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提交 5b7635f6 编写于 作者: L Liangliang He
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Merge branch 'caffe' into 'master' 

Support caffe model

See merge request !45
上级 114e6390 f64a27e5
隐藏空白更改
内联 并排
Showing with 2200 addition and 35 deletion
proto/BUILD
浏览文件 @ 5b7635f6
......@@ -18,3 +18,12 @@ py_proto_library(
srcs_version = "PY2AND3",
deps = ["@com_google_protobuf//:protobuf_python"],
)
py_proto_library(
name = "caffe_py",
srcs = ["caffe.proto"],
default_runtime = "@com_google_protobuf//:protobuf_python",
protoc = "@com_google_protobuf//:protoc",
srcs_version = "PY2AND3",
deps = ["@com_google_protobuf//:protobuf_python"],
)
proto/caffe.proto 0 → 100644
浏览文件 @ 5b7635f6
syntax = "proto2";
package caffe;
// Specifies the shape (dimensions) of a Blob.
message BlobShape {
repeated int64 dim = 1 [packed = true];
}
message BlobProto {
optional BlobShape shape = 7;
repeated float data = 5 [packed = true];
repeated float diff = 6 [packed = true];
repeated double double_data = 8 [packed = true];
repeated double double_diff = 9 [packed = true];
// 4D dimensions -- deprecated. Use "shape" instead.
optional int32 num = 1 [default = 0];
optional int32 channels = 2 [default = 0];
optional int32 height = 3 [default = 0];
optional int32 width = 4 [default = 0];
}
// The BlobProtoVector is simply a way to pass multiple blobproto instances
// around.
message BlobProtoVector {
repeated BlobProto blobs = 1;
}
message Datum {
optional int32 channels = 1;
optional int32 height = 2;
optional int32 width = 3;
// the actual image data, in bytes
optional bytes data = 4;
optional int32 label = 5;
// Optionally, the datum could also hold float data.
repeated float float_data = 6;
// If true data contains an encoded image that need to be decoded
optional bool encoded = 7 [default = false];
}
message FillerParameter {
// The filler type.
optional string type = 1 [default = 'constant'];
optional float value = 2 [default = 0]; // the value in constant filler
optional float min = 3 [default = 0]; // the min value in uniform filler
optional float max = 4 [default = 1]; // the max value in uniform filler
optional float mean = 5 [default = 0]; // the mean value in Gaussian filler
optional float std = 6 [default = 1]; // the std value in Gaussian filler
// The expected number of non-zero output weights for a given input in
// Gaussian filler -- the default -1 means don't perform sparsification.
optional int32 sparse = 7 [default = -1];
// Normalize the filler variance by fan_in, fan_out, or their average.
// Applies to 'xavier' and 'msra' fillers.
enum VarianceNorm {
FAN_IN = 0;
FAN_OUT = 1;
AVERAGE = 2;
}
optional VarianceNorm variance_norm = 8 [default = FAN_IN];
}
message NetParameter {
optional string name = 1; // consider giving the network a name
// DEPRECATED. See InputParameter. The input blobs to the network.
repeated string input = 3;
// DEPRECATED. See InputParameter. The shape of the input blobs.
repeated BlobShape input_shape = 8;
// 4D input dimensions -- deprecated. Use "input_shape" instead.
// If specified, for each input blob there should be four
// values specifying the num, channels, height and width of the input blob.
// Thus, there should be a total of (4 * #input) numbers.
repeated int32 input_dim = 4;
// Whether the network will force every layer to carry out backward operation.
// If set False, then whether to carry out backward is determined
// automatically according to the net structure and learning rates.
optional bool force_backward = 5 [default = false];
// The current "state" of the network, including the phase, level, and stage.
// Some layers may be included/excluded depending on this state and the states
// specified in the layers' include and exclude fields.
optional NetState state = 6;
// Print debugging information about results while running Net::Forward,
// Net::Backward, and Net::Update.
optional bool debug_info = 7 [default = false];
// The layers that make up the net. Each of their configurations, including
// connectivity and behavior, is specified as a LayerParameter.
repeated LayerParameter layer = 100; // ID 100 so layers are printed last.
// DEPRECATED: use 'layer' instead.
repeated V1LayerParameter layers = 2;
}
// NOTE
// Update the next available ID when you add a new SolverParameter field.
//
// SolverParameter next available ID: 43 (last added: weights)
message SolverParameter {
//////////////////////////////////////////////////////////////////////////////
// Specifying the train and test networks
//
// Exactly one train net must be specified using one of the following fields:
// train_net_param, train_net, net_param, net
// One or more test nets may be specified using any of the following fields:
// test_net_param, test_net, net_param, net
// If more than one test net field is specified (e.g., both net and
// test_net are specified), they will be evaluated in the field order given
// above: (1) test_net_param, (2) test_net, (3) net_param/net.
// A test_iter must be specified for each test_net.
// A test_level and/or a test_stage may also be specified for each test_net.
//////////////////////////////////////////////////////////////////////////////
// Proto filename for the train net, possibly combined with one or more
// test nets.
optional string net = 24;
// Inline train net param, possibly combined with one or more test nets.
optional NetParameter net_param = 25;
optional string train_net = 1; // Proto filename for the train net.
repeated string test_net = 2; // Proto filenames for the test nets.
optional NetParameter train_net_param = 21; // Inline train net params.
repeated NetParameter test_net_param = 22; // Inline test net params.
// The states for the train/test nets. Must be unspecified or
// specified once per net.
//
// By default, train_state will have phase = TRAIN,
// and all test_state's will have phase = TEST.
// Other defaults are set according to the NetState defaults.
optional NetState train_state = 26;
repeated NetState test_state = 27;
// The number of iterations for each test net.
repeated int32 test_iter = 3;
// The number of iterations between two testing phases.
optional int32 test_interval = 4 [default = 0];
optional bool test_compute_loss = 19 [default = false];
// If true, run an initial test pass before the first iteration,
// ensuring memory availability and printing the starting value of the loss.
optional bool test_initialization = 32 [default = true];
optional float base_lr = 5; // The base learning rate
// the number of iterations between displaying info. If display = 0, no info
// will be displayed.
optional int32 display = 6;
// Display the loss averaged over the last average_loss iterations
optional int32 average_loss = 33 [default = 1];
optional int32 max_iter = 7; // the maximum number of iterations
// accumulate gradients over `iter_size` x `batch_size` instances
optional int32 iter_size = 36 [default = 1];
// The learning rate decay policy. The currently implemented learning rate
// policies are as follows:
// - fixed: always return base_lr.
// - step: return base_lr * gamma ^ (floor(iter / step))
// - exp: return base_lr * gamma ^ iter
// - inv: return base_lr * (1 + gamma * iter) ^ (- power)
// - multistep: similar to step but it allows non uniform steps defined by
// stepvalue
// - poly: the effective learning rate follows a polynomial decay, to be
// zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
// - sigmoid: the effective learning rate follows a sigmod decay
// return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
//
// where base_lr, max_iter, gamma, step, stepvalue and power are defined
// in the solver parameter protocol buffer, and iter is the current iteration.
optional string lr_policy = 8;
optional float gamma = 9; // The parameter to compute the learning rate.
optional float power = 10; // The parameter to compute the learning rate.
optional float momentum = 11; // The momentum value.
optional float weight_decay = 12; // The weight decay.
// regularization types supported: L1 and L2
// controlled by weight_decay
optional string regularization_type = 29 [default = "L2"];
// the stepsize for learning rate policy "step"
optional int32 stepsize = 13;
// the stepsize for learning rate policy "multistep"
repeated int32 stepvalue = 34;
// Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,
// whenever their actual L2 norm is larger.
optional float clip_gradients = 35 [default = -1];
optional int32 snapshot = 14 [default = 0]; // The snapshot interval
// The prefix for the snapshot.
// If not set then is replaced by prototxt file path without extention.
// If is set to directory then is augmented by prototxt file name
// without extention.
optional string snapshot_prefix = 15;
// whether to snapshot diff in the results or not. Snapshotting diff will help
// debugging but the final protocol buffer size will be much larger.
optional bool snapshot_diff = 16 [default = false];
enum SnapshotFormat {
HDF5 = 0;
BINARYPROTO = 1;
}
optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];
// the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
enum SolverMode {
CPU = 0;
GPU = 1;
}
optional SolverMode solver_mode = 17 [default = GPU];
// the device_id will that be used in GPU mode. Use device_id = 0 in default.
optional int32 device_id = 18 [default = 0];
// If non-negative, the seed with which the Solver will initialize the Caffe
// random number generator -- useful for reproducible results. Otherwise,
// (and by default) initialize using a seed derived from the system clock.
optional int64 random_seed = 20 [default = -1];
// type of the solver
optional string type = 40 [default = "SGD"];
// numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
optional float delta = 31 [default = 1e-8];
// parameters for the Adam solver
optional float momentum2 = 39 [default = 0.999];
// RMSProp decay value
// MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
optional float rms_decay = 38 [default = 0.99];
// If true, print information about the state of the net that may help with
// debugging learning problems.
optional bool debug_info = 23 [default = false];
// If false, don't save a snapshot after training finishes.
optional bool snapshot_after_train = 28 [default = true];
// DEPRECATED: old solver enum types, use string instead
enum SolverType {
SGD = 0;
NESTEROV = 1;
ADAGRAD = 2;
RMSPROP = 3;
ADADELTA = 4;
ADAM = 5;
}
// DEPRECATED: use type instead of solver_type
optional SolverType solver_type = 30 [default = SGD];
// Overlap compute and communication for data parallel training
optional bool layer_wise_reduce = 41 [default = true];
// Path to caffemodel file(s) with pretrained weights to initialize finetuning.
// Tha same as command line --weights parameter for caffe train command.
// If command line --weights parameter if specified, it has higher priority
// and owerwrites this one(s).
// If --snapshot command line parameter is specified, this one(s) are ignored.
// If several model files are expected, they can be listed in a one
// weights parameter separated by ',' (like in a command string) or
// in repeated weights parameters separately.
repeated string weights = 42;
}
// A message that stores the solver snapshots
message SolverState {
optional int32 iter = 1; // The current iteration
optional string learned_net = 2; // The file that stores the learned net.
repeated BlobProto history = 3; // The history for sgd solvers
optional int32 current_step = 4 [default = 0]; // The current step for learning rate
}
enum Phase {
TRAIN = 0;
TEST = 1;
}
message NetState {
optional Phase phase = 1 [default = TEST];
optional int32 level = 2 [default = 0];
repeated string stage = 3;
}
message NetStateRule {
// Set phase to require the NetState have a particular phase (TRAIN or TEST)
// to meet this rule.
optional Phase phase = 1;
// Set the minimum and/or maximum levels in which the layer should be used.
