/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/load_save_op.h (14091B)
#ifndef CAFFE2_OPERATORS_LOAD_SAVE_OP_H_ #define CAFFE2_OPERATORS_LOAD_SAVE_OP_H_ #include #include #include #include #include "caffe2/core/blob_serialization.h" #include "caffe2/core/context.h" #include "caffe2/core/db.h" #include "caffe2/core/logging.h" #include "caffe2/core/operator.h" #include "caffe2/operators/load_save_op_util.h" #include "caffe2/utils/math.h" #include "caffe2/utils/proto_utils.h" namespace caffe2 { using db::Cursor; using db::DB; using db::Transaction; template class DBExistsOp final : public Operator { public: USE_OPERATOR_CONTEXT_FUNCTIONS; explicit DBExistsOp(const OperatorDef& operator_def, Workspace* ws) : Operator(operator_def, ws), ws_(ws), absolute_path_( this->template GetSingleArgument("absolute_path", false)), db_name_(this->template GetSingleArgument("db_name", "")), db_type_(this->template GetSingleArgument("db_type", "")) {} bool RunOnDevice() override { string full_db_name = absolute_path_ ? db_name_ : (ws_->RootFolder() + "/" + db_name_); auto* output = Output(0); output->Resize(); bool* exists = output->template mutable_data(); *exists = caffe2::db::DBExists(db_type_, full_db_name); return true; } private: Workspace* ws_; bool absolute_path_; std::string db_name_; std::string db_type_; }; template class LoadOp final : public Operator { public: USE_OPERATOR_CONTEXT_FUNCTIONS; explicit LoadOp(const OperatorDef& operator_def, Workspace* ws) : Operator(operator_def, ws), ws_(ws), absolute_path_( this->template GetSingleArgument("absolute_path", false)), add_prefix_(this->template GetSingleArgument("add_prefix", "")), strip_prefix_( this->template GetSingleArgument("strip_prefix", "")), db_name_(this->template GetSingleArgument("db", "")), db_names_(this->template GetRepeatedArgument("dbs")), db_type_(this->template GetSingleArgument("db_type", "")), db_options_(this->template GetSingleArgument("db_options", "")), keep_device_(this->template GetSingleArgument("keep_device", 0)), load_all_(this->template GetSingleArgument("load_all", 0)), allow_incomplete_( this->template GetSingleArgument("allow_incomplete", false)), blob_names_( this->template GetRepeatedArgument("source_blob_names")), shape_(this->template GetRepeatedArgument("shape")) { if (InputSize() == 0) { CAFFE_ENFORCE_GT(db_type_.size(), 0, "Must specify a db type."); if (db_names_.empty()) { CAFFE_ENFORCE_GT(db_name_.size(), 0, "Must specify a db name."); db_names_.push_back(db_name_); db_name_ = ""; } else { std::set db_name_set; for (const string& db_name : db_names_) { CAFFE_ENFORCE_GT(db_name.size(), 0, "Db name should not be empty."); CAFFE_ENFORCE( db_name_set.insert(db_name).second, "Duplicated db name: ", db_name); } db_name_ = ""; } } CAFFE_ENFORCE( // NOLINTNEXTLINE(clang-diagnostic-sign-compare) blob_names_.empty() || blob_names_.size() == OutputSize(), "Number of output blobs and source_blob_names mismatch."); CAFFE_ENFORCE( blob_names_.empty() || strip_prefix_.empty(), "strip_prefix and source_blob_names are mutually exclusive."); CAFFE_ENFORCE( blob_names_.empty() || !load_all_, "cannot load_all_ while using source_blob_names."); if (!load_all_) { // blob_names_ will be filled with ''source blob names'' in file/db // if argument source_blob_names is not given, then blob_names_ is // inferred from operator output if (blob_names_.empty()) { for (const string& name : operator_def.output()) { blob_names_.push_back(name); } } int idx = 0; std::set name_set; for (const string& name : blob_names_) { CAFFE_ENFORCE( name_set.insert(name).second, "Duplicated source blob name: ", name); output_indices_[name] = idx++; } } } void SetCurrentDevice(BlobProto* proto); bool RunOnDevice() override { int total_loaded_blobs = 0; std::unordered_map blob_states; if (InputSize() > 0) { for (int i = 0; i < InputSize(); ++i) { const db::DBReader& reader = this->template Input(i); extract(i, reader.cursor(), &blob_states, &total_loaded_blobs); } } else { // NOLINTNEXTLINE(clang-diagnostic-sign-compare) for (int i = 0; i < db_names_.size(); ++i) { string full_db_name = absolute_path_ ? db_names_[i] : (ws_->RootFolder() + "/" + db_names_[i]); std::unique_ptr in_db( caffe2::db::CreateDB(db_type_, full_db_name, caffe2::db::READ)); if (!db_options_.empty()) { in_db->SetOptions(db_options_); } CAFFE_ENFORCE( in_db.get(), "Cannot find db implementation of type ", db_type_, " (while trying to open ", full_db_name, ")"); std::unique_ptr cursor(in_db->NewCursor()); extract(i, cursor.get(), &blob_states, &total_loaded_blobs); } } load_save_op_util::validateBlobStates(blob_states); // Loaded all the needed blobs. if (!load_all_ && total_loaded_blobs == OutputSize()) { VLOG(1) << "Loaded " << total_loaded_blobs << " blobs fully from db(s)"; return true; } if (load_all_) { for (const string& name : this->debug_def().output()) { CAFFE_ENFORCE( blob_states.count(name), "Output blob name ", name, " does not exist in the db(s)."); } return true; } // Only loaded a subset of the blobs. if (allow_incomplete_) { VLOG(1) << "Loaded " << total_loaded_blobs << " blobs out of " << OutputSize() << " blobs from db(s)."; for (const auto& output_index : output_indices_) { if (!blob_states.count(output_index.first)) { const auto& blobName = output_index.first; const auto* blob = ws_->GetBlob(output_index.first); if (blob == nullptr || blob->GetRaw() == nullptr){ // If blob was not loaded in this op and // it did not exist in the workspace before, // remove it. ws_->RemoveBlob(blobName); } } } } else { for (const string& output_name : this->debug_def().output()) { if (blob_states.count(output_name) == 0) { LOG(ERROR) << "Failed to load blob: " << output_name; } } CAFFE_THROW( "Expected to load ", OutputSize(), " blobs, got ", total_loaded_blobs, " only.\n"); } return true; } private: void extract( int db_id, Cursor* cursor, std::unordered_map* blob_states, int* total_loaded_blobs) { if (load_all_) { extractAll(db_id, cursor, blob_states, total_loaded_blobs); } else { extractFrom( db_id, cursor, OperatorBase::Outputs(), blob_states, total_loaded_blobs); } } void extractAll( int db_id, Cursor* cursor, std::unordered_map* blob_states, int* total_loaded_blobs) { CAFFE_ENFORCE(cursor, "cursor is not valid"); int loaded_blobs = 0; for (; cursor->Valid(); cursor->Next()) { const auto key = load_save_op_util::buildBlobNameFromDbKey( cursor->key(), strip_prefix_, add_prefix_); if (key_to_dbid_.count(key) && key_to_dbid_[key] != db_id) { CAFFE_THROW("Duplicate Key ", key, " is found!\n"); } else { key_to_dbid_[key] = db_id; } BlobProto proto; CAFFE_ENFORCE( proto.ParseFromString(cursor->value()), "Couldn't parse Proto"); if (!keep_device_) { // If we are not keeping the device as the one specified in the // proto, we will set the current device. SetCurrentDevice(&proto); } Blob* blob = ws_->CreateBlob(key); load_save_op_util::ProcessBlob( blob, proto, blob_states, key, &loaded_blobs); } *total_loaded_blobs += loaded_blobs; } void extractFrom( int db_id, Cursor* cursor, const vector& outputs, std::unordered_map* blob_states, int* total_loaded_blobs) { CAFFE_ENFORCE(cursor); int loaded_blobs = 0; for (; cursor->Valid(); cursor->Next()) { const auto key = load_save_op_util::buildBlobNameFromDbKey( cursor->key(), strip_prefix_, add_prefix_); if (!output_indices_.count(key)) { VLOG(1) << "Key " << key << " not used. Skipping."; } else { if (key_to_dbid_.count(key) && key_to_dbid_[key] != db_id) { CAFFE_THROW("Duplicate Key ", key, " is found!