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PaddleSlim
提交 51c23929 编写于 作者: C ceci3
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fix

上级 e598cb9c
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Showing with 166 addition and 108 deletion
paddleslim/nas/search_space/__init__.py
浏览文件 @ 51c23929
......@@ -20,6 +20,10 @@ import mobilenetv1
from .mobilenetv1 import *
import resnet
from .resnet import *
import resnet_block
from .resnet_block import *
import inception_block
from .inception_block import *
import search_space_registry
from search_space_registry import *
import search_space_factory
......
paddleslim/nas/search_space/combine_search_space.py
浏览文件 @ 51c23929
......@@ -29,6 +29,7 @@ __all__ = ["CombineSearchSpace"]
_logger = get_logger(__name__, level=logging.INFO)
class CombineSearchSpace(object):
"""
Combine Search Space.
......@@ -42,11 +43,13 @@ class CombineSearchSpace(object):
for config_list in config_lists:
if isinstance(config_list, tuple):
key, config = config_list
if isinstance(config_list, str):
elif isinstance(config_list, str):
key = config_list
config = None
else:
raise NotImplementedError('the type of config is Error!!! Please check the config information. Receive the type of config is {}'.format(type(config_list)))
raise NotImplementedError(
'the type of config is Error!!! Please check the config information. Receive the type of config is {}'.
format(type(config_list)))
self.spaces.append(self._get_single_search_space(key, config))
self.init_tokens()
......@@ -61,6 +64,8 @@ class CombineSearchSpace(object):
model space(class)
"""
cls = SEARCHSPACE.get(key)
assert cls != None, '{} is NOT a correct space, the space we support is {}'.format(
key, SEARCHSPACE)
if config is None:
block_mask = None
......@@ -69,21 +74,27 @@ class CombineSearchSpace(object):
block_num = None
else:
if 'Block' not in cls.__name__:
_logger.warn('if space is not a Block space, config is useless, current space is {}'.format(cls.__name__))
block_mask = config['block_mask'] if 'block_mask' in config else None
input_size = config['input_size'] if 'input_size' in config else None
output_size = config['output_size'] if 'output_size' in config else None
_logger.warn(
'if space is not a Block space, config is useless, current space is {}'.
format(cls.__name__))
block_mask = config[
'block_mask'] if 'block_mask' in config else None
input_size = config[
'input_size'] if 'input_size' in config else None
output_size = config[
'output_size'] if 'output_size' in config else None
block_num = config['block_num'] if 'block_num' in config else None
if 'Block' in cls.__name__:
if block_mask == None and (self.block_num == None or self.input_size == None or self.output_size == None):
raise NotImplementedError("block_mask or (block num and input_size and output_size) can NOT be None at the same time in Block SPACE!")
space = cls(input_size,
output_size,
block_num,
block_mask=block_mask)
if block_mask == None and (self.block_num == None or
self.input_size == None or
self.output_size == None):
raise NotImplementedError(
"block_mask or (block num and input_size and output_size) can NOT be None at the same time in Block SPACE!"
)
space = cls(input_size, output_size, block_num, block_mask=block_mask)
return space
def init_tokens(self):
......
paddleslim/nas/search_space/inception_block.py
浏览文件 @ 51c23929
......@@ -81,7 +81,7 @@ class InceptionABlockSpace(SearchSpaceBase):
range_table_base.append(len(self.filter_num))
range_table_base.append(len(self.filter_num))
range_table_base.append(len(self.k_size))
range_table_base.append(len(self.pooltype))
range_table_base.append(len(self.pool_type))
return range_table_base
......@@ -97,51 +97,69 @@ class InceptionABlockSpace(SearchSpaceBase):
if self.block_mask != None:
for i in range(len(self.block_mask)):
self.bottleneck_params_list.append(
(self.filter_num[i * 9], self.filter_num[i * 9 + 1],
self.filter_num[i * 9 + 2], self.filter_num[i * 9 + 3],
self.filter_num[i * 9 + 4], self.filter_num[i * 9 + 5],
self.filter_num[i * 9 + 6], self.k_size[i * 9 + 7], 2 if
self.block_mask == 1 else 1, self.pool_type[i * 9 + 8]))
(self.filter_num[tokens[i * 9]],
self.filter_num[tokens[i * 9 + 1]],
