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avx_docs
liaogang 8 years ago
parent 978a6e5e9b 9bce328964
commit 84cab2c763
274 changed files with 13576 additions and 9734 deletions
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5
.clang-format
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@ -13,8 +13,6 @@
# The document of clang-format is
# http://clang.llvm.org/docs/ClangFormat.html
# http://clang.llvm.org/docs/ClangFormatStyleOptions.html
#
# TODO(yuyang18): Add python and other language code style
---
Language: Cpp
BasedOnStyle: Google
@ -22,8 +20,9 @@ IndentWidth: 2
TabWidth: 2
ContinuationIndentWidth: 4
AccessModifierOffset: -2 # The private/protected/public has no indent in class
PointerAlignment: Left # int* p/int& p, not int *p/int &p
Standard: Cpp11
AllowAllParametersOfDeclarationOnNextLine: true
BinPackParameters: false
BinPackArguments: false
...

2
.gitignore vendored
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@ -5,4 +5,6 @@ build/
.vscode
.idea
.project
.cproject
.pydevproject
Makefile

24
.pre-commit-config.yaml
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@ -0,0 +1,24 @@
- repo: https://github.com/Lucas-C/pre-commit-hooks.git
sha: c25201a00e6b0514370501050cf2a8538ac12270
hooks:
- id: remove-crlf
- repo: https://github.com/reyoung/mirrors-yapf.git
sha: v0.13.2
hooks:
- id: yapf
- repo: https://github.com/pre-commit/pre-commit-hooks
sha: 4ef03c4223ad322c7adaa6c6c0efb26b57df3b71
hooks:
- id: check-added-large-files
- id: check-merge-conflict
- id: check-symlinks
- id: detect-private-key
- id: end-of-file-fixer
# TODO(yuyang): trailing whitespace has some bugs on markdown
# files now, please not add it to pre-commit hook now
# - id: trailing-whitespace
#
# TODO(yuyang): debug-statements not fit for Paddle, because
# not all of our python code is runnable. Some are used for
# documenation
# - id: debug-statements

3
.style.yapf
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@ -0,0 +1,3 @@
[style]
based_on_style = pep8
column_limit = 80

30
demo/image_classification/data/process_cifar.py
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@ -16,7 +16,6 @@ import numpy as np
import sys
import os
import PIL.Image as Image
"""
Usage: python process_cifar input_dir output_dir
"""
@ -30,6 +29,7 @@ def mkdir_not_exist(path):
if not os.path.exists(path):
os.mkdir(path)
def create_dir_structure(output_dir):
"""
Create the directory structure for the directory.
@ -39,8 +39,8 @@ def create_dir_structure(output_dir):
mkdir_not_exist(os.path.join(output_dir, "train"))
mkdir_not_exist(os.path.join(output_dir, "test"))
def convert_batch(batch_path, label_set, label_map,
output_dir, data_split):
def convert_batch(batch_path, label_set, label_map, output_dir, data_split):
"""
Convert CIFAR batch to the structure of Paddle format.
batch_path: the batch to be converted.
@ -67,11 +67,23 @@ if __name__ == '__main__':
output_dir = sys.argv[2]
num_batch = 5
create_dir_structure(output_dir)
label_map = {0: "airplane", 1: "automobile", 2: "bird", 3: "cat", 4: "deer",
5: "dog", 6: "frog", 7: "horse", 8: "ship", 9: "truck"}
label_map = {
0: "airplane",
1: "automobile",
2: "bird",
3: "cat",
4: "deer",
5: "dog",
6: "frog",
7: "horse",
8: "ship",
9: "truck"
}
labels = {}
for i in range(1, num_batch + 1):
convert_batch(os.path.join(input_dir, "data_batch_%d" % i), labels,
label_map, output_dir, "train")
convert_batch(os.path.join(input_dir, "test_batch"), {},
label_map, output_dir, "test")
convert_batch(
os.path.join(input_dir, "data_batch_%d" % i), labels, label_map,
output_dir, "train")
convert_batch(
os.path.join(input_dir, "test_batch"), {}, label_map, output_dir,
"test")

12
demo/image_classification/image_provider.py
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@ -46,14 +46,14 @@ def hook(settings, img_size, mean_img_size, num_classes, color, meta, use_jpeg,
settings.img_mean = image_util.load_meta(settings.meta_path,
settings.mean_img_size,
settings.img_size,
settings.color)
settings.img_size, settings.color)
settings.logger.info('Image size: %s', settings.img_size)
settings.logger.info('Meta path: %s', settings.meta_path)
settings.input_types = [
dense_vector(settings.img_raw_size), # image feature
integer_value(settings.num_classes)] # labels
integer_value(settings.num_classes)
] # labels
settings.logger.info('DataProvider Initialization finished')
@ -79,8 +79,8 @@ def processData(settings, file_list):
img = image_util.decode_jpeg(data['images'][i])
else:
img = data['images'][i]
img_feat = image_util.preprocess_img(img, settings.img_mean,
settings.img_size, settings.is_train,
settings.color)
img_feat = image_util.preprocess_img(
img, settings.img_mean, settings.img_size,
settings.is_train, settings.color)
label = data['labels'][i]
yield img_feat.astype('float32'), int(label)

