filters(filters: int, arch: Tuple[float, float, float]) -> int:
+    """Returns the number output filters for a block."""
     multiplier = arch[0]
     divisor = 8
     filters *= multiplier
@@ -56,10 +56,12 @@ def round_filters(filters: int, arch: Tuple[float, float, float]) -> int:
 
 
 def round_repeats(repeats: int, arch: Tuple[float, float, float]) -> int:
+    """Returns how many times a layer should be repeated in a block."""
     return int(math.ceil(arch[1] * repeats))
 
 
-def block_args():
+def block_args() -> List[DictConfig]:
+    """Returns arguments for each efficientnet block."""
     keys = [
         "num_repeats",
         "kernel_size",
-- 
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