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提交 4f51c4d0 编写于 作者: C Channingss
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support export to onnx

上级 aafd6fe4
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Showing with 135 addition and 134 deletion
paddlex/command.py
浏览文件 @ 4f51c4d0
......@@ -60,25 +60,35 @@ def main():
print("Repo: https://github.com/PaddlePaddle/PaddleX.git")
print("Email: paddlex@baidu.com")
return
if args.export_inference:
assert args.model_dir is not None, "--model_dir should be defined while exporting inference model"
assert args.save_dir is not None, "--save_dir should be defined to save inference model"
fixed_input_shape = eval(args.fixed_input_shape)
assert len(
fixed_input_shape) == 2, "len of fixed input shape must == 2"
fixed_input_shape = None
if args.fixed_input_shape is not None:
fixed_input_shape = eval(args.fixed_input_shape)
assert len(
fixed_input_shape) == 2, "len of fixed input shape must == 2"
model = pdx.load_model(args.model_dir, fixed_input_shape)
model.export_inference_model(args.save_dir)
# if args.export_onnx:
# assert args.model_dir is not None, "--model_dir should be defined while exporting onnx model"
# assert args.save_dir is not None, "--save_dir should be defined to save onnx model"
# fixed_input_shape = eval(args.fixed_input_shape)
# assert len(
# fixed_input_shape) == 2, "len of fixed input shape must == 2"
if args.export_onnx:
assert args.model_dir is not None, "--model_dir should be defined while exporting onnx model"
assert args.save_dir is not None, "--save_dir should be defined to save onnx model"
fixed_input_shape = None
if args.fixed_input_shape is not None:
fixed_input_shape = eval(args.fixed_input_shape)
assert len(
fixed_input_shape) == 2, "len of fixed input shape must == 2"
model = pdx.load_model(args.model_dir, fixed_input_shape)
# model = pdx.load_model(args.model_dir, fixed_input_shape)
# model.export_onnx_model(args.save_dir)
model_name = os.path.basename(args.model_dir.strip('/')).split('/')[-1]
onnx_name = model_name + '.onnx'
model.export_onnx_model(args.save_dir, onnx_name=onnx_name)
if __name__ == "__main__":
......
paddlex/cv/models/base.py
浏览文件 @ 4f51c4d0
......@@ -15,6 +15,7 @@
from __future__ import absolute_import
import paddle.fluid as fluid
import os
import sys
import numpy as np
import time
import math
......@@ -327,129 +328,119 @@ class BaseAPI:
logging.info(
"Model for inference deploy saved in {}.".format(save_dir))
