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demo/nas/block_sa_nas_mobilenetv2.py 0 → 100644
浏览文件 @ 904dfaff
import sys
sys.path.append('..')
import numpy as np
import argparse
import ast
import logging
import time
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddleslim.analysis import flops
from paddleslim.nas import SANAS
from paddleslim.common import get_logger
from optimizer import create_optimizer
import imagenet_reader
_logger = get_logger(__name__, level=logging.INFO)
reduce_rate = 0.85
init_temperature = 10.24
max_flops = 321208544
server_address = ""
port = 8979
retain_epoch = 5
def create_data_loader(image_shape):
data_shape = [None] + image_shape
data = fluid.data(name='data', shape=data_shape, dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
data_loader = fluid.io.DataLoader.from_generator(
feed_list=[data, label],
capacity=1024,
use_double_buffer=True,
iterable=True)
return data_loader, data, label
def conv_bn_layer(input,
filter_size,
num_filters,
stride,
padding='SAME',
num_groups=1,
act=None,
name=None,
use_cudnn=True):
conv = fluid.layers.conv2d(
input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
param_attr=ParamAttr(name=name + '_weights'),
bias_attr=False)
bn_name = name + '_bn'
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(name=bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def search_mobilenetv2_block(config, args, image_size):
image_shape = [3, image_size, image_size]
if args.is_server:
sa_nas = SANAS(
config,
server_addr=("", port),
init_temperature=init_temperature,
reduce_rate=reduce_rate,
search_steps=args.search_steps,
is_server=True)
else:
sa_nas = SANAS(
config,
server_addr=(server_address, port),
init_temperature=init_temperature,
reduce_rate=reduce_rate,
search_steps=args.search_steps,
is_server=False)
for step in range(args.search_steps):
archs = sa_nas.next_archs()[0]
train_program = fluid.Program()
test_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
train_loader, data, label = create_data_loader(image_shape)
data = conv_bn_layer(
input=data,
num_filters=32,
filter_size=3,
stride=2,
padding='SAME',
act='relu6',
name='mobilenetv2_conv1')
data = archs(data)[0]
data = conv_bn_layer(
input=data,
num_filters=1280,
filter_size=1,
stride=1,
padding='SAME',
act='relu6',
name='mobilenetv2_last_conv')
data = fluid.layers.pool2d(
input=data,
pool_size=7,
pool_stride=1,
pool_type='avg',
global_pooling=True,
name='mobilenetv2_last_pool')
output = fluid.layers.fc(
input=data,
size=args.class_dim,
param_attr=ParamAttr(name='mobilenetv2_fc_weights'),
bias_attr=ParamAttr(name='mobilenetv2_fc_offset'))
softmax_out = fluid.layers.softmax(input=output, use_cudnn=False)
cost = fluid.layers.cross_entropy(input=softmax_out, label=label)
avg_cost = fluid.layers.mean(cost)
acc_top1 = fluid.layers.accuracy(
input=softmax_out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(
input=softmax_out, label=label, k=5)
test_program = train_program.clone(for_test=True)
optimizer = fluid.optimizer.Momentum(
learning_rate=0.1,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
optimizer.minimize(avg_cost)
current_flops = flops(train_program)
print('step: {}, current_flops: {}'.format(step, current_flops))
if current_flops > max_flops:
continue
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_program)
if args.data == 'cifar10':
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
batch_size=args.batch_size,
drop_last=True)
test_reader = paddle.batch(
paddle.dataset.cifar.test10(cycle=False),
batch_size=args.batch_size,
drop_last=False)
elif args.data == 'imagenet':
train_reader = paddle.batch(
imagenet_reader.train(),
batch_size=args.batch_size,
drop_last=True)
test_reader = paddle.batch(
imagenet_reader.val(),
batch_size=args.batch_size,
drop_last=False)
test_loader, _, _ = create_data_loader(image_shape)
train_loader.set_sample_list_generator(
train_reader,
places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
test_loader.set_sample_list_generator(test_reader, places=place)
build_strategy = fluid.BuildStrategy()
train_compiled_program = fluid.CompiledProgram(
train_program).with_data_parallel(
loss_name=avg_cost.name, build_strategy=build_strategy)
for epoch_id in range(retain_epoch):
for batch_id, data in enumerate(train_loader()):
fetches = [avg_cost.name]
s_time = time.time()
outs = exe.run(train_compiled_program,
feed=data,
fetch_list=fetches)[0]
batch_time = time.time() - s_time
if batch_id % 10 == 0:
_logger.info(
'TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}, batch_time: {}ms'.
format(step, epoch_id, batch_id, outs[0], batch_time))
reward = []
for batch_id, data in enumerate(test_loader()):
test_fetches = [avg_cost.name, acc_top1.name, acc_top5.name]
batch_reward = exe.run(test_program,
feed=data,
fetch_list=test_fetches)
reward_avg = np.mean(np.array(batch_reward), axis=1)
reward.append(reward_avg)
_logger.info(
'TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}, acc_top5: {}'.
format(step, batch_id, batch_reward[0], batch_reward[1],
batch_reward[2]))
finally_reward = np.mean(np.array(reward), axis=0)
_logger.info(
'FINAL TEST: avg_cost: {}, acc_top1: {}, acc_top5: {}'.format(
finally_reward[0], finally_reward[1], finally_reward[2]))
sa_nas.reward(float(finally_reward[1]))
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='SA NAS MobileNetV2 cifar10 argparase')
parser.add_argument(
'--use_gpu',
type=ast.literal_eval,
default=True,
help='Whether to use GPU in train/test model.')
parser.add_argument(
'--class_dim', type=int, default=1000, help='classify number.')
parser.add_argument(
'--batch_size', type=int, default=256, help='batch size.')
parser.add_argument(
'--data',
type=str,
default='cifar10',
choices=['cifar10', 'imagenet'],
help='dataset name.')
parser.add_argument(
'--is_server',
type=ast.literal_eval,
default=True,
help='Whether to start a server.')
# nas args
parser.add_argument(
'--search_steps',
type=int,
default=100,
help='controller server number.')
parser.add_argument('--lr', type=float, default=0.1, help='learning rate.')
