Merge branch 'VRandme-blur'

timm/models/helpers.py

@@ -31,9 +31,9 @@ def load_state_dict(checkpoint_path, use_ema=False):
         raise FileNotFoundError()
 
 
-def load_checkpoint(model, checkpoint_path, use_ema=False):
+def load_checkpoint(model, checkpoint_path, use_ema=False, strict=True):
     state_dict = load_state_dict(checkpoint_path, use_ema)
-    model.load_state_dict(state_dict)
+    model.load_state_dict(state_dict, strict=strict)
 
 
 def resume_checkpoint(model, checkpoint_path):

timm/models/layers/__init__.py

@@ -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 .blur_pool import BlurPool2d

timm/models/layers/anti_aliasing.py

@@ -5,12 +5,12 @@ import torch.nn.functional as F
 
 
 class AntiAliasDownsampleLayer(nn.Module):
-    def __init__(self, no_jit: bool = False, filt_size: int = 3, stride: int = 2, channels: int = 0):
+    def __init__(self, channels: int = 0, filt_size: int = 3, stride: int = 2, no_jit: bool = False):
         super(AntiAliasDownsampleLayer, self).__init__()
         if no_jit:
-            self.op = Downsample(filt_size, stride, channels)
+            self.op = Downsample(channels, filt_size, stride)
         else:
-            self.op = DownsampleJIT(filt_size, stride, channels)
+            self.op = DownsampleJIT(channels, filt_size, stride)
 
         # FIXME I should probably override _apply and clear DownsampleJIT filter cache for .cuda(), .half(), etc calls
 
@@ -20,10 +20,10 @@ class AntiAliasDownsampleLayer(nn.Module):
 
 @torch.jit.script
 class DownsampleJIT(object):
-    def __init__(self, filt_size: int = 3, stride: int = 2, channels: int = 0):
+    def __init__(self, channels: int = 0, filt_size: int = 3, stride: int = 2):
+        self.channels = channels
         self.stride = stride
         self.filt_size = filt_size
-        self.channels = channels
         assert self.filt_size == 3
         assert stride == 2
         self.filt = {}  # lazy init by device for DataParallel compat
@@ -32,8 +32,7 @@ class DownsampleJIT(object):
         filt = torch.tensor([1., 2., 1.], dtype=like.dtype, device=like.device)
         filt = filt[:, None] * filt[None, :]
         filt = filt / torch.sum(filt)
-        filt = filt[None, None, :, :].repeat((self.channels, 1, 1, 1))
-        return filt
+        return filt[None, None, :, :].repeat((self.channels, 1, 1, 1))
 
     def __call__(self, input: torch.Tensor):
         input_pad = F.pad(input, (1, 1, 1, 1), 'reflect')
@@ -42,11 +41,11 @@ class DownsampleJIT(object):
 
 
 class Downsample(nn.Module):
-    def __init__(self, filt_size=3, stride=2, channels=None):
+    def __init__(self, channels=None, filt_size=3, stride=2):
         super(Downsample, self).__init__()
+        self.channels = channels
         self.filt_size = filt_size
         self.stride = stride
-        self.channels = channels
 
         assert self.filt_size == 3
         filt = torch.tensor([1., 2., 1.])

timm/models/layers/blur_pool.py

@@ -0,0 +1,58 @@
+"""
+BlurPool layer inspired by
+ - Kornia's Max_BlurPool2d
+ - Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
+
+FIXME merge this impl with those in `anti_aliasing.py`
+
+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 typing import Dict
+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
+        filt_size (int): binomial filter size for blurring. currently supports 3 (default) and 5.
+        stride (int): downsampling filter stride
+
+    Returns:
+        torch.Tensor: the transformed tensor.
+    """
+    filt: Dict[str, torch.Tensor]
+
+    def __init__(self, channels, filt_size=3, stride=2) -> None:
+        super(BlurPool2d, self).__init__()
+        assert filt_size > 1
+        self.channels = channels
+        self.filt_size = filt_size
+        self.stride = stride
+        pad_size = [get_padding(filt_size, stride, dilation=1)] * 4
+        self.padding = nn.ReflectionPad2d(pad_size)
+        self._coeffs = torch.tensor((np.poly1d((0.5, 0.5)) ** (self.filt_size - 1)).coeffs)  # for torchscript compat
+        self.filt = {}  # lazy init by device for DataParallel compat
+
+    def _create_filter(self, like: torch.Tensor):
+        blur_filter = (self._coeffs[:, None] * self._coeffs[None, :]).to(dtype=like.dtype, device=like.device)
+        return blur_filter[None, None, :, :].repeat(self.channels, 1, 1, 1)
+
+    def _apply(self, fn):
+        # override nn.Module _apply, reset filter cache if used
+        self.filt = {}
+        super(BlurPool2d, self)._apply(fn)
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        C = input_tensor.shape[1]
+        blur_filt = self.filt.get(str(input_tensor.device), self._create_filter(input_tensor))
+        return F.conv2d(
+            self.padding(input_tensor), blur_filt, 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 +118,11 @@ 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(
+        interpolation='bicubic'),
+    'resnetblur50': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.pth',
+        interpolation='bicubic')
 }
 
