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Results 1 - 10 of 13 for 5x5x3x8xf32 (0.19 sec)
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tensorflow/compiler/mlir/lite/tests/dilated-conv.mlir
%2 = "tf.BatchToSpaceND"(%1, %cst, %cst_0) : (tensor<4x64x64x8xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<1x120x120x8xf32> func.return %2 : tensor<1x120x120x8xf32> // CHECK-LABEL: testDilatedConv // CHECK-SAME: ([[INPUT:%.*]]: tensor<1x128x128x3xf32>, [[FILTER:%.*]]: tensor<5x5x3x8xf32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 44.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/push-tpose-through-ewise.mlir
func.return %1 : tensor<5x2x3x4xf32> } // CHECK: %cst = arith.constant dense<1.000000e+00> : tensor<5x2x3x4xf32> // CHECK: %0 = "tfl.transpose"(%arg0, %arg1) : (tensor<2x3x4x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32> // CHECK: %1 = tfl.add %0, %cst {fused_activation_function = "NONE"} : tensor<5x2x3x4xf32> // CHECK: return %1 : tensor<5x2x3x4xf32> // ----- // CHECK-LABEL: doubleTposeInput
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 8.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_layout_assignment_to_nchw.mlir
} : (tensor<1x32x32x3xf32>, tensor<4xi32>, tensor<1x32x32x8xf32>) -> tensor<1x1x3x8xf32> func.return %0 : tensor<1x1x3x8xf32> } // CHECK-LABEL: func @transposeConv2DBackpropInput func.func @transposeConv2DBackpropInput( %input_sizes: tensor<4xi32>, %filter: tensor<1x1x3x8xf32>, %out_backprop: tensor<1x32x32x8xf32> ) -> tensor<1x32x32x3xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/fold_constant_transpose.mlir
// CHECK-LABEL: transpose_simple_4d func.func @transpose_simple_4d() -> tensor<5x2x3x4xf32> { %0 = stablehlo.constant dense<1.000000e+0> : tensor<2x3x4x5xf32> %1 = stablehlo.transpose %0, dims = [3, 0, 1, 2] : (tensor<2x3x4x5xf32>) -> tensor<5x2x3x4xf32> return %1 : tensor<5x2x3x4xf32> } // CHECK-DAG: %[[CONST_0:.+]] = stablehlo.constant dense<1.000000e+00> : tensor<5x2x3x4xf32> // CHECK-NOT: transpose // CHECK: return %[[CONST_0]] : tensor<5x2x3x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 12 08:06:02 UTC 2024 - 2.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/nchw_convolution_to_nhwc.mlir
%0 = stablehlo.constant() {value = dense<7.000000e+00> : tensor<8x3x3x8xf32>} : () -> tensor<8x3x3x8xf32> %2 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, f, 0, 1]x[i, 0, 1, o]->[b, f, 0, 1], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x8x4x4xf32>, tensor<8x3x3x8xf32>) -> tensor<1x8x4x4xf32> return %2 : tensor<1x8x4x4xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 25 23:00:47 UTC 2024 - 5.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_layout_assignment_to_nhwc.mlir
// dilations, etc...). This test only verifies that changing convolution data // layout will update all the attributes. // CHECK-LABEL: func @transposeConv2D func.func @transposeConv2D(%input: tensor<1x3x32x32xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<1x8x7x6xf32> { // CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}> // CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 4.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/einsum.mlir
// CHECK: return %[[v2]] : tensor<2x5x3xf32> } func.func @einsum_transposereduceddim(%arg0: tensor<2x5x7xf32>, %arg1: tensor<2x5x3x7xf32>) -> tensor<2x5x3xf32> { %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "bij,binj->bin"}: (tensor<2x5x7xf32>, tensor<2x5x3x7xf32>) -> tensor<2x5x3xf32> func.return %0 : tensor<2x5x3xf32> // CHECK-LABEL: einsum_transposereduceddim
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 25.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
%0 = "tf.Gather"(%arg0, %arg1) : (tensor<2x3x6xf32>, tensor<4x5xi32>) -> tensor<4x5x3x6xf32> func.return %0 : tensor<4x5x3x6xf32> // CHECK-LABEL:gatherHigherRankIndices // CHECK: "tfl.gather"(%arg0, %arg1) <{axis = 0 : i32, batch_dims = 0 : i32}> : (tensor<2x3x6xf32>, tensor<4x5xi32>) -> tensor<4x5x3x6xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir
func.func @FakeQuantWithMinMaxVarsPerChannel(tensor<1x2x3x8xf32>, tensor<8xf32>, tensor<8xf32>) -> tensor<1x2x3x8xf32> { ^bb0(%arg0: tensor<1x2x3x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>): %0 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg0, %arg1, %arg2) : (tensor<1x2x3x8xf32>, tensor<8xf32>, tensor<8xf32>) -> tensor<1x2x3x8xf32> func.return %0 : tensor<1x2x3x8xf32> } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 23 14:40:35 UTC 2023 - 236.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/legalize_hlo.mlir
// CHECK: }) : (tensor<5x4x3x7xf32>, tensor<2x2xi32>, tensor<2x5x3xf32>) -> tensor<5x4x3x7xf32> // CHECK: return %[[VAL_3]] : tensor<5x4x3x7xf32> // CHECK: } func.func @convert_scatter_update_to_non_trailing_operand_dimensions( %arg0: tensor<5x4x3x7xf32>, %arg1: tensor<2x2xi32>, %arg2: tensor<2x5x3xf32>) -> tensor<5x4x3x7xf32> { %0 = "mhlo.scatter"(%arg0, %arg1, %arg2) ({
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 340.2K bytes - Viewed (0)