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Results 61 - 70 of 70 for conv_3d (0.15 sec)
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tensorflow/compiler/mlir/tensorflow/transforms/optimize.td
def DefinedByConv2D : Constraint<CPred<"llvm::isa_and_nonnull<mlir::TF::Conv2DOp>($0.getDefiningOp())">>; // 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/tests/debuginfo/v1_1.0_224_frozen.wrong_attr.line.part.pbtxt.debug
key: "MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/FusedBatchNorm@" value { file_line_cols { file_index: 5 line: 362 } } } traces { key: "MobilenetV1/MobilenetV1/Conv2d_0/Conv2D@" value { file_line_cols { file_index: 2 line: 27 } } } traces { key: "input@" value { file_line_cols { file_index: 40 line: 690
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 11 15:36:55 UTC 2019 - 3.7K 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/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops_large_constants.mlir
%3 = "tf.Cast"(%2) {Truncate = false} : (tensor<960x960x3x512xi8>) -> tensor<960x960x3x512xi32> %4 = "tf.Sub"(%3, %arg5) : (tensor<960x960x3x512xi32>, tensor<512xi32>) -> tensor<960x960x3x512xi32> %5 = "tf.Conv2D"(%1, %4) {dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x2240x2240x3xi32>, tensor<960x960x3x512xi32>) -> tensor<1x2240x1120x512xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 5.9K bytes - Viewed (0) -
src/cmd/internal/goobj/builtinlist.go
{"runtime.stringtoslicerune", 1}, {"runtime.slicecopy", 1}, {"runtime.decoderune", 1}, {"runtime.countrunes", 1}, {"runtime.convT", 1}, {"runtime.convTnoptr", 1}, {"runtime.convT16", 1}, {"runtime.convT32", 1}, {"runtime.convT64", 1}, {"runtime.convTstring", 1}, {"runtime.convTslice", 1}, {"runtime.assertE2I", 1}, {"runtime.assertE2I2", 1}, {"runtime.panicdottypeE", 1}, {"runtime.panicdottypeI", 1},
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue May 21 21:08:03 UTC 2024 - 7.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize.mlir
%dq_bias = "quantfork.dcast"(%q_bias) : (tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>) -> tensor<2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 6.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/examples/mnist/ops_defs.py
derived_attrs=['T: {float, int8}'], outputs=['o: T']) def _composite_conv_add_relu(input_, filter_, bias, stride_w, stride_h, dilation_w, dilation_h, padding, act): res = tf.raw_ops.Conv2D( input=input_, filter=filter_, strides=[1, stride_w, stride_h, 1], dilations=[1, dilation_w, dilation_h, 1], padding=padding) res = tf.raw_ops.Add(x=res, y=bias) if act == 'RELU':
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 31 20:23:51 UTC 2023 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/tf_to_corert_pipeline.mlir
%outputs_6, %control_7 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<[-1, 16384]> : tensor<2xi32>} : () -> tensor<2xi32> %outputs_8, %control_9 = tf_executor.island wraps "tf.Conv2D"(%arg0, %outputs_0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1], use_cudnn_on_gpu = true} : (tensor<16x224x224x3xf32>, tensor<*xf32>) -> tensor<16x112x112x?xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 7.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_ptq.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 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.proto
// NEXT ID: 7 message UnitWiseQuantizationSpec { // Quantization unit granularity. // NEXT ID: 4 message QuantizationUnit { // Type of the op, ex: Conv2D, MatMul, Einsum... The node_name field can // be omitted if it is intended to match all nodes with this type. string op_type = 1; // Name of the node. This field accepts re2 regex format. If the node name
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 19 06:31:19 UTC 2024 - 9.2K bytes - Viewed (0)