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Results 1 - 10 of 23 for 1x112x112x6xf32 (0.2 sec)

  2. github.com/tensorflow/tensorflow

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

      return %3 : tensor<1x112x112x2xf32>
  // CHECK: %cst = arith.constant dense<0.000000e+00> : tensor<2xf32>
  // CHECK: %cst_0 = arith.constant dense<6.000000e+00> : tensor<1x3x3x2xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Wed May 29 07:26:59 UTC 2024
    - 59.8K bytes
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  3. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/tests/get-arithmetic-count.mlir 

      func.return %0 : tensor<?x32x32x16xf32>
}

func.func @testDepthwiseConv2D(tensor<1x112x112x3xf32>, tensor<1x3x3x32xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32> {
^bb0(%arg0: tensor<1x112x112x3xf32>, %arg1: tensor<1x3x3x32xf32>, %arg2: tensor<32xf32>):
  // CHECK: _arithmetic_count = 7626752 : i64
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Wed Dec 14 04:58:17 UTC 2022
    - 7.7K bytes
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  4. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/tests/prepare-quantize-signed.mlir 

    func.func @QuantizeFullyConnected(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x112x112x4xf32> {
  %w = arith.constant dense<127.0> : tensor<4x12xf32>
  %b = arith.constant dense<0.0> : tensor<4xf32>
  %fc = "tfl.fully_connected"(%arg0, %w, %b) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x224x224x3xf32>, tensor<4x12xf32>, tensor<4xf32>) -> tensor<1x112x112x4xf32>
  func.return %fc : tensor<1x112x112x4xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 02 09:41:17 UTC 2024
    - 18.4K bytes
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  5. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_move_transposes_end.mlir 

             exponential_avg_factor = 1.0 : f32,
         is_training = false
       }
        : (tensor<1x112x112x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>)
       -> (tensor<1x112x112x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>)

  func.return %2#0 : tensor<1x112x112x64xf32>
}

// CHECK-LABEL: func @fold_into_pad_with_extra_uses
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Mon Oct 30 06:52:55 UTC 2023
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  6. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/stablehlo/tests/optimize_layout.mlir 

    // CHECK-SAME:          %[[INPUT:.*]]: tensor<1x112x112x64xf32>,
// CHECK-SAME:          %[[PAD_VAL:.*]]: tensor<f32>) -> tensor<1x64x114x114xf32> {
// CHECK:           %[[PAD:.*]] = stablehlo.pad %[[INPUT]], %[[PAD_VAL]],
// CHECK:               low = [0, 1, 1, 0], high = [0, 1, 1, 0], interior = [0, 0, 0, 0]
// CHECK:               : (tensor<1x112x112x64xf32>, tensor<f32>) -> tensor<1x114x114x64xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Apr 25 21:59:06 UTC 2024
    - 2.8K bytes
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  7. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/tests/prepare-quantize-dynamic-range.mlir 

      %dconv_s = "quantfork.stats"(%dconv) {layerStats = dense<[0.000000e+00, 1.000000e+01]> : tensor<2xf32>} : (tensor<1x112x112x64xf32>) -> tensor<1x112x112x64xf32>
  %bmm = "tfl.batch_matmul"(%conv_s, %dconv_s) {adj_x = false, adj_y = true} : (tensor<1x112x112x64xf32>, tensor<1x112x112x64xf32>) -> tensor<1x112x112x112xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 02 09:41:17 UTC 2024
    - 38.2K bytes
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  8. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/tests/default_quant_params.mlir 

        %7 = "tfl.quantize"(%6) {qtype = tensor<1x112x112x32x!quant.uniform<u8:f32, 1.0>>} : (tensor<1x112x112x32xf32>) -> tensor<1x112x112x32x!quant.uniform<u8:f32, 1.0>>
    func.return %7 : tensor<1x112x112x32x!quant.uniform<u8:f32, 1.0>>

// CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %arg1, %arg2)
// CHECK-SAME: -> tensor<1x112x112x32x!quant.uniform<u8:f32, 0.0078431372549019607:128>>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 02 09:41:17 UTC 2024
    - 8.8K bytes
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  9. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/tests/prepare-quantize.mlir 

      %3 = "tfl.quantize"(%conv1) {qtype = tensor<1x112x112x32xf32>} : (tensor<1x112x112x32xf32>) -> tensor<1x112x112x32x!quant.uniform<u8:f32, 1.0>>

  %4 = "tfl.dequantize"(%3) : (tensor<1x112x112x32x!quant.uniform<u8:f32, 1.0>>) -> tensor<1x112x112x32xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 02 09:41:17 UTC 2024
    - 67.5K bytes
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  10. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range.mlir 

      func.return %conv : tensor<1x112x112x64xf32>

// CHECK: %[[b:.*]] = arith.constant dense<-1.23697901> : tensor<64xf32>
// CHECK: %[[w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32:0, {
// CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[w]], %[[b]]) <{
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 23 21:09:00 UTC 2024
    - 23.2K bytes
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  11. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/experimental/tac/tests/target-annotation.mlir 

      func.return %0 : tensor<1x112x112x32xf32>
}

// -----

func.func @testAvgPool(%arg0: tensor<256x32x32x3xf32>) -> 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
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