args = parser.parse_args()
print(args)
if args.data == 'cifar10':
image_size = 32
elif args.data == 'imagenet':
image_size = 224
else:
raise NotImplementedError(
'data must in [cifar10, imagenet], but received: {}'.format(
args.data))
# block mask means block number, 1 mean downsample, 0 means the size of feature map don't change after this block
config_info = {'block_mask': [0, 1, 1, 1, 1, 0, 1, 0]}
config = [('MobileNetV2BlockSpace', config_info)]
search_mobilenetv2_block(config, args, image_size)
demo/nas/sa_nas_mobilenetv2.py
浏览文件 @ 904dfaff
......@@ -9,7 +9,7 @@ import ast
import logging
import paddle
import paddle.fluid as fluid
from paddleslim.nas.search_space.search_space_factory import SearchSpaceFactory
from paddle.fluid.param_attr import ParamAttr
from paddleslim.analysis import flops
from paddleslim.nas import SANAS
from paddleslim.common import get_logger
......@@ -18,11 +18,18 @@ import imagenet_reader
_logger = get_logger(__name__, level=logging.INFO)
reduce_rate = 0.85
init_temperature = 10.24
max_flops = 321208544
server_address = ""
port = 8989
retain_epoch = 5
def create_data_loader(image_shape):
data_shape = [-1] + image_shape
data_shape = [None] + image_shape
data = fluid.data(name='data', shape=data_shape, dtype='float32')
label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
data_loader = fluid.io.DataLoader.from_generator(
feed_list=[data, label],
capacity=1024,
......@@ -40,6 +47,7 @@ def build_program(main_program,
with fluid.program_guard(main_program, startup_program):
data_loader, data, label = create_data_loader(image_shape)
output = archs(data)
output = fluid.layers.fc(input=output, size=args.class_dim)
softmax_out = fluid.layers.softmax(input=output, use_cudnn=False)
cost = fluid.layers.cross_entropy(input=softmax_out, label=label)
......@@ -54,24 +62,22 @@ def build_program(main_program,
def search_mobilenetv2(config, args, image_size, is_server=True):
factory = SearchSpaceFactory()
space = factory.get_search_space(config)
if is_server:
### start a server and a client
sa_nas = SANAS(
config,
server_addr=("", 8883),
init_temperature=args.init_temperature,
reduce_rate=args.reduce_rate,
server_addr=("", port),
init_temperature=init_temperature,
reduce_rate=reduce_rate,
search_steps=args.search_steps,
is_server=True)
else:
### start a client
sa_nas = SANAS(
config,
server_addr=("10.255.125.38", 8883),
init_temperature=args.init_temperature,
reduce_rate=args.reduce_rate,
server_addr=(server_address, port),
init_temperature=init_temperature,
reduce_rate=reduce_rate,
search_steps=args.search_steps,
is_server=False)
......@@ -87,7 +93,7 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
current_flops = flops(train_program)
print('step: {}, current_flops: {}'.format(step, current_flops))
if current_flops > args.max_flops:
if current_flops > max_flops:
continue
test_loader, test_avg_cost, test_acc_top1, test_acc_top5 = build_program(
......@@ -124,7 +130,6 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
batch_size=args.batch_size,
drop_last=False)
#test_loader, _, _ = create_data_loader(image_shape)
train_loader.set_sample_list_generator(
train_reader,
places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
......@@ -134,7 +139,7 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
train_compiled_program = fluid.CompiledProgram(
train_program).with_data_parallel(
loss_name=avg_cost.name, build_strategy=build_strategy)
for epoch_id in range(args.retain_epoch):
for epoch_id in range(retain_epoch):
for batch_id, data in enumerate(train_loader()):
fetches = [avg_cost.name]
s_time = time.time()
......@@ -171,6 +176,99 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
sa_nas.reward(float(finally_reward[1]))
def test_search_result(tokens, image_size, args, config):
sa_nas = SANAS(
config,
server_addr=("", 8887),
init_temperature=args.init_temperature,
reduce_rate=args.reduce_rate,
search_steps=args.search_steps,
is_server=True)
image_shape = [3, image_size, image_size]
archs = sa_nas.tokens2arch(tokens)
train_program = fluid.Program()
test_program = fluid.Program()
startup_program = fluid.Program()
train_loader, avg_cost, acc_top1, acc_top5 = build_program(
train_program, startup_program, image_shape, archs, args)
current_flops = flops(train_program)
print('current_flops: {}'.format(current_flops))
test_loader, test_avg_cost, test_acc_top1, test_acc_top5 = build_program(
test_program, startup_program, image_shape, archs, args, is_test=True)
test_program = test_program.clone(for_test=True)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_program)
if args.data == 'cifar10':
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
batch_size=args.batch_size,
drop_last=True)
test_reader = paddle.batch(
paddle.dataset.cifar.test10(cycle=False),
batch_size=args.batch_size,
drop_last=False)
elif args.data == 'imagenet':
train_reader = paddle.batch(
imagenet_reader.train(),
batch_size=args.batch_size,
drop_last=True)
test_reader = paddle.batch(
imagenet_reader.val(), batch_size=args.batch_size, drop_last=False)
train_loader.set_sample_list_generator(
train_reader,
places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
test_loader.set_sample_list_generator(test_reader, places=place)
build_strategy = fluid.BuildStrategy()
train_compiled_program = fluid.CompiledProgram(
train_program).with_data_parallel(
loss_name=avg_cost.name, build_strategy=build_strategy)
for epoch_id in range(retain_epoch):
for batch_id, data in enumerate(train_loader()):
fetches = [avg_cost.name]
s_time = time.time()
outs = exe.run(train_compiled_program,
feed=data,
fetch_list=fetches)[0]
batch_time = time.time() - s_time
if batch_id % 10 == 0:
_logger.info(
'TRAIN: epoch: {}, batch: {}, cost: {}, batch_time: {}ms'.
format(epoch_id, batch_id, outs[0], batch_time))
reward = []
for batch_id, data in enumerate(test_loader()):
test_fetches = [
test_avg_cost.name, test_acc_top1.name, test_acc_top5.name
]
batch_reward = exe.run(test_program,
feed=data,
fetch_list=test_fetches)
reward_avg = np.mean(np.array(batch_reward), axis=1)
reward.append(reward_avg)
_logger.info(
'TEST: batch: {}, avg_cost: {}, acc_top1: {}, acc_top5: {}'.
format(batch_id, batch_reward[0], batch_reward[1],
batch_reward[2]))
finally_reward = np.mean(np.array(reward), axis=0)
_logger.info(
'FINAL TEST: avg_cost: {}, acc_top1: {}, acc_top5: {}'.format(
finally_reward[0], finally_reward[1], finally_reward[2]))
if __name__ == '__main__':
parser = argparse.ArgumentParser(
......@@ -182,75 +280,25 @@ if __name__ == '__main__':
help='Whether to use GPU in train/test model.')
parser.add_argument(
'--batch_size', type=int, default=256, help='batch size.')
parser.add_argument(
'--class_dim', type=int, default=1000, help='classify number.')
parser.add_argument(
'--data',
type=str,
default='cifar10',
choices=['cifar10', 'imagenet'],
help='server address.')
# controller
parser.add_argument(
'--reduce_rate', type=float, default=0.85, help='reduce rate.')
parser.add_argument(
'--init_temperature',
type=float,
default=10.24,
help='init temperature.')
parser.add_argument(
'--is_server',
type=ast.literal_eval,
default=True,
help='Whether to start a server.')
# nas args
parser.add_argument(
'--max_flops', type=int, default=592948064, help='reduce rate.')
parser.add_argument(
'--retain_epoch', type=int, default=5, help='train epoch before val.')
parser.add_argument(
'--end_epoch', type=int, default=500, help='end epoch present client.')
parser.add_argument(
'--search_steps',
type=int,
default=100,
help='controller server number.')
parser.add_argument(
'--server_address', type=str, default=None, help='server address.')
# optimizer args
parser.add_argument(
'--lr_strategy',
type=str,
default='piecewise_decay',
help='learning rate decay strategy.')
parser.add_argument('--lr', type=float, default=0.1, help='learning rate.')
parser.add_argument(
'--l2_decay', type=float, default=1e-4, help='learning rate decay.')
parser.add_argument(
'--step_epochs',
nargs='+',
type=int,
default=[30, 60, 90],
help="piecewise decay step")
parser.add_argument(
'--momentum_rate',
type=float,
default=0.9,
help='learning rate decay.')
parser.add_argument(
'--warm_up_epochs',
type=float,
default=5.0,
help='learning rate decay.')
parser.add_argument(
'--num_epochs', type=int, default=120, help='learning rate decay.')
parser.add_argument(
'--decay_epochs', type=float, default=2.4, help='learning rate decay.')
parser.add_argument(
'--decay_rate', type=float, default=0.97, help='learning rate decay.')
parser.add_argument(
'--total_images',
type=int,
default=1281167,
help='learning rate decay.')
args = parser.parse_args()
print(args)
......@@ -261,16 +309,10 @@ if __name__ == '__main__':
image_size = 224
block_num = 6
else:
raise NotImplemented(
raise NotImplementedError(
'data must in [cifar10, imagenet], but received: {}'.format(
args.data))
config_info = {
'input_size': image_size,
'output_size': 1,
'block_num': block_num,
'block_mask': None
}
config = [('MobileNetV2Space', config_info)]
config = [('MobileNetV2Space')]
search_mobilenetv2(config, args, image_size, is_server=args.is_server)
demo/optimizer.py
浏览文件 @ 904dfaff
......@@ -23,6 +23,16 @@ import paddle.fluid.layers.ops as ops
from paddle.fluid.initializer import init_on_cpu
from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
lr_strategy = 'cosine_decay'
l2_decay = 1e-4
step_epochs = [30, 60, 90]
momentum_rate = 0.9
warm_up_epochs = 5.0
num_epochs = 120
decay_epochs = 2.4
decay_rate = 0.97
total_images = 1281167
def cosine_decay(learning_rate, step_each_epoch, epochs=120):
"""Applies cosine decay to the learning rate.