// Leave undefined to meet the rule regardless of level.
optional int32 min_level = 2;
optional int32 max_level = 3;
// Customizable sets of stages to include or exclude.
// The net must have ALL of the specified stages and NONE of the specified
// "not_stage"s to meet the rule.
// (Use multiple NetStateRules to specify conjunctions of stages.)
repeated string stage = 4;
repeated string not_stage = 5;
}
// Specifies training parameters (multipliers on global learning constants,
// and the name and other settings used for weight sharing).
message ParamSpec {
// The names of the parameter blobs -- useful for sharing parameters among
// layers, but never required otherwise. To share a parameter between two
// layers, give it a (non-empty) name.
optional string name = 1;
// Whether to require shared weights to have the same shape, or just the same
// count -- defaults to STRICT if unspecified.
optional DimCheckMode share_mode = 2;
enum DimCheckMode {
// STRICT (default) requires that num, channels, height, width each match.
STRICT = 0;
// PERMISSIVE requires only the count (num*channels*height*width) to match.
PERMISSIVE = 1;
}
// The multiplier on the global learning rate for this parameter.
optional float lr_mult = 3 [default = 1.0];
// The multiplier on the global weight decay for this parameter.
optional float decay_mult = 4 [default = 1.0];
}
// NOTE
// Update the next available ID when you add a new LayerParameter field.
//
// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
message LayerParameter {
optional string name = 1; // the layer name
optional string type = 2; // the layer type
repeated string bottom = 3; // the name of each bottom blob
repeated string top = 4; // the name of each top blob
// The train / test phase for computation.
optional Phase phase = 10;
// The amount of weight to assign each top blob in the objective.
// Each layer assigns a default value, usually of either 0 or 1,
// to each top blob.
repeated float loss_weight = 5;
// Specifies training parameters (multipliers on global learning constants,
// and the name and other settings used for weight sharing).
repeated ParamSpec param = 6;
// The blobs containing the numeric parameters of the layer.
repeated BlobProto blobs = 7;
// Specifies whether to backpropagate to each bottom. If unspecified,
// Caffe will automatically infer whether each input needs backpropagation
// to compute parameter gradients. If set to true for some inputs,
// backpropagation to those inputs is forced; if set false for some inputs,
// backpropagation to those inputs is skipped.
//
// The size must be either 0 or equal to the number of bottoms.
repeated bool propagate_down = 11;
// Rules controlling whether and when a layer is included in the network,
// based on the current NetState. You may specify a non-zero number of rules
// to include OR exclude, but not both. If no include or exclude rules are
// specified, the layer is always included. If the current NetState meets
// ANY (i.e., one or more) of the specified rules, the layer is
// included/excluded.
repeated NetStateRule include = 8;
repeated NetStateRule exclude = 9;
// Parameters for data pre-processing.
optional TransformationParameter transform_param = 100;
// Parameters shared by loss layers.
optional LossParameter loss_param = 101;
// Layer type-specific parameters.
//
// Note: certain layers may have more than one computational engine
// for their implementation. These layers include an Engine type and
// engine parameter for selecting the implementation.
// The default for the engine is set by the ENGINE switch at compile-time.
optional AccuracyParameter accuracy_param = 102;
optional ArgMaxParameter argmax_param = 103;
optional BatchNormParameter batch_norm_param = 139;
optional BiasParameter bias_param = 141;
optional ConcatParameter concat_param = 104;
optional ContrastiveLossParameter contrastive_loss_param = 105;
optional ConvolutionParameter convolution_param = 106;
optional CropParameter crop_param = 144;
optional DataParameter data_param = 107;
optional DropoutParameter dropout_param = 108;
optional DummyDataParameter dummy_data_param = 109;
optional EltwiseParameter eltwise_param = 110;
optional ELUParameter elu_param = 140;
optional EmbedParameter embed_param = 137;
optional ExpParameter exp_param = 111;
optional FlattenParameter flatten_param = 135;
optional HDF5DataParameter hdf5_data_param = 112;
optional HDF5OutputParameter hdf5_output_param = 113;
optional HingeLossParameter hinge_loss_param = 114;
optional ImageDataParameter image_data_param = 115;
optional InfogainLossParameter infogain_loss_param = 116;
optional InnerProductParameter inner_product_param = 117;
optional InputParameter input_param = 143;
optional LogParameter log_param = 134;
optional LRNParameter lrn_param = 118;
optional MemoryDataParameter memory_data_param = 119;
optional MVNParameter mvn_param = 120;
optional ParameterParameter parameter_param = 145;
optional PoolingParameter pooling_param = 121;
optional PowerParameter power_param = 122;
optional PReLUParameter prelu_param = 131;
optional PythonParameter python_param = 130;
optional RecurrentParameter recurrent_param = 146;
optional ReductionParameter reduction_param = 136;
optional ReLUParameter relu_param = 123;
optional ReshapeParameter reshape_param = 133;
optional ScaleParameter scale_param = 142;
optional SigmoidParameter sigmoid_param = 124;
optional SoftmaxParameter softmax_param = 125;
optional SPPParameter spp_param = 132;
optional SliceParameter slice_param = 126;
optional TanHParameter tanh_param = 127;
optional ThresholdParameter threshold_param = 128;
optional TileParameter tile_param = 138;
optional WindowDataParameter window_data_param = 129;
}
// Message that stores parameters used to apply transformation
// to the data layer's data
message TransformationParameter {
// For data pre-processing, we can do simple scaling and subtracting the
// data mean, if provided. Note that the mean subtraction is always carried
// out before scaling.
optional float scale = 1 [default = 1];
// Specify if we want to randomly mirror data.
optional bool mirror = 2 [default = false];
// Specify if we would like to randomly crop an image.
optional uint32 crop_size = 3 [default = 0];
// mean_file and mean_value cannot be specified at the same time
optional string mean_file = 4;
// if specified can be repeated once (would subtract it from all the channels)
// or can be repeated the same number of times as channels
// (would subtract them from the corresponding channel)
repeated float mean_value = 5;
// Force the decoded image to have 3 color channels.
optional bool force_color = 6 [default = false];
// Force the decoded image to have 1 color channels.
optional bool force_gray = 7 [default = false];
}
// Message that stores parameters shared by loss layers
message LossParameter {
// If specified, ignore instances with the given label.
optional int32 ignore_label = 1;
// How to normalize the loss for loss layers that aggregate across batches,
// spatial dimensions, or other dimensions. Currently only implemented in
// SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
enum NormalizationMode {
// Divide by the number of examples in the batch times spatial dimensions.
// Outputs that receive the ignore label will NOT be ignored in computing
// the normalization factor.
FULL = 0;
// Divide by the total number of output locations that do not take the
// ignore_label. If ignore_label is not set, this behaves like FULL.
VALID = 1;
// Divide by the batch size.
BATCH_SIZE = 2;
// Do not normalize the loss.
NONE = 3;
}
// For historical reasons, the default normalization for
// SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
optional NormalizationMode normalization = 3 [default = VALID];
// Deprecated. Ignored if normalization is specified. If normalization
// is not specified, then setting this to false will be equivalent to
// normalization = BATCH_SIZE to be consistent with previous behavior.
optional bool normalize = 2;
}
// Messages that store parameters used by individual layer types follow, in
// alphabetical order.
message AccuracyParameter {
// When computing accuracy, count as correct by comparing the true label to
// the top k scoring classes. By default, only compare to the top scoring
// class (i.e. argmax).
optional uint32 top_k = 1 [default = 1];
// The "label" axis of the prediction blob, whose argmax corresponds to the
// predicted label -- may be negative to index from the end (e.g., -1 for the
// last axis). For example, if axis == 1 and the predictions are
// (N x C x H x W), the label blob is expected to contain N*H*W ground truth
// labels with integer values in {0, 1, ..., C-1}.
optional int32 axis = 2 [default = 1];
// If specified, ignore instances with the given label.
optional int32 ignore_label = 3;
}
message ArgMaxParameter {
// If true produce pairs (argmax, maxval)
optional bool out_max_val = 1 [default = false];
optional uint32 top_k = 2 [default = 1];
// The axis along which to maximise -- may be negative to index from the
// end (e.g., -1 for the last axis).
// By default ArgMaxLayer maximizes over the flattened trailing dimensions
// for each index of the first / num dimension.
optional int32 axis = 3;
}
message ConcatParameter {
// The axis along which to concatenate -- may be negative to index from the
// end (e.g., -1 for the last axis). Other axes must have the
// same dimension for all the bottom blobs.
// By default, ConcatLayer concatenates blobs along the "channels" axis (1).
optional int32 axis = 2 [default = 1];
// DEPRECATED: alias for "axis" -- does not support negative indexing.
optional uint32 concat_dim = 1 [default = 1];
}
message BatchNormParameter {
// If false, normalization is performed over the current mini-batch
// and global statistics are accumulated (but not yet used) by a moving
// average.
// If true, those accumulated mean and variance values are used for the
// normalization.
// By default, it is set to false when the network is in the training
// phase and true when the network is in the testing phase.
optional bool use_global_stats = 1;
// What fraction of the moving average remains each iteration?
// Smaller values make the moving average decay faster, giving more
// weight to the recent values.
// Each iteration updates the moving average @f$S_{t-1}@f$ with the
// current mean @f$ Y_t @f$ by
// @f$ S_t = (1-\beta)Y_t + \beta \cdot S_{t-1} @f$, where @f$ \beta @f$
// is the moving_average_fraction parameter.
optional float moving_average_fraction = 2 [default = .999];
// Small value to add to the variance estimate so that we don't divide by
// zero.
optional float eps = 3 [default = 1e-5];
}
message BiasParameter {
// The first axis of bottom[0] (the first input Blob) along which to apply
// bottom[1] (the second input Blob). May be negative to index from the end
// (e.g., -1 for the last axis).
//
// For example, if bottom[0] is 4D with shape 100x3x40x60, the output
// top[0] will have the same shape, and bottom[1] may have any of the
// following shapes (for the given value of axis):
// (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
// (axis == 1 == -3) 3; 3x40; 3x40x60
// (axis == 2 == -2) 40; 40x60
// (axis == 3 == -1) 60
// Furthermore, bottom[1] may have the empty shape (regardless of the value of
// "axis") -- a scalar bias.
optional int32 axis = 1 [default = 1];
// (num_axes is ignored unless just one bottom is given and the bias is
// a learned parameter of the layer. Otherwise, num_axes is determined by the
// number of axes by the second bottom.)
// The number of axes of the input (bottom[0]) covered by the bias
// parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
// Set num_axes := 0, to add a zero-axis Blob: a scalar.
optional int32 num_axes = 2 [default = 1];
// (filler is ignored unless just one bottom is given and the bias is
// a learned parameter of the layer.)
