From e9753c0c1476d4c5aa614e1f65a8dd4302a1ce5b Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Fri, 25 Jun 2021 01:19:12 +0200
Subject: Efficientnet working
---
.../networks/encoders/efficientnet/efficientnet.py | 68 ++++++-------
.../networks/encoders/efficientnet/mbconv.py | 64 ++++++------
.../networks/encoders/efficientnet/utils.py | 107 +--------------------
3 files changed, 71 insertions(+), 168 deletions(-)
(limited to 'text_recognizer')
diff --git a/text_recognizer/networks/encoders/efficientnet/efficientnet.py b/text_recognizer/networks/encoders/efficientnet/efficientnet.py
index 98d58fd..b527d90 100644
--- a/text_recognizer/networks/encoders/efficientnet/efficientnet.py
+++ b/text_recognizer/networks/encoders/efficientnet/efficientnet.py
@@ -1,13 +1,9 @@
"""Efficient net."""
-from typing import Tuple
-
from torch import nn, Tensor
from .mbconv import MBConvBlock
from .utils import (
block_args,
- calculate_output_image_size,
- get_same_padding_conv2d,
round_filters,
round_repeats,
)
@@ -28,11 +24,19 @@ class EfficientNet(nn.Module):
"l2": (4.3, 5.3, 0.5),
}
- def __init__(self, arch: str, image_size: Tuple[int, int]) -> None:
+ def __init__(
+ self,
+ arch: str,
+ stochastic_dropout_rate: float = 0.2,
+ bn_momentum: float = 0.99,
+ bn_eps: float = 1.0e-3,
+ ) -> None:
super().__init__()
assert arch in self.archs, f"{arch} not a valid efficient net architecure!"
self.arch = self.archs[arch]
- self.image_size = image_size
+ self.stochastic_dropout_rate = stochastic_dropout_rate
+ self.bn_momentum = 1 - bn_momentum
+ self.bn_eps = bn_eps
self._conv_stem: nn.Sequential = None
self._blocks: nn.Sequential = None
self._conv_head: nn.Sequential = None
@@ -42,57 +46,53 @@ class EfficientNet(nn.Module):
_block_args = block_args()
in_channels = 1 # BW
out_channels = round_filters(32, self.arch)
- Conv2d = get_same_padding_conv2d(image_size=self.image_size)
self._conv_stem = nn.Sequential(
- Conv2d(
+ nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
- stride=2,
+ stride=(2, 2),
bias=False,
),
- nn.BatchNorm2d(num_features=out_channels, momentum=bn_momentum, eps=bn_eps),
+ nn.BatchNorm2d(
+ num_features=out_channels, momentum=self.bn_momentum, eps=self.bn_eps
+ ),
nn.Mish(inplace=True),
)
- image_size = calculate_output_image_size(self.image_size, 2)
self._blocks = nn.ModuleList([])
for args in _block_args:
args.in_channels = round_filters(args.in_channels, self.arch)
args.out_channels = round_filters(args.out_channels, self.arch)
- args.num_repeat = round_repeats(args.num_repeat, self.arch)
-
- self._blocks.append(
- MBConvBlock(
- **args,
- bn_momentum=bn_momentum,
- bn_eps=bn_eps,
- image_size=image_size,
- )
- )
- image_size = calculate_output_image_size(image_size, args.stride)
- if args.num_repeat > 1:
- args.in_channels = args.out_channels
- args.stride = 1
- for _ in range(args.num_repeat - 1):
+ args.num_repeats = round_repeats(args.num_repeats, self.arch)
+ for _ in range(args.num_repeats):
self._blocks.append(
MBConvBlock(
**args,
- bn_momentum=bn_momentum,
- bn_eps=bn_eps,
- image_size=image_size,
+ bn_momentum=self.bn_momentum,
+ bn_eps=self.bn_eps,
)
)
+ args.in_channels = args.out_channels
+ args.stride = 1
- in_channels = args.out_channels
+ in_channels = round_filters(320, self.arch)
out_channels = round_filters(1280, self.arch)
- Conv2d = get_same_padding_conv2d(image_size=image_size)
self._conv_head = nn.Sequential(
- Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
- nn.BatchNorm2d(num_features=out_channels, momentum=bn_momentum, eps=bn_eps),
+ nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
+ nn.BatchNorm2d(
+ num_features=out_channels, momentum=self.bn_momentum, eps=self.bn_eps
+ ),
)
def extract_features(self, x: Tensor) -> Tensor:
x = self._conv_stem(x)
+ for i, block in enumerate(self._blocks):
+ stochastic_dropout_rate = self.stochastic_dropout_rate
+ if self.stochastic_dropout_rate:
+ stochastic_dropout_rate *= i / len(self._blocks)
+ x = block(x, stochastic_dropout_rate=stochastic_dropout_rate)
+ self._conv_head(x)
+ return x
def forward(self, x: Tensor) -> Tensor:
- pass
+ return self.extract_features(x)
diff --git a/text_recognizer/networks/encoders/efficientnet/mbconv.py b/text_recognizer/networks/encoders/efficientnet/mbconv.py
index fbb3f22..e43771a 100644
--- a/text_recognizer/networks/encoders/efficientnet/mbconv.py
+++ b/text_recognizer/networks/encoders/efficientnet/mbconv.py
@@ -1,11 +1,11 @@
"""Mobile inverted residual block."""
