Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2298
Star 20931
Fork 5422
P
Paddle
未验证 提交 dce0383f 编写于 作者: T Tao Luo 提交者： GitHub
浏览文件
操作

Merge pull request #8404 from Xreki/core_refine_inference 

Refine the inference API and unittests
上级 0dbaad57 efb6ba35
隐藏空白更改
内联 并排
Showing with 450 addition and 351 deletion
paddle/fluid/framework/lod_tensor.cc
浏览文件 @ dce0383f
......@@ -31,8 +31,14 @@ std::ostream &operator<<(std::ostream &os, const LoD &lod) {
os << "{";
for (auto &v : lod) {
os << "{";
bool is_first = true;
for (auto &i : v) {
os << i << ",";
if (is_first) {
os << i;
is_first = false;
} else {
os << ", " << i;
}
}
os << "}";
}
......
paddle/fluid/inference/io.cc
浏览文件 @ dce0383f
......@@ -32,23 +32,11 @@ void ReadBinaryFile(const std::string& filename, std::string& contents) {
inputfs.close();
}
bool IsParameter(const framework::VarDesc* var,
const framework::ProgramDesc& main_program) {
if (var->Persistable()) {
// There are many unreachable variables in the program
for (size_t i = 0; i < main_program.Size(); ++i) {
const framework::BlockDesc& block = main_program.Block(i);
for (auto* op : block.AllOps()) {
if (op->Type() == framework::kFeedOpType) {
continue;
}
for (auto input_argument_name : op->InputArgumentNames()) {
if (input_argument_name == var->Name()) {
return true;
}
}
}
}
bool IsPersistable(const framework::VarDesc* var) {
if (var->Persistable() &&
var->GetType() != framework::proto::VarType::FEED_MINIBATCH &&
var->GetType() != framework::proto::VarType::FETCH_LIST) {
return true;
}
return false;
}
......@@ -65,8 +53,8 @@ void LoadPersistables(framework::Executor& executor,
std::vector<std::string> paramlist;
for (auto* var : global_block.AllVars()) {
if (IsParameter(var, main_program)) {
VLOG(3) << "parameter's name: " << var->Name();
if (IsPersistable(var)) {
VLOG(3) << "persistable variable's name: " << var->Name();
framework::VarDesc* new_var = load_block->Var(var->Name());
new_var->SetShape(var->GetShape());
......@@ -101,7 +89,6 @@ void LoadPersistables(framework::Executor& executor,
executor.Run(*load_program, &scope, 0, true, true);
VLOG(3) << "Ran loading successfully";
delete load_program;
}
......
paddle/fluid/inference/tests/book/CMakeLists.txt
浏览文件 @ dce0383f
......@@ -30,5 +30,5 @@ inference_test(label_semantic_roles)
inference_test(recognize_digits ARGS mlp conv)
inference_test(recommender_system)
#inference_test(rnn_encoder_decoder)
inference_test(understand_sentiment)
inference_test(understand_sentiment ARGS conv)
inference_test(word2vec)
paddle/fluid/inference/tests/book/test_inference_label_semantic_roles.cc
浏览文件 @ dce0383f
......@@ -32,16 +32,42 @@ TEST(inference, label_semantic_roles) {
paddle::framework::LoDTensor word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1,
ctx_p2, mark;
paddle::framework::LoD lod{{0, 4, 10}};
SetupLoDTensor(word, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(
predicate, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(ctx_n2, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(ctx_n1, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(ctx_0, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(ctx_p1, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(ctx_p2, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(mark, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
int64_t word_dict_len = 44068;
int64_t predicate_dict_len = 3162;
int64_t mark_dict_len = 2;
SetupLoDTensor(word,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(predicate,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(predicate_dict_len - 1));
SetupLoDTensor(ctx_n2,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_n1,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_0,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_p1,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_p2,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(mark,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(mark_dict_len - 1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&word);
......