// The initialization for the learned bias parameter.
// Default is the zero (0) initialization, resulting in the BiasLayer
// initially performing the identity operation.
optional FillerParameter filler = 3;
}
message ContrastiveLossParameter {
// margin for dissimilar pair
optional float margin = 1 [default = 1.0];
// The first implementation of this cost did not exactly match the cost of
// Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
// legacy_version = false (the default) uses (margin - d)^2 as proposed in the
// Hadsell paper. New models should probably use this version.
// legacy_version = true uses (margin - d^2). This is kept to support /
// reproduce existing models and results
optional bool legacy_version = 2 [default = false];
}
message ConvolutionParameter {
optional uint32 num_output = 1; // The number of outputs for the layer
optional bool bias_term = 2 [default = true]; // whether to have bias terms
// Pad, kernel size, and stride are all given as a single value for equal
// dimensions in all spatial dimensions, or once per spatial dimension.
repeated uint32 pad = 3; // The padding size; defaults to 0
repeated uint32 kernel_size = 4; // The kernel size
repeated uint32 stride = 6; // The stride; defaults to 1
// Factor used to dilate the kernel, (implicitly) zero-filling the resulting
// holes. (Kernel dilation is sometimes referred to by its use in the
// algorithme à trous from Holschneider et al. 1987.)
repeated uint32 dilation = 18; // The dilation; defaults to 1
// For 2D convolution only, the *_h and *_w versions may also be used to
// specify both spatial dimensions.
optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
optional uint32 kernel_h = 11; // The kernel height (2D only)
optional uint32 kernel_w = 12; // The kernel width (2D only)
optional uint32 stride_h = 13; // The stride height (2D only)
optional uint32 stride_w = 14; // The stride width (2D only)
optional uint32 group = 5 [default = 1]; // The group size for group conv
optional FillerParameter weight_filler = 7; // The filler for the weight
optional FillerParameter bias_filler = 8; // The filler for the bias
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 15 [default = DEFAULT];
// The axis to interpret as "channels" when performing convolution.
// Preceding dimensions are treated as independent inputs;
// succeeding dimensions are treated as "spatial".
// With (N, C, H, W) inputs, and axis == 1 (the default), we perform
// N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
// groups g>1) filters across the spatial axes (H, W) of the input.
// With (N, C, D, H, W) inputs, and axis == 1, we perform
// N independent 3D convolutions, sliding (C/g)-channels
// filters across the spatial axes (D, H, W) of the input.
optional int32 axis = 16 [default = 1];
// Whether to force use of the general ND convolution, even if a specific
// implementation for blobs of the appropriate number of spatial dimensions
// is available. (Currently, there is only a 2D-specific convolution
// implementation; for input blobs with num_axes != 2, this option is
// ignored and the ND implementation will be used.)
optional bool force_nd_im2col = 17 [default = false];
}
message CropParameter {
// To crop, elements of the first bottom are selected to fit the dimensions
// of the second, reference bottom. The crop is configured by
// - the crop `axis` to pick the dimensions for cropping
// - the crop `offset` to set the shift for all/each dimension
// to align the cropped bottom with the reference bottom.
// All dimensions up to but excluding `axis` are preserved, while
// the dimensions including and trailing `axis` are cropped.
// If only one `offset` is set, then all dimensions are offset by this amount.
// Otherwise, the number of offsets must equal the number of cropped axes to
// shift the crop in each dimension accordingly.
// Note: standard dimensions are N,C,H,W so the default is a spatial crop,
// and `axis` may be negative to index from the end (e.g., -1 for the last
// axis).
optional int32 axis = 1 [default = 2];
repeated uint32 offset = 2;
}
message DataParameter {
enum DB {
LEVELDB = 0;
LMDB = 1;
}
// Specify the data source.
optional string source = 1;
// Specify the batch size.
optional uint32 batch_size = 4;
// The rand_skip variable is for the data layer to skip a few data points
// to avoid all asynchronous sgd clients to start at the same point. The skip
// point would be set as rand_skip * rand(0,1). Note that rand_skip should not
// be larger than the number of keys in the database.
// DEPRECATED. Each solver accesses a different subset of the database.
optional uint32 rand_skip = 7 [default = 0];
optional DB backend = 8 [default = LEVELDB];
// DEPRECATED. See TransformationParameter. For data pre-processing, we can do
// simple scaling and subtracting the data mean, if provided. Note that the
// mean subtraction is always carried out before scaling.
optional float scale = 2 [default = 1];
optional string mean_file = 3;
// DEPRECATED. See TransformationParameter. Specify if we would like to randomly
// crop an image.
optional uint32 crop_size = 5 [default = 0];
// DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
// data.
optional bool mirror = 6 [default = false];
// Force the encoded image to have 3 color channels
optional bool force_encoded_color = 9 [default = false];
// Prefetch queue (Increase if data feeding bandwidth varies, within the
// limit of device memory for GPU training)
optional uint32 prefetch = 10 [default = 4];
}
message DropoutParameter {
optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
}
// DummyDataLayer fills any number of arbitrarily shaped blobs with random
// (or constant) data generated by "Fillers" (see "message FillerParameter").
message DummyDataParameter {
// This layer produces N >= 1 top blobs. DummyDataParameter must specify 1 or N
// shape fields, and 0, 1 or N data_fillers.
//
// If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
// If 1 data_filler is specified, it is applied to all top blobs. If N are
// specified, the ith is applied to the ith top blob.
repeated FillerParameter data_filler = 1;
repeated BlobShape shape = 6;
// 4D dimensions -- deprecated. Use "shape" instead.
repeated uint32 num = 2;
repeated uint32 channels = 3;
repeated uint32 height = 4;
repeated uint32 width = 5;
}
message EltwiseParameter {
enum EltwiseOp {
PROD = 0;
SUM = 1;
MAX = 2;
}
optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
repeated float coeff = 2; // blob-wise coefficient for SUM operation
// Whether to use an asymptotically slower (for >2 inputs) but stabler method
// of computing the gradient for the PROD operation. (No effect for SUM op.)
optional bool stable_prod_grad = 3 [default = true];
}
// Message that stores parameters used by ELULayer
message ELUParameter {
// Described in:
// Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
// Deep Network Learning by Exponential Linear Units (ELUs). arXiv
optional float alpha = 1 [default = 1];
}
// Message that stores parameters used by EmbedLayer
message EmbedParameter {
optional uint32 num_output = 1; // The number of outputs for the layer
// The input is given as integers to be interpreted as one-hot
// vector indices with dimension num_input. Hence num_input should be
// 1 greater than the maximum possible input value.
optional uint32 input_dim = 2;
optional bool bias_term = 3 [default = true]; // Whether to use a bias term
optional FillerParameter weight_filler = 4; // The filler for the weight
optional FillerParameter bias_filler = 5; // The filler for the bias
}
// Message that stores parameters used by ExpLayer
message ExpParameter {
// ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
// Or if base is set to the default (-1), base is set to e,
// so y = exp(shift + scale * x).
optional float base = 1 [default = -1.0];
optional float scale = 2 [default = 1.0];
optional float shift = 3 [default = 0.0];
}
/// Message that stores parameters used by FlattenLayer
message FlattenParameter {
// The first axis to flatten: all preceding axes are retained in the output.
// May be negative to index from the end (e.g., -1 for the last axis).
optional int32 axis = 1 [default = 1];
// The last axis to flatten: all following axes are retained in the output.
// May be negative to index from the end (e.g., the default -1 for the last
// axis).
optional int32 end_axis = 2 [default = -1];
}
// Message that stores parameters used by HDF5DataLayer
message HDF5DataParameter {
// Specify the data source.
optional string source = 1;
// Specify the batch size.
optional uint32 batch_size = 2;
// Specify whether to shuffle the data.
// If shuffle == true, the ordering of the HDF5 files is shuffled,
// and the ordering of data within any given HDF5 file is shuffled,
// but data between different files are not interleaved; all of a file's
// data are output (in a random order) before moving onto another file.
optional bool shuffle = 3 [default = false];
}
message HDF5OutputParameter {
optional string file_name = 1;
}
message HingeLossParameter {
enum Norm {
L1 = 1;
L2 = 2;
}
// Specify the Norm to use L1 or L2
optional Norm norm = 1 [default = L1];
}
message ImageDataParameter {
// Specify the data source.
optional string source = 1;
// Specify the batch size.
optional uint32 batch_size = 4 [default = 1];
// The rand_skip variable is for the data layer to skip a few data points
// to avoid all asynchronous sgd clients to start at the same point. The skip
// point would be set as rand_skip * rand(0,1). Note that rand_skip should not
// be larger than the number of keys in the database.
optional uint32 rand_skip = 7 [default = 0];
// Whether or not ImageLayer should shuffle the list of files at every epoch.
optional bool shuffle = 8 [default = false];
// It will also resize images if new_height or new_width are not zero.
optional uint32 new_height = 9 [default = 0];
optional uint32 new_width = 10 [default = 0];
// Specify if the images are color or gray
optional bool is_color = 11 [default = true];
// DEPRECATED. See TransformationParameter. For data pre-processing, we can do
// simple scaling and subtracting the data mean, if provided. Note that the
// mean subtraction is always carried out before scaling.
optional float scale = 2 [default = 1];
optional string mean_file = 3;
// DEPRECATED. See TransformationParameter. Specify if we would like to randomly
// crop an image.
optional uint32 crop_size = 5 [default = 0];
// DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
// data.
optional bool mirror = 6 [default = false];
optional string root_folder = 12 [default = ""];
}
message InfogainLossParameter {
// Specify the infogain matrix source.
optional string source = 1;
optional int32 axis = 2 [default = 1]; // axis of prob
}
message InnerProductParameter {
optional uint32 num_output = 1; // The number of outputs for the layer
optional bool bias_term = 2 [default = true]; // whether to have bias terms
optional FillerParameter weight_filler = 3; // The filler for the weight
optional FillerParameter bias_filler = 4; // The filler for the bias
// The first axis to be lumped into a single inner product computation;
// all preceding axes are retained in the output.
// May be negative to index from the end (e.g., -1 for the last axis).
optional int32 axis = 5 [default = 1];
// Specify whether to transpose the weight matrix or not.
// If transpose == true, any operations will be performed on the transpose
// of the weight matrix. The weight matrix itself is not going to be transposed
// but rather the transfer flag of operations will be toggled accordingly.
optional bool transpose = 6 [default = false];
}
message InputParameter {
// This layer produces N >= 1 top blob(s) to be assigned manually.
// Define N shapes to set a shape for each top.
// Define 1 shape to set the same shape for every top.
