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paddleslim/tests/quant_test/imagenet_reader.py 已删除 100644 → 0
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# 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 os
import math
import random
import functools
import numpy as np
import paddle
from PIL import Image, ImageEnhance
random.seed(0)
np.random.seed(0)
DATA_DIM = 224
THREAD = 16
BUF_SIZE = 10240
DATA_DIR = '../data/ILSVRC2012'
DATA_DIR = os.path.join(os.path.split(os.path.realpath(__file__))[0], DATA_DIR)
img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
def resize_short(img, target_size):
percent = float(target_size) / min(img.size[0], img.size[1])
resized_width = int(round(img.size[0] * percent))
resized_height = int(round(img.size[1] * percent))
img = img.resize((resized_width, resized_height), Image.LANCZOS)
return img
def crop_image(img, target_size, center):
width, height = img.size
size = target_size
if center == True:
w_start = (width - size) / 2
h_start = (height - size) / 2
else:
w_start = np.random.randint(0, width - size + 1)
h_start = np.random.randint(0, height - size + 1)
w_end = w_start + size
h_end = h_start + size
img = img.crop((w_start, h_start, w_end, h_end))
return img
def random_crop(img, size, scale=[0.08, 1.0], ratio=[3. / 4., 4. / 3.]):
aspect_ratio = math.sqrt(np.random.uniform(*ratio))
w = 1. * aspect_ratio
h = 1. / aspect_ratio
bound = min((float(img.size[0]) / img.size[1]) / (w**2),
(float(img.size[1]) / img.size[0]) / (h**2))
scale_max = min(scale[1], bound)
scale_min = min(scale[0], bound)
target_area = img.size[0] * img.size[1] * np.random.uniform(scale_min,
scale_max)
target_size = math.sqrt(target_area)
w = int(target_size * w)
h = int(target_size * h)
i = np.random.randint(0, img.size[0] - w + 1)
j = np.random.randint(0, img.size[1] - h + 1)
img = img.crop((i, j, i + w, j + h))
img = img.resize((size, size), Image.LANCZOS)
return img
def rotate_image(img):
angle = np.random.randint(-10, 11)
img = img.rotate(angle)
return img
def distort_color(img):
def random_brightness(img, lower=0.5, upper=1.5):
e = np.random.uniform(lower, upper)
return ImageEnhance.Brightness(img).enhance(e)
def random_contrast(img, lower=0.5, upper=1.5):
e = np.random.uniform(lower, upper)
return ImageEnhance.Contrast(img).enhance(e)
def random_color(img, lower=0.5, upper=1.5):
e = np.random.uniform(lower, upper)
return ImageEnhance.Color(img).enhance(e)
ops = [random_brightness, random_contrast, random_color]
np.random.shuffle(ops)
img = ops[0](img)
img = ops[1](img)
img = ops[2](img)
return img
def process_image(sample, mode, color_jitter, rotate):
img_path = sample[0]
img = Image.open(img_path)
if mode == 'train':
if rotate: img = rotate_image(img)
img = random_crop(img, DATA_DIM)
else:
img = resize_short(img, target_size=256)
img = crop_image(img, target_size=DATA_DIM, center=True)
if mode == 'train':
if color_jitter:
img = distort_color(img)
if np.random.randint(0, 2) == 1:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
if img.mode != 'RGB':
img = img.convert('RGB')
img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255
img -= img_mean
img /= img_std
if mode == 'train' or mode == 'val':
return img, sample[1]
elif mode == 'test':
return [img]
def _reader_creator(file_list,
mode,
shuffle=False,
color_jitter=False,
rotate=False,
data_dir=DATA_DIR,
batch_size=1):
def reader():
try:
with open(file_list) as flist:
full_lines = [line.strip() for line in flist]
if shuffle:
np.random.shuffle(full_lines)
if mode == 'train' and os.getenv('PADDLE_TRAINING_ROLE'):
# distributed mode if the env var `PADDLE_TRAINING_ROLE` exits
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
trainer_count = int(os.getenv("PADDLE_TRAINERS", "1"))
per_node_lines = len(full_lines) // trainer_count
lines = full_lines[trainer_id * per_node_lines:(
trainer_id + 1) * per_node_lines]
print(
"read images from %d, length: %d, lines length: %d, total: %d"
% (trainer_id * per_node_lines, per_node_lines,
len(lines), len(full_lines)))
else:
lines = full_lines
for line in lines:
if mode == 'train' or mode == 'val':
img_path, label = line.split()
img_path = os.path.join(data_dir, img_path)
yield img_path, int(label)
elif mode == 'test':
img_path = os.path.join(data_dir, line)
yield [img_path]
except Exception as e:
print("Reader failed!\n{}".format(str(e)))
os._exit(1)
mapper = functools.partial(
process_image, mode=mode, color_jitter=color_jitter, rotate=rotate)
return paddle.reader.xmap_readers(mapper, reader, THREAD, BUF_SIZE)
def train(data_dir=DATA_DIR):
file_list = os.path.join(data_dir, 'train_list.txt')
return _reader_creator(
file_list,
'train',