......@@ -152,15 +162,15 @@ class Optimizer(object):
def __init__(self, args):
self.batch_size = args.batch_size
self.lr = args.lr
self.lr_strategy = args.lr_strategy
self.l2_decay = args.l2_decay
self.momentum_rate = args.momentum_rate
self.step_epochs = args.step_epochs
self.num_epochs = args.num_epochs
self.warm_up_epochs = args.warm_up_epochs
self.decay_epochs = args.decay_epochs
self.decay_rate = args.decay_rate
self.total_images = args.total_images
self.lr_strategy = lr_strategy
self.l2_decay = l2_decay
self.momentum_rate = momentum_rate
self.step_epochs = step_epochs
self.num_epochs = num_epochs
self.warm_up_epochs = warm_up_epochs
self.decay_epochs = decay_epochs
self.decay_rate = decay_rate
self.total_images = total_images
self.step = int(math.ceil(float(self.total_images) / self.batch_size))
......@@ -295,6 +305,6 @@ class Optimizer(object):
def create_optimizer(args):
Opt = Optimizer(args)
optimizer = getattr(Opt, args.lr_strategy)()
optimizer = getattr(Opt, lr_strategy)()
return optimizer
paddleslim/nas/nas_api.md 0 → 100644
浏览文件 @ 904dfaff
# paddleslim.nas API文档
## SANAS API文档
## class SANAS
SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火算法进行模型结构搜索的算法，一般用于离散搜索任务。
---
>paddleslim.nas.SANAS(configs, server_addr, init_temperature, reduce_rate, search_steps, save_checkpoint, load_checkpoint, is_server)
**参数：**
- **configs(list<tuple>):** 搜索空间配置列表，格式是`[(key, {input_size, output_size, block_num, block_mask})]`或者`[(key)]`（MobileNetV2、MobilenetV1和ResNet的搜索空间使用和原本网络结构相同的搜索空间，所以仅需指定`key`即可）, `input_size` 和`output_size`表示输入和输出的特征图的大小，`block_num`是指搜索网络中的block数量，`block_mask`是一组由0和1组成的列表，0代表不进行下采样的block，1代表下采样的block。 更多paddleslim提供的搜索空间配置可以参考。
- **server_addr(tuple):** SANAS的地址，包括server的ip地址和端口号，如果ip地址为None或者为""的话则默认使用本机ip。默认：（"", 8881）。
- **init_temperature(float):** 基于模拟退火进行搜索的初始温度。默认：100。
- **reduce_rate(float):** 基于模拟退火进行搜索的衰减率。默认：0.85。
- **search_steps(int):** 搜索过程迭代的次数。默认：300。
- **save_checkpoint(str|None):** 保存checkpoint的文件目录，如果设置为None的话则不保存checkpoint。默认：`./nas_checkpoint`。
- **load_checkpoint(str|None):** 加载checkpoint的文件目录，如果设置为None的话则不加载checkpoint。默认：None。
- **is_server(bool):** 当前实例是否要启动一个server。默认：True。
**返回：**
一个SANAS类的实例
**示例代码：**
```
from paddleslim.nas import SANAS
config = [('MobileNetV2Space')]
sanas = SANAS(config=config)
```
---
>tokens2arch(tokens)
通过一组token得到实际的模型结构，一般用来把搜索到最优的token转换为模型结构用来做最后的训练。
**参数：**
- **tokens(list):** 一组token。
**返回**
返回一个模型结构实例。
**示例代码：**
```
import paddle.fluid as fluid
input = fluid.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
archs = sanas.token2arch(tokens)
for arch in archs:
output = arch(input)
input = output
```
---
>next_archs():
获取下一组模型结构。
**返回**
返回模型结构实例的列表，形式为list。
**示例代码：**
```
import paddle.fluid as fluid
input = fluid.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
archs = sanas.next_archs()
for arch in archs:
output = arch(input)
input = output
```
---
>reward(score):
把当前模型结构的得分情况回传。
**参数：**
**score<float>:** 当前模型的得分，分数越大越好。
**返回**
模型结构更新成功或者失败，成功则返回`True`，失败则返回`False`。
**代码示例**
```python
import numpy as np
import paddle
import paddle.fluid as fluid
from paddleslim.nas import SANAS
from paddleslim.analysis import flops
max_flops = 321208544
batch_size = 256
# 搜索空间配置
config=[('MobileNetV2Space')]
# 实例化SANAS
sa_nas = SANAS(config, server_addr=("", 8887), init_temperature=10.24, reduce_rate=0.85, search_steps=100, is_server=True)
for step in range(100):
archs = sa_nas.next_archs()
train_program = fluid.Program()
test_program = fluid.Program()
startup_program = fluid.Program()
### 构造训练program
with fluid.program_guard(train_program, startup_program):
image = fluid.data(name='image', shape=[None, 3, 32, 32], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
for arch in archs:
output = arch(image)
out = fluid.layers.fc(output, size=10, act="softmax")
softmax_out = fluid.layers.softmax(input=out, use_cudnn=False)
cost = fluid.layers.cross_entropy(input=softmax_out, label=label)
avg_cost = fluid.layers.mean(cost)
acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
### 构造测试program
test_program = train_program.clone(for_test=True)
### 定义优化器
sgd = fluid.optimizer.SGD(learning_rate=1e-3)
sgd.minimize(avg_cost)
### 增加限制条件，如果没有则进行无限制搜索
if flops(train_program) > max_flops:
continue
### 定义代码是在cpu上运行
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_program)
### 定义训练输入数据
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
batch_size=batch_size,
drop_last=True)
### 定义预测输入数据
test_reader = paddle.batch(
paddle.dataset.cifar.test10(cycle=False),
batch_size=batch_size,
drop_last=False)
train_feeder = fluid.DataFeeder([image, label], place, program=train_program)
test_feeder = fluid.DataFeeder([image, label], place, program=test_program)
### 开始训练，每个搜索结果训练5个epoch
for epoch_id in range(5):
for batch_id, data in enumerate(train_reader()):
fetches = [avg_cost.name]
outs = exe.run(train_program,
feed=train_feeder.feed(data),
fetch_list=fetches)[0]
if batch_id % 10 == 0:
print('TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}'.format(step, epoch_id, batch_id, outs[0]))
### 开始预测，得到最终的测试结果作为score回传给sa_nas
reward = []
for batch_id, data in enumerate(test_reader()):
test_fetches = [
avg_cost.name, acc_top1.name
]
batch_reward = exe.run(test_program,
feed=test_feeder.feed(data),
fetch_list=test_fetches)
reward_avg = np.mean(np.array(batch_reward), axis=1)
reward.append(reward_avg)
print('TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}'.
format(step, batch_id, batch_reward[0],batch_reward[1]))
finally_reward = np.mean(np.array(reward), axis=0)
print(
'FINAL TEST: avg_cost: {}, acc_top1: {}'.format(
finally_reward[0], finally_reward[1]))
### 回传score
sa_nas.reward(float(finally_reward[1]))
```
paddleslim/nas/sa_nas.py
浏览文件 @ 904dfaff
......@@ -40,21 +40,19 @@ class SANAS(object):
init_temperature=100,
reduce_rate=0.85,
search_steps=300,
key="sa_nas",
save_checkpoint='nas_checkpoint',
load_checkpoint=None,
is_server=False):
"""
Search a group of ratios used to prune program.
Args:
configs(list<tuple>): A list of search space configuration with format (key, input_size, output_size, block_num).
configs(list<tuple>): A list of search space configuration with format [(key, {input_size, output_size, block_num, block_mask})].
`key` is the name of search space with data type str. `input_size` and `output_size` are
input size and output size of searched sub-network. `block_num` is the number of blocks in searched network.
input size and output size of searched sub-network. `block_num` is the number of blocks in searched network, `block_mask` is a list consists by 0 and 1, 0 means normal block, 1 means reduction block.
server_addr(tuple): A tuple of server ip and server port for controller server.
init_temperature(float): The init temperature used in simulated annealing search strategy.
reduce_rate(float): The decay rate used in simulated annealing search strategy.
search_steps(int): The steps of searching.
key(str): Identity used in communication between controller server and clients.
save_checkpoint(string|None): The directory of checkpoint to save, if set to None, not save checkpoint. Default: 'nas_checkpoint'.
load_checkpoint(string|None): The directory of checkpoint to load, if set to None, not load checkpoint. Default: None.
is_server(bool): Whether current host is controller server. Default: True.
