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Mace
Mace
提交 fa4a9bf5 编写于 作者: Y yejianwu
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merge lib from libmace

上级 0b002e32 94a520f0
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mace/proto/BUILD
浏览文件 @ fa4a9bf5
......@@ -10,6 +10,15 @@ licenses(["notice"]) # Apache 2.0
load("@com_google_protobuf//:protobuf.bzl", "py_proto_library")
py_proto_library(
name = "mace_py",
srcs = ["mace.proto"],
default_runtime = "@com_google_protobuf//:protobuf_python",
protoc = "@com_google_protobuf//:protoc",
srcs_version = "PY2AND3",
deps = ["@com_google_protobuf//:protobuf_python"],
)
py_proto_library(
name = "caffe_py",
srcs = ["caffe.proto"],
......
mace/proto/caffe.proto
浏览文件 @ fa4a9bf5
......@@ -98,7 +98,7 @@ message NetParameter {
// NOTE
// Update the next available ID when you add a new SolverParameter field.
//
// SolverParameter next available ID: 41 (last added: type)
// SolverParameter next available ID: 43 (last added: weights)
message SolverParameter {
//////////////////////////////////////////////////////////////////////////////
// Specifying the train and test networks
......@@ -128,8 +128,7 @@ message SolverParameter {
// The states for the train/test nets. Must be unspecified or
// specified once per net.
//
// By default, all states will have solver = true;
// train_state will have phase = TRAIN,
// 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;
......@@ -187,7 +186,11 @@ message SolverParameter {
optional float clip_gradients = 35 [default = -1];
optional int32 snapshot = 14 [default = 0]; // The snapshot interval
optional string snapshot_prefix = 15; // The prefix for the snapshot.
// 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];
......@@ -219,7 +222,7 @@ message SolverParameter {
// RMSProp decay value
// MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
optional float rms_decay = 38;
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.
......@@ -239,6 +242,19 @@ message SolverParameter {
}
// 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
......@@ -389,16 +405,12 @@ message LayerParameter {
optional PoolingParameter pooling_param = 121;
optional PowerParameter power_param = 122;
optional PReLUParameter prelu_param = 131;
optional PSROIPoolingParameter psroi_pooling_param = 149;
optional PSROIAlignParameter psroi_align_param = 1490;
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 ROIPoolingParameter roi_pooling_param = 8266711;
optional ScaleParameter scale_param = 142;
optional ProposalParameter proposal_param = 8266713;
optional SigmoidParameter sigmoid_param = 124;
optional SoftmaxParameter softmax_param = 125;
optional SPPParameter spp_param = 132;
......@@ -407,8 +419,6 @@ message LayerParameter {
optional ThresholdParameter threshold_param = 128;
optional TileParameter tile_param = 138;
optional WindowDataParameter window_data_param = 129;
optional NNPACKConvolutionParameter nnpack_convolution_param = 204;
}
// Message that stores parameters used to apply transformation
......@@ -424,7 +434,7 @@ message TransformationParameter {
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 substract it from all the channels)
// 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;
......@@ -440,7 +450,7 @@ message LossParameter {
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 layer.
// 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
......@@ -454,6 +464,8 @@ message LossParameter {
// 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
......@@ -504,11 +516,21 @@ message ConcatParameter {
}
message BatchNormParameter {
// If false, accumulate global mean/variance values via a moving average. If
// true, use those accumulated values instead of computing mean/variance
// across the batch.
// 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;
// How much does the moving average decay each iteration?
// 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.
......@@ -590,7 +612,6 @@ message ConvolutionParameter {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
NNPACK = 3;
}
optional Engine engine = 15 [default = DEFAULT];
......@@ -660,8 +681,8 @@ message DataParameter {
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 (Number of batches to prefetch to host memory, increase if
// data access bandwidth varies).
// Prefetch queue (Increase if data feeding bandwidth varies, within the
// limit of device memory for GPU training)
optional uint32 prefetch = 10 [default = 4];
}
......@@ -808,6 +829,7 @@ message ImageDataParameter {
message InfogainLossParameter {
// Specify the infogain matrix source.
optional string source = 1;
optional int32 axis = 2 [default = 1]; // axis of prob
}
message InnerProductParameter {
......@@ -825,13 +847,6 @@ message InnerProductParameter {
// 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];
enum Engine {
DEFAULT = 0;
CAFFE = 1;
NNPACK = 2;
}
optional Engine engine = 7 [default = DEFAULT];
}
message InputParameter {
......@@ -915,7 +930,6 @@ message PoolingParameter {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
NNPACK = 3;
}
optional Engine engine = 11 [default = DEFAULT];
// If global_pooling then it will pool over the size of the bottom by doing
......@@ -930,17 +944,6 @@ message PowerParameter {
optional float shift = 3 [default = 0.0];
}
message PSROIPoolingParameter {
required float spatial_scale = 1;
required int32 output_dim = 2; // output channel number
required int32 group_size = 3; // number of groups to encode position-sensitive score maps
}
message PSROIAlignParameter {
required float spatial_scale = 1;
required int32 output_dim = 2; // output channel number
required int32 group_size = 3; // number of groups to encode position-sensitive score maps
}
message PythonParameter {
optional string module = 1;
optional string layer = 2;
......@@ -949,9 +952,7 @@ message PythonParameter {
// 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 = ''];
// Whether this PythonLayer is shared among worker solvers during data parallelism.
