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dnn: add tf.nn.conv2d support for native model
Unlike other tf.*.conv2d layers, tf.nn.conv2d does not create many nodes (within a scope) in the graph, it just acts like other layers. tf.nn.conv2d only creates one node in the graph, and no internal nodes such as 'kernel' are created. The format of native model file is also changed, a flag named has_bias is added, so change the version number. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
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@ -98,7 +98,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
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char header_expected[] = "FFMPEGDNNNATIVE";
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char *buf;
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size_t size;
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int version, header_size, major_version_expected = 0;
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int version, header_size, major_version_expected = 1;
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ConvolutionalNetwork *network = NULL;
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AVIOContext *model_file_context;
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int file_size, dnn_size, parsed_size;
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@ -38,28 +38,42 @@ int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int fil
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conv_params->input_num = (int32_t)avio_rl32(model_file_context);
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conv_params->output_num = (int32_t)avio_rl32(model_file_context);
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conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
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conv_params->has_bias = (int32_t)avio_rl32(model_file_context);
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dnn_size += 28;
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kernel_size = conv_params->input_num * conv_params->output_num *
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conv_params->kernel_size * conv_params->kernel_size;
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dnn_size += 24 + (kernel_size + conv_params->output_num << 2);
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dnn_size += kernel_size * 4;
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if (conv_params->has_bias)
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dnn_size += conv_params->output_num * 4;
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if (dnn_size > file_size || conv_params->input_num <= 0 ||
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conv_params->output_num <= 0 || conv_params->kernel_size <= 0){
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av_freep(&conv_params);
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return 0;
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}
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conv_params->kernel = av_malloc(kernel_size * sizeof(float));
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conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
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if (!conv_params->kernel || !conv_params->biases){
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av_freep(&conv_params->kernel);
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av_freep(&conv_params->biases);
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if (!conv_params->kernel) {
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av_freep(&conv_params);
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return 0;
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}
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for (int i = 0; i < kernel_size; ++i){
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for (int i = 0; i < kernel_size; ++i) {
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conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
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}
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conv_params->biases = NULL;
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if (conv_params->has_bias) {
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conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
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if (!conv_params->biases){
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av_freep(&conv_params->kernel);
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av_freep(&conv_params);
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return 0;
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}
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for (int i = 0; i < conv_params->output_num; ++i){
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conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
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}
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}
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layer->params = conv_params;
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@ -103,7 +117,10 @@ int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_
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for (int y = pad_size; y < height - pad_size; ++y) {
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for (int x = pad_size; x < width - pad_size; ++x) {
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for (int n_filter = 0; n_filter < conv_params->output_num; ++n_filter) {
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if (conv_params->has_bias)
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output[n_filter] = conv_params->biases[n_filter];
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else
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output[n_filter] = 0.f;
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for (int ch = 0; ch < conv_params->input_num; ++ch) {
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for (int kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y) {
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@ -31,6 +31,7 @@ typedef struct ConvolutionalParams{
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DNNActivationFunc activation;
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DNNConvPaddingParam padding_method;
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int32_t dilation;
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int32_t has_bias;
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float *kernel;
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float *biases;
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} ConvolutionalParams;
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@ -97,6 +97,7 @@ static int test_with_same_dilate(void)
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float bias[2] = { -1.6574852, -0.72915393 };
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params.activation = TANH;
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params.has_bias = 1;
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params.biases = bias;
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params.dilation = 2;
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params.input_num = 3;
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@ -196,6 +197,7 @@ static int test_with_valid(void)
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float bias[2] = { -0.4773722, -0.19620377 };
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params.activation = TANH;
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params.has_bias = 1;
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params.biases = bias;
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params.dilation = 1;
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params.input_num = 3;
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@ -118,7 +118,7 @@ class TFConverter:
