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Results 1 - 6 of 6 for HWIO (0.04 sec)
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tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.h
inline constexpr std::array<int64_t, 4> kNchwToNhwcPermutation = {0, 2, 3, 1}; // Permutation from the OIHW (== (output features, input features, height, // width)) tensor format to HWIO. This is commonly used to transpose convolution // weights represented as OIHW format to HWIO, which is more desirable for // certain downstream optimization passes (e.g. XLA). inline constexpr std::array<int64_t, 4> kOihwToHwioPermutation = {2, 3, 1, 0};
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 9.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/fuse_convolution_pass.cc
"non-broadcastable operands"; }); } filter_value = filter.getValue(); mul_value = multiplier.getValue(); // In MHLO, Conv filter is in HWIO format, Depthwise conv filter is in HW1O // format and backprop input conv filter is in HWOI format. // Only fuses multiplier if all dimensions other than the out channel // dimension are equal to 1.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 22:21:19 UTC 2024 - 8.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
// format HWIO to TFLite Conv2D op filter data format OHWI and return Value // for the converted filter. Requires that filter is verified by the match // method that it is a 4-D RankedTensorType. Value legalizeFilter(PatternRewriter &rewriter, Location loc, Value filter) const { // Create a constant op for HWIO to OHWI transpose permutation.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-prefer-tf2xla.mlir
%conv2d = "tf._FusedConv2D"(%input, %filter, %bias, %act, %input_scale, %side_input_scale) { data_format = "NHWC", dilations = [1, 1, 1, 1], epsilon = 9.99999974E-5 : f32, explicit_paddings = [], filter_format = "HWIO", fused_ops = ["BiasAdd", "Relu"], leakyrelu_alpha = 2.000000e-01 : f32, num_args = 2 : i64, operandSegmentSizes = array<i32: 1, 1, 2, 2>, padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true
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/tf2xla/transforms/legalize_tf.cc
int64_t num_dims = num_spatial_dims + 2; int64_t batch_dim = GetTensorBatchDimIndex(num_dims, format); int64_t feature_dim = GetTensorFeatureDimIndex(num_dims, format); // Filters data_format is always HWIO so input channels dimension is after // all spatial dimensions. int64_t kernel_input_feature_dim = num_spatial_dims; int64_t kernel_output_feature_dim = num_spatial_dims + 1;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
DefaultValuedOptionalAttr<TF_AnyStrAttrOf<["NHWC", "NCHW", "NCHW_VECT_C"]>, "\"NHWC\"">:$data_format, DefaultValuedOptionalAttr<TF_AnyStrAttrOf<["HWIO", "OIHW", "OIHW_VECT_I"]>, "\"HWIO\"">:$filter_format, DefaultValuedOptionalAttr<I64ArrayAttr, "{1, 1, 1, 1}">:$dilations, DefaultValuedOptionalAttr<BoolAttr, "true">:$use_cudnn_on_gpu,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0)