// If true, each worker solver sequentially run forward from this layer.
// This value should be set true if you are using it as a data layer.
// DEPRECATED
optional bool share_in_parallel = 4 [default = false];
}
......@@ -1083,17 +1084,6 @@ message ReshapeParameter {
optional int32 num_axes = 3 [default = -1];
}
// Message that stores parameters used by ROIPoolingLayer
message ROIPoolingParameter {
// 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 pooled_h = 1 [default = 0]; // The pooled output height
optional uint32 pooled_w = 2 [default = 0]; // The pooled output width
// Multiplicative spatial scale factor to translate ROI coords from their
// input scale to the scale used when pooling
optional float spatial_scale = 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
......@@ -1131,13 +1121,6 @@ message ScaleParameter {
optional FillerParameter bias_filler = 5;
}
// Message that stores parameters used by ProposalLayer
message ProposalParameter {
optional uint32 feat_stride = 1 [default = 16];
repeated uint32 scales = 2;
repeated float ratios = 3;
}
message SigmoidParameter {
enum Engine {
DEFAULT = 0;
......@@ -1438,22 +1421,6 @@ message PReLUParameter {
// Initial value of a_i. Default is a_i=0.25 for all i.
optional FillerParameter filler = 1;
// Whether or not slope paramters are shared across channels.
// Whether or not slope parameters are shared across channels.
optional bool channel_shared = 2 [default = false];
}
message NNPACKConvolutionParameter {
enum Algorithm {
AUTO = 0;
WINOGRAD = 1;
FFT_16x16 = 2;
FFT_8x8 = 3;
}
optional Algorithm algorithm = 1 [default=AUTO];
enum KernelTransformStrategy {
RECOMPUTE = 0;
REUSE = 1;
}
optional KernelTransformStrategy kernel_transform_strategy = 2 [default=RECOMPUTE];
}
mace/proto/mace.proto 0 → 100644
浏览文件 @ fa4a9bf5
syntax = "proto2";
package mace;
enum NetMode {
INIT = 0;
NORMAL = 1;
}
enum DeviceType {
CPU = 0; // In default, we will use CPU.
NEON = 1;
OPENCL = 2;
}
enum DataType {
DT_INVALID = 0;
// Data types that all computation devices are expected to be
// capable to support.
DT_FLOAT = 1;
DT_DOUBLE = 2;
DT_INT32 = 3;
DT_UINT8 = 4;
DT_INT16 = 5;
DT_INT8 = 6;
DT_STRING = 7;
DT_INT64 = 8;
DT_UINT16 = 9;
DT_BOOL = 10;
DT_HALF = 19;
DT_UINT32 = 22;
}
message TensorProto {
// The dimensions in the tensor.
repeated int64 dims = 1;
optional DataType data_type = 2 [default = DT_FLOAT];
// For float
repeated float float_data = 3 [packed = true];
// For int32, uint8, int8, uint16, int16, bool, and float16
// Note about float16: in storage we will basically convert float16 byte-wise
// to unsigned short and then store them in the int32_data field.
repeated int32 int32_data = 4 [packed = true];
// For bytes
optional bytes byte_data = 5;
// For strings
repeated bytes string_data = 6;
// For double
repeated double double_data = 9 [packed = true];
// For int64
repeated int64 int64_data = 10 [packed = true];
// Optionally, a name for the tensor.