// Define no shape to defer to reshaping manually.
repeated BlobShape shape = 1;
}
// Message that stores parameters used by LogLayer
message LogParameter {
// LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
// Or if base is set to the default (-1), base is set to e,
// so y = ln(shift + scale * x) = log_e(shift + scale * x)
optional float base = 1 [default = -1.0];
optional float scale = 2 [default = 1.0];
optional float shift = 3 [default = 0.0];
}
// Message that stores parameters used by LRNLayer
message LRNParameter {
optional uint32 local_size = 1 [default = 5];
optional float alpha = 2 [default = 1.];
optional float beta = 3 [default = 0.75];
enum NormRegion {
ACROSS_CHANNELS = 0;
WITHIN_CHANNEL = 1;
}
optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
optional float k = 5 [default = 1.];
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 6 [default = DEFAULT];
}
message MemoryDataParameter {
optional uint32 batch_size = 1;
optional uint32 channels = 2;
optional uint32 height = 3;
optional uint32 width = 4;
}
message MVNParameter {
// This parameter can be set to false to normalize mean only
optional bool normalize_variance = 1 [default = true];
// This parameter can be set to true to perform DNN-like MVN
optional bool across_channels = 2 [default = false];
// Epsilon for not dividing by zero while normalizing variance
optional float eps = 3 [default = 1e-9];
}
message ParameterParameter {
optional BlobShape shape = 1;
}
message PoolingParameter {
enum PoolMethod {
MAX = 0;
AVE = 1;
STOCHASTIC = 2;
}
optional PoolMethod pool = 1 [default = MAX]; // The pooling method
// Pad, kernel size, and stride are all given as a single value for equal
// dimensions in height and width or as Y, X pairs.
optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
optional uint32 pad_h = 9 [default = 0]; // The padding height
optional uint32 pad_w = 10 [default = 0]; // The padding width
optional uint32 kernel_size = 2; // The kernel size (square)
optional uint32 kernel_h = 5; // The kernel height
optional uint32 kernel_w = 6; // The kernel width
optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
optional uint32 stride_h = 7; // The stride height
optional uint32 stride_w = 8; // The stride width
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 11 [default = DEFAULT];
// If global_pooling then it will pool over the size of the bottom by doing
// kernel_h = bottom->height and kernel_w = bottom->width
optional bool global_pooling = 12 [default = false];
}
message PowerParameter {
// PowerLayer computes outputs y = (shift + scale * x) ^ power.
optional float power = 1 [default = 1.0];
optional float scale = 2 [default = 1.0];
optional float shift = 3 [default = 0.0];
}
message PythonParameter {
optional string module = 1;
optional string layer = 2;
// This value is set to the attribute `param_str` of the `PythonLayer` object
// in Python before calling the `setup()` method. This could be a number,
// string, dictionary in Python dict format, JSON, etc. You may parse this
// string in `setup` method and use it in `forward` and `backward`.
optional string param_str = 3 [default = ''];
// DEPRECATED
optional bool share_in_parallel = 4 [default = false];
}
// Message that stores parameters used by RecurrentLayer
message RecurrentParameter {
// The dimension of the output (and usually hidden state) representation --
// must be explicitly set to non-zero.
optional uint32 num_output = 1 [default = 0];
optional FillerParameter weight_filler = 2; // The filler for the weight
optional FillerParameter bias_filler = 3; // The filler for the bias
// Whether to enable displaying debug_info in the unrolled recurrent net.
optional bool debug_info = 4 [default = false];
// Whether to add as additional inputs (bottoms) the initial hidden state
// blobs, and add as additional outputs (tops) the final timestep hidden state
// blobs. The number of additional bottom/top blobs required depends on the
// recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
optional bool expose_hidden = 5 [default = false];
}
// Message that stores parameters used by ReductionLayer
message ReductionParameter {
enum ReductionOp {
SUM = 1;
ASUM = 2;
SUMSQ = 3;
MEAN = 4;
}
optional ReductionOp operation = 1 [default = SUM]; // reduction operation
// The first axis to reduce to a scalar -- may be negative to index from the
// end (e.g., -1 for the last axis).
// (Currently, only reduction along ALL "tail" axes is supported; reduction
// of axis M through N, where N < num_axes - 1, is unsupported.)
// Suppose we have an n-axis bottom Blob with shape:
// (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
// If axis == m, the output Blob will have shape
// (d0, d1, d2, ..., d(m-1)),
// and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
// times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
// If axis == 0 (the default), the output Blob always has the empty shape
// (count 1), performing reduction across the entire input --
// often useful for creating new loss functions.
optional int32 axis = 2 [default = 0];
optional float coeff = 3 [default = 1.0]; // coefficient for output
}
// Message that stores parameters used by ReLULayer
message ReLUParameter {
// Allow non-zero slope for negative inputs to speed up optimization
// Described in:
// Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
// improve neural network acoustic models. In ICML Workshop on Deep Learning
// for Audio, Speech, and Language Processing.
optional float negative_slope = 1 [default = 0];
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 2 [default = DEFAULT];
}
message ReshapeParameter {
// Specify the output dimensions. If some of the dimensions are set to 0,
// the corresponding dimension from the bottom layer is used (unchanged).
// Exactly one dimension may be set to -1, in which case its value is
// inferred from the count of the bottom blob and the remaining dimensions.
// For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
//
// layer {
// type: "Reshape" bottom: "input" top: "output"
// reshape_param { ... }
// }
//
// If "input" is 2D with shape 2 x 8, then the following reshape_param
// specifications are all equivalent, producing a 3D blob "output" with shape
// 2 x 2 x 4:
//
// reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
// reshape_param { shape { dim: 0 dim: 2 dim: 4 } }
// reshape_param { shape { dim: 0 dim: 2 dim: -1 } }
// reshape_param { shape { dim: 0 dim:-1 dim: 4 } }
//
optional BlobShape shape = 1;
// axis and num_axes control the portion of the bottom blob's shape that are
// replaced by (included in) the reshape. By default (axis == 0 and
// num_axes == -1), the entire bottom blob shape is included in the reshape,
// and hence the shape field must specify the entire output shape.
//
// axis may be non-zero to retain some portion of the beginning of the input
// shape (and may be negative to index from the end; e.g., -1 to begin the
// reshape after the last axis, including nothing in the reshape,
// -2 to include only the last axis, etc.).
//
// For example, suppose "input" is a 2D blob with shape 2 x 8.
// Then the following ReshapeLayer specifications are all equivalent,
// producing a blob "output" with shape 2 x 2 x 4:
//
// reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
// reshape_param { shape { dim: 2 dim: 4 } axis: 1 }
// reshape_param { shape { dim: 2 dim: 4 } axis: -3 }
//
// num_axes specifies the extent of the reshape.
// If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
// input axes in the range [axis, axis+num_axes].
// num_axes may also be -1, the default, to include all remaining axes
// (starting from axis).
//
// For example, suppose "input" is a 2D blob with shape 2 x 8.
// Then the following ReshapeLayer specifications are equivalent,
// producing a blob "output" with shape 1 x 2 x 8.
//
// reshape_param { shape { dim: 1 dim: 2 dim: 8 } }
// reshape_param { shape { dim: 1 dim: 2 } num_axes: 1 }
// reshape_param { shape { dim: 1 } num_axes: 0 }
//
// On the other hand, these would produce output blob shape 2 x 1 x 8:
//
// reshape_param { shape { dim: 2 dim: 1 dim: 8 } }
// reshape_param { shape { dim: 1 } axis: 1 num_axes: 0 }
//
optional int32 axis = 2 [default = 0];
optional int32 num_axes = 3 [default = -1];
}
message ScaleParameter {
// The first axis of bottom[0] (the first input Blob) along which to apply
// bottom[1] (the second input Blob). May be negative to index from the end
// (e.g., -1 for the last axis).
//
// For example, if bottom[0] is 4D with shape 100x3x40x60, the output
// top[0] will have the same shape, and bottom[1] may have any of the
// following shapes (for the given value of axis):
// (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
// (axis == 1 == -3) 3; 3x40; 3x40x60
// (axis == 2 == -2) 40; 40x60
// (axis == 3 == -1) 60
// Furthermore, bottom[1] may have the empty shape (regardless of the value of
// "axis") -- a scalar multiplier.
optional int32 axis = 1 [default = 1];
// (num_axes is ignored unless just one bottom is given and the scale is
// a learned parameter of the layer. Otherwise, num_axes is determined by the
// number of axes by the second bottom.)
// The number of axes of the input (bottom[0]) covered by the scale
// parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
// Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
optional int32 num_axes = 2 [default = 1];
// (filler is ignored unless just one bottom is given and the scale is
// a learned parameter of the layer.)
// The initialization for the learned scale parameter.
// Default is the unit (1) initialization, resulting in the ScaleLayer
// initially performing the identity operation.
optional FillerParameter filler = 3;
// Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
// may be more efficient). Initialized with bias_filler (defaults to 0).
optional bool bias_term = 4 [default = false];
optional FillerParameter bias_filler = 5;
}
message SigmoidParameter {
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 1 [default = DEFAULT];
}
message SliceParameter {
// The axis along which to slice -- may be negative to index from the end
// (e.g., -1 for the last axis).
// By default, SliceLayer concatenates blobs along the "channels" axis (1).
optional int32 axis = 3 [default = 1];
repeated uint32 slice_point = 2;
// DEPRECATED: alias for "axis" -- does not support negative indexing.
optional uint32 slice_dim = 1 [default = 1];
}
// Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
message SoftmaxParameter {
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 1 [default = DEFAULT];
// The axis along which to perform the softmax -- may be negative to index
// from the end (e.g., -1 for the last axis).
