1 /*M///////////////////////////////////////////////////////////////////////////////////////
4 //All contributions by the University of California:
5 //Copyright (c) 2014, The Regents of the University of California (Regents)
8 //All other contributions:
9 //Copyright (c) 2014, the respective contributors
10 //All rights reserved.
12 //Caffe uses a shared copyright model: each contributor holds copyright over
13 //their contributions to Caffe. The project versioning records all such
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15 //their specific copyright on a particular contribution, they should indicate
16 //their copyright solely in the commit message of the change when it is
21 //Redistribution and use in source and binary forms, with or without
22 //modification, are permitted provided that the following conditions are met:
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25 // list of conditions and the following disclaimer.
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41 //CONTRIBUTION AGREEMENT
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53 // NVidia's Caffe feature is used to store fp16 weights, https://github.com/NVIDIA/caffe:
54 // Math and storage types
59 INT = 3; // math not supported
60 UINT = 4; // math not supported
63 // Specifies the shape (dimensions) of a Blob.
65 repeated int64 dim = 1 [packed = true];
69 optional BlobShape shape = 7;
70 repeated float data = 5 [packed = true];
71 repeated float diff = 6 [packed = true];
72 repeated double double_data = 8 [packed = true];
73 repeated double double_diff = 9 [packed = true];
75 // NVidia's Caffe fields begin.
76 optional Type raw_data_type = 10;
77 optional bytes raw_data = 12 [packed = false];
78 // NVidia's Caffe fields end.
80 // 4D dimensions -- deprecated. Use "shape" instead.
81 optional int32 num = 1 [default = 0];
82 optional int32 channels = 2 [default = 0];
83 optional int32 height = 3 [default = 0];
84 optional int32 width = 4 [default = 0];
87 // The BlobProtoVector is simply a way to pass multiple blobproto instances
89 message BlobProtoVector {
90 repeated BlobProto blobs = 1;
93 message PermuteParameter {
94 // The new orders of the axes of data. Notice it should be with
95 // in the same range as the input data, and it starts from 0.
96 // Do not provide repeated order.
97 repeated uint32 order = 1;
100 // Message that stores parameters used by NormalizeBBoxLayer
101 message NormalizeBBoxParameter {
102 optional bool across_spatial = 1 [default = true];
103 // Initial value of scale. Default is 1.0 for all
104 optional FillerParameter scale_filler = 2;
105 // Whether or not scale parameters are shared across channels.
106 optional bool channel_shared = 3 [default = true];
107 // Epsilon for not dividing by zero while normalizing variance
108 optional float eps = 4 [default = 1e-10];
111 // Message that store parameters used by PriorBoxLayer
112 message PriorBoxParameter {
113 // Encode/decode type.
118 // Minimum box size (in pixels). Required!
119 optional float min_size = 1;
120 // Maximum box size (in pixels). Required!
121 optional float max_size = 2;
122 // Various of aspect ratios. Duplicate ratios will be ignored.
123 // If none is provided, we use default ratio 1.
124 repeated float aspect_ratio = 3;
125 // If true, will flip each aspect ratio.
126 // For example, if there is aspect ratio "r",
127 // we will generate aspect ratio "1.0/r" as well.
128 optional bool flip = 4 [default = true];
129 // If true, will clip the prior so that it is within [0, 1]
130 optional bool clip = 5 [default = true];
131 // Variance for adjusting the prior bboxes.
132 repeated float variance = 6;
133 // By default, we calculate img_height, img_width, step_x, step_y based on
134 // bottom[0] (feat) and bottom[1] (img). Unless these values are explicitely
136 // Explicitly provide the img_size.
137 optional uint32 img_size = 7;
138 // Either img_size or img_h/img_w should be specified; not both.
139 optional uint32 img_h = 8;
140 optional uint32 img_w = 9;
141 // Explicitly provide the step size.
142 optional float step = 10;
143 // Either step or step_h/step_w should be specified; not both.
144 optional float step_h = 11;
145 optional float step_w = 12;
146 // Offset to the top left corner of each cell.
147 optional float offset = 13 [default = 0.5];
148 // Offset to the top corner of each cell.
149 repeated float offset_h = 14;
150 // Offset to the left corner of each cell.
151 repeated float offset_w = 15;
152 // Priox boxes width (in pixels).
153 repeated float width = 16;
154 // Priox boxes height (in pixels).
155 repeated float height = 17;
158 // Message that store parameters used by DetectionOutputLayer
159 message DetectionOutputParameter {
160 // Number of classes to be predicted. Required!
161 optional uint32 num_classes = 1;
162 // If true, bounding box are shared among different classes.
163 optional bool share_location = 2 [default = true];
164 // Background label id. If there is no background class,
166 optional int32 background_label_id = 3 [default = 0];
167 // Parameters used for non maximum suppression.
168 optional NonMaximumSuppressionParameter nms_param = 4;
169 // Parameters used for saving detection results.
170 optional SaveOutputParameter save_output_param = 5;
171 // Type of coding method for bbox.
172 optional PriorBoxParameter.CodeType code_type = 6 [default = CORNER];
173 // If true, variance is encoded in target; otherwise we need to adjust the
174 // predicted offset accordingly.
175 optional bool variance_encoded_in_target = 8 [default = false];
176 // Number of total bboxes to be kept per image after nms step.
177 // -1 means keeping all bboxes after nms step.
178 optional int32 keep_top_k = 7 [default = -1];
179 // Only consider detections whose confidences are larger than a threshold.
180 // If not provided, consider all boxes.
181 optional float confidence_threshold = 9;
185 optional int32 channels = 1;
186 optional int32 height = 2;
187 optional int32 width = 3;
188 // the actual image data, in bytes
189 optional bytes data = 4;
190 optional int32 label = 5;
191 // Optionally, the datum could also hold float data.
192 repeated float float_data = 6;
193 // If true data contains an encoded image that need to be decoded
194 optional bool encoded = 7 [default = false];
197 message FillerParameter {
199 optional string type = 1 [default = 'constant'];
200 optional float value = 2 [default = 0]; // the value in constant filler
201 optional float min = 3 [default = 0]; // the min value in uniform filler
202 optional float max = 4 [default = 1]; // the max value in uniform filler
203 optional float mean = 5 [default = 0]; // the mean value in Gaussian filler
204 optional float std = 6 [default = 1]; // the std value in Gaussian filler
205 // The expected number of non-zero output weights for a given input in
206 // Gaussian filler -- the default -1 means don't perform sparsification.
207 optional int32 sparse = 7 [default = -1];
208 // Normalize the filler variance by fan_in, fan_out, or their average.
209 // Applies to 'xavier' and 'msra' fillers.
215 optional VarianceNorm variance_norm = 8 [default = FAN_IN];
218 message NetParameter {
219 optional string name = 1; // consider giving the network a name
220 // DEPRECATED. See InputParameter. The input blobs to the network.
221 repeated string input = 3;
222 // DEPRECATED. See InputParameter. The shape of the input blobs.
223 repeated BlobShape input_shape = 8;
225 // 4D input dimensions -- deprecated. Use "input_shape" instead.
226 // If specified, for each input blob there should be four
227 // values specifying the num, channels, height and width of the input blob.
228 // Thus, there should be a total of (4 * #input) numbers.
229 repeated int32 input_dim = 4;
231 // Whether the network will force every layer to carry out backward operation.
