/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators/
/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators
NameSizeModeActions
abs_op.h7050644editdlrm
accumulate_op.h10730644editdlrm
accuracy_op.h6520644editdlrm
acos_op.h7110644editdlrm
activation_ops_cudnn.h41220644editdlrm
affine_channel_op.h34500644editdlrm
alias_with_name.h12340644editdlrm
apmeter_op.h10270644editdlrm
arg_ops.h23190644editdlrm
asin_op.h7110644editdlrm
assert_op.h13350644editdlrm
async_net_barrier_op.h9040644editdlrm
atan_op.h7110644editdlrm
batch_box_cox_op.h22870644editdlrm
batch_bucketize_op.h7200644editdlrm
batch_gather_ops.h52640644editdlrm
batch_matmul_op.h96020644editdlrm
batch_moments_op.h33640644editdlrm
batch_permutation_op.h9540644editdlrm
batch_sparse_to_dense_op.h61470644editdlrm
bbox_transform_op.h26680644editdlrm
bisect_percentile_op.h49210644editdlrm
boolean_mask_ops.h26650644editdlrm
boolean_unmask_ops.h3780644editdlrm
box_with_nms_limit_op.h49600644editdlrm
bucketize_op.h13610644editdlrm
byte_weight_dequant_op.h17220644editdlrm
cast_op.h13930644editdlrm
cbrt_op.h7230644editdlrm
cc_bmm_bg_op.h38940644editdlrm
ceil_op.h7820644editdlrm
channel_backprop_stats_op.h7370644editdlrm
channel_shuffle_op.h19020644editdlrm
channel_stats_op.h18070644editdlrm
clip_op.h16390644editdlrm
collect_and_distribute_fpn_rpn_proposals_op.h68750644editdlrm
concat_split_op.h118500644editdlrm
conditional_op.h4870644editdlrm
conv_op.h31250644editdlrm
conv_op_cache_cudnn.h19350644editdlrm
conv_op_impl.h287290644editdlrm
conv_op_shared.h6720644editdlrm
conv_pool_op_base.h321090644editdlrm
conv_transpose_op.h17270644editdlrm
conv_transpose_op_impl.h182640644editdlrm
conv_transpose_op_mobile.h14700644editdlrm
conv_transpose_op_mobile_impl.h195870644editdlrm
conv_transpose_unpool_op_base.h103030644editdlrm
copy_op.h12960644editdlrm
copy_rows_to_tensor_op.h25990644editdlrm
cosh_op.h7110644editdlrm
cosine_embedding_criterion_op.h11270644editdlrm
cos_op.h7050644editdlrm
counter_ops.h45960644editdlrm
create_scope_op.h52320644editdlrm
cross_entropy_op.h44200644editdlrm
ctc_beam_search_decoder_op.h11020644editdlrm
ctc_greedy_decoder_op.h8170644editdlrm
cube_op.h7230644editdlrm
dataset_ops.h55010644editdlrm
data_couple.h4640644editdlrm
deform_conv_op.h35430644editdlrm
deform_conv_op_impl.h131710644editdlrm
dense_vector_to_id_list_op.h17970644editdlrm
distance_op.h84190644editdlrm
do_op.h69810644editdlrm
dropout_op.h15160644editdlrm
elementwise_add_op.h20240644editdlrm
elementwise_div_op.h12240644editdlrm
elementwise_linear_op.h11700644editdlrm
elementwise_logical_ops.h50830644editdlrm
elementwise_mul_op.h12240644editdlrm
elementwise_ops.h191150644editdlrm
elementwise_ops_utils.h10080644editdlrm
elementwise_op_test.h92370644editdlrm
elementwise_sub_op.h20250644editdlrm
elu_op.h8750644editdlrm
enforce_finite_op.h23030644editdlrm
ensure_clipped_op.h16080644editdlrm
ensure_cpu_output_op.h14650644editdlrm
erf_op.h7510644editdlrm
expand_op.h38770644editdlrm
expand_squeeze_dims_op.h34510644editdlrm
exp_op.h4250644editdlrm
fc_inference.h7750644editdlrm
feature_maps_ops.h324370644editdlrm
feed_blob_op.h8020644editdlrm
