you need to get below files first.
| #!/usr/bin/env python | |
| # coding: utf-8 | |
| import os | |
| import numpy as np | |
| import json | |
| import tensorflow as tf | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '0' | |
| schema = "schema.fbs" | |
| binary = "flatc" | |
| model_path = "mediapipe/models/facedetector_front.tflite" | |
| output_pb_path = "facedetector_front.pb" | |
| output_savedmodel_path = "saved_model" | |
| model_json_path = "/tmp/facedetector_front.json" | |
| num_tensors = 176 | |
| output_node_names = ['classificators', 'regressors'] | |
| def gen_model_json(): | |
| if not os.path.exists(model_json_path): | |
| cmd = (binary + | |
| " -t --strict-json --defaults-json -o /tmp {schema} -- {input}". | |
| format(input=model_path, schema=schema)) | |
| os.system(cmd) | |
| def parse_json(): | |
| j = json.load(open(model_json_path)) | |
| op_types = [v['builtin_code'] for v in j['operator_codes']] | |
| # print('op types:', op_types) | |
| ops = j['subgraphs'][0]['operators'] | |
| # print('num of ops:', len(ops)) | |
| return ops, op_types | |
| def make_graph(ops, op_types, interpreter): | |
| tensors = {} | |
| input_details = interpreter.get_input_details() | |
| output_details = interpreter.get_output_details() | |
| # print(input_details) | |
| for input_detail in input_details: | |
| tensors[input_detail['index']] = tf.placeholder( | |
| dtype=input_detail['dtype'], | |
| shape=input_detail['shape'], | |
| name=input_detail['name']) | |
| for index, op in enumerate(ops): | |
| print('op: ', op) | |
| op_type = op_types[op['opcode_index']] | |
| if op_type == 'CONV_2D': | |
| input_tensor = tensors[op['inputs'][0]] | |
| weights_detail = interpreter._get_tensor_details(op['inputs'][1]) | |
| bias_detail = interpreter._get_tensor_details(op['inputs'][2]) | |
| output_detail = interpreter._get_tensor_details(op['outputs'][0]) | |
| # print('weights_detail: ', weights_detail) | |
| # print('bias_detail: ', bias_detail) | |
| # print('output_detail: ', output_detail) | |
| weights_array = interpreter.get_tensor(weights_detail['index']) | |
| weights_array = np.transpose(weights_array, (1, 2, 3, 0)) | |
| bias_array = interpreter.get_tensor(bias_detail['index']) | |
| weights = tf.Variable(weights_array, name=weights_detail['name']) | |
| bias = tf.Variable(bias_array, name=bias_detail['name']) | |
| options = op['builtin_options'] | |
| output_tensor = tf.nn.conv2d( | |
| input_tensor, | |
| weights, | |
| strides=[1, options['stride_h'], options['stride_w'], 1], | |
| padding=options['padding'], | |
| dilations=[ | |
| 1, options['dilation_h_factor'], | |
| options['dilation_w_factor'], 1 | |
| ], | |
| name=output_detail['name'] + '/conv2d') | |
| output_tensor = tf.add( | |
| output_tensor, bias, name=output_detail['name']) | |
| tensors[output_detail['index']] = output_tensor | |
| elif op_type == 'DEPTHWISE_CONV_2D': | |
| input_tensor = tensors[op['inputs'][0]] | |
| weights_detail = interpreter._get_tensor_details(op['inputs'][1]) | |
| bias_detail = interpreter._get_tensor_details(op['inputs'][2]) | |
| output_detail = interpreter._get_tensor_details(op['outputs'][0]) | |
| # print('weights_detail: ', weights_detail) | |
| # print('bias_detail: ', bias_detail) | |
| # print('output_detail: ', output_detail) | |
| weights_array = interpreter.get_tensor(weights_detail['index']) | |
| weights_array = np.transpose(weights_array, (1, 2, 3, 0)) | |
| bias_array = interpreter.get_tensor(bias_detail['index']) | |
| weights = tf.Variable(weights_array, name=weights_detail['name']) | |
| bias = tf.Variable(bias_array, name=bias_detail['name']) | |
| options = op['builtin_options'] | |
| output_tensor = tf.nn.depthwise_conv2d( | |
| input_tensor, | |
| weights, | |
| strides=[1, options['stride_h'], options['stride_w'], 1], | |
| padding=options['padding'], | |
| # dilations=[ | |
| # 1, options['dilation_h_factor'], | |
| # options['dilation_w_factor'], 1 | |
| # ], | |
| name=output_detail['name'] + '/depthwise_conv2d') | |
| output_tensor = tf.add( | |
| output_tensor, bias, name=output_detail['name']) | |
| tensors[output_detail['index']] = output_tensor | |
| elif op_type == 'MAX_POOL_2D': | |
| input_tensor = tensors[op['inputs'][0]] | |
| output_detail = interpreter._get_tensor_details(op['outputs'][0]) | |
| options = op['builtin_options'] | |
| output_tensor = tf.nn.max_pool( | |
| input_tensor, | |
| ksize=[ | |
| 1, options['filter_height'], options['filter_width'], 1 | |
| ], | |
| strides=[1, options['stride_h'], options['stride_w'], 1], | |
| padding=options['padding'], | |
