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Results 1 - 10 of 10 for MaxPool (0.13 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize.mlir
func.return %a: tensor<*xf32> } // CHECK-LABEL: same_scale_test // CHECK: %[[maxpool:.*]] = "tf.MaxPool" // CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[maxpool]]) // CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10> // CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]]) // CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Dec 29 02:42:57 UTC 2022 - 2.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_ptq.mlir
// CHECK: %[[q0:.*]] = "quantfork.qcast"(%arg0) // CHECK: %[[dq0:.*]] = "quantfork.dcast"(%[[q0]]) // CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1> // CHECK: %[[maxpool:.*]] = "tf.MaxPool"(%[[dq0]]) // CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[maxpool]]) // CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1> // CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]]) // CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 01 10:21:29 UTC 2023 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_move_transposes_end.mlir
func.func @fold_into_max_pool(%arg0: tensor<1x64x112x112xf32>) -> tensor<1x56x56x64xf32> { // MaxPool operand transpose must be folded into the op and MaxPool // must use NCHW data format with updated kernel size and strides. // CHECK: %[[RES_PERM:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_layout_assignment_to_nhwc.mlir
// CHECK: %[[R0:.*]] = "tf.Transpose"(%arg0, %[[CST]]) // CHECK: %[[R1:.*]] = "tf.MaxPool"(%[[R0]]) <{data_format = "NHWC", explicit_paddings = [], ksize = [1, 3, 3, 1], padding = "SAME", strides = [1, 2, 2, 1]}> // CHECK: %[[CST_0:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}> // CHECK: "tf.Transpose"(%[[R1]], %[[CST_0]]) %0 = "tf.MaxPool"(%arg0) { data_format = "NCHW", ksize = [1, 1, 3, 3],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 4.5K bytes - Viewed (0) -
tensorflow/compiler/jit/tests/keras_imagenet_main_graph_mode.golden_summary
BiasAdd 1 BiasAddGrad 1 Cast 3 Const 357 Conv2D 53 Conv2DBackpropFilter 53 Conv2DBackpropInput 52 DivNoNan 1 Equal 1 FusedBatchNorm 53 FusedBatchNormGrad 53 Identity 2 MatMul 3 MaxPool 1 MaxPoolGrad 1 Mean 1 Mul 164 Pad 1 ReadVariableOp 646 Relu 49 ReluGrad 49 Reshape 2 ResourceApplyKerasMomentum 161 ShapeN 50 Softmax 1 SparseSoftmaxCrossEntropyWithLogits 1
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 06 10:38:14 UTC 2023 - 740 bytes - Viewed (0) -
tensorflow/compiler/jit/tests/keras_imagenet_main.golden_summary
AssignAddVariableOp 1 BiasAdd 1 BiasAddGrad 1 Cast 115 Const 407 Conv2D 53 Conv2DBackpropFilter 53 Conv2DBackpropInput 52 Equal 1 FusedBatchNormGradV2 53 FusedBatchNormV2 53 MatMul 3 MaxPool 1 MaxPoolGrad 1 Mean 1 Mul 218 Pad 2 ReadVariableOp 538 Relu 49 ReluGrad 49 Reshape 2 ResourceApplyKerasMomentum 161 Slice 1 Softmax 1 SparseSoftmaxCrossEntropyWithLogits 1
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 06 10:38:14 UTC 2023 - 874 bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize.mlir
%0 = "quantfork.qcast"(%arg0) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>> %1 = "quantfork.dcast"(%0) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32> %2 = "tf.MaxPool"(%1) {data_format = "NHWC", device = "", explicit_paddings = [], ksize = [1, 2, 2, 1], padding = "VALID", strides = [1, 2, 2, 1]} : (tensor<*xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 6.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_op_interfaces.td
let description = [{ Operation supports folding operand(s) transposes into the operation itself. (1) Operation might have layout dependent operands and results... Example: MaxPool(Transpose($arg, $perm)) -> Transpose(MaxPool($arg, $perm)) (2) ... or it might have only layout dependent operands: Example: Mean(Transpose($arg, $reduction_dims)) -> Mean($arg, Transpose($reduction_dims))
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Nov 30 19:07:07 UTC 2022 - 6.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_to_nhwc.mlir
%7 = "tf.Relu"(%6) : (tensor<?x64x112x112xf32>) -> tensor<?x64x112x112xf32> %8 = "tf.MaxPool"(%7) { data_format = "NCHW", ksize = [1, 1, 3, 3], padding = "SAME", strides = [1, 1, 2, 2] } : (tensor<?x64x112x112xf32>) -> tensor<?x64x56x56xf32> // CHECK: %[[MAX_POOL:[0-9]*]] = "tf.MaxPool" // CHECK-SAME: data_format = "NHWC" // CHECK-SAME: ksize = [1, 3, 3, 1]
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/tfr/examples/mnist/ops_defs.py
def _composite_max_pool(input_, stride_w, stride_h, filter_width, filter_height, padding): ksize = [1, filter_width, filter_height, 1] strides = [1, stride_w, stride_h, 1] return tf.raw_ops.MaxPool( input=input_, ksize=ksize, strides=strides, padding=padding) @tf.RegisterGradient('NewMaxPool') def _max_pool_grad(op: ops.Operation, grad): filter_width = op.get_attr('filter_width')
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 31 20:23:51 UTC 2023 - 6.8K bytes - Viewed (0)