shuffle=True,
color_jitter=False,
rotate=False,
data_dir=data_dir)
def val(data_dir=DATA_DIR):
file_list = os.path.join(data_dir, 'val_list.txt')
return _reader_creator(file_list, 'val', shuffle=False, data_dir=data_dir)
def test(data_dir=DATA_DIR):
file_list = os.path.join(data_dir, 'test_list.txt')
return _reader_creator(file_list, 'test', shuffle=False, data_dir=data_dir)
paddleslim/tests/quant_test/models/__init__.py 已删除 100644 → 0
浏览文件 @ 60b33d7c
# 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 .mobilenet import MobileNet
from .resnet import ResNet34, ResNet50
from .mobilenet_v2 import MobileNetV2
__all__ = ['MobileNet', 'ResNet34', 'ResNet50', 'MobileNetV2']
\ No newline at end of file
paddleslim/tests/quant_test/models/mobilenet.py 已删除 100644 → 0
浏览文件 @ 60b33d7c
# 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 paddle.fluid as fluid
from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr
__all__ = ['MobileNet']
train_parameters = {
"input_size": [3, 224, 224],
"input_mean": [0.485, 0.456, 0.406],
"input_std": [0.229, 0.224, 0.225],
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": 256,
"epochs": [10, 16, 30],
"steps": [0.1, 0.01, 0.001, 0.0001]
}
}
class MobileNet():
def __init__(self):
self.params = train_parameters
def net(self, input, class_dim=1000, scale=1.0):
# conv1: 112x112
input = self.conv_bn_layer(
input,
filter_size=3,
channels=3,
num_filters=int(32 * scale),
stride=2,
padding=1,
name="conv1")
# 56x56
input = self.depthwise_separable(
input,
num_filters1=32,
num_filters2=64,
num_groups=32,
stride=1,
scale=scale,
name="conv2_1")
input = self.depthwise_separable(
input,
num_filters1=64,
num_filters2=128,
num_groups=64,
stride=2,
scale=scale,
name="conv2_2")
# 28x28
input = self.depthwise_separable(
input,
num_filters1=128,
num_filters2=128,
num_groups=128,
stride=1,
scale=scale,
name="conv3_1")
input = self.depthwise_separable(
input,
num_filters1=128,
num_filters2=256,
num_groups=128,
stride=2,
scale=scale,
name="conv3_2")
# 14x14
input = self.depthwise_separable(
input,
num_filters1=256,
num_filters2=256,
num_groups=256,
stride=1,
scale=scale,
name="conv4_1")
input = self.depthwise_separable(
input,
num_filters1=256,
num_filters2=512,
num_groups=256,
stride=2,
scale=scale,
name="conv4_2")
# 14x14
for i in range(5):
input = self.depthwise_separable(
input,
num_filters1=512,
num_filters2=512,
num_groups=512,
stride=1,
scale=scale,
name="conv5" + "_" + str(i + 1))
# 7x7
input = self.depthwise_separable(
input,
num_filters1=512,
num_filters2=1024,
num_groups=512,
stride=2,
scale=scale,
name="conv5_6")
input = self.depthwise_separable(
input,
num_filters1=1024,
num_filters2=1024,
num_groups=1024,
stride=1,
scale=scale,
name="conv6")
input = fluid.layers.pool2d(
input=input,
pool_size=0,
pool_stride=1,
pool_type='avg',
global_pooling=True)
output = fluid.layers.fc(input=input,
size=class_dim,
act='softmax',
param_attr=ParamAttr(
initializer=MSRA(), name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
return output
def conv_bn_layer(self,
input,
filter_size,
num_filters,
stride,
padding,
channels=None,
num_groups=1,
act='relu',
use_cudnn=True,
name=None):
conv = fluid.layers.conv2d(
input=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(
initializer=MSRA(), 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 depthwise_separable(self,
input,
num_filters1,
num_filters2,
num_groups,
stride,
scale,
name=None):
depthwise_conv = self.conv_bn_layer(
input=input,
filter_size=3,
num_filters=int(num_filters1 * scale),
stride=stride,
padding=1,
num_groups=int(num_groups * scale),
use_cudnn=False,
name=name + "_dw")
pointwise_conv = self.conv_bn_layer(
input=depthwise_conv,
filter_size=1,
num_filters=int(num_filters2 * scale),
stride=1,
padding=0,
name=name + "_sep")
return pointwise_conv
paddleslim/tests/quant_test/models/mobilenet_v2.py 已删除 100644 → 0
浏览文件 @ 60b33d7c
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# 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 paddle.fluid as fluid
from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr
__all__ = ['MobileNetV2', 'MobileNetV2_x0_25, ''MobileNetV2_x0_5', 'MobileNetV2_x1_0', 'MobileNetV2_x1_5',
'MobileNetV2_x2_0',
'MobileNetV2_scale']
train_parameters = {
"input_size": [3, 224, 224],
"input_mean": [0.485, 0.456, 0.406],
"input_std": [0.229, 0.224, 0.225],
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": 256,
"epochs": [30, 60, 90],
"steps": [0.1, 0.01, 0.001, 0.0001]
}
}
class MobileNetV2():
def __init__(self, scale=1.0, change_depth=False):
self.params = train_parameters
self.scale = scale
self.change_depth = change_depth
def net(self, input, class_dim=1000):