60
demo/image_classification/image_util.py
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@ -16,17 +16,20 @@ import numpy as np
from PIL import Image
from cStringIO import StringIO
def resize_image(img, target_size):
"""
Resize an image so that the shorter edge has length target_size.
img: the input image to be resized.
target_size: the target resized image size.
"""
percent = (target_size/float(min(img.size[0], img.size[1])))
resized_size = int(round(img.size[0] * percent)), int(round(img.size[1] * percent))
percent = (target_size / float(min(img.size[0], img.size[1])))
resized_size = int(round(img.size[0] * percent)), int(
round(img.size[1] * percent))
img = img.resize(resized_size, Image.ANTIALIAS)
return img
def flip(im):
"""
Return the flipped image.
@ -38,6 +41,7 @@ def flip(im):
else:
return im[:, ::-1]
def crop_img(im, inner_size, color=True, test=True):
"""
Return cropped image.
@ -50,20 +54,22 @@ def crop_img(im, inner_size, color=True, test=True):
If True, crop the center of images.
"""
if color:
height, width = max(inner_size, im.shape[1]), max(inner_size, im.shape[2])
height, width = max(inner_size, im.shape[1]), max(inner_size,
im.shape[2])
padded_im = np.zeros((3, height, width))
startY = (height - im.shape[1]) / 2
startX = (width - im.shape[2]) / 2
endY, endX = startY + im.shape[1], startX + im.shape[2]
padded_im[:, startY: endY, startX: endX] = im
padded_im[:, startY:endY, startX:endX] = im
else:
im = im.astype('float32')
height, width = max(inner_size, im.shape[0]), max(inner_size, im.shape[1])
height, width = max(inner_size, im.shape[0]), max(inner_size,
im.shape[1])
padded_im = np.zeros((height, width))
startY = (height - im.shape[0]) / 2
startX = (width - im.shape[1]) / 2
endY, endX = startY + im.shape[0], startX + im.shape[1]
padded_im[startY: endY, startX: endX] = im
padded_im[startY:endY, startX:endX] = im
if test:
startY = (height - inner_size) / 2
startX = (width - inner_size) / 2
@ -72,19 +78,21 @@ def crop_img(im, inner_size, color=True, test=True):
startX = np.random.randint(0, width - inner_size + 1)
endY, endX = startY + inner_size, startX + inner_size
if color:
pic = padded_im[:, startY: endY, startX: endX]
pic = padded_im[:, startY:endY, startX:endX]
else:
pic = padded_im[startY: endY, startX: endX]
pic = padded_im[startY:endY, startX:endX]
if (not test) and (np.random.randint(2) == 0):
pic = flip(pic)
return pic
def decode_jpeg(jpeg_string):
np_array = np.array(Image.open(StringIO(jpeg_string)))
if len(np_array.shape) == 3:
np_array = np.transpose(np_array, (2, 0, 1))
return np_array
def preprocess_img(im, img_mean, crop_size, is_train, color=True):
"""
Does data augmentation for images.
@ -99,6 +107,7 @@ def preprocess_img(im, img_mean, crop_size, is_train, color=True):
pic -= img_mean
return pic.flatten()
def load_meta(meta_path, mean_img_size, crop_size, color=True):
"""
Return the loaded meta file.
@ -109,17 +118,18 @@ def load_meta(meta_path, mean_img_size, crop_size, color=True):
mean = np.load(meta_path)['data_mean']
border = (mean_img_size - crop_size) / 2
if color:
assert(mean_img_size * mean_img_size * 3 == mean.shape[0])
assert (mean_img_size * mean_img_size * 3 == mean.shape[0])
mean = mean.reshape(3, mean_img_size, mean_img_size)
mean = mean[:, border: border + crop_size,
border: border + crop_size].astype('float32')
mean = mean[:, border:border + crop_size, border:border +
crop_size].astype('float32')
else:
assert(mean_img_size * mean_img_size == mean.shape[0])
assert (mean_img_size * mean_img_size == mean.shape[0])
mean = mean.reshape(mean_img_size, mean_img_size)
mean = mean[border: border + crop_size,
border: border + crop_size].astype('float32')
mean = mean[border:border + crop_size, border:border +
crop_size].astype('float32')
return mean
def load_image(img_path, is_color=True):
"""
Load image and return.
@ -130,6 +140,7 @@ def load_image(img_path, is_color=True):
img.load()
return img
def oversample(img, crop_dims):
"""
image : iterable of (H x W x K) ndarrays
@ -152,26 +163,29 @@ def oversample(img, crop_dims):
for j in w_indices:
crops_ix[curr] = (i, j, i + crop_dims[0], j + crop_dims[1])
curr += 1
crops_ix[4] = np.tile(im_center, (1, 2)) + np.concatenate([
-crop_dims / 2.0,
crop_dims / 2.0
])
crops_ix[4] = np.tile(im_center, (1, 2)) + np.concatenate(
[-crop_dims / 2.0, crop_dims / 2.0])
crops_ix = np.tile(crops_ix, (2, 1))
# Extract crops
crops = np.empty((10 * len(img), crop_dims[0], crop_dims[1],
im_shape[-1]), dtype=np.float32)
crops = np.empty(
(10 * len(img), crop_dims[0], crop_dims[1], im_shape[-1]),
dtype=np.float32)
ix = 0
for im in img:
for crop in crops_ix:
crops[ix] = im[crop[0]:crop[2], crop[1]:crop[3], :]
ix += 1
crops[ix-5:ix] = crops[ix-5:ix, :, ::-1, :] # flip for mirrors
crops[ix - 5:ix] = crops[ix - 5:ix, :, ::-1, :] # flip for mirrors
return crops
class ImageTransformer:
def __init__(self, transpose = None,
channel_swap = None, mean = None, is_color = True):
def __init__(self,
transpose=None,
channel_swap=None,
mean=None,
is_color=True):
self.transpose = transpose
self.channel_swap = None
self.mean = None

