Skip to content

M
models

PaddlePaddle / models
1 年多 前同步成功

通知 222
Star 6828
Fork 2962
M
models
提交 a0692c17 编写于 作者: W wanghaoshuang
浏览文件
操作

Merge branch 'develop' of https://github.com/PaddlePaddle/models into parallel_executor 

And some fix:
1. Remove some arguments
2. Rename 'inference' to 'infer'
3. Refine document
上级 2d985018 609dc345
展开全部 隐藏空白更改
内联 并排
Showing with 3574 addition and 1364 deletion
.travis.yml
浏览文件 @ a0692c17
......@@ -17,7 +17,7 @@ addons:
- python-pip
- python2.7-dev
- clang-format-3.8
ssh_known_hosts: 52.76.173.135
ssh_known_hosts: 13.229.163.131
before_install:
- if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
- sudo pip install -U virtualenv pre-commit pip
......
fluid/DeepASR/data_utils/async_data_reader.py
浏览文件 @ a0692c17
......@@ -15,9 +15,7 @@ from multiprocessing import Manager, Process
import data_utils.augmentor.trans_mean_variance_norm as trans_mean_variance_norm
import data_utils.augmentor.trans_add_delta as trans_add_delta
from data_utils.util import suppress_complaints, suppress_signal
from data_utils.util import SharedNDArray, SharedMemoryPoolManager
from data_utils.util import DaemonProcessGroup, batch_to_ndarray
from data_utils.util import CriticalException, ForceExitWrapper, EpochEndSignal
from data_utils.util import CriticalException, ForceExitWrapper
class SampleInfo(object):
......@@ -32,11 +30,12 @@ class SampleInfo(object):
label_bin_path (str): File containing the label data.
label_size (int): Byte count of the sample's label data.
label_frame_num (int): Label number of the sample.
sample_name (str): Key of the sample
"""
def __init__(self, feature_bin_path, feature_start, feature_size,
feature_frame_num, feature_dim, label_bin_path, label_start,
label_size, label_frame_num):
label_size, label_frame_num, sample_name):
self.feature_bin_path = feature_bin_path
self.feature_start = feature_start
self.feature_size = feature_size
......@@ -47,6 +46,7 @@ class SampleInfo(object):
self.label_start = label_start
self.label_size = label_size
self.label_frame_num = label_frame_num
self.sample_name = sample_name
class SampleInfoBucket(object):
......@@ -69,8 +69,8 @@ class SampleInfoBucket(object):
split_sentence_threshold(int): Sentence whose length larger than
the value will trigger split operation.
split_sub_sentence_len(int): sub-sentence length is equal to
(split_sub_sentence_len + \
rand() % split_perturb).
(split_sub_sentence_len
+ rand() % split_perturb).
"""
def __init__(self,
......@@ -104,24 +104,33 @@ class SampleInfoBucket(object):
feature_bin_path = self._feature_bin_paths[block_idx]
feature_desc_path = self._feature_desc_paths[block_idx]
label_desc_lines = open(label_desc_path).readlines()
feature_desc_lines = open(feature_desc_path).readlines()
sample_num = int(label_desc_lines[0].split()[1])
assert sample_num == int(feature_desc_lines[0].split()[1])
label_desc_lines = []
if label_desc_path != "":
label_desc_lines = open(label_desc_path).readlines()
sample_num = int(feature_desc_lines[0].split()[1])
if label_desc_path != "":
assert sample_num == int(label_desc_lines[0].split()[1])
for i in xrange(sample_num):
feature_desc_split = feature_desc_lines[i + 1].split()
sample_name = feature_desc_split[0]
feature_start = int(feature_desc_split[2])
feature_size = int(feature_desc_split[3])
feature_frame_num = int(feature_desc_split[4])
feature_dim = int(feature_desc_split[5])
label_desc_split = label_desc_lines[i + 1].split()
label_start = int(label_desc_split[2])
label_size = int(label_desc_split[3])
label_frame_num = int(label_desc_split[4])
assert feature_frame_num == label_frame_num
label_start = -1
label_size = -1
label_frame_num = feature_frame_num
if label_desc_path != "":
label_desc_split = label_desc_lines[i + 1].split()
label_start = int(label_desc_split[2])
label_size = int(label_desc_split[3])
label_frame_num = int(label_desc_split[4])
assert feature_frame_num == label_frame_num
if self._split_sentence_threshold == -1 or \
self._split_perturb == -1 or \
......@@ -131,7 +140,7 @@ class SampleInfoBucket(object):
SampleInfo(feature_bin_path, feature_start,
feature_size, feature_frame_num, feature_dim,
label_bin_path, label_start, label_size,
label_frame_num))
label_frame_num, sample_name))
#split sentence
else:
cur_frame_pos = 0
......@@ -152,16 +161,19 @@ class SampleInfoBucket(object):
* feature_dim * 4, cur_frame_len * feature_dim *
4, cur_frame_len, feature_dim, label_bin_path,
label_start + cur_frame_pos * 4, cur_frame_len *
4, cur_frame_len))
4, cur_frame_len, sample_name))
remain_frame_num -= cur_frame_len
cur_frame_pos += cur_frame_len
if remain_frame_num <= 0:
break
return sample_info_list
class EpochEndSignal():
pass
class AsyncDataReader(object):
"""DataReader provides basic audio sample preprocessing pipeline including
data loading and data augmentation.
......@@ -190,7 +202,7 @@ class AsyncDataReader(object):
def __init__(self,
feature_file_list,
label_file_list,
label_file_list="",
drop_frame_len=512,
proc_num=10,
sample_buffer_size=1024,
......@@ -213,25 +225,30 @@ class AsyncDataReader(object):
self._sample_info_buffer_size = sample_info_buffer_size
self._batch_buffer_size = batch_buffer_size
self._proc_num = proc_num
if self._proc_num <= 2:
raise ValueError("Value of `proc_num` should be greater than 2.")
self._sample_proc_num = self._proc_num - 2
self._verbose = verbose
self._force_exit = ForceExitWrapper(self._manager.Value('b', False))
def generate_bucket_list(self, is_shuffle):
if self._block_info_list is None:
block_feature_info_lines = open(self._feature_file_list).readlines()
block_label_info_lines = open(self._label_file_list).readlines()
assert len(block_feature_info_lines) == len(block_label_info_lines)
self._block_info_list = []
for i in xrange(0, len(block_feature_info_lines), 2):
block_info = (block_feature_info_lines[i],
block_feature_info_lines[i + 1],
block_label_info_lines[i],
block_label_info_lines[i + 1])
self._block_info_list.append(
map(lambda line: line.strip(), block_info))
if self._label_file_list != "":
block_label_info_lines = open(self._label_file_list).readlines()
assert len(block_feature_info_lines) == len(
block_label_info_lines)
for i in xrange(0, len(block_feature_info_lines), 2):
block_info = (block_feature_info_lines[i],
block_feature_info_lines[i + 1],
block_label_info_lines[i],
block_label_info_lines[i + 1])
self._block_info_list.append(
map(lambda line: line.strip(), block_info))
else:
for i in xrange(0, len(block_feature_info_lines), 2):
block_info = (block_feature_info_lines[i],
block_feature_info_lines[i + 1], "", "")
self._block_info_list.append(
map(lambda line: line.strip(), block_info))
if is_shuffle:
self._rng.shuffle(self._block_info_list)
......@@ -251,23 +268,13 @@ class AsyncDataReader(object):
def set_transformers(self, transformers):
self._transformers = transformers
def recycle(self, *args):
for shared_ndarray in args:
if not isinstance(shared_ndarray, SharedNDArray):
raise Value("Only support recycle SharedNDArray object.")
shared_ndarray.recycle(self._pool_manager.pool)
def _start_async_processing(self):
def _sample_generator(self):
sample_info_queue = self._manager.Queue(self._sample_info_buffer_size)
sample_queue = self._manager.Queue(self._sample_buffer_size)
self._order_id = 0
@suppress_complaints(verbose=self._verbose, notify=self._force_exit)
def ordered_feeding_task(sample_info_queue):
if self._verbose == 0:
signal.signal(signal.SIGTERM, suppress_signal)
signal.signal(signal.SIGINT, suppress_signal)
for sample_info_bucket in self._bucket_list:
try:
sample_info_list = \
......@@ -280,12 +287,13 @@ class AsyncDataReader(object):
sample_info_queue.put((sample_info, self._order_id))
self._order_id += 1
for i in xrange(self._sample_proc_num):
for i in xrange(self._proc_num):
sample_info_queue.put(EpochEndSignal())
feeding_proc = DaemonProcessGroup(
proc_num=1, target=ordered_feeding_task, args=(sample_info_queue, ))
feeding_proc.start_all()
feeding_thread = Thread(
target=ordered_feeding_task, args=(sample_info_queue, ))
feeding_thread.daemon = True
feeding_thread.start()
@suppress_complaints(verbose=self._verbose, notify=self._force_exit)
def ordered_processing_task(sample_info_queue, sample_queue, out_order):
......@@ -313,25 +321,32 @@ class AsyncDataReader(object):
sample_info.feature_size)
assert sample_info.feature_frame_num \
* sample_info.feature_dim * 4 == len(feature_bytes), \
(sample_info.feature_bin_path,
sample_info.feature_frame_num,
sample_info.feature_dim,
len(feature_bytes))
label_bytes = read_bytes(sample_info.label_bin_path,
sample_info.label_start,
sample_info.label_size)
assert sample_info.label_frame_num * 4 == len(label_bytes), (
sample_info.label_bin_path, sample_info.label_array,
len(label_bytes))
label_array = struct.unpack('I' * sample_info.label_frame_num,
label_bytes)
label_data = np.array(
label_array, dtype='int64').reshape(
(sample_info.label_frame_num, 1))
* sample_info.feature_dim * 4 \
== len(feature_bytes), \
(sample_info.feature_bin_path,
sample_info.feature_frame_num,
sample_info.feature_dim,
len(feature_bytes))
label_data = None
if sample_info.label_bin_path != "":
label_bytes = read_bytes(sample_info.label_bin_path,
sample_info.label_start,
sample_info.label_size)
assert sample_info.label_frame_num * 4 == len(
label_bytes), (sample_info.label_bin_path,
sample_info.label_array,
len(label_bytes))
label_array = struct.unpack(
'I' * sample_info.label_frame_num, label_bytes)
label_data = np.array(
label_array, dtype='int64').reshape(
(sample_info.label_frame_num, 1))
else:
label_data = np.zeros(
(sample_info.label_frame_num, 1), dtype='int64')
feature_frame_num = sample_info.feature_frame_num
feature_dim = sample_info.feature_dim
......@@ -341,12 +356,11 @@ class AsyncDataReader(object):
feature_data = np.array(
feature_array, dtype='float32').reshape((
sample_info.feature_frame_num, sample_info.feature_dim))
sample_data = (feature_data, label_data)
sample_data = (feature_data, label_data,
sample_info.sample_name)
for transformer in self._transformers:
# @TODO(pkuyym) to make transfomer only accept feature_data
sample_data = transformer.perform_trans(sample_data)
while order_id != out_order[0]:
time.sleep(0.001)
......@@ -362,74 +376,77 @@ class AsyncDataReader(object):
out_order = self._manager.list([0])
args = (sample_info_queue, sample_queue, out_order)
sample_proc = DaemonProcessGroup(
proc_num=self._sample_proc_num,
target=ordered_processing_task,
args=args)
sample_proc.start_all()
workers = [
Process(
target=ordered_processing_task, args=args)
for _ in xrange(self._proc_num)
]
return sample_queue
for w in workers:
w.daemon = True
w.start()
def batch_iterator(self, batch_size, minimum_batch_size):
@suppress_complaints(verbose=self._verbose, notify=self._force_exit)
def batch_assembling_task(sample_queue, batch_queue, pool):
def conv_to_shared(ndarray):
while self._force_exit == False:
try:
(name, shared_ndarray) = pool.popitem()
except Exception as e:
time.sleep(0.001)
finished_proc_num = 0
while self._force_exit == False:
try:
sample = sample_queue.get_nowait()
except Queue.Empty:
time.sleep(0.001)
else:
if isinstance(sample, EpochEndSignal):
finished_proc_num += 1
if finished_proc_num >= self._proc_num:
break
else:
shared_ndarray.copy(ndarray)
return shared_ndarray
continue
if self._verbose == 0:
signal.signal(signal.SIGTERM, suppress_signal)
signal.signal(signal.SIGINT, suppress_signal)
yield sample
def batch_iterator(self, batch_size, minimum_batch_size):
def batch_to_ndarray(batch_samples, lod):
assert len(batch_samples)
frame_dim = batch_samples[0][0].shape[1]
batch_feature = np.zeros((lod[-1], frame_dim), dtype="float32")
batch_label = np.zeros((lod[-1], 1), dtype="int64")
start = 0
name_lst = []
for sample in batch_samples:
frame_num = sample[0].shape[0]
batch_feature[start:start + frame_num, :] = sample[0]
batch_label[start:start + frame_num, :] = sample[1]
start += frame_num
name_lst.append(sample[2])
return (batch_feature, batch_label, name_lst)
@suppress_complaints(verbose=self._verbose, notify=self._force_exit)
def batch_assembling_task(sample_generator, batch_queue):
batch_samples = []
lod = [0]
done_num = 0
while done_num < self._sample_proc_num:
sample = sample_queue.get()
if isinstance(sample, EpochEndSignal):
done_num += 1
else:
batch_samples.append(sample)
lod.append(lod[-1] + sample[0].shape[0])
if len(batch_samples) == batch_size:
feature, label = batch_to_ndarray(batch_samples, lod)
feature = conv_to_shared(feature)
label = conv_to_shared(label)
lod = conv_to_shared(np.array(lod).astype('int64'))
batch_queue.put((feature, label, lod))
batch_samples = []
lod = [0]
for sample in sample_generator():
batch_samples.append(sample)
lod.append(lod[-1] + sample[0].shape[0])
if len(batch_samples) == batch_size:
(batch_feature, batch_label, name_lst) = batch_to_ndarray(
batch_samples, lod)
batch_queue.put((batch_feature, batch_label, lod, name_lst))
batch_samples = []
lod = [0]
if len(batch_samples) >= minimum_batch_size:
(feature, label) = batch_to_ndarray(batch_samples, lod)
feature = conv_to_shared(feature)
label = conv_to_shared(label)
lod = conv_to_shared(np.array(lod).astype('int64'))
batch_queue.put((feature, label, lod))
(batch_feature, batch_label, name_lst) = batch_to_ndarray(
batch_samples, lod)
batch_queue.put((batch_feature, batch_label, lod, name_lst))
batch_queue.put(EpochEndSignal())
sample_queue = self._start_async_processing()
batch_queue = self._manager.Queue(self._batch_buffer_size)
batch_queue = Queue.Queue(self._batch_buffer_size)
self._pool_manager = SharedMemoryPoolManager(self._batch_buffer_size *
3, self._manager)
assembling_proc = DaemonProcessGroup(
proc_num=1,
assembling_thread = Thread(
target=batch_assembling_task,
args=(sample_queue, batch_queue, self._pool_manager.pool))
assembling_proc.start_all()
args=(self._sample_generator, batch_queue))
assembling_thread.daemon = True
assembling_thread.start()
while self._force_exit == False:
try:
......@@ -440,6 +457,3 @@ class AsyncDataReader(object):
if isinstance(batch_data, EpochEndSignal):
break
yield batch_data
# clean the shared memory
del self._pool_manager
fluid/DeepASR/data_utils/augmentor/tests/test_data_trans.py
浏览文件 @ a0692c17
......@@ -22,7 +22,7 @@ class TestTransMeanVarianceNorm(unittest.TestCase):
feature = np.zeros((2, 120), dtype="float32")
feature.fill(1)
trans = trans_mean_variance_norm.TransMeanVarianceNorm(self._file_path)
(feature1, label1) = trans.perform_trans((feature, None))
(feature1, label1, name) = trans.perform_trans((feature, None, None))
(mean, var) = trans.get_mean_var()
feature_flat1 = feature1.flatten()
feature_flat = feature.flatten()
......@@ -70,7 +70,7 @@ class TestTransAddDelta(unittest.TestCase):
feature[2, 0:40].fill(3)
feature[3, 0:40].fill(4)
trans = trans_add_delta.TransAddDelta()
(feature, label) = trans.perform_trans((feature, None))
(feature, label, name) = trans.perform_trans((feature, None, None))
self.assertAlmostEqual(feature.shape[0], 4)
self.assertAlmostEqual(feature.shape[1], 120)
self.assertAlmostEqual(1.0, feature[0][0])
......@@ -93,7 +93,7 @@ class TestTransSplict(unittest.TestCase):
feature[i, :].fill(i)
trans = trans_splice.TransSplice()
(feature, label) = trans.perform_trans((feature, None))
(feature, label, name) = trans.perform_trans((feature, None, None))
self.assertEqual(feature.shape[1], 110)
for i in xrange(8):
......
