- Sort Score
- Result 10 results
- Languages All
Results 51 - 60 of 62 for DRconv (0.25 sec)
-
src/cmd/vendor/golang.org/x/sys/unix/zerrors_zos_s390x.go
{259, "EDC5259I", "A CUN_RS_NO_CONVERSION error was issued by Unicode Services."}, {260, "EDC5260I", "A CUN_RS_TABLE_NOT_ALIGNED error was issued by Unicode Services."}, {262, "EDC5262I", "An iconv() function encountered an unexpected error while using Unicode Services."}, {265, "EDC5265I", "The named attribute not available."}, {1000, "EDC8000I", "A bad socket-call constant was found in the IUCV header."},
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed May 08 16:12:58 UTC 2024 - 39.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
auto pad_output_type = UnrankedTensorType::get(elem_type); input = rewriter.create<TF::PadOp>(op->getLoc(), pad_output_type, input, padding_const); // Set Conv padding to `VALID` since padding has been handled by Pad op. state.padding = rewriter.getStringAttr("VALID"); } auto conv_op = static_cast<const ConcreteType *>(this)->createTFLOp(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
ConstBoolAttrTrue, $asymmetric_quantize_inputs), [(HasRank<2> $input), (AreLastTwoDimsTransposed $perm_value), (IsBoolAttrEqual<"false"> $adj_y)]>; // Replace conv-->transpose-->add with conv-->add-->transpose // The bias needs only reshape (i.e. ReshapeNCHWBiasToNHWC) and not transpose // because the bias's shape simply changes from NxCx1x1 to Nx1x1xC. def ReorderNCHWTransposeAdd : Pat <
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
}; using ConvertConv2DDynamic = ConvertConvDynamic<TF::Conv2DOp, /*num_spatial_dims=*/2>; // Converts the TensorFlow conv op in template to the generic HLO conv op by // converting TensorFlow op attributes to HLO op attributes. // // Sample result for Conv2D: // // %conv = "mhlo.convolution"(%input, %filter) { // strides = [1, 2], // paddings = [[1, 0], [1, 1]], // ... // } //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (0) -
src/runtime/iface.go
uint32Type *_type = efaceOf(&uint32Eface)._type uint64Type *_type = efaceOf(&uint64Eface)._type stringType *_type = efaceOf(&stringEface)._type sliceType *_type = efaceOf(&sliceEface)._type ) // The conv and assert functions below do very similar things. // The convXXX functions are guaranteed by the compiler to succeed. // The assertXXX functions may fail (either panicking or returning false,
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed May 29 17:58:53 UTC 2024 - 22.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_driver.cc
// For now, restrict scale adjustment to ops with affine quantized weights, // and having weights and biases as constants. This currently only applies to // FC and Conv* ops. Restriction for the weight can be relaxed if there are // needs for adjusting scale of variable weights. auto affine_op = dyn_cast<AffineQuantizedOpInterface>(op);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 38.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf.mlir
// CHECK-DAG: %[[ONE:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32> // CHECK: %[[CONV:.*]] = mhlo.convert %arg0 : (tensor<3xi32>) -> tensor<3xi64> // CHECK: %[[F32:.*]] = "mhlo.rng"(%[[ZERO]], %[[ONE]], %[[CONV]]) {{.*UNIFORM.*}} -> tensor<12x?x64xf32> %0 = "tf.RandomUniform"(%arg0) : (tensor<3xi32>) -> tensor<12x?x64xf32> // CHECK: return %[[F32]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo.cc
(i != out_batch_dim && out_type.isDynamicDim(i))) { return false; } } } // All ones in "lhs_dilation" means this "mhlo.conv" op should be // converted to "tf.Conv2D" or "tf.DepthwiseConv2dNativeOp". auto lhs_dilation = conv_op.getLhsDilation().value(); if (!lhs_dilation.isSplat() || lhs_dilation.getSplatValue<int64_t>() != 1)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 154.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
// CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[q]]) // CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[dq]], %[[cst]]) // CHECK: return %[[conv]] : tensor<256x8x7x3xf32> } // CHECK-LABEL: @fuseMulIntoFullyConnectedWithOptionalAttribute
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/compose_uniform_quantized_type_pass.cc
// Replace filter uses with uniform quantized filter. rewriter.replaceAllUsesWith(filter_op->getResult(0), quantized_filter_constant_op.getResult()); // Replace conv op with a new convolution op that has quantized output type. // Quantize -> Dequantize following r3. auto output_uniform_quantize_call_op = cast<func::CallOp>( *combined_scale_multiply_op.getResult().user_begin());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 64.6K bytes - Viewed (0)