45
demo/image_classification/prediction.py
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@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os,sys
import os, sys
import numpy as np
import logging
from PIL import Image
@ -24,9 +24,11 @@ from py_paddle import swig_paddle, DataProviderConverter
from paddle.trainer.PyDataProvider2 import dense_vector
from paddle.trainer.config_parser import parse_config
logging.basicConfig(format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
logging.basicConfig(
format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
logging.getLogger().setLevel(logging.INFO)
class ImageClassifier():
def __init__(self,
train_conf,
@ -58,8 +60,8 @@ class ImageClassifier():
self.oversample = oversample
self.is_color = is_color
self.transformer = image_util.ImageTransformer(is_color = is_color)
self.transformer.set_transpose((2,0,1))
self.transformer = image_util.ImageTransformer(is_color=is_color)
self.transformer.set_transpose((2, 0, 1))
self.mean_file = mean_file
mean = np.load(self.mean_file)['data_mean']
@ -69,7 +71,8 @@ class ImageClassifier():
conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (gpu)
conf = parse_config(train_conf, conf_args)
swig_paddle.initPaddle("--use_gpu=%d" % (gpu))
self.network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
self.network.loadParameters(self.model_dir)
@ -90,14 +93,14 @@ class ImageClassifier():
# image_util.resize_image: short side is self.resize_dim
image = image_util.resize_image(image, self.resize_dim)
image = np.array(image)
input = np.zeros((1, image.shape[0], image.shape[1], 3),
dtype=np.float32)
input = np.zeros(
(1, image.shape[0], image.shape[1], 3), dtype=np.float32)
input[0] = image.astype(np.float32)
input = image_util.oversample(input, self.crop_dims)
else:
image = image.resize(self.crop_dims, Image.ANTIALIAS)
input = np.zeros((1, self.crop_dims[0], self.crop_dims[1], 3),
dtype=np.float32)
input = np.zeros(
(1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32)
input[0] = np.array(image).astype(np.float32)
data_in = []
@ -133,18 +136,20 @@ class ImageClassifier():
lab = np.argsort(-prob)
logging.info("Label of %s is: %d", image, lab[0])
if __name__ == '__main__':
image_size=32
crop_size=32
multi_crop=True
config="vgg_16_cifar.py"
output_layer="__fc_layer_1__"
mean_path="data/cifar-out/batches/batches.meta"
model_path=sys.argv[1]
image=sys.argv[2]
use_gpu=bool(int(sys.argv[3]))
obj = ImageClassifier(train_conf=config,
image_size = 32
crop_size = 32
multi_crop = True
config = "vgg_16_cifar.py"
output_layer = "__fc_layer_1__"
mean_path = "data/cifar-out/batches/batches.meta"
model_path = sys.argv[1]
image = sys.argv[2]
use_gpu = bool(int(sys.argv[3]))
obj = ImageClassifier(
train_conf=config,
model_dir=model_path,
resize_dim=image_size,
crop_dim=crop_size,

30
demo/image_classification/preprocess.py
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@ -19,22 +19,34 @@ from optparse import OptionParser
def option_parser():
parser = OptionParser(usage="usage: python preprcoess.py "\
"-i data_dir [options]")
parser.add_option("-i", "--input", action="store",
dest="input", help="Input data directory.")
parser.add_option("-s", "--size", action="store",
dest="size", help="Processed image size.")
parser.add_option("-c", "--color", action="store",
dest="color", help="whether to use color images.")
parser.add_option(
"-i",
"--input",
action="store",
dest="input",
help="Input data directory.")
parser.add_option(
"-s",
"--size",
action="store",
dest="size",
help="Processed image size.")
parser.add_option(
"-c",
"--color",
action="store",
dest="color",
help="whether to use color images.")
return parser.parse_args()
if __name__ == '__main__':
options, args = option_parser()
data_dir = options.input
processed_image_size = int(options.size)
color = options.color == "1"
data_creator = ImageClassificationDatasetCreater(data_dir,
processed_image_size,
color)
data_creator = ImageClassificationDatasetCreater(
data_dir, processed_image_size, color)
data_creator.train_list_name = "train.txt"
data_creator.test_list_name = "test.txt"
data_creator.num_per_batch = 1000