......
paddleslim/nas/search_space/__init__.py
浏览文件 @ 904dfaff
......@@ -15,11 +15,16 @@
from .mobilenetv2 import MobileNetV2Space
from .mobilenetv1 import MobileNetV1Space
from .resnet import ResNetSpace
from .mobilenet_block import MobileNetV1BlockSpace, MobileNetV2BlockSpace
from .resnet_block import ResNetBlockSpace
from .inception_block import InceptionABlockSpace, InceptionCBlockSpace
from .search_space_registry import SEARCHSPACE
from .search_space_factory import SearchSpaceFactory
from .search_space_base import SearchSpaceBase
__all__ = [
'MobileNetV1Space', 'MobileNetV2Space', 'ResNetSpace', 'SearchSpaceBase',
'MobileNetV1Space', 'MobileNetV2Space', 'ResNetSpace',
'MobileNetV1BlockSpace', 'MobileNetV2BlockSpace', 'ResNetBlockSpace',
'InceptionABlockSpace', 'InceptionCBlockSpace', 'SearchSpaceBase',
'SearchSpaceFactory', 'SEARCHSPACE'
]
paddleslim/nas/search_space/combine_search_space.py
浏览文件 @ 904dfaff
......@@ -19,12 +19,16 @@ from __future__ import print_function
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
import logging
from ...common import get_logger
from .search_space_base import SearchSpaceBase
from .search_space_registry import SEARCHSPACE
from .base_layer import conv_bn_layer
__all__ = ["CombineSearchSpace"]
_logger = get_logger(__name__, level=logging.INFO)
class CombineSearchSpace(object):
"""
......@@ -37,7 +41,15 @@ class CombineSearchSpace(object):
self.lens = len(config_lists)
self.spaces = []
for config_list in config_lists:
key, config = config_list
if isinstance(config_list, tuple):
key, config = config_list
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)))
self.spaces.append(self._get_single_search_space(key, config))
self.init_tokens()
......@@ -52,11 +64,37 @@ class CombineSearchSpace(object):
model space(class)
"""
cls = SEARCHSPACE.get(key)
block_mask = config['block_mask'] if 'block_mask' in config else None
space = cls(config['input_size'],
config['output_size'],
config['block_num'],
block_mask=block_mask)
assert cls != None, '{} is NOT a correct space, the space we support is {}'.format(
key, SEARCHSPACE)
if config is None:
block_mask = None
input_size = None
output_size = None
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
block_num = config['block_num'] if 'block_num' in config else None
if 'Block' in cls.__name__:
if block_mask == None and (block_num == None or
input_size == None or
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 0 → 100644
浏览文件 @ 904dfaff
此差异已折叠。
paddleslim/nas/search_space/mobilenet_block.py 0 → 100644
浏览文件 @ 904dfaff
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from .search_space_base import SearchSpaceBase
from .base_layer import conv_bn_layer
from .search_space_registry import SEARCHSPACE
from .utils import compute_downsample_num, check_points
__all__ = ["MobileNetV1BlockSpace", "MobileNetV2BlockSpace"]
@SEARCHSPACE.register
class MobileNetV2BlockSpace(SearchSpaceBase):
def __init__(self,
input_size,
output_size,
block_num,
block_mask=None,
scale=1.0):
super(MobileNetV2BlockSpace, 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
self.downsample_num = compute_downsample_num(self.input_size,
self.output_size)
if self.block_num != None:
assert self.downsample_num <= self.block_num, 'downsample numeber must be LESS THAN OR EQUAL TO block_num, but NOW: downsample numeber is {}, block_num is {}'.format(
self.downsample_num, self.block_num)
# self.filter_num means channel number
self.filter_num = np.array([
3, 4, 8, 12, 16, 24, 32, 48, 64, 80, 96, 128, 144, 160, 192, 224,
256, 320, 384, 512
]) # 20
# self.k_size means kernel size
self.k_size = np.array([3, 5]) #2
# self.multiply means expansion_factor of each _inverted_residual_unit
self.multiply = np.array([1, 2, 3, 4, 5, 6]) #6
# self.repeat means repeat_num _inverted_residual_unit in each _invresi_blocks
self.repeat = np.array([1, 2, 3, 4, 5, 6]) #6
self.scale = scale
def init_tokens(self):
if self.block_mask != None:
return [0] * (len(self.block_mask) * 4)
else:
return [0] * (self.block_num * 4)
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_num
for i in range(range_table_length):
range_table_base.append(len(self.multiply))
range_table_base.append(len(self.filter_num))
range_table_base.append(len(self.repeat))
range_table_base.append(len(self.k_size))
return range_table_base
def token2arch(self, tokens=None):
"""
return mobilenetv2 net_arch function
"""
if tokens == None:
tokens = self.init_tokens()
self.bottleneck_params_list = []
if self.block_mask != None:
for i in range(len(self.block_mask)):
self.bottleneck_params_list.append(
(self.multiply[tokens[i * 4]],
self.filter_num[tokens[i * 4 + 1]],
self.repeat[tokens[i * 4 + 2]], 2
if self.block_mask[i] == 1 else 1,
self.k_size[tokens[i * 4 + 3]]))
else:
repeat_num = int(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.multiply[tokens[i * 4]],
self.filter_num[tokens[i * 4 + 1]],
self.repeat[tokens[i * 4 + 2]], 2,
self.k_size[tokens[i * 4 + 3]]))
### 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.multiply[tokens[kk * 4]],
self.filter_num[tokens[kk * 4 + 1]],
self.repeat[tokens[kk * 4 + 2]], 1,
self.k_size[tokens[kk * 4 + 3]]))
if self.downsample_num - i <= num_minus:
j = self.downsample_num * (repeat_num - 1) + i
self.bottleneck_params_list.append(
(self.multiply[tokens[j * 4]],
self.filter_num[tokens[j * 4 + 1]],
self.repeat[tokens[j * 4 + 2]], 1,
self.k_size[tokens[j * 4 + 3]]))
if self.downsample_num == 0 and self.block_num != 0:
for i in range(len(self.block_num)):
self.bottleneck_params_list.append(
(self.multiply[tokens[i * 4]],
self.filter_num[tokens[i * 4 + 1]],
self.repeat[tokens[i * 4 + 2]], 1,
self.k_size[tokens[i * 4 + 3]]))
def net_arch(input, return_mid_layer=False, return_block=None):
# all padding is 'SAME' in the conv2d, can compute the actual padding automatic.
# bottleneck sequences
in_c = int(32 * self.scale)
mid_layer = dict()
layer_count = 0
depthwise_conv = None
for i, layer_setting in enumerate(self.bottleneck_params_list):
t, c, n, s, k = layer_setting
if s == 2:
layer_count += 1
if check_points((layer_count - 1), return_block):
mid_layer[layer_count - 1] = depthwise_conv
input, depthwise_conv = self._invresi_blocks(
input=input,
in_c=in_c,
t=t,
c=int(c * self.scale),
n=n,
s=s,
k=k,
name='mobilenetv2_' + str(i + 1))
in_c = int(c * self.scale)
if check_points(layer_count, return_block):
mid_layer[layer_count] = depthwise_conv
if return_mid_layer:
return input, mid_layer
else:
return input,
return net_arch
def _shortcut(self, input, data_residual):
"""Build shortcut layer.
Args:
input(Variable): input.
data_residual(Variable): residual layer.
Returns:
Variable, layer output.
"""
return fluid.layers.elementwise_add(input, data_residual)
def _inverted_residual_unit(self,
input,
num_in_filter,
num_filters,
ifshortcut,
stride,
filter_size,
expansion_factor,
reduction_ratio=4,
name=None):
"""Build inverted residual unit.
Args:
input(Variable), input.
num_in_filter(int), number of in filters.
num_filters(int), number of filters.
ifshortcut(bool), whether using shortcut.
stride(int), stride.
filter_size(int), filter size.
padding(str|int|list), padding.
expansion_factor(float), expansion factor.
name(str), name.
Returns:
Variable, layers output.
"""
num_expfilter = int(round(num_in_filter * expansion_factor))
channel_expand = conv_bn_layer(
input=input,
num_filters=num_expfilter,
filter_size=1,
stride=1,
padding='SAME',
num_groups=1,
act='relu6',
name=name + '_expand')
bottleneck_conv = conv_bn_layer(
input=channel_expand,
num_filters=num_expfilter,
filter_size=filter_size,
stride=stride,
padding='SAME',
num_groups=num_expfilter,
act='relu6',
name=name + '_dwise',
use_cudnn=False)
depthwise_output = bottleneck_conv
linear_out = conv_bn_layer(
input=bottleneck_conv,
num_filters=num_filters,
filter_size=1,
stride=1,
padding='SAME',
num_groups=1,
act=None,
name=name + '_linear')
out = linear_out
if ifshortcut:
out = self._shortcut(input=input, data_residual=out)
return out, depthwise_output
def _invresi_blocks(self, input, in_c, t, c, n, s, k, name=None):
"""Build inverted residual blocks.
Args:
input: Variable, input.
in_c: int, number of in filters.
t: float, expansion factor.
c: int, number of filters.
n: int, number of layers.
s: int, stride.
k: int, filter size.
name: str, name.