// Any other axes will be evaluated as independent softmaxes.
optional int32 axis = 2 [default = 1];
}
message TanHParameter {
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 1 [default = DEFAULT];
}
// Message that stores parameters used by TileLayer
message TileParameter {
// The index of the axis to tile.
optional int32 axis = 1 [default = 1];
// The number of copies (tiles) of the blob to output.
optional int32 tiles = 2;
}
// Message that stores parameters used by ThresholdLayer
message ThresholdParameter {
optional float threshold = 1 [default = 0]; // Strictly positive values
}
message WindowDataParameter {
// Specify the data source.
optional string source = 1;
// For data pre-processing, we can do simple scaling and subtracting the
// data mean, if provided. Note that the mean subtraction is always carried
// out before scaling.
optional float scale = 2 [default = 1];
optional string mean_file = 3;
// Specify the batch size.
optional uint32 batch_size = 4;
// Specify if we would like to randomly crop an image.
optional uint32 crop_size = 5 [default = 0];
// Specify if we want to randomly mirror data.
optional bool mirror = 6 [default = false];
// Foreground (object) overlap threshold
optional float fg_threshold = 7 [default = 0.5];
// Background (non-object) overlap threshold
optional float bg_threshold = 8 [default = 0.5];
// Fraction of batch that should be foreground objects
optional float fg_fraction = 9 [default = 0.25];
// Amount of contextual padding to add around a window
// (used only by the window_data_layer)
optional uint32 context_pad = 10 [default = 0];
// Mode for cropping out a detection window
// warp: cropped window is warped to a fixed size and aspect ratio
// square: the tightest square around the window is cropped
optional string crop_mode = 11 [default = "warp"];
// cache_images: will load all images in memory for faster access
optional bool cache_images = 12 [default = false];
// append root_folder to locate images
optional string root_folder = 13 [default = ""];
}
message SPPParameter {
enum PoolMethod {
MAX = 0;
AVE = 1;
STOCHASTIC = 2;
}
optional uint32 pyramid_height = 1;
optional PoolMethod pool = 2 [default = MAX]; // The pooling method
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 6 [default = DEFAULT];
}
// DEPRECATED: use LayerParameter.
message V1LayerParameter {
repeated string bottom = 2;
repeated string top = 3;
optional string name = 4;
repeated NetStateRule include = 32;
repeated NetStateRule exclude = 33;
enum LayerType {
NONE = 0;
ABSVAL = 35;
ACCURACY = 1;
ARGMAX = 30;
BNLL = 2;
CONCAT = 3;
CONTRASTIVE_LOSS = 37;
CONVOLUTION = 4;
DATA = 5;
DECONVOLUTION = 39;
DROPOUT = 6;
DUMMY_DATA = 32;
EUCLIDEAN_LOSS = 7;
ELTWISE = 25;
EXP = 38;
FLATTEN = 8;
HDF5_DATA = 9;
HDF5_OUTPUT = 10;
HINGE_LOSS = 28;
IM2COL = 11;
IMAGE_DATA = 12;
INFOGAIN_LOSS = 13;
INNER_PRODUCT = 14;
LRN = 15;
MEMORY_DATA = 29;
MULTINOMIAL_LOGISTIC_LOSS = 16;
MVN = 34;
POOLING = 17;
POWER = 26;
RELU = 18;
SIGMOID = 19;
SIGMOID_CROSS_ENTROPY_LOSS = 27;
SILENCE = 36;
SOFTMAX = 20;
SOFTMAX_LOSS = 21;
SPLIT = 22;
SLICE = 33;
TANH = 23;
WINDOW_DATA = 24;
THRESHOLD = 31;
}
optional LayerType type = 5;
repeated BlobProto blobs = 6;
repeated string param = 1001;
repeated DimCheckMode blob_share_mode = 1002;
enum DimCheckMode {
STRICT = 0;
PERMISSIVE = 1;
}
repeated float blobs_lr = 7;
repeated float weight_decay = 8;
repeated float loss_weight = 35;
optional AccuracyParameter accuracy_param = 27;
optional ArgMaxParameter argmax_param = 23;
optional ConcatParameter concat_param = 9;
optional ContrastiveLossParameter contrastive_loss_param = 40;
optional ConvolutionParameter convolution_param = 10;
optional DataParameter data_param = 11;
optional DropoutParameter dropout_param = 12;
optional DummyDataParameter dummy_data_param = 26;
optional EltwiseParameter eltwise_param = 24;
optional ExpParameter exp_param = 41;
optional HDF5DataParameter hdf5_data_param = 13;
optional HDF5OutputParameter hdf5_output_param = 14;
optional HingeLossParameter hinge_loss_param = 29;
optional ImageDataParameter image_data_param = 15;
optional InfogainLossParameter infogain_loss_param = 16;
optional InnerProductParameter inner_product_param = 17;
optional LRNParameter lrn_param = 18;
optional MemoryDataParameter memory_data_param = 22;
optional MVNParameter mvn_param = 34;
optional PoolingParameter pooling_param = 19;
optional PowerParameter power_param = 21;
optional ReLUParameter relu_param = 30;
optional SigmoidParameter sigmoid_param = 38;
optional SoftmaxParameter softmax_param = 39;
optional SliceParameter slice_param = 31;
optional TanHParameter tanh_param = 37;
optional ThresholdParameter threshold_param = 25;
optional WindowDataParameter window_data_param = 20;
optional TransformationParameter transform_param = 36;
optional LossParameter loss_param = 42;
optional V0LayerParameter layer = 1;
}
// DEPRECATED: V0LayerParameter is the old way of specifying layer parameters
// in Caffe. We keep this message type around for legacy support.
message V0LayerParameter {
optional string name = 1; // the layer name
optional string type = 2; // the string to specify the layer type
// Parameters to specify layers with inner products.
optional uint32 num_output = 3; // The number of outputs for the layer
optional bool biasterm = 4 [default = true]; // whether to have bias terms
optional FillerParameter weight_filler = 5; // The filler for the weight
optional FillerParameter bias_filler = 6; // The filler for the bias
optional uint32 pad = 7 [default = 0]; // The padding size
optional uint32 kernelsize = 8; // The kernel size
optional uint32 group = 9 [default = 1]; // The group size for group conv
optional uint32 stride = 10 [default = 1]; // The stride
enum PoolMethod {
MAX = 0;
AVE = 1;
STOCHASTIC = 2;
}
optional PoolMethod pool = 11 [default = MAX]; // The pooling method
optional float dropout_ratio = 12 [default = 0.5]; // dropout ratio
optional uint32 local_size = 13 [default = 5]; // for local response norm
optional float alpha = 14 [default = 1.]; // for local response norm
optional float beta = 15 [default = 0.75]; // for local response norm
optional float k = 22 [default = 1.];
// For data layers, specify the data source
optional string source = 16;
// For data pre-processing, we can do simple scaling and subtracting the
// data mean, if provided. Note that the mean subtraction is always carried
// out before scaling.
optional float scale = 17 [default = 1];
optional string meanfile = 18;
// For data layers, specify the batch size.
optional uint32 batchsize = 19;
// For data layers, specify if we would like to randomly crop an image.
optional uint32 cropsize = 20 [default = 0];
// For data layers, specify if we want to randomly mirror data.
optional bool mirror = 21 [default = false];
// The blobs containing the numeric parameters of the layer
repeated BlobProto blobs = 50;
// The ratio that is multiplied on the global learning rate. If you want to
// set the learning ratio for one blob, you need to set it for all blobs.
repeated float blobs_lr = 51;
// The weight decay that is multiplied on the global weight decay.
repeated float weight_decay = 52;
// The rand_skip variable is for the data layer to skip a few data points
// to avoid all asynchronous sgd clients to start at the same point. The skip
// point would be set as rand_skip * rand(0,1). Note that rand_skip should not
// be larger than the number of keys in the database.
optional uint32 rand_skip = 53 [default = 0];
// Fields related to detection (det_*)
// foreground (object) overlap threshold
optional float det_fg_threshold = 54 [default = 0.5];
// background (non-object) overlap threshold
optional float det_bg_threshold = 55 [default = 0.5];
// Fraction of batch that should be foreground objects
optional float det_fg_fraction = 56 [default = 0.25];
// optional bool OBSOLETE_can_clobber = 57 [default = true];
// Amount of contextual padding to add around a window
// (used only by the window_data_layer)
optional uint32 det_context_pad = 58 [default = 0];
// Mode for cropping out a detection window
// warp: cropped window is warped to a fixed size and aspect ratio
// square: the tightest square around the window is cropped
optional string det_crop_mode = 59 [default = "warp"];
// For ReshapeLayer, one needs to specify the new dimensions.
optional int32 new_num = 60 [default = 0];
optional int32 new_channels = 61 [default = 0];
optional int32 new_height = 62 [default = 0];
optional int32 new_width = 63 [default = 0];
// Whether or not ImageLayer should shuffle the list of files at every epoch.
// It will also resize images if new_height or new_width are not zero.
optional bool shuffle_images = 64 [default = false];
// For ConcatLayer, one needs to specify the dimension for concatenation, and
// the other dimensions must be the same for all the bottom blobs.
// By default it will concatenate blobs along the channels dimension.
optional uint32 concat_dim = 65 [default = 1];
optional HDF5OutputParameter hdf5_output_param = 1001;
}
message PReLUParameter {
// Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
// Surpassing Human-Level Performance on ImageNet Classification, 2015.
// Initial value of a_i. Default is a_i=0.25 for all i.
optional FillerParameter filler = 1;
// Whether or not slope parameters are shared across channels.
optional bool channel_shared = 2 [default = false];
}
python/tools/BUILD
浏览文件 @ 5b7635f6
......@@ -13,6 +13,18 @@ py_library(
],
)
py_library(
name = "caffe_converter_lib",
srcs = [
"caffe_converter_lib.py",
],
srcs_version = "PY2AND3",
deps = [
":memory_optimizer",
"//lib/proto:caffe_py",
],
)
py_library(
name = "source_converter_lib",
srcs = [
......@@ -25,11 +37,12 @@ py_library(
)
py_binary(
name = "tf_converter",
srcs = ["tf_converter.py"],
name = "converter",
srcs = ["converter.py"],
srcs_version = "PY2AND3",
deps = [
":tf_converter_lib",
":caffe_converter_lib",
":source_converter_lib",
"@six_archive//:six",
],
......
python/tools/caffe_converter_lib.py 0 → 100644
浏览文件 @ 5b7635f6
from lib.proto import mace_pb2
from lib.proto import caffe_pb2
from lib.python.tools import memory_optimizer
import google.protobuf.text_format
import numpy as np
import math
pooling_type_mode = {
'AvgPool': 1,
'MaxPool': 2
}
buffer_type_map = {
'CONV2D_FILTER' : 0,
'IN_OUT_CHANNEL' : 1,
'ARGUMENT' : 2,
'IN_OUT_HEIGHT' : 3,
'IN_OUT_WIDTH' : 4,
'WINOGRAD_FILTER' : 5,
'DW_CONV2D_FILTER' : 6,
'WEIGHT_HEIGHT' : 7,
}
data_type_map = {
'DT_HALF' : mace_pb2.DT_HALF,
'DT_FLOAT': mace_pb2.DT_FLOAT
}
activation_name_map = {
'ReLU' : 'RELU',
'Sigmoid' : 'SIGMOID',
'TanH' : 'TANH',
}
MACE_INPUT_NODE_NAME = "mace_input_node"
MACE_OUTPUT_NODE_NAME = "mace_output_node"
OPENCL_IMAGE_MAX_SIZE = 16384
class Operator(object):
def __init__(self, name, type, layer):
self.name = name
self.type = type
self.layer = layer
self.parents = []
self.children = []
self.data = []
self.output_shape = []
def add_parent(self, parent_op):
assert parent_op not in self.parents
self.parents.append(parent_op)
if self not in parent_op.children:
parent_op.children.append(self)
def add_child(self, child_op):
assert child_op not in self.children
self.children.append(child_op)
if self not in child_op.parents:
child_op.parents.append(self)
def get_single_parent(self):
if len(self.parents) != 1:
raise Exception('Operation %s expected single parent, but got %s'
% (self.name, len(self.parents)))
return self.parents[0]
def BlobToNPArray(blob):
if blob.num != 0:
return (np.asarray(blob.data, dtype=np.float32).