42
demo/image_classification/vgg_16_cifar.py
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@ -18,14 +18,20 @@ is_predict = get_config_arg("is_predict", bool, False)
####################Data Configuration ##################
if not is_predict:
data_dir='data/cifar-out/batches/'
meta_path=data_dir+'batches.meta'
args = {'meta':meta_path,'mean_img_size': 32,
'img_size': 32,'num_classes': 10,
'use_jpeg': 1,'color': "color"}
define_py_data_sources2(train_list="train.list",
data_dir = 'data/cifar-out/batches/'
meta_path = data_dir + 'batches.meta'
args = {
'meta': meta_path,
'mean_img_size': 32,
'img_size': 32,
'num_classes': 10,
'use_jpeg': 1,
'color': "color"
}
define_py_data_sources2(
train_list="train.list",
test_list="train.list",
module='image_provider',
obj='processData',
@ -33,21 +39,17 @@ if not is_predict:
######################Algorithm Configuration #############
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128)
)
batch_size=128,
learning_rate=0.1 / 128.0,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * 128))
#######################Network Configuration #############
data_size=3*32*32
label_size=10
img = data_layer(name='image',
size=data_size)
data_size = 3 * 32 * 32
label_size = 10
img = data_layer(name='image', size=data_size)
# small_vgg is predefined in trainer_config_helpers.networks
predict = small_vgg(input_image=img,
num_channels=3,
num_classes=label_size)
predict = small_vgg(input_image=img, num_channels=3, num_classes=label_size)
if not is_predict:
lbl = data_layer(name="label", size=label_size)

1
demo/introduction/README.md
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@ -1,4 +1,3 @@
This folder contains scripts used in PaddlePaddle introduction.
- use `bash train.sh` to train a simple linear regression model
- use `python evaluate_model.py` to read model parameters. You can see that `w` and `b` are very close to [2, 0.3].

6
demo/introduction/dataprovider.py
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@ -15,10 +15,10 @@
from paddle.trainer.PyDataProvider2 import *
import random
# define data types of input: 2 real numbers
@provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False)
@provider(input_types=[dense_vector(1), dense_vector(1)], use_seq=False)
def process(settings, input_file):
for i in xrange(2000):
x = random.random()
yield [x], [2*x+0.3]
yield [x], [2 * x + 0.3]

3
demo/introduction/evaluate_model.py
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@ -23,14 +23,17 @@ Usage:
import numpy as np
import os
def load(file_name):
with open(file_name, 'rb') as f:
f.read(16) # skip header for float type.
return np.fromfile(f, dtype=np.float32)
def main():
print 'w=%.6f, b=%.6f from pass 29' % (load('output/pass-00029/w'),
load('output/pass-00029/b'))
if __name__ == '__main__':
main()

19
demo/introduction/trainer_config.py
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@ -16,9 +16,14 @@ from paddle.trainer_config_helpers import *
# 1. read data. Suppose you saved above python code as dataprovider.py
data_file = 'empty.list'
with open(data_file, 'w') as f: f.writelines(' ')
define_py_data_sources2(train_list=data_file, test_list=None,
module='dataprovider', obj='process',args={})
with open(data_file, 'w') as f:
f.writelines(' ')
define_py_data_sources2(
train_list=data_file,
test_list=None,
module='dataprovider',
obj='process',
args={})
# 2. learning algorithm
settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer())
@ -26,7 +31,11 @@ settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer())
# 3. Network configuration
x = data_layer(name='x', size=1)
y = data_layer(name='y', size=1)
y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
y_predict = fc_layer(
input=x,
param_attr=ParamAttr(name='w'),
size=1,
act=LinearActivation(),
bias_attr=ParamAttr(name='b'))
cost = regression_cost(input=y_predict, label=y)
outputs(cost)

4
demo/mnist/data/generate_list.py
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@ -13,9 +13,9 @@
# limitations under the License.
o = open("./" + "train.list", "w")
o.write("./data/raw_data/train" +"\n")
o.write("./data/raw_data/train" + "\n")
o.close()
o = open("./" + "test.list", "w")
o.write("./data/raw_data/t10k" +"\n")
o.write("./data/raw_data/t10k" + "\n")
o.close()

1
demo/mnist/data/get_mnist_data.sh
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@ -19,4 +19,3 @@ done
cd $DIR
rm -f *.list
python generate_list.py

7
demo/mnist/mnist_provider.py
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@ -2,10 +2,9 @@ from paddle.trainer.PyDataProvider2 import *
# Define a py data provider
@provider(input_types={
'pixel': dense_vector(28 * 28),
'label': integer_value(10)
})
@provider(
input_types={'pixel': dense_vector(28 * 28),
'label': integer_value(10)})
def process(settings, filename): # settings is not used currently.
imgf = filename + "-images-idx3-ubyte"
labelf = filename + "-labels-idx1-ubyte"

25
demo/mnist/vgg_16_mnist.py
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@ -18,32 +18,29 @@ is_predict = get_config_arg("is_predict", bool, False)
####################Data Configuration ##################
if not is_predict:
data_dir='./data/'
define_py_data_sources2(train_list= data_dir + 'train.list',
test_list= data_dir + 'test.list',
data_dir = './data/'
define_py_data_sources2(
train_list=data_dir + 'train.list',
test_list=data_dir + 'test.list',
module='mnist_provider',
obj='process')
######################Algorithm Configuration #############
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128)
)
batch_size=128,
learning_rate=0.1 / 128.0,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * 128))
#######################Network Configuration #############
data_size=1*28*28
label_size=10
data_size = 1 * 28 * 28
label_size = 10
img = data_layer(name='pixel', size=data_size)
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img,
num_channels=1,
num_classes=label_size)
predict = small_vgg(input_image=img, num_channels=1, num_classes=label_size)
if not is_predict:
lbl = data_layer(name="label", size=label_size)

