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Merge branch 'blur' of https://github.com/VRandme/pytorch-image-models into VRandme-blur

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pull/136/head
Ross Wightman 5 years ago
parent 7a9942a75e 1a9ab07307
commit 9590f301a9
3 changed files with 115 additions and 9 deletions
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3
timm/models/layers/__init__.py
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@ -17,4 +17,5 @@ from .drop import DropBlock2d, DropPath, drop_block_2d, drop_path
from .test_time_pool import TestTimePoolHead, apply_test_time_pool
from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
from .anti_aliasing import AntiAliasDownsampleLayer
from .space_to_depth import SpaceToDepthModule
from .space_to_depth import SpaceToDepthModule
from .blurpool import BlurPool2d

55
timm/models/layers/blurpool.py
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@ -0,0 +1,55 @@
"""
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
Hacked together by Chris Ha and Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .padding import get_padding
class BlurPool2d(nn.Module):
r"""Creates a module that computes blurs and downsample a given feature map.
See :cite:`zhang2019shiftinvar` for more details.
Corresponds to the Downsample class, which does blurring and subsampling
Args:
channels = Number of input channels
blur_filter_size (int): binomial filter size for blurring. currently supports 3 (default) and 5.
stride (int): downsampling filter stride
Shape:
Returns:
torch.Tensor: the transformed tensor.
Examples:
"""
def __init__(self, channels, blur_filter_size=3, stride=2) -> None:
super(BlurPool2d, self).__init__()
assert blur_filter_size > 1
self.channels = channels
self.blur_filter_size = blur_filter_size
self.stride = stride
pad_size = [get_padding(blur_filter_size, stride, dilation=1)] * 4
self.padding = nn.ReflectionPad2d(pad_size)
blur_matrix = (np.poly1d((0.5, 0.5)) ** (blur_filter_size - 1)).coeffs
blur_filter = torch.Tensor(blur_matrix[:, None] * blur_matrix[None, :])
self.blur_filter = blur_filter[None, None, :, :]
def _apply(self, fn):
# override nn.Module _apply to prevent need for blur_filter to be registered as a buffer,
# this keeps it out of state dict, but allows .cuda(), .type(), etc to work as expected
super(BlurPool2d, self)._apply(fn)
self.blur_filter = fn(self.blur_filter)
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: # type: ignore
C = input_tensor.shape[1]
return F.conv2d(
self.padding(input_tensor),
self.blur_filter.type(input_tensor.dtype).expand(C, -1, -1, -1), stride=self.stride, groups=C)