Returns:
Variable, layers output.
"""
first_block, depthwise_output = self._inverted_residual_unit(
input=input,
num_in_filter=in_c,
num_filters=c,
ifshortcut=False,
stride=s,
filter_size=k,
expansion_factor=t,
name=name + '_1')
last_residual_block = first_block
last_c = c
for i in range(1, n):
last_residual_block, depthwise_output = self._inverted_residual_unit(
input=last_residual_block,
num_in_filter=last_c,
num_filters=c,
ifshortcut=True,
stride=1,
filter_size=k,
expansion_factor=t,
name=name + '_' + str(i + 1))
return last_residual_block, depthwise_output
@SEARCHSPACE.register
class MobileNetV1BlockSpace(SearchSpaceBase):
def __init__(self,
input_size,
output_size,
block_num,
block_mask=None,
scale=1.0):
super(MobileNetV1BlockSpace, 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)
if self.block_num != None:
assert self.downsample_num <= self.block_num, 'downsample numeber must be LESS THAN OR EQUAL TO block_num, but NOW: downsample numeber is {}, block_num is {}'.format(
self.downsample_num, self.block_num)
# self.filter_num means channel number
self.filter_num = np.array([
3, 4, 8, 12, 16, 24, 32, 48, 64, 80, 96, 128, 144, 160, 192, 224,
256, 320, 384, 512, 576, 640, 768, 1024, 1048
])
self.k_size = np.array([3, 5])
self.scale = scale
def init_tokens(self):
if self.block_mask != None:
return [0] * (len(self.block_mask) * 3)
else:
return [0] * (self.block_num * 3)
def range_table(self):
range_table_base = []
if self.block_mask != None:
for i in range(len(self.block_mask)):
range_table_base.append(len(self.filter_num))
range_table_base.append(len(self.filter_num))
range_table_base.append(len(self.k_size))
else:
for i in range(self.block_num):
range_table_base.append(len(self.filter_num))
range_table_base.append(len(self.filter_num))
range_table_base.append(len(self.k_size))
return range_table_base
def token2arch(self, tokens=None):
if tokens == None:
tokens = self.init_tokens()
self.bottleneck_params_list = []
if self.block_mask != None:
for i in range(len(self.block_mask)):
self.bottleneck_params_list.append(
(self.filter_num[tokens[i * 3]],
self.filter_num[tokens[i * 3 + 1]], 2
if self.block_mask[i] == 1 else 1,
self.k_size[tokens[i * 3 + 2]]))
else:
repeat_num = int(self.block_num / self.downsample_num)
num_minus = self.block_num % self.downsample_num
for i in range(self.downsample_num):
### if block_num > downsample_num, add stride=1 block at last (block_num-downsample_num) layers
self.bottleneck_params_list.append(
(self.filter_num[tokens[i * 3]],
self.filter_num[tokens[i * 3 + 1]], 2,
self.k_size[tokens[i * 3 + 2]]))
### 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[tokens[kk * 3]],
self.filter_num[tokens[kk * 3 + 1]], 1,
self.k_size[tokens[kk * 3 + 2]]))
if self.downsample_num - i <= num_minus:
j = self.downsample_num * (repeat_num - 1) + i
self.bottleneck_params_list.append(
(self.filter_num[tokens[j * 3]],
self.filter_num[tokens[j * 3 + 1]], 1,
self.k_size[tokens[j * 3 + 2]]))
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[tokens[i * 3]],
self.filter_num[tokens[i * 3 + 1]], 1,
self.k_size[tokens[i * 3 + 2]]))
def net_arch(input, return_mid_layer=False, return_block=None):
mid_layer = dict()
layer_count = 0
for i, layer_setting in enumerate(self.bottleneck_params_list):
filter_num1, filter_num2, stride, kernel_size = layer_setting
if stride == 2:
layer_count += 1
if check_points((layer_count - 1), return_block):
mid_layer[layer_count - 1] = input
input = self._depthwise_separable(
input=input,
num_filters1=filter_num1,
num_filters2=filter_num2,
stride=stride,
scale=self.scale,
kernel_size=kernel_size,
name='mobilenetv1_{}'.format(str(i + 1)))
if return_mid_layer:
return input, mid_layer
else:
return input,
return net_arch
def _depthwise_separable(self,
input,
num_filters1,
num_filters2,
stride,
scale,
kernel_size,
name=None):
num_groups = input.shape[1]
s_oc = int(num_filters1 * scale)
if s_oc > num_groups:
output_channel = s_oc - (s_oc % num_groups)
else:
output_channel = num_groups
depthwise_conv = conv_bn_layer(
input=input,
filter_size=kernel_size,
num_filters=output_channel,
stride=stride,
num_groups=num_groups,
use_cudnn=False,
name=name + '_dw')
pointwise_conv = conv_bn_layer(
input=depthwise_conv,
filter_size=1,
num_filters=int(num_filters2 * scale),
stride=1,
name=name + '_sep')
return pointwise_conv
paddleslim/nas/search_space/mobilenetv1.py
浏览文件 @ 904dfaff
......@@ -22,24 +22,16 @@ from paddle.fluid.param_attr import ParamAttr
from .search_space_base import SearchSpaceBase
from .base_layer import conv_bn_layer
from .search_space_registry import SEARCHSPACE
from .utils import check_points
__all__ = ["MobileNetV1Space"]
@SEARCHSPACE.register
class MobileNetV1Space(SearchSpaceBase):
def __init__(self,
input_size,
output_size,
block_num,
block_mask,
scale=1.0,
class_dim=1000):
def __init__(self, input_size, output_size, block_num, block_mask):
super(MobileNetV1Space, self).__init__(input_size, output_size,
block_num, block_mask)
assert self.block_mask == None, 'MobileNetV1Space will use origin MobileNetV1 as seach space, so use input_size, output_size and block_num to search'
self.scale = scale
self.class_dim = class_dim
# self.head_num means the channel of first convolution
self.head_num = np.array([3, 4, 8, 12, 16, 24, 32]) # 7
# self.filter_num1 ~ self.filtet_num9 means channel of the following convolution
......@@ -67,9 +59,6 @@ class MobileNetV1Space(SearchSpaceBase):
# self.repeat means repeat_num in forth downsample
self.repeat = np.array([1, 2, 3, 4, 5, 6]) #6
assert self.block_num < 6, 'MobileNetV1: block number must less than 6, but receive block number is {}'.format(
self.block_num)
def init_tokens(self):
"""
The initial token.