scale = self.scale
change_depth = self.change_depth
# if change_depth is True, the new depth is 1.4 times as deep as before.
bottleneck_params_list = [
(1, 16, 1, 1),
(6, 24, 2, 2),
(6, 32, 3, 2),
(6, 64, 4, 2),
(6, 96, 3, 1),
(6, 160, 3, 2),
(6, 320, 1, 1),
] if change_depth == False else [
(1, 16, 1, 1),
(6, 24, 2, 2),
(6, 32, 5, 2),
(6, 64, 7, 2),
(6, 96, 5, 1),
(6, 160, 3, 2),
(6, 320, 1, 1),
]
# conv1
input = self.conv_bn_layer(
input,
num_filters=int(32 * scale),
filter_size=3,
stride=2,
padding=1,
if_act=True,
name='conv1_1')
# bottleneck sequences
i = 1
in_c = int(32 * scale)
for layer_setting in bottleneck_params_list:
t, c, n, s = layer_setting
i += 1
input = self.invresi_blocks(
input=input,
in_c=in_c,
t=t,
c=int(c * scale),
n=n,
s=s,
name='conv' + str(i))
in_c = int(c * scale)
# last_conv
input = self.conv_bn_layer(
input=input,
num_filters=int(1280 * scale) if scale > 1.0 else 1280,
filter_size=1,
stride=1,
padding=0,
if_act=True,
name='conv9')
input = fluid.layers.pool2d(
input=input,
pool_size=7,
pool_stride=1,
pool_type='avg',
global_pooling=True)
output = fluid.layers.fc(input=input,
size=class_dim,
act='softmax',
param_attr=ParamAttr(name='fc10_weights'),
bias_attr=ParamAttr(name='fc10_offset'))
return output
def conv_bn_layer(self,
input,
filter_size,
num_filters,
stride,
padding,
channels=None,
num_groups=1,
if_act=True,
name=None,
use_cudnn=True):
conv = fluid.layers.conv2d(
input=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'
bn = fluid.layers.batch_norm(
input=conv,
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')
if if_act:
return fluid.layers.relu6(bn)
else:
return bn
def shortcut(self, input, data_residual):
return fluid.layers.elementwise_add(input, data_residual)
def inverted_residual_unit(self,
input,
num_in_filter,
num_filters,
ifshortcut,
stride,
filter_size,
padding,
expansion_factor,
name=None):
num_expfilter = int(round(num_in_filter * expansion_factor))
channel_expand = self.conv_bn_layer(
input=input,
num_filters=num_expfilter,
filter_size=1,
stride=1,
padding=0,
num_groups=1,
if_act=True,
name=name + '_expand')
bottleneck_conv = self.conv_bn_layer(
input=channel_expand,
num_filters=num_expfilter,
filter_size=filter_size,
stride=stride,
padding=padding,
num_groups=num_expfilter,
if_act=True,
name=name + '_dwise',
use_cudnn=False)
linear_out = self.conv_bn_layer(
input=bottleneck_conv,
num_filters=num_filters,
filter_size=1,
stride=1,
padding=0,
num_groups=1,
if_act=False,
name=name + '_linear')
if ifshortcut:
out = self.shortcut(input=input, data_residual=linear_out)
return out
else:
return linear_out
def invresi_blocks(self, input, in_c, t, c, n, s, name=None):
first_block = self.inverted_residual_unit(
input=input,
num_in_filter=in_c,
num_filters=c,
ifshortcut=False,
stride=s,
filter_size=3,
padding=1,
expansion_factor=t,
name=name + '_1')
last_residual_block = first_block
last_c = c
for i in range(1, n):
last_residual_block = self.inverted_residual_unit(
input=last_residual_block,
num_in_filter=last_c,
num_filters=c,
ifshortcut=True,
stride=1,
filter_size=3,
padding=1,
expansion_factor=t,
name=name + '_' + str(i + 1))
return last_residual_block
def MobileNetV2_x0_25():
model = MobileNetV2(scale=0.25)
return model
def MobileNetV2_x0_5():
model = MobileNetV2(scale=0.5)
return model
def MobileNetV2_x1_0():
model = MobileNetV2(scale=1.0)
return model
def MobileNetV2_x1_5():
model = MobileNetV2(scale=1.5)
return model
def MobileNetV2_x2_0():
model = MobileNetV2(scale=2.0)
return model
def MobileNetV2_scale():
model = MobileNetV2(scale=1.2, change_depth=True)
return model
paddleslim/tests/quant_test/models/resnet.py 已删除 100644 → 0
浏览文件 @ 60b33d7c
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# 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 paddle
