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Results 21 - 28 of 28 for 1x16x1x1xf32 (0.22 sec)
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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/quantization/stablehlo/tests/bridge/optimize.mlir
return %2 : tensor<?x2x2x1xi8> } // ----- // CHECK-LABEL: func @convolution_add_add_f32 func.func @convolution_add_add_f32( %lhs: tensor<?x3x2x1xf32>, %rhs: tensor<2x1x1x1xf32>, %zp_offset: tensor<?x2x2x1xf32>, %bias: tensor<1xf32> ) -> tensor<?x2x2x1xf32> { // CHECK-DAG: %[[conv:.*]] = mhlo.convolution // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[conv:.*]], %[[zp_offset:.*]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Feb 24 02:26:47 UTC 2024 - 10.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
func.func @testSelectV2With4DBroadcasting(%cond : tensor<1x1x3x1xi1>, %arg0 : tensor<1x1x1x4xf32>, %arg1 : tensor<1x2x1x1xf32>) -> tensor<1x2x3x4xf32> { // CHECK: "tfl.select_v2"(%arg0, %arg1, %arg2) %0 = "tfl.select_v2"(%cond, %arg0, %arg1): (tensor<1x1x3x1xi1>, tensor<1x1x1x4xf32>, tensor<1x2x1x1xf32>) -> tensor<1x2x3x4xf32> func.return %0 : tensor<1x2x3x4xf32> } // -----
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/prepare-quantize-signed.mlir
%w = arith.constant dense<[[[[0.0]]], [[[127.0]]], [[[-127.0]]]]> : tensor<3x1x1x1xf32> %b = arith.constant dense<0.0> : tensor<3xf32> %conv = "tfl.conv_2d"(%arg0, %w, %b) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x5x5x1xf32>, tensor<3x1x1x1xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32> func.return %conv : tensor<1x5x5x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.4K 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/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/lite/stablehlo/tests/legalize_hlo.mlir
// CHECK: } func.func @convert_avgpool_reshape_broadcast(%arg0: tensor<4x16x16x8xf32>) -> tensor<4x8x8x8xf32> { %0 = mhlo.constant dense<1.000000e+00> : tensor<1x16x16x1xf32> %1 = mhlo.constant dense<0.000000e+00> : tensor<f32> %2 = "mhlo.reduce_window"(%arg0, %1) ({ ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): %7 = mhlo.add %arg1, %arg2 : tensor<f32>
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)