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Results 41 - 50 of 77 for conv_2d (0.72 sec)
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tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo.cc
auto conv_result = rewriter.create<mhlo::ConvolutionOp>( conv_op.getLoc(), new_output_type, sliced_input, sliced_kernel, conv_op.getWindowStridesAttr(), conv_op.getPaddingAttr(), conv_op.getLhsDilationAttr(), conv_op.getRhsDilationAttr(), conv_op.getWindowReversalAttr(), conv_op.getDimensionNumbers(), 1, 1, conv_op.getPrecisionConfigAttr()); conv_results.push_back(conv_result);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 154.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tpu_space_to_depth_pass.cc
} } // Handle Conv2D input, stride and filter. HandleConv2DInput(conv2d, block_size); HandleConv2DStride(conv2d); HandleConv2DFilter(conv2d, block_size); // Book keeping new filter shape for backprop filter rewrite. // Filter shape is defined in HandleConv2DFilter, thus it is RankedTensorType. filter_shape = mlir::cast<RankedTensorType>(conv2d.getFilter().getType()).getShape();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 29.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/ops/tf_op_quant_spec.cc
if (function_name.contains("with_bias")) { spec->biases_params[2] = {{0, 1}, quant::GetUniformQuantizedTypeForBias}; } } else if (function_name.contains("conv2d")) { spec->coeff_op_quant_dim[1] = 3; if (function_name.contains("with_bias")) { spec->biases_params[2] = {{0, 1}, quant::GetUniformQuantizedTypeForBias}; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/dilated_conv.h
// // // SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> BatchToSpaceND -> BiasAdd // // SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> Pad -> BatchToSpaceND -> // BiasAdd // // SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> BiasAdd -> BatchToSpaceND // // SpaceToBatchND -> Conv2D -> Pad -> BatchToSpaceND -> BiasAdd // // SpaceToBatchND -> Conv2D -> BatchToSpaceND -> BiasAdd // //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 20K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/fallback_to_flex_ops_default.mlir
%cst_0 = "tf.Const"() {value = dense<-1.000000e+00> : tensor<f32>} : () -> tensor<f32> %cst_1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32> %0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<1x3x4x3xf32>, tensor<1x1x3x2xf32>) -> tensor<1x3x4x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/fused_kernel_matcher.cc
// Performs a fusion of the following pattern(s), if possible: // Conv2D + BiasAdd + <Activation> -> _FusedConv2D class FuseConv2DBiasAdd : public FuseContractionWithBiasAdd<Conv2DOp, _FusedConv2DOp> { public: using FuseContractionWithBiasAdd<Conv2DOp, _FusedConv2DOp>::FuseContractionWithBiasAdd; // Verify that the Conv2D and BiasAdd data formats match. This is necessary
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 14.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/components/tf_to_stablehlo.mlir
%cst_1 = "tf.Const"() {value = dense<[0.1, 0.2]> : tensor<2xf32>} : () -> tensor<2xf32> %cst_2 = "tf.Const"() {value = dense<[0.3, 0.4]> : tensor<2xf32>} : () -> tensor<2xf32> %0 = "tf.Conv2D"(%arg_0, %cst_0) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 08 20:05:12 UTC 2024 - 13.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
in the flatbuffer. }]; let arguments = (ins I32Attr:$buffer_index); let results = (outs AnyTensor:$output); } def TFL_Conv2DOp : TFL_ConvOp<"conv_2d", "Convolution", 0, [DeclareOpInterfaceMethods<InferTypeOpInterface>, DeclareOpInterfaceMethods<TFL_ArithmeticCount>, DynamicRangeQuantizedOpInterface]> { let arguments = (
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test_base.py
save_options = None if has_func_alias: save_options = tensorflow.saved_model.SaveOptions( function_aliases={FUNC_ALIAS: model.conv2d} ) saved_model_save.save( model, saved_model_path, signatures=model.conv2d.get_concrete_function( tensor_spec.TensorSpec( shape=input_shape, dtype=dtypes.float32, name='input_tensor' )
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 18.2K bytes - Viewed (0) -
tensorflow/compiler/jit/node_matchers_test.cc
Output const_0d = ops::Const(root.WithOpName("const_0d"), 42); Output const_2d = ops::Const(root.WithOpName("const_2d"), {{1, 2}, {4, 3}}); EXPECT_THAT(const_0d.node(), NodeWith(ConstantValue(42))); EXPECT_THAT(const_0d.node(), NodeWith(ConstantValue(42), Name("const_0d"))); EXPECT_THAT(const_2d.node(), NodeWith(ConstantValue({{1, 2}, {4, 3}})));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 20 14:43:57 UTC 2024 - 9.1K bytes - Viewed (0)