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Results 111 - 120 of 185 for conv2 (0.07 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/fake_quant_e2e_flow.mlir
%1 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {device = "", max = 2.000000e-01 : f32, min = -1.000000e-01 : f32, narrow_range = false, num_bits = 8 : i64} : (tensor<1x3x4x3xf32>) -> tensor<*xf32> %2 = "tf.Conv2D"(%1, %0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 3.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_ptq_per_channel.mlir
%1 = "quantfork.stats"(%arg0) {layerStats = dense<[1.27501142, 149.824783]> : tensor<2xf32>} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 01 10:21:29 UTC 2023 - 4.2K bytes - Viewed (0) -
src/encoding/base64/base64_test.go
got := tt.enc.EncodeToString([]byte(p.decoded)) testEqual(t, "Encode(%q) = %q, want %q", p.decoded, got, tt.conv(p.encoded)) dst := tt.enc.AppendEncode([]byte("lead"), []byte(p.decoded)) testEqual(t, `AppendEncode("lead", %q) = %q, want %q`, p.decoded, string(dst), "lead"+tt.conv(p.encoded)) } } } func TestEncoder(t *testing.T) { for _, p := range pairs { bb := &strings.Builder{}
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Sun Sep 03 18:57:29 UTC 2023 - 15.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_xla.mlir
%conv = "tf.Conv2D"(%dq_input, %dq_weight) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 11.4K bytes - Viewed (0) -
subprojects/diagnostics/src/integTest/groovy/org/gradle/api/tasks/diagnostics/DependencyReportTaskIntegrationTest.groovy
configurations { conf1 conf2 } dependencies { conf1 'org:toplevel1:1.0' conf2 'org:toplevel2:1.0' } """ when: run "dependencies", "--configuration", "conf2" then: output.contains """ conf2 \\--- org:toplevel2:1.0 +--- org:leaf3:1.0
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Wed Oct 25 05:32:54 UTC 2023 - 31.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/quantize_preprocess.cc
mlir::mhlo::createLegalizeDotToDotGeneralPass()); // Unfuse mhlo BatchNorm to primitive ops. pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createUnfuseBatchNormPass()); // Fuse Conv + Mul to Conv. pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createFuseConvolutionPass()); // Fold broadcast_in_dim + Mul. pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createFoldBroadcastPass());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 12:49:45 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/optimize.td
// Checks if the value has only one user. def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>; // If we see a Conv2D op followed by Mul, then multiply the filter // with the value in Mul. def FuseMulAndConv2D : Pat<(TF_MulOp:$mul (TF_Conv2DOp:$conv $input, (Arith_ConstantOp:$filter F32ElementsAttr:$filter_value),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 22 07:31:23 UTC 2023 - 5.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/README.md
In this pass, every op will be targeted with the user specified targets based on the device capabilites. For example, If the user specified the desired targets are "GPU", "CPU", `conv2d` can run on both "GPU" and "CPU", we will annotate the op `conv2d` with "GPU" since it's preferred; `pack` can only run on "CPU", so we will annotate the op with "CPU" since "GPU" does not support this op. #### Raise Target Subgraphs Pass
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 29 18:32:13 UTC 2022 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/shape_inference.mlir
func.return %arg0 : tensor<*xi32> } // Test conv2d inferReturnTypes can infer some information when input or // filter does not have fully static shape. // CHECK-LABEL: func @conv2d_unranked_input_and_filter func.func @conv2d_unranked_input_and_filter(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> { // CHECK: "tf.Conv2D" // CHECK-SAME: -> tensor<?x?x?x?xf32>
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/cc/gradients/nn_grad.cc
op.input(2), strides, padding, filter_attrs)); Conv2D::Attrs conv_attrs; conv_attrs.use_cudnn_on_gpu_ = use_cudnn_on_gpu; conv_attrs.explicit_paddings_ = explicit_paddings; conv_attrs.data_format_ = data_format; conv_attrs.dilations_ = dilations; grad_outputs->push_back( Conv2D(scope, grad_inputs[0], op.input(1), strides, padding, conv_attrs)); return scope.status(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 27 23:34:33 UTC 2022 - 24.5K bytes - Viewed (0)