232 // If set False, then whether to carry out backward is determined
233 // automatically according to the net structure and learning rates.
234 optional bool force_backward = 5 [default = false];
235 // The current "state" of the network, including the phase, level, and stage.
236 // Some layers may be included/excluded depending on this state and the states
237 // specified in the layers' include and exclude fields.
238 optional NetState state = 6;
240 // Print debugging information about results while running Net::Forward,
241 // Net::Backward, and Net::Update.
242 optional bool debug_info = 7 [default = false];
244 // The layers that make up the net. Each of their configurations, including
245 // connectivity and behavior, is specified as a LayerParameter.
246 repeated LayerParameter layer = 100; // ID 100 so layers are printed last.
248 // DEPRECATED: use 'layer' instead.
249 repeated V1LayerParameter layers = 2;
253 // Update the next available ID when you add a new SolverParameter field.
255 // SolverParameter next available ID: 41 (last added: type)
256 message SolverParameter {
257 //////////////////////////////////////////////////////////////////////////////
258 // Specifying the train and test networks
260 // Exactly one train net must be specified using one of the following fields:
261 // train_net_param, train_net, net_param, net
262 // One or more test nets may be specified using any of the following fields:
263 // test_net_param, test_net, net_param, net
264 // If more than one test net field is specified (e.g., both net and
265 // test_net are specified), they will be evaluated in the field order given
266 // above: (1) test_net_param, (2) test_net, (3) net_param/net.
267 // A test_iter must be specified for each test_net.
268 // A test_level and/or a test_stage may also be specified for each test_net.
269 //////////////////////////////////////////////////////////////////////////////
271 // Proto filename for the train net, possibly combined with one or more
273 optional string net = 24;
274 // Inline train net param, possibly combined with one or more test nets.
275 optional NetParameter net_param = 25;
277 optional string train_net = 1; // Proto filename for the train net.
278 repeated string test_net = 2; // Proto filenames for the test nets.
279 optional NetParameter train_net_param = 21; // Inline train net params.
280 repeated NetParameter test_net_param = 22; // Inline test net params.
282 // The states for the train/test nets. Must be unspecified or
283 // specified once per net.
285 // By default, all states will have solver = true;
286 // train_state will have phase = TRAIN,
287 // and all test_state's will have phase = TEST.
288 // Other defaults are set according to the NetState defaults.
289 optional NetState train_state = 26;
290 repeated NetState test_state = 27;
292 // The number of iterations for each test net.
293 repeated int32 test_iter = 3;
295 // The number of iterations between two testing phases.
296 optional int32 test_interval = 4 [default = 0];
297 optional bool test_compute_loss = 19 [default = false];
298 // If true, run an initial test pass before the first iteration,
299 // ensuring memory availability and printing the starting value of the loss.
300 optional bool test_initialization = 32 [default = true];
301 optional float base_lr = 5; // The base learning rate
302 // the number of iterations between displaying info. If display = 0, no info
303 // will be displayed.
304 optional int32 display = 6;
305 // Display the loss averaged over the last average_loss iterations
306 optional int32 average_loss = 33 [default = 1];
307 optional int32 max_iter = 7; // the maximum number of iterations
308 // accumulate gradients over `iter_size` x `batch_size` instances
309 optional int32 iter_size = 36 [default = 1];
311 // The learning rate decay policy. The currently implemented learning rate
312 // policies are as follows:
313 // - fixed: always return base_lr.
314 // - step: return base_lr * gamma ^ (floor(iter / step))
315 // - exp: return base_lr * gamma ^ iter
316 // - inv: return base_lr * (1 + gamma * iter) ^ (- power)
317 // - multistep: similar to step but it allows non uniform steps defined by
319 // - poly: the effective learning rate follows a polynomial decay, to be
320 // zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
321 // - sigmoid: the effective learning rate follows a sigmod decay
322 // return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
324 // where base_lr, max_iter, gamma, step, stepvalue and power are defined
325 // in the solver parameter protocol buffer, and iter is the current iteration.
326 optional string lr_policy = 8;
327 optional float gamma = 9; // The parameter to compute the learning rate.
328 optional float power = 10; // The parameter to compute the learning rate.
329 optional float momentum = 11; // The momentum value.
330 optional float weight_decay = 12; // The weight decay.
331 // regularization types supported: L1 and L2
332 // controlled by weight_decay
333 optional string regularization_type = 29 [default = "L2"];
334 // the stepsize for learning rate policy "step"
335 optional int32 stepsize = 13;
336 // the stepsize for learning rate policy "multistep"
337 repeated int32 stepvalue = 34;
339 // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,
340 // whenever their actual L2 norm is larger.
341 optional float clip_gradients = 35 [default = -1];
343 optional int32 snapshot = 14 [default = 0]; // The snapshot interval
344 optional string snapshot_prefix = 15; // The prefix for the snapshot.
345 // whether to snapshot diff in the results or not. Snapshotting diff will help
346 // debugging but the final protocol buffer size will be much larger.
347 optional bool snapshot_diff = 16 [default = false];
348 enum SnapshotFormat {
352 optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];
353 // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
358 optional SolverMode solver_mode = 17 [default = GPU];
359 // the device_id will that be used in GPU mode. Use device_id = 0 in default.
360 optional int32 device_id = 18 [default = 0];
361 // If non-negative, the seed with which the Solver will initialize the Caffe
362 // random number generator -- useful for reproducible results. Otherwise,
363 // (and by default) initialize using a seed derived from the system clock.
364 optional int64 random_seed = 20 [default = -1];
366 // type of the solver
367 optional string type = 40 [default = "SGD"];
369 // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
370 optional float delta = 31 [default = 1e-8];
371 // parameters for the Adam solver
372 optional float momentum2 = 39 [default = 0.999];
374 // RMSProp decay value
375 // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
376 optional float rms_decay = 38 [default = 0.99];
378 // If true, print information about the state of the net that may help with
379 // debugging learning problems.
380 optional bool debug_info = 23 [default = false];
382 // If false, don't save a snapshot after training finishes.
383 optional bool snapshot_after_train = 28 [default = true];
385 // DEPRECATED: old solver enum types, use string instead
394 // DEPRECATED: use type instead of solver_type
395 optional SolverType solver_type = 30 [default = SGD];
398 // A message that stores the solver snapshots
399 message SolverState {
400 optional int32 iter = 1; // The current iteration
401 optional string learned_net = 2; // The file that stores the learned net.
402 repeated BlobProto history = 3; // The history for sgd solvers
403 optional int32 current_step = 4 [default = 0]; // The current step for learning rate
412 optional Phase phase = 1 [default = TEST];
413 optional int32 level = 2 [default = 0];
414 repeated string stage = 3;
417 message NetStateRule {
418 // Set phase to require the NetState have a particular phase (TRAIN or TEST)
419 // to meet this rule.
420 optional Phase phase = 1;
422 // Set the minimum and/or maximum levels in which the layer should be used.
423 // Leave undefined to meet the rule regardless of level.
424 optional int32 min_level = 2;
425 optional int32 max_level = 3;
427 // Customizable sets of stages to include or exclude.
428 // The net must have ALL of the specified stages and NONE of the specified
429 // "not_stage"s to meet the rule.
430 // (Use multiple NetStateRules to specify conjunctions of stages.)
