Move efficientnet from encoder dir

1 files changed, 0 insertions, 85 deletions

diff --git a/text_recognizer/networks/encoders/efficientnet/utils.py b/text_recognizer/networks/encoders/efficientnet/utils.py
deleted file mode 100644
index 2b1aebb..0000000
--- a/text_recognizer/networks/encoders/efficientnet/utils.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""Util functions for efficient net."""
-import math
-from typing import List, Tuple
-
-from omegaconf import DictConfig, OmegaConf
-import torch
-from torch import Tensor
-
-
-def stochastic_depth(x: Tensor, p: float, training: bool) -> Tensor:
-    """Stochastic 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 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
-    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:
-    """Returns how many times a layer should be repeated in a block."""
-    return int(math.ceil(arch[1] * repeats))
-
-
-def block_args() -> List[DictConfig]:
-    """Returns arguments for each efficientnet block."""
-    keys = [
-        "num_repeats",
-        "kernel_size",
-        "stride",
-        "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],
-        [1, 3, (1, 1), 6, 112, 320, 0.25],
-    ]
-    block_args_ = []
-    for row in args:
-        block_args_.append(OmegaConf.create(dict(zip(keys, row))))
-    return block_args_