fluid/DeepASR/data_utils/augmentor/trans_add_delta.py
浏览文件 @ a0692c17
......@@ -32,9 +32,9 @@ class TransAddDelta(object):
Args:
sample(object,tuple): contain feature numpy and label numpy
Returns:
(feature, label)
(feature, label, name)
"""
(feature, label) = sample
(feature, label, name) = sample
frame_dim = feature.shape[1]
d_frame_dim = frame_dim * 3
head_filled = 5
......@@ -64,7 +64,7 @@ class TransAddDelta(object):
start * d_frame_dim + 2 * frame_dim, frame_dim, nframe,
d_frame_dim)
mat.shape = tmp_shape
return (mat[head_filled:mat.shape[0] - tail_filled, :], label)
return (mat[head_filled:mat.shape[0] - tail_filled, :], label, name)
def _regress(self, data_in, start_in, data_out, start_out, size, n, step):
""" regress
......
fluid/DeepASR/data_utils/augmentor/trans_mean_variance_norm.py
浏览文件 @ a0692c17
......@@ -53,9 +53,9 @@ class TransMeanVarianceNorm(object):
Args:
sample(object):input sample, contain feature numpy and label numpy
Returns:
(feature, label)
(feature, label, name)
"""
(feature, label) = sample
(feature, label, name) = sample
shape = feature.shape
assert len(shape) == 2
nfeature_len = shape[0] * shape[1]
......@@ -68,4 +68,4 @@ class TransMeanVarianceNorm(object):
feature[ncur_idx:ncur_idx + self._nLen] = block
ncur_idx += self._nLen
feature = feature.reshape(shape)
return (feature, label)
return (feature, label, name)
fluid/DeepASR/data_utils/augmentor/trans_splice.py
浏览文件 @ a0692c17
......@@ -30,9 +30,9 @@ class TransSplice(object):
Args:
sample(object): input sample(feature, label)
Return:
(feature, label)
(feature, label, name)
"""
(feature, label) = sample
(feature, label, name) = sample
nframe_num = feature.shape[0]
nframe_dim = feature.shape[1]
nnew_frame_dim = nframe_dim * (
......@@ -61,4 +61,4 @@ class TransSplice(object):
np.copyto(ret[i * nnew_frame_dim:(i + 1) * nnew_frame_dim],
mat[i * nframe_dim:i * nframe_dim + nnew_frame_dim])
ret = ret.reshape((nframe_num, nnew_frame_dim))
return (ret, label)
return (ret, label, name)
fluid/DeepASR/data_utils/util.py
浏览文件 @ a0692c17
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys, time
import sys
from six import reraise
from tblib import Traceback
from multiprocessing import Manager, Process
import posix_ipc, mmap
import numpy as np
......@@ -37,19 +35,6 @@ def lodtensor_to_ndarray(lod_tensor):
return ret, lod_tensor.lod()
def batch_to_ndarray(batch_samples, lod):
frame_dim = batch_samples[0][0].shape[1]
batch_feature = np.zeros((lod[-1], frame_dim), dtype="float32")
batch_label = np.zeros((lod[-1], 1), dtype="int64")
start = 0
for sample in batch_samples:
frame_num = sample[0].shape[0]
batch_feature[start:start + frame_num, :] = sample[0]
batch_label[start:start + frame_num, :] = sample[1]
start += frame_num
return (batch_feature, batch_label)
def split_infer_result(infer_seq, lod):
infer_batch = []
for i in xrange(0, len(lod[0]) - 1):
......@@ -57,127 +42,10 @@ def split_infer_result(infer_seq, lod):
return infer_batch
class DaemonProcessGroup(object):
def __init__(self, proc_num, target, args):
self._proc_num = proc_num
self._workers = [
Process(
target=target, args=args) for _ in xrange(self._proc_num)
]
def start_all(self):
for w in self._workers:
w.daemon = True
w.start()
@property
def proc_num(self):
return self._proc_num
class EpochEndSignal(object):
pass
class CriticalException(Exception):
pass
class SharedNDArray(object):
"""SharedNDArray utilizes shared memory to avoid data serialization when
data object shared among different processes. We can reconstruct the
`ndarray` when memory address, shape and dtype provided.
Args:
name (str): Address name of shared memory.
whether_verify (bool): Whether to validate the writing operation.
"""
def __init__(self, name, whether_verify=False):
self._name = name
self._shm = None
self._buf = None
self._array = np.zeros(1, dtype=np.float32)
self._inited = False
self._whether_verify = whether_verify
def zeros_like(self, shape, dtype):
size = int(np.prod(shape)) * np.dtype(dtype).itemsize
if self._inited:
self._shm = posix_ipc.SharedMemory(self._name)
else:
self._shm = posix_ipc.SharedMemory(
self._name, posix_ipc.O_CREAT, size=size)
self._buf = mmap.mmap(self._shm.fd, size)
self._array = np.ndarray(shape, dtype, self._buf, order='C')
def copy(self, ndarray):
size = int(np.prod(ndarray.shape)) * np.dtype(ndarray.dtype).itemsize
self.zeros_like(ndarray.shape, ndarray.dtype)
self._array[:] = ndarray
self._buf.flush()
self._inited = True
if self._whether_verify:
shm = posix_ipc.SharedMemory(self._name)
buf = mmap.mmap(shm.fd, size)
array = np.ndarray(ndarray.shape, ndarray.dtype, buf, order='C')
np.testing.assert_array_equal(array, ndarray)
@property
def ndarray(self):
return self._array
def recycle(self, pool):
self._buf.close()
self._shm.close_fd()
self._inited = False
pool[self._name] = self
def __getstate__(self):
return (self._name, self._array.shape, self._array.dtype, self._inited,
self._whether_verify)
def __setstate__(self, state):
self._name = state[0]
self._inited = state[3]
self.zeros_like(state[1], state[2])
self._whether_verify = state[4]
class SharedMemoryPoolManager(object):
"""SharedMemoryPoolManager maintains a multiprocessing.Manager.dict object.
All available addresses are allocated once and will be reused. Though this
class is not process-safe, the pool can be shared between processes. All
shared memory should be unlinked before the main process exited.
Args:
pool_size (int): Size of shared memory pool.
manager (dict): A multiprocessing.Manager object, the pool is
maintained by the proxy process.
name_prefix (str): Address prefix of shared memory.
"""
def __init__(self, pool_size, manager, name_prefix='/deep_asr'):
self._names = []
self._dict = manager.dict()
self._time_prefix = time.strftime('%Y%m%d%H%M%S')
for i in xrange(pool_size):
name = name_prefix + '_' + self._time_prefix + '_' + str(i)
self._dict[name] = SharedNDArray(name)
self._names.append(name)
@property
def pool(self):
return self._dict
def __del__(self):
for name in self._names:
# have to unlink the shared memory
posix_ipc.unlink_shared_memory(name)
def suppress_signal(signo, stack_frame):
pass
......
fluid/DeepASR/decoder/post_decode_faster.cc
浏览文件 @ a0692c17
......@@ -21,14 +21,15 @@ using fst::StdArc;
Decoder::Decoder(std::string word_syms_filename,
std::string fst_in_filename,
std::string logprior_rxfilename) {
std::string logprior_rxfilename,
kaldi::BaseFloat acoustic_scale) {
const char* usage =
"Decode, reading log-likelihoods (of transition-ids or whatever symbol "
"is on the graph) as matrices.";
kaldi::ParseOptions po(usage);
binary = true;
acoustic_scale = 1.5;
this->acoustic_scale = acoustic_scale;
allow_partial = true;
kaldi::FasterDecoderOptions decoder_opts;
decoder_opts.Register(&po, true); // true == include obscure settings.
......
fluid/DeepASR/decoder/post_decode_faster.h
浏览文件 @ a0692c17
......@@ -29,7 +29,8 @@ class Decoder {
public:
Decoder(std::string word_syms_filename,
std::string fst_in_filename,
std::string logprior_rxfilename);
std::string logprior_rxfilename,
kaldi::BaseFloat acoustic_scale);
~Decoder();
// Interface to accept the scores read from specifier and return
......
fluid/DeepASR/decoder/pybind.cc
浏览文件 @ a0692c17
......@@ -23,7 +23,7 @@ PYBIND11_MODULE(post_decode_faster, m) {
m.doc() = "Decoder for Deep ASR model";
py::class_<Decoder>(m, "Decoder")
.def(py::init<std::string, std::string, std::string>())
.def(py::init<std::string, std::string, std::string, kaldi::BaseFloat>())
.def("decode",
(std::vector<std::string> (Decoder::*)(std::string)) &
Decoder::decode,
......
fluid/DeepASR/infer.py
浏览文件 @ a0692c17
......@@ -8,7 +8,7 @@ import paddle.fluid as fluid
import data_utils.augmentor.trans_mean_variance_norm as trans_mean_variance_norm
import data_utils.augmentor.trans_add_delta as trans_add_delta
import data_utils.augmentor.trans_splice as trans_splice
import data_utils.data_reader as reader
import data_utils.async_data_reader as reader
from data_utils.util import lodtensor_to_ndarray
from data_utils.util import split_infer_result
......@@ -79,12 +79,13 @@ def infer(args):
trans_splice.TransSplice()
]
infer_data_reader = reader.DataReader(args.infer_feature_lst,
args.infer_label_lst)
infer_data_reader = reader.AsyncDataReader(args.infer_feature_lst,
args.infer_label_lst)
infer_data_reader.set_transformers(ltrans)
feature_t = fluid.LoDTensor()
one_batch = infer_data_reader.batch_iterator(args.batch_size, 1).next()
(features, labels, lod) = one_batch
feature_t.set(features, place)
feature_t.set_lod([lod])
......
fluid/DeepASR/infer_by_ckpt.py
浏览文件 @ a0692c17
......@@ -106,6 +106,11 @@ def parse_args():
type=str,
default="./decoder/logprior",
help="The log prior probs for training data. (default: %(default)s)")
parser.add_argument(
'--acoustic_scale',
type=float,
default=0.2,
help="Scaling factor for acoustic likelihoods. (default: %(default)f)")
args = parser.parse_args()
return args
......@@ -143,6 +148,10 @@ def infer_from_ckpt(args):
# load checkpoint.
fluid.io.load_persistables(exe, args.checkpoint)
# init decoder
decoder = Decoder(args.vocabulary, args.graphs, args.log_prior,
args.acoustic_scale)
ltrans = [
trans_add_delta.TransAddDelta(2, 2),
trans_mean_variance_norm.TransMeanVarianceNorm(args.mean_var),
......@@ -162,12 +171,10 @@ def infer_from_ckpt(args):
args.minimum_batch_size)):
# load_data
(features, labels, lod) = batch_data
feature_t.set(features.ndarray, place)
feature_t.set_lod([lod.ndarray])
label_t.set(labels.ndarray, place)
label_t.set_lod([lod.ndarray])
infer_data_reader.recycle(features, labels, lod)
feature_t.set(features, place)
feature_t.set_lod([lod])
label_t.set(labels, place)
label_t.set_lod([lod])
results = exe.run(infer_program,
feed={"feature": feature_t,
......@@ -181,7 +188,7 @@ def infer_from_ckpt(args):
infer_batch = split_infer_result(probs, lod)
for index, sample in enumerate(infer_batch):
key = "utter#%d" % (batch_id * args.batch_size + index)
print(key, ": ", decoder.decode(key, sample), "\n")
print(key, ": ", decoder.decode(key, sample).encode("utf8"), "\n")
print(np.mean(infer_costs), np.mean(infer_accs))
......
fluid/DeepASR/tools/profile.py
浏览文件 @ a0692c17
......@@ -168,15 +168,13 @@ def profile(args):
start_time = time.time()
frames_seen = 0
# load_data
(features, labels, lod) = batch_data
feature_t.set(features.ndarray, place)
feature_t.set_lod([lod.ndarray])
label_t.set(labels.ndarray, place)
label_t.set_lod([lod.ndarray])
(features, labels, lod, _) = batch_data
feature_t.set(features, place)
feature_t.set_lod([lod])
label_t.set(labels, place)
label_t.set_lod([lod])
frames_seen += lod.ndarray[-1]
data_reader.recycle(features, labels, lod)
frames_seen += lod[-1]
outs = exe.run(fluid.default_main_program(),
feed={"feature": feature_t,
......
fluid/DeepASR/train.py
浏览文件 @ a0692c17
......@@ -192,13 +192,11 @@ def train(args):
test_data_reader.batch_iterator(args.batch_size,
args.minimum_batch_size)):
# load_data
(features, labels, lod) = batch_data
feature_t.set(features.ndarray, place)
feature_t.set_lod([lod.ndarray])
label_t.set(labels.ndarray, place)
label_t.set_lod([lod.ndarray])
test_data_reader.recycle(features, labels, lod)
(features, labels, lod, _) = batch_data
feature_t.set(features, place)
feature_t.set_lod([lod])
label_t.set(labels, place)
label_t.set_lod([lod])
cost, acc = exe.run(test_program,
feed={"feature": feature_t,
......@@ -212,6 +210,7 @@ def train(args):
# train data reader
train_data_reader = reader.AsyncDataReader(args.train_feature_lst,
args.train_label_lst, -1)
train_data_reader.set_transformers(ltrans)
# train
for pass_id in xrange(args.pass_num):
......@@ -220,13 +219,11 @@ def train(args):
train_data_reader.batch_iterator(args.batch_size,
args.minimum_batch_size)):
# load_data
(features, labels, lod) = batch_data
feature_t.set(features.ndarray, place)
feature_t.set_lod([lod.ndarray])
label_t.set(labels.ndarray, place)
label_t.set_lod([lod.ndarray])
train_data_reader.recycle(features, labels, lod)
(features, labels, lod, name_lst) = batch_data
feature_t.set(features, place)
feature_t.set_lod([lod])
label_t.set(labels, place)
label_t.set_lod([lod])
to_print = batch_id > 0 and (batch_id % args.print_per_batches == 0)
outs = exe.run(fluid.default_main_program(),
......
fluid/adversarial/README.md
浏览文件 @ a0692c17
......@@ -4,10 +4,109 @@ The minimum PaddlePaddle version needed for the code sample in this directory is
# Advbox
Advbox is a Python toolbox to create adversarial examples that fool neural networks. It requires Python and paddle.
Advbox is a toolbox to generate adversarial examples that fool neural networks and Advbox can benchmark the robustness of machine learning models.
## How to use
The Advbox is based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) Fluid and is under continual development, always welcoming contributions of the latest method of adversarial attacks and defenses.
1. train a model and save it's parameters. (like fluid_mnist.py)
2. load the parameters which is trained in step1, then reconstruct the model.(like mnist_tutorial_fgsm.py)
3. use advbox to generate the adversarial sample.