reshape(blob.num, blob.channels, blob.height, blob.width))
else:
return np.asarray(blob.data, dtype=np.float32).reshape(blob.shape.dim)
def CommonConvert(op, mace_type, dt):
op_def = mace_pb2.OperatorDef()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = dt
data_format_arg = op_def.arg.add()
data_format_arg.name = 'data_format'
data_format_arg.s = 'NHWC'
op_def.name = op.name
op_def.type = mace_type
op_def.input.extend([parent.name+':0' for parent in op.parents])
return op_def
class Shapes(object):
@staticmethod
def conv_pool_shape(input_shape, filter_shape, paddings, strides, dilations, round_func):
output_shape = np.zeros_like(input_shape)
output_shape[0] = input_shape[0]
output_shape[1] = int(round_func((input_shape[1] + paddings[0] - filter_shape[0]
- (filter_shape[0] - 1) * (dilations[0] - 1)) / float(strides[0]))) + 1
output_shape[2] = int(round_func((input_shape[2] + paddings[1] - filter_shape[1]
- (filter_shape[1] - 1) * (dilations[1] - 1)) / float(strides[1]))) + 1
output_shape[3] = filter_shape[2]
return output_shape
@staticmethod
def fully_connected_shape(input_shape, weight_shape):
return [input_shape[0], 1, 1, weight_shape[0]]
@staticmethod
def concat_shape(input_shapes, axis):
output_shape = None
for input_shape in input_shapes:
if output_shape is None:
output_shape = list(input_shape)
else:
output_shape[axis] += input_shape[axis]
return output_shape
class CaffeConverter(object):
def __init__(self, caffe_net, weights, net_def, dt, device, winograd):
self.net_def = net_def
self.caffe_net = caffe_net
self.weights = weights
self.dt = dt
self.device = device
self.winograd = winograd
self.resolved_ops = set()
layers = caffe_net.layer
# remove train layers and dropout
layers = self.remove_unused_layers(layers)
# Construct graph
# Only support single-output layer
# layer with single output often use the same top name.
self.ops = [Operator(layer.name, layer.type, layer) for layer in layers]
self.ops_map = {op.name : op for op in self.ops}
output_op = {}
for layer in layers:
op = self.ops_map[layer.name]
for input_name in layer.bottom:
assert input_name != layer.name
parent_op = output_op.get(input_name)
if parent_op is None:
parent_op = self.ops_map[input_name]
op.add_parent(parent_op)
if len(layer.top) > 1:
raise Exception('Only support single-output layers')
for output_name in layer.top:
if output_name == layer.name:
continue
output_op[output_name] = op
# Load weights
weights_layers = weights.layer
for layer in weights_layers:
if not layer.blobs:
continue
if layer.name in self.ops_map:
op = self.ops_map[layer.name]
op.data = [BlobToNPArray(blob) for blob in layer.blobs]
# toposort ops
self.ops = self.toposort_ops()
def remove_unused_layers(self, layers):
phase_map = {0: 'train', 1: 'test'}
test_layers_names = set()
test_layers = []
for layer in layers:
phase = 'test'
if len(layer.include):
phase = phase_map[layer.include[0].phase]
if len(layer.exclude):
phase = phase_map[layer.exclude[0].phase]
if phase == 'test' and layer.type != 'Dropout':
test_layers.append(layer)
assert layer.name not in test_layers_names
test_layers_names.add(layer.name)
return test_layers
def toposort_ops(self):
sorted_ops = []
temp_visited = set()
visited = set()
def search(op):
if op.name in temp_visited:
raise Exception("The model is not DAG")
if op.name in visited:
return
temp_visited.add(op.name)
for parent_op in op.parents:
search(parent_op)
temp_visited.remove(op.name)
sorted_ops.append(op)
visited.add(op.name)
for op in self.ops:
search(op)
return sorted_ops
def add_buffer_to_image(self, input_name, input_type):
output_name = input_name[:-2] + "_b2i" + input_name[-2:]
op_def = self.net_def.op.add()
op_def.name = output_name[:-2]
op_def.type = 'BufferToImage'
op_def.input.extend([input_name])
op_def.output.extend([output_name])
arg = op_def.arg.add()
arg.name = 'buffer_type'
arg.i = buffer_type_map[input_type]
arg = op_def.arg.add()
arg.name = 'mode'
arg.i = 0
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
return output_name
def add_image_to_buffer(self, input_name, input_type):
output_name = input_name[:-2] + "_i2b" + input_name[-2:]
op_def = self.net_def.op.add()
op_def.name = output_name[:-2]
op_def.type = 'ImageToBuffer'
op_def.input.extend([input_name])
op_def.output.extend([output_name])
arg = op_def.arg.add()
arg.name = 'buffer_type'
arg.i = buffer_type_map[input_type]
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
return output_name
def add_input_transform(self, name):
new_input_name = MACE_INPUT_NODE_NAME + ":0"
op_def = self.net_def.op.add()
op_def.name = name
op_def.type = 'BufferToImage'
op_def.input.extend([new_input_name])
if name not in self.ops_map:
raise Exception("Input name not in the model")
top_name = self.ops_map[name].layer.top[0]
op_def.output.extend([top_name+':0'])
epsilon_arg = op_def.arg.add()
epsilon_arg.name = 'buffer_type'
epsilon_arg.i = buffer_type_map['IN_OUT_CHANNEL']
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
def add_output_transform(self, name):
output_name = MACE_OUTPUT_NODE_NAME + ":0"
op_def = self.net_def.op.add()
op_def.name = output_name[:-2]
op_def.type = 'ImageToBuffer'
op_def.input.extend([name+':0'])
op_def.output.extend([output_name])
epsilon_arg = op_def.arg.add()
epsilon_arg.name = 'buffer_type'
epsilon_arg.i = buffer_type_map['IN_OUT_CHANNEL']
def add_tensor(self, name, value):
tensor = self.net_def.tensors.add()
tensor.name = name
shape = list(value.shape)
tensor.dims.extend(shape)
tensor.data_type = mace_pb2.DT_FLOAT
tensor.float_data.extend(value.flat)
@staticmethod
def add_output_shape(op_def, output_shape):
mace_output_shape = mace_pb2.OutputShape()
mace_output_shape.dims.extend(output_shape)
op_def.output_shape.extend([mace_output_shape])
def add_stride_pad_kernel_arg(self, param, op_def):
try:
if len(param.stride) > 1 or len(param.kernel_size) > 1 or len(param.pad) > 1:
raise Exception('Mace does not support multiple stride/kernel_size/pad')
stride = [param.stride[0], param.stride[0]] if len(param.stride) else [1, 1]
pad = [param.pad[0] * 2, param.pad[0] * 2] if len(param.pad) else [0, 0]
kernel = [param.kernel_size[0], param.kernel_size[0]] if len(param.kernel_size) else [0, 0]
except TypeError:
stride = [param.stride, param.stride]
pad = [param.pad * 2, param.pad * 2]
kernel = [param.kernel_size, param.kernel_size]
strides_arg = op_def.arg.add()
strides_arg.name = 'strides'
if param.HasField("stride_h") or param.HasField("stride_w"):
stride = [param.stride_h, param.stride_w]
strides_arg.ints.extend(stride)
# Pad
padding_arg = op_def.arg.add()
padding_arg.name = 'padding_values'
if param.HasField("pad_h") or param.HasField("pad_w"):
pad = [param.pad_h * 2, param.pad_w * 2]
padding_arg.ints.extend(pad)
# kernel
if op_def.type == 'Pooling':
kernel_arg = op_def.arg.add()
kernel_arg.name = 'kernels'
if param.HasField("kernel_h") or param.HasField("kernel_w"):
kernel = [param.kernel_h, param.kernel_w]
kernel_arg.ints.extend(kernel)
return pad, stride, kernel
def convert_conv2d(self, op):
op_def = CommonConvert(op, 'Conv2D', self.dt)
param = op.layer.convolution_param
# Add filter
weight_tensor_name = op.name + '_weight:0'
weight_data = op.data[0].transpose((2, 3, 0, 1))
self.add_tensor(weight_tensor_name, weight_data)
if self.device == 'gpu':
buffer_type = "CONV2D_FILTER"
output_name = self.add_buffer_to_image(weight_tensor_name, buffer_type)
op_def.input.extend([output_name])
else:
op_def.input.extend([weight_tensor_name])
# Add Bias
if len(op.data) == 2:
bias_tensor_name = op.name + '_bias:0'
bias_data = op.data[1]
self.add_tensor(bias_tensor_name, bias_data)
if self.device == 'gpu':
output_name = self.add_buffer_to_image(bias_tensor_name, "ARGUMENT")
op_def.input.extend([output_name])
else:
op_def.input.extend([bias_tensor_name])
paddings, strides, _ = self.add_stride_pad_kernel_arg(param, op_def)
dilations = [1, 1]
if len(param.dilation) > 0:
dilation_arg = op_def.arg.add()
dilation_arg.name = 'dilations'
if len(param.dilation) == 1:
dilations = [param.dilation[0], param.dilation[0]]
elif len(param.dilation) == 2:
dilations = [param.dilation[0], param.dilation[1]]
dilation_arg.ints.extend(dilations)
final_op = op
self.resolved_ops.add(op.name)
output_shape = Shapes.conv_pool_shape(op.get_single_parent().output_shape,
weight_data.shape,
paddings, strides, dilations,
math.floor)
op.output_shape = output_shape
if len(self.ops_map[final_op.name].children) == 1 \
and self.ops_map[final_op.name].children[0].type in activation_name_map:
activation_op = self.ops_map[final_op.name].children[0]
op_def.type = "FusedConv2D"
fused_act_arg = op_def.arg.add()
fused_act_arg.name = 'activation'
fused_act_arg.s = activation_name_map[activation_op.type]
final_op = activation_op
final_op.output_shape = output_shape
self.resolved_ops.add(activation_op.name)
op_def.output.extend([final_op.name+':0'])
self.add_output_shape(op_def, output_shape)
self.net_def.op.extend([op_def])
def convert_batchnorm(self, op):
if len(op.children) != 1 or op.children[0].type != 'Scale':
raise Exception('Now only support BatchNorm+Scale')
op_def = CommonConvert(op, 'FoldedBatchNorm', self.dt)
scale_op = op.children[0]
epsilon_value = op.layer.batch_norm_param.eps
if op.data[2][0] != 0:
mean_value = (1. / op.data[2][0]) * op.data[0]
var_value = (1. / op.data[2][0]) * op.data[1]
else:
raise RuntimeError('scalar is zero.')