35
demo/model_zoo/embedding/extract_para.py
Unescape View File

@ -12,7 +12,6 @@
# 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.
"""
Example:
python extract_para.py --preModel PREMODEL --preDict PREDICT \
@ -29,6 +28,7 @@ Options:
from optparse import OptionParser
import struct
def get_row_index(preDict, usrDict):
"""
Get the row positions for all words in user dictionary from pre-trained dictionary.
@ -47,7 +47,9 @@ def get_row_index(preDict, usrDict):
pos.append(index[word])
return pos
def extract_parameters_by_usrDict(preModel, preDict, usrModel, usrDict, paraDim):
def extract_parameters_by_usrDict(preModel, preDict, usrModel, usrDict,
paraDim):
"""
Extract desired parameters from a pretrained embedding model based on user dictionary
"""
@ -70,6 +72,7 @@ def extract_parameters_by_usrDict(preModel, preDict, usrModel, usrDict, paraDim)
print "extract parameters finish, total", len(rowIndex), "lines"
fi.close()
def main():
"""
Main entry for running paraconvert.py
@ -78,19 +81,33 @@ def main():
"python %prog --preModel PREMODEL --preDict PREDICT" \
" --usrModel USRMODEL --usrDict USRDICT -d DIM"
parser = OptionParser(usage)
parser.add_option("--preModel", action="store", dest="preModel",
parser.add_option(
"--preModel",
action="store",
dest="preModel",
help="the name of pretrained embedding model")
parser.add_option("--preDict", action="store", dest="preDict",
parser.add_option(
"--preDict",
action="store",
dest="preDict",
help="the name of pretrained dictionary")
parser.add_option("--usrModel", action="store", dest="usrModel",
parser.add_option(
"--usrModel",
action="store",
dest="usrModel",
help="the name of output usr embedding model")
parser.add_option("--usrDict", action="store", dest="usrDict",
parser.add_option(
"--usrDict",
action="store",
dest="usrDict",
help="the name of user specified dictionary")
parser.add_option("-d", action="store", dest="dim",
help="dimension of parameter")
parser.add_option(
"-d", action="store", dest="dim", help="dimension of parameter")
(options, args) = parser.parse_args()
extract_parameters_by_usrDict(options.preModel, options.preDict,
options.usrModel, options.usrDict, int(options.dim))
options.usrModel, options.usrDict,
int(options.dim))
if __name__ == '__main__':
main()

26
demo/model_zoo/embedding/paraconvert.py
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@ -12,7 +12,6 @@
# 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.
"""
Example:
python paraconvert.py --b2t -i INPUT -o OUTPUT -d DIM
@ -29,6 +28,7 @@ Options:
from optparse import OptionParser
import struct
def binary2text(input, output, paraDim):
"""
Convert a binary parameter file of embedding model to be a text file.
@ -76,6 +76,7 @@ def binary2text(input, output, paraDim):
fo.close()
print "binary2text finish, total", line, "lines"
def get_para_count(input):
"""
Compute the total number of embedding parameters in input text file.
@ -90,6 +91,7 @@ def get_para_count(input):
numRows += 1
return numRows * paraDim
def text2binary(input, output, paddle_head=True):
"""
Convert a text parameter file of embedding model to be a binary file.
@ -123,6 +125,7 @@ def text2binary(input, output, paddle_head=True):
fo.close()
print "text2binary finish, total", count, "lines"
def main():
"""
Main entry for running paraconvert.py
@ -131,21 +134,26 @@ def main():
"python %prog --b2t -i INPUT -o OUTPUT -d DIM \n" \
"python %prog --t2b -i INPUT -o OUTPUT"
parser = OptionParser(usage)
parser.add_option("--b2t", action="store_true",
parser.add_option(
"--b2t",
action="store_true",
help="convert parameter file of embedding model from binary to text")
parser.add_option("--t2b", action="store_true",
parser.add_option(
"--t2b",
action="store_true",
help="convert parameter file of embedding model from text to binary")
parser.add_option("-i", action="store", dest="input",
help="input parameter file name")
parser.add_option("-o", action="store", dest="output",
help="output parameter file name")
parser.add_option("-d", action="store", dest="dim",
help="dimension of parameter")
parser.add_option(
"-i", action="store", dest="input", help="input parameter file name")
parser.add_option(
"-o", action="store", dest="output", help="output parameter file name")
parser.add_option(
"-d", action="store", dest="dim", help="dimension of parameter")
(options, args) = parser.parse_args()
if options.b2t:
binary2text(options.input, options.output, options.dim)
if options.t2b:
text2binary(options.input, options.output)
if __name__ == '__main__':
main()