## Overview
[Szegedy et al.](https://arxiv.org/abs/1312.6199) discovered an intriguing properties of deep neural networks in the context of image classification for the first time. They showed that despite the state-of-the-art deep networks are surprisingly susceptible to adversarial attacks in the form of small perturbations to images that remain (almost) imperceptible to human vision system. These perturbations are found by optimizing the input to maximize the prediction error and the images modified by these perturbations are called as `adversarial examples`. The profound implications of these results triggered a wide interest of researchers in adversarial attacks and their defenses for deep learning in general.
Advbox is similar to [Foolbox](https://github.com/bethgelab/foolbox) and [CleverHans](https://github.com/tensorflow/cleverhans). CleverHans only supports TensorFlow framework while foolbox interfaces with many popular machine learning frameworks such as PyTorch, Keras, TensorFlow, Theano, Lasagne and MXNet. However, these two great libraries don't support PaddlePaddle, an easy-to-use, efficient, flexible and scalable deep learning platform which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
## Usage
Advbox provides many stable reference implementations of modern methods to generate adversarial examples such as FGSM, DeepFool, JSMA. When you want to benchmark the robustness of your neural networks , you can use the advbox to generate some adversarial examples and benchmark the networks. Some tips of using Advbox:
1. Train a model and save the parameters.
2. Load the parameters which has been trained，then reconstruct the model.
3. Use advbox to generate the adversarial samples.
#### Dependencies
* PaddlePaddle: [the lastest develop branch](http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html)
* Python 2.x
#### Structure
Network models, attack method's implements and the criterion that defines adversarial examples are three essential elements to generate adversarial examples. Misclassification is adopted as the adversarial criterion for briefness in Advbox.
The structure of Advbox module are as follows:
.
├── advbox
| ├── __init__.py
| ├── attack
| ├── __init__.py
| ├── base.py
| ├── deepfool.py
| ├── gradient_method.py
| ├── lbfgs.py
| └── saliency.py
| ├── models
| ├── __init__.py
| ├── base.py
| └── paddle.py
| └── adversary.py
├── tutorials
| ├── __init__.py
| ├── mnist_model.py
| ├── mnist_tutorial_lbfgs.py
| ├── mnist_tutorial_fgsm.py
| ├── mnist_tutorial_bim.py
| ├── mnist_tutorial_ilcm.py
| ├── mnist_tutorial_mifgsm.py
| ├── mnist_tutorial_jsma.py
| └── mnist_tutorial_deepfool.py
└── README.md
**advbox.attack**
Advbox implements several popular adversarial attacks which search adversarial examples. Each attack method uses a distance measure(L1, L2, etc.) to quantify the size of adversarial perturbations. Advbox is easy to craft adversarial example as some attack methods could perform internal hyperparameter tuning to find the minimum perturbation.
**advbox.model**
Advbox implements interfaces to PaddlePaddle. Additionally, other deep learning framworks such as TensorFlow can also be defined and employed. The module is use to compute predictions and gradients for given inputs in a specific framework.
**advbox.adversary**
Adversary contains the original object, the target and the adversarial examples. It provides the misclassification as the criterion to accept a adversarial example.
## Tutorials
The `./tutorials/` folder provides some tutorials to generate adversarial examples on the MNIST dataset. You can slightly modify the code to apply to other dataset. These attack methods are supported in Advbox:
* [L-BFGS](https://arxiv.org/abs/1312.6199)
* [FGSM](https://arxiv.org/abs/1412.6572)
* [BIM](https://arxiv.org/abs/1607.02533)
* [ILCM](https://arxiv.org/abs/1607.02533)
* [MI-FGSM](https://arxiv.org/pdf/1710.06081.pdf)
* [JSMA](https://arxiv.org/pdf/1511.07528)
* [DeepFool](https://arxiv.org/abs/1511.04599)
## Testing
Benchmarks on a vanilla CNN model.
> MNIST
| adversarial attacks | fooling rate (non-targeted) | fooling rate (targeted) | max_epsilon | iterations | Strength |
|:-----:| :----: | :---: | :----: | :----: | :----: |
|L-BFGS| --- | 89.2% | --- | One shot | *** |
|FGSM| 57.8% | 26.55% | 0.3 | One shot| *** |
|BIM| 97.4% | --- | 0.1 | 100 | **** |
|ILCM| --- | 100.0% | 0.1 | 100 | **** |
|MI-FGSM| 94.4% | 100.0% | 0.1 | 100 | **** |
|JSMA| 96.8% | 90.4%| 0.1 | 2000 | *** |
|DeepFool| 97.7% | 51.3% | --- | 100 | **** |
* The strength (higher for more asterisks) is based on the impression from the reviewed literature.
---
## References
* [Intriguing properties of neural networks](https://arxiv.org/abs/1312.6199), C. Szegedy et al., arxiv 2014
* [Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572), I. Goodfellow et al., ICLR 2015
* [Adversarial Examples In The Physical World](https://arxiv.org/pdf/1607.02533v3.pdf), A. Kurakin et al., ICLR workshop 2017
* [Boosting Adversarial Attacks with Momentum](https://arxiv.org/abs/1710.06081), Yinpeng Dong et al., arxiv 2018
* [The Limitations of Deep Learning in Adversarial Settings](https://arxiv.org/abs/1511.07528), N. Papernot et al., ESSP 2016
* [DeepFool: a simple and accurate method to fool deep neural networks](https://arxiv.org/abs/1511.04599), S. Moosavi-Dezfooli et al., CVPR 2016
* [Foolbox: A Python toolbox to benchmark the robustness of machine learning models](https://arxiv.org/abs/1707.04131), Jonas Rauber et al., arxiv 2018
* [CleverHans: An adversarial example library for constructing attacks, building defenses, and benchmarking both](https://github.com/tensorflow/cleverhans#setting-up-cleverhans)
* [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://arxiv.org/abs/1801.00553), Naveed Akhtar, Ajmal Mian, arxiv 2018
fluid/adversarial/advbox/attacks/gradient_method.py
浏览文件 @ a0692c17
......@@ -14,7 +14,8 @@ __all__ = [
'GradientMethodAttack', 'FastGradientSignMethodAttack', 'FGSM',
'FastGradientSignMethodTargetedAttack', 'FGSMT',
'BasicIterativeMethodAttack', 'BIM',
'IterativeLeastLikelyClassMethodAttack', 'ILCM'
'IterativeLeastLikelyClassMethodAttack', 'ILCM', 'MomentumIteratorAttack',
'MIFGSM'
]
......@@ -32,7 +33,12 @@ class GradientMethodAttack(Attack):
super(GradientMethodAttack, self).__init__(model)
self.support_targeted = support_targeted
def _apply(self, adversary, norm_ord=np.inf, epsilons=0.01, steps=100):
def _apply(self,
adversary,
norm_ord=np.inf,
epsilons=0.01,
steps=1,
epsilon_steps=100):
"""
Apply the gradient attack method.
:param adversary(Adversary):
......@@ -41,8 +47,11 @@ class GradientMethodAttack(Attack):
Order of the norm, such as np.inf, 1, 2, etc. It can't be 0.
:param epsilons(list|tuple|int):
Attack step size (input variation).
Largest step size if epsilons is not iterable.
:param steps:
The number of iterator steps.
The number of attack iteration.
:param epsilon_steps:
The number of Epsilons' iteration for each attack iteration.
:return:
adversary(Adversary): The Adversary object.
"""
......@@ -55,7 +64,7 @@ class GradientMethodAttack(Attack):
"This attack method doesn't support targeted attack!")
if not isinstance(epsilons, Iterable):
epsilons = np.linspace(epsilons, epsilons + 1e-10, num=steps)
epsilons = np.linspace(0, epsilons, num=epsilon_steps)
pre_label = adversary.original_label
min_, max_ = self.model.bounds()
......@@ -65,30 +74,33 @@ class GradientMethodAttack(Attack):
self.model.channel_axis() == adversary.original.shape[0] or
self.model.channel_axis() == adversary.original.shape[-1])
step = 1
adv_img = adversary.original
for epsilon in epsilons[:steps]:
for epsilon in epsilons[:]:
step = 1
adv_img = adversary.original
if epsilon == 0.0:
continue
if adversary.is_targeted_attack:
gradient = -self.model.gradient(adv_img, adversary.target_label)
else:
gradient = self.model.gradient(adv_img,
adversary.original_label)
if norm_ord == np.inf:
gradient_norm = np.sign(gradient)
else:
gradient_norm = gradient / self._norm(gradient, ord=norm_ord)
adv_img = adv_img + epsilon * gradient_norm * (max_ - min_)
adv_img = np.clip(adv_img, min_, max_)
adv_label = np.argmax(self.model.predict(adv_img))
logging.info('step={}, epsilon = {:.5f}, pre_label = {}, '
'adv_label={}'.format(step, epsilon, pre_label,
adv_label))
if adversary.try_accept_the_example(adv_img, adv_label):
return adversary
step += 1
for i in range(steps):
if adversary.is_targeted_attack:
gradient = -self.model.gradient(adv_img,
adversary.target_label)
else:
gradient = self.model.gradient(adv_img,
adversary.original_label)
if norm_ord == np.inf:
gradient_norm = np.sign(gradient)
else:
gradient_norm = gradient / self._norm(
gradient, ord=norm_ord)
adv_img = adv_img + epsilon * gradient_norm * (max_ - min_)
adv_img = np.clip(adv_img, min_, max_)
adv_label = np.argmax(self.model.predict(adv_img))
logging.info('step={}, epsilon = {:.5f}, pre_label = {}, '
'adv_label={}'.format(step, epsilon, pre_label,
adv_label))
if adversary.try_accept_the_example(adv_img, adv_label):
return adversary
step += 1
return adversary
@staticmethod
......@@ -113,7 +125,7 @@ class FastGradientSignMethodTargetedAttack(GradientMethodAttack):
Paper link: https://arxiv.org/abs/1412.6572
"""
def _apply(self, adversary, epsilons=0.03):
def _apply(self, adversary, epsilons=0.01):
return GradientMethodAttack._apply(
self,
adversary=adversary,
......@@ -144,7 +156,7 @@ class IterativeLeastLikelyClassMethodAttack(GradientMethodAttack):
Paper link: https://arxiv.org/abs/1607.02533
"""
def _apply(self, adversary, epsilons=0.001, steps=1000):
def _apply(self, adversary, epsilons=0.01, steps=1000):
return GradientMethodAttack._apply(
self,
adversary=adversary,
......@@ -164,7 +176,103 @@ class BasicIterativeMethodAttack(IterativeLeastLikelyClassMethodAttack):
super(BasicIterativeMethodAttack, self).__init__(model, False)
class MomentumIteratorAttack(GradientMethodAttack):
"""
The Momentum Iterative Fast Gradient Sign Method (Dong et al. 2017).
This method won the first places in NIPS 2017 Non-targeted Adversarial
Attacks and Targeted Adversarial Attacks. The original paper used
hard labels for this attack; no label smoothing. inf norm.
Paper link: https://arxiv.org/pdf/1710.06081.pdf
"""
def __init__(self, model, support_targeted=True):
"""
:param model(model): The model to be attacked.
:param support_targeted(bool): Does this attack method support targeted.
"""
super(MomentumIteratorAttack, self).__init__(model)
self.support_targeted = support_targeted
def _apply(self,
adversary,
norm_ord=np.inf,
epsilons=0.1,
steps=100,
epsilon_steps=100,
decay_factor=1):
"""
Apply the momentum iterative gradient attack method.
:param adversary(Adversary):
The Adversary object.
:param norm_ord(int):
Order of the norm, such as np.inf, 1, 2, etc. It can't be 0.
:param epsilons(list|tuple|float):
Attack step size (input variation).
Largest step size if epsilons is not iterable.
:param epsilon_steps:
The number of Epsilons' iteration for each attack iteration.
:param steps:
The number of attack iteration.
:param decay_factor:
The decay factor for the momentum term.
:return:
adversary(Adversary): The Adversary object.
"""
if norm_ord == 0:
raise ValueError("L0 norm is not supported!")
if not self.support_targeted:
if adversary.is_targeted_attack:
raise ValueError(
"This attack method doesn't support targeted attack!")
assert self.model.channel_axis() == adversary.original.ndim
assert (self.model.channel_axis() == 1 or
self.model.channel_axis() == adversary.original.shape[0] or
self.model.channel_axis() == adversary.original.shape[-1])
if not isinstance(epsilons, Iterable):
epsilons = np.linspace(0, epsilons, num=epsilon_steps)
min_, max_ = self.model.bounds()
pre_label = adversary.original_label
for epsilon in epsilons[:]:
if epsilon == 0.0:
continue
step = 1
adv_img = adversary.original
momentum = 0
for i in range(steps):
if adversary.is_targeted_attack:
gradient = -self.model.gradient(adv_img,
adversary.target_label)
else:
gradient = self.model.gradient(adv_img, pre_label)
# normalize gradient
velocity = gradient / self._norm(gradient, ord=1)
momentum = decay_factor * momentum + velocity
if norm_ord == np.inf:
normalized_grad = np.sign(momentum)
else:
normalized_grad = self._norm(momentum, ord=norm_ord)
perturbation = epsilon * normalized_grad
adv_img = adv_img + perturbation
adv_img = np.clip(adv_img, min_, max_)
adv_label = np.argmax(self.model.predict(adv_img))
logging.info(
'step={}, epsilon = {:.5f}, pre_label = {}, adv_label={}'
.format(step, epsilon, pre_label, adv_label))
if adversary.try_accept_the_example(adv_img, adv_label):
return adversary
step += 1
return adversary
FGSM = FastGradientSignMethodAttack
FGSMT = FastGradientSignMethodTargetedAttack
BIM = BasicIterativeMethodAttack
ILCM = IterativeLeastLikelyClassMethodAttack
MIFGSM = MomentumIteratorAttack
fluid/adversarial/mnist_tutorial_fgsm.py 已删除 100644 → 0
浏览文件 @ 2d985018
"""
FGSM demos on mnist using advbox tool.
"""
import matplotlib.pyplot as plt
import paddle.v2 as paddle
import paddle.fluid as fluid
from advbox.adversary import Adversary
from advbox.attacks.gradient_method import FGSM
from advbox.models.paddle import PaddleModel
def cnn_model(img):
"""
Mnist cnn model
Args:
img(Varaible): the input image to be recognized
Returns:
Variable: the label prediction
"""
# conv1 = fluid.nets.conv2d()
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
num_filters=20,
filter_size=5,
pool_size=2,
pool_stride=2,
act='relu')
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
num_filters=50,
filter_size=5,
pool_size=2,
pool_stride=2,
act='relu')
logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
return logits
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(
feed_list=[IMG_NAME, LABEL_NAME],
place=place,
program=fluid.default_main_program())
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(fluid.default_main_program(), IMG_NAME, LABEL_NAME,
logits.name, avg_cost.name, (-1, 1))
att = FGSM(m)
for data in train_reader():
# fgsm attack
adversary = att(Adversary(data[0][0], data[0][1]))
if adversary.is_successful():
plt.imshow(adversary.target, cmap='Greys_r')
plt.show()
# np.save('adv_img', adversary.target)
break
if __name__ == '__main__':
main()
fluid/adversarial/mnist_tutorial_jsma.py 已删除 100644 → 0
浏览文件 @ 2d985018
"""
FGSM demos on mnist using advbox tool.