431 repeated string stage = 4;
432 repeated string not_stage = 5;
435 // Specifies training parameters (multipliers on global learning constants,
436 // and the name and other settings used for weight sharing).
438 // The names of the parameter blobs -- useful for sharing parameters among
439 // layers, but never required otherwise. To share a parameter between two
440 // layers, give it a (non-empty) name.
441 optional string name = 1;
443 // Whether to require shared weights to have the same shape, or just the same
444 // count -- defaults to STRICT if unspecified.
445 optional DimCheckMode share_mode = 2;
447 // STRICT (default) requires that num, channels, height, width each match.
449 // PERMISSIVE requires only the count (num*channels*height*width) to match.
453 // The multiplier on the global learning rate for this parameter.
454 optional float lr_mult = 3 [default = 1.0];
456 // The multiplier on the global weight decay for this parameter.
457 optional float decay_mult = 4 [default = 1.0];
461 // Update the next available ID when you add a new LayerParameter field.
463 // LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
464 message LayerParameter {
465 optional string name = 1; // the layer name
466 optional string type = 2; // the layer type
467 repeated string bottom = 3; // the name of each bottom blob
468 repeated string top = 4; // the name of each top blob
470 // The train / test phase for computation.
471 optional Phase phase = 10;
473 // The amount of weight to assign each top blob in the objective.
474 // Each layer assigns a default value, usually of either 0 or 1,
476 repeated float loss_weight = 5;
478 // Specifies training parameters (multipliers on global learning constants,
479 // and the name and other settings used for weight sharing).
480 repeated ParamSpec param = 6;
482 // The blobs containing the numeric parameters of the layer.
483 repeated BlobProto blobs = 7;
485 // Specifies whether to backpropagate to each bottom. If unspecified,
486 // Caffe will automatically infer whether each input needs backpropagation
487 // to compute parameter gradients. If set to true for some inputs,
488 // backpropagation to those inputs is forced; if set false for some inputs,
489 // backpropagation to those inputs is skipped.
491 // The size must be either 0 or equal to the number of bottoms.
492 repeated bool propagate_down = 11;
494 // Rules controlling whether and when a layer is included in the network,
495 // based on the current NetState. You may specify a non-zero number of rules
496 // to include OR exclude, but not both. If no include or exclude rules are
497 // specified, the layer is always included. If the current NetState meets
498 // ANY (i.e., one or more) of the specified rules, the layer is
499 // included/excluded.
500 repeated NetStateRule include = 8;
501 repeated NetStateRule exclude = 9;
503 // Parameters for data pre-processing.
504 optional TransformationParameter transform_param = 100;
506 // Parameters shared by loss layers.
507 optional LossParameter loss_param = 101;
509 // Layer type-specific parameters.
511 // Note: certain layers may have more than one computational engine
512 // for their implementation. These layers include an Engine type and
513 // engine parameter for selecting the implementation.
514 // The default for the engine is set by the ENGINE switch at compile-time.
515 optional AccuracyParameter accuracy_param = 102;
516 optional ArgMaxParameter argmax_param = 103;
517 optional BatchNormParameter batch_norm_param = 139;
518 optional BiasParameter bias_param = 141;
519 optional ConcatParameter concat_param = 104;
520 optional ContrastiveLossParameter contrastive_loss_param = 105;
521 optional ConvolutionParameter convolution_param = 106;
522 optional CropParameter crop_param = 144;
523 optional DataParameter data_param = 107;
524 optional DetectionOutputParameter detection_output_param = 147;
525 optional DropoutParameter dropout_param = 108;
526 optional DummyDataParameter dummy_data_param = 109;
527 optional EltwiseParameter eltwise_param = 110;
528 optional ELUParameter elu_param = 140;
529 optional EmbedParameter embed_param = 137;
530 optional ExpParameter exp_param = 111;
531 optional FlattenParameter flatten_param = 135;
532 optional HDF5DataParameter hdf5_data_param = 112;
533 optional HDF5OutputParameter hdf5_output_param = 113;
534 optional HingeLossParameter hinge_loss_param = 114;
535 optional ImageDataParameter image_data_param = 115;
536 optional InfogainLossParameter infogain_loss_param = 116;
537 optional InnerProductParameter inner_product_param = 117;
538 optional InputParameter input_param = 143;
539 optional LogParameter log_param = 134;
540 optional LRNParameter lrn_param = 118;
541 optional MemoryDataParameter memory_data_param = 119;
542 optional MVNParameter mvn_param = 120;
543 optional NormalizeBBoxParameter norm_param = 149;
544 optional PermuteParameter permute_param = 148;
545 optional ParameterParameter parameter_param = 145;
546 optional PoolingParameter pooling_param = 121;
547 optional PowerParameter power_param = 122;
548 optional PReLUParameter prelu_param = 131;
549 optional PriorBoxParameter prior_box_param = 150;
550 optional PythonParameter python_param = 130;
551 optional RecurrentParameter recurrent_param = 146;
552 optional ReductionParameter reduction_param = 136;
553 optional ReLUParameter relu_param = 123;
554 optional ReshapeParameter reshape_param = 133;
555 optional ROIPoolingParameter roi_pooling_param = 8266711; // https://github.com/rbgirshick/caffe-fast-rcnn/tree/fast-rcnn
556 optional ScaleParameter scale_param = 142;
557 optional SigmoidParameter sigmoid_param = 124;
558 optional SoftmaxParameter softmax_param = 125;
559 optional SPPParameter spp_param = 132;
560 optional SliceParameter slice_param = 126;
561 optional TanHParameter tanh_param = 127;
562 optional ThresholdParameter threshold_param = 128;
563 optional TileParameter tile_param = 138;
564 optional WindowDataParameter window_data_param = 129;
567 // Message that stores parameters used to apply transformation
568 // to the data layer's data
569 message TransformationParameter {
570 // For data pre-processing, we can do simple scaling and subtracting the
571 // data mean, if provided. Note that the mean subtraction is always carried
572 // out before scaling.
573 optional float scale = 1 [default = 1];
574 // Specify if we want to randomly mirror data.
575 optional bool mirror = 2 [default = false];
576 // Specify if we would like to randomly crop an image.
577 optional uint32 crop_size = 3 [default = 0];
578 // mean_file and mean_value cannot be specified at the same time
579 optional string mean_file = 4;
580 // if specified can be repeated once (would subtract it from all the channels)
581 // or can be repeated the same number of times as channels
582 // (would subtract them from the corresponding channel)
583 repeated float mean_value = 5;
584 // Force the decoded image to have 3 color channels.
585 optional bool force_color = 6 [default = false];
586 // Force the decoded image to have 1 color channels.
587 optional bool force_gray = 7 [default = false];
590 // Message that stores parameters shared by loss layers
591 message LossParameter {
592 // If specified, ignore instances with the given label.
593 optional int32 ignore_label = 1;
594 // How to normalize the loss for loss layers that aggregate across batches,
595 // spatial dimensions, or other dimensions. Currently only implemented in
596 // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
597 enum NormalizationMode {
598 // Divide by the number of examples in the batch times spatial dimensions.
599 // Outputs that receive the ignore label will NOT be ignored in computing
600 // the normalization factor.
602 // Divide by the total number of output locations that do not take the
603 // ignore_label. If ignore_label is not set, this behaves like FULL.
605 // Divide by the batch size.
607 // Do not normalize the loss.
