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Results 21 - 29 of 29 for 1x1x1x96xf32 (0.15 sec)
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tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/fallback_to_flex_ops_default.mlir
func.func @depth_to_space(%arg0: tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> { %0 = "tf.DepthToSpace"(%arg0) {block_size = 2: i64, data_format = "NHWC"}: (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> func.return %0 : tensor<1x2x2x1xf32> // CHECK: %[[CUSTOM_0:.*]] = "tfl.custom"(%arg0) <{custom_code = "FlexDepthToSpace", custom_option = #tfl<const_bytes : "{{.*}}">}> : (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range.mlir
// CustomOpNotWeightOnly-LABEL: QuantizeCustomOp func.func @QuantizeCustomOp(%arg0: tensor<1x1x1x1xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input", outputs = "custom_op"}} { %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>} : (tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32> %w = arith.constant dense<127.0> : tensor<1024x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 23 21:09:00 UTC 2024 - 23.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
func.func @testSpaceToDepthF32(%arg0: tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32> { // CHECK: %[[ARG:.*]]: tensor<1x2x2x1xf32> // CHECK: "tfl.space_to_depth"(%[[ARG]]) <{block_size = 2 : i32}> : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32> %0 = "tfl.space_to_depth"(%arg0) {block_size = 2: i32} : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32> func.return %0 : tensor<1x1x1x4xf32> } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
// CHECK-LABEL: @NotReorderReshapeAddIfNotTailingDimAfter func.func @NotReorderReshapeAddIfNotTailingDimAfter(%arg0: tensor<1x30x1x96xf32>) -> tensor<1x30x96xf32> { %cst = arith.constant dense<2.0> : tensor<1x30x96xf32> %shape = arith.constant dense<[1, 30, 96]> : tensor<3xi32> %1 = "tfl.reshape"(%arg0, %shape) : (tensor<1x30x1x96xf32>, tensor<3xi32>) -> tensor<1x30x96xf32>
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/tests/prepare-quantize.mlir
} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> func.return %1 : tensor<1x1x1x16xf32> // CHECK: %0 = "tfl.dequantize"(%arg0) // CHECK: %1 = "tfl.average_pool_2d"(%0) // CHECK: %2 = "tfl.quantize"(%1) // CHECK: %3 = "tfl.dequantize"(%2) // CHECK: return %3 : tensor<1x1x1x16xf32> } // CHECK-LABEL: QuantizeMaximum
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 67.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/shape_inference.mlir
func.func @simple_folding(%arg0: tensor<1x1x1x1xi32>, %arg1: tensor<1x1x1x1xf32>) -> tensor<?x?x?x?xf32> { // CHECK: %[[SHAPE:.*]] = "tf.Shape" // CHECK: %[[CONV:.*]] = "tf.Conv2DBackpropInput"(%[[SHAPE]] // CHECK-SAME: (tensor<4xi32>, tensor<1x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32> // CHECK: return %[[CONV]] : tensor<1x1x1x1xf32> %0 = "tf.Shape"(%arg0) : (tensor<1x1x1x1xi32>) -> tensor<4xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 23 17:24:10 UTC 2024 - 167.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
// Move binary op batched RHS before reshape: // binary(reshape(lhs), rhs) => reshape(binary(lhs, flatten(rhs))) // Pattern targetted here is as follows- // [input, lhr, rhs] == [<1x1024x128>, <1x1024x8x16>, <1x1x8x16xf32>] // This is valid only when the- // 1.last dimension of lhs is equal to the number of elements in constant rhs. // 2.Reduded shape of rhs, here <8x16> is equal to last dimensions of lhs.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/legalize_hlo.mlir
// CHECK: %[[VAL_2:.*]] = "tf.Reshape"(%[[VAL_0]], %[[VAL_1]]) : (tensor<3x1x16xf32>, tensor<4xi64>) -> tensor<3x1x1x16xf32> // CHECK: %[[VAL_3:.*]] = arith.constant dense<[3, 8, 8, 16]> : tensor<4xi64> // CHECK: %[[VAL_4:.*]] = "tf.BroadcastTo"(%[[VAL_2]], %[[VAL_3]]) : (tensor<3x1x1x16xf32>, tensor<4xi64>) -> tensor<3x8x8x16xf32> // CHECK: return %[[VAL_4]] : tensor<3x8x8x16xf32> // CHECK: }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 340.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf.mlir
} // ----- // CHECK-LABEL: @conv2d_backprop_filter_grouped func.func @conv2d_backprop_filter_grouped( %input: tensor<1x2x2x2xf32>, %out_backprop: tensor<1x1x1x2xf32> ) -> tensor<2x2x1x2xf32> { // CHECK: mhlo.convolution(%arg0, %arg1) // CHECK-SAME: batch_group_count = 2 : i64 // CHECK-SAME: feature_group_count = 1 : i64
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0)