optional string name = 7;
optional uint32 node_id = 100;
}
message Argument {
optional string name = 1;
optional float f = 2;
optional int64 i = 3;
optional bytes s = 4;
repeated float floats = 5;
repeated int64 ints = 6;
repeated bytes strings = 7;
}
// for hexagon mace-nnlib
message NodeInput {
optional int32 node_id = 1;
optional int32 output_port = 2;
}
message OutputShape {
repeated int64 dims = 1;
}
message OperatorDef {
repeated string input = 1;
repeated string output = 2;
optional string name = 3;
optional string type = 4;
repeated Argument arg = 5;
repeated OutputShape output_shape = 6;
repeated DataType output_type = 7;
repeated int32 mem_id = 10;
// for hexagon mace-nnlib
optional uint32 node_id = 100;
optional uint32 op_id = 101;
optional uint32 padding = 102;
repeated NodeInput node_input = 103;
repeated int32 out_max_byte_size = 104; // only support 32-bit len
}
// for memory optimization
message MemoryBlock {
optional int32 mem_id = 1;
optional uint32 x = 2;
optional uint32 y = 3;
}
message MemoryArena {
repeated MemoryBlock mem_block = 1;
}
// for hexagon mace-nnlib
message InputInfo {
optional string name = 1;
optional int32 node_id = 2;
repeated int32 dims = 3;
optional int32 max_byte_size = 4; // only support 32-bit len
optional DataType data_type = 5 [default = DT_FLOAT];
}
message OutputInfo {
optional string name = 1;
optional int32 node_id = 2;
repeated int32 dims = 3;
optional int32 max_byte_size = 4; // only support 32-bit len
optional DataType data_type = 5 [default = DT_FLOAT];
}
message NetDef {
optional string name = 1;
repeated OperatorDef op = 2;
optional string version = 3;
repeated Argument arg = 4;
repeated TensorProto tensors = 5;
// for mem optimization
optional MemoryArena mem_arena = 10;
// for hexagon mace-nnlib
repeated InputInfo input_info = 100;
repeated OutputInfo output_info = 101;
}
mace/python/tools/BUILD
浏览文件 @ fa4a9bf5
py_binary(
name = "caffe_ops_stats",
srcs = ["caffe_ops_stats.py"],
py_library(
name = "tf_converter_lib",
srcs = [
"convert_util.py",
"graph_util.py",
"tf_converter_lib.py",
"tf_dsp_converter_lib.py",
],
srcs_version = "PY2AND3",
deps = [
":memory_optimizer",
"//mace/proto:mace_py",
],
)
py_library(
name = "caffe_converter_lib",
srcs = [
"caffe_converter_lib.py",
],
srcs_version = "PY2AND3",
deps = [
":memory_optimizer",
"//mace/proto:caffe_py",
],
)
py_library(
name = "source_converter_lib",
srcs = [
"source_converter_lib.py",
],
srcs_version = "PY2AND3",
deps = [
"//mace/proto:mace_py",
],
)
py_binary(
name = "converter",
srcs = ["converter.py"],
srcs_version = "PY2AND3",
deps = [
":tf_converter_lib",
":caffe_converter_lib",
":source_converter_lib",
"@six_archive//:six",
],
)
py_binary(
name = "memory_optimizer",
srcs = ["memory_optimizer.py"],
srcs_version = "PY2AND3",
deps = [
"//mace/proto:mace_py",
],
)
mace/python/tools/binary_codegen.py 0 → 100644
浏览文件 @ fa4a9bf5
import argparse
import os
import sys
import struct
import jinja2
import numpy as np
# python mace/python/tools/binary_codegen.py \
# --binary_dirs=${BIN_FILE} \
# --binary_file_name=mace_run.config \
# --output_path=${CODE_GEN_PATH} --variable_name=kTuningParamsData
FLAGS = None
def generate_cpp_source():
data_map = {}
for binary_dir in FLAGS.binary_dirs.split(","):
binary_path = os.path.join(binary_dir, FLAGS.binary_file_name)
if not os.path.exists(binary_path):
continue
with open(binary_path, "rb") as f:
binary_array = np.fromfile(f, dtype=np.uint8)
idx = 0
size, = struct.unpack("Q", binary_array[idx:idx+8])
print size
idx += 8
for _ in xrange(size):
key_size, = struct.unpack("i", binary_array[idx:idx+4])
idx += 4
key, = struct.unpack(str(key_size) + "s", binary_array[idx:idx+key_size])
idx += key_size
params_size, = struct.unpack("i", binary_array[idx:idx+4])
idx += 4
data_map[key] = []
count = params_size / 4
params = struct.unpack(str(count) + "i", binary_array[idx:idx+params_size])
for i in params:
data_map[key].append(i)
idx += params_size
env = jinja2.Environment(loader=jinja2.FileSystemLoader(sys.path[0]))
return env.get_template('str2vec_maps.cc.tmpl').render(
maps = data_map,
data_type = 'unsigned int',
variable_name = FLAGS.variable_name
)
def main(unused_args):
cpp_binary_source = generate_cpp_source()
if os.path.isfile(FLAGS.output_path):
os.remove(FLAGS.output_path)
w_file = open(FLAGS.output_path, "w")
w_file.write(cpp_binary_source)
w_file.close()
def parse_args():
"""Parses command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
"--binary_dirs",
type=str,
default="cl_bin0/,cl_bin1/",
help="The binaries file path.")
parser.add_argument(
"--binary_file_name",
type=str,
default="mace_run.config",
help="The binary file name.")
parser.add_argument(
"--output_path",
type=str,
default="",
help="The path of generated C++ source file which contains the binary.")
