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Results 1 - 10 of 81 for conv3d (0.23 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions.mlir
%0 = "tf.Conv3D"(%arg0, %cst) { data_format = "NDHWC", device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1, 1] } : (tensor<1x3x4x3x3xf32>, tensor<2x3x3x3x2xf32>) -> tensor<1x3x2x3x2xf32> %1 = "tf.Relu"(%0) {device = ""} : (tensor<1x3x2x3x2xf32>) -> tensor<1x3x2x3x2xf32> %2 = "tf.Conv3D"(%arg0, %cst) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 26.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-dynamic-range.mlir
// CHECK: %[[conv3d:.*]] = "tfl.conv_3d"(%arg0, %[[w]], %[[const]]) <{dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<?x28x28x28x8xf32>, tensor<3x3x3x8x16xf32>, none) -> tensor<?x26x26x26x16xf32> // CHECK: %2 = "tfl.shape"(%[[conv3d]]) : (tensor<?x26x26x26x16xf32>) -> tensor<5xi64>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 38.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/lift_quantizable_spots_as_functions.cc
} else if (function_name.contains("conv2d")) { // For Conv2D, the channel dimension must be static to calculate the // feature group count. if (!HasStaticShapeAtDims(call_op->getOperand(0), /*dims=*/3)) { return absl::InternalError( "The channel dimension of Conv2D is required to be static."); } } else if (function_name.contains("conv3d")) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 16.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/legalize_tf.cc
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()); rewriter.replaceOpWithNewOp<TFL::Conv3DOp>( op, tf_op.getType(), tf_op.getInput(), tf_op.getFilter(),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 20 20:06:54 UTC 2024 - 45.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library.mlir
equation = "", attr_map = "equation:0" } : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32> func.return %4 : tensor<*xi32> } for main_op in ["Conv2D", "DepthwiseConv2D", "MatMul", "Conv3D", "BatchMatMul", "Einsum"] { parameters[ {"quantized_ops": ["${main_op}", "BiasAdd"], "act_func": "internal_requantize_no_activation_fn", "output_type": "i8"},
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jan 08 01:16:10 UTC 2024 - 30.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions_drq.mlir
func.func @lift_float_conv3d(%arg0: tensor<1x3x4x3x3xf32>) -> (tensor<1x3x2x3x2xf32>) { %cst = "tf.Const"() {device = "", value = dense<1.0> : tensor<2x3x3x3x2xf32>} : () -> tensor<2x3x3x3x2xf32> %0 = "tf.Conv3D"(%arg0, %cst) { data_format = "NDHWC", device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1, 1] } : (tensor<1x3x4x3x3xf32>, tensor<2x3x3x3x2xf32>) -> tensor<1x3x2x3x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 11.8K 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/quantization/tensorflow/passes/replace_cast_hacks_with_tf_xla_ops.td
(IsInt8ElementType $filter), (IsConstTensor $filter), (IsInt32ElementType $conv), (HasStaticShapeConstraint $filter), (HasStaticShapeAtDimsConstraint<"3"> $input)], [], (addBenefit 10)>; // Convert Conv2D with hybrid inputs (f32 activation/int8 weight) to XlaConv def ConvertTFConv2DToXLAConvOpWeightOnly : Pat< (TF_Conv2DOp:$conv $input, (TF_MulOp (TF_CastOp (TF_IdentityOp $filter), $truncate1), $scale),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun Dec 10 05:52:02 UTC 2023 - 21.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema_v3b.fbs
table Conv2DOptions { padding:Padding; stride_w:int; stride_h:int; fused_activation_function:ActivationFunctionType; dilation_w_factor:int = 1; dilation_h_factor:int = 1; } // Options for both Conv3D and Conv3DTranspose. table Conv3DOptions { padding:Padding; stride_d:int; stride_w:int; stride_h:int; fused_activation_function:ActivationFunctionType; dilation_d_factor:int = 1;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 14:28:27 UTC 2024 - 30K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema.fbs
fused_activation_function:ActivationFunctionType; dilation_w_factor:int = 1; dilation_h_factor:int = 1; // Parameters for Conv2D version 8 or above. // When set, quantized_bias_type defines the dtype for both bias and accumulator. quantized_bias_type: TensorType; } // Options for both Conv3D and Conv3DTranspose. table Conv3DOptions { padding:Padding; stride_d:int; stride_w:int; stride_h:int;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 41.7K bytes - Viewed (0)