mirror of https://github.com/tensorflow/tfjs.git
73 lines
2.6 KiB
Python
Executable File
73 lines
2.6 KiB
Python
Executable File
# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Example showing how to use tfjs binary in python code."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import subprocess
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def predict(binary_path,
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model_path,
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inputs_dir,
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outputs_dir,
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backend=None,
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tf_output_name_file=None):
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"""Use tfjs binary to make inference and store output in file.
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Args:
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binary_path: Path to the nodejs binary. The path can be an absolute path
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(preferred) or a relative path from this python script's current
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directory.
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model_path: Directory to TensorFlow.js model's json file.
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inputs_dir: Directory to the inputs files, including data, shape and dtype
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files.
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outputs_dir: Directory to write the outputs files, including data, shape
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and dtype files.
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backend: Optional. Choose which TensorFlow.js backend to use. Supported
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backends include cpu and wasm. Default: cpu
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tf_output_name_file: Optional. File name of the tf_output_name, if file does
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not exist, will use the default outputs of the model.
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"""
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model_path_option = '--model_path=' + model_path
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inputs_dir_option = '--inputs_dir=' + inputs_dir
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outputs_dir_option = '--outputs_dir=' + outputs_dir
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tfjs_inference_command = [
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binary_path, model_path_option, inputs_dir_option,
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outputs_dir_option
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]
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if tf_output_name_file:
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tf_output_name_file_option = '--tf_output_name_file=' + tf_output_name_file
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tfjs_inference_command.append(tf_output_name_file_option)
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if backend:
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backend_option = '--backend=' + backend
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tfjs_inference_command.append(backend_option)
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popen = subprocess.Popen(
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tfjs_inference_command,
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stdin=subprocess.PIPE,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE)
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stdout, stderr = popen.communicate()
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if popen.returncode != 0:
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raise ValueError('Inference failed with status %d\nstderr:\n%s' %
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(popen.returncode, stderr.decode()))
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