"""
import matplotlib.pyplot as plt
import paddle.v2 as paddle
import paddle.fluid as fluid
import numpy as np
from advbox import Adversary
from advbox.attacks.saliency import SaliencyMapAttack
from advbox.models.paddle import PaddleModel
def cnn_model(img):
"""
Mnist cnn model
Args:
img(Varaible): the input image to be recognized
Returns:
Variable: the label prediction
"""
# conv1 = fluid.nets.conv2d()
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
num_filters=20,
filter_size=5,
pool_size=2,
pool_stride=2,
act='relu')
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
num_filters=50,
filter_size=5,
pool_size=2,
pool_stride=2,
act='relu')
logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
return logits
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(
feed_list=[IMG_NAME, LABEL_NAME],
place=place,
program=fluid.default_main_program())
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(fluid.default_main_program(), IMG_NAME, LABEL_NAME,
logits.name, avg_cost.name, (-1, 1))
attack = SaliencyMapAttack(m)
total_num = 0
success_num = 0
for data in train_reader():
total_num += 1
# adversary.set_target(True, target_label=target_label)
jsma_attack = attack(Adversary(data[0][0], data[0][1]))
if jsma_attack is not None and jsma_attack.is_successful():
# plt.imshow(jsma_attack.target, cmap='Greys_r')
# plt.show()
success_num += 1
print('original_label=%d, adversary examples label =%d' %
(data[0][1], jsma_attack.adversarial_label))
# np.save('adv_img', jsma_attack.adversarial_example)
print('total num = %d, success num = %d ' % (total_num, success_num))
if total_num == 100:
break
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/__init__.py 0 → 100644
浏览文件 @ a0692c17
"""
A set of tutorials for generating adversarial examples with advbox.
"""
\ No newline at end of file
fluid/adversarial/fluid_mnist.py → fluid/adversarial/tutorials/mnist_model.py
浏览文件 @ a0692c17
......@@ -30,8 +30,9 @@ def mnist_cnn_model(img):
pool_size=2,
pool_stride=2,
act='relu')
fc = fluid.layers.fc(input=conv_pool_2, size=50, act='relu')
logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
logits = fluid.layers.fc(input=fc, size=10, act='softmax')
return logits
......@@ -60,7 +61,10 @@ def main():
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
exe.run(fluid.default_startup_program())
......@@ -74,9 +78,11 @@ def main():
feed=feeder.feed(data),
fetch_list=[avg_cost, batch_acc, batch_size])
pass_acc.add(value=acc, weight=b_size)
pass_acc_val = pass_acc.eval()[0]
print("pass_id=" + str(pass_id) + " acc=" + str(acc[0]) +
" pass_acc=" + str(pass_acc.eval()[0]))
if loss < LOSS_THRESHOLD and pass_acc > ACC_THRESHOLD:
" pass_acc=" + str(pass_acc_val))
if loss < LOSS_THRESHOLD and pass_acc_val > ACC_THRESHOLD:
# early stop
break
print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc.eval()[
......
fluid/adversarial/tutorials/mnist_tutorial_bim.py 0 → 100644
浏览文件 @ a0692c17
"""
BIM tutorial on mnist using advbox tool.
BIM method iteratively take multiple small steps while adjusting the direction after each step.
It only supports non-targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.gradient_method import BIM
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = BIM(m)
attack_config = {"epsilons": 0.1, "steps": 100}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# BIM non-targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# BIM non-targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("bim attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_deepfool.py 0 → 100644
浏览文件 @ a0692c17
"""
DeepFool tutorial on mnist using advbox tool.
Deepfool is a simple and accurate adversarial attack method.
It supports both targeted attack and non-targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.deepfool import DeepFoolAttack
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = DeepFoolAttack(m)
attack_config = {"iterations": 100, "overshoot": 9}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# DeepFool non-targeted attack
adversary = attack(adversary, **attack_config)
# DeepFool targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# DeepFool non-targeted attack
adversary = attack(adversary, **attack_config)
# DeepFool targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("deelfool attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_fgsm.py 0 → 100644
浏览文件 @ a0692c17
"""
FGSM tutorial on mnist using advbox tool.
FGSM method is non-targeted attack while FGSMT is targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import numpy as np
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.gradient_method import FGSM
from advbox.attacks.gradient_method import FGSMT
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = FGSM(m)
# attack = FGSMT(m)
attack_config = {"epsilons": 0.3}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# FGSM non-targeted attack
adversary = attack(adversary, **attack_config)
# FGSMT targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# FGSM non-targeted attack
adversary = attack(adversary, **attack_config)
# FGSMT targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("fgsm attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_ilcm.py 0 → 100644
浏览文件 @ a0692c17
"""
ILCM tutorial on mnist using advbox tool.
ILCM method extends "BIM" to support targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.gradient_method import ILCM
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = ILCM(m)
attack_config = {"epsilons": 0.1, "steps": 100}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
tlabel = 0
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# ILCM targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
tlabel = 0
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# ILCM targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("ilcm attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_jsma.py 0 → 100644
浏览文件 @ a0692c17
"""
JSMA tutorial on mnist using advbox tool.
JSMA method supports both targeted attack and non-targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.saliency import JSMA
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = JSMA(m)
attack_config = {
"max_iter": 2000,
"theta": 0.1,
"max_perturbations_per_pixel": 7
}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# JSMA non-targeted attack
adversary = attack(adversary, **attack_config)
# JSMA targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
# JSMA may return None
if adversary is not None and adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# JSMA non-targeted attack
adversary = attack(adversary, **attack_config)
# JSMA targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
# JSMA may return None
if adversary is not None and adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("jsma attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_lbfgs.py 0 → 100644
浏览文件 @ a0692c17
"""
LBFGS tutorial on mnist using advbox tool.
LBFGS method only supports targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.lbfgs import LBFGS
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = LBFGS(m)
attack_config = {"epsilon": 0.001, }
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# LBFGS targeted attack
tlabel = 0
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# LBFGS targeted attack
tlabel = 0
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("lbfgs attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_mifgsm.py 0 → 100644
浏览文件 @ a0692c17
"""
MIFGSM tutorial on mnist using advbox tool.
MIFGSM is a broad class of momentum iterative gradient-based methods based on FSGM.
It supports non-targeted attack and targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import numpy as np
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.gradient_method import MIFGSM
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = MIFGSM(m)
attack_config = {
"norm_ord": np.inf,
"epsilons": 0.1,
"steps": 100,
"decay_factor": 1
}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# MIFGSM non-targeted attack
adversary = attack(adversary, **attack_config)
# MIFGSM targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# MIFGSM non-targeted attack
adversary = attack(adversary, **attack_config)
# MIFGSM targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("mifgsm attack done")
if __name__ == '__main__':
main()
fluid/image_classification/caffe2fluid/README.md
浏览文件 @ a0692c17
......@@ -18,19 +18,19 @@ This tool is used to convert a Caffe model to Fluid model
### Tested models
- Lenet on mnist dataset
- Lenet
- ResNets:(ResNet-50, ResNet-101, ResNet-152)
model addr: `https://onedrive.live.com/?authkey=%21AAFW2-FVoxeVRck&id=4006CBB8476FF777%2117887&cid=4006CBB8476FF777`_
[model addr](https://onedrive.live.com/?authkey=%21AAFW2-FVoxeVRck&id=4006CBB8476FF777%2117887&cid=4006CBB8476FF777)
- GoogleNet:
model addr: `https://gist.github.com/jimmie33/7ea9f8ac0da259866b854460f4526034`_
[model addr](https://gist.github.com/jimmie33/7ea9f8ac0da259866b854460f4526034)
- VGG:
model addr: `https://gist.github.com/ksimonyan/211839e770f7b538e2d8`_
[model addr](https://gist.github.com/ksimonyan/211839e770f7b538e2d8)
- AlexNet:
model addr: `https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet`_
[model addr](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet)
### Notes
Some of this code come from here: https://github.com/ethereon/caffe-tensorflow
fluid/image_classification/caffe2fluid/examples/imagenet/compare.py 0 → 100644
浏览文件 @ a0692c17
#!/usr/bin/python
#
#a tool to compare tensors in two files or two directories
#
import sys
import os
def walk_dir(rootdir):
for subdir, dirs, files in os.walk(rootdir):
for file in files:
yield file
def calc_diff(f1, f2):
import numpy as np
d1 = np.load(f1).flatten()
d2 = np.load(f2).flatten()
d1_num = reduce(lambda x, y: x * y, d1.shape)
d2_num = reduce(lambda x, y: x * y, d2.shape)
if d1_num != d2_num:
print d1.shape
print d2.shape
assert (d1_num == d2_num), "their shape is not consistent"
try:
df = np.abs(d1 - d2)
max_df = np.max(df)
sq_df = np.mean(df * df)
return max_df, sq_df
except Exception as e:
return -1.0, -1.0
def compare(path1, path2):
def diff(f1, f2):
max_df, sq_df = calc_diff(f1, f2)
print('compare %s <=> %s with result[max_df:%.4e, sq_df:%.4e]' %
(f1, f2, max_df, sq_df))
assert (max_df < 1e-5), \
'max_df is too large with value[%.6e]' % (max_df)
assert (sq_df < 1e-10), \
'sq_df is too large with value[%.6e]' % (sq_df)
if os.path.exists(path1) is False:
print('not found %s' % (path1))
return 1
elif os.path.exists(path2) is False:
print('not found %s' % (path2))
return 1
if path1.find('.npy') > 0 and path2.find('.npy') > 0:
diff(path1, path2)
return
for f in walk_dir(path2):
if f.find('.npy') < 0:
continue
f1 = os.path.join(path1, f)
f2 = os.path.join(path2, f)
diff(f1, f2)
print('all checking succeed to pass')
return 0
if __name__ == "__main__":
if len(sys.argv) == 1:
path1 = 'lenet.tf/results'
path2 = 'lenet.paddle/results'
elif len(sys.argv) == 3:
path1 = sys.argv[1]
path2 = sys.argv[2]
else:
print('usage:')
print(' %s [path1] [path2]' % (sys.argv[0]))
exit(1)
print('compare inner result in %s %s' % (path1, path2))
exit(compare(path1, path2))
fluid/image_classification/caffe2fluid/examples/imagenet/diff.sh 0 → 100644
浏览文件 @ a0692c17
#!/bin/bash
#
#function:
# a tool used to check the difference of models' results generated by caffe model and paddle model
#
#howto:
# bash diff.sh resnet50 #when this has been finished, you can get the difference in precision
#
#notes:
# 0, in order to infer using caffe, we need pycaffe installed
# 1, prepare your caffe model in 'models.caffe/', eg: 'model.caffe/resnet101/resnet101.[prototxt|caffemodel]'
# 2, converted paddle model will be in 'models'
# 3, results of layers will be stored in 'results/${model_name}.[paddle|caffe]'
# 4, only the last layer will be checked by default
model_name="resnet50"
results_root="results/"
if [[ -n $1 ]];then
if [ $1 = "-h" ];then
echo "usage:"
echo " bash $0 [model_name]"
echo " eg:bash $0 resnet50"
exit 0
fi
model_name=$1
fi
mkdir -p $results_root
model_prototxt="models.caffe/$model_name/${model_name}.prototxt"
model_caffemodel="models.caffe/${model_name}/${model_name}.caffemodel"
#1, dump layers' results from paddle
paddle_results="$results_root/${model_name}.paddle"
rm -rf $paddle_results
rm -rf "results.paddle"
bash run.sh $model_name ./models.caffe/$model_name ./models/$model_name
if [[ $? -ne 0 ]] || [[ ! -e "results.paddle" ]];then
echo "not found paddle's results, maybe failed to convert"
exit 1
fi
mv results.paddle $paddle_results
#2, dump layers' results from caffe
caffe_results="$results_root/${model_name}.caffe"
rm -rf $caffe_results
rm -rf "results.caffe"
cfpython ./infer.py caffe $model_prototxt $model_caffemodel $paddle_results/data.npy
if [[ $? -ne 0 ]] || [[ ! -e "results.caffe" ]];then
echo "not found caffe's results, maybe failed to do inference with caffe"
exit 1
fi
mv results.caffe $caffe_results
#3, extract layer names
cat $model_prototxt | grep name | perl -ne 'if(/^\s*name:\s+\"([^\"]+)/){ print $1."\n";}' >.layer_names
#4, compare one by one
for i in $(cat ".layer_names" | tail -n1);do
echo "process $i"
python compare.py $caffe_results/${i}.npy $paddle_results/${i}.npy
done
fluid/image_classification/caffe2fluid/examples/imagenet/infer.py
浏览文件 @ a0692c17
......@@ -10,8 +10,11 @@ import os
import sys
import inspect
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
def import_fluid():
import paddle.fluid as fluid
return fluid
def load_data(imgfile, shape):
......@@ -52,8 +55,10 @@ def build_model(net_file, net_name):
print(e)
return None
input_name = 'data'
input_shape = MyNet.input_shapes()[input_name]
fluid = import_fluid()
inputs_dict = MyNet.input_shapes()
input_name = inputs_dict.keys()[0]
input_shape = inputs_dict[input_name]
images = fluid.layers.data(name='image', shape=input_shape, dtype='float32')
#label = fluid.layers.data(name='label', shape=[1], dtype='int64')
......@@ -64,7 +69,7 @@ def build_model(net_file, net_name):
def dump_results(results, names, root):
if os.path.exists(root) is False:
os.path.mkdir(root)
os.mkdir(root)
for i in range(len(names)):
n = names[i]
......@@ -73,9 +78,12 @@ def dump_results(results, names, root):
np.save(filename + '.npy', res)
def infer(net_file, net_name, model_file, imgfile, debug=False):
def infer(net_file, net_name, model_file, imgfile, debug=True):
""" do inference using a model which consist 'xxx.py' and 'xxx.npy'
"""
fluid = import_fluid()
#1, build model
net, input_shape = build_model(net_file, net_name)
prediction = net.get_output()
......@@ -109,34 +117,79 @@ def infer(net_file, net_name, model_file, imgfile, debug=False):
fetch_list=fetch_list_var)
if debug is True:
dump_path = 'results.layers'
dump_path = 'results.paddle'
dump_results(results, fetch_list_name, dump_path)
print('all results dumped to [%s]' % (dump_path))
print('all result of layers dumped to [%s]' % (dump_path))
else:
result = results[0]
print('predicted class:', np.argmax(result))
return 0
def caffe_infer(prototxt, caffemodel, datafile):
""" do inference using pycaffe for debug,
all intermediate results will be dumpped to 'results.caffe'
"""
import caffe
net = caffe.Net(prototxt, caffemodel, caffe.TEST)
input_layer = net.blobs.keys()[0]
print('got name of input layer is:%s' % (input_layer))
input_shape = list(net.blobs[input_layer].data.shape[1:])
if '.npy' in datafile:
np_images = np.load(datafile)
else:
np_images = load_data(datafile, input_shape)
inputs = {input_layer: np_images}
net.forward_all(**inputs)
results = []
names = []
for k, v in net.blobs.items():
k = k.rstrip('_output')
k = k.replace('/', '_')
names.append(k)
results.append(v.data.copy())
dump_path = 'results.caffe'
dump_results(results, names, dump_path)
print('all result of layers dumped to [%s]' % (dump_path))
return 0
if __name__ == "__main__":
""" maybe more convenient to use 'run.sh' to call this tool
"""
net_file = 'models/resnet50/resnet50.py'
weight_file = 'models/resnet50/resnet50.npy'
imgfile = 'data/65.jpeg'
datafile = 'data/65.jpeg'
net_name = 'ResNet50'
argc = len(sys.argv)
if argc == 5:
if sys.argv[1] == 'caffe':
if len(sys.argv) != 5:
print('usage:')
print('\tpython %s caffe [prototxt] [caffemodel] [datafile]' %
(sys.argv[0]))
sys.exit(1)
prototxt = sys.argv[2]
caffemodel = sys.argv[3]
datafile = sys.argv[4]
sys.exit(caffe_infer(prototxt, caffemodel, datafile))
elif argc == 5:
net_file = sys.argv[1]
weight_file = sys.argv[2]
imgfile = sys.argv[3]
datafile = sys.argv[3]
net_name = sys.argv[4]
elif argc > 1:
print('usage:')
print('\tpython %s [net_file] [weight_file] [imgfile] [net_name]' %
print('\tpython %s [net_file] [weight_file] [datafile] [net_name]' %
(sys.argv[0]))
print('\teg:python %s %s %s %s %s' % (sys.argv[0], net_file,
weight_file, imgfile, net_name))
weight_file, datafile, net_name))
sys.exit(1)
infer(net_file, net_name, weight_file, imgfile)
infer(net_file, net_name, weight_file, datafile)
fluid/image_classification/caffe2fluid/examples/imagenet/run.sh
浏览文件 @ a0692c17
......@@ -3,7 +3,7 @@
#function:
# a tool used to:
# 1, convert a caffe model
# 2, do inference using this model
# 2, do inference(only in fluid) using this model
#
#usage:
# bash run.sh resnet50 ./models.caffe/resnet50 ./models/resnet50
......@@ -65,7 +65,12 @@ if [[ -z $only_convert ]];then
PYTHON=`which python`
fi
imgfile="data/65.jpeg"
net_name=`grep "name" $proto_file | head -n1 | perl -ne 'if(/\"([^\"]+)\"/){ print $1."\n";}'`
#FIX ME:
# only look the first line in prototxt file for the name of this network, maybe not correct
net_name=`grep "name" $proto_file | head -n1 | perl -ne 'if(/^\s*name\s*:\s*\"([^\"]+)\"/){ print $1."\n";}'`
if [[ -z $net_name ]];then
net_name="MyNet"
fi
$PYTHON ./infer.py $net_file $weight_file $imgfile $net_name
ret=$?