filler_op.h184310644editdlrm
find_duplicate_elements_op.h15630644editdlrm
find_op.h20550644editdlrm
flatten_op.h15250644editdlrm
flexible_top_k.h9360644editdlrm
floor_op.h7880644editdlrm
free_op.h7770644editdlrm
fully_connected_op.h93510644editdlrm
fused_rowwise_8bit_conversion_ops.h66010644editdlrm
fused_rowwise_nbitfake_conversion_ops.h43750644editdlrm
fused_rowwise_nbit_conversion_ops.h87230644editdlrm
fused_rowwise_random_quantization_ops.h26070644editdlrm
gather_fused_8bit_rowwise_op.h21790644editdlrm
gather_op.h75050644editdlrm
gather_ranges_to_dense_op.h81880644editdlrm
gelu_op.h14520644editdlrm
generate_proposals_op.h62560644editdlrm
generate_proposals_op_util_boxes.h143090644editdlrm
generate_proposals_op_util_nms.h262140644editdlrm
generate_proposals_op_util_nms_gpu.h21280644editdlrm
given_tensor_byte_string_to_uint8_fill_op.h21500644editdlrm
given_tensor_fill_op.h30020644editdlrm
glu_op.h14580644editdlrm
group_norm_op.h89670644editdlrm
gru_unit_op.h66260644editdlrm
half_float_ops.h27320644editdlrm
hard_sigmoid_op.h9940644editdlrm
heatmap_max_keypoint_op.h9390644editdlrm
histogram_op.h24210644editdlrm
h_softmax_op.h49540644editdlrm
if_op.h17640644editdlrm
im2col_op.h89430644editdlrm
index_hash_ops.h22320644editdlrm
index_ops.h31550644editdlrm
inference_lstm_op.h98810644editdlrm
instance_norm_op.h74410644editdlrm
integral_image_op.h9230644editdlrm
is_empty_op.h5580644editdlrm
jsd_op.h7210644editdlrm
key_split_ops.h14000644editdlrm
layer_norm_op.h80980644editdlrm
leaky_relu_op.h11110644editdlrm
lengths_pad_op.h25740644editdlrm
lengths_reducer_fused_8bit_rowwise_ops.h55320644editdlrm
lengths_reducer_fused_nbit_rowwise_ops.h234650644editdlrm
lengths_reducer_ops.h233150644editdlrm
lengths_reducer_rowwise_8bit_ops.h61800644editdlrm
lengths_tile_op.h5820644editdlrm
lengths_top_k_op.h13580644editdlrm
length_split_op.h22590644editdlrm
listwise_l2r_op.h16770644editdlrm
load_save_op.h140910644editdlrm
load_save_op_util.h16420644editdlrm
locally_connected_op.h38720644editdlrm
locally_connected_op_impl.h264950644editdlrm
locally_connected_op_util.h13320644editdlrm
local_response_normalization_op.h28040644editdlrm
log1p_op.h7170644editdlrm
logit_op.h11290644editdlrm
log_op.h4310644editdlrm
loss_op.h10580644editdlrm
lpnorm_op.h12790644editdlrm
lstm_unit_op.h67330644editdlrm
lstm_utils.h94240644editdlrm
map_ops.h80110644editdlrm
margin_ranking_criterion_op.h11130644editdlrm
matmul_op.h28430644editdlrm
max_pool_with_index_gpu.h11550644editdlrm
mean_op.h32520644editdlrm
merge_id_lists_op.h25700644editdlrm
minmax_ops.h38290644editdlrm
mish_op.h7940644editdlrm
mod_op.h9840644editdlrm
moments_op.h40510644editdlrm
multi_class_accuracy_op.h5390644editdlrm
negate_gradient_op.h5660644editdlrm
negative_op.h4510644editdlrm
ngram_ops.h26440644editdlrm
normalize_l1_op.h10750644editdlrm
normalize_op.h30130644editdlrm
no_default_engine_op.h10630644editdlrm
numpy_tile_op.h36430644editdlrm
one_hot_ops.h25620644editdlrm
onnx_while_op.h106550644editdlrm
operator_fallback_gpu.h41550644editdlrm
op_utils_cudnn.h21120644editdlrm
order_switch_ops.h21490644editdlrm
pack_rnn_sequence_op.h30740644editdlrm