-from typing import Any, Optional, Tuple
+from typing import Any, Optional, Union, Tuple
import torch
from torch import nn, Tensor
-from torch.nn import functional as F
+import torch.nn.functional as F
-from .utils import calculate_output_image_size, drop_connection, get_same_padding_conv2d
+from .utils import stochastic_depth
class MBConvBlock(nn.Module):
@@ -16,22 +16,30 @@ class MBConvBlock(nn.Module):
in_channels: int,
out_channels: int,
kernel_size: int,
- stride: int,
+ stride: Union[Tuple[int, int], int],
bn_momentum: float,
bn_eps: float,
se_ratio: float,
expand_ratio: int,
- image_size: Optional[Tuple[int, int]],
*args: Any,
**kwargs: Any,
) -> None:
super().__init__()
self.kernel_size = kernel_size
+ self.stride = (stride, ) * 2 if isinstance(stride, int) else stride
self.bn_momentum = bn_momentum
self.bn_eps = bn_eps
- self.in_channels = self.in_channels
+ self.in_channels = in_channels
self.out_channels = out_channels
+ if self.stride == (2, 2):
+ self.pad = [
+ (self.kernel_size - 1) // 2 - 1,
+ (self.kernel_size - 1) // 2,
+ ] * 2
+ else:
+ self.pad = [(self.kernel_size - 1) // 2] * 4
+
# Placeholders for layers.
self._inverted_bottleneck: nn.Sequential = None
self._depthwise: nn.Sequential = None
@@ -39,7 +47,6 @@ class MBConvBlock(nn.Module):
self._pointwise: nn.Sequential = None
self._build(
- image_size=image_size,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
@@ -50,11 +57,10 @@ class MBConvBlock(nn.Module):
def _build(
self,
- image_size: Optional[Tuple[int, int]],
in_channels: int,
out_channels: int,
kernel_size: int,
- stride: int,
+ stride: Union[Tuple[int, int], int],
expand_ratio: int,
se_ratio: float,
) -> None:
@@ -62,7 +68,6 @@ class MBConvBlock(nn.Module):
inner_channels = in_channels * expand_ratio
self._inverted_bottleneck = (
self._configure_inverted_bottleneck(
- image_size=image_size,
in_channels=in_channels,
out_channels=inner_channels,
)
@@ -71,7 +76,6 @@ class MBConvBlock(nn.Module):
)
self._depthwise = self._configure_depthwise(
- image_size=image_size,
in_channels=inner_channels,
out_channels=inner_channels,
groups=inner_channels,
@@ -79,7 +83,6 @@ class MBConvBlock(nn.Module):
stride=stride,
)
- image_size = calculate_output_image_size(image_size, stride)
self._squeeze_excite = (
self._configure_squeeze_excite(
in_channels=inner_channels,
@@ -91,19 +94,17 @@ class MBConvBlock(nn.Module):
)
self._pointwise = self._configure_pointwise(
- image_size=image_size, in_channels=inner_channels, out_channels=out_channels
+ in_channels=inner_channels, out_channels=out_channels
)
def _configure_inverted_bottleneck(
self,
- image_size: Optional[Tuple[int, int]],
in_channels: int,
out_channels: int,
) -> nn.Sequential:
"""Expansion phase."""
