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Results 51 - 60 of 101 for conv2 (0.09 sec)
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src/compress/gzip/issue14937_test.go
// has a zero MTIME. This is a requirement for the Debian maintainers // to be able to have deterministic packages. // // To patch a .gz file, use the following command: // // $ dd if=/dev/zero bs=1 seek=4 count=4 conv=notrunc of=filename.gz // // See https://golang.org/issue/14937. func TestGZIPFilesHaveZeroMTimes(t *testing.T) { // To avoid spurious false positives due to untracked GZIP files that
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed Apr 10 16:37:53 UTC 2024 - 2K 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) -
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/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/compiler/mlir/quantization/stablehlo/tests/passes/insert_calibration_statistics_saver_with_skipping.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/testing/test_lift_quantizable_spots_as_functions_with_quantization_specs.cc
// Configure `QuantizationSpecs` to apply `StaticRangePtq` to compute heavy // units. constexpr absl::string_view kSpecsStaticRangePtqToComputeHeavy = R"pb(specs [ { matcher { function_name { regex: "^.*(conv|dot|gather).*" } } method { static_range_ptq {} } }])pb"; class TestLiftQuantizableSpotsAsFunctionsWithQuantizationSpecsPass : public impl::
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 28 23:21:42 UTC 2024 - 5.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/ops/tf_op_quant_spec.cc
if (function_name.contains("with_bias")) { spec->biases_params[2] = {{0, 1}, quant::GetUniformQuantizedTypeForBias}; } } else if (function_name.contains("conv2d")) { spec->coeff_op_quant_dim[1] = 3; if (function_name.contains("with_bias")) { spec->biases_params[2] = {{0, 1}, quant::GetUniformQuantizedTypeForBias}; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.3K bytes - Viewed (0) -
src/cmd/compile/internal/walk/expr.go
if types.IsComplex[et] && n.Op() == ir.ODIV { t := n.Type() call := mkcall("complex128div", types.Types[types.TCOMPLEX128], init, typecheck.Conv(n.X, types.Types[types.TCOMPLEX128]), typecheck.Conv(n.Y, types.Types[types.TCOMPLEX128])) return typecheck.Conv(call, t) } // Nothing to do for float divisions. if types.IsFloat[et] { return n } // rewrite 64-bit div and mod on 32-bit architectures.
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Mar 04 17:34:01 UTC 2024 - 27.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant-4bit.mlir
// CHECK: %[[DEQUANTIZE:.*]] = "tfl.dequantize"(%[[QUANTIZE]]) // CHECK: %[[CONV:.*]] = "tfl.conv_2d"(%arg0, %[[DEQUANTIZE]], %[[CONSTANT]]) // CHECK: return %[[CONV]] } // CHECK-LABEL: perChannelFakeQuantWithConv2D func.func @perChannelFakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 22K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/cc/config.cc
return spec; } QuantizationSpec GetDefaultWeightOnlyPtqSpec() { QuantizationSpec spec{}; spec.mutable_matcher()->mutable_function_name()->set_regex( "^.*(conv|dot_general).*"); WeightOnlyPtq& weight_only_ptq_spec = *spec.mutable_method()->mutable_weight_only_ptq(); if (auto [iter, inserted] = weight_only_ptq_spec.mutable_input_quantized_types()->try_emplace(1);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 8.3K bytes - Viewed (0)