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Results 21 - 30 of 55 for 1x1x3x3xf32 (0.35 sec)
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tensorflow/compiler/mlir/lite/tests/quantize-numeric-verify.mlir
%4 = "quantfork.stats"(%3) { layerStats = dense<[0.0, 4.0]> : tensor<2xf32> } : (tensor<?x1x1x3xf32>) -> tensor<?x1x1x3xf32> %5 = "tfl.sqrt"(%4) : (tensor<?x1x1x3xf32>) -> tensor<?x1x1x3xf32> %6 = "quantfork.stats"(%5) { layerStats = dense<[0.0, 2.0]> : tensor<2xf32> } : (tensor<?x1x1x3xf32>) -> tensor<?x1x1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 15.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/fold_broadcast.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 4.1K 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/quantization/tensorflow/tests/quantize_composite_functions_weight_only.mlir
// ----- module { func.func @conv(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>, tensor<*xf32>) { %weight = "tf.Const"() {value = dense<2.000000e+00> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32> %1 = "tf.PartitionedCall"(%arg0, %weight) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_conv2d_fn_1} : (tensor<1x2x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 11.3K 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/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
return %2 : tensor<1x2x2x3xf32> } func.func private @quantize_i8(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<f32>, %arg2: tensor<i32>) -> tensor<1x3x4x3xi8> { %0 = "tf.Div"(%arg0, %arg1) : (tensor<1x3x4x3xf32>, tensor<f32>) -> tensor<1x3x4x3xf32> %1 = "tf.Round"(%0) : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/fold-constants-to-subgraph.mlir
%0 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<16x3x3x3xf32>} : () -> tensor<16x3x3x3xf32> %1 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<16xf32>} : () -> tensor<16xf32> %2 = func.call @fold_all_test(%arg0, %0, %1) : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x30x30x16xf32> func.return %2 : tensor<256x30x30x16xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 10.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/get-op-cost.mlir
func.return %0 : tensor<256x32x32x16xf32> } func.func @testConv2DGPU(tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x32x32x16xf32> { ^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>): // CHECK: tac.cost = 0x4C300000
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 05:29:10 UTC 2022 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/cast_bf16_ops_to_f32.mlir
// RUN: tf-quant-opt %s -quant-cast-bf16-ops-to-f32 | FileCheck %s func.func @cast_bf16_conv_to_fp32(%arg0: tensor<1x3x4x3xf32>) -> (tensor<1x3x2x2xf32>) { %cst = "tf.Const"() {device = "", value = dense_resource<__elided__> : tensor<2x3x3x2xbf16>} : () -> tensor<2x3x3x2xbf16> %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xbf16>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_drq_per_channel.mlir
module { func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) { %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32> %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32> func.return %1: tensor<*xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.8K bytes - Viewed (0)