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Results 121 - 130 of 173 for conv_3d (0.12 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_ptq_per_channel.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 - 4.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/debuginfo/v1_1.0_224_frozen.wrong_attr.stack.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 } file_line_cols { file_index: 3 line: 28 } file_line_cols { file_index: 4
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 11 15:36:55 UTC 2019 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/post-quantize.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 19.9K 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/lite/tests/quantize.mlir
%4 = "tfl.dequantize"(%3) : (tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.1>>) -> tensor<32x3x3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 23:10:13 UTC 2024 - 39.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composit_functions_debugging.mlir
// TF-DAG: "tf.DumpTensor"(%[[conv0_float]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "conv_with_dump", log_dir_path = "/tmp/dumps/composite_conv2d_with_bias_and_relu6_fn_2", node_name = "Conv2D"}> {device = ""}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Nov 06 01:23:21 UTC 2023 - 80.5K bytes - Viewed (0) -
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/experimental/tac/README.md
In this pass, every op will be targeted with the user specified targets based on the device capabilites. For example, If the user specified the desired targets are "GPU", "CPU", `conv2d` can run on both "GPU" and "CPU", we will annotate the op `conv2d` with "GPU" since it's preferred; `pack` can only run on "CPU", so we will annotate the op with "CPU" since "GPU" does not support this op. #### Raise Target Subgraphs Pass
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 29 18:32:13 UTC 2022 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/shape_inference.mlir
func.return %arg0 : tensor<*xi32> } // Test conv2d inferReturnTypes can infer some information when input or // filter does not have fully static shape. // CHECK-LABEL: func @conv2d_unranked_input_and_filter func.func @conv2d_unranked_input_and_filter(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> { // CHECK: "tf.Conv2D" // CHECK-SAME: -> tensor<?x?x?x?xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 23 17:24:10 UTC 2024 - 167.4K bytes - Viewed (0) -
tensorflow/cc/gradients/nn_grad.cc
op.input(2), strides, padding, filter_attrs)); Conv2D::Attrs conv_attrs; conv_attrs.use_cudnn_on_gpu_ = use_cudnn_on_gpu; conv_attrs.explicit_paddings_ = explicit_paddings; conv_attrs.data_format_ = data_format; conv_attrs.dilations_ = dilations; grad_outputs->push_back( Conv2D(scope, grad_inputs[0], op.input(1), strides, padding, conv_attrs)); return scope.status(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 27 23:34:33 UTC 2022 - 24.5K bytes - Viewed (0)