paddle/fluid/inference/tests/book/test_inference_understand_sentiment.cc
浏览文件 @ dce0383f
......@@ -31,7 +31,12 @@ TEST(inference, understand_sentiment) {
paddle::framework::LoDTensor words;
paddle::framework::LoD lod{{0, 4, 10}};
SetupLoDTensor(words, lod, static_cast<int64_t>(0), static_cast<int64_t>(10));
int64_t word_dict_len = 5147;
SetupLoDTensor(words,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&words);
......
paddle/fluid/inference/tests/book/test_inference_word2vec.cc
浏览文件 @ dce0383f
......@@ -31,12 +31,12 @@ TEST(inference, word2vec) {
paddle::framework::LoDTensor first_word, second_word, third_word, fourth_word;
paddle::framework::LoD lod{{0, 1}};
int64_t dict_size = 2072; // Hard-coding the size of dictionary
int64_t dict_size = 2073; // The size of dictionary
SetupLoDTensor(first_word, lod, static_cast<int64_t>(0), dict_size);
SetupLoDTensor(second_word, lod, static_cast<int64_t>(0), dict_size);
SetupLoDTensor(third_word, lod, static_cast<int64_t>(0), dict_size);
SetupLoDTensor(fourth_word, lod, static_cast<int64_t>(0), dict_size);
SetupLoDTensor(first_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(second_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(third_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(fourth_word, lod, static_cast<int64_t>(0), dict_size - 1);
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&first_word);
......
paddle/fluid/inference/tests/test_helper.h
浏览文件 @ dce0383f
......@@ -101,8 +101,8 @@ void TestInference(const std::string& dirname,
if (IsCombined) {
// All parameters are saved in a single file.
// Hard-coding the file names of program and parameters in unittest.
// Users are free to specify different filename
// (provided: the filenames are changed in the python api as well: io.py)
// The file names should be consistent with that used in Python API
// `fluid.io.save_inference_model`.
std::string prog_filename = "__model_combined__";
std::string param_filename = "__params_combined__";
inference_program = paddle::inference::Load(executor,
......
python/paddle/fluid/io.py
浏览文件 @ dce0383f
......@@ -68,7 +68,7 @@ def save_vars(executor,
main_program=None,
vars=None,
predicate=None,
save_file_name=None):
filename=None):
"""
Save variables to directory by executor.
......@@ -80,8 +80,8 @@ def save_vars(executor,
as a bool. If it returns true, the corresponding input variable will be saved.
:param vars: variables need to be saved. If vars is specified, program & predicate
will be ignored
:param save_file_name: The name of a single file that all vars are saved to.
If it is None, save variables to separate files.
:param filename: The name of a single file that all vars are saved to.
If it is None, save variables to separate files.
:return: None
"""
......@@ -95,7 +95,7 @@ def save_vars(executor,
executor,
dirname=dirname,
vars=filter(predicate, main_program.list_vars()),
save_file_name=save_file_name)
filename=filename)
else:
save_program = Program()
save_block = save_program.global_block()
......@@ -103,7 +103,7 @@ def save_vars(executor,
save_var_map = {}
for each_var in vars:
new_var = _clone_var_in_block_(save_block, each_var)
if save_file_name is None:
if filename is None:
save_block.append_op(
type='save',
inputs={'X': [new_var]},
......@@ -112,7 +112,7 @@ def save_vars(executor,
else:
save_var_map[new_var.name] = new_var
if save_file_name is not None:
if filename is not None:
save_var_list = []
for name in sorted(save_var_map.keys()):
save_var_list.append(save_var_map[name])
......@@ -121,12 +121,12 @@ def save_vars(executor,
type='save_combine',
inputs={'X': save_var_list},
outputs={},
attrs={'file_path': os.path.join(dirname, save_file_name)})
attrs={'file_path': os.path.join(dirname, filename)})
executor.run(save_program)
def save_params(executor, dirname, main_program=None, save_file_name=None):
def save_params(executor, dirname, main_program=None, filename=None):
"""
Save all parameters to directory with executor.
