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Searched refs:channel_multiplier (Results 1 – 7 of 7) sorted by relevance

/external/tensorflow/tensorflow/lite/delegates/gpu/common/tasks/
Ddepthwise_conv.cc31 bool IsSpecializedCase(int channel_multiplier) { in IsSpecializedCase() argument
32 return channel_multiplier == 1 || channel_multiplier == 2 || in IsSpecializedCase()
33 channel_multiplier == 4; in IsSpecializedCase()
36 std::string GetSrcValue(int channel_multiplier, const std::string coords) { in GetSrcValue() argument
38 if (channel_multiplier == 1) { in GetSrcValue()
40 } else if (channel_multiplier == 2) { in GetSrcValue()
45 } else if (channel_multiplier == 4) { in GetSrcValue()
70 const OperationDef& op_def, bool stride_correction, int channel_multiplier, in GenerateDepthwiseConvolutionCode() argument
208 c += GetSrcValue(channel_multiplier, coords); in GenerateDepthwiseConvolutionCode()
/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_DepthwiseConv2dNative.pbtxt40 `[filter_height, filter_width, in_channels, channel_multiplier]`, containing
43 `channel_multiplier` channels for each), then concatenates the results
44 together. Thus, the output has `in_channels * channel_multiplier` channels.
48 for q in 0..channel_multiplier-1
49 output[b, i, j, k * channel_multiplier + q] =
/external/tensorflow/tensorflow/lite/delegates/hexagon/builders/
Dconv_2d_builder.cc146 int channel_multiplier = 1; in PopulateSubGraph() local
161 channel_multiplier = conv_params->depth_multiplier; in PopulateSubGraph()
218 if (channel_multiplier > 1 && input_depth_size == 1) { in PopulateSubGraph()
222 } else if (channel_multiplier > 1) { in PopulateSubGraph()
Dconv_2d_helpers.cc206 const int channel_multiplier = weights_depth_size / input_depth; in InitializeWeightsNodes() local
208 channel_multiplier}; in InitializeWeightsNodes()
/external/tensorflow/tensorflow/python/tools/
Doptimize_for_inference_lib.py365 channel_multiplier = weights.shape[3]
367 current_scale = scale_value[it.multi_index[2] * channel_multiplier +
/external/tensorflow/tensorflow/compiler/xla/g3doc/
Doperation_semantics.md811 `[filter_height, filter_width, in_channels, channel_multiplier]` to
812 `[filter_height, filter_width, 1, in_channels * channel_multiplier]`. For more
/external/tensorflow/tensorflow/compiler/mlir/tensorflow/ir/
Dtf_generated_ops.td3044 `[filter_height, filter_width, in_channels, channel_multiplier]`, containing
3047 `channel_multiplier` channels for each), then concatenates the results
3048 together. Thus, the output has `in_channels * channel_multiplier` channels.
3052 for q in 0..channel_multiplier-1
3053 output[b, i, j, k * channel_multiplier + q] =