self.filter_num[tokens[i * 9 + 2]],
self.filter_num[tokens[i * 9 + 3]],
self.filter_num[tokens[i * 9 + 4]],
self.filter_num[tokens[i * 9 + 5]],
self.filter_num[tokens[i * 9 + 6]],
self.k_size[tokens[i * 9 + 7]], 2 if self.block_mask == 1
else 1, self.pool_type[tokens[i * 9 + 8]]))
else:
repeat_num = self.block_num / self.downsample_num
num_minus = self.block_num % self.downsample_num
### if block_num > downsample_num, add stride=1 block at last (block_num-downsample_num) layers
for i in range(self.downsample_num):
self.bottleneck_params_list.append(
(self.filter_num[i * 9], self.filter_num[i * 9 + 1],
self.filter_num[i * 9 + 2], self.filter_num[i * 9 + 3],
self.filter_num[i * 9 + 4], self.filter_num[i * 9 + 5],
self.filter_num[i * 9 + 6], self.k_size[i * 9 + 7], 2,
self.pool_type[i * 9 + 8]))
(self.filter_num[tokens[i * 9]],
self.filter_num[tokens[i * 9 + 1]],
self.filter_num[tokens[i * 9 + 2]],
self.filter_num[tokens[i * 9 + 3]],
self.filter_num[tokens[i * 9 + 4]],
self.filter_num[tokens[i * 9 + 5]],
self.filter_num[tokens[i * 9 + 6]],
self.k_size[tokens[i * 9 + 7]], 2,
self.pool_type[tokens[i * 9 + 8]]))
### if block_num / downsample_num > 1, add (block_num / downsample_num) times stride=1 block
for k in range(repeat_num - 1):
kk = k * self.downsample_num + i
self.bottleneck_params_list.append(
(self.filter_num[kk * 9], self.filter_num[kk * 9 + 1],
self.filter_num[kk * 9 + 2],
self.filter_num[kk * 9 + 3],
self.filter_num[kk * 9 + 4],
self.filter_num[kk * 9 + 5],
self.filter_num[kk * 9 + 6], self.k_size[kk * 9 + 7],
1, self.pool_type[kk * 9 + 8]))
(self.filter_num[tokens[kk * 9]],
self.filter_num[tokens[kk * 9 + 1]],
self.filter_num[tokens[kk * 9 + 2]],
self.filter_num[tokens[kk * 9 + 3]],
self.filter_num[tokens[kk * 9 + 4]],
self.filter_num[tokens[kk * 9 + 5]],
self.filter_num[tokens[kk * 9 + 6]],
self.k_size[tokens[kk * 9 + 7]], 1,
self.pool_type[tokens[kk * 9 + 8]]))
if self.downsample_num - i <= num_minus:
j = self.downsample_num * repeat_num + i
self.bottleneck_params_list.append((
self.filter_num[j * 9], self.filter_num[j * 9 + 1],
self.filter_num[j * 9 + 2], self.filter_num[j * 9 + 3],
self.filter_num[j * 9 + 4], self.filter_num[j * 9 + 5],
self.filter_num[j * 9 + 6], self.k_size[j * 9 + 7], 1,
self.pool_type[j * 9 + 8]))
self.bottleneck_params_list.append(
(self.filter_num[tokens[j * 9]],
self.filter_num[tokens[j * 9 + 1]],
self.filter_num[tokens[j * 9 + 2]],
self.filter_num[tokens[j * 9 + 3]],
self.filter_num[tokens[j * 9 + 4]],
self.filter_num[tokens[j * 9 + 5]],
self.filter_num[tokens[j * 9 + 6]],
self.k_size[tokens[j * 9 + 7]], 1,
self.pool_type[tokens[j * 9 + 8]]))
if self.downsample_num == 0 and self.block_num != 0:
for i in range(len(self.block_num)):
self.bottleneck_params_list.append((
self.filter_num[i * 9], self.filter_num[i * 9 + 1],
self.filter_num[i * 9 + 2], self.filter_num[i * 9 + 3],
self.filter_num[i * 9 + 4], self.filter_num[i * 9 + 5],
self.filter_num[i * 9 + 6], self.k_size[i * 9 + 7], 1,
self.pool_type[i * 9 + 8]))
self.bottleneck_params_list.append(
(self.filter_num[tokens[i * 9]],
self.filter_num[tokens[i * 9 + 1]],
self.filter_num[tokens[i * 9 + 2]],
self.filter_num[tokens[i * 9 + 3]],
self.filter_num[tokens[i * 9 + 4]],
self.filter_num[tokens[i * 9 + 5]],
self.filter_num[tokens[i * 9 + 6]],
self.k_size[tokens[i * 9 + 7]], 1,
self.pool_type[tokens[i * 9 + 8]]))
def net_arch(input, return_mid_layer=False, return_block=[]):
assert isinstance(return_block,
......@@ -169,7 +187,7 @@ class InceptionABlockSpace(SearchSpaceBase):
if return_mid_layer:
return input, mid_layer
else:
return input
return input,
return net_arch
......@@ -247,7 +265,7 @@ class InceptionABlockSpace(SearchSpaceBase):
@SEARCHSPACE.register
class InceptionCBlockSpace(SearchSpaceBase):
def __init__(self, input_size, output_size, block_num, block_mask):
super(InceptionABlockSpace, self).__init__(input_size, output_size,
super(InceptionCBlockSpace, self).__init__(input_size, output_size,
block_num, block_mask)
if self.block_mask == None:
# use input_size and output_size to compute self.downsample_num
......@@ -274,9 +292,9 @@ class InceptionCBlockSpace(SearchSpaceBase):
The initial token.
