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PaddleSeg
提交 78791df2 编写于 作者: C chenguowei01
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add lr_mult_list

上级 5d19dfa6
隐藏空白更改
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Showing with 140 addition and 123 deletion
pdseg/models/backbone/resnet_vd.py
浏览文件 @ 78791df2
......@@ -40,11 +40,21 @@ train_parameters = {
class ResNet():
def __init__(self, layers=50, scale=1.0, stem=None):
def __init__(self,
layers=50,
scale=1.0,
stem=None,
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
self.params = train_parameters
self.layers = layers
self.scale = scale
self.stem = stem
self.lr_mult_list = lr_mult_list
assert len(
self.lr_mult_list
) == 5, "lr_mult_list length in ResNet must be 5 but got {}!!".format(
len(self.lr_mult_list))
self.curr_stage = 0
def net(self,
input,
......@@ -86,42 +96,37 @@ class ResNet():
num_filters = [64, 128, 256, 512]
if self.stem == 'icnet' or self.stem == 'pspnet' or self.stem == 'deeplab':
conv = self.conv_bn_layer(
input=input,
num_filters=int(32 * self.scale),
filter_size=3,
stride=2,
act='relu',
name="conv1_1")
conv = self.conv_bn_layer(
input=conv,
num_filters=int(32 * self.scale),
filter_size=3,
stride=1,
act='relu',
name="conv1_2")
conv = self.conv_bn_layer(
input=conv,
num_filters=int(64 * self.scale),
filter_size=3,
stride=1,
act='relu',
name="conv1_3")
conv = self.conv_bn_layer(input=input,
num_filters=int(32 * self.scale),
filter_size=3,
stride=2,
act='relu',
name="conv1_1")
conv = self.conv_bn_layer(input=conv,
num_filters=int(32 * self.scale),
filter_size=3,
stride=1,
act='relu',
name="conv1_2")
conv = self.conv_bn_layer(input=conv,
num_filters=int(64 * self.scale),
filter_size=3,
stride=1,
act='relu',
name="conv1_3")
else:
conv = self.conv_bn_layer(
input=input,
num_filters=int(64 * self.scale),
filter_size=7,
stride=2,
act='relu',
name="conv1")
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
conv = self.conv_bn_layer(input=input,
num_filters=int(64 * self.scale),
filter_size=7,
stride=2,
act='relu',
name="conv1")
conv = fluid.layers.pool2d(input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
layer_count = 1
if check_points(layer_count, decode_points):
......@@ -132,6 +137,7 @@ class ResNet():
if layers >= 50:
for block in range(len(depth)):
self.curr_stage += 1
for i in range(depth[block]):
if layers in [101, 152] and block == 2:
if i == 0:
......@@ -164,8 +170,10 @@ class ResNet():
np.ceil(
np.array(conv.shape[2:]).astype('int32') / 2))
pool = fluid.layers.pool2d(
input=conv, pool_size=7, pool_type='avg', global_pooling=True)
pool = fluid.layers.pool2d(input=conv,
pool_size=7,
pool_type='avg',
global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
......@@ -174,6 +182,7 @@ class ResNet():
initializer=fluid.initializer.Uniform(-stdv, stdv)))
else:
for block in range(len(depth)):
self.curr_stage += 1
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
conv = self.basic_block(
......@@ -189,8 +198,10 @@ class ResNet():
if check_points(layer_count, end_points):
return conv, decode_ends
pool = fluid.layers.pool2d(
input=conv, pool_size=7, pool_type='avg', global_pooling=True)
pool = fluid.layers.pool2d(input=conv,
pool_size=7,
pool_type='avg',
global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
......@@ -217,23 +228,25 @@ class ResNet():
act=None,
name=None):
lr_mult = self.lr_mult_list[self.curr_stage]
if self.stem == 'pspnet':
bias_attr = ParamAttr(name=name + "_biases")
else:
bias_attr = False
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2 if dilation == 1 else 0,
dilation=dilation,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=bias_attr,
name=name + '.conv2d.output.1')
conv = fluid.layers.conv2d(input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) //
2 if dilation == 1 else 0,
dilation=dilation,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights",