gamma_value = scale_op.data[0]
beta_value = np.zeros_like(mean_value)
if len(scale_op.data) == 2:
beta_value = scale_op.data[1]
scale_value = (
(1.0 / np.vectorize(math.sqrt)(var_value + epsilon_value)) *
gamma_value)
offset_value = (-mean_value * scale_value) + beta_value
input_names = [op.name+'_scale:0', op.name+'_offset:0']
self.add_tensor(input_names[0], scale_value)
self.add_tensor(input_names[1], offset_value)
if self.device == 'gpu':
for name in input_names:
output_name = self.add_buffer_to_image(name, "ARGUMENT")
op_def.input.extend([output_name])
else:
op_def.input.extend([name for name in input_names])
self.resolved_ops.add(op.name)
self.resolved_ops.add(scale_op.name)
final_op = scale_op
output_shape = op.get_single_parent().output_shape
if len(self.ops_map[final_op.name].children) == 1 \
and self.ops_map[final_op.name].children[0].type in activation_name_map:
activation_op = self.ops_map[final_op.name].children[0]
fused_act_arg = op_def.arg.add()
fused_act_arg.name = 'activation'
fused_act_arg.s = activation_name_map[activation_op.type]
final_op = activation_op
final_op.output_shape = output_shape
self.resolved_ops.add(activation_op.name)
op_def.output.extend([final_op.name + ':0'])
self.add_output_shape(op_def, output_shape)
self.net_def.op.extend([op_def])
def convert_inner_product(self, op):
param = op.layer.inner_product_param
try:
if param.axis != 1 or param.transpose:
raise ValueError('Do not support non-default axis and transpose '
'case for innner product')
except AttributeError:
pass
op_def = CommonConvert(op, 'FC', self.dt)
weight_tensor_name = op.name + '_weight:0'
if op.data[0].ndim not in [2, 4]:
raise ValueError('Unexpected weigth ndim.')
if op.data[0].ndim == 4 and list(op.data[0].shape[:2] != [1, 1]):
raise ValueError('Do not support 4D weight with shape [1, 1, *, *]')
input_shape = op.get_single_parent().output_shape
weight_data = op.data[0].reshape(-1, op.data[0].shape[-1])
assert weight_data.shape[1] == (input_shape[1] * input_shape[2] * input_shape[3])
weight_data = weight_data.reshape(-1, input_shape[3], input_shape[1], input_shape[2])
weight_data = weight_data.transpose((0, 2, 3, 1)).reshape(weight_data.shape[0], -1)
self.add_tensor(weight_tensor_name, weight_data)
if self.device == 'gpu':
buffer_type = "WEIGHT_HEIGHT"
output_name = self.add_buffer_to_image(weight_tensor_name, buffer_type)
op_def.input.extend([output_name])
else:
op_def.input.extend([weight_tensor_name])
# Add Bias
if len(op.data) == 2:
bias_tensor_name = op.name + '_bias:0'
bias_data = op.data[1]
self.add_tensor(bias_tensor_name, bias_data)
if self.device == 'gpu':
output_name = self.add_buffer_to_image(bias_tensor_name, "ARGUMENT")
op_def.input.extend([output_name])
else:
op_def.input.extend([bias_tensor_name])
output_shape = Shapes.fully_connected_shape(input_shape, weight_data.shape)
op.output_shape = output_shape
self.resolved_ops.add(op.name)
op_def.output.extend([op.name + ':0'])
self.add_output_shape(op_def, output_shape)
self.net_def.op.extend([op_def])
def convert_pooling(self, op):
op_def = CommonConvert(op, 'Pooling', self.dt)
param = op.layer.pooling_param
paddings, strides, kernels = self.add_stride_pad_kernel_arg(param, op_def)
if param.pool == caffe_pb2.PoolingParameter.MAX:
pooling_type = "MaxPool"
elif param.pool == caffe_pb2.PoolingParameter.AVE:
pooling_type = "AvgPool"
pooling_type_arg = op_def.arg.add()
pooling_type_arg.name = 'pooling_type'
pooling_type_arg.i = pooling_type_mode[pooling_type]
input_shape = op.get_single_parent().output_shape
filter_shape = [kernels[0], kernels[1], input_shape[3], input_shape[3]]
output_shape = Shapes.conv_pool_shape(input_shape, filter_shape,
paddings, strides, [1, 1], math.ceil)
op.output_shape = output_shape
op_def.output.extend([op.name + ':0'])
self.add_output_shape(op_def, output_shape)
self.net_def.op.extend([op_def])
self.resolved_ops.add(op.name)
def convert_activation(self, op):
op_def = CommonConvert(op, 'Activation', self.dt)
activation_arg = op_def.arg.add()
activation_arg.name = 'activation'
activation_arg.s = activation_name_map[op.type]
op_def.output.extend([op.name + ':0'])
output_shape = op.get_single_parent().output_shape
op.output_shape = output_shape
self.add_output_shape(op_def, output_shape)
self.net_def.op.extend([op_def])
self.resolved_ops.add(op.name)
def convert_prelu(self, op):
op_def = CommonConvert(op, 'Activation', self.dt)
activation_arg = op_def.arg.add()
activation_arg.name = 'activation'
activation_arg.s = 'PRELU'
alpha_tensor_name = op.name + '_alpha:0'
alpha_data = op.data[0]
self.add_tensor(alpha_tensor_name, alpha_data)
if self.device == 'gpu':
output_name = self.add_buffer_to_image(alpha_tensor_name, "ARGUMENT")
op_def.input.extend([output_name])
else:
op_def.input.extend([alpha_tensor_name])
op_def.output.extend([op.name + ':0'])
output_shape = op.get_single_parent().output_shape
op.output_shape = output_shape
self.add_output_shape(op_def, output_shape)
self.net_def.op.extend([op_def])
self.resolved_ops.add(op.name)
def convert_add(self, op):
op_def = CommonConvert(op, 'AddN', self.dt)
op_def.output.extend([op.name + ':0'])
output_shape = op.parents[0].output_shape
op.output_shape = output_shape
self.add_output_shape(op_def, output_shape)
self.net_def.op.extend([op_def])
self.resolved_ops.add(op.name)
def convert_concat(self, op):
op_def = CommonConvert(op, 'Concat', self.dt)
axis_arg = op_def.arg.add()
axis_arg.name = 'axis'
axis_arg.i = 3
try:
if op.layer.concat_param.HasFeild('axis'):
axis_arg.i = op.concat_param.axis
elif op.layer.concat_param.HasFeild('concat_dim'):
axis_arg.i = op.concat_param.concat_dim
except AttributeError:
pass
input_shapes = []
for parent in op.parents:
input_shapes.append(parent.output_shape)
output_shape = Shapes.concat_shape(input_shapes, axis_arg.i)
op.output_shape = output_shape
self.add_output_shape(op_def, output_shape)
op_def.output.extend([op.name + ':0'])
self.net_def.op.extend([op_def])
self.resolved_ops.add(op.name)
def convert_eltwise(self, op):
op_def = CommonConvert(op, 'Eltwise', self.dt)
param = op.layer.eltwise_param
type_arg = op_def.arg.add()
type_arg.name = 'type'
type_arg.i = param.operation
if len(param.coeff) > 0:
coeff_arg = op_def.arg.add()
coeff_arg.name = 'coeff'
coeff_arg.ints.extend(list(param.coeff))
output_shape = op.parents[0].output_shape
op.output_shape = output_shape
self.add_output_shape(op_def, output_shape)
op_def.output.extend([op.name + ':0'])
self.net_def.op.extend([op_def])
self.resolved_ops.add(op.name)
def convert_normal_op(self, op):
op_def = CommonConvert(op, op.type, self.dt)
output_shape = op.parents[0].output_shape
op.output_shape = output_shape
self.add_output_shape(op_def, output_shape)
op_def.output.extend([op.name + ':0'])
self.net_def.op.extend([op_def])
self.resolved_ops.add(op.name)
def replace_in_out_name(self, input_name, output_name):
input_name = input_name + ":0"
output_name = output_name + ":0"
for op in self.net_def.op:
if len(op.input) > 0 and op.input[0] == input_name:
op.input[0] = MACE_INPUT_NODE_NAME + ":0"
if len(op.output) > 0 and op.output[0] == output_name:
op.output[0] = MACE_OUTPUT_NODE_NAME + ":0"
def add_input_op_shape(self, input_node, input_shape):
if not input_shape:
input_shape = []
if self.caffe_net.input_dim:
input_shape = self.caffe_net.input_dim
elif self.caffe_net.input_shape:
input_shape = self.caffe_net.input_shape[0].dim
elif self.caffe_net.layer[0].input_param.shape:
input_shape = self.caffe_net.layer[0].input_param.shape[0].dim
input_op = self.ops_map[input_node]
input_op.output_shape = input_shape
def convert(self, input_node, input_shape, output_node):
if self.device == 'gpu':
self.add_input_transform(input_node)
assert self.ops[0].type == 'Input'
self.add_input_op_shape(input_node, input_shape)
for op in self.ops:
if op.name in self.resolved_ops:
continue
if op.type == 'Input':
self.resolved_ops.add(op.name)
elif op.type == 'Convolution':
self.convert_conv2d(op)
elif op.type == 'BatchNorm':
self.convert_batchnorm(op)
elif op.type == 'InnerProduct':
self.convert_inner_product(op)
elif op.type == 'Pooling':
self.convert_pooling(op)
elif op.type == 'PReLU':
self.convert_prelu(op)
elif op.type in ['ReLU', 'Sigmoid', 'TanH']:
self.convert_activation(op)
elif op.type == 'Add':
self.convert_add(op)
elif op.type == 'Concat':
self.convert_concat(op)
elif op.type == 'Eltwise':
self.convert_eltwise(op)
elif op.type in ['Softmax']:
self.convert_normal_op(op)
else:
raise Exception('Unknown Op: %s, type: %s' % (op.name, op.type))
if self.device == 'gpu':
self.add_output_transform(output_node)
if self.device == 'cpu':
self.replace_in_out_name(input_node, output_node)
for op in self.ops:
if op.name not in self.resolved_ops:
print 'Unresolve Op: %s with type %s' % (op.name, op.type)
def convert_to_mace_pb(model_file, weight_file, input_node, input_shape, output_node, data_type, device, winograd):
net_def = mace_pb2.NetDef()
dt = data_type_map[data_type]
caffe_net = caffe_pb2.NetParameter()
with open(model_file, "r") as f:
google.protobuf.text_format.Merge(str(f.read()), caffe_net)
weights = caffe_pb2.NetParameter()
with open(weight_file, "rb") as f:
weights.MergeFromString(f.read())
converter = CaffeConverter(caffe_net, weights, net_def, dt, device, winograd)
converter.convert(input_node, input_shape, output_node)
print "PB Converted."