"""
if self.block_mask != None:
return [0] * (len(self.block_mask) * 9)
return [0] * (len(self.block_mask) * 11)
else:
return [0] * (self.block_num * 9)
return [0] * (self.block_num * 11)
def range_table(self):
"""
......@@ -297,7 +315,7 @@ class InceptionCBlockSpace(SearchSpaceBase):
range_table_base.append(len(self.filter_num))
range_table_base.append(len(self.filter_num))
range_table_base.append(len(self.k_size))
range_table_base.append(len(self.pooltype))
range_table_base.append(len(self.pool_type))
return range_table_base
......@@ -313,63 +331,79 @@ class InceptionCBlockSpace(SearchSpaceBase):
if self.block_mask != None:
for i in range(len(self.block_mask)):
self.bottleneck_params_list.append(
(self.filter_num[i * 11], self.filter_num[i * 11 + 1],
self.filter_num[i * 11 + 2], self.filter_num[i * 11 + 3],
self.filter_num[i * 11 + 4], self.filter_num[i * 11 + 5],
self.filter_num[i * 11 + 6], self.filter_num[i * 11 + 7],
self.filter_num[i * 11 + 8], self.k_size[i * 11 + 9], 2 if
self.block_mask == 1 else 1, self.pool_type[i * 11 + 10]))
(self.filter_num[tokens[i * 11]],
self.filter_num[tokens[i * 11 + 1]],
self.filter_num[tokens[i * 11 + 2]],
self.filter_num[tokens[i * 11 + 3]],
self.filter_num[tokens[i * 11 + 4]],
self.filter_num[tokens[i * 11 + 5]],
self.filter_num[tokens[i * 11 + 6]],
self.filter_num[tokens[i * 11 + 7]],
self.filter_num[tokens[i * 11 + 8]],
self.k_size[tokens[i * 11 + 9]], 2 if self.block_mask == 1
else 1, self.pool_type[tokens[i * 11 + 10]]))
else:
repeat_num = self.block_num / self.downsample_num
num_minus = self.block_num % self.downsample_num
### if block_num > downsample_num, add stride=1 block at last (block_num-downsample_num) layers
for i in range(self.downsample_num):
self.bottleneck_params_list.append(
(self.filter_num[i * 11], self.filter_num[i * 11 + 1],
self.filter_num[i * 11 + 2], self.filter_num[i * 11 + 3],
self.filter_num[i * 11 + 4], self.filter_num[i * 11 + 5],
self.filter_num[i * 11 + 6], self.filter_num[i * 11 + 7],
self.filter_num[i * 11 + 8], self.k_size[i * 11 + 9], 2,
self.pool_type[i * 11 + 10]))
(self.filter_num[tokens[i * 11]],
self.filter_num[tokens[i * 11 + 1]],
self.filter_num[tokens[i * 11 + 2]],
self.filter_num[tokens[i * 11 + 3]],
self.filter_num[tokens[i * 11 + 4]],
self.filter_num[tokens[i * 11 + 5]],
self.filter_num[tokens[i * 11 + 6]],
self.filter_num[tokens[i * 11 + 7]],
self.filter_num[tokens[i * 11 + 8]],
self.k_size[tokens[i * 11 + 9]], 2,
self.pool_type[tokens[i * 11 + 10]]))
### if block_num / downsample_num > 1, add (block_num / downsample_num) times stride=1 block
for k in range(repeat_num - 1):
kk = k * self.downsample_num + i
self.bottleneck_params_list.append((
self.filter_num[kk * 11], self.filter_num[kk * 11 + 1],
self.filter_num[kk * 11 + 2],
self.filter_num[kk * 11 + 3],
self.filter_num[kk * 11 + 4],
self.filter_num[kk * 11 + 5],
self.filter_num[kk * 11 + 6],
self.filter_num[kk * 11 + 7],
self.filter_num[kk * 11 + 8], self.k_size[kk * 11 + 9],
1, self.pool_type[kk * 11 + 10]))
self.bottleneck_params_list.append(
(self.filter_num[tokens[kk * 11]],
self.filter_num[tokens[kk * 11 + 1]],
self.filter_num[tokens[kk * 11 + 2]],
self.filter_num[tokens[kk * 11 + 3]],
self.filter_num[tokens[kk * 11 + 4]],