learning_rate=lr_mult),
bias_attr=bias_attr,
name=name + '.conv2d.output.1')
if name == "conv1":
bn_name = "bn_" + name
......@@ -243,8 +256,9 @@ class ResNet():
input=conv,
act=act,
name=bn_name + '.output.1',
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
param_attr=ParamAttr(name=bn_name + '_scale',
learning_rate=lr_mult),
bias_attr=ParamAttr(bn_name + '_offset', learning_rate=lr_mult),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance',
)
......@@ -257,24 +271,24 @@ class ResNet():
groups=1,
act=None,
name=None):
pool = fluid.layers.pool2d(
input=input,
pool_size=2,
pool_stride=2,
pool_padding=0,
pool_type='avg',
ceil_mode=True)
conv = fluid.layers.conv2d(
input=pool,
num_filters=num_filters,
filter_size=filter_size,
stride=1,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
lr_mult = self.lr_mult_list[self.curr_stage]
pool = fluid.layers.pool2d(input=input,
pool_size=2,
pool_stride=2,
pool_padding=0,
pool_type='avg',
ceil_mode=True)
conv = fluid.layers.conv2d(input=pool,
num_filters=num_filters,
filter_size=filter_size,
stride=1,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights",
learning_rate=lr_mult),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
else:
......@@ -282,8 +296,9 @@ class ResNet():
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
param_attr=ParamAttr(name=bn_name + '_scale',
learning_rate=lr_mult),
bias_attr=ParamAttr(bn_name + '_offset', learning_rate=lr_mult),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
......@@ -294,8 +309,11 @@ class ResNet():
if is_first or stride == 1:
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
else:
return self.conv_bn_layer_new(
input, ch_out, 1, stride, name=name)
return self.conv_bn_layer_new(input,
ch_out,
1,
stride,
name=name)
elif is_first:
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
else:
......@@ -308,60 +326,59 @@ class ResNet():
name,
is_first=False,
dilation=1):
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=1,
dilation=1,
stride=1,
act='relu',
name=name + "_branch2a")
conv0 = self.conv_bn_layer(input=input,
num_filters=num_filters,
filter_size=1,
dilation=1,
stride=1,
act='relu',
name=name + "_branch2a")
if dilation > 1:
conv0 = self.zero_padding(conv0, dilation)
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
dilation=dilation,
stride=stride,
act='relu',
name=name + "_branch2b")
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters * 4,
dilation=1,
filter_size=1,
act=None,
name=name + "_branch2c")
short = self.shortcut(
input,
num_filters * 4,
stride,
is_first=is_first,
name=name + "_branch1")
conv1 = self.conv_bn_layer(input=conv0,
num_filters=num_filters,
filter_size=3,
dilation=dilation,
stride=stride,
act='relu',
name=name + "_branch2b")
conv2 = self.conv_bn_layer(input=conv1,
num_filters=num_filters * 4,
dilation=1,
filter_size=1,
act=None,
name=name + "_branch2c")
short = self.shortcut(input,
num_filters * 4,
stride,
is_first=is_first,
name=name + "_branch1")
print(input.shape, short.shape, conv2.shape)
print(stride)
return fluid.layers.elementwise_add(
x=short, y=conv2, act='relu', name=name + ".add.output.5")
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):
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=3,
act='relu',
stride=stride,
name=name + "_branch2a")
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
act=None,
name=name + "_branch2b")
short = self.shortcut(
input, num_filters, stride, is_first, name=name + "_branch1")
conv0 = self.conv_bn_layer(input=input,
num_filters=num_filters,
filter_size=3,
act='relu',
stride=stride,
name=name + "_branch2a")
conv1 = self.conv_bn_layer(input=conv0,
num_filters=num_filters,
filter_size=3,
act=None,
name=name + "_branch2b")
short = self.shortcut(input,
num_filters,
stride,
is_first,
name=name + "_branch1")
return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
......
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