610 // For historical reasons, the default normalization for
611 // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
612 optional NormalizationMode normalization = 3 [default = VALID];
613 // Deprecated. Ignored if normalization is specified. If normalization
614 // is not specified, then setting this to false will be equivalent to
615 // normalization = BATCH_SIZE to be consistent with previous behavior.
616 optional bool normalize = 2;
619 // Messages that store parameters used by individual layer types follow, in
620 // alphabetical order.
622 message AccuracyParameter {
623 // When computing accuracy, count as correct by comparing the true label to
624 // the top k scoring classes. By default, only compare to the top scoring
625 // class (i.e. argmax).
626 optional uint32 top_k = 1 [default = 1];
628 // The "label" axis of the prediction blob, whose argmax corresponds to the
629 // predicted label -- may be negative to index from the end (e.g., -1 for the
630 // last axis). For example, if axis == 1 and the predictions are
631 // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
632 // labels with integer values in {0, 1, ..., C-1}.
633 optional int32 axis = 2 [default = 1];
635 // If specified, ignore instances with the given label.
636 optional int32 ignore_label = 3;
639 message ArgMaxParameter {
640 // If true produce pairs (argmax, maxval)
641 optional bool out_max_val = 1 [default = false];
642 optional uint32 top_k = 2 [default = 1];
643 // The axis along which to maximise -- may be negative to index from the
644 // end (e.g., -1 for the last axis).
645 // By default ArgMaxLayer maximizes over the flattened trailing dimensions
646 // for each index of the first / num dimension.
647 optional int32 axis = 3;
650 message ConcatParameter {
651 // The axis along which to concatenate -- may be negative to index from the
652 // end (e.g., -1 for the last axis). Other axes must have the
653 // same dimension for all the bottom blobs.
654 // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
655 optional int32 axis = 2 [default = 1];
657 // DEPRECATED: alias for "axis" -- does not support negative indexing.
658 optional uint32 concat_dim = 1 [default = 1];
661 message BatchNormParameter {
662 // If false, accumulate global mean/variance values via a moving average. If
663 // true, use those accumulated values instead of computing mean/variance
665 optional bool use_global_stats = 1;
666 // How much does the moving average decay each iteration?
667 optional float moving_average_fraction = 2 [default = .999];
668 // Small value to add to the variance estimate so that we don't divide by
670 optional float eps = 3 [default = 1e-5];
673 message BiasParameter {
674 // The first axis of bottom[0] (the first input Blob) along which to apply
675 // bottom[1] (the second input Blob). May be negative to index from the end
676 // (e.g., -1 for the last axis).
678 // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
679 // top[0] will have the same shape, and bottom[1] may have any of the
680 // following shapes (for the given value of axis):
681 // (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
682 // (axis == 1 == -3) 3; 3x40; 3x40x60
683 // (axis == 2 == -2) 40; 40x60
684 // (axis == 3 == -1) 60
685 // Furthermore, bottom[1] may have the empty shape (regardless of the value of
686 // "axis") -- a scalar bias.
687 optional int32 axis = 1 [default = 1];
689 // (num_axes is ignored unless just one bottom is given and the bias is
690 // a learned parameter of the layer. Otherwise, num_axes is determined by the
691 // number of axes by the second bottom.)
692 // The number of axes of the input (bottom[0]) covered by the bias
693 // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
694 // Set num_axes := 0, to add a zero-axis Blob: a scalar.
695 optional int32 num_axes = 2 [default = 1];
697 // (filler is ignored unless just one bottom is given and the bias is
698 // a learned parameter of the layer.)
699 // The initialization for the learned bias parameter.
700 // Default is the zero (0) initialization, resulting in the BiasLayer
701 // initially performing the identity operation.
702 optional FillerParameter filler = 3;
705 message ContrastiveLossParameter {
706 // margin for dissimilar pair
707 optional float margin = 1 [default = 1.0];
708 // The first implementation of this cost did not exactly match the cost of
709 // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
710 // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
711 // Hadsell paper. New models should probably use this version.
712 // legacy_version = true uses (margin - d^2). This is kept to support /
713 // reproduce existing models and results
714 optional bool legacy_version = 2 [default = false];
717 message ConvolutionParameter {
718 optional uint32 num_output = 1; // The number of outputs for the layer
719 optional bool bias_term = 2 [default = true]; // whether to have bias terms
721 // Pad, kernel size, and stride are all given as a single value for equal
722 // dimensions in all spatial dimensions, or once per spatial dimension.
723 repeated uint32 pad = 3; // The padding size; defaults to 0
724 repeated uint32 kernel_size = 4; // The kernel size
725 repeated uint32 stride = 6; // The stride; defaults to 1
726 // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
727 // holes. (Kernel dilation is sometimes referred to by its use in the
728 // algorithme à trous from Holschneider et al. 1987.)
729 repeated uint32 dilation = 18; // The dilation; defaults to 1
731 // For 2D convolution only, the *_h and *_w versions may also be used to
732 // specify both spatial dimensions.
733 optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
734 optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
735 optional uint32 kernel_h = 11; // The kernel height (2D only)
736 optional uint32 kernel_w = 12; // The kernel width (2D only)
737 optional uint32 stride_h = 13; // The stride height (2D only)
738 optional uint32 stride_w = 14; // The stride width (2D only)
740 optional uint32 group = 5 [default = 1]; // The group size for group conv
742 optional FillerParameter weight_filler = 7; // The filler for the weight
743 optional FillerParameter bias_filler = 8; // The filler for the bias
749 optional Engine engine = 15 [default = DEFAULT];
751 // The axis to interpret as "channels" when performing convolution.
752 // Preceding dimensions are treated as independent inputs;
753 // succeeding dimensions are treated as "spatial".
754 // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
755 // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
756 // groups g>1) filters across the spatial axes (H, W) of the input.
757 // With (N, C, D, H, W) inputs, and axis == 1, we perform
758 // N independent 3D convolutions, sliding (C/g)-channels
759 // filters across the spatial axes (D, H, W) of the input.
760 optional int32 axis = 16 [default = 1];
762 // Whether to force use of the general ND convolution, even if a specific
763 // implementation for blobs of the appropriate number of spatial dimensions
764 // is available. (Currently, there is only a 2D-specific convolution
765 // implementation; for input blobs with num_axes != 2, this option is
766 // ignored and the ND implementation will be used.)
767 optional bool force_nd_im2col = 17 [default = false];
770 message CropParameter {
771 // To crop, elements of the first bottom are selected to fit the dimensions
772 // of the second, reference bottom. The crop is configured by
773 // - the crop `axis` to pick the dimensions for cropping
774 // - the crop `offset` to set the shift for all/each dimension
775 // to align the cropped bottom with the reference bottom.
776 // All dimensions up to but excluding `axis` are preserved, while
777 // the dimensions including and trailing `axis` are cropped.
778 // If only one `offset` is set, then all dimensions are offset by this amount.
779 // Otherwise, the number of offsets must equal the number of cropped axes to
780 // shift the crop in each dimension accordingly.
781 // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
782 // and `axis` may be negative to index from the end (e.g., -1 for the last
784 optional int32 axis = 1 [default = 2];
785 repeated uint32 offset = 2;
788 message DataParameter {
793 // Specify the data source.
794 optional string source = 1;
795 // Specify the batch size.
