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Results 1 - 9 of 9 for 256x32x32x3xf32 (0.22 sec)
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tensorflow/compiler/mlir/lite/experimental/tac/tests/target-annotation.mlir
func.func @testConv(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>) -> tensor<256x30x30x16xf32> { // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 19:32:06 UTC 2023 - 6.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant_4bit.mlir
// CHECK: %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) // CHECK: return %0 : tensor<8xf32> } // CHECK-LABEL: fakeQuantWithConv2D func.func @fakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) { ^bb0(%arg: tensor<256x32x32x3xf32>) : %in = arith.constant dense<0.0> : tensor<3x3x3x16xf32> %min = arith.constant dense<0.0> : tensor<f32> %max = arith.constant dense<15.0> : tensor<f32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant.mlir
// CHECK: %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) // CHECK: return %0 : tensor<8xf32> } // CHECK-LABEL: fakeQuantWithConv2D func.func @fakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) { ^bb0(%arg: tensor<256x32x32x3xf32>) : %in = arith.constant dense<0.0> : tensor<3x3x3x16xf32> %min = arith.constant dense<0.0> : tensor<f32> %max = arith.constant dense<255.0> : tensor<f32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/import_json.json
// CHECK: return %[[RES0]] : tensor<256x32x32x16xf32> { "version": 3, "operator_codes": [ { "builtin_code": "CONV_2D" } ], "subgraphs": [ { "tensors": [
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/optimize.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 3.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize-after-quantization.mlir
// CHECK-LABEL: fuseMulIntoPerTensorConv2dWithQDQs func.func @fuseMulIntoPerTensorConv2dWithQDQs(%arg0: tensor<256x32x32x3xf32>) -> tensor<256x8x7x3xf32> { %cst = arith.constant dense<1.5> : tensor<3xf32> %cst_0 = arith.constant dense<[1.0, 2.0, 3.0]> : tensor<3xf32> %w = arith.constant dense<2.0> : tensor<3x3x3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 1.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/optional_input.json
// CHECK: return %[[RES0]] : tensor<256x32x32x16xf32> { "version": 3, "operator_codes": [ { "builtin_code": "CONV_2D" } ], "subgraphs": [ { "tensors": [
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/get-arithmetic-count.mlir
// RUN: tf-opt -split-input-file -verify-diagnostics -tfl-get-arithmetic-count %s | FileCheck %s func.func @testConv2D(tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x32x32x16xf32> { ^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>): // CHECK: _arithmetic_count = 230686720 : i64
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 14 04:58:17 UTC 2022 - 7.7K 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)