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Results 21 - 30 of 70 for conv_3d (0.25 sec)
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tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/dynamic_shape.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 07:35:24 UTC 2022 - 716 bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/BUILD
":test_utilities", ], driver = "@llvm-project//mlir:run_lit.sh", size_override = { "quant_stats.pbtxt": "medium", }, tags_override = { "add.pbtxt": ["no_rocm"], "conv_2d.pbtxt": ["no_rocm"], "fake_quant_per_channel.pbtxt": ["no_rocm"], }, test_file_exts = [ "pbtxt", ], ) # Bundle together all of the test utilities that are used by tests. filegroup(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 08 15:18:46 UTC 2023 - 1.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_to_nchw.mlir
%4 = "tf.Transpose"(%2, %3) : (tensor<1x32x32x8xf32>, tensor<4xi32>) -> tensor<1x8x32x32xf32> // Check that Conv2D computed in NCHW format, and all redundant transpose // operations removed from the function. // CHECK: %[[CONV:[0-9]*]] = "tf.Conv2D"(%arg0, %arg1) // CHECK-SAME: data_format = "NCHW" // CHECK-SAME: -> tensor<1x8x32x32xf32> // CHECK: return %[[CONV]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 05:47:26 UTC 2022 - 1.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/fake_quant_e2e_xla.mlir
return %3 : tensor<?x?x?x2xf32> } // CHECK-LABEL: func @conv_with_dynamic_shape // The Conv2D should not be quantized since it has dynamic channel. // CHECK: "tf.Conv2D" // CHECK-SAME: (tensor<?x?x?x?xf32>, tensor<2x3x3x2xf32>) -> tensor<?x?x?x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 7.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_to_nhwc.mlir
%5 = "tf.Conv2D"(%4, %arg3) { data_format = "NCHW", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 2] } : (tensor<?x3x230x230xf32>, tensor<7x7x3x64xf32>) -> tensor<?x64x112x112xf32> // CHECK: %[[CONV0:[0-9]*]] = "tf.Conv2D" // CHECK-SAME: %[[PAD]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 7.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_layout_assignment_to_nchw.mlir
// CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}> // CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]]) // CHECK: %[[CONV2D:[0-9]*]] = "tf.Conv2D"(%[[ARG_TRANSPOSE]], %arg1) // CHECK-SAME: data_format = "NCHW" // CHECK-SAME: dilations = [1, 4, 2, 3] // CHECK-SAME: explicit_paddings = [1, 2, 7, 8, 3, 4, 5, 6] // CHECK-SAME: padding = "EXPLICIT"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_layout_assignment_gpu_cc_70.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 21 08:41:18 UTC 2022 - 8.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_driver_test.cc
} func.func private @composite_fn_1(%arg0: tensor<1x4x4x3xf32>, %arg1: tensor<3x1x1x3xf32>, %arg2: tensor<3xf32>) -> tensor<1x4x4x3xf32> attributes {tf_quant.composite_function} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/optimize.mlir
%filter = arith.constant dense<2.0> : tensor<3x3x3x16xf32> %bias = arith.constant dense<3.0> : tensor<16xf32> %value = arith.constant dense<4.0> : tensor<16xf32> %0 = "tf.Conv2D"(%arg, %filter) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
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/tensorflow/tests/layout_optimization_layout_assignment_to_nhwc.mlir
// CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}> // CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]]) // CHECK: %[[CONV2D:[0-9]*]] = "tf.Conv2D"(%[[ARG_TRANSPOSE]], %arg1) // CHECK-SAME: data_format = "NHWC" // CHECK-SAME: dilations = [1, 3, 4, 2] // CHECK-SAME: explicit_paddings = [1, 2, 5, 6, 7, 8, 3, 4] // CHECK-SAME: padding = "EXPLICIT"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 4.5K bytes - Viewed (0)