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Results 141 - 150 of 202 for conv3d (0.1 sec)
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tensorflow/compiler/mlir/lite/experimental/tac/tests/fold-constants-to-subgraph.mlir
} // ALL-LABEL: @fold_all_test func.func @fold_all_test(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>) -> tensor<256x30x30x16xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 10.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0) -
subprojects/core/src/main/java/org/gradle/configuration/internal/ListenerBuildOperationDecorator.java
* <p> * Works in conjunction with {@link UserCodeApplicationContext} to attach the current user code application ID * to the listener, in order to convey it as part of the operation details. * This allows tracking the listener back to the plugin or script that <i>registered</i> it. */ @ServiceScope(Scope.CrossBuildSession.class) public interface ListenerBuildOperationDecorator {
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Thu Apr 04 06:02:19 UTC 2024 - 3.5K bytes - Viewed (0) -
tensorflow/cc/gradients/nn_grad_test.cc
#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/lib/random/random.h" namespace tensorflow { namespace { using ops::AvgPool; using ops::AvgPool3D; using ops::BiasAdd; using ops::Conv2D; using ops::Conv2DBackpropInput; using ops::DepthwiseConv2dNative; using ops::Elu; using ops::FractionalAvgPool; using ops::FractionalMaxPool; using ops::FusedBatchNormV3; using ops::L2Loss; using ops::LogSoftmax;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 22 20:45:22 UTC 2022 - 15K 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/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/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/passes/prepare_lifting.cc
// - Conv2DOp // - Conv3DOp // - DepthwiseConv2dNativeOp // - MatMulOp // - BatchMatMulV2Op LogicalResult MatchSupportedAffineOp(Operation* op, Value& binding_output, Value& binding_input, Value& binding_weight) { bool is_supported_affine_op = false; if (llvm::isa<TF::Conv2DOp, TF::Conv3DOp, TF::DepthwiseConv2dNativeOp>(op)) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 13.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization.td
tensor<64x3x3x3xf32> %conv = "tfl.conv_2d"(%input_act, %w, %bias) but if it is supported, it will be rewritten as: %q_w = "tfl.pseudo_qconst"() { qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>> %conv = "tfl.conv_2d"(%input_act, %q_w, %bias)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 07:39:40 UTC 2024 - 8.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
// Only rank size four input will be only available by the tf.Conv2D // operator verification. if (!input_type || input_type.isDynamicDim(3)) { return failure(); } // Check if the given op is based on grouped convolution. // Dim size zero will be verified by the tf.Conv2D operator verification. if (input_type.getDimSize(3) % filter_type.getDimSize(2) != 0) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0)