"""
......@@ -136,11 +136,10 @@ def save_params(executor, dirname, main_program=None, save_file_name=None):
main_program=main_program,
vars=None,
predicate=is_parameter,
save_file_name=save_file_name)
filename=filename)
def save_persistables(executor, dirname, main_program=None,
save_file_name=None):
def save_persistables(executor, dirname, main_program=None, filename=None):
"""
Save all persistables to directory with executor.
"""
......@@ -150,7 +149,7 @@ def save_persistables(executor, dirname, main_program=None,
main_program=main_program,
vars=None,
predicate=is_persistable,
save_file_name=save_file_name)
filename=filename)
def load_vars(executor,
......@@ -158,7 +157,7 @@ def load_vars(executor,
main_program=None,
vars=None,
predicate=None,
load_file_name=None):
filename=None):
"""
Load variables from directory by executor.
......@@ -170,8 +169,8 @@ def load_vars(executor,
as a bool. If it returns true, the corresponding input variable will be loaded.
:param vars: variables need to be loaded. If vars is specified, program &
predicate will be ignored
:param load_file_name: The name of the single file that all vars are loaded from.
If it is None, load variables from separate files.
:param filename: The name of the single file that all vars are loaded from.
If it is None, load variables from separate files.
:return: None
"""
......@@ -185,7 +184,7 @@ def load_vars(executor,
executor,
dirname=dirname,
vars=filter(predicate, main_program.list_vars()),
load_file_name=load_file_name)
filename=filename)
else:
load_prog = Program()
load_block = load_prog.global_block()
......@@ -194,7 +193,7 @@ def load_vars(executor,
for each_var in vars:
assert isinstance(each_var, Variable)
new_var = _clone_var_in_block_(load_block, each_var)
if load_file_name is None:
if filename is None:
load_block.append_op(
type='load',
inputs={},
......@@ -203,7 +202,7 @@ def load_vars(executor,
else:
load_var_map[new_var.name] = new_var
if load_file_name is not None:
if filename is not None:
load_var_list = []
for name in sorted(load_var_map.keys()):
load_var_list.append(load_var_map[name])
......@@ -212,12 +211,12 @@ def load_vars(executor,
type='load_combine',
inputs={},
outputs={"Out": load_var_list},
attrs={'file_path': os.path.join(dirname, load_file_name)})
attrs={'file_path': os.path.join(dirname, filename)})
executor.run(load_prog)
def load_params(executor, dirname, main_program=None, load_file_name=None):
def load_params(executor, dirname, main_program=None, filename=None):
"""
load all parameters from directory by executor.
"""
......@@ -226,11 +225,10 @@ def load_params(executor, dirname, main_program=None, load_file_name=None):
dirname=dirname,
main_program=main_program,
predicate=is_parameter,
load_file_name=load_file_name)
filename=filename)
def load_persistables(executor, dirname, main_program=None,
load_file_name=None):
def load_persistables(executor, dirname, main_program=None, filename=None):
"""
load all persistables from directory by executor.
"""
......@@ -239,7 +237,7 @@ def load_persistables(executor, dirname, main_program=None,
dirname=dirname,
main_program=main_program,
predicate=is_persistable,
load_file_name=load_file_name)
filename=filename)
def get_inference_program(target_vars, main_program=None):
......@@ -299,7 +297,8 @@ def save_inference_model(dirname,
target_vars,
executor,
main_program=None,
save_file_name=None):
model_filename=None,
params_filename=None):
"""
Build a model especially for inference,
and save it to directory by the executor.
......@@ -310,8 +309,11 @@ def save_inference_model(dirname,
:param executor: executor that save inference model
:param main_program: original program, which will be pruned to build the inference model.
Default default_main_program().
:param save_file_name: The name of a single file that all parameters are saved to.
If it is None, save parameters to separate files.
:param model_filename: The name of file to save inference program.
If not specified, default filename `__model__` will be used.
:param params_filename: The name of file to save parameters.
It is used for the case that all parameters are saved in a single binary file.
If not specified, parameters are considered saved in separate files.