import paddle.fluid as fluid
import math
from paddle.fluid.param_attr import ParamAttr
__all__ = ["ResNet", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]
train_parameters = {
"input_size": [3, 224, 224],
"input_mean": [0.485, 0.456, 0.406],
"input_std": [0.229, 0.224, 0.225],
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": 256,
"epochs": [10, 16, 30],
"steps": [0.1, 0.01, 0.001, 0.0001]
}
}
class ResNet():
def __init__(self, layers=50, prefix_name=''):
self.params = train_parameters
self.layers = layers
self.prefix_name = prefix_name
def net(self, input, class_dim=1000, conv1_name='conv1', fc_name='res_fc'):
layers = self.layers
prefix_name = self.prefix_name if self.prefix_name is '' else self.prefix_name + '_'
supported_layers = [34, 50, 101, 152]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers, layers)
if layers == 34 or layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
num_filters = [64, 128, 256, 512]
# TODO(wanghaoshuang@baidu.com):
# fix name("conv1") conflict between student and teacher in distillation.
conv = self.conv_bn_layer(
input=input,
num_filters=64,
filter_size=7,
stride=2,
act='relu',
name=prefix_name + conv1_name)
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
for block in range(len(depth)):
for i in range(depth[block]):
if layers in [101, 152] and block == 2:
if i == 0:
conv_name = "res" + str(block + 2) + "a"
else:
conv_name = "res" + str(block + 2) + "b" + str(i)
else:
conv_name = "res" + str(block + 2) + chr(97 + i)
conv_name = prefix_name + conv_name
conv = self.bottleneck_block(
input=conv,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
name=conv_name)
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,
size=class_dim,
act='softmax',
name=prefix_name + fc_name,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv,
stdv)))
return out
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False,
name=name + '.conv2d.output.1')
if self.prefix_name == '':
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
else:
if name.split("_")[1] == "conv1":
bn_name = name.split("_", 1)[0] + "_bn_" + name.split("_", 1)[1]
else:
bn_name = name.split("_", 1)[0] + "_bn" + name.split("_",
1)[1][3:]
return fluid.layers.batch_norm(
input=conv,
act=act,
name=bn_name + '.output.1',
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance', )
def shortcut(self, input, ch_out, stride, name):
ch_in = input.shape[1]
if ch_in != ch_out or stride != 1:
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
else:
return input
def bottleneck_block(self, input, num_filters, stride, name):
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=1,
act='relu',
name=name + "_branch2a")
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu',
name=name + "_branch2b")
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters * 4,
filter_size=1,
act=None,
name=name + "_branch2c")
short = self.shortcut(
input, num_filters * 4, stride, name=name + "_branch1")
return fluid.layers.elementwise_add(
x=short, y=conv2, act='relu', name=name + ".add.output.5")
def ResNet34(prefix_name=''):
model = ResNet(layers=34, prefix_name=prefix_name)
return model
def ResNet50(prefix_name=''):
model = ResNet(layers=50, prefix_name=prefix_name)
return model
def ResNet101():
model = ResNet(layers=101)
return model
def ResNet152():
model = ResNet(layers=152)
return model
paddleslim/tests/quant_test/quanter_test.py 已删除 100644 → 0
浏览文件 @ 60b33d7c
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# 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 os
import numpy as np
import time
import functools
import paddle
import paddle.fluid as fluid
import argparse
import subprocess
import sys
sys.path.append('../..')
sys.path.append('.')
