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"""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
|