796 optional uint32 batch_size = 4;
797 // The rand_skip variable is for the data layer to skip a few data points
798 // to avoid all asynchronous sgd clients to start at the same point. The skip
799 // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
800 // be larger than the number of keys in the database.
801 // DEPRECATED. Each solver accesses a different subset of the database.
802 optional uint32 rand_skip = 7 [default = 0];
803 optional DB backend = 8 [default = LEVELDB];
804 // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
805 // simple scaling and subtracting the data mean, if provided. Note that the
806 // mean subtraction is always carried out before scaling.
807 optional float scale = 2 [default = 1];
808 optional string mean_file = 3;
809 // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
811 optional uint32 crop_size = 5 [default = 0];
812 // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
814 optional bool mirror = 6 [default = false];
815 // Force the encoded image to have 3 color channels
816 optional bool force_encoded_color = 9 [default = false];
817 // Prefetch queue (Number of batches to prefetch to host memory, increase if
818 // data access bandwidth varies).
819 optional uint32 prefetch = 10 [default = 4];
822 message NonMaximumSuppressionParameter {
823 // Threshold to be used in nms.
824 optional float nms_threshold = 1 [default = 0.3];
825 // Maximum number of results to be kept.
826 optional int32 top_k = 2;
827 // Parameter for adaptive nms.
828 optional float eta = 3 [default = 1.0];
831 message SaveOutputParameter {
832 // Output directory. If not empty, we will save the results.
833 optional string output_directory = 1;
834 // Output name prefix.
835 optional string output_name_prefix = 2;
837 // VOC - PASCAL VOC output format.
838 // COCO - MS COCO output format.
839 optional string output_format = 3;
840 // If you want to output results, must also provide the following two files.
841 // Otherwise, we will ignore saving results.
843 optional string label_map_file = 4;
844 // A file which contains a list of names and sizes with same order
845 // of the input DB. The file is in the following format:
848 optional string name_size_file = 5;
849 // Number of test images. It can be less than the lines specified in
850 // name_size_file. For example, when we only want to evaluate on part
851 // of the test images.
852 optional uint32 num_test_image = 6;
855 message DropoutParameter {
856 optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
859 // DummyDataLayer fills any number of arbitrarily shaped blobs with random
860 // (or constant) data generated by "Fillers" (see "message FillerParameter").
861 message DummyDataParameter {
862 // This layer produces N >= 1 top blobs. DummyDataParameter must specify 1 or N
863 // shape fields, and 0, 1 or N data_fillers.
865 // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
866 // If 1 data_filler is specified, it is applied to all top blobs. If N are
867 // specified, the ith is applied to the ith top blob.
868 repeated FillerParameter data_filler = 1;
869 repeated BlobShape shape = 6;
871 // 4D dimensions -- deprecated. Use "shape" instead.
872 repeated uint32 num = 2;
873 repeated uint32 channels = 3;
874 repeated uint32 height = 4;
875 repeated uint32 width = 5;
878 message EltwiseParameter {
884 optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
885 repeated float coeff = 2; // blob-wise coefficient for SUM operation
887 // Whether to use an asymptotically slower (for >2 inputs) but stabler method
888 // of computing the gradient for the PROD operation. (No effect for SUM op.)
889 optional bool stable_prod_grad = 3 [default = true];
892 // Message that stores parameters used by ELULayer
893 message ELUParameter {
895 // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
896 // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
897 optional float alpha = 1 [default = 1];
900 // Message that stores parameters used by EmbedLayer
901 message EmbedParameter {
902 optional uint32 num_output = 1; // The number of outputs for the layer
903 // The input is given as integers to be interpreted as one-hot
904 // vector indices with dimension num_input. Hence num_input should be
905 // 1 greater than the maximum possible input value.
906 optional uint32 input_dim = 2;
908 optional bool bias_term = 3 [default = true]; // Whether to use a bias term
909 optional FillerParameter weight_filler = 4; // The filler for the weight
910 optional FillerParameter bias_filler = 5; // The filler for the bias
914 // Message that stores parameters used by ExpLayer
915 message ExpParameter {
916 // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
917 // Or if base is set to the default (-1), base is set to e,
918 // so y = exp(shift + scale * x).
919 optional float base = 1 [default = -1.0];
920 optional float scale = 2 [default = 1.0];
921 optional float shift = 3 [default = 0.0];
924 /// Message that stores parameters used by FlattenLayer
925 message FlattenParameter {
926 // The first axis to flatten: all preceding axes are retained in the output.
927 // May be negative to index from the end (e.g., -1 for the last axis).
928 optional int32 axis = 1 [default = 1];
930 // The last axis to flatten: all following axes are retained in the output.
931 // May be negative to index from the end (e.g., the default -1 for the last
933 optional int32 end_axis = 2 [default = -1];
936 // Message that stores parameters used by HDF5DataLayer
937 message HDF5DataParameter {
938 // Specify the data source.
939 optional string source = 1;
940 // Specify the batch size.
941 optional uint32 batch_size = 2;
943 // Specify whether to shuffle the data.
944 // If shuffle == true, the ordering of the HDF5 files is shuffled,
945 // and the ordering of data within any given HDF5 file is shuffled,
946 // but data between different files are not interleaved; all of a file's
947 // data are output (in a random order) before moving onto another file.
948 optional bool shuffle = 3 [default = false];
951 message HDF5OutputParameter {
952 optional string file_name = 1;
955 message HingeLossParameter {
960 // Specify the Norm to use L1 or L2
961 optional Norm norm = 1 [default = L1];
964 message ImageDataParameter {
965 // Specify the data source.
966 optional string source = 1;
967 // Specify the batch size.
968 optional uint32 batch_size = 4 [default = 1];
969 // The rand_skip variable is for the data layer to skip a few data points
970 // to avoid all asynchronous sgd clients to start at the same point. The skip
971 // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
972 // be larger than the number of keys in the database.
973 optional uint32 rand_skip = 7 [default = 0];
974 // Whether or not ImageLayer should shuffle the list of files at every epoch.
975 optional bool shuffle = 8 [default = false];
976 // It will also resize images if new_height or new_width are not zero.
977 optional uint32 new_height = 9 [default = 0];
978 optional uint32 new_width = 10 [default = 0];
979 // Specify if the images are color or gray
980 optional bool is_color = 11 [default = true];
981 // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
982 // simple scaling and subtracting the data mean, if provided. Note that the
983 // mean subtraction is always carried out before scaling.
984 optional float scale = 2 [default = 1];
985 optional string mean_file = 3;
986 // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
988 optional uint32 crop_size = 5 [default = 0];
989 // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
991 optional bool mirror = 6 [default = false];
992 optional string root_folder = 12 [default = ""];
995 message InfogainLossParameter {
996 // Specify the infogain matrix source.
997 optional string source = 1;
1000 message InnerProductParameter {
1001 optional uint32 num_output = 1; // The number of outputs for the layer
1002 optional bool bias_term = 2 [default = true]; // whether to have bias terms
1003 optional FillerParameter weight_filler = 3; // The filler for the weight
1004 optional FillerParameter bias_filler = 4; // The filler for the bias
1006 // The first axis to be lumped into a single inner product computation;
1007 // all preceding axes are retained in the output.
1008 // May be negative to index from the end (e.g., -1 for the last axis).
1009 optional int32 axis = 5 [default = 1];
1010 // Specify whether to transpose the weight matrix or not.