111
demo/model_zoo/resnet/classify.py
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@ -26,16 +26,22 @@ from py_paddle import swig_paddle, DataProviderConverter
from paddle.trainer.PyDataProvider2 import dense_vector
from paddle.trainer.config_parser import parse_config
logging.basicConfig(format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
logging.basicConfig(
format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
logging.getLogger().setLevel(logging.INFO)
class ImageClassifier():
def __init__(self, train_conf, model_dir=None,
resize_dim=256, crop_dim=224,
def __init__(self,
train_conf,
model_dir=None,
resize_dim=256,
crop_dim=224,
use_gpu=True,
mean_file=None,
output_layer=None,
oversample=False, is_color=True):
oversample=False,
is_color=True):
"""
train_conf: network configure.
model_dir: string, directory of model.
@ -62,9 +68,9 @@ class ImageClassifier():
assert isinstance(self.output_layer, basestring)
self.output_layer = self.output_layer.split(",")
self.transformer = image_util.ImageTransformer(is_color = is_color)
self.transformer.set_transpose((2,0,1))
self.transformer.set_channel_swap((2,1,0))
self.transformer = image_util.ImageTransformer(is_color=is_color)
self.transformer.set_transpose((2, 0, 1))
self.transformer.set_channel_swap((2, 1, 0))
self.mean_file = mean_file
if self.mean_file is not None:
@ -74,12 +80,13 @@ class ImageClassifier():
else:
# if you use three mean value, set like:
# this three mean value is calculated from ImageNet.
self.transformer.set_mean(np.array([103.939,116.779,123.68]))
self.transformer.set_mean(np.array([103.939, 116.779, 123.68]))
conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (int(use_gpu))
conf = parse_config(train_conf, conf_args)
swig_paddle.initPaddle("--use_gpu=%d" % (int(use_gpu)))
self.network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
self.network.loadParameters(self.model_dir)
@ -105,14 +112,14 @@ class ImageClassifier():
# image_util.resize_image: short side is self.resize_dim
image = image_util.resize_image(image, self.resize_dim)
image = np.array(image)
input = np.zeros((1, image.shape[0], image.shape[1], 3),
dtype=np.float32)
input = np.zeros(
(1, image.shape[0], image.shape[1], 3), dtype=np.float32)
input[0] = image.astype(np.float32)
input = image_util.oversample(input, self.crop_dims)
else:
image = image.resize(self.crop_dims, Image.ANTIALIAS)
input = np.zeros((1, self.crop_dims[0], self.crop_dims[1], 3),
dtype=np.float32)
input = np.zeros(
(1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32)
input[0] = np.array(image).astype(np.float32)
data_in = []
@ -172,7 +179,7 @@ class ImageClassifier():
logging.info("Label of %s is: %d", image, lab[0])
return results
def extract(self, data_file, output_dir, batch_size = 10000):
def extract(self, data_file, output_dir, batch_size=10000):
"""
extract and save features of output layers, which are
specify in Outputs() in network configure.
@ -197,7 +204,7 @@ class ImageClassifier():
image_feature[file_name] = feature
sample_num += 1
if sample_num == batch_size:
batch_name = os.path.join(output_dir, 'batch_%d' %(batch_num))
batch_name = os.path.join(output_dir, 'batch_%d' % (batch_num))
self.save_file(image_feature, batch_name)
logging.info('Finish batch %d', batch_num)
batch_num += 1
@ -206,7 +213,7 @@ class ImageClassifier():
if idx % 1000 == 0:
logging.info('%d/%d, %s', idx, len(image_files), file_name)
if sample_num > 0:
batch_name = os.path.join(output_dir, 'batch_%d' %(batch_num))
batch_name = os.path.join(output_dir, 'batch_%d' % (batch_num))
self.save_file(image_feature, batch_name)
logging.info('Finish batch %d', batch_num)
logging.info('Done: make image feature batch')
@ -215,37 +222,63 @@ class ImageClassifier():
of = open(file, 'wb')
cPickle.dump(data, of, protocol=cPickle.HIGHEST_PROTOCOL)
def option_parser():
"""
Main entry for predciting
"""
usage = "%prog -c config -i data_list -w model_dir [options]"
parser = OptionParser(usage="usage: %s" % usage)
parser.add_option("-j", "--job",
action="store", dest="job_type",
parser.add_option(
"-j",
"--job",
action="store",
dest="job_type",
help="job type: predict, extract\
predict: predicting,\
extract: extract features")
parser.add_option("-c", "--conf",
action="store", dest="train_conf",
parser.add_option(
"-c",
"--conf",
action="store",
dest="train_conf",
help="network config")
parser.add_option("-i", "--data",
action="store", dest="data_file",
help="image list")
parser.add_option("-w", "--model",
action="store", dest="model_path",
default=None, help="model path")
parser.add_option("-g", "--use_gpu", action="store",
dest="use_gpu", default=True,
parser.add_option(
"-i", "--data", action="store", dest="data_file", help="image list")
parser.add_option(
"-w",
"--model",
action="store",
dest="model_path",
default=None,
help="model path")
parser.add_option(
"-g",
"--use_gpu",
action="store",
dest="use_gpu",
default=True,
help="Whether to use gpu mode.")
parser.add_option("-o", "--output_dir",
action="store", dest="output_dir",
default="output", help="output path")
parser.add_option("-m", "--mean", action="store",
dest="mean", default=None,
parser.add_option(
"-o",
"--output_dir",
action="store",
dest="output_dir",
default="output",
help="output path")
parser.add_option(
"-m",
"--mean",
action="store",
dest="mean",
default=None,
help="mean file.")
parser.add_option("-p", "--multi_crop", action="store_true",
dest="multi_crop", default=False,
parser.add_option(
"-p",
"--multi_crop",
action="store_true",
dest="multi_crop",
default=False,
help="Wether to use multiple crops on image.")
parser.add_option("-l", "--output_layer", action="store",
dest="output_layer", default=None,
@ -254,13 +287,15 @@ def option_parser():
"classification probability, output in resnet.py.")
return parser.parse_args()
def main():
"""
1. parse input arguments.
2. predicting or extract features according job type.
"""
options, args = option_parser()
obj = ImageClassifier(options.train_conf,
obj = ImageClassifier(
options.train_conf,
options.model_path,
use_gpu=options.use_gpu,
mean_file=options.mean,
@ -270,8 +305,8 @@ def main():
obj.predict(options.data_file)
elif options.job_type == "extract":
obj.extract(options.data_file,
options.output_dir)
obj.extract(options.data_file, options.output_dir)
if __name__ == '__main__':
main()

1
demo/model_zoo/resnet/example/__init__.py
Unescape View File

@ -11,4 +11,3 @@
# 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.