self.filter_num[tokens[kk * 11 + 5]],
self.filter_num[tokens[kk * 11 + 6]],
self.filter_num[tokens[kk * 11 + 7]],
self.filter_num[tokens[kk * 11 + 8]],
self.k_size[tokens[kk * 11 + 9]], 1,
self.pool_type[tokens[kk * 11 + 10]]))
if self.downsample_num - i <= num_minus:
j = self.downsample_num * repeat_num + i
self.bottleneck_params_list.append(
(self.filter_num[j * 11], self.filter_num[j * 11 + 1],
self.filter_num[j * 11 + 2],
self.filter_num[j * 11 + 3],
self.filter_num[j * 11 + 4],
self.filter_num[j * 11 + 5],
self.filter_num[j * 11 + 6],
self.filter_num[j * 11 + 7],
self.filter_num[j * 11 + 8], self.k_size[j * 11 + 9],
1, self.pool_type[j * 11 + 10]))
(self.filter_num[tokens[j * 11]],
self.filter_num[tokens[j * 11 + 1]],
self.filter_num[tokens[j * 11 + 2]],
self.filter_num[tokens[j * 11 + 3]],
self.filter_num[tokens[j * 11 + 4]],
self.filter_num[tokens[j * 11 + 5]],
self.filter_num[tokens[j * 11 + 6]],
self.filter_num[tokens[j * 11 + 7]],
self.filter_num[tokens[j * 11 + 8]],
self.k_size[tokens[j * 11 + 9]], 1,
self.pool_type[tokens[j * 11 + 10]]))
if self.downsample_num == 0 and self.block_num != 0:
for i in range(len(self.block_num)):
self.bottleneck_params_list.append(
(self.filter_num[i * 11], self.filter_num[i * 11 + 1],
self.filter_num[i * 11 + 2],
self.filter_num[i * 11 + 3],
self.filter_num[i * 11 + 4],
self.filter_num[i * 11 + 5],
self.filter_num[i * 11 + 6],
self.filter_num[i * 11 + 7],
self.filter_num[i * 11 + 8], self.k_size[i * 11 + 9],
1, self.pool_type[i * 11 + 10]))
(self.filter_num[tokens[i * 11]],
self.filter_num[tokens[i * 11 + 1]],
self.filter_num[tokens[i * 11 + 2]],
self.filter_num[tokens[i * 11 + 3]],
self.filter_num[tokens[i * 11 + 4]],
self.filter_num[tokens[i * 11 + 5]],
self.filter_num[tokens[i * 11 + 6]],
self.filter_num[tokens[i * 11 + 7]],
self.filter_num[tokens[i * 11 + 8]],
self.k_size[tokens[i * 11 + 9]], 1,
self.pool_type[tokens[i * 11 + 10]]))
def net_arch(input, return_mid_layer=False, return_block=[]):
assert isinstance(return_block,
......@@ -397,7 +431,7 @@ class InceptionCBlockSpace(SearchSpaceBase):
if return_mid_layer:
return input, mid_layer
else:
return input
return input,
return net_arch
......
paddleslim/nas/search_space/mobilenet_block.py
浏览文件 @ 51c23929
......@@ -334,7 +334,7 @@ class MobileNetV1BlockSpace(SearchSpaceBase):
if tokens == None:
tokens = self.init_tokens()
self.bottleneck_param_list = []
self.bottleneck_params_list = []
if self.block_mask != None:
for i in range(len(self.block_mask)):
self.bottleneck_params_list.append(
......@@ -391,7 +391,6 @@ class MobileNetV1BlockSpace(SearchSpaceBase):
input=input,
num_filters1=filter_num1,
num_filters2=filter_num2,
num_groups=filter_num1,
stride=stride,
scale=self.scale,
kernel_size=kernel_size,
......@@ -408,17 +407,17 @@ class MobileNetV1BlockSpace(SearchSpaceBase):
input,
num_filters1,
num_filters2,
num_groups,
stride,
scale,
kernel_size,
name=None):
num_groups = input.shape[1]
depthwise_conv = conv_bn_layer(
input=input,
filter_size=kernel_size,
num_filters=int(num_filters1 * scale),
stride=stride,
num_groups=int(num_groups * scale),
num_groups=num_groups,
use_cudnn=False,
name=name + '_dw')
pointwise_conv = conv_bn_layer(
......