1011 // If transpose == true, any operations will be performed on the transpose
1012 // of the weight matrix. The weight matrix itself is not going to be transposed
1013 // but rather the transfer flag of operations will be toggled accordingly.
1014 optional bool transpose = 6 [default = false];
1017 message InputParameter {
1018 // This layer produces N >= 1 top blob(s) to be assigned manually.
1019 // Define N shapes to set a shape for each top.
1020 // Define 1 shape to set the same shape for every top.
1021 // Define no shape to defer to reshaping manually.
1022 repeated BlobShape shape = 1;
1025 // Message that stores parameters used by LogLayer
1026 message LogParameter {
1027 // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
1028 // Or if base is set to the default (-1), base is set to e,
1029 // so y = ln(shift + scale * x) = log_e(shift + scale * x)
1030 optional float base = 1 [default = -1.0];
1031 optional float scale = 2 [default = 1.0];
1032 optional float shift = 3 [default = 0.0];
1035 // Message that stores parameters used by LRNLayer
1036 message LRNParameter {
1037 optional uint32 local_size = 1 [default = 5];
1038 optional float alpha = 2 [default = 1.];
1039 optional float beta = 3 [default = 0.75];
1041 ACROSS_CHANNELS = 0;
1044 optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
1045 optional float k = 5 [default = 1.];
1051 optional Engine engine = 6 [default = DEFAULT];
1054 message MemoryDataParameter {
1055 optional uint32 batch_size = 1;
1056 optional uint32 channels = 2;
1057 optional uint32 height = 3;
1058 optional uint32 width = 4;
1061 message MVNParameter {
1062 // This parameter can be set to false to normalize mean only
1063 optional bool normalize_variance = 1 [default = true];
1065 // This parameter can be set to true to perform DNN-like MVN
1066 optional bool across_channels = 2 [default = false];
1068 // Epsilon for not dividing by zero while normalizing variance
1069 optional float eps = 3 [default = 1e-9];
1072 message ParameterParameter {
1073 optional BlobShape shape = 1;
1076 message PoolingParameter {
1082 optional PoolMethod pool = 1 [default = MAX]; // The pooling method
1083 // Pad, kernel size, and stride are all given as a single value for equal
1084 // dimensions in height and width or as Y, X pairs.
1085 optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
1086 optional uint32 pad_h = 9 [default = 0]; // The padding height
1087 optional uint32 pad_w = 10 [default = 0]; // The padding width
1088 optional uint32 kernel_size = 2; // The kernel size (square)
1089 optional uint32 kernel_h = 5; // The kernel height
1090 optional uint32 kernel_w = 6; // The kernel width
1091 optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
1092 optional uint32 stride_h = 7; // The stride height
1093 optional uint32 stride_w = 8; // The stride width
1099 optional Engine engine = 11 [default = DEFAULT];
1100 // If global_pooling then it will pool over the size of the bottom by doing
1101 // kernel_h = bottom->height and kernel_w = bottom->width
1102 optional bool global_pooling = 12 [default = false];
1103 // Specify floor/ceil mode
1104 // source: https://github.com/BVLC/caffe/pull/3057
1105 optional bool ceil_mode = 13 [default = true];
1108 message PowerParameter {
1109 // PowerLayer computes outputs y = (shift + scale * x) ^ power.
1110 optional float power = 1 [default = 1.0];
1111 optional float scale = 2 [default = 1.0];
1112 optional float shift = 3 [default = 0.0];
1115 message PythonParameter {
1116 optional string module = 1;
1117 optional string layer = 2;
1118 // This value is set to the attribute `param_str` of the `PythonLayer` object
1119 // in Python before calling the `setup()` method. This could be a number,
1120 // string, dictionary in Python dict format, JSON, etc. You may parse this
1121 // string in `setup` method and use it in `forward` and `backward`.
1122 optional string param_str = 3 [default = ''];
1123 // Whether this PythonLayer is shared among worker solvers during data parallelism.
1124 // If true, each worker solver sequentially run forward from this layer.
1125 // This value should be set true if you are using it as a data layer.
1126 optional bool share_in_parallel = 4 [default = false];
1129 // Message that stores parameters used by RecurrentLayer
1130 message RecurrentParameter {
1131 // The dimension of the output (and usually hidden state) representation --
1132 // must be explicitly set to non-zero.
1133 optional uint32 num_output = 1 [default = 0];
1135 optional FillerParameter weight_filler = 2; // The filler for the weight
1136 optional FillerParameter bias_filler = 3; // The filler for the bias
1138 // Whether to enable displaying debug_info in the unrolled recurrent net.
1139 optional bool debug_info = 4 [default = false];
1141 // Whether to add as additional inputs (bottoms) the initial hidden state
1142 // blobs, and add as additional outputs (tops) the final timestep hidden state
1143 // blobs. The number of additional bottom/top blobs required depends on the
1144 // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
1145 optional bool expose_hidden = 5 [default = false];
1148 // Message that stores parameters used by ReductionLayer
1149 message ReductionParameter {
1157 optional ReductionOp operation = 1 [default = SUM]; // reduction operation
1159 // The first axis to reduce to a scalar -- may be negative to index from the
1160 // end (e.g., -1 for the last axis).
1161 // (Currently, only reduction along ALL "tail" axes is supported; reduction
1162 // of axis M through N, where N < num_axes - 1, is unsupported.)
1163 // Suppose we have an n-axis bottom Blob with shape:
1164 // (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
1165 // If axis == m, the output Blob will have shape
1166 // (d0, d1, d2, ..., d(m-1)),
1167 // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
1168 // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
1169 // If axis == 0 (the default), the output Blob always has the empty shape
1170 // (count 1), performing reduction across the entire input --
1171 // often useful for creating new loss functions.
1172 optional int32 axis = 2 [default = 0];
1174 optional float coeff = 3 [default = 1.0]; // coefficient for output
1177 // Message that stores parameters used by ReLULayer
1178 message ReLUParameter {
1179 // Allow non-zero slope for negative inputs to speed up optimization
1181 // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
1182 // improve neural network acoustic models. In ICML Workshop on Deep Learning
1183 // for Audio, Speech, and Language Processing.
1184 optional float negative_slope = 1 [default = 0];
1190 optional Engine engine = 2 [default = DEFAULT];
1193 message ReshapeParameter {
1194 // Specify the output dimensions. If some of the dimensions are set to 0,
1195 // the corresponding dimension from the bottom layer is used (unchanged).
1196 // Exactly one dimension may be set to -1, in which case its value is
1197 // inferred from the count of the bottom blob and the remaining dimensions.
1198 // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
1201 // type: "Reshape" bottom: "input" top: "output"
1202 // reshape_param { ... }
1205 // If "input" is 2D with shape 2 x 8, then the following reshape_param
1206 // specifications are all equivalent, producing a 3D blob "output" with shape
1209 // reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
1210 // reshape_param { shape { dim: 0 dim: 2 dim: 4 } }
1211 // reshape_param { shape { dim: 0 dim: 2 dim: -1 } }
1212 // reshape_param { shape { dim: 0 dim:-1 dim: 4 } }
1214 optional BlobShape shape = 1;
1216 // axis and num_axes control the portion of the bottom blob's shape that are
1217 // replaced by (included in) the reshape. By default (axis == 0 and
1218 // num_axes == -1), the entire bottom blob shape is included in the reshape,
1219 // and hence the shape field must specify the entire output shape.