:return: None
"""
......@@ -342,15 +344,19 @@ def save_inference_model(dirname,
prepend_feed_ops(inference_program, feeded_var_names)
append_fetch_ops(inference_program, fetch_var_names)
if save_file_name == None:
model_file_name = dirname + "/__model__"
if model_filename is not None:
model_filename = os.path.basename(model_filename)
else:
model_file_name = dirname + "/__model_combined__"
model_filename = "__model__"
model_filename = os.path.join(dirname, model_filename)
with open(model_file_name, "wb") as f:
if params_filename is not None:
params_filename = os.path.basename(params_filename)
with open(model_filename, "wb") as f:
f.write(inference_program.desc.serialize_to_string())
save_persistables(executor, dirname, inference_program, save_file_name)
save_persistables(executor, dirname, inference_program, params_filename)
def get_feed_targets_names(program):
......@@ -371,15 +377,21 @@ def get_fetch_targets_names(program):
return fetch_targets_names
def load_inference_model(dirname, executor, load_file_name=None):
def load_inference_model(dirname,
executor,
model_filename=None,
params_filename=None):
"""
Load inference model from a directory
:param dirname: directory path
:param executor: executor that load inference model
:param load_file_name: The name of the single file that all parameters are loaded from.
If it is None, load parameters from separate files.
:param model_filename: The name of file to load inference program.
If not specified, default filename `__model__` will be used.
:param params_filename: The name of file to load parameters.
It is used for the case that all parameters are saved in a single binary file.
If not specified, parameters are considered saved in separate files.
:return: [program, feed_target_names, fetch_targets]
program: program especially for inference.
feed_target_names: Names of variables that need to feed data
......@@ -388,16 +400,20 @@ def load_inference_model(dirname, executor, load_file_name=None):
if not os.path.isdir(dirname):
raise ValueError("There is no directory named '%s'", dirname)
if load_file_name == None:
model_file_name = dirname + "/__model__"
if model_filename is not None:
model_filename = os.path.basename(model_filename)
else:
model_file_name = dirname + "/__model_combined__"
model_filename = "__model__"
model_filename = os.path.join(dirname, model_filename)
if params_filename is not None:
params_filename = os.path.basename(params_filename)
with open(model_file_name, "rb") as f:
with open(model_filename, "rb") as f:
program_desc_str = f.read()
program = Program.parse_from_string(program_desc_str)
load_persistables(executor, dirname, program, load_file_name)
load_persistables(executor, dirname, program, params_filename)
feed_target_names = get_feed_targets_names(program)
fetch_target_names = get_fetch_targets_names(program)
......
python/paddle/fluid/tests/book/notest_rnn_encoder_decoer.py
浏览文件 @ dce0383f
......@@ -228,32 +228,34 @@ def infer(use_cuda, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
word_data = create_random_lodtensor(lod, place, low=0, high=1)
trg_word = create_random_lodtensor(lod, place, low=0, high=1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'source_sequence'
assert feed_target_names[1] == 'target_sequence'
results = exe.run(inference_program,
feed={
feed_target_names[0]: word_data,
feed_target_names[1]: trg_word,
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference shape: ", np_data.shape)
print("Inference results: ", np_data)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
word_data = create_random_lodtensor(lod, place, low=0, high=1)
trg_word = create_random_lodtensor(lod, place, low=0, high=1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'source_sequence'
assert feed_target_names[1] == 'target_sequence'
results = exe.run(inference_program,
feed={
feed_target_names[0]: word_data,
feed_target_names[1]: trg_word,
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference shape: ", np_data.shape)
print("Inference results: ", np_data)
def main(use_cuda):
......
python/paddle/fluid/tests/book/test_fit_a_line.py
浏览文件 @ dce0383f
......@@ -72,23 +72,26 @@ def infer(use_cuda, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_x},
fetch_list=fetch_targets)
print("infer shape: ", results[0].shape)
print("infer results: ", results[0])
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
tensor_x = numpy.random.uniform(0, 10,
[batch_size, 13]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_x},
fetch_list=fetch_targets)
print("infer shape: ", results[0].shape)
print("infer results: ", results[0])
def main(use_cuda):
......