import imagenet_reader as reader
import models
from utility import add_arguments, print_arguments
from utility import save_persistable_nodes, load_persistable_nodes
import quant
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('batch_size', int, 256, "Minibatch size.")
add_arg('use_gpu', bool, True, "Whether to use GPU or not.")
add_arg('total_images', int, 1281167, "Training image number.")
add_arg('num_epochs', int, 120, "number of epochs.")
add_arg('class_dim', int, 1000, "Class number.")
add_arg('image_shape', str, "3,224,224", "input image size")
add_arg('model_save_dir', str, "output", "model save directory")
add_arg('pretrained_fp32_model', str, None, "Whether to use the pretrained float32 model to initialize the weights.")
add_arg('checkpoint', str, None, "Whether to resume the training process from the checkpoint.")
add_arg('lr', float, 0.1, "set learning rate.")
add_arg('lr_strategy', str, "piecewise_decay", "Set the learning rate decay strategy.")
add_arg('model', str, "SE_ResNeXt50_32x4d", "Set the network to use.")
add_arg('data_dir', str, "./data/ILSVRC2012", "The ImageNet dataset root dir.")
add_arg('act_quant_type', str, "abs_max", "quantization type for activation, valid value:'abs_max','range_abs_max', 'moving_average_abs_max'" )
add_arg('wt_quant_type', str, "abs_max", "quantization type for weight, valid value:'abs_max','channel_wise_abs_max'" )
# yapf: enabl
def optimizer_setting(params):
ls = params["learning_strategy"]
if ls["name"] == "piecewise_decay":
if "total_images" not in params:
total_images = 1281167
else:
total_images = params["total_images"]
batch_size = ls["batch_size"]
step = int(total_images / batch_size + 1)
bd = [step * e for e in ls["epochs"]]
print("decay list:{}".format(bd))
base_lr = params["lr"]
lr = []
lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
elif ls["name"] == "cosine_decay":
if "total_images" not in params:
total_images = 1281167
else:
total_images = params["total_images"]
batch_size = ls["batch_size"]
step = int(total_images / batch_size + 1)
lr = params["lr"]
num_epochs = params["num_epochs"]
optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.cosine_decay(
learning_rate=lr, step_each_epoch=step, epochs=num_epochs),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(4e-5))
elif ls["name"] == "exponential_decay":
if "total_images" not in params:
total_images = 1281167
else:
total_images = params["total_images"]
batch_size = ls["batch_size"]
step = int(total_images / batch_size +1)
lr = params["lr"]
num_epochs = params["num_epochs"]
learning_decay_rate_factor=ls["learning_decay_rate_factor"]
num_epochs_per_decay = ls["num_epochs_per_decay"]
NUM_GPUS = 1
optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.exponential_decay(
learning_rate = lr * NUM_GPUS,
decay_steps = step * num_epochs_per_decay / NUM_GPUS,
decay_rate = learning_decay_rate_factor),
momentum=0.9,
regularization = fluid.regularizer.L2Decay(4e-5))
else:
lr = params["lr"]
optimizer = fluid.optimizer.Momentum(
learning_rate=lr,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
return optimizer
def net_config(image, label, model, args):
model_list = [m for m in dir(models) if "__" not in m]
assert args.model in model_list,"{} is not lists: {}".format(
args.model, model_list)
class_dim = args.class_dim
model_name = args.model
if model_name == "GoogleNet":
out0, out1, out2 = model.net(input=image, class_dim=class_dim)
cost0 = fluid.layers.cross_entropy(input=out0, label=label)
cost1 = fluid.layers.cross_entropy(input=out1, label=label)
cost2 = fluid.layers.cross_entropy(input=out2, label=label)
avg_cost0 = fluid.layers.mean(x=cost0)
avg_cost1 = fluid.layers.mean(x=cost1)
avg_cost2 = fluid.layers.mean(x=cost2)
avg_cost = avg_cost0 + 0.3 * avg_cost1 + 0.3 * avg_cost2
acc_top1 = fluid.layers.accuracy(input=out0, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out0, label=label, k=5)
out = out0
else:
out = model.net(input=image, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
return out, avg_cost, acc_top1, acc_top5
def build_program(is_train, main_prog, startup_prog, args):
image_shape = [int(m) for m in args.image_shape.split(",")]
model_name = args.model
model_list = [m for m in dir(models) if "__" not in m]
assert model_name in model_list, "{} is not in lists: {}".format(args.model,
model_list)