parser.add_argument(
"--variable_name",
type=str,
default="kTuningParamsData",
help="global variable name.")
return parser.parse_known_args()
if __name__ == '__main__':
FLAGS, unparsed = parse_args()
main(unused_args=[sys.argv[0]] + unparsed)
mace/python/tools/caffe_converter_lib.py 0 → 100644
浏览文件 @ fa4a9bf5
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_map = {}
def add_parent(self, parent_op):
self.parents.append(parent_op)
parent_op.children.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)
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
@staticmethod
def slice_shape(input_shape, num_output):
return [input_shape[0], input_shape[1], input_shape[2], input_shape[3]/num_output]
# outputs' name is [op.name + '_' + #]
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()
self.ops = []
self.inputs_map = {} # caffe op name -> mace inputs' name
# Add Input operations
top_name_map = {}
inputs = caffe_net.input
for input in inputs:
self.ops.extend([Operator(input, 'Input', None)])
top_name_map[input] = input
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.extend([Operator(layer.name, layer.type, layer) for layer in layers])
self.ops_map = {op.name : op for op in self.ops}
output_op_map = {}
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_map.get(input_name)
if parent_op is None:
parent_op = self.ops_map[input_name]
op.add_parent(parent_op)
if op.name not in self.inputs_map:
self.inputs_map[op.name] = []
self.inputs_map[op.name].extend([top_name_map[input_name]])
for i in range(len(layer.top)):
output_name = layer.top[i]
if len(layer.top) == 1:
top_name_map[output_name] = op.name
else:
top_name_map[output_name] = op.name + '_' + str(i)
if output_name == layer.name:
continue
output_op_map[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 CommonConvert(self, op, mace_type):
op_def = mace_pb2.OperatorDef()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.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([name+':0' for name in self.inputs_map[op.name]])
return op_def
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, names, is_single):
for name in names:
if is_single:
new_input_name = MACE_INPUT_NODE_NAME + ":0"
else:
new_input_name = MACE_INPUT_NODE_NAME + '_' + name + ":0"
op_def = self.net_def.op.add()
op_def.name = name
op_def.type = 'BufferToImage'
op_def.input.extend([new_input_name])
op_def.output.extend([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, names, is_single):
for name in names:
if is_single:
output_name = MACE_OUTPUT_NODE_NAME + ":0"
else:
output_name = MACE_OUTPUT_NODE_NAME + '_' + 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 = self.CommonConvert(op, 'Conv2D')
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].reshape(-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_map[op.layer.bottom[0]],
weight_data.shape,
paddings, strides, dilations,
math.floor)
op.output_shape_map[op.layer.top[0]] = 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_map[final_op.layer.top[0]] = 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 = self.CommonConvert(op, 'FoldedBatchNorm')
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).reshape(-1)
offset_value = ((-mean_value * scale_value) + beta_value).reshape(-1)
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_map[op.layer.bottom[0]]
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_map[final_op.layer.top[0]] = 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 = self.CommonConvert(op, 'FC')
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_map[op.layer.bottom[0]]
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':
if (weight_data.shape[0] + 3) / 4 > OPENCL_IMAGE_MAX_SIZE \
or weight_data.shape[1] > OPENCL_IMAGE_MAX_SIZE:
raise Exception('Mace gpu do not support FC with weight shape: '
+str(weight_data.shape))
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].reshape(-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])
self.resolved_ops.add(op.name)
output_shape = Shapes.fully_connected_shape(input_shape, weight_data.shape)
op.output_shape_map[op.layer.top[0]] = output_shape
final_op = op
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_map[final_op.layer.top[0]] = 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_pooling(self, op):
op_def = self.CommonConvert(op, 'Pooling')
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_map[op.layer.bottom[0]]
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_map[op.layer.top[0]] = 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 = self.CommonConvert(op, 'Activation')
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_map[op.layer.bottom[0]]
op.output_shape_map[op.layer.top[0]] = 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 = self.CommonConvert(op, 'Activation')
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].reshape(-1)
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_map[op.layer.bottom[0]]
op.output_shape_map[op.layer.top[0]] = 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 = self.CommonConvert(op, 'AddN')
op_def.output.extend([op.name + ':0'])
output_shape = op.parents[0].output_shape_map[op.layer.bottom[0]]
op.output_shape_map[op.layer.top[0]] = 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 = self.CommonConvert(op, 'Concat')
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 i in range(len(op.parents)):