......@@ -90,11 +79,7 @@ class MobileNetV1Space(SearchSpaceBase):
8, 10, 0, # 512, 1024, 3
10, 10, 0] # 1024, 1024, 3
# yapf: enable
if self.block_num < 5:
self.token_len = 1 + (self.block_num * 2 - 1) * 3
else:
self.token_len = 2 + (self.block_num * 2 - 1) * 3
return base_init_tokens[:self.token_len]
return base_init_tokens
def range_table(self):
"""
......@@ -113,65 +98,92 @@ class MobileNetV1Space(SearchSpaceBase):
len(self.filter_num8), len(self.filter_num9), len(self.k_size),
len(self.filter_num9), len(self.filter_num9), len(self.k_size)]
# yapf: enable
return base_range_table[:self.token_len]
return base_range_table
def token2arch(self, tokens=None):
if tokens is None:
tokens = self.tokens()
bottleneck_param_list = []
if self.block_num >= 1:
# tokens[0] = 32
# 32, 64
bottleneck_param_list.append(
(self.filter_num1[tokens[1]], self.filter_num2[tokens[2]], 1,
self.k_size[tokens[3]]))
if self.block_num >= 2:
# 64 128 128 128
bottleneck_param_list.append(
(self.filter_num2[tokens[4]], self.filter_num3[tokens[5]], 2,
self.k_size[tokens[6]]))
bottleneck_param_list.append(
(self.filter_num3[tokens[7]], self.filter_num4[tokens[8]], 1,
self.k_size[tokens[9]]))
if self.block_num >= 3:
# 128 256 256 256
bottleneck_param_list.append(
(self.filter_num4[tokens[10]], self.filter_num5[tokens[11]], 2,
self.k_size[tokens[12]]))
bottleneck_param_list.append(
(self.filter_num5[tokens[13]], self.filter_num6[tokens[14]], 1,
self.k_size[tokens[15]]))
if self.block_num >= 4:
# 256 512 (512 512) * 5
bottleneck_param_list.append(
(self.filter_num6[tokens[16]], self.filter_num7[tokens[17]], 2,
self.k_size[tokens[18]]))
for i in range(self.repeat[tokens[19]]):
bottleneck_param_list.append(
(self.filter_num7[tokens[20]],
self.filter_num8[tokens[21]], 1, self.k_size[tokens[22]]))
if self.block_num >= 5:
# 512 1024 1024 1024
bottleneck_param_list.append(
(self.filter_num8[tokens[23]], self.filter_num9[tokens[24]], 2,
self.k_size[tokens[25]]))
bottleneck_param_list.append(
(self.filter_num9[tokens[26]], self.filter_num9[tokens[27]], 1,
self.k_size[tokens[28]]))
def net_arch(input):
self.bottleneck_param_list = []
# tokens[0] = 32
# 32, 64
self.bottleneck_param_list.append(
(self.filter_num1[tokens[1]], self.filter_num2[tokens[2]], 1,
self.k_size[tokens[3]]))
# 64 128 128 128
self.bottleneck_param_list.append(
(self.filter_num2[tokens[4]], self.filter_num3[tokens[5]], 2,
self.k_size[tokens[6]]))
self.bottleneck_param_list.append(
(self.filter_num3[tokens[7]], self.filter_num4[tokens[8]], 1,
self.k_size[tokens[9]]))
# 128 256 256 256
self.bottleneck_param_list.append(
(self.filter_num4[tokens[10]], self.filter_num5[tokens[11]], 2,
self.k_size[tokens[12]]))
self.bottleneck_param_list.append(
(self.filter_num5[tokens[13]], self.filter_num6[tokens[14]], 1,
self.k_size[tokens[15]]))
# 256 512 (512 512) * 5
self.bottleneck_param_list.append(
(self.filter_num6[tokens[16]], self.filter_num7[tokens[17]], 2,
self.k_size[tokens[18]]))
for i in range(self.repeat[tokens[19]]):
self.bottleneck_param_list.append(
(self.filter_num7[tokens[20]], self.filter_num8[tokens[21]], 1,
self.k_size[tokens[22]]))
# 512 1024 1024 1024
self.bottleneck_param_list.append(
(self.filter_num8[tokens[23]], self.filter_num9[tokens[24]], 2,
self.k_size[tokens[25]]))
self.bottleneck_param_list.append(
(self.filter_num9[tokens[26]], self.filter_num9[tokens[27]], 1,
self.k_size[tokens[28]]))
def _modify_bottle_params(output_stride=None):
if output_stride is not None and output_stride % 2 != 0:
raise Exception("output stride must to be even number")
if output_stride is None:
return
else:
stride = 2
for i, layer_setting in enumerate(self.bottleneck_params_list):
f1, f2, s, ks = layer_setting
stride = stride * s
if stride > output_stride:
s = 1
self.bottleneck_params_list[i] = (f1, f2, s, ks)
def net_arch(input,
scale=1.0,
return_block=None,
end_points=None,
output_stride=None):
self.scale = scale
_modify_bottle_params(output_stride)
decode_ends = dict()
input = conv_bn_layer(
input=input,
filter_size=3,
num_filters=self.head_num[tokens[0]],
stride=2,
name='mobilenetv1')
name='mobilenetv1_conv1')
for i, layer_setting in enumerate(bottleneck_param_list):
layer_count = 1
for i, layer_setting in enumerate(self.bottleneck_param_list):
filter_num1, filter_num2, stride, kernel_size = layer_setting
if stride == 2:
layer_count += 1
### return_block and end_points means block num
if check_points((layer_count - 1), return_block):
decode_ends[layer_count - 1] = input
if check_points((layer_count - 1), end_points):
return input, decode_ends
input = self._depthwise_separable(
input=input,
num_filters1=filter_num1,
......@@ -182,18 +194,15 @@ class MobileNetV1Space(SearchSpaceBase):
kernel_size=kernel_size,
name='mobilenetv1_{}'.format(str(i + 1)))
if self.output_size == 1:
print('NOTE: if output_size is 1, add fc layer in the end!!!')
input = fluid.layers.fc(
input=input,
size=self.class_dim,
param_attr=ParamAttr(name='mobilenetv2_fc_weights'),
bias_attr=ParamAttr(name='mobilenetv2_fc_offset'))
else:
assert self.output_size == input.shape[2], \
("output_size must EQUAL to input_size / (2^block_num)."
"But receive input_size={}, output_size={}, block_num={}".format(
self.input_size, self.output_size, self.block_num))
### return_block and end_points means block num
if check_points(layer_count, end_points):
return input, decode_ends
input = fluid.layers.pool2d(
input=input,
pool_type='avg',
global_pooling=True,
name='mobilenetv1_last_pool')
return input
......@@ -208,12 +217,20 @@ class MobileNetV1Space(SearchSpaceBase):
scale,
kernel_size,
name=None):
num_groups = input.shape[1]
s_oc = int(num_filters1 * scale)
if s_oc > num_groups:
output_channel = s_oc - (s_oc % num_groups)
else:
output_channel = num_groups
depthwise_conv = conv_bn_layer(
input=input,
filter_size=kernel_size,
num_filters=int(num_filters1 * scale),
num_filters=output_channel,
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/mobilenetv2.py
浏览文件 @ 904dfaff
......@@ -22,22 +22,16 @@ from paddle.fluid.param_attr import ParamAttr
from .search_space_base import SearchSpaceBase
from .base_layer import conv_bn_layer
from .search_space_registry import SEARCHSPACE
from .utils import check_points
__all__ = ["MobileNetV2Space"]
@SEARCHSPACE.register
class MobileNetV2Space(SearchSpaceBase):
def __init__(self,
input_size,
output_size,
block_num,
block_mask=None,
scale=1.0,
class_dim=1000):
def __init__(self, input_size, output_size, block_num, block_mask=None):
super(MobileNetV2Space, self).__init__(input_size, output_size,
block_num, block_mask)
assert self.block_mask == None, 'MobileNetV2Space will use origin MobileNetV2 as seach space, so use input_size, output_size and block_num to search'
# self.head_num means the first convolution channel
self.head_num = np.array([3, 4, 8, 12, 16, 24, 32]) #7
# self.filter_num1 ~ self.filter_num6 means following convlution channel
......@@ -56,11 +50,6 @@ class MobileNetV2Space(SearchSpaceBase):
self.multiply = np.array([1, 2, 3, 4, 6]) #5
# self.repeat means repeat_num _inverted_residual_unit in each _invresi_blocks
self.repeat = np.array([1, 2, 3, 4, 5, 6]) #6
self.scale = scale
self.class_dim = class_dim
assert self.block_num < 7, 'MobileNetV2: block number must less than 7, but receive block number is {}'.format(
self.block_num)
def init_tokens(self):
"""
......@@ -80,13 +69,7 @@ class MobileNetV2Space(SearchSpaceBase):
4, 9, 0, 0] # 6, 320, 1
# yapf: enable
if self.block_num < 5:
self.token_len = 1 + (self.block_num - 1) * 4
