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Results 11 - 20 of 29 for Convolution (0.21 sec)
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tensorflow/compiler/mlir/lite/tests/end2end/conv_2d_nchw.pbtxt
type: DT_FLOAT } } attr { key: "_class" value { list { s: "loc:@conv_net_2d/conv_2d_0/w" } } } } node { name: "conv_net_2d_1/conv_2d_0/convolution" op: "Conv2D" input: "input" input: "conv_net_2d/conv_2d_0/w/read" attr { key: "T" value { type: DT_FLOAT } } attr { key: "data_format" value {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Dec 03 03:26:13 UTC 2021 - 3.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/components/pre_calibration_component.mlir
// CHECK: } // CHECK: } // ----- // Tests that `stablehlo.convolution` with NCHW format is converted to NHWC. func.func @main(%arg0: tensor<1x8x4x4xf32>) -> tensor<1x8x4x4xf32> { %0 = stablehlo.constant() {value = dense<3.000000e+00> : tensor<8x8x3x3xf32>} : () -> tensor<8x8x3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 5.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/conv_2d.pbtxt
type: DT_FLOAT } } attr { key: "_class" value { list { s: "loc:@conv_net_2d/conv_2d_0/w" } } } } node { name: "conv_net_2d_1/conv_2d_0/convolution" op: "Conv2D" input: "input" input: "conv_net_2d/conv_2d_0/w/read" attr { key: "T" value { type: DT_FLOAT } } attr { key: "data_format" value {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jun 28 06:29:38 UTC 2019 - 3.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/fuse_convolution_pass.cc
: public PassWrapper<FuseMhloConvolutionPass, OperationPass<func::FuncOp>> { public: StringRef getArgument() const final { return "fuse-mhlo-convolution-pass"; } StringRef getDescription() const final { return "Fuses MHLO binary element-wise ops and convolution op"; } void runOnOperation() override { RewritePatternSet patterns(&getContext());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 22:21:19 UTC 2024 - 8.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_layout_assignment_to_nhwc.mlir
// RUN: tf-opt %s -tf-layout-assignment=force-data-format=NHWC -verify-diagnostics | FileCheck %s --dump-input=always // IMPORTANT: Tensor shapes do not match convolution parameters (stride, // dilations, etc...). This test only verifies that changing convolution data // layout will update all the attributes. // CHECK-LABEL: func @transposeConv2D func.func @transposeConv2D(%input: tensor<1x3x32x32xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<1x8x7x6xf32> {
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/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize_per_channel.mlir
%1 = "quantfork.stats"(%arg0) {layerStats = dense<[1.27501142, 2.824783]> : tensor<2xf32>} : (tensor<1x3x2x3xf32>) -> tensor<1x3x2x3xf32> %2 = stablehlo.convolution(%1, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = { stride = [1, 1], pad = [[0, 0], [1, 1]], lhs_dilate = [1, 1], rhs_dilate = [1, 1] } {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 26 07:48:15 UTC 2024 - 8.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/preprocess_op.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-preprocess-op | FileCheck %s module { // For UniformQuantized depthwise convolution, tensor shape should have // transformed from [H,W,C,M] to [H,W,1,CxM], func.func @depthwise_conv(%arg0: tensor<1x3x4x3xf32>) -> (tensor<*xf32>) { %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<6xf32>} : () -> tensor<6xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/order_by_dialect.mlir
%4 = "tf.ReadVariableOp"(%arg1) : (tensor<!tf_type.resource<tensor<3x3x1x5xf32>>>) -> tensor<3x3x1x5xf32> %5 = "tf.ReadVariableOp"(%arg3) : (tensor<!tf_type.resource<tensor<3920x10xf32>>>) -> tensor<3920x10xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 7.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_layout_assignment_to_nchw.mlir
// IMPORTANT: In the following Conv2D tests tensor shapes do not match // convolution parameters (stride, dilations, etc...). This test only verifies // that changing convolution data layout will update all the attributes. // CHECK-LABEL: func @transposeConv2D
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/lite/stablehlo/transforms/passes.h
std::unique_ptr<Pass> createUnfuseBatchNormPass(); // Creates a pass which constant folds broadcast_in_dim op conditionally. std::unique_ptr<Pass> createFoldBroadcastPass(); // Creates a pass which fuses MHLO binary element-wise ops and convolution op. std::unique_ptr<Pass> createFuseConvolutionPass(); // Creates a pass which applies various optimizations on MHLO IR. std::unique_ptr<Pass> createOptimizePass();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 21:59:06 UTC 2024 - 3.2K bytes - Viewed (0)