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Results 1 - 9 of 9 for 1x1x3x128xf32 (0.35 sec)
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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/lite/stablehlo/tests/fuse_mhlo_convolution.mlir
// CHECK-DAG: %[[CST:.+]] = mhlo.constant dense<[1.000000e-01, 2.000000e-01]> : tensor<2xf32> // CHECK-DAG: %[[CST_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[CST]]) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<2xf32>) -> tensor<1x1x3x2xf32> // CHECK-DAG: %[[NEW_FILTER:.+]] = mhlo.multiply %[[CST_BCAST]], %[[FILTER]] : tensor<1x1x3x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 4.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/fallback_to_flex_ops_default.mlir
%1 = "tf.Maximum"(%0, %cst_0) : (tensor<1x3x4x2xf32>, tensor<f32>) -> tensor<1x3x4x2xf32> %2 = "tf.Minimum"(%1, %cst_1) : (tensor<1x3x4x2xf32>, tensor<f32>) -> tensor<1x3x4x2xf32> func.return %2 : tensor<1x3x4x2xf32> // CHECK-DAG: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<1x1x3x2xf32>}> : () -> tensor<1x1x3x2xf32>
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/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/tests/quantize-dynamic-range.mlir
func.func @QuantizeMatmulWithActConst(%arg0: tensor<1x3x3x512xf32>) -> tensor<1x3x3x12xf32> { %w = arith.constant dense<127.0> : tensor<512x12xf32> %mm = "tfl.batch_matmul"(%arg0, %w) {adj_x = false, adj_y = false} : (tensor<1x3x3x512xf32>, tensor<512x12xf32>) -> tensor<1x3x3x12xf32> func.return %mm : tensor<1x3x3x12xf32>
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/prepare-quantize-dynamic-range.mlir
func.func @NotQuantizeBatchMatmulWithConstAct(%arg0: tensor<1x1x3x512xf32>) -> tensor<1x1x12x3xf32> { %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 1.000000e+01]> : tensor<2xf32>} : (tensor<1x1x3x512xf32>) -> tensor<1x1x3x512xf32> %w = arith.constant dense<127.0> : tensor<1x1x12x512xf32> %mm = "tfl.batch_matmul"(%w, %0) {adj_x = false, adj_y = true} : (tensor<1x1x12x512xf32>, tensor<1x1x3x512xf32>) -> tensor<1x1x12x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 38.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
%894 = "tfl.reshape"(%arg0, %cst_10) : (tensor<128xf32>, tensor<4xi32>) -> tensor<1x1x1x128xf32> %895 = "tfl.mul"(%894, %arg1) {fused_activation_function = "NONE"} : (tensor<1x1x1x128xf32>, tensor<1x512x512x128xf32>) -> tensor<1x512x512x128xf32> %896 = "tfl.mul"(%arg2, %894) {fused_activation_function = "NONE"} : (tensor<1x512x512x128xf32>, tensor<1x1x1x128xf32>) -> tensor<1x512x512x128xf32>
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-tf.mlir
// CHECK: %1 = "tfl.depthwise_conv_2d"(%arg0, %0, %[[CONSTANT]]) <{depth_multiplier = 4 : i32, dilation_h_factor = 2 : i32, dilation_w_factor = 3 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 4 : i32, stride_w = 5 : i32}> : (tensor<256x32x32x3xf32>, tensor<1x3x3x12xf32>, tensor<12xf32>) -> tensor<256x30x30x12xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
} // ----- func.func @testMaxUnpooling2D(%arg0: tensor<1x8x8x128xf32>, %arg1: tensor<1x8x8x128xf32>) -> tensor<1x8x8x128xf32> { // custom op for "tfl.max_unpooling_2d"(%arg0, %arg1) {filter_h = 2 : i32, filter_w = 2 : i32, padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x8x8x128xf32>, tensor<1x8x8x128xf32>) -> (tensor<1x8x8x128xf32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0)