pack_segments.h27290644editdlrm
pad_op.h29020644editdlrm
partition_ops.h99580644editdlrm
percentile_op.h10090644editdlrm
perplexity_op.h4470644editdlrm
piecewise_linear_transform_op.h82810644editdlrm
pool_op.h85250644editdlrm
pool_op_util.h11050644editdlrm
pow_op.h46770644editdlrm
prefetch_op.h46610644editdlrm
prelu_op.h10670644editdlrm
prepend_dim_op.h27600644editdlrm
quantile_op.h41200644editdlrm
quant_decode_op.h53700644editdlrm
rank_loss_op.h8200644editdlrm
reciprocal_op.h7210644editdlrm
reducer_functors.h245560644editdlrm
reduce_front_back_max_ops.h43990644editdlrm
reduce_front_back_sum_mean_ops.h53370644editdlrm
reduce_ops.h99620644editdlrm
reduction_ops.h59440644editdlrm
relu_n_op.h9900644editdlrm
relu_op.h6240644editdlrm
remove_data_blocks_op.h26510644editdlrm
replace_nan_op.h11700644editdlrm
reshape_op.h57230644editdlrm
resize_3d_op.h26770644editdlrm
resize_op.h23070644editdlrm
reverse_packed_segs_op.h27720644editdlrm
rmac_regions_op.h7080644editdlrm
rms_norm_op.h29680644editdlrm
roi_align_gradient_op.h14860644editdlrm
roi_align_op.h28570644editdlrm
roi_align_rotated_gradient_op.h13690644editdlrm
roi_align_rotated_op.h16360644editdlrm
roi_pool_op.h25030644editdlrm
rowmul_op.h19470644editdlrm
rsqrt_op.h7290644editdlrm
scale_blobs_op.h14580644editdlrm
scale_op.h10190644editdlrm
segment_reduction_op.h710220644editdlrm
self_binning_histogram_op.h62580644editdlrm
selu_op.h15450644editdlrm
sequence_ops.h82640644editdlrm
shape_op.h16380644editdlrm
sigmoid_op.h6390644editdlrm
sinh_op.h7110644editdlrm
sinusoid_position_encoding_op.h28340644editdlrm
sin_op.h7050644editdlrm
slice_op.h100710644editdlrm
softmax_op.h11740644editdlrm
softmax_utils.h4470644editdlrm
softmax_with_loss_op.h28830644editdlrm
softplus_op.h7810644editdlrm
softsign_op.h6750644editdlrm
space_batch_op.h68480644editdlrm
sparse_dropout_with_replacement_op.h11220644editdlrm
sparse_itemwise_dropout_with_replacement_op.h11630644editdlrm
sparse_lp_regularizer_op.h11300644editdlrm
sparse_normalize_op.h8340644editdlrm
sparse_to_dense_mask_op.h100510644editdlrm
sparse_to_dense_op.h39770644editdlrm
spatial_batch_norm_op.h151750644editdlrm
spatial_softmax_with_loss_op.h21820644editdlrm
sqrt_op.h4480644editdlrm
sqr_op.h4310644editdlrm
square_root_divide_op.h18570644editdlrm
stats_put_ops.h28130644editdlrm
stop_gradient.h5480644editdlrm
string_ops.h20670644editdlrm
stump_func_op.h21120644editdlrm
summarize_op.h18750644editdlrm
swish_op.h7720644editdlrm
tanh_op.h7230644editdlrm
tan_op.h7050644editdlrm
tensor_protos_db_input.h36330644editdlrm
text_file_reader_utils.h29000644editdlrm
thresholded_relu_op.h11370644editdlrm
tile_op.h87410644editdlrm
top_k.h10610644editdlrm
transpose_op.h20820644editdlrm
tt_linear_op.h65010644editdlrm
unique_ops.h16660644editdlrm
unsafe_coalesce.h24810644editdlrm
upsample_op.h22460644editdlrm
utility_ops.h499940644editdlrm
variable_length_sequence_padding.h13780644editdlrm
weighted_multi_sampling_op.h6020644editdlrm
weighted_sample_op.h7390644editdlrm
while_op.h19610644editdlrm
zero_gradient_op.h3470644editdlrm
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators/gather_ranges_to_dense_op.h (8188B)