- Conv2d = get_same_padding_conv2d(image_size=image_size)
return nn.Sequential(
- Conv2d(
+ nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
@@ -117,16 +118,14 @@ class MBConvBlock(nn.Module):
def _configure_depthwise(
self,
- image_size: Optional[Tuple[int, int]],
in_channels: int,
out_channels: int,
groups: int,
kernel_size: int,
- stride: int,
+ stride: Union[Tuple[int, int], int],
) -> nn.Sequential:
- Conv2d = get_same_padding_conv2d(image_size=image_size)
return nn.Sequential(
- Conv2d(
+ nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
@@ -143,16 +142,15 @@ class MBConvBlock(nn.Module):
def _configure_squeeze_excite(
self, in_channels: int, out_channels: int, se_ratio: float
) -> nn.Sequential:
- Conv2d = get_same_padding_conv2d(image_size=(1, 1))
num_squeezed_channels = max(1, int(in_channels * se_ratio))
return nn.Sequential(
- Conv2d(
+ nn.Conv2d(
in_channels=in_channels,
out_channels=num_squeezed_channels,
kernel_size=1,
),
nn.Mish(inplace=True),
- Conv2d(
+ nn.Conv2d(
in_channels=num_squeezed_channels,
out_channels=out_channels,
kernel_size=1,
@@ -160,11 +158,10 @@ class MBConvBlock(nn.Module):
)
def _configure_pointwise(
- self, image_size: Optional[Tuple[int, int]], in_channels: int, out_channels: int
+ self, in_channels: int, out_channels: int
) -> nn.Sequential:
- Conv2d = get_same_padding_conv2d(image_size=image_size)
return nn.Sequential(
- Conv2d(
+ nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
@@ -176,20 +173,23 @@ class MBConvBlock(nn.Module):
)
def _stochastic_depth(
- self, x: Tensor, residual: Tensor, drop_connection_rate: Optional[float]
+ self, x: Tensor, residual: Tensor, stochastic_dropout_rate: Optional[float]
) -> Tensor:
- if self.id_skip and self.stride == 1 and self.in_channels == self.out_channels:
- if drop_connection_rate:
- x = drop_connection(x, p=drop_connection_rate, training=self.training)
+ if self.stride == (1, 1) and self.in_channels == self.out_channels:
+ if stochastic_dropout_rate:
+ x = stochastic_depth(
+ x, p=stochastic_dropout_rate, training=self.training
+ )
x += residual
return x
def forward(
- self, x: Tensor, drop_connection_rate: Optional[float] = None
+ self, x: Tensor, stochastic_dropout_rate: Optional[float] = None
) -> Tensor:
residual = x
if self._inverted_bottleneck is not None:
x = self._inverted_bottleneck(x)
+ x = F.pad(x, self.pad)
x = self._depthwise(x)
@@ -201,5 +201,5 @@ class MBConvBlock(nn.Module):
x = self._pointwise(x)
# Stochastic depth
- x = self._stochastic_depth(x, residual, drop_connection_rate)
+ x = self._stochastic_depth(x, residual, stochastic_dropout_rate)
return x
diff --git a/text_recognizer/networks/encoders/efficientnet/utils.py b/text_recognizer/networks/encoders/efficientnet/utils.py
index ff52485..6f293db 100644
--- a/text_recognizer/networks/encoders/efficientnet/utils.py
+++ b/text_recognizer/networks/encoders/efficientnet/utils.py
@@ -1,27 +1,15 @@
"""Util functions for efficient net."""
from functools import partial
import math
-from typing import Any, Optional, Tuple, Type
+from typing import Any, Optional, Union, Tuple, Type
from omegaconf import OmegaConf
import torch
-from torch import nn, Tensor
-import torch.functional as F
+from torch import Tensor
-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.
+def stochastic_depth(x: Tensor, p: float, training: bool) -> Tensor:
+ """Stochastic connection.
Drops the entire convolution with a given survival probability.
@@ -57,91 +45,6 @@ def drop_connection(x: Tensor, p: float, training: bool) -> 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
-
-
def round_filters(filters: int, arch: Tuple[float, float, float]) -> int:
multiplier = arch[0]
divisor = 8
@@ -160,7 +63,7 @@ def block_args():
keys = [
"num_repeats",
"kernel_size",
- "strides",
+ "stride",
"expand_ratio",
"in_channels",
"out_channels",
--
cgit v1.2.3-70-g09d2