paddleslim/nas/search_space/resnet_block.py
浏览文件 @ 51c23929
......@@ -29,9 +29,9 @@ __all__ = ["ResNetBlockSpace"]
@SEARCHSPACE.register
class ResNetBlockSpace(SearchSpaceBase):
def __init__(input_size, output_size, block_num, block_mask):
super(ResNetSpace, self).__init__(input_size, output_size, block_num,
block_mask)
def __init__(self, input_size, output_size, block_num, block_mask=None):
super(ResNetBlockSpace, self).__init__(input_size, output_size,
block_num, block_mask)
# use input_size and output_size to compute self.downsample_num
self.downsample_num = compute_downsample_num(self.input_size,
self.output_size)
......@@ -52,6 +52,14 @@ class ResNetBlockSpace(SearchSpaceBase):
def range_table(self):
range_table_base = []
if self.block_mask != None:
range_table_length = len(self.block_mask)
else:
range_table_length = self.block_mum
for i in range(range_table_length):
range_table_base.append(len(self.filter_num))
range_table_base.append(len(self.k_size))
return range_table_base
......@@ -63,39 +71,39 @@ class ResNetBlockSpace(SearchSpaceBase):
if self.block_mask != None:
for i in range(len(self.block_mask)):
self.bottleneck_params_list.append(
(self.num_filters[tokens[i * 2]],
self.kernel_size[tokens[i * 2 + 1]], 2
(self.filter_num[tokens[i * 2]],
self.k_size[tokens[i * 2 + 1]], 2
if self.block_mask[i] == 1 else 1))
else:
repeat_num = self.block_num / self.downsample_num
num_minus = self.block_num % self.downsample_num
for i in range(self.downsample_num):
self.bottleneck_params_list.append(
self.num_filters[tokens[i * 2]],
self.kernel_size[tokens[i * 2 + 1]], 2)
self.filter_num[tokens[i * 2]],
self.k_size[tokens[i * 2 + 1]], 2)
for k in range(repeat_num - 1):
kk = k * self.downsample_num + i
self.bottleneck_params_list.append(
self.num_filters[tokens[kk * 2]],
self.kernel_size[tokens[kk * 2 + 1]], 1)
self.filter_num[tokens[kk * 2]],
self.k_size[tokens[kk * 2 + 1]], 1)
if self.downsample_num - i <= num_minus:
j = self.downsample_num * repeat_num + i
self.bottleneck_params_list.append(
self.num_filters[tokens[j * 2]],
self.kernel_size[tokens[j * 2 + 1]], 1)
self.filter_num[tokens[j * 2]],
self.k_size[tokens[j * 2 + 1]], 1)
if self.downsample_num == 0 and self.block_num != 0:
for i in range(len(self.block_num)):
self.bottleneck_params_list.append(
self.num_filters[tokens[i * 2]],
self.kernel_size[tokens[i * 2 + 1]], 1)
self.filter_num[tokens[i * 2]],
self.k_size[tokens[i * 2 + 1]], 1)
def net_arch(input, return_mid_layer=False, return_block=[]):
assert isinstance(return_block,
list), 'return_block must be a list.'
layer_count = 0
mid_layer = dict()
for layer_setting in self.bottleneck_params_list:
for i, layer_setting in enumerate(self.bottleneck_params_list):
filter_num, k_size, stride = layer_setting
if stride == 2:
layer_count += 1
......@@ -138,6 +146,7 @@ class ResNetBlockSpace(SearchSpaceBase):
input=input,
num_filters=num_filters,
filter_size=1,
stride=1,
act='relu',
name=name + '_bottleneck_conv0')
conv1 = conv_bn_layer(
......@@ -151,6 +160,7 @@ class ResNetBlockSpace(SearchSpaceBase):
input=conv1,
num_filters=num_filters * 4,
filter_size=1,
stride=1,
act=None,
name=name + '_bottleneck_conv2')
......
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