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Merge pull request #1358 from TeslaZhao/develop
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Modify model conversion interfaces and model load methods
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@bjjwwang
bjjwwang committed Aug 20, 2021
2 parents 5e0a8ec + 60aede0 commit d2d9590
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Showing 5 changed files with 195 additions and 63 deletions.
119 changes: 94 additions & 25 deletions python/paddle_serving_app/local_predict.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@
from .proto import general_model_config_pb2 as m_config
import paddle.inference as paddle_infer
import logging
import glob

logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("LocalPredictor")
Expand Down Expand Up @@ -51,6 +52,23 @@ def __init__(self):
self.fetch_names_to_idx_ = {}
self.fetch_names_to_type_ = {}

def search_suffix_files(self, model_path, target_suffix):
"""
Find all files with the suffix xxx in the specified directory.
Args:
model_path: model directory, not None.
target_suffix: filenames with target suffix, not None. e.g: *.pdmodel
Returns:
file_list, None, [] or [path, ] .
"""
if model_path is None or target_suffix is None:
return None

file_list = glob.glob(os.path.join(model_path, target_suffix))
return file_list

def load_model_config(self,
model_path,
use_gpu=False,
Expand Down Expand Up @@ -97,11 +115,30 @@ def load_model_config(self,
f = open(client_config, 'r')
model_conf = google.protobuf.text_format.Merge(
str(f.read()), model_conf)

# Init paddle_infer config
# Paddle's model files and parameter files have multiple naming rules:
# 1) __model__, __params__
# 2) *.pdmodel, *.pdiparams
# 3) __model__, conv2d_1.w_0, conv2d_2.w_0, fc_1.w_0, conv2d_1.b_0, ...
pdmodel_file_list = self.search_suffix_files(model_path, "*.pdmodel")
pdiparams_file_list = self.search_suffix_files(model_path,
"*.pdiparams")
if os.path.exists(os.path.join(model_path, "__params__")):
# case 1) initializing
config = paddle_infer.Config(
os.path.join(model_path, "__model__"),
os.path.join(model_path, "__params__"))
elif pdmodel_file_list and len(
pdmodel_file_list) > 0 and pdiparams_file_list and len(
pdiparams_file_list) > 0:
# case 2) initializing
logger.info("pdmodel_file_list:{}, pdiparams_file_list:{}".format(
pdmodel_file_list, pdiparams_file_list))
config = paddle_infer.Config(pdmodel_file_list[0],
pdiparams_file_list[0])
else:
# case 3) initializing.
config = paddle_infer.Config(model_path)

logger.info(
Expand Down Expand Up @@ -201,25 +238,18 @@ def predict(self, feed=None, fetch=None, batch=False, log_id=0):
Run model inference by Paddle Inference API.
Args:
feed: feed var
fetch: fetch var
feed: feed var list, None is not allowed.
fetch: fetch var list, None allowed. when it is None, all fetch
vars are returned. Otherwise, return fetch specified result.
batch: batch data or not, False default.If batch is False, a new
dimension is added to header of the shape[np.newaxis].
log_id: for logging
Returns:
fetch_map: dict
"""
if feed is None or fetch is None:
raise ValueError("You should specify feed and fetch for prediction.\
log_id:{}".format(log_id))
fetch_list = []
if isinstance(fetch, str):
fetch_list = [fetch]
elif isinstance(fetch, list):
fetch_list = fetch
else:
raise ValueError("Fetch only accepts string and list of string.\
if feed is None:
raise ValueError("You should specify feed vars for prediction.\
log_id:{}".format(log_id))

feed_batch = []
Expand All @@ -231,18 +261,20 @@ def predict(self, feed=None, fetch=None, batch=False, log_id=0):
raise ValueError("Feed only accepts dict and list of dict.\
log_id:{}".format(log_id))

fetch_names = []
fetch_list = []
if fetch is not None:
if isinstance(fetch, str):
fetch_list = [fetch]
elif isinstance(fetch, list):
fetch_list = fetch

# Filter invalid fetch names
fetch_names = []
for key in fetch_list:
if key in self.fetch_names_:
fetch_names.append(key)

if len(fetch_names) == 0:
raise ValueError(
"Fetch names should not be empty or out of saved fetch list.\
log_id:{}".format(log_id))

# Assemble the input data of paddle predictor
# Assemble the input data of paddle predictor, and filter invalid inputs.
input_names = self.predictor.get_input_names()
for name in input_names:
if isinstance(feed[name], list):
Expand Down Expand Up @@ -282,11 +314,15 @@ def predict(self, feed=None, fetch=None, batch=False, log_id=0):
input_tensor_handle.copy_from_cpu(feed[name][np.newaxis, :])
else:
input_tensor_handle.copy_from_cpu(feed[name])

# set output tensor handlers
output_tensor_handles = []
output_name_to_index_dict = {}
output_names = self.predictor.get_output_names()
for output_name in output_names:
for i, output_name in enumerate(output_names):
output_tensor_handle = self.predictor.get_output_handle(output_name)
output_tensor_handles.append(output_tensor_handle)
output_name_to_index_dict[output_name] = i

