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Results 51 - 60 of 66 for conv4 (0.15 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/passes/replace_cast_hacks_with_tf_xla_ops.cc
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h" #include "xla/xla_data.pb.h" namespace mlir::quant { namespace { constexpr StringRef kTfQuantCreatedEinsum = "__tf_quant_created_einsum"; // Replaces mixed-type Conv and Matmul cast hacks with TF XLA ops. // TODO(b/228403741): Support conversion for dynamic-shaped TF ops. class ReplaceCastHacksWithTFXLAOpsPass : public PassWrapper<ReplaceCastHacksWithTFXLAOpsPass,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 47.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize.cc
if (elements_depth == 1) { return true; } // In TFLite Conv2D uses OHWI format for filter, and 1HWO for Depthwise Conv. // For conv: // Check if last dimension in filter equals the first dimension // For depthwise conv: // Check if the first in filter dimension equals the first dimension. if (filter_shape.empty() || (is_depthwise ? filter_shape.back() != elements_depth
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 102.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/passes.td
Option<"is_signed_", "is-signed", "bool", "false", "Is the corresponding integer signed">, ]; } def IdentifyDilatedConvPass : Pass<"tfl-identify-dilated-conv", "mlir::func::FuncOp"> { let summary = "Convert dense tensor to sparse format."; let constructor = "CreateIdentifyDilatedConvPass()"; let dependentDialects = ["TFL::TensorFlowLiteDialect"]; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 22.6K bytes - Viewed (0) -
pilot/pkg/serviceregistry/kube/controller/controller.go
for _, svc := range services { hostname := kube.ServiceHostname(svc.Name, svc.Namespace, c.opts.DomainSuffix) c.Lock() conv, f := c.servicesMap[hostname] c.Unlock() if !f { return } shard := model.ShardKeyFromRegistry(c) endpoints := c.buildEndpointsForService(conv, true) if len(endpoints) > 0 { c.opts.XDSUpdater.EDSCacheUpdate(shard, string(hostname), svc.Namespace, endpoints) }
Registered: Fri Jun 14 15:00:06 UTC 2024 - Last Modified: Thu May 23 21:07:03 UTC 2024 - 41.2K 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/tests/prepare-tf.mlir
module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} { func.func @conv(tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>, tensor<256x3x32x32xf32>) -> (tensor<256x8x7x16xf32>, tensor<256x16x32x32xf32>, tensor<256x8x6x16xf32>, tensor<256x32x32x16xf32>, tensor<256x32x32x16xf32>) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K 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) -
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)