input_shapes.append(op.parents[i].output_shape_map[op.layer.bottom[i]])
output_shape = Shapes.concat_shape(input_shapes, axis_arg.i)
op.output_shape_map[op.layer.top[0]] = 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 = self.CommonConvert(op, 'Eltwise')
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_map[op.layer.bottom[0]]
op.output_shape_map[op.layer.top[0]] = 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_slice(self, op):
op_def = self.CommonConvert(op, 'Slice')
if op.layer.HasField('slice_param'):
param = op.layer.slice_param
if param.HasField('axis') and param.axis != 1:
raise Exception('Mace do not support slice with axis ' + str(param.axis))
if len(param.slice_point) > 0:
raise Exception('Mace do not support slice with slice_point')
input_shape = op.parents[0].output_shape_map[op.layer.bottom[0]]
num_outputs = len(op.layer.top)
if (input_shape[3] % num_outputs) != 0 or \
(self.device == 'gpu' and ((input_shape[3] / num_outputs) % 4 != 0)) :
raise Exception('Mace do not support slice with input shape '
+ str(input_shape) + ' and number of output ' + str(num_outputs))
output_shape = Shapes.slice_shape(input_shape, num_outputs)
for i in range(len(op.layer.top)):
op.output_shape_map[op.layer.top[i]] = output_shape
self.add_output_shape(op_def, output_shape)
op_def.output.extend([op.name + '_' + str(i) + ':0'])
self.net_def.op.extend([op_def])
self.resolved_ops.add(op.name)
def convert_normal_op(self, op):
op_def = self.CommonConvert(op, op.type)
output_shape = op.parents[0].output_shape_map[op.layer.bottom[0]]
op.output_shape_map[op.layer.top[0]] = 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_names, output_names, is_single):
in_names = set([input_name + ":0" for input_name in input_names])
out_names = set([output_name + ":0" for output_name in output_names])
if is_single:
for op in self.net_def.op:
if len(op.input) > 0 and op.input[0] in in_names:
op.input[0] = MACE_INPUT_NODE_NAME + ':0'
if len(op.output) > 0 and op.output[0] in out_names:
op.output[0] = MACE_OUTPUT_NODE_NAME + ':0'
else:
for op in self.net_def.op:
if len(op.input) > 0 and op.input[0] in in_names:
op.input[0] = MACE_INPUT_NODE_NAME + '_' + op.input[0]
if len(op.output) > 0 and op.output[0] in out_names:
op.output[0] = MACE_OUTPUT_NODE_NAME + '_' + op.output[0]
def add_input_op_shape(self, input_nodes, input_shapes):
assert len(input_nodes) == len(input_shapes)
for i in range(len(input_nodes)):
input_op = self.ops_map[input_nodes[i]]
if input_op.layer is not None:
input_op.output_shape_map[input_op.layer.top[0]] = input_shapes[i]
else:
input_op.output_shape_map[input_op.name] = input_shapes[i]
def convert(self, input_nodes, input_shapes, output_nodes):
is_single = len(input_nodes) == 1 and len(output_nodes) == 1
if self.device == 'gpu':
self.add_input_transform(input_nodes, is_single)
assert self.ops[0].type == 'Input'
self.add_input_op_shape(input_nodes, input_shapes)
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)
elif op.type == 'Slice':
self.convert_slice(op)
else:
raise Exception('Unknown Op: %s, type: %s' % (op.name, op.type))
if self.device == 'gpu':
self.add_output_transform(output_nodes, is_single)
if self.device == 'cpu':
self.replace_in_out_name(input_nodes, output_nodes, is_single)
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_str, input_shape_str, output_node_str, 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())
input_nodes = [x for x in input_node_str.split(',')]
input_shapes = []
if input_shape_str != "":
input_shape_strs = [x for x in input_shape_str.split(':')]
for shape_str in input_shape_strs:
input_shapes.extend([[int(x) for x in shape_str.split(',')]])
output_nodes = [x for x in output_node_str.split(',')]
assert len(input_nodes) == len(input_shapes)
converter = CaffeConverter(caffe_net, weights, net_def, dt, device, winograd)
converter.convert(input_nodes, input_shapes, output_nodes)
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
mace/python/tools/convert_util.py 0 → 100644
浏览文件 @ fa4a9bf5
import tensorflow as tf
from lib.proto import mace_pb2
TF_DTYPE_2_MACE_DTYPE_MAP = {
tf.float32: mace_pb2.DT_FLOAT,
tf.double: mace_pb2.DT_DOUBLE,
tf.half: mace_pb2.DT_HALF,
tf.int64: mace_pb2.DT_INT64,
tf.int32: mace_pb2.DT_INT32,
tf.qint32: mace_pb2.DT_INT32,
tf.int16: mace_pb2.DT_INT16,
tf.qint16: mace_pb2.DT_INT16,
tf.int8: mace_pb2.DT_INT8,
tf.qint8: mace_pb2.DT_INT8,
tf.quint16: mace_pb2.DT_UINT16,
tf.uint16: mace_pb2.DT_UINT16,
tf.quint8: mace_pb2.DT_UINT8,
tf.uint8: mace_pb2.DT_UINT8,
tf.string: mace_pb2.DT_STRING,
tf.bool: mace_pb2.DT_BOOL,
}
def tf_dtype_2_mace_dtype(tf_dtype):
mace_dtype = TF_DTYPE_2_MACE_DTYPE_MAP.get(tf_dtype, None)
if not mace_dtype:
raise Exception("Not supported tensorflow dtype: " + tf_dtype)
return mace_dtype
mace/python/tools/converter.py 0 → 100644
浏览文件 @ fa4a9bf5
import argparse
import sys
import hashlib
import os.path
from lib.python.tools import source_converter_lib
# ./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
def file_checksum(fname):
hash_func = hashlib.sha256()
with open(fname, "rb") as f:
for chunk in iter(lambda: f.read(4096), b""):
hash_func.update(chunk)
return hash_func.hexdigest()
def main(unused_args):
if not os.path.isfile(FLAGS.model_file):
print("Input graph file '" + FLAGS.model_file + "' does not exist!")
