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Results 11 - 20 of 28 for 1x1x1x16xi32 (0.14 sec)
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tensorflow/compiler/mlir/lite/stablehlo/tests/compose-uniform-quantized-type.mlir
%1 = stablehlo.constant dense<1.000000e+03> : tensor<1x1x1x1xf32> // Input inverse scale. %2 = stablehlo.constant dense<-128> : tensor<1x1x1x1xi8> // Input zero point. %3 = stablehlo.constant dense<1> : tensor<3x3x4x4xi8> // Quantized filter tensor. %4 = stablehlo.constant dense<3.000000e+03> : tensor<1x1x1x4xf32> %5 = stablehlo.constant dense<4.000000e+03> : tensor<1x1x1x1xf32> // Output inverse scale.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 37K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
func.func @add_with_i32_five_dim_broadcasting(tensor<1x1x1x1x1xi32>, tensor<1xi32>) -> tensor<1x1x1x1x1xi32> { ^bb0(%arg0: tensor<1x1x1x1x1xi32>, %arg1: tensor<1xi32>): // CHECK: tfl.add(%arg0, %arg1) <{fused_activation_function = "RELU6"}> %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1x1x1x1x1xi32>, tensor<1xi32>) -> tensor<1x1x1x1x1xi32> func.return %0#0 : tensor<1x1x1x1x1xi32> } // -----
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/tests/prepare-quantize-dynamic-range.mlir
// MinElement-LABEL: QuantizeCustomOp func.func @QuantizeCustomOp(%arg0: tensor<1x1x1x1xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) attributes {tf.entry_function = {inputs = "input", outputs = "custom_op"}} { %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>} : (tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32> %w_1 = arith.constant dense<127.0> : tensor<4096x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 38.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize.mlir
func.return %1 : tensor<1x1x1x16xf32> // CHECK: %[[avgp:.*]] = "tfl.average_pool_2d"(%arg0) // CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[avgp]]) : (tensor<1x1x1x16x!quant.uniform<u8:f32, 7.812500e-03:128>>) -> tensor<1x1x1x16xf32> // CHECK: return %[[dq]] : tensor<1x1x1x16xf32> } // CHECK-LABEL: QuantizeReshape2D
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 23:10:13 UTC 2024 - 39.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/fold_broadcast.mlir
%0 = mhlo.constant dense<[[[[0, 1, 2, 3]]]]> : tensor<1x1x1x4xi32> %1 = mhlo.constant dense<[[[[0, 1, 2, 3]], [[0, 1, 2, 3]]]]> : tensor<1x2x1x4xi32> %2 = "mhlo.broadcast_in_dim"(%0) <{broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>}> : (tensor<1x1x1x4xi32>) -> tensor<1x2x1x4xi32> %3 = mhlo.multiply %1, %2 : tensor<1x2x1x4xi32>
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/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops_large_constants.mlir
return %15 : tensor<1x2240x1120x512xi8> } // CHECK-LABEL: func @conv_with_filter_larger_than_1GB // CHECK-DAG: %[[CONST:.*]] = "tf.Const"() <{value = dense<-237772800> : tensor<1x1x1x512xi32>}> : () -> tensor<1x1x1x512xi32> // CHECK: %[[PADV2_0:.*]] = "tf.PadV2" // CHECK: %[[XLACONVV2_0:.*]] = "tf.XlaConvV2"(%[[PADV2_0]] // CHECK: %[[SUB_0:.*]] = "tf.Sub"(%[[XLACONVV2_0]], %[[CONST]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 5.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-composite-functions-tf.mlir
%7 = "tf.FloorDiv"(%arg1, %5) {device = ""} : (tensor<1x1x2x1xi32>, tensor<i32>) -> tensor<1x1x2x1xi32> %8 = "tf.FloorMod"(%7, %4) {device = ""} : (tensor<1x1x2x1xi32>, tensor<i32>) -> tensor<1x1x2x1xi32> %9 = "tf.FloorDiv"(%arg1, %4) {device = ""} : (tensor<1x1x2x1xi32>, tensor<i32>) -> tensor<1x1x2x1xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 122.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/get-alternative-subgraph.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/device-transform-gpu.mlir
// CHECK: %[[VAL_6:.*]] = "tfl.reshape"(%[[VAL_1]], %[[VAL_2]]) : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32> // CHECK: %[[VAL_7:.*]] = "tfl.concatenation"(%[[VAL_5]], %[[VAL_6]]) <{axis = 3 : i32, fused_activation_function = "NONE"}> : (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 - 15.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/README.md
%1 = "tfl.reshape"(%arg1, %cst) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32> %2 = "tfl.concatenation"(%0, %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: Tue Mar 29 18:32:13 UTC 2022 - 11.6K bytes - Viewed (0)