if device == 'gpu':
print "start optimize memory."
mem_optimizer = memory_optimizer.MemoryOptimizer(net_def)
mem_optimizer.optimize()
print "Memory optimization done."
return net_def
python/tools/tf_converter.py → python/tools/converter.py
浏览文件 @ 5b7635f6
import argparse
import sys
import hashlib
import tensorflow as tf
from tensorflow import gfile
from lib.proto import mace_pb2
from lib.python.tools import tf_converter_lib
from lib.python.tools import tf_dsp_converter_lib
import os.path
from lib.python.tools import source_converter_lib
# ./bazel-bin/mace/python/tools/tf_converter --input quantized_test.pb --output quantized_test_dsp.pb --runtime dsp --input_dim input_node,1,28,28,3
# ./bazel-bin/mace/python/tools/tf_converter --model_file quantized_test.pb --output quantized_test_dsp.pb --runtime dsp --input_dim input_node,1,28,28,3
FLAGS = None
......@@ -20,38 +16,57 @@ def file_checksum(fname):
return hash_func.hexdigest()
def main(unused_args):
if not gfile.Exists(FLAGS.input):
print("Input graph file '" + FLAGS.input + "' does not exist!")
if not os.path.isfile(FLAGS.model_file):
print("Input graph file '" + FLAGS.model_file + "' does not exist!")
return -1
model_checksum = file_checksum(FLAGS.input)
model_checksum = file_checksum(FLAGS.model_file)
if FLAGS.model_checksum != "" and FLAGS.model_checksum != model_checksum:
print("Model checksum mismatch: %s != %s" % (model_checksum, FLAGS.model_checksum))
return -1
input_graph_def = tf.GraphDef()
with gfile.Open(FLAGS.input, "rb") as f:
data = f.read()
input_graph_def.ParseFromString(data)
if FLAGS.platform == 'caffe':
if not os.path.isfile(FLAGS.weight_file):
print("Input weight file '" + FLAGS.weight_file + "' does not exist!")
return -1
weight_checksum = file_checksum(FLAGS.weight_file)
if FLAGS.weight_checksum != "" and FLAGS.weight_checksum != weight_checksum:
print("Weight checksum mismatch: %s != %s" % (weight_checksum, FLAGS.weight_checksum))
return -1
if FLAGS.runtime == 'dsp':
print("DSP not support caffe model yet.")
return -1
if FLAGS.runtime == 'dsp':
output_graph_def = tf_dsp_converter_lib.convert_to_mace_pb(
input_graph_def, FLAGS.input_node, FLAGS.output_node, FLAGS.dsp_mode)
else:
input_shape = []
if FLAGS.input_shape != "":
input_shape.extend([int(x) for x in FLAGS.input_shape.split(',')])
output_graph_def = tf_converter_lib.convert_to_mace_pb(
input_graph_def, FLAGS.input_node, input_shape, FLAGS.output_node,
from lib.python.tools import caffe_converter_lib
output_graph_def = caffe_converter_lib.convert_to_mace_pb(
FLAGS.model_file, FLAGS.weight_file, FLAGS.input_node, input_shape, FLAGS.output_node,
FLAGS.data_type, FLAGS.runtime, FLAGS.winograd)
elif FLAGS.platform == 'tensorflow':
if FLAGS.runtime == 'dsp':
from lib.python.tools import tf_dsp_converter_lib
output_graph_def = tf_dsp_converter_lib.convert_to_mace_pb(
FLAGS.model_file, FLAGS.input_node, FLAGS.output_node, FLAGS.dsp_mode)
else:
input_shape = []
if FLAGS.input_shape != "":
input_shape.extend([int(x) for x in FLAGS.input_shape.split(',')])
from lib.python.tools import tf_converter_lib
output_graph_def = tf_converter_lib.convert_to_mace_pb(
FLAGS.model_file, FLAGS.input_node, input_shape, FLAGS.output_node,
FLAGS.data_type, FLAGS.runtime, FLAGS.winograd)
if FLAGS.output_type == 'source':
source_converter_lib.convert_to_source(output_graph_def, model_checksum, FLAGS.template, FLAGS.obfuscate,
FLAGS.model_tag, FLAGS.output, FLAGS.runtime, FLAGS.embed_model_data)
else:
with gfile.GFile(FLAGS.output, "wb") as f:
with open(FLAGS.output, "wb") as f:
f.write(output_graph_def.SerializeToString())
with gfile.GFile(FLAGS.output + '_txt', "wb") as f:
with open(FLAGS.output + '_txt', "wb") as f:
# output_graph_def.ClearField('tensors')
f.write(str(output_graph_def))
print("Model conversion is completed.")
......@@ -69,15 +84,25 @@ def parse_args():
parser = argparse.ArgumentParser()
parser.register("type", "bool", lambda v: v.lower() == "true")
parser.add_argument(
"--input",
"--model_file",
type=str,
default="",
help="TensorFlow \'GraphDef\' file to load, Caffe prototxt file to load.")
parser.add_argument(
"--weight_file",
type=str,
default="",
help="TensorFlow \'GraphDef\' file to load.")
help="Caffe data file to load.")
parser.add_argument(
"--model_checksum",
type=str,
default="",
help="Model file sha256 checksum")
parser.add_argument(
"--weight_checksum",
type=str,
default="",
help="Weight file sha256 checksum")
parser.add_argument(
"--output",
type=str,
......@@ -142,6 +167,11 @@ def parse_args():
type=str,
default="",
help="input shape.")
parser.add_argument(
"--platform",
type=str,
default="tensorflow",
help="tensorflow/caffe")
parser.add_argument(
"--embed_model_data",
type=str2bool,
......
python/tools/source_converter_lib.py
浏览文件 @ 5b7635f6
import struct
import os
import uuid
import numpy as np
import hashlib
from tensorflow import gfile
from lib.proto import mace_pb2
from jinja2 import Environment, FileSystemLoader
......@@ -82,7 +80,6 @@ def rename_tensor(net_def):
class TensorInfo:
def __init__(self, id, t, runtime):
self.id = id
self.name = t.name
self.data_type = mace_pb2.DataType.Name(t.data_type)
if t.data_type == mace_pb2.DT_FLOAT:
if runtime == 'gpu':
......@@ -136,7 +133,7 @@ def convert_to_source(net_def, mode_pb_checksum, template, obfuscate, model_tag,
)
model_data.extend(tensor_info.data)
offset += len(tensor_info.data)
with gfile.GFile(output_dir + 'tensor' + str(counter) + '.cc', "wb") as f:
with open(output_dir + 'tensor' + str(counter) + '.cc', "wb") as f:
f.write(source)
counter += 1
......@@ -148,7 +145,7 @@ def convert_to_source(net_def, mode_pb_checksum, template, obfuscate, model_tag,
model_data_size = offset,
model_data = model_data
)
with gfile.GFile(output_dir + 'tensor_data' + '.cc', "wb") as f:
with open(output_dir + 'tensor_data' + '.cc', "wb") as f:
f.write(source)
if not embed_model_data:
f = open(output_dir + model_tag + '.data', "wb")
......@@ -167,7 +164,7 @@ def convert_to_source(net_def, mode_pb_checksum, template, obfuscate, model_tag,
mode = 2,
runtime = runtime,
)
with gfile.GFile(output_dir + 'op' + str(counter) + '.cc', "wb") as f:
with open(output_dir + 'op' + str(counter) + '.cc', "wb") as f:
f.write(source)
counter += 1
......@@ -181,5 +178,5 @@ def convert_to_source(net_def, mode_pb_checksum, template, obfuscate, model_tag,
runtime = runtime,
model_pb_checksum = mode_pb_checksum
)
with gfile.GFile(output, "wb") as f:
with open(output, "wb") as f:
f.write(source)
python/tools/tf_converter_lib.py
浏览文件 @ 5b7635f6
......@@ -3,6 +3,7 @@ import tensorflow as tf
import numpy as np
import math
import copy
from tensorflow import gfile
from lib.python.tools import memory_optimizer
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import tensor_shape_pb2
......@@ -993,10 +994,15 @@ def add_shape_info(input_graph_def, input_node, input_shape):
return inputs_replaced_graph
def convert_to_mace_pb(input_graph_def, input_node, input_shape, output_node, data_type, device, winograd):
def convert_to_mace_pb(model_file, input_node, input_shape, output_node, data_type, device, winograd):
net_def = mace_pb2.NetDef()
dt = data_type_map[data_type]
input_graph_def = tf.GraphDef()
with gfile.Open(model_file, "rb") as f:
data = f.read()
input_graph_def.ParseFromString(data)
input_graph_def = add_shape_info(input_graph_def, input_node, input_shape)
with tf.Session() as session:
with session.graph.as_default() as graph:
......@@ -1006,7 +1012,7 @@ def convert_to_mace_pb(input_graph_def, input_node, input_shape, output_node, da
converter.convert(input_node, output_node)
optimizer = Optimizer(net_def, device)
net_def = optimizer.optimize()
print "PB Converted."
print "Model Converted."
if device == 'gpu':
print "start optimize memory."
mem_optimizer = memory_optimizer.MemoryOptimizer(net_def)
......
python/tools/tf_dsp_converter_lib.py
浏览文件 @ 5b7635f6
from lib.proto import mace_pb2
import tensorflow as tf
from tensorflow import gfile
from operator import mul
from dsp_ops import DspOps
from lib.python.tools import graph_util
......@@ -359,12 +360,17 @@ def fuse_quantize(net_def, input_node, output_node):
new_net_def.op.extend(new_ops)
return new_net_def
def convert_to_mace_pb(input_graph_def, input_node, output_node, dsp_mode):
def convert_to_mace_pb(model_file, input_node, output_node, dsp_mode):
"""
nnlib does not have batch norm, so use tensorflow optimizer to fold
batch norm with convolution. The fold optimization reorders ops, so
we sort ops first by topology.
"""
input_graph_def = tf.GraphDef()
with gfile.Open(model_file, "rb") as f:
data = f.read()
input_graph_def.ParseFromString(data)
input_graph_def = graph_util.sort_tf_graph(input_graph_def)
net_def = mace_pb2.NetDef()
......
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