sys.exit(-1)
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))
sys.exit(-1)
if FLAGS.platform == 'caffe':
if not os.path.isfile(FLAGS.weight_file):
print("Input weight file '" + FLAGS.weight_file + "' does not exist!")
sys.exit(-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))
sys.exit(-1)
if FLAGS.runtime == 'dsp':
print("DSP not support caffe model yet.")
sys.exit(-1)
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, FLAGS.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:
from lib.python.tools import tf_converter_lib
output_graph_def = tf_converter_lib.convert_to_mace_pb(
FLAGS.model_file, FLAGS.input_node, FLAGS.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 open(FLAGS.output, "wb") as f:
f.write(output_graph_def.SerializeToString())
with open(FLAGS.output + '_txt', "wb") as f:
# output_graph_def.ClearField('tensors')
f.write(str(output_graph_def))
print("Model conversion is completed.")
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def parse_args():
"""Parses command line arguments."""
parser = argparse.ArgumentParser()
parser.register("type", "bool", lambda v: v.lower() == "true")
parser.add_argument(
"--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="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,
default="",
help="File to save the output graph to.")
parser.add_argument(
"--runtime",
type=str,
default="cpu",
help="Runtime: cpu/gpu/dsp")
parser.add_argument(
"--input_node",
type=str,
default="input_node",
help="e.g., input_node")
parser.add_argument(
"--output_node",
type=str,
default="softmax",
help="e.g., softmax")
parser.add_argument(
"--data_type",
type=str,
default='DT_FLOAT',
help="e.g., DT_HALF/DT_FLOAT")
parser.add_argument(
"--output_type",
type=str,
default="pb",
help="output type: source/pb")
parser.add_argument(
"--template",
type=str,
default="",
help="template path")
parser.add_argument(
"--obfuscate",
type=str2bool,
nargs='?',
const=False,
default=False,
help="obfuscate model names")
parser.add_argument(
"--model_tag",
type=str,
default="",
help="model tag for generated function and namespace")
parser.add_argument(
"--winograd",
type=str2bool,
nargs='?',
const=False,
default=False,
help="open winograd convolution or not")
parser.add_argument(
"--dsp_mode",
type=int,
default=0,
help="dsp run mode, defalut=0")
parser.add_argument(
"--input_shape",
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,
default=True,
help="input shape.")
return parser.parse_known_args()
if __name__ == '__main__':
FLAGS, unparsed = parse_args()
main(unused_args=[sys.argv[0]] + unparsed)
mace/python/tools/dsp_ops.py 0 → 100644
浏览文件 @ fa4a9bf5
class DspOps(object):
def __init__(self):
self.dsp_ops = {
'INPUT': 'INPUT"',
'OUTPUT': 'OUTPUT',
'NoOp': 'Nop',
'FLATTEN': 'Flatten',
'Identity': 'Nop',
'Placeholder': 'INPUT',
'Const': 'Const',
'QuantizedConv2D': 'QuantizedConv2d_8x8to32',
'QuantizedMatMul': 'QuantizedMatMul_8x8to32',
'QuantizeDownAndShrinkRange': 'QuantizeDownAndShrinkRange_32to8',
'QuantizedRelu': 'QuantizedRelu_8',
'QuantizedReluX': 'QuantizedReluX_8',
'QuantizedMaxPool': 'QuantizedMaxPool_8',
'QuantizedAvgPool': 'QuantizedAvgPool_8',
'QuantizedConcat': 'QuantizedConcat_8',
'QuantizedBiasAdd': 'QuantizedBiasAdd_8p8to32',
'QuantizedResizeBilinear' : 'QuantizedResizeBilinear_8',
'QuantizedSpaceToBatchND': 'QuantizedSpaceToBatchND_8',
'QuantizedBatchToSpaceND': 'QuantizedBatchToSpaceND_8',
'QuantizedSoftmax': 'QuantizedSoftmax_8',
'Min': 'Min_f',
'Max': 'Max_f',
'QuantizeV2': 'Quantize',
'Dequantize': 'Dequantize',
'Softmax': 'Softmax_f',
'Reshape': 'Reshape',
'QuantizedReshape': 'QuantizedReshape',
'Sigmoid': 'Sigmoid_f',
'Slice': 'Slice_f',
'Add': 'Add_f',
'Mul': 'Mul_f',
'Requantize': 'Requantize_32to8',
'RequantizationRange': 'RequantizationRange_32',
'Sub': 'Sub_f',
'Pack': 'Pack_int32',