# def export_onnx_model(self, save_dir, onnx_model=None):
# from fluid.utils import op_io_info, init_name_prefix
# from onnx import helper, checker
# import fluid_onnx.ops as ops
# from fluid_onnx.variables import paddle_variable_to_onnx_tensor, paddle_onnx_weight
# from debug.model_check import debug_model, Tracke
# place = fluid.CPUPlace()
# exe = fluid.Executor(place)
# inference_scope = fluid.core.Scope()
# with fluid.scope_guard(inference_scope):
# test_input_names = [
# var.name for var in list(self.test_inputs.values())
# ]
# inputs_outputs_list = ["fetch", "feed"]
# weights, weights_value_info = [], []
# global_block = self.test_program.global_block()
# for var_name in global_block.vars:
# var = global_block.var(var_name)
# if var_name not in feed_fetch_list\
# and var.persistable:
# weight, val_info = paddle_onnx_weight(
# var=var, scope=inference_scope)
# weights.append(weight)
# weights_value_info.append(val_info)
# # Create inputs
# inputs = [
# paddle_variable_to_onnx_tensor(v, global_block)
# for v in test_input_names
# ]
# print("load the model parameter done.")
# onnx_nodes = []
# op_check_list = []
# op_trackers = []
# nms_first_index = -1
# nms_outputs = []
# for block in inference_program.blocks:
# for op in block.ops:
# if op.type in ops.node_maker:
# # TODO(kuke): deal with the corner case that vars in
# # different blocks have the same name
# node_proto = ops.node_maker[str(op.type)](operator=op,
# block=block)
# op_outputs = []
# last_node = None
# if isinstance(node_proto, tuple):
# onnx_nodes.extend(list(node_proto))
# last_node = list(node_proto)
# else:
# onnx_nodes.append(node_proto)
# last_node = [node_proto]
# tracker = Tracker(str(op.type), last_node)
# op_trackers.append(tracker)
# op_check_list.append(str(op.type))
# if op.type == "multiclass_nms" and nms_first_index < 0:
# nms_first_index = 0
# if nms_first_index >= 0:
# _, _, output_op = op_io_info(op)
# for output in output_op:
# nms_outputs.extend(output_op[output])
# else:
# if op.type not in ['feed', 'fetch']:
# op_check_list.append(op.type)
# print('The operator sets to run test case.')
# print(set(op_check_list))
# # Create outputs
# # Get the new names for outputs if they've been renamed in nodes' making
# renamed_outputs = op_io_info.get_all_renamed_outputs()
# test_outputs = list(self.test_outputs.values())
# test_outputs_names = [var.name for var in self.test_outpus.values]
# test_outputs_names = [
# name if name not in renamed_outputs else renamed_outputs[name]
# for name in test_outputs_names
# ]
# outputs = [
# paddle_variable_to_onnx_tensor(v, global_block)
# for v in test_outputs_names
# ]
# # Make graph
# #model_name = os.path.basename(args.fluid_model.strip('/')).split('.')[0]
# model_name = 'test'
# onnx_graph = helper.make_graph(
# nodes=onnx_nodes,
# name=model_name,
# initializer=weights,
# inputs=inputs + weights_value_info,
# outputs=outputs)
# # Make model
# onnx_model = helper.make_model(onnx_graph, producer_name='PaddlePaddle')
# # Model check
# checker.check_model(onnx_model)
# # Print model
# #if to_print_model:
# # print("The converted model is:\n{}".format(onnx_model))
# # Save converted model
#
# if onnx_model is not None:
# try:
# onnx_model_file = osp.join(save_dir, onnx_model)
# with open(onnx_model_file, 'wb') as f:
# f.write(onnx_model.SerializeToString())
# print("Saved converted model to path: %s" % onnx_model_file)
# # If in debug mode, need to save op list, add we will check op
# #if args.debug:
# # op_check_list = list(set(op_check_list))
# # check_outputs = []
# # for node_proto in onnx_nodes:
# # check_outputs.extend(node_proto.output)
# # print("The num of %d operators need to check, and %d op outputs need to check."\
# # %(len(op_check_list), len(check_outputs)))
# # debug_model(op_check_list, op_trackers, nms_outputs, args)
# except Exception as e:
# print(e)
# print(
# "Convert Failed! Please use the debug message to find error."
# )
# sys.exit(-1)
def export_onnx_model(self, save_dir, onnx_name=None):
from fluid.utils import op_io_info, init_name_prefix
from onnx import helper, checker
import fluid_onnx.ops as ops
from fluid_onnx.variables import paddle_variable_to_onnx_tensor, paddle_onnx_weight
from debug.model_check import debug_model, Tracker
place = fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.global_scope()
with fluid.scope_guard(inference_scope):
test_input_names = [
var.name for var in list(self.test_inputs.values())
]
inputs_outputs_list = ["fetch", "feed"]
weights, weights_value_info = [], []
global_block = self.test_prog.global_block()
for var_name in global_block.vars:
var = global_block.var(var_name)
if var_name not in test_input_names\
and var.persistable:
weight, val_info = paddle_onnx_weight(
var=var, scope=inference_scope)
weights.append(weight)
weights_value_info.append(val_info)
# Create inputs
inputs = [
paddle_variable_to_onnx_tensor(v, global_block)
for v in test_input_names
]
print("load the model parameter done.")
onnx_nodes = []
op_check_list = []
op_trackers = []
nms_first_index = -1
nms_outputs = []
for block in self.test_prog.blocks:
for op in block.ops:
if op.type in ops.node_maker:
# TODO(kuke): deal with the corner case that vars in
# different blocks have the same name
node_proto = ops.node_maker[str(op.type)](
operator=op, block=block)
op_outputs = []
last_node = None
if isinstance(node_proto, tuple):
onnx_nodes.extend(list(node_proto))
last_node = list(node_proto)
else:
onnx_nodes.append(node_proto)
last_node = [node_proto]
tracker = Tracker(str(op.type), last_node)
op_trackers.append(tracker)
op_check_list.append(str(op.type))
if op.type == "multiclass_nms" and nms_first_index < 0:
nms_first_index = 0
if nms_first_index >= 0:
_, _, output_op = op_io_info(op)
for output in output_op:
nms_outputs.extend(output_op[output])
else:
if op.type not in ['feed', 'fetch']:
op_check_list.append(op.type)
print('The operator sets to run test case.')
print(set(op_check_list))
# Create outputs
# Get the new names for outputs if they've been renamed in nodes' making
renamed_outputs = op_io_info.get_all_renamed_outputs()
test_outputs = list(self.test_outputs.values())
test_outputs_names = [
var.name for var in self.test_outputs.values()
]
test_outputs_names = [
name if name not in renamed_outputs else renamed_outputs[name]
for name in test_outputs_names
]
outputs = [
paddle_variable_to_onnx_tensor(v, global_block)
for v in test_outputs_names
]
# Make graph
onnx_name = 'test'
onnx_graph = helper.make_graph(
nodes=onnx_nodes,
name=onnx_name,
initializer=weights,
inputs=inputs + weights_value_info,
outputs=outputs)
# Make model
onnx_model = helper.make_model(
onnx_graph, producer_name='PaddlePaddle')
# Model check
checker.check_model(onnx_model)
# Print model
#if to_print_model:
# print("The converted model is:\n{}".format(onnx_model))
# Save converted model
if onnx_model is not None:
try:
onnx_model_file = osp.join(save_dir, onnx_name)
with open(onnx_model_file, 'wb') as f:
f.write(onnx_model.SerializeToString())
print(
"Saved converted model to path: %s" % onnx_model_file)
except Exception as e:
print(e)
print(
"Convert Failed! Please use the debug message to find error."
)
sys.exit(-1)
def train_loop(self,
num_epochs,
......
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