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Results 1 - 6 of 6 for 16x1x1x8xf32 (0.18 sec)
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tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/simple-graph.mlir
// CHECK: [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32> // CHECK: [[VAL_2:%.*]] = "tfl.concatenation"([[VAL_0]], [[VAL_1]]) <{axis = 3 : i32, fused_activation_function = "NONE"}> {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<1x1x1x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.6K 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/tensorflow/tests/layout_optimization_to_nchw.mlir
// RUN: tf-opt %s -tf-layout-optimization=force-data-format=NCHW -verify-diagnostics | FileCheck %s --dump-input=always // CHECK-LABEL: func @transposeConv2D func.func @transposeConv2D(%arg0: tensor<1x3x32x32xf32>, %arg1: tensor<1x1x3x8xf32>) -> tensor<1x8x32x32xf32> { // Convert input: NCHW -> NHWC %0 = "tf.Const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 05:47:26 UTC 2022 - 1.3K 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/lite/quantization/tensorflow/tests/fallback_to_flex_ops_legacy.mlir
func.func @depth_to_space(%arg0: tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> { %0 = "tf.DepthToSpace"(%arg0) {block_size = 2: i64, data_format = "NHWC"}: (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> func.return %0 : tensor<1x2x2x1xf32> // CHECK: %[[CUSTOM_0:.*]] = "tfl.custom"(%arg0) <{custom_code = "FlexDepthToSpace", custom_option = #tfl<const_bytes : "{{.*}}">}> : (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_move_transposes_end.mlir
} // CHECK-LABEL: func @move_across_broadcastable_op func.func @move_across_broadcastable_op(%arg0: tensor<1x4x1x8xf32>, %arg1: tensor<1x4x4x8xf32>) -> tensor<1x8x4x4xf32> { // CHECK: %[[RES_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> // CHECK: %[[ADD:[0-9]*]] = "tf.AddV2"(%arg0, %arg1) : (tensor<1x4x1x8xf32>, tensor<1x4x4x8xf32>) -> tensor<1x4x4x8xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9.5K bytes - Viewed (0)