model = models.__dict__[model_name]()
with fluid.program_guard(main_prog, startup_prog):
py_reader = fluid.layers.py_reader(
capacity=16,
shapes=[[-1] + image_shape, [-1, 1]],
lod_levels=[0, 0],
dtypes=["float32", "int64"],
use_double_buffer=True)
with fluid.unique_name.guard():
image, label = fluid.layers.read_file(py_reader)
out, avg_cost, acc_top1, acc_top5 = net_config(image, label, model, args)
avg_cost.persistable = True
acc_top1.persistable = True
acc_top5.persistable = True
if is_train:
params = model.params
params["total_images"] = args.total_images
params["lr"] = args.lr
params["num_epochs"] = args.num_epochs
params["learning_strategy"]["batch_size"] = args.batch_size
params["learning_strategy"]["name"] = args.lr_strategy
optimizer = optimizer_setting(params)
optimizer.minimize(avg_cost)
global_lr = optimizer._global_learning_rate()
if is_train:
return image, out, py_reader, avg_cost, acc_top1, acc_top5, global_lr
else:
return image, out, py_reader, avg_cost, acc_top1, acc_top5
def train(args):
############################################################################################################
# 1. quantization configs
############################################################################################################
quant_config = {
# weight quantize type, default is 'abs_max'
'weight_quantize_type': 'abs_max',
# activation quantize type, default is 'abs_max'
'activation_quantize_type': 'abs_max',
# weight quantize bit num, default is 8
'weight_bits': 8,
# activation quantize bit num, default is 8
'activation_bits': 8,
# op of name_scope in not_quant_pattern list, will not quantized
'not_quant_pattern': ['skip_quant_dd'],
# op of types in quantize_op_types, will quantized
'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'],
# data type after quantization, default is 'int8'
'dtype': 'int8',
# window size for 'range_abs_max' quantization. defaulf is 10000
'window_size': 10000,
# The decay coefficient of moving average, default is 0.9
'moving_rate': 0.9,
# if set quant_weight_only True, then only quantize parameters of layers which need quantization,
# and insert anti-quantization op for parameters of these layers.
'quant_weight_only': False
}
# parameters from arguments
model_name = args.model
pretrained_fp32_model = args.pretrained_fp32_model
checkpoint = args.checkpoint
model_save_dir = args.model_save_dir
data_dir = args.data_dir
activation_quant_type = args.act_quant_type
weight_quant_type = args.wt_quant_type
print("Using %s as the actiavtion quantize type." % activation_quant_type)
print("Using %s as the weight quantize type." % weight_quant_type)
startup_prog = fluid.Program()
train_prog = fluid.Program()
test_prog = fluid.Program()
_, _, train_py_reader, train_cost, train_acc1, train_acc5, global_lr = build_program(
is_train=True,
main_prog=train_prog,
startup_prog=startup_prog,
args=args)
image, out, test_py_reader, test_cost, test_acc1, test_acc5 = build_program(
is_train=False,
main_prog=test_prog,
startup_prog=startup_prog,
args=args)
test_prog = test_prog.clone(for_test=True)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
scope = fluid.global_scope()
exe = fluid.Executor(place)
# load checkpoint todo
exe.run(startup_prog)
if pretrained_fp32_model:
def if_exist(var):
return os.path.exists(os.path.join(pretrained_fp32_model, var.name))
fluid.io.load_vars(
exe, pretrained_fp32_model, main_program=train_prog, predicate=if_exist)
if args.use_gpu:
visible_device = os.getenv('CUDA_VISIBLE_DEVICES')
if visible_device:
device_num = len(visible_device.split(','))
else:
device_num = subprocess.check_output(
['nvidia-smi', '-L']).decode().count('\n')
else:
device_num = 1
train_batch_size = args.batch_size / device_num
test_batch_size = 1 if activation_quant_type == 'abs_max' else 8
train_reader = paddle.batch(
reader.train(data_dir=data_dir), batch_size=train_batch_size, drop_last=True)
test_reader = paddle.batch(reader.val(data_dir=data_dir), batch_size=test_batch_size)
train_py_reader.decorate_paddle_reader(train_reader)
test_py_reader.decorate_paddle_reader(test_reader)
train_fetch_list = [train_cost.name, train_acc1.name, train_acc5.name, global_lr.name]
test_fetch_list = [test_cost.name, test_acc1.name, test_acc5.name]
############################################################################################################