 
@@ -131,7 +136,7 @@ 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, aa_layer=None, drop_block=None, drop_path=None):
         super(BasicBlock, self).__init__()
 
         assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
@@ -139,12 +144,15 @@ class BasicBlock(nn.Module):
         first_planes = planes // reduce_first
         outplanes = planes * self.expansion
         first_dilation = first_dilation or dilation
+        use_aa = aa_layer is not None
 
         self.conv1 = nn.Conv2d(
-            inplanes, first_planes, kernel_size=3, stride=stride, padding=first_dilation,
+            inplanes, first_planes, kernel_size=3, stride=1 if use_aa else stride, padding=first_dilation,
             dilation=first_dilation, bias=False)
         self.bn1 = norm_layer(first_planes)
         self.act1 = act_layer(inplace=True)
+        self.aa = aa_layer(channels=first_planes) if stride == 2 and use_aa 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 +177,8 @@ class BasicBlock(nn.Module):
         if self.drop_block is not None:
             x = self.drop_block(x)
         x = self.act1(x)
+        if self.aa is not None:
+            x = self.aa(x)
 
         x = self.conv2(x)
         x = self.bn2(x)
@@ -195,22 +205,26 @@ 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, aa_layer=None, drop_block=None, drop_path=None):
         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
+        use_aa = aa_layer is not None
 
         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 use_aa else stride,
             padding=first_dilation, dilation=first_dilation, groups=cardinality, bias=False)
         self.bn2 = norm_layer(width)
         self.act2 = act_layer(inplace=True)
+        self.aa = aa_layer(channels=width) if stride == 2 and use_aa else None
+
         self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
         self.bn3 = norm_layer(outplanes)
 
@@ -240,6 +254,8 @@ class Bottleneck(nn.Module):
         if self.drop_block is not None:
             x = self.drop_block(x)
         x = self.act2(x)
+        if self.aa is not None:
+            x = self.aa(x)
 
         x = self.conv3(x)
         x = self.bn3(x)
@@ -353,8 +369,9 @@ class ResNet(nn.Module):
         Whether to use average pooling for projection skip connection between stages/downsample.
     output_stride : int, default 32
         Set the output stride of the network, 32, 16, or 8. Typically used in segmentation.
-    act_layer : class, activation layer
-    norm_layer : class, normalization layer
+    act_layer : nn.Module, activation layer
+    norm_layer : nn.Module, normalization layer
+    aa_layer : nn.Module, anti-aliasing layer
     drop_rate : float, default 0.
         Dropout probability before classifier, for training
     global_pool : str, default 'avg'
@@ -363,7 +380,7 @@ class ResNet(nn.Module):
     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.,
+                 act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_rate=0.0, drop_path_rate=0.,
                  drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None):
         block_args = block_args or dict()
         self.num_classes = num_classes
@@ -393,7 +410,14 @@ 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 aa_layer is not None:
+            self.maxpool = nn.Sequential(*[
+                nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
+                aa_layer(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
@@ -410,7 +434,7 @@ class ResNet(nn.Module):
             assert output_stride == 32
         layer_args = list(zip(channels, layers, strides, dilations))
         layer_kwargs = dict(
-            reduce_first=block_reduce_first, act_layer=act_layer, norm_layer=norm_layer,
+            reduce_first=block_reduce_first, act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer,
             avg_down=avg_down, down_kernel_size=down_kernel_size, drop_path=dp, **block_args)
         self.layer1 = self._make_layer(block, *layer_args[0], **layer_kwargs)
         self.layer2 = self._make_layer(block, *layer_args[1], **layer_kwargs)
@@ -1114,3 +1138,29 @@ 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, aa_layer=BlurPool2d, **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, aa_layer=BlurPool2d, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model