fi
......
fluid/image_classification/caffe2fluid/kaffe/graph.py
浏览文件 @ a0692c17
......@@ -52,7 +52,10 @@ class Graph(object):
def __init__(self, nodes=None, name=None):
self.nodes = nodes or []
self.node_lut = {node.name: node for node in self.nodes}
self.name = name
if name is None or name == '':
self.name = 'MyNet'
else:
self.name = name
def add_node(self, node):
self.nodes.append(node)
......
fluid/image_classification/caffe2fluid/kaffe/paddle/network.py
浏览文件 @ a0692c17
......@@ -4,7 +4,7 @@ import numpy as np
def import_fluid():
import paddle.v2.fluid as fluid
import paddle.fluid as fluid
return fluid
......@@ -64,7 +64,7 @@ class Network(object):
if os.path.isdir(data_path):
assert (exe is not None), \
'must provide a executor to load fluid model'
fluid.io.load_persistables_if_exist(executor=exe, dirname=data_path)
fluid.io.load_persistables(executor=exe, dirname=data_path)
return True
#load model from a npy file
......@@ -161,56 +161,28 @@ class Network(object):
output = fluid.layers.relu(x=input)
return output
def _adjust_pad_if_needed(self, i_hw, k_hw, s_hw, p_hw):
#adjust the padding if needed
i_h, i_w = i_hw
k_h, k_w = k_hw
s_h, s_w = s_hw
p_h, p_w = p_hw
def is_consistent(i, k, s, p):
o = i + 2 * p - k
if o % s == 0:
return True
else:
return False
real_p_h = 0
real_p_w = 0
if is_consistent(i_h, k_h, s_h, p_h) is False:
real_p_h = int(k_h / 2)
if is_consistent(i_w, k_w, s_w, p_w) is False:
real_p_w = int(k_w / 2)
return [real_p_h, real_p_w]
def pool(self, pool_type, input, k_h, k_w, s_h, s_w, name, padding):
# Get the number of channels in the input
in_hw = input.shape[2:]
k_hw = [k_h, k_w]
s_hw = [s_h, s_w]
if padding is None:
#fix bug about the difference between conv and pool
#more info: https://github.com/BVLC/caffe/issues/1318
padding = self._adjust_pad_if_needed(in_hw, k_hw, s_hw, [0, 0])
fluid = import_fluid()
output = fluid.layers.pool2d(
input=input,
pool_size=k_hw,
pool_stride=s_hw,
pool_padding=padding,
ceil_mode=True,
pool_type=pool_type)
return output
@layer
def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=None):
def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=[0, 0]):
return self.pool('max', input, k_h, k_w, s_h, s_w, name, padding)
@layer
def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=None):
def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=[0, 0]):
return self.pool('avg', input, k_h, k_w, s_h, s_w, name, padding)
@layer
......@@ -258,7 +230,12 @@ class Network(object):
return output
@layer
def batch_normalization(self, input, name, scale_offset=True, relu=False):
def batch_normalization(self,
input,
name,
scale_offset=True,
eps=1e-5,
relu=False):
# NOTE: Currently, only inference is supported
fluid = import_fluid()
prefix = name + '_'
......@@ -276,7 +253,7 @@ class Network(object):
bias_attr=bias_attr,
moving_mean_name=mean_name,
moving_variance_name=variance_name,
epsilon=1e-5,
epsilon=eps,
act='relu' if relu is True else None)
return output
......
fluid/image_classification/caffe2fluid/kaffe/paddle/transformer.py
浏览文件 @ a0692c17
......@@ -142,7 +142,13 @@ class TensorFlowMapper(NodeMapper):
def map_batch_norm(self, node):
scale_offset = len(node.data) == 4
kwargs = {} if scale_offset else {'scale_offset': False}
#this default value comes from caffe's param in batch_norm
default_eps = 1e-5
kwargs = {'scale_offset': scale_offset}
if node.parameters.eps != default_eps:
kwargs['eps'] = node.parameters.eps
return MaybeActivated(
node, default=False)('batch_normalization', **kwargs)
......@@ -236,7 +242,7 @@ class TensorFlowEmitter(object):
func_def = self.statement('@classmethod')
func_def += self.statement('def convert(cls, npy_model, fluid_path):')
self.indent()
func_def += self.statement('import paddle.v2.fluid as fluid')
func_def += self.statement('fluid = import_fluid()')
for l in codes:
func_def += self.statement(l)
return '\n' + func_def
......
fluid/image_classification/se_resnext.py
浏览文件 @ a0692c17
import os
import paddle.v2 as paddle
import paddle.fluid as fluid
import reader
def conv_bn_layer(input, num_filters, filter_size, stride=1, groups=1,
......@@ -65,20 +63,44 @@ def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio):
return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
def SE_ResNeXt(input, class_dim, infer=False):
cardinality = 64
reduction_ratio = 16
depth = [3, 8, 36, 3]
num_filters = [128, 256, 512, 1024]
conv = conv_bn_layer(
input=input, num_filters=64, filter_size=3, stride=2, act='relu')
conv = conv_bn_layer(
input=conv, num_filters=64, filter_size=3, stride=1, act='relu')
conv = conv_bn_layer(
input=conv, num_filters=128, filter_size=3, stride=1, act='relu')
conv = fluid.layers.pool2d(
input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
def SE_ResNeXt(input, class_dim, infer=False, layers=50):
supported_layers = [50, 152]
if layers not in supported_layers:
print("supported layers are", supported_layers, "but input layer is",
layers)
exit()
if layers == 50:
cardinality = 32
reduction_ratio = 16
depth = [3, 4, 6, 3]
num_filters = [128, 256, 512, 1024]
conv = conv_bn_layer(
input=input, num_filters=64, filter_size=7, stride=2, act='relu')
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
elif layers == 152:
cardinality = 64
reduction_ratio = 16
depth = [3, 8, 36, 3]
num_filters = [128, 256, 512, 1024]
conv = conv_bn_layer(
input=input, num_filters=64, filter_size=3, stride=2, act='relu')
conv = conv_bn_layer(
input=conv, num_filters=64, filter_size=3, stride=1, act='relu')
conv = conv_bn_layer(
input=conv, num_filters=128, filter_size=3, stride=1, act='relu')
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]):
......@@ -97,93 +119,3 @@ def SE_ResNeXt(input, class_dim, infer=False):
drop = pool
out = fluid.layers.fc(input=drop, size=class_dim, act='softmax')
return out
def train(learning_rate,
batch_size,
num_passes,
init_model=None,
model_save_dir='model',
parallel=True):
class_dim = 1000
image_shape = [3, 224, 224]
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if parallel:
places = fluid.layers.get_places()
pd = fluid.layers.ParallelDo(places)
with pd.do():
image_ = pd.read_input(image)
label_ = pd.read_input(label)
out = SE_ResNeXt(input=image_, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=out, label=label_)
avg_cost = fluid.layers.mean(x=cost)
accuracy = fluid.layers.accuracy(input=out, label=label_)
pd.write_output(avg_cost)
pd.write_output(accuracy)
avg_cost, accuracy = pd()
avg_cost = fluid.layers.mean(x=avg_cost)
accuracy = fluid.layers.mean(x=accuracy)
else:
out = SE_ResNeXt(input=image, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost)
accuracy = fluid.layers.accuracy(input=out, label=label)
optimizer = fluid.optimizer.Momentum(
learning_rate=learning_rate,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
opts = optimizer.minimize(avg_cost)
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
inference_program = fluid.io.get_inference_program([avg_cost, accuracy])
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if init_model is not None:
fluid.io.load_persistables(exe, init_model)
train_reader = paddle.batch(reader.train(), batch_size=batch_size)
test_reader = paddle.batch(reader.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
for pass_id in range(num_passes):
for batch_id, data in enumerate(train_reader()):
loss = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
print("Pass {0}, batch {1}, loss {2}".format(pass_id, batch_id,
float(loss[0])))
total_loss = 0.0
total_acc = 0.0
total_batch = 0
for data in test_reader():
loss, acc = exe.run(inference_program,
feed=feeder.feed(data),
fetch_list=[avg_cost, accuracy])
total_loss += float(loss)
total_acc += float(acc)
total_batch += 1
print("End pass {0}, test_loss {1}, test_acc {2}".format(
pass_id, total_loss / total_batch, total_acc / total_batch))
model_path = os.path.join(model_save_dir, str(pass_id))
fluid.io.save_inference_model(model_path, ['image'], [out], exe)
if __name__ == '__main__':
train(
learning_rate=0.1,
batch_size=8,
num_passes=100,
init_model=None,
parallel=False)
fluid/image_classification/train.py 0 → 100644
浏览文件 @ a0692c17
import os
import numpy as np
import time
import sys
import paddle.v2 as paddle
import paddle.fluid as fluid
from se_resnext import SE_ResNeXt
import reader
import argparse
import functools
from utility import add_arguments, print_arguments
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('num_layers', int, 50, "How many layers for SE-ResNeXt model.")
add_arg('with_mem_opt', bool, True, "Whether to use memory optimization or not.")
add_arg('parallel_exe', bool, True, "Whether to use ParallelExecutor to train or not.")
def train_paralle_do(args,
learning_rate,
batch_size,
num_passes,
init_model=None,
model_save_dir='model',
parallel=True,
use_nccl=True,
lr_strategy=None,
layers=50):
class_dim = 1000
image_shape = [3, 224, 224]
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if parallel:
places = fluid.layers.get_places()
pd = fluid.layers.ParallelDo(places, use_nccl=use_nccl)
with pd.do():
image_ = pd.read_input(image)
label_ = pd.read_input(label)
out = SE_ResNeXt(input=image_, class_dim=class_dim, layers=layers)
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)
pd.write_output(avg_cost)
pd.write_output(acc_top1)
pd.write_output(acc_top5)
avg_cost, acc_top1, acc_top5 = pd()
avg_cost = fluid.layers.mean(x=avg_cost)
acc_top1 = fluid.layers.mean(x=acc_top1)
acc_top5 = fluid.layers.mean(x=acc_top5)
else:
out = SE_ResNeXt(input=image, class_dim=class_dim, layers=layers)
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)
if lr_strategy is None:
optimizer = fluid.optimizer.Momentum(
learning_rate=learning_rate,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
else:
bd = lr_strategy["bd"]
lr = lr_strategy["lr"]
optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
inference_program = fluid.default_main_program().clone(for_test=True)
opts = optimizer.minimize(avg_cost)
if args.with_mem_opt:
fluid.memory_optimize(fluid.default_main_program())
fluid.memory_optimize(inference_program)
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if init_model is not None:
fluid.io.load_persistables(exe, init_model)
train_reader = paddle.batch(reader.train(), batch_size=batch_size)
test_reader = paddle.batch(reader.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
for pass_id in range(num_passes):
train_info = [[], [], []]
test_info = [[], [], []]
for batch_id, data in enumerate(train_reader()):
t1 = time.time()
loss, acc1, acc5 = exe.run(
fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost, acc_top1, acc_top5])
t2 = time.time()
period = t2 - t1
train_info[0].append(loss[0])
train_info[1].append(acc1[0])
train_info[2].append(acc5[0])
if batch_id % 10 == 0:
print("Pass {0}, trainbatch {1}, loss {2}, \
acc1 {3}, acc5 {4} time {5}"
.format(pass_id, \
batch_id, loss[0], acc1[0], acc5[0], \
"%2.2f sec" % period))
sys.stdout.flush()
train_loss = np.array(train_info[0]).mean()
train_acc1 = np.array(train_info[1]).mean()
train_acc5 = np.array(train_info[2]).mean()
for data in test_reader():
t1 = time.time()
loss, acc1, acc5 = exe.run(
inference_program,
feed=feeder.feed(data),
fetch_list=[avg_cost, acc_top1, acc_top5])
t2 = time.time()
period = t2 - t1
test_info[0].append(loss[0])
test_info[1].append(acc1[0])
test_info[2].append(acc5[0])
if batch_id % 10 == 0:
print("Pass {0},testbatch {1},loss {2}, \
acc1 {3},acc5 {4},time {5}"
.format(pass_id, \
batch_id, loss[0], acc1[0], acc5[0], \
"%2.2f sec" % period))
sys.stdout.flush()
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()
model_path = os.path.join(model_save_dir, str(pass_id))
if not os.path.isdir(model_path):
os.makedirs(model_path)
fluid.io.save_persistables(exe, model_path)
def train_parallel_exe(args,
learning_rate,
batch_size,
num_passes,
init_model=None,
model_save_dir='model',
parallel=True,
use_nccl=True,
lr_strategy=None,
layers=50):
class_dim = 1000
image_shape = [3, 224, 224]
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
out = SE_ResNeXt(input=image, class_dim=class_dim, layers=layers)
cost = fluid.layers.cross_entropy(input=out, label=label)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
avg_cost = fluid.layers.mean(x=cost)
test_program = fluid.default_main_program().clone(for_test=True)
if lr_strategy is None:
optimizer = fluid.optimizer.Momentum(
learning_rate=learning_rate,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
else:
bd = lr_strategy["bd"]
lr = lr_strategy["lr"]
optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
opts = optimizer.minimize(avg_cost)
if args.with_mem_opt:
fluid.memory_optimize(fluid.default_main_program())
fluid.memory_optimize(test_program)
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if init_model is not None:
fluid.io.load_persistables(exe, init_model)
train_reader = paddle.batch(reader.train(), batch_size=batch_size)
test_reader = paddle.batch(reader.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
test_exe = fluid.ParallelExecutor(
use_cuda=True,
main_program=test_program,
share_vars_from=train_exe)
fetch_list = [avg_cost.name, acc_top1.name, acc_top5.name]
for pass_id in range(num_passes):
train_info = [[], [], []]
test_info = [[], [], []]
for batch_id, data in enumerate(train_reader()):
t1 = time.time()
loss, acc1, acc5 = train_exe.run(
fetch_list,
feed_dict=feeder.feed(data))
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)
if batch_id % 10 == 0:
print("Pass {0}, trainbatch {1}, loss {2}, \
acc1 {3}, acc5 {4} time {5}"
.format(pass_id, \
batch_id, loss, acc1, acc5, \
"%2.2f sec" % period))
sys.stdout.flush()
train_loss = np.array(train_info[0]).mean()
train_acc1 = np.array(train_info[1]).mean()
train_acc5 = np.array(train_info[2]).mean()
for data in test_reader():
t1 = time.time()
loss, acc1, acc5 = test_exe.run(
fetch_list,
feed_dict=feeder.feed(data))
t2 = time.time()
period = t2 - t1
loss = np.mean(np.array(loss))
acc1 = np.mean(np.array(acc1))
acc5 = np.mean(np.array(acc5))
test_info[0].append(loss)
test_info[1].append(acc1)
test_info[2].append(acc5)
if batch_id % 10 == 0:
print("Pass {0},testbatch {1},loss {2}, \
acc1 {3},acc5 {4},time {5}"
.format(pass_id, \
batch_id, loss, acc1, acc5, \
"%2.2f sec" % period))
sys.stdout.flush()
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()
model_path = os.path.join(model_save_dir, str(pass_id))
if not os.path.isdir(model_path):
os.makedirs(model_path)
fluid.io.save_persistables(exe, model_path)
if __name__ == '__main__':
args = parser.parse_args()
print_arguments(args)
epoch_points = [30, 60, 90]
total_images = 1281167
batch_size = args.batch_size
step = int(total_images / batch_size + 1)
bd = [e * step for e in epoch_points]
lr = [0.1, 0.01, 0.001, 0.0001]
lr_strategy = {"bd": bd, "lr": lr}
use_nccl = True
# layers: 50, 152
layers = args.num_layers
method = train_parallel_exe if args.parallel_exe else train_parallel_do
method(args,
learning_rate=0.1,
batch_size=batch_size,
num_passes=120,
init_model=None,
parallel=True,
use_nccl=True,
lr_strategy=lr_strategy,
layers=layers)
fluid/ocr_recognition/dummy_reader.py → fluid/image_classification/utility.py
浏览文件 @ a0692c17
"""A dummy reader for test."""