'StridedSlice': 'StridedSlice_f',
'ExpandDims': 'ExpandDims_f',
'QuantizedMul': 'QuantizedMul_8x8to32',
'QuantizedAdd': 'QuantizedAdd_8p8to32',
'Pad': 'Pad_f',
'SpaceToBatchND': 'SpaceToBatchND_f',
'BatchToSpaceND': 'BatchToSpaceND_f',
'ResizeBilinear': 'ResizeBilinear_f',
'ConcatV2': 'ConcatV2_f',
'Conv2DBackpropInput': 'Deconv_f',
'Tanh': 'Tanh_f',
'Split': 'Split_f',
'Transpose': 'Transpose_f',
'Concat': 'Concat_f',
'AddN': 'AddN_f',
}
def has_op(self, tf_op):
return tf_op in self.dsp_ops
def map_nn_op(self, tf_op):
if tf_op not in self.dsp_ops:
raise Exception('Could not map nn op for: ', tf_op)
return self.dsp_ops[tf_op]
mace/python/tools/graph_util.py 0 → 100644
浏览文件 @ fa4a9bf5
import tensorflow as tf
from lib.proto import mace_pb2
from collections import OrderedDict
def sort_tf_node(node, nodes_map, ordered_nodes_map):
if node.name not in ordered_nodes_map:
for input_tensor_name in node.input:
input_node_name = input_tensor_name.split(':')[
0] if ':' in input_tensor_name else input_tensor_name
if input_node_name not in nodes_map or input_node_name in ordered_nodes_map:
continue
input_node = nodes_map[input_node_name]
sort_tf_node(input_node, nodes_map, ordered_nodes_map)
ordered_nodes_map[node.name] = node
def sort_tf_graph(graph_def):
nodes_map = {}
ordered_nodes_map = OrderedDict()
for node in graph_def.node:
nodes_map[node.name] = node
for node in graph_def.node:
sort_tf_node(node, nodes_map, ordered_nodes_map)
sorted_graph = tf.GraphDef()
sorted_graph.node.extend([node for node in ordered_nodes_map.values()])
return sorted_graph
def sort_mace_node(node, nodes_map, ordered_nodes_map):
if node.name not in ordered_nodes_map:
for input_tensor_name in node.input:
input_node_name = input_tensor_name.split(':')[
0] if ':' in input_tensor_name else input_tensor_name
if input_node_name not in nodes_map or input_node_name in ordered_nodes_map:
continue
input_node = nodes_map[input_node_name]
sort_mace_node(input_node, nodes_map, ordered_nodes_map)
ordered_nodes_map[node.name] = node
def sort_mace_graph(graph_def, output_name):
nodes_map = {}
ordered_nodes_map = OrderedDict()
for node in graph_def.op:
nodes_map[node.name] = node
sort_mace_node(nodes_map[output_name], nodes_map, ordered_nodes_map)
sorted_graph = mace_pb2.NetDef()
sorted_graph.tensors.extend(graph_def.tensors)
sorted_graph.op.extend([node for node in ordered_nodes_map.values()])
return sorted_graph
mace/python/tools/memory_optimizer.py 0 → 100644
浏览文件 @ fa4a9bf5
import sys
import operator
from lib.proto import mace_pb2
class MemoryOptimizer(object):
def __init__(self, net_def):
self.net_def = net_def
self.idle_mem = set()
self.op_mem = {} # op_name->mem_id
self.mem_block = {} # mem_id->[x, y]
self.total_mem_count = 0
self.ref_counter = {}
consumers = {}
for op in net_def.op:
if self.is_buffer_image_op(op):
continue
for ipt in op.input:
if ipt not in consumers:
consumers[ipt] = []
consumers[ipt].append(op)
# only ref op's output tensor
for op in net_def.op:
if self.is_buffer_image_op(op):
continue
for output in op.output:
tensor_name = output
if tensor_name in consumers:
self.ref_counter[tensor_name] = len(consumers[tensor_name])
else:
self.ref_counter[tensor_name] = 0
def is_buffer_image_op(self, op):
return op.type == 'BufferToImage' or op.type == 'ImageToBuffer'
def get_mem_size(self, op_type, output_shape):
mem_size = [0, 0]
if op_type == 'WinogradTransform' or op_type == 'GEMM':
mem_size[0] = output_shape[2] * output_shape[3]
mem_size[1] = output_shape[0] * int((output_shape[1]+3)/4)
else:
mem_size[0] = output_shape[2] * int((output_shape[3]+3)/4)
mem_size[1] = output_shape[0] * output_shape[1]
return mem_size
def optimize(self):
for op in self.net_def.op:
if self.is_buffer_image_op(op):
continue
if not op.output_shape:
print('WARNING: There is no output shape information to do memory optimization.')
