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[IE Tools] Update tools with new Python API (#944)

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Anastasia Kuporosova 2020-07-08 13:38:49 +03:00 committed by GitHub
parent 499c976194
commit c39e32a47b
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GPG Key ID: 4AEE18F83AFDEB23
6 changed files with 6 additions and 136 deletions
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6
inference-engine/tools/cross_check_tool/cross_check_tool.py
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@ -77,7 +77,7 @@ def get_net_copy_with_output(model: str, output: str, core: IECore):
def get_model_info(net: IENetwork):
layers = net.layers
precision = layers[list(layers.keys())[0]].out_data[0].precision
return layers, net.inputs, net.outputs, precision
return layers, net.input_info, net.outputs, precision
###
@ -222,8 +222,8 @@ def two_ir_mode(args):
def dump_mode(args):
core = get_plugin(args.device, args.l, args.config)
net = get_net(model=args.model, core=core)
out_layers = get_layers_list(net.layers, net.inputs, net.outputs, args.layers)
inputs = input_processing(args.model, net.inputs, args.input)
out_layers = get_layers_list(net.layers, net.input_info, net.outputs, args.layers)
inputs = input_processing(args.model, net.input_info, args.input)
dump_dict = {}
for out_layer in out_layers:
log.info('Layer {} processing'.format(out_layer))

6
inference-engine/tools/cross_check_tool/utils.py
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@ -320,7 +320,7 @@ def read_multi_input_file(input_file: str, net_inputs: dict):
', '.join(net_inputs.keys())))
if 'blob' in npz[net_input].item(0):
just_blob = npz[net_input].item(0)['blob']
network_shape = net_inputs[net_input].shape
network_shape = net_inputs[net_input].input_data.shape
log.info('Layer {} shape = {}, input blob from multi-input file shape = {}'
''.format(net_input, network_shape, just_blob.shape))
try:
@ -344,7 +344,7 @@ def read_image_file(input_file: str, net_inputs: dict):
if image is None:
raise Exception('Can not read input image ' + input_file)
only_layer_name = list(net_inputs.keys())[0]
shape = net_inputs[only_layer_name].shape
shape = net_inputs[only_layer_name].input_data.shape
if len(shape) != 4:
raise Exception('Can not interpret input shape as image')
n, c, h, w = shape
@ -361,7 +361,7 @@ def input_processing(model_path: str, net_inputs: dict, input_file: str, layers_
inputs = dict()
if input_file is None:
for net_input in net_inputs:
inputs[net_input] = np.clip(np.random.normal(0.5, 0.1, size=net_inputs[net_input].shape), 0, 1)
inputs[net_input] = np.clip(np.random.normal(0.5, 0.1, size=net_inputs[net_input].input_data.shape), 0, 1)
dump_output_file(model_path + '_random_input_dump.npz', {inp: {'blob': inputs[inp]} for inp in inputs})
return inputs
try:

6
tools/.gitignore vendored
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@ -1,8 +1,2 @@
accuracy_checker
accuracy_checker.log
i8_normalized.dot
openvino.tools.benchmark.log
__pycache__/
accuracy_checker.local/
accuracy_checker.original/

1
tools/README.md
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@ -2,7 +2,6 @@
## General
`openvino.tools` package includes:
* openvino.tools.accuracy_checker
* openvino.tools.benchmark
Please, refer to https://docs.openvinotoolkit.org for details.

17
tools/__init__.py
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@ -1,17 +0,0 @@
"""
Copyright (C) 2018-2020 Intel Corporation
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
__version__ = "0.0.1"

106
tools/python_api_reproducer.py
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@ -1,106 +0,0 @@
import argparse
import logging as log
import sys
log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
import os
import numpy as np
from openvino.inference_engine import IENetwork
def python_api_infer(net, feed_dict, device, lib, api, nireq, outputs_to_add: list = None):
"""
Function to perform IE inference using python API "in place"
:param net: IENetwork instance
:param feed_dict: Dict which contains mapping between input blob and input data
:param device: Device name for inference
:param lib: Absolute path to custom kernel lib
:param outputs_to_add: Layer names list to take output from
:param api: Defines use synchronous infer or asynchronous
:param nireq: Number of infer requests to create for asynchronous infer
:return: Dict containing out blob name and out data
"""
from openvino.inference_engine import IECore
ie = IECore()
if outputs_to_add:
net.add_outputs(outputs_to_add)
exec_net = ie.load_network(net, device, num_requests=nireq)
if api == "async":
res = []
for i in range(nireq):
reqest_handler = exec_net.start_async(request_id=i, inputs=feed_dict)
reqest_handler.wait()
res.append(reqest_handler.outputs)
else:
res = exec_net.infer(inputs=feed_dict)
del net
# It's important to delete executable network first to avoid double free in plugin offloading.
# Issue relates ony for hetero and Myriad plugins
del exec_net
del ie
return res
def cli_parser():
parser = argparse.ArgumentParser(description='Python_api reproducer')
parser.add_argument('-i', dest='feed_dict', required=True, help='Path to input data in .npz format')
parser.add_argument('-m', dest='ir_path', required=True, help='Path to XML file of IR')
parser.add_argument('-d', dest='device', required=True, help='Target device to infer on')
parser.add_argument('-api', dest='api', default='sync', help='')
parser.add_argument('-nireq', dest='nireq', default=1, help='')
parser.add_argument('-r', dest='out_path', default=None,
help='Dumps results to the output folder')
parser.add_argument('--out_layers', dest='out_layers', default=[],
help='Names of layers to dump inference results. Example: "input,conv3d"')
parser.add_argument('--dump_all_layers', dest='dump_all_layers', default=False, action="store_true",
help='Bool value to dump inference results from all layers')
args = parser.parse_args()
feed_dict = args.feed_dict
ir_path = args.ir_path
device = args.device
lib = args.lib
api = args.api
nireq = int(args.nireq)
out_path = args.out_path
if out_path and not os.path.exists(out_path):
os.makedirs(out_path)
out_layers = args.out_layers.split(",") if args.out_layers else args.out_layers
dump_all_layers = args.dump_all_layers
if out_layers and dump_all_layers:
raise AttributeError('CMD arguments "out_layers" and "dump_all_layers" were specified together. '
'Please, specify only one argument')
return feed_dict, ir_path, device, lib, api, nireq, out_path, out_layers, dump_all_layers
if __name__ == "__main__":
feed_dict, ir_path, device, lib, api, nireq, out_path, out_layers, dump_all_layers = cli_parser()
bin_path = os.path.splitext(ir_path)[0] + '.bin'
feed_dict = dict(np.load(feed_dict))
network = IENetwork(model=ir_path, weights=bin_path)
if dump_all_layers:
out_layers = list(network.layers.keys())
results = python_api_infer(net=network, feed_dict=feed_dict, device=device, lib=lib, api=api, nireq=nireq,
outputs_to_add=out_layers)
if out_path:
if api == "async":
for i, result in enumerate(results):
dump_path = os.path.join(out_path, "dump_req{}.npz".format(str(i)))
np.savez(dump_path, **result)
log.info("Path for inference results for {} request: {}".format(str(i), dump_path))
else:
dump_path = os.path.join(out_path, "dump.npz")
np.savez(os.path.join(out_path, "dump.npz"), **results)
log.info("Path for inference results: {}".format(dump_path))
else:
log.info("Inference results won't be saved in the file. "
"To do it need to specify '-r' option.")
log.info("Inference results:")
log.info(results)
log.info("SUCCESS!")