"""Contains common utility functions."""
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
......@@ -13,40 +13,50 @@
#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 numpy as np
import paddle.v2 as paddle
from paddle.fluid import core
DATA_SHAPE = [1, 512, 512]
NUM_CLASSES = 20
def print_arguments(args):
"""Print argparse's arguments.
def _read_creater(num_sample=1024, min_seq_len=1, max_seq_len=10):
def reader():
for i in range(num_sample):
sequence_len = np.random.randint(min_seq_len, max_seq_len)
x = np.random.uniform(0.1, 1, DATA_SHAPE).astype("float32")
y = np.random.randint(0, NUM_CLASSES + 1,
[sequence_len]).astype("int32")
yield x, y
Usage:
return reader
.. code-block:: python
parser = argparse.ArgumentParser()
parser.add_argument("name", default="Jonh", type=str, help="User name.")
args = parser.parse_args()
print_arguments(args)
def train(batch_size, num_sample=128):
"""Get train dataset reader."""
return paddle.batch(_read_creater(num_sample=num_sample), batch_size)
:param args: Input argparse.Namespace for printing.
:type args: argparse.Namespace
"""
print("----------- Configuration Arguments -----------")
for arg, value in sorted(vars(args).iteritems()):
print("%s: %s" % (arg, value))
print("------------------------------------------------")
def test(batch_size=1, num_sample=16):
"""Get test dataset reader."""
return paddle.batch(_read_creater(num_sample=num_sample), batch_size)
def add_arguments(argname, type, default, help, argparser, **kwargs):
"""Add argparse's argument.
Usage:
def data_shape():
"""Get image shape in CHW order."""
return DATA_SHAPE
.. code-block:: python
def num_classes():
"""Get number of total classes."""
return NUM_CLASSES
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)
fluid/neural_machine_translation/transformer/config.py
浏览文件 @ a0692c17
......@@ -15,6 +15,9 @@ class TrainTaskConfig(object):
# the parameters for learning rate scheduling.
warmup_steps = 4000
# the flag indicating to use average loss or sum loss when training.
use_avg_cost = False
# the directory for saving trained models.
model_dir = "trained_models"
......@@ -22,8 +25,7 @@ class TrainTaskConfig(object):
class InferTaskConfig(object):
use_gpu = False
# the number of examples in one run for sequence generation.
# currently the batch size can only be set to 1.
batch_size = 1
batch_size = 10
# the parameters for beam search.
beam_size = 5
......@@ -31,37 +33,38 @@ class InferTaskConfig(object):
# the number of decoded sentences to output.
n_best = 1
# the flags indicating whether to output the special tokens.
output_bos = False
output_eos = False
output_unk = False
# the directory for loading the trained model.
model_path = "trained_models/pass_1.infer.model"
class ModelHyperParams(object):
# Dictionary size for source and target language. This model directly uses
# paddle.dataset.wmt16 in which <bos>, <eos> and <unk> token has
# alreay been added, but the <pad> token is not added. Transformer requires
# sequences in a mini-batch are padded to have the same length. A <pad> token is
# added into the original dictionary in paddle.dateset.wmt16.
# This model directly uses paddle.dataset.wmt16 in which <bos>, <eos> and
# <unk> token has alreay been added. As for the <pad> token, any token
# included in dict can be used to pad, since the paddings' loss will be
# masked out and make no effect on parameter gradients.
# size of source word dictionary.
src_vocab_size = 10000
# index for <pad> token in source language.
src_pad_idx = src_vocab_size
# size of target word dictionay
trg_vocab_size = 10000
# index for <pad> token in target language.
trg_pad_idx = trg_vocab_size
# index for <bos> token
bos_idx = 0
# index for <eos> token
eos_idx = 1
# index for <unk> token
unk_idx = 2
# position value corresponding to the <pad> token.
pos_pad_idx = 0
# max length of sequences. It should plus 1 to include position
# padding token for position encoding.
# max length of sequences.
# The size of position encoding table should at least plus 1, since the
# sinusoid position encoding starts from 1 and 0 can be used as the padding
# token for position encoding.
max_length = 50
# the dimension for word embeddings, which is also the last dimension of
......@@ -92,7 +95,10 @@ pos_enc_param_names = (
encoder_input_data_names = (
"src_word",
"src_pos",
"src_slf_attn_bias", )
"src_slf_attn_bias",
"src_data_shape",
"src_slf_attn_pre_softmax_shape",
"src_slf_attn_post_softmax_shape", )
# Names of all data layers in decoder listed in order.
decoder_input_data_names = (
......@@ -100,6 +106,11 @@ decoder_input_data_names = (
"trg_pos",
"trg_slf_attn_bias",
"trg_src_attn_bias",
"trg_data_shape",
"trg_slf_attn_pre_softmax_shape",
"trg_slf_attn_post_softmax_shape",
"trg_src_attn_pre_softmax_shape",
"trg_src_attn_post_softmax_shape",
"enc_output", )
# Names of label related data layers listed in order.
......
fluid/neural_machine_translation/transformer/infer.py
浏览文件 @ a0692c17
import numpy as np
import paddle.v2 as paddle
import paddle
import paddle.fluid as fluid
import model
......@@ -11,10 +11,26 @@ from config import InferTaskConfig, ModelHyperParams, \
from train import pad_batch_data
def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
decoder, dec_in_names, dec_out_names, beam_size, max_length,
n_best, batch_size, n_head, src_pad_idx, trg_pad_idx,
bos_idx, eos_idx):
def translate_batch(exe,
src_words,
encoder,
enc_in_names,
enc_out_names,
decoder,
dec_in_names,
dec_out_names,
beam_size,
max_length,
n_best,
batch_size,
n_head,
d_model,
src_pad_idx,
trg_pad_idx,
bos_idx,
eos_idx,
unk_idx,
output_unk=True):
"""
Run the encoder program once and run the decoder program multiple times to
implement beam search externally.
......@@ -25,9 +41,21 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
src_pad_idx,
n_head,
is_target=False,
return_pos=True,
is_label=False,
return_attn_bias=True,
return_max_len=True)
return_max_len=False)
# Append the data shape input to reshape the output of embedding layer.
enc_in_data = enc_in_data + [
np.array(
[-1, enc_in_data[2].shape[-1], d_model], dtype="int32")
]
# Append the shape inputs to reshape before and after softmax in encoder
# self attention.
enc_in_data = enc_in_data + [
np.array(
[-1, enc_in_data[2].shape[-1]], dtype="int32"), np.array(
enc_in_data[2].shape, dtype="int32")
]
enc_output = exe.run(encoder,
feed=dict(zip(enc_in_names, enc_in_data)),
fetch_list=enc_out_names)[0]
......@@ -35,13 +63,18 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
# Beam Search.
# To store the beam info.
scores = np.zeros((batch_size, beam_size), dtype="float32")
prev_branchs = [[]] * batch_size
next_ids = [[]] * batch_size
# Use beam_map to map the instance idx in batch to beam idx, since the
prev_branchs = [[] for i in range(batch_size)]
next_ids = [[] for i in range(batch_size)]
# Use beam_inst_map to map beam idx to the instance idx in batch, since the
# size of feeded batch is changing.
beam_map = range(batch_size)
beam_inst_map = {
beam_idx: inst_idx
for inst_idx, beam_idx in enumerate(range(batch_size))
}
# Use active_beams to recode the alive.
active_beams = range(batch_size)
def beam_backtrace(prev_branchs, next_ids, n_best=beam_size, add_bos=True):
def beam_backtrace(prev_branchs, next_ids, n_best=beam_size):
"""
Decode and select n_best sequences for one instance by backtrace.
"""
......@@ -53,7 +86,8 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
seq.append(next_ids[j][k])
k = prev_branchs[j][k]
seq = seq[::-1]
seq = [bos_idx] + seq if add_bos else seq
# Add the <bos>, since next_ids don't include the <bos>.
seq = [bos_idx] + seq
seqs.append(seq)
return seqs
......@@ -64,8 +98,8 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
trg_words = np.array(
[[bos_idx]] * batch_size * beam_size, dtype="int64")
trg_pos = np.array([[1]] * batch_size * beam_size, dtype="int64")
src_max_length, src_slf_attn_bias, trg_max_len = enc_in_data[
-1], enc_in_data[-2], 1
src_max_length, src_slf_attn_bias, trg_max_len = enc_in_data[2].shape[
-1], enc_in_data[2], 1
# This is used to remove attention on subsequent words.
trg_slf_attn_bias = np.ones((batch_size * beam_size, trg_max_len,
trg_max_len))
......@@ -75,22 +109,47 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
[-1e9]).astype("float32")
# This is used to remove attention on the paddings of source sequences.
trg_src_attn_bias = np.tile(
src_slf_attn_bias[:, :, ::src_max_length, :],
[beam_size, 1, trg_max_len, 1])
enc_output = np.tile(enc_output, [beam_size, 1, 1])
return trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, enc_output
src_slf_attn_bias[:, :, ::src_max_length, :][:, np.newaxis],
[1, beam_size, 1, trg_max_len, 1]).reshape([
-1, src_slf_attn_bias.shape[1], trg_max_len,
src_slf_attn_bias.shape[-1]
])
# Append the shape input to reshape the output of embedding layer.
trg_data_shape = np.array(
[batch_size * beam_size, trg_max_len, d_model], dtype="int32")
# Append the shape inputs to reshape before and after softmax in
# decoder self attention.
trg_slf_attn_pre_softmax_shape = np.array(
[-1, trg_slf_attn_bias.shape[-1]], dtype="int32")
trg_slf_attn_post_softmax_shape = np.array(
trg_slf_attn_bias.shape, dtype="int32")
# Append the shape inputs to reshape before and after softmax in
# encoder-decoder attention.
trg_src_attn_pre_softmax_shape = np.array(
[-1, trg_src_attn_bias.shape[-1]], dtype="int32")
trg_src_attn_post_softmax_shape = np.array(
trg_src_attn_bias.shape, dtype="int32")
enc_output = np.tile(
enc_output[:, np.newaxis], [1, beam_size, 1, 1]).reshape(
[-1, enc_output.shape[-2], enc_output.shape[-1]])
return trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, \
trg_data_shape, trg_slf_attn_pre_softmax_shape, \
trg_slf_attn_post_softmax_shape, trg_src_attn_pre_softmax_shape, \
trg_src_attn_post_softmax_shape, enc_output
def update_dec_in_data(dec_in_data, next_ids, active_beams):
def update_dec_in_data(dec_in_data, next_ids, active_beams, beam_inst_map):
"""
Update the input data of decoder mainly by slicing from the previous
input data and dropping the finished instance beams.
"""
trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, enc_output = dec_in_data
trg_cur_len = len(next_ids[0]) + 1 # include the <bos>
trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, \
trg_data_shape, trg_slf_attn_pre_softmax_shape, \
trg_slf_attn_post_softmax_shape, trg_src_attn_pre_softmax_shape, \
trg_src_attn_post_softmax_shape, enc_output = dec_in_data
trg_cur_len = trg_slf_attn_bias.shape[-1] + 1
trg_words = np.array(
[
beam_backtrace(
prev_branchs[beam_idx], next_ids[beam_idx], add_bos=True)
beam_backtrace(prev_branchs[beam_idx], next_ids[beam_idx])
for beam_idx in active_beams
],
dtype="int64")
......@@ -98,6 +157,7 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
trg_pos = np.array(
[range(1, trg_cur_len + 1)] * len(active_beams) * beam_size,
dtype="int64").reshape([-1, 1])
active_beams = [beam_inst_map[beam_idx] for beam_idx in active_beams]
active_beams_indice = (
(np.array(active_beams) * beam_size)[:, np.newaxis] +
np.array(range(beam_size))[np.newaxis, :]).flatten()
......@@ -112,8 +172,27 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
trg_src_attn_bias = np.tile(trg_src_attn_bias[
active_beams_indice, :, ::trg_src_attn_bias.shape[2], :],
[1, 1, trg_cur_len, 1])
# Append the shape input to reshape the output of embedding layer.
trg_data_shape = np.array(
[len(active_beams) * beam_size, trg_cur_len, d_model],
dtype="int32")
# Append the shape inputs to reshape before and after softmax in
# decoder self attention.
trg_slf_attn_pre_softmax_shape = np.array(
[-1, trg_slf_attn_bias.shape[-1]], dtype="int32")
trg_slf_attn_post_softmax_shape = np.array(
trg_slf_attn_bias.shape, dtype="int32")
# Append the shape inputs to reshape before and after softmax in
# encoder-decoder attention.
trg_src_attn_pre_softmax_shape = np.array(
[-1, trg_src_attn_bias.shape[-1]], dtype="int32")
trg_src_attn_post_softmax_shape = np.array(
trg_src_attn_bias.shape, dtype="int32")
enc_output = enc_output[active_beams_indice, :, :]
return trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, enc_output
return trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, \
trg_data_shape, trg_slf_attn_pre_softmax_shape, \
trg_slf_attn_post_softmax_shape, trg_src_attn_pre_softmax_shape, \
trg_src_attn_post_softmax_shape, enc_output
dec_in_data = init_dec_in_data(batch_size, beam_size, enc_in_data,
enc_output)
......@@ -122,13 +201,18 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
feed=dict(zip(dec_in_names, dec_in_data)),
fetch_list=dec_out_names)[0]
predict_all = np.log(
predict_all.reshape([len(beam_map) * beam_size, i + 1, -1])[:,
-1, :])
predict_all = (predict_all + scores[beam_map].reshape(
[len(beam_map) * beam_size, -1])).reshape(
[len(beam_map), beam_size, -1])
predict_all.reshape([len(beam_inst_map) * beam_size, i + 1, -1])
[:, -1, :])
predict_all = (predict_all + scores[active_beams].reshape(
[len(beam_inst_map) * beam_size, -1])).reshape(
[len(beam_inst_map), beam_size, -1])
if not output_unk: # To exclude the <unk> token.
