def test_convert_lenet(self, output):
"""Test LeNet script of the PyTorch convert to MindSpore script"""
script_filename = "lenet_script.py"
expect_filename = "lenet_converted.py"
files_config = {
'root_path': self.script_dir,
'in_files': [os.path.join(self.script_dir, script_filename)],
'outfile_dir': output,
'report_dir': output
}
main(files_config)
assert os.path.isfile(os.path.join(output, script_filename))
with open(os.path.join(output, script_filename)) as converted_f:
converted_source = converted_f.readlines()
with open(os.path.join(self.script_dir, expect_filename)) as expect_f:
expect_source = expect_f.readlines()
diff = difflib.ndiff(converted_source, expect_source)
diff_lines = 0
for line in diff:
if line.startswith('+'):
diff_lines += 1
converted_ratio = 100 - (diff_lines * 100) / (len(expect_source))
assert converted_ratio >= 80
def _run(in_files, out_dir, in_module, report):
"""
Run converter command.
Args:
in_files (str): The file path or directory to convert.
out_dir (str): The output directory to save converted file.
in_module (str): The module name to convert.
report (str): The report file path.
"""
files_config = {
'root_path': in_files if in_files else '',
'in_files': [],
'outfile_dir': out_dir,
'report_dir': report,
'in_module': in_module
}
if os.path.isfile(in_files):
files_config['root_path'] = os.path.dirname(in_files)
files_config['in_files'] = [in_files]
else:
for root_dir, _, files in os.walk(in_files):
for file in files:
files_config['in_files'].append(os.path.join(root_dir, file))
main(files_config)
def _run(in_files, model_file, shape, input_nodes, output_nodes, out_dir,
report, project_path):
"""
Run converter command.
Args:
in_files (str): The file path or directory to convert.
model_file(str): The pytorch .pth to convert on graph based schema.
shape(list): The input tensor shape of module_file.
input_nodes(str): The input node(s) name of Tensorflow model, split by ','.
output_nodes(str): The output node(s) name of Tensorflow model, split by ','.
out_dir (str): The output directory to save converted file.
report (str): The report file path.
project_path(str): Pytorch scripts project path.
"""
if in_files:
files_config = {
'root_path': in_files,
'in_files': [],
'outfile_dir': out_dir,
'report_dir': report if report else out_dir
}
if os.path.isfile(in_files):
files_config['root_path'] = os.path.dirname(in_files)
files_config['in_files'] = [in_files]
else:
for root_dir, _, files in os.walk(in_files):
for file in files:
files_config['in_files'].append(
os.path.join(root_dir, file))
main(files_config)
log_console.info("\n")
log_console.info("MindConverter: conversion is completed.")
log_console.info("\n")
elif model_file:
file_config = {
'model_file': model_file,
'shape': shape if shape else [],
'input_nodes': input_nodes,
'output_nodes': output_nodes,
'outfile_dir': out_dir,
'report_dir': report if report else out_dir
}
if project_path:
paths = sys.path
if project_path not in paths:
sys.path.append(project_path)
main_graph_base_converter(file_config)
log_console.info("\n")
log_console.info("MindConverter: conversion is completed.")
log_console.info("\n")
else:
error_msg = "`--in_file` and `--model_file` should be set at least one."
error = FileNotFoundError(error_msg)
log.error(str(error))
log.exception(error)
raise error
def _run(in_files, model_file, shape, input_nodes, output_nodes, out_dir,
report):
"""
Run converter command.
Args:
in_files (str): The file path or directory to convert.
model_file (str): The model to convert on graph based schema.
shape (Sequence[tuple]): The input tensor shape of the model.
input_nodes (Sequence[str]): The input node(s) name of model.
output_nodes (Sequence[str]): The output node(s) name of model.
out_dir (str): The output directory to save converted file.
report (str): The report file path.
"""
if in_files:
files_config = {
'root_path': in_files,
'in_files': [],
'outfile_dir': out_dir,
'report_dir': report if report else out_dir
}
if os.path.isfile(in_files):
files_config['root_path'] = os.path.dirname(in_files)
files_config['in_files'] = [in_files]
else:
for root_dir, _, files in os.walk(in_files):
for file in files:
files_config['in_files'].append(
os.path.join(root_dir, file))
main(files_config)
log_console.info("MindConverter: conversion is completed.")
elif model_file:
file_config = {
'model_file': model_file,
'shape': shape if shape else [],
'input_nodes': input_nodes,
'output_nodes': output_nodes,
'outfile_dir': out_dir,
'report_dir': report if report else out_dir
}
main_graph_base_converter(file_config)
log_console.info("MindConverter: conversion is completed.")
else:
error_msg = "`--in_file` and `--model_file` should be set at least one."
error = FileNotFoundError(error_msg)
log.error(str(error))
log_console.error(str(error))
sys.exit(-1)