#ifndef CAFFE2_OPERATORS_GATHER_RANGES_TO_DENSE_OPS_H_ #define CAFFE2_OPERATORS_GATHER_RANGES_TO_DENSE_OPS_H_ #include #include "caffe2/core/common_omp.h" #include "caffe2/core/context.h" #include "caffe2/core/export_caffe2_op_to_c10.h" #include "caffe2/core/logging.h" #include "caffe2/core/operator.h" #include "caffe2/core/types.h" #include "caffe2/utils/math.h" #include "caffe2/utils/proto_utils.h" #include #include #include C10_DECLARE_EXPORT_CAFFE2_OP_TO_C10(GatherRangesToDense); namespace caffe2 { template class GatherRangesToDenseOp final : public Operator { public: USE_OPERATOR_CONTEXT_FUNCTIONS; template explicit GatherRangesToDenseOp(Args&&... args) : Operator(std::forward(args)...), lengths_(this->template GetRepeatedArgument("lengths")), minObservation_(this->template GetSingleArgument( "min_observation", 10000)), maxMismatchedRatio_(this->template GetSingleArgument( "max_mismatched_ratio", 0.01)), maxEmptyRatio_( this->template GetSingleArgument("max_empty_ratio", 1.0)) { CAFFE_ENFORCE_GT(lengths_.size(), 0, "There has to be at least one length"); for (auto length : lengths_) { CAFFE_ENFORCE_GT(length, 0, "Each length should be positive"); } CAFFE_ENFORCE_GT( minObservation_, 0, "The number of observations is at least 1"); // Initialize the empty and mismatch counter. for (int i = 0; i < OutputSize(); ++i) { emptyRanges_.push_back(0); mismatchedRanges_.push_back(0); mismatchedLengths_.push_back(set()); } } ~GatherRangesToDenseOp() noexcept override { if (totalRanges_ > minObservation_) { string debugString; if (this->has_debug_def()) { debugString = "Info from operator: " + ProtoDebugString(this->debug_def()); } else { debugString = "Info from operator: no op def"; } LOG(INFO) << "In GatherRangesToDenseOp:\n" << " Lifetime empty ranges for each feature is " << emptyRanges_ << ".\n" << " Lifetime mismatched ranges for each feature is " << mismatchedRanges_ << ".\n" << " With a total of " << totalRanges_ << " examples.\n" << debugString; } } bool RunOnDevice() override { return DispatchHelper>::call( this, this->template Input(RANGES, CPU)); } template bool DoRunWithType() { auto& data = Input(DATA); auto& ranges = Input(RANGES); CAFFE_ENFORCE_EQ(data.dim(), 1, "Data has to be 1-D"); CAFFE_ENFORCE_EQ(ranges.dim(), 3, "Ranges has to be 3-D"); if (InputSize() == 3) { auto& key = Input(KEY); CAFFE_ENFORCE_EQ(key.dim(), 1, "Key has to be 1-D"); CAFFE_ENFORCE( key.dtype().template Match(), "Key has to be type int64_t"); } CAFFE_ENFORCE_EQ( ranges.size(1), lengths_.size(), "Number of ranges should match number of lengths"); CAFFE_ENFORCE_EQ( ranges.size(1), OutputSize(), "Number of ranges should match number of outputs"); CAFFE_ENFORCE_EQ( ranges.size(2), 2, "Ranges last dimension should be of size 2"); auto* rawData = static_cast(data.raw_data()); auto* rangesData = ranges.template data(); int rangesDataOffset = 0; auto itemsize = data.dtype().itemsize(); auto batchSize = ranges.size(0); vector outputDims{batchSize, 0}; vector outputRawData; for (int i = 0; i < OutputSize(); ++i) { auto* output = Output(i); outputDims[1] = lengths_[i]; output->Resize(outputDims); char* ptr = static_cast(output->raw_mutable_data(data.dtype())); memset(ptr, 0, output->nbytes()); outputRawData.push_back(ptr); } for (int i = 0; i < batchSize; ++i) { for (int j = 0; j < OutputSize(); ++j) { auto rangeStart = rangesData[rangesDataOffset++]; auto rangeLength = rangesData[rangesDataOffset++]; if (rangeLength == 0) { // empty range, will be filled with zeros emptyRanges_[j]++; continue; } if (rangeLength != lengths_[j]) { // Range lengths missmatch for output #, will be filled with zeros // Note, empty ranges are not counted as mismatched because empty // are more common and more tolerable. mismatchedRanges_[j]++; mismatchedLengths_[j].insert(rangeLength); continue; } if (InputSize() == 2) { context_.CopyItemsSameDevice( data.dtype(), rangeLength, rawData + rangeStart * itemsize, outputRawData[j] + i * itemsize * lengths_[j]); } else { auto& key = Input(KEY); auto* key_data = key.template data(); vector> buffer; for (int b_i = 0; b_i < rangeLength; ++b_i) { int64_t one_key_item = key_data[rangeStart + b_i]; auto* one_data_item = rawData + (rangeStart + b_i) * itemsize; buffer.emplace_back(one_key_item, one_data_item); } std::sort( buffer.begin(), buffer.end(), [](const std::pair& left, const std::pair& right) { return left.first < right.first; }); for (int b_i = 0; b_i < rangeLength; ++b_i) { // Since this CPU only, directly copy to the destination. std::memcpy( outputRawData[j] + (i * lengths_[j] + b_i) * itemsize, buffer[b_i].second, itemsize); } } } } CAFFE_ENFORCE_EQ(rangesDataOffset, ranges.numel()); // Check whether the empty and mismatch ratio exceeded the threshold. totalRanges_ += batchSize; for (int j = 0; j < OutputSize(); ++j) { // Only check when the ratio is not set to allow all mismatches. if (maxMismatchedRatio_ < 1.0) { CAFFE_ENFORCE_GE( std::max(totalRanges_, minObservation_) * maxMismatchedRatio_, mismatchedRanges_[j], "Ratio of range length mismatch for feature at index ", j, " is ", (static_cast(mismatchedRanges_[j]) / static_cast(totalRanges_)), " (", mismatchedRanges_[j], "/", totalRanges_, ") which exceeds ", maxMismatchedRatio_, ". The incorrect lengths include: ", mismatchedLengths_[j]); } // Only check when the ratio is not set to allow all examples to be empty. if (maxEmptyRatio_ < 1.0) { CAFFE_ENFORCE_GE( std::max(totalRanges_, minObservation_) * maxEmptyRatio_, emptyRanges_[j], "Ratio of empty ranges for feature at index ", j, " is ", (static_cast(emptyRanges_[j]) / static_cast(totalRanges_)), " (", emptyRanges_[j], "/", totalRanges_, ") which exceeds ", maxEmptyRatio_); } } return true; } INPUT_TAGS(DATA, RANGES, KEY); private: vector lengths_; int64_t totalRanges_ = 0; vector emptyRanges_; vector mismatchedRanges_; vector> mismatchedLengths_; // To avoid false alarm due to insufficient sample (e.g., first batch being // mismatched and causing 100% to be mismatched), use a threshold to ensure // enough samples are gathered before decideding whether there is an alarm or // not. int64_t minObservation_ = 0; float maxMismatchedRatio_ = 0; float maxEmptyRatio_ = 0; }; } // namespace caffe2 #endif // CAFFE2_OPERATORS_GATHER_RANGES_TO_DENSE_OPS_H_