# 2. quantization transform programs (training aware)
# Make some quantization transforms in the graph before training and testing.
# According to the weight and activation quantization type, the graph will be added
# some fake quantize operators and fake dequantize operators.
############################################################################################################
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False
test_prog = quant.quanter.quant_aware(test_prog, scope, place, quant_config, for_test=True)
train_prog = quant.quanter.quant_aware(train_prog, scope, place, quant_config, for_test=False)
train_prog = train_prog.with_data_parallel(loss_name=train_cost.name, build_strategy=build_strategy)
#train_prog_binary = train_prog_binary.with_data_parallel(loss_name=train_cost.name)
params = models.__dict__[args.model]().params
for pass_id in range(params["num_epochs"]):
train_py_reader.start()
train_info = [[], [], []]
test_info = [[], [], []]
train_time = []
batch_id = 0
try:
while True:
t1 = time.time()
loss, acc1, acc5, lr = exe.run(train_prog, fetch_list=train_fetch_list)
t2 = time.time()
period = t2 - t1
loss = np.mean(np.array(loss))
acc1 = np.mean(np.array(acc1))
acc5 = np.mean(np.array(acc5))
train_info[0].append(loss)
train_info[1].append(acc1)
train_info[2].append(acc5)
lr = np.mean(np.array(lr))
train_time.append(period)
if batch_id % 10 == 0:
print("Pass {0}, trainbatch {1}, loss {2}, \
acc1 {3}, acc5 {4}, lr {5}, time {6}"
.format(pass_id, batch_id, loss, acc1, acc5, "%.6f" %
lr, "%2.2f sec" % period))
sys.stdout.flush()
batch_id += 1
except fluid.core.EOFException:
train_py_reader.reset()
train_loss = np.array(train_info[0]).mean()
train_acc1 = np.array(train_info[1]).mean()
train_acc5 = np.array(train_info[2]).mean()
test_py_reader.start()
test_batch_id = 0
try:
while True:
t1 = time.time()
loss, acc1, acc5 = exe.run(program=test_prog,
fetch_list=test_fetch_list)
t2 = time.time()
period = t2 - t1
loss = np.mean(loss)
acc1 = np.mean(acc1)
acc5 = np.mean(acc5)
test_info[0].append(loss)
test_info[1].append(acc1)
test_info[2].append(acc5)
if test_batch_id % 10 == 0:
print("Pass {0},testbatch {1},loss {2}, \
acc1 {3},acc5 {4},time {5}"
.format(pass_id, test_batch_id, loss, acc1, acc5,
"%2.2f sec" % period))
sys.stdout.flush()
test_batch_id += 1
except fluid.core.EOFException:
test_py_reader.reset()
test_loss = np.array(test_info[0]).mean()
test_acc1 = np.array(test_info[1]).mean()
test_acc5 = np.array(test_info[2]).mean()
print("End pass {0}, train_loss {1}, train_acc1 {2}, train_acc5 {3}, "
"test_loss {4}, test_acc1 {5}, test_acc5 {6}".format(
pass_id, train_loss, train_acc1, train_acc5, test_loss,
test_acc1, test_acc5))
sys.stdout.flush()
# save checkpoints todo
# save_checkpoint_path = os.path.join(model_save_dir, model_name, str(pass_id))
# if not os.path.isdir(save_checkpoint_path):
# os.makedirs(save_checkpoint_path)
# save_persistable_nodes(exe, save_checkpoint_path, main_graph)
############################################################################################################
# 3. Freeze the graph after training by adjusting the quantize
# operators' order for the inference.
# The dtype of float_program's weights is float32, but in int8 range.
############################################################################################################
float_program, int8_program = quant.quanter.convert(test_prog, scope, place, quant_config, save_int8=True)
############################################################################################################
# 4. Save inference model
############################################################################################################
model_path = os.path.join(model_save_dir, model_name, args.act_quant_type)
float_path = os.path.join(model_path, 'float')
int8_path = os.path.join(model_path, 'int8')
if not os.path.isdir(model_path):
os.makedirs(model_path)
fluid.io.save_inference_model(
dirname=float_path,
feeded_var_names=[image.name],
target_vars=[out], executor=exe,
main_program=float_program)
fluid.io.save_inference_model(
dirname=int8_path,
feeded_var_names=[image.name],
target_vars=[out], executor=exe,
main_program=int8_program)
def main():
args = parser.parse_args()
print_arguments(args)
train(args)
if __name__ == '__main__':
main()
paddleslim/tests/quant_test/run_quant.sh 已删除 100644 → 0
浏览文件 @ 60b33d7c
#!/usr/bin/env bash
source /home/wsz/anaconda2/bin/activate py27_paddle1.6
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#MobileNet v1:
nohup python quanter_test.py \
--model=MobileNet \
--pretrained_fp32_model='../../../../pretrain/MobileNetV1_pretrained/' \
--use_gpu=True \
--data_dir='/home/ssd8/wsz/tianfei01/traindata/imagenet/' \
--batch_size=2048 \
--total_images=1281167 \
--class_dim=1000 \
--image_shape=3,224,224 \
--model_save_dir=output/ \
--lr_strategy=piecewise_decay \
--num_epochs=20 \
--lr=0.0001 \
--act_quant_type=abs_max \
--wt_quant_type=abs_max 2>&1 &
#ResNet50:
#python quanter_test.py \
# --model=ResNet50 \
# --pretrained_fp32_model=${pretrain_dir}/ResNet50_pretrained \
# --use_gpu=True \
# --data_dir=${data_dir} \
# --batch_size=128 \
# --total_images=1281167 \
# --class_dim=1000 \
# --image_shape=3,224,224 \
# --model_save_dir=output/ \
# --lr_strategy=piecewise_decay \
# --num_epochs=20 \
# --lr=0.0001 \
# --act_quant_type=abs_max \
# --wt_quant_type=abs_max
paddleslim/tests/quant_test/utility.py 已删除 100644 → 0
浏览文件 @ 60b33d7c
"""Contains common utility functions."""