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @ dce0383f
......@@ -174,22 +174,26 @@ def infer(use_cuda, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension of conv should be 4-D or 5-D.
tensor_img = numpy.random.rand(1, 3, 32, 32).astype("float32")
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
print("infer results: ", results[0])
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [0, 1.0].
batch_size = 1
tensor_img = numpy.random.rand(batch_size, 3, 32, 32).astype("float32")
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
print("infer results: ", results[0])
def main(net_type, use_cuda):
......
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @ dce0383f
......@@ -26,7 +26,7 @@ import unittest
word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_len = len(verb_dict)
pred_dict_len = len(verb_dict)
mark_dict_len = 2
word_dim = 32
......@@ -53,7 +53,7 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
# 8 features
predicate_embedding = fluid.layers.embedding(
input=predicate,
size=[pred_len, word_dim],
size=[pred_dict_len, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr='vemb')
......@@ -234,6 +234,7 @@ def train(use_cuda, save_dirname=None):
# Set the threshold low to speed up the CI test
if float(pass_precision) > 0.05:
if save_dirname is not None:
# TODO(liuyiqun): Change the target to crf_decode
fluid.io.save_inference_model(save_dirname, [
'word_data', 'verb_data', 'ctx_n2_data',
'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
......@@ -251,51 +252,60 @@ def infer(use_cuda, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
ts_word = create_random_lodtensor(lod, place, low=0, high=1)
ts_pred = create_random_lodtensor(lod, place, low=0, high=1)
ts_ctx_n2 = create_random_lodtensor(lod, place, low=0, high=1)
ts_ctx_n1 = create_random_lodtensor(lod, place, low=0, high=1)
ts_ctx_0 = create_random_lodtensor(lod, place, low=0, high=1)
ts_ctx_p1 = create_random_lodtensor(lod, place, low=0, high=1)
ts_ctx_p2 = create_random_lodtensor(lod, place, low=0, high=1)
ts_mark = create_random_lodtensor(lod, place, low=0, high=1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'word_data'
assert feed_target_names[1] == 'verb_data'
assert feed_target_names[2] == 'ctx_n2_data'
assert feed_target_names[3] == 'ctx_n1_data'
assert feed_target_names[4] == 'ctx_0_data'
assert feed_target_names[5] == 'ctx_p1_data'
assert feed_target_names[6] == 'ctx_p2_data'
assert feed_target_names[7] == 'mark_data'
results = exe.run(inference_program,
feed={
feed_target_names[0]: ts_word,
feed_target_names[1]: ts_pred,
feed_target_names[2]: ts_ctx_n2,
feed_target_names[3]: ts_ctx_n1,
feed_target_names[4]: ts_ctx_0,
feed_target_names[5]: ts_ctx_p1,
feed_target_names[6]: ts_ctx_p2,
feed_target_names[7]: ts_mark
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print("Inference results: ", np_data)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
word = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
pred = create_random_lodtensor(
lod, place, low=0, high=pred_dict_len - 1)
ctx_n2 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_n1 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_0 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_p1 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_p2 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
mark = create_random_lodtensor(
lod, place, low=0, high=mark_dict_len - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'word_data'
assert feed_target_names[1] == 'verb_data'
assert feed_target_names[2] == 'ctx_n2_data'
assert feed_target_names[3] == 'ctx_n1_data'
assert feed_target_names[4] == 'ctx_0_data'
assert feed_target_names[5] == 'ctx_p1_data'
assert feed_target_names[6] == 'ctx_p2_data'
assert feed_target_names[7] == 'mark_data'
results = exe.run(inference_program,
feed={
feed_target_names[0]: word,
feed_target_names[1]: pred,
feed_target_names[2]: ctx_n2,
feed_target_names[3]: ctx_n1,
feed_target_names[4]: ctx_0,
feed_target_names[5]: ctx_p1,
feed_target_names[6]: ctx_p2,
feed_target_names[7]: mark
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
def main(use_cuda):
......
