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Results 11 - 20 of 21 for 256x32x32x2xf32 (0.2 sec)

  2. github.com/tensorflow/tensorflow

    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)
  3. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/tensorflow/tests/optimize.mlir 

    func.func @convbiasaddmul(%arg: tensor<256x32x32x3xf32>) -> tensor<256x8x7x16xf32> {
  %filter = arith.constant dense<2.0> : tensor<3x3x3x16xf32>
  %bias = arith.constant dense<3.0> : tensor<16xf32>
  %value = arith.constant dense<4.0> : tensor<16xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Fri Jan 05 18:35:42 UTC 2024
    - 3.3K bytes
    - Viewed (0)
  4. github.com/tensorflow/tensorflow

    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)
  5. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/experimental/tac/tests/fold-constants-to-subgraph.mlir 

    }

// -----

module {
func.func @main(%arg0: tensor<256x32x32x3xf32>) -> tensor<256x30x30x16xf32> {
  %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>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 02 09:41:17 UTC 2024
    - 10.5K bytes
    - Viewed (0)
  6. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant-4bit.mlir 

    // CHECK:  %[[R2:.*]] = "tf.Reshape"(%[[FQ]], %cst)
// CHECK-SAME: tensor<2x1xf32>
}

// 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: Thu May 02 09:41:17 UTC 2024
    - 22K bytes
    - Viewed (0)
  7. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant.mlir 

    // CHECK:  %[[R2:.*]] = "tf.Reshape"(%[[FQ]], %cst)
// CHECK-SAME: tensor<2x1xf32>
}

// 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: Thu May 02 09:41:17 UTC 2024
    - 20.4K bytes
    - Viewed (0)
  8. github.com/tensorflow/tensorflow

    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)
  9. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/tests/prepare-tf-with-allowing-bf16-and-f16-type-legalization.mlir 

      // CHECK: "tf.FusedBatchNormV3"
}

// CHECK-LABEL: depthwise_conv_2d_bf16
func.func @depthwise_conv_2d_bf16(%arg0 : tensor<256x32x32x3xbf16>, %arg1 : tensor<3x3x3x4xf32>, %arg2 : tensor<256x3x32x32xf32>) -> tensor<256x30x30x12xbf16> {
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 26 23:53:32 UTC 2022
    - 2.2K bytes
    - Viewed (0)
  10. github.com/tensorflow/tensorflow

    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)
  11. github.com/tensorflow/tensorflow

    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
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