[Feature] Add fast_conv_bn_eval option in ConvModule for fast validation and...

[Feature] Add fast_conv_bn_eval option in ConvModule for fast validation and training in Eval mode (#2807)

mmcv/cnn/bricks/conv_module.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
+from functools import partial
 from typing import Dict, Optional, Tuple, Union
 
 import torch
@@ -14,6 +15,55 @@ from .norm import build_norm_layer
 from .padding import build_padding_layer
 
 
+def fast_conv_bn_eval_forward(bn: _BatchNorm, conv: nn.modules.conv._ConvNd,
+                              x: torch.Tensor):
+    """
+    Implementation based on https://arxiv.org/abs/2305.11624
+    "Tune-Mode ConvBN Blocks For Efficient Transfer Learning"
+    It leverages the associative law between convolution and affine transform,
+    i.e., normalize (weight conv feature) = (normalize weight) conv feature.
+    It works for Eval mode of ConvBN blocks during validation, and can be used
+    for training as well. It reduces memory and computation cost.
+
+    Args:
+        bn (_BatchNorm): a BatchNorm module.
+        conv (nn._ConvNd): a conv module
+        x (torch.Tensor): Input feature map.
+    """
+    # These lines of code are designed to deal with various cases
+    # like bn without affine transform, and conv without bias
+    weight_on_the_fly = conv.weight
+    if conv.bias is not None:
+        bias_on_the_fly = conv.bias
+    else:
+        bias_on_the_fly = torch.zeros_like(bn.running_var)
+
+    if bn.weight is not None:
+        bn_weight = bn.weight
+    else:
+        bn_weight = torch.ones_like(bn.running_var)
+
+    if bn.bias is not None:
+        bn_bias = bn.bias
+    else:
+        bn_bias = torch.zeros_like(bn.running_var)
+
+    # shape of [C_out, 1, 1, 1] in Conv2d
+    weight_coeff = torch.rsqrt(bn.running_var +
+                               bn.eps).reshape([-1] + [1] *
+                                               (len(conv.weight.shape) - 1))
+    # shape of [C_out, 1, 1, 1] in Conv2d
+    coefff_on_the_fly = bn_weight.view_as(weight_coeff) * weight_coeff
+
+    # shape of [C_out, C_in, k, k] in Conv2d
+    weight_on_the_fly = weight_on_the_fly * coefff_on_the_fly
+    # shape of [C_out] in Conv2d
+    bias_on_the_fly = bn_bias + coefff_on_the_fly.flatten() *\
+        (bias_on_the_fly - bn.running_mean)
+
+    return conv._conv_forward(x, weight_on_the_fly, bias_on_the_fly)
+
+
 @MODELS.register_module()
 class ConvModule(nn.Module):
     """A conv block that bundles conv/norm/activation layers.
@@ -65,6 +115,9 @@ class ConvModule(nn.Module):
             sequence of "conv", "norm" and "act". Common examples are
             ("conv", "norm", "act") and ("act", "conv", "norm").
             Default: ('conv', 'norm', 'act').
+        fast_conv_bn_eval (bool): Whether use fast conv when the consecutive
+            bn is in eval mode (either training or testing), as proposed in
+            https://arxiv.org/abs/2305.11624 . Default: False.
     """
 
     _abbr_ = 'conv_block'
@@ -84,7 +137,8 @@ class ConvModule(nn.Module):
                  inplace: bool = True,
                  with_spectral_norm: bool = False,
                  padding_mode: str = 'zeros',
-                 order: tuple = ('conv', 'norm', 'act')):
+                 order: tuple = ('conv', 'norm', 'act'),
+                 fast_conv_bn_eval: bool = False):
         super().__init__()
         assert conv_cfg is None or isinstance(conv_cfg, dict)
         assert norm_cfg is None or isinstance(norm_cfg, dict)
@@ -155,6 +209,16 @@ class ConvModule(nn.Module):
         else:
             self.norm_name = None  # type: ignore
 
