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Results 31 - 40 of 43 for conv_2d (0.23 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/add_dump_tensor_op.mlir
// IntPerLayer-DAG: "tf.DumpTensor"(%[[output0_unquantized]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "multiple_conv2d", log_dir_path = "/tmp/dumps/composite_conv2d_with_bias_and_relu6_fn_2", node_name = "Conv2D"}>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 22:55:22 UTC 2024 - 37.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test_base.py
save_options = None if has_func_alias: save_options = tensorflow.saved_model.SaveOptions( function_aliases={FUNC_ALIAS: model.conv2d} ) saved_model_save.save( model, saved_model_path, signatures=model.conv2d.get_concrete_function( tensor_spec.TensorSpec( shape=input_shape, dtype=dtypes.float32, name='input_tensor' )
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 18.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/lift_quantizable_spots_as_functions_fusion.td
def LiftConvWithBiasDynamic : Pat< (StableHLO_AddOp:$res (StableHLO_ConvolutionOp:$conv_0 $lhs, $rhs, $window_strides, $padding, $lhs_dilation, $rhs_dilation, $window_reversal, $dimension_numbers, $feature_group_count, $batch_group_count, $precision_config), (StableHLO_DynamicBroadcastInDimOp $bias, (Shape_ShapeOfOp $conv_1), $_, $_, $_)), (LiftAsTFXlaCallModule<"composite_conv_with_bias_dynamic_fn">
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 04 07:19:09 UTC 2024 - 23.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/insert_custom_aggregation_ops.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 32.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/replace_cast_hacks_with_tf_xla_ops.cc
// Input: [N, H, W, C] for Conv2D or [N, D, H, W, C] for Conv3D. dnums.set_input_batch_dimension(0); dnums.set_input_feature_dimension(num_dims - 1); // Kernel: [K, K, I, O] for Conv2D or [K, K, K, I, O] for Conv3D. dnums.set_kernel_input_feature_dimension(num_dims - 2); dnums.set_kernel_output_feature_dimension(num_dims - 1); // Output: [N, H, W, C] for Conv2D or [N, D, H, W, C] for Conv3D.
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/prepare_patterns.td
(UpdateShapeWithAxis<-1> $qtype, $old_value))), [(CanUpdateShapeWithAxis<-1> $qtype, $old_value)]>; // The axis is set to 0 because the transpose is from the legalization of // tf.conv2d and the new channel axis is the first dimension. def ReorderTransposeDequantQuantUsedByConv : Pat<(TF_TransposeOp:$old_value (TFL_DequantizeOp (TFL_QuantizeOp $input, $qtype)), $perm),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 10.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_xla.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 - 11.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-prefer-tf2xla.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 15.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tf_tfl_passes.cc
// Canonicalization includes const folding, which is utilized here to optimize // away ops that can't get constant folded after PrepareTF pass. For example, // tf.Conv2D is split into tf.Transpose and tfl.Conv2D. pass_manager->addNestedPass<mlir::func::FuncOp>( mlir::createCanonicalizerPass()); pass_manager->addNestedPass<mlir::func::FuncOp>(mlir::createCSEPass());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 18:45:51 UTC 2024 - 25.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/legalize_tf.cc
dilation_width_factor = rewriter.getI32IntegerAttr(1); } StringAttr padding; if (!TFPaddingIsSameOrValid(op, &padding)) return failure(); // TensorFlow Conv3D has no bias, optimization patterns will fuse Conv3D // with other ops can fill the bias. Value none = rewriter.create<TFL::NoValueOp>( op->getLoc(), rewriter.getNoneType(), rewriter.getUnitAttr());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 20 20:06:54 UTC 2024 - 45.2K bytes - Viewed (0)