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @ dce0383f
......@@ -78,7 +78,12 @@ def conv_net(img, label):
return loss_net(conv_pool_2, label)
def train(nn_type, use_cuda, parallel, save_dirname, save_param_filename):
def train(nn_type,
use_cuda,
parallel,
save_dirname=None,
model_filename=None,
params_filename=None):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
......@@ -146,7 +151,8 @@ def train(nn_type, use_cuda, parallel, save_dirname, save_param_filename):
fluid.io.save_inference_model(
save_dirname, ["img"], [prediction],
exe,
save_file_name=save_param_filename)
model_filename=model_filename,
params_filename=params_filename)
return
else:
print(
......@@ -158,54 +164,62 @@ def train(nn_type, use_cuda, parallel, save_dirname, save_param_filename):
raise AssertionError("Loss of recognize digits is too large")
def infer(use_cuda, save_dirname=None, param_filename=None):
def infer(use_cuda,
save_dirname=None,
model_filename=None,
params_filename=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names, fetch_targets
] = fluid.io.load_inference_model(save_dirname, exe, param_filename)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [-1.0, 1.0].
batch_size = 1
tensor_img = numpy.random.uniform(-1.0, 1.0,
[batch_size, 1, 28, 28]).astype("float32")
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
print("infer results: ", results[0])
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(
save_dirname, exe, model_filename, params_filename)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [-1.0, 1.0].
batch_size = 1
tensor_img = numpy.random.uniform(
-1.0, 1.0, [batch_size, 1, 28, 28]).astype("float32")
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
print("infer results: ", results[0])
def main(use_cuda, parallel, nn_type, combine):
save_dirname = None
model_filename = None
params_filename = None
if not use_cuda and not parallel:
save_dirname = "recognize_digits_" + nn_type + ".inference.model"
save_filename = None
if combine == True:
save_filename = "__params_combined__"
else:
save_dirname = None
save_filename = None
model_filename = "__model_combined__"
params_filename = "__params_combined__"
train(
nn_type=nn_type,
use_cuda=use_cuda,
parallel=parallel,
save_dirname=save_dirname,
save_param_filename=save_filename)
model_filename=model_filename,
params_filename=params_filename)
infer(
use_cuda=use_cuda,
save_dirname=save_dirname,
param_filename=save_filename)
model_filename=model_filename,
params_filename=params_filename)
class TestRecognizeDigits(unittest.TestCase):
......
python/paddle/fluid/tests/book/test_recommender_system.py
浏览文件 @ dce0383f
......@@ -251,13 +251,6 @@ def infer(use_cuda, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
def create_lod_tensor(data, lod=None):
tensor = fluid.LoDTensor()
if lod is None:
......@@ -275,44 +268,53 @@ def infer(use_cuda, save_dirname=None):
tensor.set(flattened_data, place)
return tensor
# Use the first data from paddle.dataset.movielens.test() as input
assert feed_target_names[0] == "user_id"
user_id = create_lod_tensor([[1]])
assert feed_target_names[1] == "gender_id"
gender_id = create_lod_tensor([[1]])
assert feed_target_names[2] == "age_id"
age_id = create_lod_tensor([[0]])
assert feed_target_names[3] == "job_id"
job_id = create_lod_tensor([[10]])
assert feed_target_names[4] == "movie_id"
movie_id = create_lod_tensor([[783]])
assert feed_target_names[5] == "category_id"
category_id = create_lod_tensor([[10], [8], [9]], [[0, 3]])
assert feed_target_names[6] == "movie_title"
movie_title = create_lod_tensor([[1069], [4140], [2923], [710], [988]],
[[0, 5]])
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: user_id,
feed_target_names[1]: gender_id,
feed_target_names[2]: age_id,
feed_target_names[3]: job_id,
feed_target_names[4]: movie_id,
feed_target_names[5]: category_id,
feed_target_names[6]: movie_title
},
fetch_list=fetch_targets,
return_numpy=False)
print("inferred score: ", np.array(results[0]))
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# Use the first data from paddle.dataset.movielens.test() as input
assert feed_target_names[0] == "user_id"
user_id = create_lod_tensor([[1]])
assert feed_target_names[1] == "gender_id"
gender_id = create_lod_tensor([[1]])
assert feed_target_names[2] == "age_id"
age_id = create_lod_tensor([[0]])
assert feed_target_names[3] == "job_id"
job_id = create_lod_tensor([[10]])
assert feed_target_names[4] == "movie_id"
movie_id = create_lod_tensor([[783]])
assert feed_target_names[5] == "category_id"
category_id = create_lod_tensor([[10], [8], [9]], [[0, 3]])
assert feed_target_names[6] == "movie_title"
movie_title = create_lod_tensor([[1069], [4140], [2923], [710], [988]],
[[0, 5]])
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: user_id,
feed_target_names[1]: gender_id,
feed_target_names[2]: age_id,
feed_target_names[3]: job_id,
feed_target_names[4]: movie_id,
feed_target_names[5]: category_id,
feed_target_names[6]: movie_title
},
fetch_list=fetch_targets,
return_numpy=False)
print("inferred score: ", np.array(results[0]))
def main(use_cuda):
......
