# Copyright (c) 2019 Guo Yejun## This file is part of FFmpeg.## FFmpeg is free software; you can redistribute it and/or# modify it under the terms of the GNU Lesser General Public# License as published by the Free Software Foundation; either# version 2.1 of the License, or (at your option) any later version.## FFmpeg is distributed in the hope that it will be useful,# but WITHOUT ANY WARRANTY; without even the implied warranty of# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU# Lesser General Public License for more details.## You should have received a copy of the GNU Lesser General Public# License along with FFmpeg; if not, write to the Free Software# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA# ============================================================================== import tensorflow as tfimport numpy as npimport sys, struct __all__ = ['convert_from_tensorflow'] class TFConverter:    def __init__(self, graph_def, nodes, outfile, dump4tb):        self.graph_def = graph_def        self.nodes = nodes        self.outfile = outfile        self.dump4tb = dump4tb        self.layer_number = 0        self.output_names = []        self.name_node_dict = {}        self.edges = {}        self.conv_activations = {'Relu':0, 'Tanh':1, 'Sigmoid':2, 'LeakyRelu':4}        self.conv_paddings = {'VALID':0, 'SAME':1}        self.converted_nodes = set()        self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3}        self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}      def dump_for_tensorboard(self):        graph = tf.get_default_graph()        tf.import_graph_def(self.graph_def, name="")        tf.summary.FileWriter('/tmp/graph', graph)        print('graph saved, run "tensorboard --logdir=/tmp/graph" to see it')      def get_conv2d_params(self, node):        knode = self.name_node_dict[node.input[1]]        bnode = None        activation = 'None'        next = self.edges[node.name][0]        if next.op == 'BiasAdd':            self.converted_nodes.add(next.name)            bnode = self.name_node_dict[next.input[1]]            next = self.edges[next.name][0]        if next.op in self.conv_activations:            self.converted_nodes.add(next.name)            activation = next.op        return knode, bnode, activation      def dump_conv2d_to_file(self, node, f):        assert(node.op == 'Conv2D')        self.layer_number = self.layer_number + 1        self.converted_nodes.add(node.name)        knode, bnode, activation = self.get_conv2d_params(node)         dilation = node.attr['dilations'].list.i[0]        padding = node.attr['padding'].s        padding = self.conv_paddings[padding.decode("utf-8")]         ktensor = knode.attr['value'].tensor        filter_height = ktensor.tensor_shape.dim[0].size        filter_width = ktensor.tensor_shape.dim[1].size        in_channels = ktensor.tensor_shape.dim[2].size        out_channels = ktensor.tensor_shape.dim[3].size        kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32)        kernel = kernel.reshape(filter_height, filter_width, in_channels, out_channels)        kernel = np.transpose(kernel, [3, 0, 1, 2])         np.array([self.op2code[node.op], dilation, padding, self.conv_activations[activation], in_channels, out_channels, filter_height], dtype=np.uint32).tofile(f)        kernel.tofile(f)         btensor = bnode.attr['value'].tensor        if btensor.tensor_shape.dim[0].size == 1:            bias = struct.pack("f", btensor.float_val[0])        else:            bias = btensor.tensor_content        f.write(bias)      def dump_depth2space_to_file(self, node, f):        assert(node.op == 'DepthToSpace')        self.layer_number = self.layer_number + 1        block_size = node.attr['block_size'].i        np.array([self.op2code[node.op], block_size], dtype=np.uint32).tofile(f)        self.converted_nodes.add(node.name)      def dump_mirrorpad_to_file(self, node, f):        assert(node.op == 'MirrorPad')        self.layer_number = self.layer_number + 1        mode = node.attr['mode'].s        mode = self.mirrorpad_mode[mode.decode("utf-8")]        np.array([self.op2code[node.op], mode], dtype=np.uint32).tofile(f)        pnode = self.name_node_dict[node.input[1]]        self.converted_nodes.add(pnode.name)        paddings = pnode.attr['value'].tensor.tensor_content        f.write(paddings)        self.converted_nodes.add(node.name)      def generate_layer_number(self):        # in current hard code implementation, the layer number is the first data written to the native model file        # it is not easy to know it at the beginning time in the general converter, so first do a dry run for compatibility        # will be refined later.        with open('/tmp/tmp.model', 'wb') as f:            self.dump_layers_to_file(f)        self.converted_nodes.clear()      def dump_layers_to_file(self, f):        for node in self.nodes:            if node.name in self.converted_nodes:                continue            if node.op == 'Conv2D':                self.dump_conv2d_to_file(node, f)            elif node.op == 'DepthToSpace':                self.dump_depth2space_to_file(node, f)            elif node.op == 'MirrorPad':                self.dump_mirrorpad_to_file(node, f)      def dump_to_file(self):        self.generate_layer_number()        with open(self.outfile, 'wb') as f:            np.array([self.layer_number], dtype=np.uint32).tofile(f)            self.dump_layers_to_file(f)      def generate_name_node_dict(self):        for node in self.nodes:            self.name_node_dict[node.name] = node      def generate_output_names(self):        used_names = []        for node in self.nodes:            for input in node.input:                used_names.append(input)         for node in self.nodes:            if node.name not in used_names:                self.output_names.append(node.name)      def remove_identity(self):        id_nodes = []        id_dict = {}        for node in self.nodes:            if node.op == 'Identity':                name = node.name                input = node.input[0]                id_nodes.append(node)                # do not change the output name                if name in self.output_names:                    self.name_node_dict[input].name = name                    self.name_node_dict[name] = self.name_node_dict[input]                    del self.name_node_dict[input]                else:                    id_dict[name] = input         for idnode in id_nodes:            self.nodes.remove(idnode)         for node in self.nodes:            for i in range(len(node.input)):                input = node.input[i]                if input in id_dict:                    node.input[i] = id_dict[input]      def generate_edges(self):        for node in self.nodes:            for input in node.input:                if input in self.edges:                    self.edges[input].append(node)                else:                    self.edges[input] = [node]      def run(self):        self.generate_name_node_dict()        self.generate_output_names()        self.remove_identity()        self.generate_edges()         if self.dump4tb:            self.dump_for_tensorboard()         self.dump_to_file()  def convert_from_tensorflow(infile, outfile, dump4tb):    with open(infile, 'rb') as f:        # read the file in .proto format        graph_def = tf.GraphDef()        graph_def.ParseFromString(f.read())        nodes = graph_def.node     converter = TFConverter(graph_def, nodes, outfile, dump4tb)    converter.run()