predict_all[:, :, unk_idx] = -1e9
active_beams = []
for inst_idx, beam_idx in enumerate(beam_map):
for beam_idx in range(batch_size):
if not beam_inst_map.has_key(beam_idx):
continue
inst_idx = beam_inst_map[beam_idx]
predict = (predict_all[inst_idx, :, :]
if i != 0 else predict_all[inst_idx, 0, :]).flatten()
top_k_indice = np.argpartition(predict, -beam_size)[-beam_size:]
......@@ -141,13 +225,20 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
next_ids[beam_idx].append(top_scores_ids % predict_all.shape[-1])
if next_ids[beam_idx][-1][0] != eos_idx:
active_beams.append(beam_idx)
beam_map = active_beams
if len(beam_map) == 0:
if len(active_beams) == 0:
break
dec_in_data = update_dec_in_data(dec_in_data, next_ids, active_beams)
dec_in_data = update_dec_in_data(dec_in_data, next_ids, active_beams,
beam_inst_map)
beam_inst_map = {
beam_idx: inst_idx
for inst_idx, beam_idx in enumerate(active_beams)
}
# Decode beams and select n_best sequences for each instance by backtrace.
seqs = [beam_backtrace(prev_branchs[beam_idx], next_ids[beam_idx], n_best)]
seqs = [
beam_backtrace(prev_branchs[beam_idx], next_ids[beam_idx], n_best)
for beam_idx in range(batch_size)
]
return seqs, scores[:, :n_best].tolist()
......@@ -155,29 +246,24 @@ def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
def main():
place = fluid.CUDAPlace(0) if InferTaskConfig.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# The current program desc is coupled with batch_size and the only
# supported batch size is 1 currently.
encoder_program = fluid.Program()
model.batch_size = InferTaskConfig.batch_size
with fluid.program_guard(main_program=encoder_program):
enc_output = encoder(
ModelHyperParams.src_vocab_size + 1,
ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
ModelHyperParams.n_head, ModelHyperParams.d_key,
ModelHyperParams.d_value, ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
ModelHyperParams.src_pad_idx, ModelHyperParams.pos_pad_idx)
model.batch_size = InferTaskConfig.batch_size * InferTaskConfig.beam_size
ModelHyperParams.src_vocab_size, ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer, ModelHyperParams.n_head,
ModelHyperParams.d_key, ModelHyperParams.d_value,
ModelHyperParams.d_model, ModelHyperParams.d_inner_hid,
ModelHyperParams.dropout)
decoder_program = fluid.Program()
with fluid.program_guard(main_program=decoder_program):
predict = decoder(
ModelHyperParams.trg_vocab_size + 1,
ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
ModelHyperParams.n_head, ModelHyperParams.d_key,
ModelHyperParams.d_value, ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
ModelHyperParams.trg_pad_idx, ModelHyperParams.pos_pad_idx)
ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer, ModelHyperParams.n_head,
ModelHyperParams.d_key, ModelHyperParams.d_value,
ModelHyperParams.d_model, ModelHyperParams.d_inner_hid,
ModelHyperParams.dropout)
# Load model parameters of encoder and decoder separately from the saved
# transformer model.
......@@ -214,17 +300,51 @@ def main():
trg_idx2word = paddle.dataset.wmt16.get_dict(
"de", dict_size=ModelHyperParams.trg_vocab_size, reverse=True)
def post_process_seq(seq,
bos_idx=ModelHyperParams.bos_idx,
eos_idx=ModelHyperParams.eos_idx,
output_bos=InferTaskConfig.output_bos,
output_eos=InferTaskConfig.output_eos):
"""
Post-process the beam-search decoded sequence. Truncate from the first
<eos> and remove the <bos> and <eos> tokens currently.
"""
eos_pos = len(seq) - 1
for i, idx in enumerate(seq):
if idx == eos_idx:
eos_pos = i
break
seq = seq[:eos_pos + 1]
return filter(
lambda idx: (output_bos or idx != bos_idx) and \
(output_eos or idx != eos_idx),
seq)
for batch_id, data in enumerate(test_data()):
batch_seqs, batch_scores = translate_batch(
exe, [item[0] for item in data], encoder_program,
encoder_input_data_names, [enc_output.name], decoder_program,
decoder_input_data_names, [predict.name], InferTaskConfig.beam_size,
InferTaskConfig.max_length, InferTaskConfig.n_best,
len(data), ModelHyperParams.n_head, ModelHyperParams.src_pad_idx,
ModelHyperParams.trg_pad_idx, ModelHyperParams.bos_idx,
ModelHyperParams.eos_idx)
exe,
[item[0] for item in data],
encoder_program,
encoder_input_data_names,
[enc_output.name],
decoder_program,
decoder_input_data_names,
[predict.name],
InferTaskConfig.beam_size,
InferTaskConfig.max_length,
InferTaskConfig.n_best,
len(data),
ModelHyperParams.n_head,
ModelHyperParams.d_model,
ModelHyperParams.eos_idx, # Use eos_idx to pad.
ModelHyperParams.eos_idx, # Use eos_idx to pad.
ModelHyperParams.bos_idx,
ModelHyperParams.eos_idx,
ModelHyperParams.unk_idx,
output_unk=InferTaskConfig.output_unk)
for i in range(len(batch_seqs)):
seqs = batch_seqs[i]
# Post-process the beam-search decoded sequences.
seqs = map(post_process_seq, batch_seqs[i])
scores = batch_scores[i]
for seq in seqs:
print(" ".join([trg_idx2word[idx] for idx in seq]))
......
fluid/neural_machine_translation/transformer/train.py
浏览文件 @ a0692c17
import os
import time
import numpy as np
import paddle.v2 as paddle
import paddle
import paddle.fluid as fluid
from model import transformer, position_encoding_init
......@@ -14,7 +15,7 @@ def pad_batch_data(insts,
pad_idx,
n_head,
is_target=False,
return_pos=True,
is_label=False,
return_attn_bias=True,
return_max_len=True):
"""
......@@ -23,14 +24,20 @@ def pad_batch_data(insts,
"""
return_list = []
max_len = max(len(inst) for inst in insts)
# Any token included in dict can be used to pad, since the paddings' loss
# will be masked out by weights and make no effect on parameter gradients.
inst_data = np.array(
[inst + [pad_idx] * (max_len - len(inst)) for inst in insts])
return_list += [inst_data.astype("int64").reshape([-1, 1])]
if return_pos:
inst_pos = np.array([[
pos_i + 1 if w_i != pad_idx else 0 for pos_i, w_i in enumerate(inst)
] for inst in inst_data])
if is_label: # label weight
inst_weight = np.array(
[[1.] * len(inst) + [0.] * (max_len - len(inst)) for inst in insts])
return_list += [inst_weight.astype("float32").reshape([-1, 1])]
else: # position data
inst_pos = np.array([
range(1, len(inst) + 1) + [0] * (max_len - len(inst))
for inst in insts
])
return_list += [inst_pos.astype("int64").reshape([-1, 1])]
if return_attn_bias:
if is_target:
......@@ -56,7 +63,7 @@ def pad_batch_data(insts,
def prepare_batch_input(insts, input_data_names, src_pad_idx, trg_pad_idx,
max_length, n_head):
n_head, d_model):
"""
Put all padded data needed by training into a dict.
"""
......@@ -66,13 +73,40 @@ def prepare_batch_input(insts, input_data_names, src_pad_idx, trg_pad_idx,
[inst[1] for inst in insts], trg_pad_idx, n_head, is_target=True)
trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
[1, 1, trg_max_len, 1]).astype("float32")
lbl_word = pad_batch_data([inst[2] for inst in insts], trg_pad_idx, n_head,
False, False, False, False)
lbl_weight = (lbl_word != trg_pad_idx).astype("float32").reshape([-1, 1])
# These shape tensors are used in reshape_op.
src_data_shape = np.array([len(insts), src_max_len, d_model], dtype="int32")
trg_data_shape = np.array([len(insts), trg_max_len, d_model], dtype="int32")
src_slf_attn_pre_softmax_shape = np.array(
[-1, src_slf_attn_bias.shape[-1]], dtype="int32")
src_slf_attn_post_softmax_shape = np.array(
src_slf_attn_bias.shape, dtype="int32")
trg_slf_attn_pre_softmax_shape = np.array(
[-1, trg_slf_attn_bias.shape[-1]], dtype="int32")
trg_slf_attn_post_softmax_shape = np.array(
trg_slf_attn_bias.shape, dtype="int32")
trg_src_attn_pre_softmax_shape = np.array(
[-1, trg_src_attn_bias.shape[-1]], dtype="int32")
trg_src_attn_post_softmax_shape = np.array(
trg_src_attn_bias.shape, dtype="int32")
lbl_word, lbl_weight = pad_batch_data(
[inst[2] for inst in insts],
trg_pad_idx,
n_head,
is_target=False,
is_label=True,
return_attn_bias=False,
return_max_len=False)
input_dict = dict(
zip(input_data_names, [
src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
src_word, src_pos, src_slf_attn_bias, src_data_shape,
src_slf_attn_pre_softmax_shape, src_slf_attn_post_softmax_shape,
trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias,
trg_data_shape, trg_slf_attn_pre_softmax_shape,
trg_slf_attn_post_softmax_shape, trg_src_attn_pre_softmax_shape,
trg_src_attn_post_softmax_shape, lbl_word, lbl_weight
]))
return input_dict
......@@ -81,14 +115,12 @@ def main():
place = fluid.CUDAPlace(0) if TrainTaskConfig.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
cost, predict = transformer(
ModelHyperParams.src_vocab_size + 1,
ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer, ModelHyperParams.n_head,
ModelHyperParams.d_key, ModelHyperParams.d_value,
ModelHyperParams.d_model, ModelHyperParams.d_inner_hid,
ModelHyperParams.dropout, ModelHyperParams.src_pad_idx,
ModelHyperParams.trg_pad_idx, ModelHyperParams.pos_pad_idx)
sum_cost, avg_cost, predict, token_num = transformer(
ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
ModelHyperParams.n_head, ModelHyperParams.d_key,
ModelHyperParams.d_value, ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid, ModelHyperParams.dropout)
lr_scheduler = LearningRateScheduler(ModelHyperParams.d_model,
TrainTaskConfig.warmup_steps, place,
......@@ -98,7 +130,7 @@ def main():
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
optimizer.minimize(cost)
optimizer.minimize(avg_cost if TrainTaskConfig.use_avg_cost else sum_cost)
train_data = paddle.batch(
paddle.reader.shuffle(
......@@ -110,27 +142,31 @@ def main():
# Program to do validation.
test_program = fluid.default_main_program().clone()
with fluid.program_guard(test_program):
test_program = fluid.io.get_inference_program([cost])
test_program = fluid.io.get_inference_program([avg_cost])
val_data = paddle.batch(
paddle.dataset.wmt16.validation(ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size),
batch_size=TrainTaskConfig.batch_size)
def test(exe):
test_costs = []
test_total_cost = 0
test_total_token = 0
for batch_id, data in enumerate(val_data()):
if len(data) != TrainTaskConfig.batch_size:
continue
data_input = prepare_batch_input(
data, encoder_input_data_names + decoder_input_data_names[:-1] +
label_data_names, ModelHyperParams.src_pad_idx,
ModelHyperParams.trg_pad_idx, ModelHyperParams.max_length,
ModelHyperParams.n_head)
test_cost = exe.run(test_program,
feed=data_input,
fetch_list=[cost])[0]
test_costs.append(test_cost)
return np.mean(test_costs)
label_data_names, ModelHyperParams.eos_idx,
ModelHyperParams.eos_idx, ModelHyperParams.n_head,
ModelHyperParams.d_model)
test_sum_cost, test_token_num = exe.run(
test_program,
feed=data_input,
fetch_list=[sum_cost, token_num],
use_program_cache=True)
test_total_cost += test_sum_cost
test_total_token += test_token_num
test_avg_cost = test_total_cost / test_total_token
test_ppl = np.exp([min(test_avg_cost, 100)])
return test_avg_cost, test_ppl
# Initialize the parameters.
exe.run(fluid.framework.default_startup_program())
......@@ -142,27 +178,30 @@ def main():
ModelHyperParams.d_model), place)
for pass_id in xrange(TrainTaskConfig.pass_num):
pass_start_time = time.time()
for batch_id, data in enumerate(train_data()):
# The current program desc is coupled with batch_size, thus all
# mini-batches must have the same number of instances currently.
if len(data) != TrainTaskConfig.batch_size:
continue
data_input = prepare_batch_input(
data, encoder_input_data_names + decoder_input_data_names[:-1] +
label_data_names, ModelHyperParams.src_pad_idx,
ModelHyperParams.trg_pad_idx, ModelHyperParams.max_length,
ModelHyperParams.n_head)
label_data_names, ModelHyperParams.eos_idx,
ModelHyperParams.eos_idx, ModelHyperParams.n_head,
ModelHyperParams.d_model)
lr_scheduler.update_learning_rate(data_input)
outs = exe.run(fluid.framework.default_main_program(),
feed=data_input,
fetch_list=[cost],
fetch_list=[sum_cost, avg_cost],
use_program_cache=True)
cost_val = np.array(outs[0])
print("pass_id = " + str(pass_id) + " batch = " + str(batch_id) +
" cost = " + str(cost_val))
sum_cost_val, avg_cost_val = np.array(outs[0]), np.array(outs[1])
print("epoch: %d, batch: %d, sum loss: %f, avg loss: %f, ppl: %f" %
(pass_id, batch_id, sum_cost_val, avg_cost_val,
np.exp([min(avg_cost_val[0], 100)])))
# Validate and save the model for inference.
val_cost = test(exe)
print("pass_id = " + str(pass_id) + " val_cost = " + str(val_cost))
val_avg_cost, val_ppl = test(exe)
pass_end_time = time.time()
time_consumed = pass_end_time - pass_start_time
print("epoch: %d, val avg loss: %f, val ppl: %f, "
"consumed %fs" % (pass_id, val_avg_cost, val_ppl, time_consumed))
fluid.io.save_inference_model(
os.path.join(TrainTaskConfig.model_dir,
"pass_" + str(pass_id) + ".infer.model"),
......
fluid/object_detection/.gitignore 0 → 100644
浏览文件 @ a0692c17
./data/pascalvoc/VOCdevkit/
data/pascalvoc/test.txt
data/pascalvoc/trainval.txt
pretrained/ssd_mobilenet_v1_coco.tar.gz
pretrained/ssd_mobilenet_v1_coco
pretrained/mobilenet_v1_imagenet.tar.gz
pretrained/mobilenet_v1_imagenet
log*
fluid/object_detection/README.md
浏览文件 @ a0692c17
......@@ -2,7 +2,99 @@ The minimum PaddlePaddle version needed for the code sample in this directory is
---
# MobileNet-SSD
## SSD Object Detection
This model built with paddle fluid is still under active development and is not
the final version. We welcome feedbacks.
### Introduction
[Single Shot MultiBox Detector (SSD)](https://arxiv.org/abs/1512.02325) framework for object detection is based on a feed-forward convolutional network. The early network is a standard convolutional architecture for image classification, such as VGG, ResNet, or MobileNet, which is als called base network. In this tutorial we used [MobileNet](https://arxiv.org/abs/1704.04861).
### Data Preparation
You can use [PASCAL VOC dataset](http://host.robots.ox.ac.uk/pascal/VOC/) or [MS-COCO dataset](http://cocodataset.org/#download).
#### PASCAL VOC Dataset
If you want to train model on PASCAL VOC dataset, please download datset at first, skip this step if you already have one.
```bash
cd data/pascalvoc
./download.sh
```
The command `download.sh` also will create training and testing file lists.
#### MS-COCO Dataset
If you want to train model on MS-COCO dataset, please download datset at first, skip this step if you already have one.
```
cd data/coco
./download.sh
```
### Train
#### Download the Pre-trained Model.
We provide two pre-trained models. The one is MobileNet-v1 SSD trained on COCO dataset, but removed the convolutional predictors for COCO dataset. This model can be used to initialize the models when training other dataset, like PASCAL VOC. Then other pre-trained model is MobileNet v1 trained on ImageNet 2012 dataset, but removed the last weights and bias in Fully-Connected layer.
Declaration: the MobileNet-v1 SSD model is converted by [TensorFlow model](https://github.com/tensorflow/models/blob/f87a58cd96d45de73c9a8330a06b2ab56749a7fa/research/object_detection/g3doc/detection_model_zoo.md). The MobileNet v1 model is converted [Caffe](https://github.com/shicai/MobileNet-Caffe).
- Download MobileNet-v1 SSD:
```
./pretrained/download_coco.sh
```
- Download MobileNet-v1:
```
./pretrained/download_imagenet.sh
```
#### Train on PASCAL VOC
- Train on one device (/GPU).
```python
env CUDA_VISIABLE_DEVICES=0 python -u train.py --parallel=False --data='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/'
```
- Train on multi devices (/GPUs).