| name=output_detail['name']) | |
| tensors[output_detail['index']] = output_tensor | |
| elif op_type == 'PAD': | |
| input_tensor = tensors[op['inputs'][0]] | |
| output_detail = interpreter._get_tensor_details(op['outputs'][0]) | |
| paddings_detail = interpreter._get_tensor_details(op['inputs'][1]) | |
| # print('output_detail:', output_detail) | |
| # print('paddings_detail:', paddings_detail) | |
| paddings_array = interpreter.get_tensor(paddings_detail['index']) | |
| paddings = tf.Variable( | |
| paddings_array, name=paddings_detail['name']) | |
| output_tensor = tf.pad( | |
| input_tensor, paddings, name=output_detail['name']) | |
| tensors[output_detail['index']] = output_tensor | |
| elif op_type == 'RELU': | |
| output_detail = interpreter._get_tensor_details(op['outputs'][0]) | |
| input_tensor = tensors[op['inputs'][0]] | |
| output_tensor = tf.nn.relu( | |
| input_tensor, name=output_detail['name']) | |
| tensors[output_detail['index']] = output_tensor | |
| elif op_type == 'RESHAPE': | |
| input_tensor = tensors[op['inputs'][0]] | |
| output_detail = interpreter._get_tensor_details(op['outputs'][0]) | |
| options = op['builtin_options'] | |
| output_tensor = tf.reshape( | |
| input_tensor, options['new_shape'], name=output_detail['name']) | |
| tensors[output_detail['index']] = output_tensor | |
| elif op_type == 'ADD': | |
| output_detail = interpreter._get_tensor_details(op['outputs'][0]) | |
| input_tensor_0 = tensors[op['inputs'][0]] | |
| input_tensor_1 = tensors[op['inputs'][1]] | |
| output_tensor = tf.add( | |
| input_tensor_0, input_tensor_1, name=output_detail['name']) | |
| tensors[output_detail['index']] = output_tensor | |
| elif op_type == 'CONCATENATION': | |
| output_detail = interpreter._get_tensor_details(op['outputs'][0]) | |
| input_tensor_0 = tensors[op['inputs'][0]] | |
| input_tensor_1 = tensors[op['inputs'][1]] | |
| options = op['builtin_options'] | |
| output_tensor = tf.concat([input_tensor_0, input_tensor_1], | |
| options['axis'], | |
| name=output_detail['name']) | |
| tensors[output_detail['index']] = output_tensor | |
| else: | |
| raise ValueError(op_type) | |
| def main(): | |
| gen_model_json() | |
| ops, op_types = parse_json() | |
| interpreter = tf.lite.Interpreter(model_path) | |
| interpreter.allocate_tensors() | |
| # input_details = interpreter.get_input_details() | |
| # output_details = interpreter.get_output_details() | |
| # print(input_details) | |
| # print(output_details) | |
| # for i in range(num_tensors): | |
| # detail = interpreter._get_tensor_details(i) | |
| # print(detail) | |
| make_graph(ops, op_types, interpreter) | |
| config = tf.ConfigProto() | |
| config.gpu_options.allow_growth = True | |
| graph = tf.get_default_graph() | |
| # writer = tf.summary.FileWriter(os.path.splitext(output_pb_path)[0]) | |
| # writer.add_graph(graph) | |
| # writer.flush() | |
| # writer.close() | |
| with tf.Session(config=config, graph=graph) as sess: | |
| sess.run(tf.global_variables_initializer()) | |
| graph_def = tf.graph_util.convert_variables_to_constants( | |
| sess=sess, | |
| input_graph_def=graph.as_graph_def(), | |
| output_node_names=output_node_names) | |
| with tf.gfile.GFile(output_pb_path, 'wb') as f: | |
| f.write(graph_def.SerializeToString()) | |
| tf.saved_model.simple_save( | |
| sess, | |
| output_savedmodel_path, | |
| inputs={'input': graph.get_tensor_by_name('input:0')}, | |
| outputs={ | |
| 'classificators': graph.get_tensor_by_name('classificators:0'), | |
| 'regressors': graph.get_tensor_by_name('regressors:0') | |
| }) | |
| if __name__ == '__main__': | |
| main() |
@SocialKitLtd Run download.sh or download_new.sh to download all the materials you need.
@PINTO0309 On which of the folders? Sorry, I don't really have deep understand ML yet:
01_float32
@PINTO0309 Works!! :) Katsuya, I sent you an email from [email protected]. Did you get it by chance?
These locations are now not valid-
flatc: https://github.com/tensorflow/tensorflow/blob/release_1.14.0/tensorflow/lite/tools/visualize.py#L38
shema: https://github.com/tensorflow/tensorflow/blob/release_1.14.0/tensorflow/lite/tools/visualize.py#L37
They are still valid at:
flatc: https://github.com/tensorflow/tensorflow/blob/v1.14.0/tensorflow/lite/tools/visualize.py#L38
schema: https://github.com/tensorflow/tensorflow/blob/v1.14.0/tensorflow/lite/tools/visualize.py#L37
RuntimeError: Sorry, schema file cannot be found at '../schema/schema.fbs' . where do i get this file?
@PINTO0309 Thanks! But I can't seems to find the .pb model. Is it there? Or I'm miss understanding what you wrote