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"""Util functions for efficient net."""
from functools import partial
import math
from typing import Any, Optional, Union, Tuple, Type
from omegaconf import 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:
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",
"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],
[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_
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