1221 // axis may be non-zero to retain some portion of the beginning of the input
1222 // shape (and may be negative to index from the end; e.g., -1 to begin the
1223 // reshape after the last axis, including nothing in the reshape,
1224 // -2 to include only the last axis, etc.).
1226 // For example, suppose "input" is a 2D blob with shape 2 x 8.
1227 // Then the following ReshapeLayer specifications are all equivalent,
1228 // producing a blob "output" with shape 2 x 2 x 4:
1230 // reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
1231 // reshape_param { shape { dim: 2 dim: 4 } axis: 1 }
1232 // reshape_param { shape { dim: 2 dim: 4 } axis: -3 }
1234 // num_axes specifies the extent of the reshape.
1235 // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
1236 // input axes in the range [axis, axis+num_axes].
1237 // num_axes may also be -1, the default, to include all remaining axes
1238 // (starting from axis).
1240 // For example, suppose "input" is a 2D blob with shape 2 x 8.
1241 // Then the following ReshapeLayer specifications are equivalent,
1242 // producing a blob "output" with shape 1 x 2 x 8.
1244 // reshape_param { shape { dim: 1 dim: 2 dim: 8 } }
1245 // reshape_param { shape { dim: 1 dim: 2 } num_axes: 1 }
1246 // reshape_param { shape { dim: 1 } num_axes: 0 }
1248 // On the other hand, these would produce output blob shape 2 x 1 x 8:
1250 // reshape_param { shape { dim: 2 dim: 1 dim: 8 } }
1251 // reshape_param { shape { dim: 1 } axis: 1 num_axes: 0 }
1253 optional int32 axis = 2 [default = 0];
1254 optional int32 num_axes = 3 [default = -1];
1257 message ScaleParameter {
1258 // The first axis of bottom[0] (the first input Blob) along which to apply
1259 // bottom[1] (the second input Blob). May be negative to index from the end
1260 // (e.g., -1 for the last axis).
1262 // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
1263 // top[0] will have the same shape, and bottom[1] may have any of the
1264 // following shapes (for the given value of axis):
1265 // (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
1266 // (axis == 1 == -3) 3; 3x40; 3x40x60
1267 // (axis == 2 == -2) 40; 40x60
1268 // (axis == 3 == -1) 60
1269 // Furthermore, bottom[1] may have the empty shape (regardless of the value of
1270 // "axis") -- a scalar multiplier.
1271 optional int32 axis = 1 [default = 1];
1273 // (num_axes is ignored unless just one bottom is given and the scale is
1274 // a learned parameter of the layer. Otherwise, num_axes is determined by the
1275 // number of axes by the second bottom.)
1276 // The number of axes of the input (bottom[0]) covered by the scale
1277 // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
1278 // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
1279 optional int32 num_axes = 2 [default = 1];
1281 // (filler is ignored unless just one bottom is given and the scale is
1282 // a learned parameter of the layer.)
1283 // The initialization for the learned scale parameter.
1284 // Default is the unit (1) initialization, resulting in the ScaleLayer
1285 // initially performing the identity operation.
1286 optional FillerParameter filler = 3;
1288 // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
1289 // may be more efficient). Initialized with bias_filler (defaults to 0).
1290 optional bool bias_term = 4 [default = false];
1291 optional FillerParameter bias_filler = 5;
1294 message SigmoidParameter {
1300 optional Engine engine = 1 [default = DEFAULT];
1303 message SliceParameter {
1304 // The axis along which to slice -- may be negative to index from the end
1305 // (e.g., -1 for the last axis).
1306 // By default, SliceLayer concatenates blobs along the "channels" axis (1).
1307 optional int32 axis = 3 [default = 1];
1308 repeated uint32 slice_point = 2;
1310 // DEPRECATED: alias for "axis" -- does not support negative indexing.
1311 optional uint32 slice_dim = 1 [default = 1];
1314 // Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
1315 message SoftmaxParameter {
1321 optional Engine engine = 1 [default = DEFAULT];
1323 // The axis along which to perform the softmax -- may be negative to index
1324 // from the end (e.g., -1 for the last axis).
1325 // Any other axes will be evaluated as independent softmaxes.
1326 optional int32 axis = 2 [default = 1];
1329 message TanHParameter {
1335 optional Engine engine = 1 [default = DEFAULT];
1338 // Message that stores parameters used by TileLayer
1339 message TileParameter {
1340 // The index of the axis to tile.
1341 optional int32 axis = 1 [default = 1];
1343 // The number of copies (tiles) of the blob to output.
1344 optional int32 tiles = 2;
1347 // Message that stores parameters used by ThresholdLayer
1348 message ThresholdParameter {
1349 optional float threshold = 1 [default = 0]; // Strictly positive values
1352 message WindowDataParameter {
1353 // Specify the data source.
1354 optional string source = 1;
1355 // For data pre-processing, we can do simple scaling and subtracting the
1356 // data mean, if provided. Note that the mean subtraction is always carried
1357 // out before scaling.
1358 optional float scale = 2 [default = 1];
1359 optional string mean_file = 3;
1360 // Specify the batch size.
1361 optional uint32 batch_size = 4;
1362 // Specify if we would like to randomly crop an image.
1363 optional uint32 crop_size = 5 [default = 0];
1364 // Specify if we want to randomly mirror data.
1365 optional bool mirror = 6 [default = false];
1366 // Foreground (object) overlap threshold
1367 optional float fg_threshold = 7 [default = 0.5];
1368 // Background (non-object) overlap threshold
1369 optional float bg_threshold = 8 [default = 0.5];
1370 // Fraction of batch that should be foreground objects
1371 optional float fg_fraction = 9 [default = 0.25];
1372 // Amount of contextual padding to add around a window
1373 // (used only by the window_data_layer)
1374 optional uint32 context_pad = 10 [default = 0];
1375 // Mode for cropping out a detection window
1376 // warp: cropped window is warped to a fixed size and aspect ratio
1377 // square: the tightest square around the window is cropped
1378 optional string crop_mode = 11 [default = "warp"];
1379 // cache_images: will load all images in memory for faster access
1380 optional bool cache_images = 12 [default = false];
1381 // append root_folder to locate images
1382 optional string root_folder = 13 [default = ""];
1385 message SPPParameter {
1391 optional uint32 pyramid_height = 1;
1392 optional PoolMethod pool = 2 [default = MAX]; // The pooling method
1398 optional Engine engine = 6 [default = DEFAULT];
1401 // DEPRECATED: use LayerParameter.