# Run inference
self.predictor.run()
Expand All @@ -296,10 +332,43 @@ def predict(self, feed=None, fetch=None, batch=False, log_id=0):
for output_tensor_handle in output_tensor_handles:
output = output_tensor_handle.copy_to_cpu()
outputs.append(output)
outputs_len = len(outputs)

# Copy fetch vars. If fetch is None, it will copy all results from output_tensor_handles.
# Otherwise, it will copy the fields specified from output_tensor_handles.
fetch_map = {}
for i, name in enumerate(fetch):
fetch_map[name] = outputs[i]
if len(output_tensor_handles[i].lod()) > 0:
fetch_map[name + ".lod"] = np.array(output_tensor_handles[i]
.lod()[0]).astype('int32')
if fetch is None:
for i, name in enumerate(output_names):
fetch_map[name] = outputs[i]
if len(output_tensor_handles[i].lod()) > 0:
fetch_map[name + ".lod"] = np.array(output_tensor_handles[
i].lod()[0]).astype('int32')
else:
# Because the save_inference_model interface will increase the scale op
# in the network, the name of fetch_var is different from that in prototxt.
# Therefore, it is compatible with v0.6.x and the previous model save format,
# and here is compatible with the results that do not match.
fetch_match_num = 0
for i, name in enumerate(fetch):
output_index = output_name_to_index_dict.get(name)
if output_index is None:
continue

fetch_map[name] = outputs[output_index]
fetch_match_num += 1
if len(output_tensor_handles[output_index].lod()) > 0:
fetch_map[name + ".lod"] = np.array(output_tensor_handles[
output_index].lod()[0]).astype('int32')

# Compatible with v0.6.x and lower versions model saving formats.
if fetch_match_num == 0:
logger.debug("fetch match num is 0. Retrain the model please!")
for i, name in enumerate(fetch):
if i >= outputs_len:
break
fetch_map[name] = outputs[i]
if len(output_tensor_handles[i].lod()) > 0:
fetch_map[name + ".lod"] = np.array(
output_tensor_handles[i].lod()[0]).astype('int32')

return fetch_map
37 changes: 25 additions & 12 deletions python/paddle_serving_client/io/__init__.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,6 @@ def save_dygraph_model(serving_model_folder, client_config_folder, model):
}
config = model_conf.GeneralModelConfig()

#int64 = 0; float32 = 1; int32 = 2;
for key in feed_var_dict:
feed_var = model_conf.FeedVar()
feed_var.alias_name = key
Expand Down Expand Up @@ -127,7 +126,6 @@ def save_dygraph_model(serving_model_folder, client_config_folder, model):
def var_type_conversion(dtype):
"""
Variable type conversion
Args:
dtype: type of core.VarDesc.VarType.xxxxx
(https://github.com/PaddlePaddle/Paddle/blob/release/2.1/python/paddle/framework/dtype.py)
Expand Down Expand Up @@ -184,7 +182,9 @@ def save_model(server_model_folder,
main_program=None,
encryption=False,
key_len=128,
encrypt_conf=None):
encrypt_conf=None,
model_filename=None,
params_filename=None):
executor = Executor(place=CPUPlace())

feed_var_names = [feed_var_dict[x].name for x in feed_var_dict]
Expand All @@ -194,15 +194,27 @@ def save_model(server_model_folder,
target_vars.append(fetch_var_dict[key])
target_var_names.append(key)

if not os.path.exists(server_model_folder):
os.makedirs(server_model_folder)
if not encryption:
save_inference_model(
server_model_folder,
feed_var_names,
target_vars,
executor,
model_filename="__model__",
params_filename="__params__",
main_program=main_program)
if not model_filename:
model_filename = "model.pdmodel"
if not params_filename:
params_filename = "params.pdiparams"

new_model_path = os.path.join(server_model_folder, model_filename)
new_params_path = os.path.join(server_model_folder, params_filename)

with open(new_model_path, "wb") as new_model_file:
new_model_file.write(main_program.desc.serialize_to_string())

paddle.static.save_vars(
executor=executor,
dirname=server_model_folder,
main_program=main_program,
vars=None,
predicate=paddle.static.io.is_persistable,
filename=params_filename)
else:
if encrypt_conf == None:
aes_cipher = CipherFactory.create_cipher()
Expand Down Expand Up @@ -296,7 +308,8 @@ def inference_model_to_serving(dirname,
}
fetch_dict = {x.name: x for x in fetch_targets}
save_model(serving_server, serving_client, feed_dict, fetch_dict,
inference_program, encryption, key_len, encrypt_conf)
inference_program, encryption, key_len, encrypt_conf,
model_filename, params_filename)
feed_names = feed_dict.keys()
fetch_names = fetch_dict.keys()
return feed_names, fetch_names

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