```python
env CUDA_VISIABLE_DEVICES=0,1 python -u train.py --batch_size=64 --data='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/'
```
#### Train on MS-COCO
- Train on one device (/GPU).
```python
env CUDA_VISIABLE_DEVICES=0 python -u train.py --parallel=False --data='coco' --pretrained_model='pretrained/mobilenet_imagenet/'
```
- Train on multi devices (/GPUs).
```python
env CUDA_VISIABLE_DEVICES=0,1 python -u train.py --batch_size=64 --data='coco' --pretrained_model='pretrained/mobilenet_imagenet/'
```
TBD
### Evaluate
```python
env CUDA_VISIABLE_DEVICES=0 python eval.py --model='model/90' --test_list=''
```
TBD
### Infer and Visualize
```python
env CUDA_VISIABLE_DEVICES=0 python infer.py --batch_size=2 --model='model/90' --test_list=''
```
TBD
### Released Model
| Model | Pre-trained Model | Training data | Test data | mAP |
|:------------------------:|:------------------:|:----------------:|:------------:|:----:|
|MobileNet-v1-SSD 300x300 | COCO MobileNet SSD | VOC07+12 trainval| VOC07 test | xx% |
|MobileNet-v1-SSD 300x300 | ImageNet MobileNet | VOC07+12 trainval| VOC07 test | xx% |
|MobileNet-v1-SSD 300x300 | ImageNet MobileNet | MS-COCO trainval | MS-COCO test | xx% |
TBD
fluid/object_detection/data/prepare_voc_data.py → fluid/object_detection/data/pascalvoc/create_list.py
浏览文件 @ a0692c17
......@@ -60,4 +60,5 @@ def prepare_filelist(devkit_dir, years, output_dir):
ftest.write(item[0] + ' ' + item[1] + '\n')
prepare_filelist(devkit_dir, years, '.')
if __name__ == '__main__':
prepare_filelist(devkit_dir, years, '.')
fluid/object_detection/data/pascalvoc/download.sh 0 → 100755
浏览文件 @ a0692c17
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd "$DIR"
# Download the data.
echo "Downloading..."
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
# Extract the data.
echo "Extractint..."
tar -xf VOCtrainval_11-May-2012.tar
tar -xf VOCtrainval_06-Nov-2007.tar
tar -xf VOCtest_06-Nov-2007.tar
echo "Creating data lists..."
python create_list.py
fluid/object_detection/image_util.py
浏览文件 @ a0692c17
......@@ -85,8 +85,7 @@ def satisfy_sample_constraint(sampler, sample_bbox, bbox_labels):
return False
def generate_batch_samples(batch_sampler, bbox_labels, image_width,
image_height):
def generate_batch_samples(batch_sampler, bbox_labels):
sampled_bbox = []
index = []
c = 0
......@@ -216,9 +215,9 @@ def distort_image(img, settings):
def expand_image(img, bbox_labels, img_width, img_height, settings):
prob = random.uniform(0, 1)
if prob < settings._hue_prob:
expand_ratio = random.uniform(1, settings._expand_max_ratio)
if expand_ratio - 1 >= 0.01:
if prob < settings._expand_prob:
if _expand_max_ratio - 1 >= 0.01:
expand_ratio = random.uniform(1, settings._expand_max_ratio)
height = int(img_height * expand_ratio)
width = int(img_width * expand_ratio)
h_off = math.floor(random.uniform(0, height - img_height))
......@@ -231,5 +230,5 @@ def expand_image(img, bbox_labels, img_width, img_height, settings):
expand_img = Image.fromarray(expand_img)
expand_img.paste(img, (int(w_off), int(h_off)))
bbox_labels = transform_labels(bbox_labels, expand_bbox)
return expand_img, bbox_labels
return img, bbox_labels
return expand_img, bbox_labels, width, height
return img, bbox_labels, img_width, img_height
fluid/object_detection/load_model.py 已删除 100644 → 0
浏览文件 @ 2d985018
import paddle.v2 as paddle
import paddle.fluid as fluid
import numpy as np
# From npy
def load_vars():
vars = {}
name_map = {}
with open('./ssd_mobilenet_v1_coco/names.map', 'r') as map_file:
for param in map_file:
fd_name, tf_name = param.strip().split('\t')
name_map[fd_name] = tf_name
tf_vars = np.load(
'./ssd_mobilenet_v1_coco/ssd_mobilenet_v1_coco_2017_11_17.npy').item()
for fd_name in name_map:
tf_name = name_map[fd_name]
tf_var = tf_vars[tf_name]
if len(tf_var.shape) == 4 and 'depthwise' in tf_name:
vars[fd_name] = np.transpose(tf_var, (2, 3, 0, 1))
elif len(tf_var.shape) == 4:
vars[fd_name] = np.transpose(tf_var, (3, 2, 0, 1))
else:
vars[fd_name] = tf_var
return vars
def load_and_set_vars(place):
vars = load_vars()
for k, v in vars.items():
t = fluid.global_scope().find_var(k).get_tensor()
#print(np.array(t).shape, v.shape, k)
assert np.array(t).shape == v.shape
t.set(v, place)
# From Paddle V1
def load_paddlev1_vars(place):
vars = {}
name_map = {}
with open('./caffe2paddle/names.map', 'r') as map_file:
for param in map_file:
fd_name, tf_name = param.strip().split('\t')
name_map[fd_name] = tf_name
from operator import mul
def load(file_name, shape):
with open(file_name, 'rb') as f:
f.read(16)
arr = np.fromfile(f, dtype=np.float32)
#print(arr.size, reduce(mul, shape), file_name)
assert arr.size == reduce(mul, shape)
return arr.reshape(shape)
for fd_name in name_map:
v1_name = name_map[fd_name]
t = fluid.global_scope().find_var(fd_name).get_tensor()
shape = np.array(t).shape
v1_var = load('./caffe2paddle/' + v1_name, shape)
t.set(v1_var, place)
if __name__ == "__main__":
load_vars()
fluid/object_detection/mobilenet_ssd.py
浏览文件 @ a0692c17
......@@ -27,12 +27,7 @@ def conv_bn(input,
bias_attr=False)
parameter_attr = ParamAttr(learning_rate=0.1, initializer=MSRA())
bias_attr = ParamAttr(learning_rate=0.2)
return fluid.layers.batch_norm(
input=conv,
act=act,
epsilon=0.00001,
param_attr=parameter_attr,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=conv, act=act)
def depthwise_separable(input, num_filters1, num_filters2, num_groups, stride,
......@@ -76,7 +71,7 @@ def extra_block(input, num_filters1, num_filters2, num_groups, stride, scale):
return normal_conv
def mobile_net(img, img_shape, scale=1.0):
def mobile_net(num_classes, img, img_shape, scale=1.0):
# 300x300
tmp = conv_bn(img, 3, int(32 * scale), 2, 1, 3)
# 150x150
......@@ -104,10 +99,11 @@ def mobile_net(img, img_shape, scale=1.0):
module16 = extra_block(module15, 128, 256, 1, 2, scale)
# 2x2
module17 = extra_block(module16, 64, 128, 1, 2, scale)
mbox_locs, mbox_confs, box, box_var = fluid.layers.multi_box_head(
inputs=[module11, module13, module14, module15, module16, module17],
image=img,
num_classes=21,
num_classes=num_classes,
min_ratio=20,
max_ratio=90,
min_sizes=[60.0, 105.0, 150.0, 195.0, 240.0, 285.0],
......
fluid/object_detection/pretrained/download_coco.sh 0 → 100755
浏览文件 @ a0692c17
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd "$DIR"
# Download the data.
echo "Downloading..."
wget http://paddlemodels.bj.bcebos.com/ssd_mobilenet_v1_coco.tar.gz
echo "Extractint..."
tar -xf ssd_mobilenet_v1_coco.tar.gz
fluid/object_detection/pretrained/download_imagenet.sh 0 → 100755
浏览文件 @ a0692c17
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd "$DIR"
# Download the data.
echo "Downloading..."
wget http://paddlemodels.bj.bcebos.com/mobilenet_v1_imagenet.tar.gz
echo "Extractint..."
tar -xf mobilenet_v1_imagenet.tar.gz
fluid/object_detection/reader.py
浏览文件 @ a0692c17
此差异已折叠。
fluid/object_detection/train.py
浏览文件 @ a0692c17
此差异已折叠。
fluid/ocr_recognition/README.md
浏览文件 @ a0692c17
# OCR Model

[toc]
This model built with paddle fluid is still under active development and is not
the final version. We welcome feedbacks.
运行本目录下的程序示例需要使用PaddlePaddle develop最新版本。如果您的PaddlePaddle安装版本低于此要求，请按照安装文档中的说明更新PaddlePaddle安装版本。
# Optical Character Recognition
这里将介绍如何在PaddlePaddle Fluid下使用CRNN-CTC 和 CRNN-Attention模型对图片中的文字内容进行识别。
## 1. CRNN-CTC
本章的任务是识别含有单行汉语字符图片，首先采用卷积将图片转为特征图, 然后使用`im2sequence op`将特征图转为序列，通过`双向GRU`学习到序列特征。训练过程选用的损失函数为CTC(Connectionist Temporal Classification) loss，最终的评估指标为样本级别的错误率。
本路径下各个文件的作用如下：
- **ctc_reader.py :** 下载、读取、处理数据。提供方法`train()` 和 `test()` 分别产生训练集和测试集的数据迭代器。
- **crnn_ctc_model.py :** 在该脚本中定义了训练网络、预测网络和evaluate网络。
- **ctc_train.py :** 用于模型的训练，可通过命令`python train.py --help` 获得使用方法。
- **infer.py :** 加载训练好的模型文件，对新数据进行预测。可通过命令`python infer.py --help` 获得使用方法。
- **eval.py :** 评估模型在指定数据集上的效果。可通过命令`python infer.py --help` 获得使用方法。
- **utility.py :** 实现的一些通用方法，包括参数配置、tensor的构造等。
### 1.1 数据
数据的下载和简单预处理都在`ctc_reader.py`中实现。
#### 1.1.1 数据格式
我们使用的训练和测试数据如`图1`所示，每张图片包含单行不定长的中文字符串，这些图片都是经过检测算法进行预框选处理的。
<p align="center">
<img src="images/demo.jpg" width="620" hspace='10'/> <br/>
<strong>图 1</strong>
</p>
在训练集中，每张图片对应的label是汉字在词典中的索引。 `图1` 对应的label如下所示：
```
3835,8371,7191,2369,6876,4162,1938,168,1517,4590,3793
```
在上边这个label中，`3835` 表示字符‘两’的索引，`4590` 表示中文字符逗号的索引。
#### 1.1.2 数据准备
**A. 训练集**
我们需要把所有参与训练的图片放入同一个文件夹，暂且记为`train_images`。然后用一个list文件存放每张图片的信息，包括图片大小、图片名称和对应的label，这里暂记该list文件为`train_list`，其格式如下所示：
```
185 48 00508_0215.jpg 7740,5332,2369,3201,4162
48 48 00197_1893.jpg 6569
338 48 00007_0219.jpg 4590,4788,3015,1994,3402,999,4553
150 48 00107_4517.jpg 5936,3382,1437,3382
...
157 48 00387_0622.jpg 2397,1707,5919,1278
```
<center>文件train_list</center>
上述文件中的每一行表示一张图片，每行被空格分为四列，前两列分别表示图片的宽和高，第三列表示图片的名称，第四列表示该图片对应的sequence label。
最终我们应有以下类似文件结构：
```
|-train_data
|- train_list
|- train_imags
|- 00508_0215.jpg
|- 00197_1893.jpg
|- 00007_0219.jpg
| ...
```
在训练时，我们通过选项`--train_images` 和 `--train_list` 分别设置准备好的`train_images` 和`train_list`。
>**注：** 如果`--train_images` 和 `--train_list`都未设置或设置为None， ctc_reader.py会自动下载使用[示例数据](http://cloud.dlnel.org/filepub/?uuid=df937251-3c0b-480d-9a7b-0080dfeee65c)，并将其缓存到`$HOME/.cache/paddle/dataset/ctc_data/data/` 路径下。
**B. 测试集和评估集**
测试集、评估集的准备方式与训练集相同。
在训练阶段，测试集的路径通过train.py的选项`--test_images` 和 `--test_list` 来设置。
在评估时，评估集的路径通过eval.py的选项`--input_images_dir` 和`--input_images_list` 来设置。
**C. 待预测数据集**
预测支持三种形式的输入：
第一种：设置`--input_images_dir`和`--input_images_list`, 与训练集类似, 只不过list文件中的最后一列可以放任意占位字符或字符串，如下所示：
```
185 48 00508_0215.jpg s
48 48 00197_1893.jpg s
338 48 00007_0219.jpg s
...
```
第二种：仅设置`--input_images_list`, 其中list文件中只需放图片的完整路径，如下所示：
```
data/test_images/00000.jpg
data/test_images/00001.jpg
data/test_images/00003.jpg
```
第三种：从stdin读入一张图片的path，然后进行一次inference.
#### 1.2 训练
使用默认数据在GPU单卡上训练:
```
env CUDA_VISIABLE_DEVICES=0 python ctc_train.py
```
使用默认数据在GPU多卡上训练:
```
env CUDA_VISIABLE_DEVICES=0,1,2,3 python ctc_train.py --parallel=True
```
执行`python ctc_train.py --help`可查看更多使用方式和参数详细说明。
图2为使用默认参数和默认数据集训练的收敛曲线，其中横坐标轴为训练迭代次数，纵轴为在测试集上的样本级错误率。在45轮迭代训练中，最低错误率为第42轮的21.11%.
<p align="center">
<img src="images/train.jpg" width="620" hspace='10'/> <br/>
<strong>图 2</strong>
</p>
### 1.3 评估
通过以下命令调用评估脚本用指定数据集对模型进行评估：
```
env CUDA_VISIBLE_DEVICE=0 python eval.py \
--model_path="./models/model_0" \
--input_images_dir="./eval_data/images/" \
--input_images_list="./eval_data/eval_list\" \
```
执行`python ctc_train.py --help`可查看参数详细说明。
### 1.4 预测
从标准输入读取一张图片的路径，并对齐进行预测：
```
env CUDA_VISIBLE_DEVICE=0 python infer.py \
--model_path="models/model_00044_15000"
```
执行上述命令进行预测的效果如下：
```
----------- Configuration Arguments -----------
use_gpu: True
input_images_dir: None
input_images_list: None
model_path: /home/work/models/fluid/ocr_recognition/models/model_00052_15000
------------------------------------------------
Init model from: /home/work/models/fluid/ocr_recognition/models/model_00052_15000.
Please input the path of image: /home/work/models/fluid/ocr_recognition/data/test_images/00001_0060.jpg
result: [3298 2371 4233 6514 2378 3298 2363]
Please input the path of image: /home/work/models/fluid/ocr_recognition/data/test_images/00001_0429.jpg
result: [2067 2067 8187 8477 5027 7191 2431 1462]
```
从文件中批量读取图片路径，并对其进行预测：
```
env CUDA_VISIBLE_DEVICE=0 python infer.py \
--model_path="models/model_00044_15000" \
--input_images_list="data/test.list"
```
fluid/ocr_recognition/ctc_reader.py
浏览文件 @ a0692c17
此差异已折叠。
fluid/ocr_recognition/ctc_train.py
浏览文件 @ a0692c17
此差异已折叠。
fluid/ocr_recognition/eval.py
浏览文件 @ a0692c17
此差异已折叠。
fluid/ocr_recognition/infer.py 0 → 100644
浏览文件 @ a0692c17
此差异已折叠。
fluid/ocr_recognition/inference.py 已删除 100644 → 0
浏览文件 @ 2d985018
此差异已折叠。
fluid/ocr_recognition/load_model.py 已删除 100644 → 0
浏览文件 @ 2d985018
此差异已折叠。
fluid/policy_gradient/brain.py
浏览文件 @ a0692c17
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或