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

timm/models/layers/__init__.py

@@ -18,3 +18,4 @@ 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 .blurpool import BlurPool2d

timm/models/layers/blurpool.py

@@ -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)

timm/models/resnet.py

@@ -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
@@ -393,6 +413,13 @@ 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)
+        # 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
@@ -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