else:
self.token_len = 1 + (self.block_num + 2 *
(self.block_num - 5)) * 4
return init_token_base[:self.token_len]
return init_token_base
def range_table(self):
"""
......@@ -102,9 +85,8 @@ class MobileNetV2Space(SearchSpaceBase):
len(self.multiply), len(self.filter_num4), len(self.repeat), len(self.k_size),
len(self.multiply), len(self.filter_num5), len(self.repeat), len(self.k_size),
len(self.multiply), len(self.filter_num6), len(self.repeat), len(self.k_size)]
range_table_base = list(np.array(range_table_base) - 1)
# yapf: enable
return range_table_base[:self.token_len]
return range_table_base
def token2arch(self, tokens=None):
"""
......@@ -115,35 +97,29 @@ class MobileNetV2Space(SearchSpaceBase):
tokens = self.init_tokens()
self.bottleneck_params_list = []
if self.block_num >= 1:
self.bottleneck_params_list.append(
(1, self.head_num[tokens[0]], 1, 1, 3))
if self.block_num >= 2:
self.bottleneck_params_list.append(
(self.multiply[tokens[1]], self.filter_num1[tokens[2]],
self.repeat[tokens[3]], 2, self.k_size[tokens[4]]))
if self.block_num >= 3:
self.bottleneck_params_list.append(
(self.multiply[tokens[5]], self.filter_num1[tokens[6]],
self.repeat[tokens[7]], 2, self.k_size[tokens[8]]))
if self.block_num >= 4:
self.bottleneck_params_list.append(
(self.multiply[tokens[9]], self.filter_num2[tokens[10]],
self.repeat[tokens[11]], 2, self.k_size[tokens[12]]))
if self.block_num >= 5:
self.bottleneck_params_list.append(
(self.multiply[tokens[13]], self.filter_num3[tokens[14]],
self.repeat[tokens[15]], 2, self.k_size[tokens[16]]))
self.bottleneck_params_list.append(
(self.multiply[tokens[17]], self.filter_num4[tokens[18]],
self.repeat[tokens[19]], 1, self.k_size[tokens[20]]))
if self.block_num >= 6:
self.bottleneck_params_list.append(
(self.multiply[tokens[21]], self.filter_num5[tokens[22]],
self.repeat[tokens[23]], 2, self.k_size[tokens[24]]))
self.bottleneck_params_list.append(
(self.multiply[tokens[25]], self.filter_num6[tokens[26]],
self.repeat[tokens[27]], 1, self.k_size[tokens[28]]))
self.bottleneck_params_list.append(
(1, self.head_num[tokens[0]], 1, 1, 3))
self.bottleneck_params_list.append(
(self.multiply[tokens[1]], self.filter_num1[tokens[2]],
self.repeat[tokens[3]], 2, self.k_size[tokens[4]]))
self.bottleneck_params_list.append(
(self.multiply[tokens[5]], self.filter_num1[tokens[6]],
self.repeat[tokens[7]], 2, self.k_size[tokens[8]]))
self.bottleneck_params_list.append(
(self.multiply[tokens[9]], self.filter_num2[tokens[10]],
self.repeat[tokens[11]], 2, self.k_size[tokens[12]]))
self.bottleneck_params_list.append(
(self.multiply[tokens[13]], self.filter_num3[tokens[14]],
self.repeat[tokens[15]], 2, self.k_size[tokens[16]]))
self.bottleneck_params_list.append(
(self.multiply[tokens[17]], self.filter_num4[tokens[18]],
self.repeat[tokens[19]], 1, self.k_size[tokens[20]]))
self.bottleneck_params_list.append(
(self.multiply[tokens[21]], self.filter_num5[tokens[22]],
self.repeat[tokens[23]], 2, self.k_size[tokens[24]]))
self.bottleneck_params_list.append(
(self.multiply[tokens[25]], self.filter_num6[tokens[26]],
self.repeat[tokens[27]], 1, self.k_size[tokens[28]]))
def _modify_bottle_params(output_stride=None):
if output_stride is not None and output_stride % 2 != 0:
......@@ -160,9 +136,11 @@ class MobileNetV2Space(SearchSpaceBase):
self.bottleneck_params_list[i] = (t, c, n, s, ks)
def net_arch(input,
scale=1.0,
return_block=None,
end_points=None,
decode_points=None,
output_stride=None):
self.scale = scale
_modify_bottle_params(output_stride)
decode_ends = dict()
......@@ -185,21 +163,23 @@ class MobileNetV2Space(SearchSpaceBase):
stride=2,
padding='SAME',
act='relu6',
name='mobilenetv2_conv1_1')
name='mobilenetv2_conv1')
layer_count = 1
if check_points(layer_count, decode_points):
decode_ends[layer_count] = input
if check_points(layer_count, end_points):
return input, decode_ends
depthwise_output = None
# bottleneck sequences
i = 1
in_c = int(32 * self.scale)
for layer_setting in self.bottleneck_params_list:
for i, layer_setting in enumerate(self.bottleneck_params_list):
t, c, n, s, k = layer_setting
i += 1
#print(input)
if s == 2:
layer_count += 1
### return_block and end_points means block num
if check_points((layer_count - 1), return_block):
decode_ends[layer_count - 1] = depthwise_output
if check_points((layer_count - 1), end_points):
return input, decode_ends
input, depthwise_output = self._invresi_blocks(
input=input,
in_c=in_c,
......@@ -210,14 +190,13 @@ class MobileNetV2Space(SearchSpaceBase):
k=k,
name='mobilenetv2_conv' + str(i))
in_c = int(c * self.scale)
layer_count += 1
### decode_points and end_points means block num
if check_points(layer_count, decode_points):
decode_ends[layer_count] = depthwise_output
### return_block and end_points means block num
if check_points(layer_count, return_block):
decode_ends[layer_count] = depthwise_output
if check_points(layer_count, end_points):
return input, decode_ends
if check_points(layer_count, end_points):
return input, decode_ends
# last conv
input = conv_bn_layer(
......@@ -232,25 +211,10 @@ class MobileNetV2Space(SearchSpaceBase):
input = fluid.layers.pool2d(
input=input,
pool_size=7,
pool_stride=1,
pool_type='avg',
global_pooling=True,
name='mobilenetv2_last_pool')
# if output_size is 1, add fc layer in the end
if self.output_size == 1:
input = fluid.layers.fc(
input=input,
size=self.class_dim,
param_attr=ParamAttr(name='mobilenetv2_fc_weights'),
bias_attr=ParamAttr(name='mobilenetv2_fc_offset'))
else:
assert self.output_size == input.shape[2], \
("output_size must EQUAL to input_size / (2^block_num)."
"But receive input_size={}, output_size={}, block_num={}".format(
self.input_size, self.output_size, self.block_num))
return input
return net_arch
......
paddleslim/nas/search_space/resnet.py
浏览文件 @ 904dfaff
......@@ -22,22 +22,16 @@ from paddle.fluid.param_attr import ParamAttr
from .search_space_base import SearchSpaceBase
from .base_layer import conv_bn_layer
from .search_space_registry import SEARCHSPACE
from .utils import check_points
__all__ = ["ResNetSpace"]
@SEARCHSPACE.register
class ResNetSpace(SearchSpaceBase):
def __init__(self,
input_size,
output_size,
block_num,
block_mask=None,
extract_feature=False,
class_dim=1000):
def __init__(self, input_size, output_size, block_num, block_mask=None):
super(ResNetSpace, self).__init__(input_size, output_size, block_num,
block_mask)
assert self.block_mask == None, 'ResNetSpace will use origin ResNet as seach space, so use input_size, output_size and block_num to search'
# self.filter_num1 ~ self.filter_num4 means convolution channel
self.filter_num1 = np.array([48, 64, 96, 128, 160, 192, 224]) #7
self.filter_num2 = np.array([64, 96, 128, 160, 192, 256, 320]) #7
......@@ -48,31 +42,24 @@ class ResNetSpace(SearchSpaceBase):
self.repeat2 = [2, 3, 4, 5, 6, 7] #6
self.repeat3 = [2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24] #13
self.repeat4 = [2, 3, 4, 5, 6, 7] #6
self.class_dim = class_dim
self.extract_feature = extract_feature
assert self.block_num < 5, 'ResNet: block number must less than 5, but receive block number is {}'.format(
self.block_num)
def init_tokens(self):
"""
The initial token.
return 2 * self.block_num, 2 means depth and num_filter
"""
init_token_base = [0, 0, 0, 0, 0, 0, 0, 0]
self.token_len = self.block_num * 2
return init_token_base[:self.token_len]
return init_token_base
def range_table(self):
"""
Get range table of current search space, constrains the range of tokens.