python/paddle/fluid/tests/book/test_understand_sentiment.py
浏览文件 @ dce0383f
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -193,36 +193,39 @@ def train(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
net_method.__name__))
def infer(use_cuda, save_dirname=None):
def infer(word_dict, use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
word_dict = paddle.dataset.imdb.word_dict()
tensor_words = create_random_lodtensor(
lod, place, low=0, high=len(word_dict) - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == "words"
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_words},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print("Inference results: ", np_data)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
word_dict_len = len(word_dict)
lod = [0, 4, 10]
tensor_words = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == "words"
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_words},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print("Inference results: ", np_data)
def main(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
......@@ -258,7 +261,7 @@ class TestUnderstandSentiment(unittest.TestCase):
self.word_dict,
net_method=convolution_net,
use_cuda=False,
save_dirname="understand_sentiment.inference.model")
save_dirname="understand_sentiment_conv.inference.model")
def test_conv_cpu_parallel(self):
with self.new_program_scope():
......@@ -271,7 +274,11 @@ class TestUnderstandSentiment(unittest.TestCase):
@unittest.skip(reason="make CI faster")
def test_stacked_lstm_cpu(self):
with self.new_program_scope():
main(self.word_dict, net_method=stacked_lstm_net, use_cuda=False)
main(
self.word_dict,
net_method=stacked_lstm_net,
use_cuda=False,
save_dirname="understand_sentiment_stacked_lstm.inference.model")
def test_stacked_lstm_cpu_parallel(self):
with self.new_program_scope():
......@@ -287,7 +294,7 @@ class TestUnderstandSentiment(unittest.TestCase):
self.word_dict,
net_method=convolution_net,
use_cuda=True,
save_dirname="understand_sentiment.inference.model")
save_dirname="understand_sentiment_conv.inference.model")
def test_conv_gpu_parallel(self):
with self.new_program_scope():
......@@ -300,7 +307,11 @@ class TestUnderstandSentiment(unittest.TestCase):
@unittest.skip(reason="make CI faster")
def test_stacked_lstm_gpu(self):
with self.new_program_scope():
main(self.word_dict, net_method=stacked_lstm_net, use_cuda=True)
main(
self.word_dict,
net_method=stacked_lstm_net,
use_cuda=True,
save_dirname="understand_sentiment_stacked_lstm.inference.model")
def test_stacked_lstm_gpu_parallel(self):
with self.new_program_scope():
......
