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Results 1 - 6 of 6 for 1x128x128x1xf32 (0.26 sec)
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tensorflow/compiler/mlir/lite/tests/dilated-conv.mlir
// CHECK-NEXT: [[RESULT:%.*]] = "tf.BiasAdd"([[CONV]], [[BIAS]]) : (tensor<1x128x128x8xf32>, tensor<8xf32>) -> tensor<1x128x128x8xf32> // CHECK-NEXT: return [[RESULT]] : tensor<1x128x128x8xf32> } func.func @testDilatedDepthWiseConvWithPad(%arg0: tensor<1x128x128x3xf32>, %arg1: tensor<5x5x3x8xf32>, %arg2: tensor<8xf32>) -> tensor<1x128x128x8xf32> { %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 44.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/raise-target-subgraphs.mlir
%7 = "tfl.add"(%1, %6) {tac.device = "GPU", tac.inference_type = "FLOAT", fused_activation_function = "NONE"} : (tensor<1x128x128xf32>, tensor<1x128x128xf32>) -> tensor<1x128x128xf32> func.return %7 : tensor<1x128x128xf32> } // CHECK: func @norm1(%[[VAL_0:.*]]: tensor<1x128x128xf32>) -> tensor<1x128x128xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/get-alternative-subgraph.mlir
func.func private @func_20_GPU_FLOAT(%arg0: tensor<128x128xf32>, %arg1: tensor<3xi32>) -> tensor<1x128x128xf32> attributes {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_20"} { %0 = "tfl.reshape"(%arg0, %arg1) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<128x128xf32>, tensor<3xi32>) -> tensor<1x128x128xf32> func.return %0 : tensor<1x128x128xf32> }
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/tensorflow/tests/tf_saved_model/keras.py
return model class TestModule(tf.Module): def __init__(self): super(TestModule, self).__init__() self.model = mnist_model() # CHECK: func {{@[a-zA-Z_0-9]+}}(%arg0: tensor<1x28x28x1xf32> {tf._user_specified_name = "x", tf_saved_model.index_path = [0]} # CHECK: attributes {{.*}} tf_saved_model.exported_names = ["my_predict"] @tf.function(input_signature=[ tf.TensorSpec([1, 28, 28, 1], tf.float32),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 1.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
%3 = "tfl.mul"(%2, %six) {fused_activation_function = "NONE"} : (tensor<1x128x128x3xf32>, tensor<f32>) -> tensor<1x128x128x3xf32> func.return %3 : tensor<1x128x128x3xf32> // CHECK: %0 = "tfl.hard_swish"(%arg0) : (tensor<1x128x128x3xf32>) -> tensor<1x128x128x3xf32> } // CHECK-LABEL: @HardSwishPatternThree func.func @HardSwishPatternThree(%arg0: tensor<1x128x128x3xf32>) -> tensor<1x128x128x3xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/legalize_hlo.mlir
// CHECK: %[[VAL_8:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}> : () -> tensor<4xi64> // CHECK: %[[VAL_9:.*]] = "tf.Transpose"(%[[VAL_7:.*]], %[[VAL_8:.*]]) : (tensor<1x128x128x64xf32>, tensor<4xi64>) -> tensor<1x64x128x128xf32> // CHECK: return %[[VAL_9:.*]] : tensor<1x64x128x128xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 340.2K bytes - Viewed (0)