"""
#2 * self.block_num, 2 means depth and num_filter
range_table_base = [
len(self.filter_num1), len(self.repeat1), len(self.filter_num2),
len(self.repeat2), len(self.filter_num3), len(self.repeat3),
len(self.filter_num4), len(self.repeat4)
]
return range_table_base[:self.token_len]
return range_table_base
def token2arch(self, tokens=None):
"""
......@@ -83,28 +70,27 @@ class ResNetSpace(SearchSpaceBase):
depth = []
num_filters = []
if self.block_num >= 1:
filter1 = self.filter_num1[tokens[0]]
repeat1 = self.repeat1[tokens[1]]
num_filters.append(filter1)
depth.append(repeat1)
if self.block_num >= 2:
filter2 = self.filter_num2[tokens[2]]
repeat2 = self.repeat2[tokens[3]]
num_filters.append(filter2)
depth.append(repeat2)
if self.block_num >= 3:
filter3 = self.filter_num3[tokens[4]]
repeat3 = self.repeat3[tokens[5]]
num_filters.append(filter3)
depth.append(repeat3)
if self.block_num >= 4:
filter4 = self.filter_num4[tokens[6]]
repeat4 = self.repeat4[tokens[7]]
num_filters.append(filter4)
depth.append(repeat4)
def net_arch(input):
filter1 = self.filter_num1[tokens[0]]
repeat1 = self.repeat1[tokens[1]]
num_filters.append(filter1)
depth.append(repeat1)
filter2 = self.filter_num2[tokens[2]]
repeat2 = self.repeat2[tokens[3]]
num_filters.append(filter2)
depth.append(repeat2)
filter3 = self.filter_num3[tokens[4]]
repeat3 = self.repeat3[tokens[5]]
num_filters.append(filter3)
depth.append(repeat3)
filter4 = self.filter_num4[tokens[6]]
repeat4 = self.repeat4[tokens[7]]
num_filters.append(filter4)
depth.append(repeat4)
def net_arch(input, return_block=None, end_points=None):
decode_ends = dict()
conv = conv_bn_layer(
input=input,
filter_size=5,
......@@ -112,24 +98,26 @@ class ResNetSpace(SearchSpaceBase):
stride=2,
act='relu',
name='resnet_conv0')
layer_count = 1
for block in range(len(depth)):
for i in range(depth[block]):
stride = 2 if i == 0 and block != 0 else 1
if stride == 2:
layer_count += 1
if check_points((layer_count - 1), return_block):
decode_ends[layer_count - 1] = conv
if check_points((layer_count - 1), end_points):
return conv, decode_ends
conv = self._bottleneck_block(
input=conv,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
stride=stride,
name='resnet_depth{}_block{}'.format(i, block))
if self.output_size == 1:
conv = fluid.layers.fc(
input=conv,
size=self.class_dim,
act=None,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(0.0,
0.01)),
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.ConstantInitializer(0)))
if check_points(layer_count, end_points):
return conv, decode_ends
return conv
......
paddleslim/nas/search_space/resnet_block.py 0 → 100644
浏览文件 @ 904dfaff
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from .search_space_base import SearchSpaceBase
from .base_layer import conv_bn_layer
from .search_space_registry import SEARCHSPACE
from .utils import compute_downsample_num, check_points
__all__ = ["ResNetBlockSpace"]
@SEARCHSPACE.register
class ResNetBlockSpace(SearchSpaceBase):
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)
if self.block_num != None:
assert self.downsample_num <= self.block_num, 'downsample numeber must be LESS THAN OR EQUAL TO block_num, but NOW: downsample numeber is {}, block_num is {}'.format(
self.downsample_num, self.block_num)
self.filter_num = np.array(
[48, 64, 96, 128, 160, 192, 224, 256, 320, 384, 512, 640])
self.repeat = np.array([0, 1, 2])
self.k_size = np.array([3, 5])
def init_tokens(self):
if self.block_mask != None:
return [0] * (len(self.block_mask) * 6)
else:
return [0] * (self.block_num * 6)
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_num
for i in range(range_table_length):
range_table_base.append(len(self.filter_num))
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.repeat))
range_table_base.append(len(self.repeat))
return range_table_base
def token2arch(self, tokens=None):
if tokens == None:
tokens = self.init_tokens()
self.bottleneck_params_list = []
if self.block_mask != None:
for i in range(len(self.block_mask)):
self.bottleneck_params_list.append(
(self.filter_num[tokens[i * 6]],
self.filter_num[tokens[i * 6 + 1]],
self.filter_num[tokens[i * 6 + 2]],
self.k_size[tokens[i * 6 + 3]],
self.repeat[tokens[i * 6 + 4]],
self.repeat[tokens[i * 6 + 5]], 2
if self.block_mask[i] == 1 else 1))
else:
repeat_num = int(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.filter_num[tokens[i * 6]],
self.filter_num[tokens[i * 6 + 1]],
self.filter_num[tokens[i * 6 + 2]],
self.k_size[tokens[i * 6 + 3]],
self.repeat[tokens[i * 6 + 4]],
self.repeat[tokens[i * 6 + 5]], 2))
for k in range(repeat_num - 1):
kk = k * self.downsample_num + i
self.bottleneck_params_list.append(
(self.filter_num[tokens[kk * 6]],
self.filter_num[tokens[kk * 6 + 1]],
self.filter_num[tokens[kk * 6 + 2]],
self.k_size[tokens[kk * 6 + 3]],
self.repeat[tokens[kk * 6 + 4]],
self.repeat[tokens[kk * 6 + 5]], 1))
if self.downsample_num - i <= num_minus:
j = self.downsample_num * (repeat_num - 1) + i
self.bottleneck_params_list.append(
(self.filter_num[tokens[j * 6]],
self.filter_num[tokens[j * 6 + 1]],
self.filter_num[tokens[j * 6 + 2]],
self.k_size[tokens[j * 6 + 3]],
self.repeat[tokens[j * 6 + 4]],
self.repeat[tokens[j * 6 + 5]], 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.filter_num[tokens[i * 6]],
self.filter_num[tokens[i * 6 + 1]],
self.filter_num[tokens[i * 6 + 2]],
self.k_size[tokens[i * 6 + 3]],
self.repeat[tokens[i * 6 + 4]],
self.repeat[tokens[i * 6 + 5]], 1))
def net_arch(input, return_mid_layer=False, return_block=None):
layer_count = 0
mid_layer = dict()
for i, layer_setting in enumerate(self.bottleneck_params_list):
filter_num1, filter_num2, filter_num3, k_size, repeat1, repeat2, stride = layer_setting
if stride == 2:
layer_count += 1
if check_points((layer_count - 1), return_block):
mid_layer[layer_count - 1] = input
input = self._bottleneck_block(
input=input,
num_filters1=filter_num1,
num_filters2=filter_num3,
num_filters3=filter_num3,
kernel_size=k_size,
repeat1=repeat1,
repeat2=repeat2,
stride=stride,
name='resnet' + str(i + 1))
if return_mid_layer:
return input, mid_layer
else:
return input,
return net_arch
def _shortcut(self, input, ch_out, stride, name=None):
ch_in = input.shape[1]
if ch_in != ch_out or stride != 1:
return conv_bn_layer(
input=input,
filter_size=1,
num_filters=ch_out,
stride=stride,
name=name + '_shortcut')
else:
return input
def _bottleneck_block(self,
input,
num_filters1,
num_filters2,
num_filters3,
kernel_size,
repeat1,
repeat2,
stride,
name=None):
short = self._shortcut(input, num_filters3, stride, name=name)
for i in range(repeat1):
input = conv_bn_layer(
input=input,
num_filters=num_filters1,
filter_size=1,
stride=1,
act='relu',
name=name + '_bottleneck_conv0_{}'.format(str(i)))
input = conv_bn_layer(
input=input,
num_filters=num_filters2,
filter_size=kernel_size,
stride=stride,
act='relu',
name=name + '_bottleneck_conv1')
for i in range(repeat2):
input = conv_bn_layer(
input=input,
num_filters=num_filters3,
filter_size=1,
stride=1,
act=None,
name=name + '_bottleneck_conv2_{}'.format(str(i)))
return fluid.layers.elementwise_add(
x=short, y=input, act='relu', name=name + '_bottleneck_add')
paddleslim/nas/search_space/search_space_base.py
浏览文件 @ 904dfaff
......@@ -12,8 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from ...common import get_logger
__all__ = ['SearchSpaceBase']
_logger = get_logger(__name__, level=logging.INFO)
class SearchSpaceBase(object):
"""Controller for Neural Architecture Search.
......@@ -26,12 +30,13 @@ class SearchSpaceBase(object):
self.output_size = output_size
self.block_num = block_num
self.block_mask = block_mask
if self.block_mask is not None:
if self.block_mask != None:
assert isinstance(self.block_mask,
list), 'Block_mask must be a list.'
print(
_logger.warn(
"If block_mask is NOT None, we will use block_mask as major configs!"
)
self.block_num = None
def init_tokens(self):
"""Get init tokens in search space.
......
paddleslim/nas/search_space/utils.py 0 → 100644
浏览文件 @ 904dfaff
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
def compute_downsample_num(input_size, output_size):
downsample_num = 0
while input_size > output_size:
input_size = math.ceil(float(input_size) / 2.0)
downsample_num += 1
if input_size != output_size:
raise NotImplementedError(
'output_size must can downsample by input_size!!!')
return downsample_num
def check_points(count, points):
if points is None:
return False
else:
if isinstance(points, list):
return (True if count in points else False)
else:
return (True if count == points else False)
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