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Results 11 - 17 of 17 for 256x32x32x3xf32 (0.17 sec)
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tensorflow/compiler/mlir/lite/tests/optimize.mlir
padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32 } : (tensor<256x32x32x3xf32>, tensor<2x3x3x3xf32>, tensor<2xf32>) -> tensor<256x32x32x2xf32> %1 = "tfl.add"(%0, %cst) {fused_activation_function = "NONE"} : (tensor<256x32x32x2xf32>, tensor<1x1x1x2xf32>) -> tensor<256x32x32x2xf32> func.return %1 : tensor<256x32x32x2xf32> // CHECK-DAG: %cst = arith.constant dense<[2.000000e+00, 4.000000e+00]> : tensor<2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir
// ----- // CHECK-LABEL: func @testValidConv2D func.func @testValidConv2D(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<256x32x32x16xf32> { %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x32x32x16xf32> func.return %0 : tensor<256x32x32x16xf32> } // ----- // CHECK-LABEL: func @testValidDynamicConv2D
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/tests/ops.mlir
func.return %0 : tensor<256x32x32x16xf32> } // ----- func.func @testConv2D4DBias(tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<1x1x1x16xf32>) -> tensor<256x32x32x16xf32> { ^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<1x1x1x16xf32>):
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/raise-target-subgraphs.mlir
// CHECK: return %[[VAL_5]] : tensor<256x30x30x16xf32> // CHECK: }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/shape_inference.mlir
} // CHECK-LABEL: func @conv2d_unranked_filter func.func @conv2d_unranked_filter(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> { // CHECK: "tf.Conv2D" // CHECK-SAME: -> tensor<256x?x?x?xf32> %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<*xf32>) -> tensor<*xf32> func.return %0 : tensor<*xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 23 17:24:10 UTC 2024 - 167.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize_no_verify.mlir
// TFLite runtime restrictions. // RUN: tf-opt %s -tfl-optimize | FileCheck %s // CHECK-LABEL: fuseScalarAddIntoConv2dHalf func.func @fuseScalarAddIntoConv2dHalf(%arg0: tensor<256x32x32x3xf16>, %arg1: tensor<16x3x3x3xf16>) -> tensor<256x8x7x16xf16> { %cst = arith.constant dense<1.5> : tensor<f16>
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/tf2xla/tests/legalize-tf.mlir
func.func @conv_explicit_paddings(%arg0: tensor<256x32x32x6xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<256x9x7x16xf32> { // CHECK: mhlo.convolution(%arg0, %arg1) // CHECK-SAME{LITERAL}: pad = [[6, 0], [3, 3]] %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "EXPLICIT", explicit_paddings = [0, 0, 6, 0, 3, 3, 0, 0], strides = [1, 4, 5, 1]} : (tensor<256x32x32x6xf32>, tensor<3x3x3x16xf32>) -> tensor<256x9x7x16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0)