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return knode, bnode, dnode, anode
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def dump_conv2d_to_file(self, node, f):
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def dump_complex_conv2d_to_file(self, node, f):
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assert(node.op == 'Conv2D')
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self.layer_number = self.layer_number + 1
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self.converted_nodes.add(node.name)
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@ -153,7 +153,8 @@ class TFConverter:
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kernel = kernel.reshape(filter_height, filter_width, in_channels, out_channels)
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kernel = np.transpose(kernel, [3, 0, 1, 2])
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np.array([self.op2code[node.op], dilation, padding, self.conv_activations[activation], in_channels, out_channels, filter_height], dtype=np.uint32).tofile(f)
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has_bias = 1
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np.array([self.op2code[node.op], dilation, padding, self.conv_activations[activation], in_channels, out_channels, filter_height, has_bias], dtype=np.uint32).tofile(f)
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kernel.tofile(f)
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btensor = bnode.attr['value'].tensor
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@ -173,6 +174,41 @@ class TFConverter:
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np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
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def dump_simple_conv2d_to_file(self, node, f):
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assert(node.op == 'Conv2D')
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self.layer_number = self.layer_number + 1
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self.converted_nodes.add(node.name)
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node0 = self.name_node_dict[node.input[0]]
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node1 = self.name_node_dict[node.input[1]]
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if node0.op == 'Const':
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knode = node0
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input_name = node.input[1]
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else:
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knode = node1
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input_name = node.input[0]
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ktensor = knode.attr['value'].tensor
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filter_height = ktensor.tensor_shape.dim[0].size
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filter_width = ktensor.tensor_shape.dim[1].size
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in_channels = ktensor.tensor_shape.dim[2].size
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out_channels = ktensor.tensor_shape.dim[3].size
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kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32)
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kernel = kernel.reshape(filter_height, filter_width, in_channels, out_channels)
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kernel = np.transpose(kernel, [3, 0, 1, 2])
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has_bias = 0
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dilation = 1
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padding = node.attr['padding'].s.decode("utf-8")
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np.array([self.op2code[node.op], dilation, self.conv_paddings[padding], self.conv_activations['None'],
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in_channels, out_channels, filter_height, has_bias], dtype=np.uint32).tofile(f)
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kernel.tofile(f)
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input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT)
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output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
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np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
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def dump_depth2space_to_file(self, node, f):
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assert(node.op == 'DepthToSpace')
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self.layer_number = self.layer_number + 1
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@ -222,10 +258,12 @@ class TFConverter:
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scope_name = TFConverter.get_scope_name(node.name)
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if scope_name in self.conv2d_scope_names:
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if node.op == 'Conv2D':
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self.dump_conv2d_to_file(node, f)
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self.dump_complex_conv2d_to_file(node, f)
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continue
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if node.op == 'DepthToSpace':
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if node.op == 'Conv2D':
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self.dump_simple_conv2d_to_file(node, f)
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elif node.op == 'DepthToSpace':
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self.dump_depth2space_to_file(node, f)
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elif node.op == 'MirrorPad':
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self.dump_mirrorpad_to_file(node, f)
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@ -312,10 +350,16 @@ class TFConverter:
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def generate_conv2d_scope_info(self):
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# conv2d is a sub block in graph, get the scope name
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# mostly, conv2d is a sub block in graph, get the scope name
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for node in self.nodes:
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if node.op == 'Conv2D':
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scope = TFConverter.get_scope_name(node.name)
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# for the case tf.nn.conv2d is called directly
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if scope == '':
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continue
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# for the case tf.nn.conv2d is called within a scope
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if scope + '/kernel' not in self.name_node_dict:
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continue
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self.conv2d_scope_names.add(scope)
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# get the input name to the conv2d sub block
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@ -20,7 +20,7 @@
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str = 'FFMPEGDNNNATIVE'
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# increase major and reset minor when we have to re-convert the model file
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major = 0
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major = 1
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# increase minor when we don't have to re-convert the model file
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minor = 2
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minor = 0
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