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Revert "add fuse_bn_add_act_ops args" #4914

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Oct 27, 2020
Merged

Revert "add fuse_bn_add_act_ops args" #4914

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9c6dc32
Revert "add fuse_bn_add_act_ops args (#4864)"
zhangting2020 Oct 22, 2020
48818f6
Merge branch 'develop' into revert-4864-fuse_bn_add_act
zhangting2020 Oct 27, 2020
4705d1a
Update utility.py
zhangting2020 Oct 27, 2020
79fcca2
Update utility.py
zhangting2020 Oct 27, 2020
e537e07
Update utility.py
zhangting2020 Oct 27, 2020
e6828ba
Update utility.py
zhangting2020 Oct 27, 2020
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7 changes: 1 addition & 6 deletions PaddleCV/image_classification/build_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,14 +39,9 @@ def _basic_model(data, model, args, is_train):
image_in = fluid.layers.transpose(
image, [0, 2, 3, 1]) if args.data_format == 'NHWC' else image
image_in.stop_gradient = image.stop_gradient
# fuse_bn_add_act only supports amp training
fuse_bn_add_act=False
if is_train and args.fuse_bn_add_act_ops:
fuse_bn_add_act=True
net_out = model.net(input=image_in,
class_dim=args.class_dim,
data_format=args.data_format,
fuse_bn_add_act=fuse_bn_add_act)
data_format=args.data_format)
else:
net_out = model.net(input=image, class_dim=args.class_dim)
softmax_out = fluid.layers.softmax(net_out, use_cudnn=False)
Expand Down
132 changes: 31 additions & 101 deletions PaddleCV/image_classification/models/resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ class ResNet():
def __init__(self, layers=50):
self.layers = layers

def net(self, input, class_dim=1000, data_format="NCHW", fuse_bn_add_act=False):
def net(self, input, class_dim=1000, data_format="NCHW"):
layers = self.layers
supported_layers = [18, 34, 50, 101, 152]
assert layers in supported_layers, \
Expand Down Expand Up @@ -77,8 +77,7 @@ def net(self, input, class_dim=1000, data_format="NCHW", fuse_bn_add_act=False):
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
name=conv_name,
data_format=data_format,
fuse_bn_add_act=fuse_bn_add_act)
data_format=data_format)

pool = fluid.layers.pool2d(
input=conv, pool_type='avg', global_pooling=True, data_format=data_format)
Expand All @@ -98,8 +97,7 @@ def net(self, input, class_dim=1000, data_format="NCHW", fuse_bn_add_act=False):
stride=2 if i == 0 and block != 0 else 1,
is_first=block == i == 0,
name=conv_name,
data_format=data_format,
fuse_bn_add_act=fuse_bn_add_act)
data_format=data_format)

pool = fluid.layers.pool2d(
input=conv, pool_type='avg', global_pooling=True, data_format=data_format)
Expand Down Expand Up @@ -157,7 +155,7 @@ def shortcut(self, input, ch_out, stride, is_first, name, data_format):
else:
return input

def bottleneck_block(self, input, num_filters, stride, name, data_format, fuse_bn_add_act):
def bottleneck_block(self, input, num_filters, stride, name, data_format):
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
Expand All @@ -173,56 +171,26 @@ def bottleneck_block(self, input, num_filters, stride, name, data_format, fuse_b
act='relu',
name=name + "_branch2b",
data_format=data_format)
if not fuse_bn_add_act:
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters * 4,
filter_size=1,
act=None,
name=name + "_branch2c",
data_format=data_format)
short = self.shortcut(
input,
num_filters * 4,
stride,
is_first=False,
name=name + "_branch1",
data_format=data_format)
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters * 4,
filter_size=1,
act=None,
name=name + "_branch2c",
data_format=data_format)

return fluid.layers.elementwise_add(
x=short, y=conv2, act='relu', name=name + ".add.output.5")
else:
conv2 = fluid.layers.conv2d(
input=conv1,
num_filters=num_filters * 4,
filter_size=1,
act=None,
param_attr=ParamAttr(name=name + "_branch2c" + "_weights"),
bias_attr=False,
name=name + '_branch2c' + '.conv2d.output.1',
data_format=data_format)
short = self.shortcut(
input,
num_filters * 4,
stride,
is_first=False,
name=name + "_branch1",
data_format=data_format)
name = name + "_branch2c"
bn_name = "bn" + name[3:]
short = fluid.contrib.layers.fused_bn_add_act(
conv2,
short,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance',
name=name + ".add.output.5")
short = self.shortcut(
input,
num_filters * 4,
stride,
is_first=False,
name=name + "_branch1",
data_format=data_format)

return short
return fluid.layers.elementwise_add(
x=short, y=conv2, act='relu', name=name + ".add.output.5")

def basic_block(self, input, num_filters, stride, is_first, name,
data_format, fuse_bn_add_act):
def basic_block(self, input, num_filters, stride, is_first, name, data_format):
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
Expand All @@ -231,54 +199,16 @@ def basic_block(self, input, num_filters, stride, is_first, name,
stride=stride,
name=name + "_branch2a",
data_format=data_format)
if not fuse_bn_add_act:
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
act=None,
name=name + "_branch2b",
data_format=data_format)
short = self.shortcut(
input,
num_filters,
stride,
is_first,
name=name + "_branch1",
data_format=data_format)

return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
else:
conv1 = fluid.layers.conv2d(
input=conv0,
num_filters=num_filters,
filter_size=3,
stride=1,
padding=1,
groups=1,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False,
name=name + '_branch2b' + '.conv2d.output.1',
data_format=data_format)
short = self.shortcut(
input,
num_filters,
stride,
is_first,
name=name + "_branch1",
data_format=data_format)
name = name + "_branch2b"
bn_name = "bn" + name[3:]
short = fluid.contrib.layers.fused_bn_add_act(
conv1,
short,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')

return short
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
act=None,
name=name + "_branch2b",
data_format=data_format)
short = self.shortcut(
input, num_filters, stride, is_first, name=name + "_branch1", data_format=data_format)
return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')


def ResNet18():
Expand Down
2 changes: 1 addition & 1 deletion PaddleCV/image_classification/utils/utility.py
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Original file line number Diff line number Diff line change
Expand Up @@ -147,7 +147,7 @@ def parse_args():
add_arg('fuse_bn_act_ops', bool, False, "Whether to use batch_norm and act fusion.")
add_arg('fuse_bn_add_act_ops', bool, False, "Whether to use batch_norm, elementwise_add and act fusion. This is only used for AMP training.")
add_arg('enable_addto', bool, False, "Whether to enable the addto strategy for gradient accumulation or not. This is only used for AMP training.")

add_arg('use_label_smoothing', bool, False, "Whether to use label_smoothing")
add_arg('label_smoothing_epsilon', float, 0.1, "The value of label_smoothing_epsilon parameter")
#NOTE: (2019/08/08) temporary disable use_distill
Expand Down