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#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 distutils.util
import os
import numpy as np
import six
import logging
import paddle.fluid as fluid
import paddle.compat as cpt
from paddle.fluid import core
from paddle.fluid.framework import Program
logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
_logger = logging.getLogger(__name__)
_logger.setLevel(logging.INFO)
def print_arguments(args):
"""Print argparse's arguments.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
parser.add_argument("name", default="Jonh", type=str, help="User name.")
args = parser.parse_args()
print_arguments(args)
:param args: Input argparse.Namespace for printing.
:type args: argparse.Namespace
"""
print("----------- Configuration Arguments -----------")
for arg, value in sorted(six.iteritems(vars(args))):
print("%s: %s" % (arg, value))
print("------------------------------------------------")
def add_arguments(argname, type, default, help, argparser, **kwargs):
"""Add argparse's argument.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
add_argument("name", str, "Jonh", "User name.", parser)
args = parser.parse_args()
"""
type = distutils.util.strtobool if type == bool else type
argparser.add_argument(
"--" + argname,
default=default,
type=type,
help=help + ' Default: %(default)s.',
**kwargs)
def save_persistable_nodes(executor, dirname, graph):
"""
Save persistable nodes to the given directory by the executor.
Args:
executor(Executor): The executor to run for saving node values.
dirname(str): The directory path.
graph(IrGraph): All the required persistable nodes in the graph will be saved.
"""
persistable_node_names = set()
persistable_nodes = []
all_persistable_nodes = graph.all_persistable_nodes()
for node in all_persistable_nodes:
name = cpt.to_text(node.name())
if name not in persistable_node_names:
persistable_node_names.add(name)
persistable_nodes.append(node)
program = Program()
var_list = []
for node in persistable_nodes:
var_desc = node.var()
if var_desc.type() == core.VarDesc.VarType.RAW or \
var_desc.type() == core.VarDesc.VarType.READER:
continue
var = program.global_block().create_var(
name=var_desc.name(),
shape=var_desc.shape(),
dtype=var_desc.dtype(),
type=var_desc.type(),
lod_level=var_desc.lod_level(),
persistable=var_desc.persistable())
var_list.append(var)
fluid.io.save_vars(executor=executor, dirname=dirname, vars=var_list)
def load_persistable_nodes(executor, dirname, graph):
"""
Load persistable node values from the given directory by the executor.
Args:
executor(Executor): The executor to run for loading node values.
dirname(str): The directory path.
graph(IrGraph): All the required persistable nodes in the graph will be loaded.
"""
persistable_node_names = set()
persistable_nodes = []
all_persistable_nodes = graph.all_persistable_nodes()
for node in all_persistable_nodes:
name = cpt.to_text(node.name())
if name not in persistable_node_names:
persistable_node_names.add(name)
persistable_nodes.append(node)
program = Program()
var_list = []
def _exist(var):
return os.path.exists(os.path.join(dirname, var.name))
def _load_var(name, scope):
return np.array(scope.find_var(name).get_tensor())
def _store_var(name, array, scope, place):
tensor = scope.find_var(name).get_tensor()
tensor.set(array, place)
for node in persistable_nodes:
var_desc = node.var()
if var_desc.type() == core.VarDesc.VarType.RAW or \
var_desc.type() == core.VarDesc.VarType.READER:
continue
var = program.global_block().create_var(
name=var_desc.name(),
shape=var_desc.shape(),
dtype=var_desc.dtype(),
type=var_desc.type(),
lod_level=var_desc.lod_level(),
persistable=var_desc.persistable())
if _exist(var):
var_list.append(var)
else:
_logger.info("Cannot find the var %s!!!" %(node.name()))
fluid.io.load_vars(executor=executor, dirname=dirname, vars=var_list)
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