1402 message V1LayerParameter {
1403 repeated string bottom = 2;
1404 repeated string top = 3;
1405 optional string name = 4;
1406 repeated NetStateRule include = 32;
1407 repeated NetStateRule exclude = 33;
1415 CONTRASTIVE_LOSS = 37;
1434 MULTINOMIAL_LOGISTIC_LOSS = 16;
1440 SIGMOID_CROSS_ENTROPY_LOSS = 27;
1450 optional LayerType type = 5;
1451 repeated BlobProto blobs = 6;
1452 repeated string param = 1001;
1453 repeated DimCheckMode blob_share_mode = 1002;
1458 repeated float blobs_lr = 7;
1459 repeated float weight_decay = 8;
1460 repeated float loss_weight = 35;
1461 optional AccuracyParameter accuracy_param = 27;
1462 optional ArgMaxParameter argmax_param = 23;
1463 optional ConcatParameter concat_param = 9;
1464 optional ContrastiveLossParameter contrastive_loss_param = 40;
1465 optional ConvolutionParameter convolution_param = 10;
1466 optional DataParameter data_param = 11;
1467 optional DropoutParameter dropout_param = 12;
1468 optional DummyDataParameter dummy_data_param = 26;
1469 optional EltwiseParameter eltwise_param = 24;
1470 optional ExpParameter exp_param = 41;
1471 optional HDF5DataParameter hdf5_data_param = 13;
1472 optional HDF5OutputParameter hdf5_output_param = 14;
1473 optional HingeLossParameter hinge_loss_param = 29;
1474 optional ImageDataParameter image_data_param = 15;
1475 optional InfogainLossParameter infogain_loss_param = 16;
1476 optional InnerProductParameter inner_product_param = 17;
1477 optional LRNParameter lrn_param = 18;
1478 optional MemoryDataParameter memory_data_param = 22;
1479 optional MVNParameter mvn_param = 34;
1480 optional PoolingParameter pooling_param = 19;
1481 optional PowerParameter power_param = 21;
1482 optional ReLUParameter relu_param = 30;
1483 optional SigmoidParameter sigmoid_param = 38;
1484 optional SoftmaxParameter softmax_param = 39;
1485 optional SliceParameter slice_param = 31;
1486 optional TanHParameter tanh_param = 37;
1487 optional ThresholdParameter threshold_param = 25;
1488 optional WindowDataParameter window_data_param = 20;
1489 optional TransformationParameter transform_param = 36;
1490 optional LossParameter loss_param = 42;
1491 optional V0LayerParameter layer = 1;
1494 // DEPRECATED: V0LayerParameter is the old way of specifying layer parameters
1495 // in Caffe. We keep this message type around for legacy support.
1496 message V0LayerParameter {
1497 optional string name = 1; // the layer name
1498 optional string type = 2; // the string to specify the layer type
1500 // Parameters to specify layers with inner products.
1501 optional uint32 num_output = 3; // The number of outputs for the layer
1502 optional bool biasterm = 4 [default = true]; // whether to have bias terms
1503 optional FillerParameter weight_filler = 5; // The filler for the weight
1504 optional FillerParameter bias_filler = 6; // The filler for the bias
1506 optional uint32 pad = 7 [default = 0]; // The padding size
1507 optional uint32 kernelsize = 8; // The kernel size
1508 optional uint32 group = 9 [default = 1]; // The group size for group conv
1509 optional uint32 stride = 10 [default = 1]; // The stride
1515 optional PoolMethod pool = 11 [default = MAX]; // The pooling method
1516 optional float dropout_ratio = 12 [default = 0.5]; // dropout ratio
1518 optional uint32 local_size = 13 [default = 5]; // for local response norm
1519 optional float alpha = 14 [default = 1.]; // for local response norm
1520 optional float beta = 15 [default = 0.75]; // for local response norm
1521 optional float k = 22 [default = 1.];
1523 // For data layers, specify the data source
1524 optional string source = 16;
1525 // For data pre-processing, we can do simple scaling and subtracting the
1526 // data mean, if provided. Note that the mean subtraction is always carried
1527 // out before scaling.
1528 optional float scale = 17 [default = 1];
1529 optional string meanfile = 18;
1530 // For data layers, specify the batch size.
1531 optional uint32 batchsize = 19;
1532 // For data layers, specify if we would like to randomly crop an image.
1533 optional uint32 cropsize = 20 [default = 0];
1534 // For data layers, specify if we want to randomly mirror data.
1535 optional bool mirror = 21 [default = false];
1537 // The blobs containing the numeric parameters of the layer
1538 repeated BlobProto blobs = 50;
1539 // The ratio that is multiplied on the global learning rate. If you want to
1540 // set the learning ratio for one blob, you need to set it for all blobs.
1541 repeated float blobs_lr = 51;
1542 // The weight decay that is multiplied on the global weight decay.
1543 repeated float weight_decay = 52;
1545 // The rand_skip variable is for the data layer to skip a few data points
1546 // to avoid all asynchronous sgd clients to start at the same point. The skip
1547 // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
1548 // be larger than the number of keys in the database.
1549 optional uint32 rand_skip = 53 [default = 0];
1551 // Fields related to detection (det_*)
1552 // foreground (object) overlap threshold
1553 optional float det_fg_threshold = 54 [default = 0.5];
1554 // background (non-object) overlap threshold
1555 optional float det_bg_threshold = 55 [default = 0.5];
1556 // Fraction of batch that should be foreground objects
1557 optional float det_fg_fraction = 56 [default = 0.25];
1559 // optional bool OBSOLETE_can_clobber = 57 [default = true];
1561 // Amount of contextual padding to add around a window
1562 // (used only by the window_data_layer)
1563 optional uint32 det_context_pad = 58 [default = 0];
1565 // Mode for cropping out a detection window
1566 // warp: cropped window is warped to a fixed size and aspect ratio
1567 // square: the tightest square around the window is cropped
1568 optional string det_crop_mode = 59 [default = "warp"];
1570 // For ReshapeLayer, one needs to specify the new dimensions.
1571 optional int32 new_num = 60 [default = 0];
1572 optional int32 new_channels = 61 [default = 0];
1573 optional int32 new_height = 62 [default = 0];
1574 optional int32 new_width = 63 [default = 0];
1576 // Whether or not ImageLayer should shuffle the list of files at every epoch.
1577 // It will also resize images if new_height or new_width are not zero.
1578 optional bool shuffle_images = 64 [default = false];
1580 // For ConcatLayer, one needs to specify the dimension for concatenation, and
1581 // the other dimensions must be the same for all the bottom blobs.
1582 // By default it will concatenate blobs along the channels dimension.
1583 optional uint32 concat_dim = 65 [default = 1];
1585 optional HDF5OutputParameter hdf5_output_param = 1001;
1588 message PReLUParameter {
1589 // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
1590 // Surpassing Human-Level Performance on ImageNet Classification, 2015.
1592 // Initial value of a_i. Default is a_i=0.25 for all i.
1593 optional FillerParameter filler = 1;
1594 // Whether or not slope parameters are shared across channels.
1595 optional bool channel_shared = 2 [default = false];
1598 // The normalized bounding box [0, 1] w.r.t. the input image size.
1599 message NormalizedBBox {
1600 optional float xmin = 1;
1601 optional float ymin = 2;
1602 optional float xmax = 3;
1603 optional float ymax = 4;
1604 optional int32 label = 5;
1605 optional bool difficult = 6;
1606 optional float score = 7;
1607 optional float size = 8;
1610 // origin: https://github.com/rbgirshick/caffe-fast-rcnn/tree/fast-rcnn
1611 // Message that stores parameters used by ROIPoolingLayer
1612 message ROIPoolingParameter {
1613 // Pad, kernel size, and stride are all given as a single value for equal
1614 // dimensions in height and width or as Y, X pairs.
1615 optional uint32 pooled_h = 1 [default = 0]; // The pooled output height
1616 optional uint32 pooled_w = 2 [default = 0]; // The pooled output width
1617 // Multiplicative spatial scale factor to translate ROI coords from their
1618 // input scale to the scale used when pooling
1619 optional float spatial_scale = 3 [default = 1];