15
demo/model_zoo/resnet/example/image_list_provider.py
Unescape View File

@ -16,8 +16,7 @@ from paddle.utils.image_util import *
from paddle.trainer.PyDataProvider2 import *
def hook(settings, image_size, crop_size, color, file_list,
is_train, **kwargs):
def hook(settings, image_size, crop_size, color, file_list, is_train, **kwargs):
"""
Description: Init with a list of data file
file_list is the name list of input files.
@ -58,7 +57,7 @@ def hook(settings, image_size, crop_size, color, file_list,
sz = settings.crop_size * settings.crop_size
settings.img_mean = np.zeros(sz * 3, dtype=np.single)
for idx, value in enumerate(settings.mean_value):
settings.img_mean[idx * sz: (idx + 1) * sz] = value
settings.img_mean[idx * sz:(idx + 1) * sz] = value
settings.img_mean = settings.img_mean.reshape(3, settings.crop_size,
settings.crop_size)
@ -69,7 +68,8 @@ def hook(settings, image_size, crop_size, color, file_list,
settings.input_types = [
dense_vector(settings.img_input_size), # image feature
integer_value(1)] # labels
integer_value(1)
] # labels
settings.logger.info('Image short side: %s', settings.img_size)
settings.logger.info('Crop size: %s', settings.crop_size)
@ -97,9 +97,6 @@ def processData(settings, file_list):
# swap channel
if settings.is_swap_channel:
img = img[settings.swap_channel, :, :]
img_feat = preprocess_img(img,
settings.img_mean,
settings.crop_size,
settings.is_train,
settings.color)
img_feat = preprocess_img(img, settings.img_mean, settings.crop_size,
settings.is_train, settings.color)
yield img_feat.tolist(), int(lab.strip())

6
demo/model_zoo/resnet/load_feature.py
Unescape View File

@ -17,9 +17,11 @@ import sys
import cPickle
import logging
logging.basicConfig(format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
logging.basicConfig(
format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
logging.getLogger().setLevel(logging.INFO)
def load_feature_c(file):
"""
Load feature extracted by C++ interface.
@ -38,6 +40,7 @@ def load_feature_c(file):
f.close()
return features
def load_feature_py(feature_dir):
"""
Load feature extracted by python interface.
@ -54,6 +57,7 @@ def load_feature_py(feature_dir):
logging.info('Load feature file %s', file_name)
return features
if __name__ == '__main__':
print load_feature_py(sys.argv[1])
#print load_feature_c(sys.argv[1])