+        # fast_conv_bn_eval works for conv + bn
+        # with `track_running_stats` option
+        if fast_conv_bn_eval and self.norm and isinstance(
+                self.norm, _BatchNorm) and self.norm.track_running_stats:
+            self.fast_conv_bn_eval_forward = partial(fast_conv_bn_eval_forward,
+                                                     self.norm, self.conv)
+        else:
+            self.fast_conv_bn_eval_forward = None  # type: ignore
+        self.original_conv_forward = self.conv.forward
+
         # build activation layer
         if self.with_activation:
             act_cfg_ = act_cfg.copy()  # type: ignore
@@ -200,13 +264,77 @@ class ConvModule(nn.Module):
                 x: torch.Tensor,
                 activate: bool = True,
                 norm: bool = True) -> torch.Tensor:
-        for layer in self.order:
+        layer_index = 0
+        while layer_index < len(self.order):
+            layer = self.order[layer_index]
             if layer == 'conv':
                 if self.with_explicit_padding:
                     x = self.padding_layer(x)
+                # if the next operation is norm and we have a norm layer in
+                # eval mode and we have enabled fast_conv_bn_eval for the conv
+                # operator, then activate the optimized forward and skip the
+                # next norm operator since it has been fused
+                if layer_index + 1 < len(self.order) and \
+                        self.order[layer_index + 1] == 'norm' and norm and \
+                        self.with_norm and not self.norm.training and \
+                        self.fast_conv_bn_eval_forward is not None:
+                    self.conv.forward = self.fast_conv_bn_eval_forward
+                    layer_index += 1
+                else:
+                    self.conv.forward = self.original_conv_forward
                 x = self.conv(x)
             elif layer == 'norm' and norm and self.with_norm:
                 x = self.norm(x)
             elif layer == 'act' and activate and self.with_activation:
                 x = self.activate(x)
+            layer_index += 1
         return x
+
+    @staticmethod
+    def create_from_conv_bn(conv: torch.nn.modules.conv._ConvNd,
+                            bn: torch.nn.modules.batchnorm._BatchNorm,
+                            fast_conv_bn_eval=True) -> 'ConvModule':
+        """Create a ConvModule from a conv and a bn module."""
+        self = ConvModule.__new__(ConvModule)
+        super(ConvModule, self).__init__()
+
+        self.conv_cfg = None
+        self.norm_cfg = None
+        self.act_cfg = None
+        self.inplace = False
+        self.with_spectral_norm = False
+        self.with_explicit_padding = False
+        self.order = ('conv', 'norm', 'act')
+
+        self.with_norm = True
+        self.with_activation = False
+        self.with_bias = conv.bias is not None
+
+        # build convolution layer
+        self.conv = conv
+        # export the attributes of self.conv to a higher level for convenience
+        self.in_channels = self.conv.in_channels
+        self.out_channels = self.conv.out_channels
+        self.kernel_size = self.conv.kernel_size
+        self.stride = self.conv.stride
+        self.padding = self.conv.padding
+        self.dilation = self.conv.dilation
+        self.transposed = self.conv.transposed
+        self.output_padding = self.conv.output_padding
+        self.groups = self.conv.groups
+
+        # build normalization layers
+        self.norm_name, norm = 'bn', bn
+        self.add_module(self.norm_name, norm)
+
+        # fast_conv_bn_eval works for conv + bn
+        # with `track_running_stats` option
+        if fast_conv_bn_eval and self.norm and isinstance(
+                self.norm, _BatchNorm) and self.norm.track_running_stats:
+            self.fast_conv_bn_eval_forward = partial(fast_conv_bn_eval_forward,
+                                                     self.norm, self.conv)
+        else:
+            self.fast_conv_bn_eval_forward = None  # type: ignore
+        self.original_conv_forward = self.conv.forward
+
+        return self

tests/test_cnn/test_conv_module.py

@@ -75,6 +75,16 @@ def test_conv_module():
     output = conv(x)
     assert output.shape == (1, 8, 255, 255)
 
+    # conv + norm with fast mode
+    conv = ConvModule(
+        3, 8, 2, norm_cfg=dict(type='BN'), fast_conv_bn_eval=True)
+    conv.norm.eval()
+    x = torch.rand(1, 3, 256, 256)
+    fast_mode_output = conv(x)
+    conv.conv.forward = conv.original_conv_forward
+    plain_implementation = conv.activate(conv.norm(conv.conv(x)))
+    assert torch.allclose(fast_mode_output, plain_implementation, atol=1e-5)
+
     # conv + act
     conv = ConvModule(3, 8, 2)
     assert conv.with_activation