\n"); } else { key_to_dbid_[key] = db_id; } VLOG(2) << "Deserializing blob " << key; BlobProto proto; CAFFE_ENFORCE(proto.ParseFromString(cursor->value())); if (!keep_device_) { // If we are not keeping the device as the one specified in the // proto, we will set the current device. SetCurrentDevice(&proto); } auto blobIndex = output_indices_[key]; Blob* blob = outputs.at(blobIndex); load_save_op_util::ProcessBlob( blob, proto, blob_states, key, &loaded_blobs); if (*total_loaded_blobs + loaded_blobs == OutputSize()) { break; } } } *total_loaded_blobs += loaded_blobs; } private: Workspace* ws_; bool absolute_path_; string add_prefix_; string strip_prefix_; string db_name_; std::vector db_names_; string db_type_; std::string db_options_; bool keep_device_; bool load_all_; bool allow_incomplete_; std::map output_indices_; std::map key_to_dbid_; std::vector blob_names_; std::vector shape_; }; namespace internal { class TORCH_API SaveOpImpl { public: SaveOpImpl(OperatorBase* op, const OperatorDef& operator_def, Workspace* ws); bool RunOnDevice(); private: OperatorBase* operator_; std::string strip_prefix_; std::string full_db_name_; std::string db_type_; std::string db_options_; std::vector blob_names_; SerializationOptions options_; }; } // namespace internal template class SaveOp final : public Operator { public: USE_OPERATOR_CONTEXT_FUNCTIONS; explicit SaveOp(const OperatorDef& operator_def, Workspace* ws) : Operator(operator_def, ws), impl_(this, operator_def, ws) {} bool RunOnDevice() override { return impl_.RunOnDevice(); } private: internal::SaveOpImpl impl_; }; template std::string FormatString(const std::string& pattern, Ts... values) { // Start with an initial buffer size that is probably enough most of the time. std::string buffer(256, '\0'); auto bytes_written = snprintf(&buffer[0], buffer.size(), pattern.c_str(), values...); if (bytes_written < 0) { throw std::runtime_error("FormatString failed"); } // NOLINTNEXTLINE(clang-diagnostic-sign-compare) if (bytes_written > buffer.size()) { // Our initial buffer size wasn't enough, resize and run again. buffer.resize(bytes_written + 1); bytes_written = snprintf(&buffer[0], buffer.size(), pattern.c_str(), values...); if (bytes_written < 0) { throw std::runtime_error("FormatString failed"); } } // Truncate the string to the correct size to trim off the nul terminator. buffer.resize(bytes_written); return buffer; } // CheckpointOp is a wrapper over a SaveFloatTensorOp that basically allows // flexible naming over iterations. // The file pattern in db_name should be a format string that can be passed into // sprintf with an int argument specifying the current iteration. An example: // "/path/to/my/checkpoint/checkpoint_at_%d.pb" template class CheckpointOp final : public Operator { public: explicit CheckpointOp(const OperatorDef& operator_def, Workspace* ws) : Operator(operator_def, ws), db_pattern_(this->template GetSingleArgument("db", "")), every_(this->template GetSingleArgument("every", 1)), ws_(ws), save_op_def_(operator_def) { CAFFE_ENFORCE_GT( db_pattern_.size(), 0, "Must specify a checkpoint file pattern."); CAFFE_ENFORCE_GT(every_, 0, "Checkpoint interval should be positive."); if (every_ == 1) { // Just issue a warning, but it's totally legal so we don't do anything. LOG(WARNING) << "It seems that we are checkpointting every iteration. " << "Is that intended?"; } save_op_def_.set_type("Save"); } USE_OPERATOR_CONTEXT_FUNCTIONS; bool RunOnDevice() override { int64_t iter = this->template Input(0, CPU).template data()[0]; if (iter % every_ == 0) { GetMutableArgument("db", true, &save_op_def_) ->set_s(FormatString(db_pattern_, iter)); SaveOp sub_op(save_op_def_, ws_); return sub_op.Run(); } else { return true; } } private: string db_pattern_; int every_; Workspace* ws_; OperatorDef save_op_def_; }; } // namespace caffe2 #endif // CAFFE2_OPERATORS_LOAD_SAVE_OP_H_