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @ dce0383f
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# # Licensed under the Apache License, Version 2.0 (the "License");
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
......@@ -21,6 +22,7 @@ import sys
def create_random_lodtensor(lod, place, low, high):
# The range of data elements is [low, high]
data = np.random.random_integers(low, high, [lod[-1], 1]).astype("int64")
res = fluid.LoDTensor()
res.set(data, place)
......@@ -28,54 +30,7 @@ def create_random_lodtensor(lod, place, low, high):
return res
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict) - 1
# Setup input, by creating 4 words, and setting up lod required for
# lookup_table_op
lod = [0, 1]
first_word = create_random_lodtensor(lod, place, low=0, high=dict_size)
second_word = create_random_lodtensor(lod, place, low=0, high=dict_size)
third_word = create_random_lodtensor(lod, place, low=0, high=dict_size)
fourth_word = create_random_lodtensor(lod, place, low=0, high=dict_size)
assert feed_target_names[0] == 'firstw'
assert feed_target_names[1] == 'secondw'
assert feed_target_names[2] == 'thirdw'
assert feed_target_names[3] == 'forthw'
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: first_word,
feed_target_names[1]: second_word,
feed_target_names[2]: third_word,
feed_target_names[3]: fourth_word
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print("Inference results: ", np_data)
def train(use_cuda, is_sparse, parallel, save_dirname):
def train(use_cuda, is_sparse, is_parallel, save_dirname):
PASS_NUM = 100
EMBED_SIZE = 32
HIDDEN_SIZE = 256
......@@ -130,7 +85,7 @@ def train(use_cuda, is_sparse, parallel, save_dirname):
forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
if not parallel:
if not is_parallel:
avg_cost, predict_word = __network__(
[first_word, second_word, third_word, forth_word, next_word])
else:
......@@ -176,11 +131,67 @@ def train(use_cuda, is_sparse, parallel, save_dirname):
raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))
def main(use_cuda, is_sparse, parallel):
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
# Setup inputs, by creating 4 words, the lod of which should be [0, 1]
lod = [0, 1]
first_word = create_random_lodtensor(
lod, place, low=0, high=dict_size - 1)
second_word = create_random_lodtensor(
lod, place, low=0, high=dict_size - 1)
third_word = create_random_lodtensor(
lod, place, low=0, high=dict_size - 1)
fourth_word = create_random_lodtensor(
lod, place, low=0, high=dict_size - 1)
assert feed_target_names[0] == 'firstw'
assert feed_target_names[1] == 'secondw'
assert feed_target_names[2] == 'thirdw'
assert feed_target_names[3] == 'forthw'
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: first_word,
feed_target_names[1]: second_word,
feed_target_names[2]: third_word,
feed_target_names[3]: fourth_word
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
def main(use_cuda, is_sparse, is_parallel):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_dirname = "word2vec.inference.model"
train(use_cuda, is_sparse, parallel, save_dirname)
if not is_parallel:
save_dirname = "word2vec.inference.model"
else:
save_dirname = None
train(use_cuda, is_sparse, is_parallel, save_dirname)
infer(use_cuda, save_dirname)
......@@ -193,10 +204,10 @@ class W2VTest(unittest.TestCase):
pass
def inject_test_method(use_cuda, is_sparse, parallel):
def inject_test_method(use_cuda, is_sparse, is_parallel):
fn_name = "test_{0}_{1}_{2}".format("cuda" if use_cuda else "cpu", "sparse"
if is_sparse else "dense", "parallel"
if parallel else "normal")
if is_parallel else "normal")
def __impl__(*args, **kwargs):
prog = fluid.Program()
......@@ -204,10 +215,12 @@ def inject_test_method(use_cuda, is_sparse, parallel):
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
with fluid.program_guard(prog, startup_prog):
main(use_cuda=use_cuda, is_sparse=is_sparse, parallel=parallel)
main(
use_cuda=use_cuda,
is_sparse=is_sparse,
is_parallel=is_parallel)
# run only 2 cases: use_cuda is either True or False
if is_sparse == False and parallel == False:
if use_cuda and is_sparse:
fn = __impl__
else:
# skip the other test when on CI server
......@@ -219,8 +232,8 @@ def inject_test_method(use_cuda, is_sparse, parallel):
for use_cuda in (False, True):
for is_sparse in (False, True):
for parallel in (False, True):
inject_test_method(use_cuda, is_sparse, parallel)
for is_parallel in (False, True):
inject_test_method(use_cuda, is_sparse, is_parallel)
if __name__ == '__main__':
unittest.main()
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