123
demo/model_zoo/resnet/resnet.py
Unescape View File

@ -13,7 +13,6 @@
# limitations under the License.
from paddle.trainer_config_helpers import *
"""
paper: https://arxiv.org/abs/1512.03385
"""
@ -28,11 +27,15 @@ if not is_predict and data_provider:
# mean.meta size : 3 x 224 x 224.
# If you use three mean value, set like:
# "mean_value:103.939,116.779,123.68;"
args={
args = {
'mean_meta': "model/mean_meta_224/mean.meta",
'image_size': 224, 'crop_size': 224,
'color': True,'swap_channel:': [2, 1, 0]}
define_py_data_sources2(train_list,
'image_size': 224,
'crop_size': 224,
'color': True,
'swap_channel:': [2, 1, 0]
}
define_py_data_sources2(
train_list,
'example/test.list',
module="example.image_list_provider",
obj="processData",
@ -54,12 +57,16 @@ Settings(
learning_method='momentum',
learning_rate_decay_a=0.5,
learning_rate_decay_b=1200000 * 10,
learning_rate_schedule="discexp",
)
learning_rate_schedule="discexp", )
def conv_bn_layer(name, input, filter_size, num_filters,
stride, padding, channels=None,
def conv_bn_layer(name,
input,
filter_size,
num_filters,
stride,
padding,
channels=None,
active_type=ReluActivation()):
"""
A wrapper for conv layer with batch normalization layers.
@ -67,7 +74,8 @@ def conv_bn_layer(name, input, filter_size, num_filters,
conv layer has no activation.
"""
tmp = img_conv_layer(name=name + "_conv",
tmp = img_conv_layer(
name=name + "_conv",
input=input,
filter_size=filter_size,
num_channels=channels,
@ -76,10 +84,8 @@ def conv_bn_layer(name, input, filter_size, num_filters,
padding=padding,
act=LinearActivation(),
bias_attr=False)
return batch_norm_layer(name=name + "_bn",
input=tmp,
act=active_type,
use_global_stats=is_test)
return batch_norm_layer(
name=name + "_bn", input=tmp, act=active_type, use_global_stats=is_test)
def bottleneck_block(name, input, num_filters1, num_filters2):
@ -88,19 +94,22 @@ def bottleneck_block(name, input, num_filters1, num_filters2):
Last conv_bn_layer has no activation.
Addto layer has activation of relu.
"""
last_name = conv_bn_layer(name=name + '_branch2a',
last_name = conv_bn_layer(
name=name + '_branch2a',
input=input,
filter_size=1,
num_filters=num_filters1,
stride=1,
padding=0)
last_name = conv_bn_layer(name=name + '_branch2b',
last_name = conv_bn_layer(
name=name + '_branch2b',
input=last_name,
filter_size=3,
num_filters=num_filters1,
stride=1,
padding=1)
last_name = conv_bn_layer(name=name + '_branch2c',
last_name = conv_bn_layer(
name=name + '_branch2c',
input=last_name,
filter_size=1,
num_filters=num_filters2,
@ -108,9 +117,8 @@ def bottleneck_block(name, input, num_filters1, num_filters2):
padding=0,
active_type=LinearActivation())
return addto_layer(name=name + "_addto",
input=[input, last_name],
act=ReluActivation())
return addto_layer(
name=name + "_addto", input=[input, last_name], act=ReluActivation())
def mid_projection(name, input, num_filters1, num_filters2, stride=2):
@ -123,7 +131,8 @@ def mid_projection(name, input, num_filters1, num_filters2, stride=2):
branch2x: bottleneck building block, shortcuts are identity.
"""
# stride = 2
branch1 = conv_bn_layer(name=name + '_branch1',
branch1 = conv_bn_layer(
name=name + '_branch1',
input=input,
filter_size=1,
num_filters=num_filters2,
@ -131,20 +140,23 @@ def mid_projection(name, input, num_filters1, num_filters2, stride=2):
padding=0,
active_type=LinearActivation())
last_name = conv_bn_layer(name=name + '_branch2a',
last_name = conv_bn_layer(
name=name + '_branch2a',
input=input,
filter_size=1,
num_filters=num_filters1,
stride=stride,
padding=0)
last_name = conv_bn_layer(name=name + '_branch2b',
last_name = conv_bn_layer(
name=name + '_branch2b',
input=last_name,
filter_size=3,
num_filters=num_filters1,
stride=1,
padding=1)
last_name = conv_bn_layer(name=name + '_branch2c',
last_name = conv_bn_layer(
name=name + '_branch2c',
input=last_name,
filter_size=1,
num_filters=num_filters2,
@ -152,9 +164,8 @@ def mid_projection(name, input, num_filters1, num_filters2, stride=2):
padding=0,
active_type=LinearActivation())
return addto_layer(name=name + "_addto",
input=[branch1, last_name],
act=ReluActivation())
return addto_layer(
name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
@ -168,7 +179,9 @@ def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
# For ImageNet
# conv1: 112x112
img = data_layer(name='input', size=224 * 224 * 3)
tmp = conv_bn_layer("conv1", img,
tmp = conv_bn_layer(
"conv1",
img,
filter_size=7,
channels=3,
num_filters=64,
@ -177,58 +190,56 @@ def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
# conv2_x: 56x56
tmp = mid_projection(name="res2_1",
input=tmp,
num_filters1=64,
num_filters2=256,
stride=1)
tmp = mid_projection(
name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
for i in xrange(2, res2_num + 1, 1):
tmp = bottleneck_block(name="res2_" + str(i),
input=tmp,
num_filters1=64,
num_filters2=256)
tmp = bottleneck_block(
name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
# conv3_x: 28x28
tmp = mid_projection(name="res3_1",
tmp = mid_projection(
name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
for i in xrange(2, res3_num + 1, 1):
tmp = bottleneck_block(
name="res3_" + str(i),
input=tmp,
num_filters1=128,
num_filters2=512)
for i in xrange(2, res3_num + 1, 1):
tmp = bottleneck_block(name="res3_" + str(i),
input=tmp, num_filters1=128,
num_filters2=512)
# conv4_x: 14x14
tmp = mid_projection(name="res4_1", input=tmp,
num_filters1=256, num_filters2=1024)
tmp = mid_projection(
name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
for i in xrange(2, res4_num + 1, 1):
tmp = bottleneck_block(name="res4_" + str(i),
tmp = bottleneck_block(
name="res4_" + str(i),
input=tmp,
num_filters1=256,
num_filters2=1024)
# conv5_x: 7x7
tmp = mid_projection(name="res5_1", input=tmp,
num_filters1=512, num_filters2=2048)
tmp = mid_projection(
name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
for i in xrange(2, res5_num + 1, 1):
tmp = bottleneck_block(name="res5_" + str(i),
input=tmp, num_filters1=512,
tmp = bottleneck_block(
name="res5_" + str(i),
input=tmp,
num_filters1=512,
num_filters2=2048)
tmp = img_pool_layer(name='avgpool',
tmp = img_pool_layer(
name='avgpool',
input=tmp,
pool_size=7,
stride=1,
pool_type=AvgPooling())
output = fc_layer(name='output',
input=tmp,
size=1000,
act=SoftmaxActivation())
output = fc_layer(
name='output', input=tmp, size=1000, act=SoftmaxActivation())
if not is_predict:
classification_cost(input=output, label=data_layer(name='label',
size=1))
classification_cost(
input=output, label=data_layer(
name='label', size=1))
def res_net_50():

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