66
timm/models/resnet.py
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@ -12,7 +12,7 @@ import torch.nn.functional as F
from .registry import register_model
from .helpers import load_pretrained, adapt_model_from_file
from .layers import SelectAdaptivePool2d, DropBlock2d, DropPath, AvgPool2dSame, create_attn
from .layers import SelectAdaptivePool2d, DropBlock2d, DropPath, AvgPool2dSame, create_attn, BlurPool2d
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
__all__ = ['ResNet', 'BasicBlock', 'Bottleneck'] # model_registry will add each entrypoint fn to this
@ -118,6 +118,8 @@ default_cfgs = {
'ecaresnet101d_pruned': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth',
interpolation='bicubic'),
'resnetblur18': _cfg(),
'resnetblur50': _cfg()
}
@ -131,7 +133,7 @@ class BasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
attn_layer=None, drop_block=None, drop_path=None):
attn_layer=None, drop_block=None, drop_path=None, blur=False):
super(BasicBlock, self).__init__()
assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
@ -141,10 +143,12 @@ class BasicBlock(nn.Module):
first_dilation = first_dilation or dilation
self.conv1 = nn.Conv2d(
inplanes, first_planes, kernel_size=3, stride=stride, padding=first_dilation,
inplanes, first_planes, kernel_size=3, stride=1 if blur else stride, padding=first_dilation,
dilation=first_dilation, bias=False)
self.bn1 = norm_layer(first_planes)
self.act1 = act_layer(inplace=True)
self.blurpool = BlurPool2d(channels=first_planes) if stride == 2 and blur else None
self.conv2 = nn.Conv2d(
first_planes, outplanes, kernel_size=3, padding=dilation, dilation=dilation, bias=False)
self.bn2 = norm_layer(outplanes)
@ -169,6 +173,8 @@ class BasicBlock(nn.Module):
if self.drop_block is not None:
x = self.drop_block(x)
x = self.act1(x)
if self.blurpool is not None:
x = self.blurpool(x)
x = self.conv2(x)
x = self.bn2(x)
@ -195,22 +201,26 @@ class Bottleneck(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
attn_layer=None, drop_block=None, drop_path=None):
attn_layer=None, drop_block=None, drop_path=None, blur=False):
super(Bottleneck, self).__init__()
width = int(math.floor(planes * (base_width / 64)) * cardinality)
first_planes = width // reduce_first
outplanes = planes * self.expansion
first_dilation = first_dilation or dilation
self.blur = blur
self.conv1 = nn.Conv2d(inplanes, first_planes, kernel_size=1, bias=False)
self.bn1 = norm_layer(first_planes)
self.act1 = act_layer(inplace=True)
self.conv2 = nn.Conv2d(
first_planes, width, kernel_size=3, stride=stride,
first_planes, width, kernel_size=3, stride=1 if blur else stride,
padding=first_dilation, dilation=first_dilation, groups=cardinality, bias=False)
self.bn2 = norm_layer(width)
self.act2 = act_layer(inplace=True)
self.blurpool = BlurPool2d(channels=width) if stride == 2 and blur else None
self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
self.bn3 = norm_layer(outplanes)
@ -240,6 +250,8 @@ class Bottleneck(nn.Module):
if self.drop_block is not None:
x = self.drop_block(x)
x = self.act2(x)
if self.blurpool is not None:
x = self.blurpool(x)
x = self.conv3(x)
x = self.bn3(x)
@ -359,12 +371,19 @@ class ResNet(nn.Module):
Dropout probability before classifier, for training
global_pool : str, default 'avg'
Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax'
blur : str, default ''
Location of Blurring:
* '', default - Not applied
* 'max' - only stem layer MaxPool will be blurred
* 'strided' - only strided convolutions in the downsampling blocks (assembled-cnn style)
* 'max_strided' - on both stem MaxPool and strided convolutions (zhang2019shiftinvar style for ResNets)
"""
def __init__(self, block, layers, num_classes=1000, in_chans=3,
cardinality=1, base_width=64, stem_width=64, stem_type='',
block_reduce_first=1, down_kernel_size=1, avg_down=False, output_stride=32,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, drop_rate=0.0, drop_path_rate=0.,
drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None):
drop_block_rate=0., global_pool='avg', blur='', zero_init_last_bn=True, block_args=None):
block_args = block_args or dict()
self.num_classes = num_classes
deep_stem = 'deep' in stem_type
@ -373,6 +392,7 @@ class ResNet(nn.Module):
self.base_width = base_width
self.drop_rate = drop_rate
self.expansion = block.expansion
self.blur = 'strided' in blur
super(ResNet, self).__init__()
# Stem
@ -393,7 +413,14 @@ class ResNet(nn.Module):
self.conv1 = nn.Conv2d(in_chans, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = norm_layer(self.inplanes)
self.act1 = act_layer(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# Stem Pooling
if 'max' in blur :
self.maxpool = nn.Sequential(*[
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
BlurPool2d(channels=self.inplanes, stride=2)
])
else :
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# Feature Blocks
dp = DropPath(drop_path_rate) if drop_path_rate else None
@ -445,7 +472,7 @@ class ResNet(nn.Module):
block_kwargs = dict(
cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
dilation=dilation, **kwargs)
dilation=dilation, blur=self.blur, **kwargs)
layers = [block(self.inplanes, planes, stride, downsample, first_dilation=first_dilation, **block_kwargs)]
self.inplanes = planes * block.expansion
layers += [block(self.inplanes, planes, **block_kwargs) for _ in range(1, blocks)]
@ -1114,3 +1141,26 @@ def ecaresnet101d_pruned(pretrained=False, num_classes=1000, in_chans=3, **kwarg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def resnetblur18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-18 model with blur anti-aliasing
"""
default_cfg = default_cfgs['resnetblur18']
model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans, blur='max_strided',**kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def resnetblur50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-50 model with blur anti-aliasing
"""
default_cfg = default_cfgs['resnetblur50']
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, blur='max_strided', **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model

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