return
if len(op.output_shape) != len(op.output):
print('WARNING: the number of output shape is not equal to the number of output.')
return
for i in range(len(op.output)):
if len(self.idle_mem) == 0:
# allocate new mem
mem_id = self.total_mem_count
self.total_mem_count += 1
else:
# reuse mem
mem_id = self.idle_mem.pop()
op.mem_id.extend([mem_id])
self.op_mem[op.output[i]] = mem_id
if mem_id not in self.mem_block:
self.mem_block[mem_id] = [0, 0]
mem_size = self.mem_block[mem_id]
op_mem_size = self.get_mem_size(op.type, op.output_shape[i].dims)
mem_size[0] = max(mem_size[0], op_mem_size[0])
mem_size[1] = max(mem_size[1], op_mem_size[1])
# de-ref input tensor mem
for ipt in op.input:
if ipt in self.ref_counter:
self.ref_counter[ipt] -= 1
if self.ref_counter[ipt] == 0:
self.idle_mem.add(self.op_mem[ipt])
elif self.ref_counter[ipt] < 0:
raise Exception('ref count is less than 0')
for mem in self.mem_block:
arena = self.net_def.mem_arena
block = arena.mem_block.add()
block.mem_id = mem
block.x = self.mem_block[mem][0]
block.y = self.mem_block[mem][1]
print('total op: %d', len(self.net_def.op))
origin_mem_size = 0
optimized_mem_size = 0
for op in self.net_def.op:
if self.is_buffer_image_op(op):
continue
origin_mem_size += reduce(operator.mul, op.output_shape[0].dims, 1)
for mem in self.mem_block:
print mem, self.mem_block[mem]
optimized_mem_size += reduce(operator.mul, self.mem_block[mem], 4)
print('origin mem: %d, optimized mem: %d', origin_mem_size, optimized_mem_size)
def optimize_memory(net_def):
mem_optimizer = MemoryOptimizer(net_def)
mem_optimizer.optimize()
mace/python/tools/model.template 0 → 100644
浏览文件 @ fa4a9bf5
//
// Copyright (c) 2017 XiaoMi All rights reserved.
// Generated by the mace converter. DO NOT EDIT!
//
#include <vector>
#include <string>
#include "mace/public/mace.h"
#include "mace/utils/env_time.h"
#include "mace/utils/logging.h"
namespace mace {
namespace {{tag}} {
{% for tensor in tensors %}
extern void CreateTensor{{ tensor.id }}(std::vector<mace::ConstTensor> &tensors,
const unsigned char *model_data);
{% endfor %}
{% for i in range(net.op|length) %}
extern void CreateOperator{{i}}(mace::OperatorDef &op);
{% endfor %}
} // namespace {{ tag }}
namespace {
{% if net.arg|length != 0 %}
void CreateNetArg(mace::NetDef &net_def) {
net_def.mutable_arg().reserve({{ net.arg|length }});
mace::Argument *arg = nullptr;
{% for arg in net.arg %}
arg = net_def.add_arg();
arg->set_name({{ arg.name|tojson }});
{%- if arg.HasField('f') %}
arg->set_f({{ arg.f }});
{% endif %}
{%- if arg.HasField('i') %}
arg->set_i({{ arg.i }});
{% endif %}
{%- if arg.HasField('s') %}
arg->set_s({{ arg.s|tojson }});
{% endif %}
{% if arg.floats|length != 0 %}
arg->set_floats({ {{ arg.floats|join(', ') }} });
{% endif %}
{% if arg.ints|length != 0 %}
arg->set_ints({ {{ arg.ints|join(', ') }} });
{% endif %}
{% if arg.strings|length != 0 %}
arg->set_strings({ {{ arg.strings|stringfy() }} });
{% endif %}
{% endfor %}
}
{% endif %}
{% if net.output_info | length > 0 %}
void CreateOutputInfo(mace::NetDef &net_def) {
std::vector<std::vector<int>> dims { {{net.output_info | map(attribute='dims') | join(', ') | replace('[', '{') | replace(']', '}') }} };
std::vector<int> data_types_int { {{ net.output_info | map(attribute='data_type') | join(', ') }} };
std::vector<mace::DataType> data_types({{ net.output_info | length }});
for (int k = 0; k < {{ net.output_info | length }}; ++k) {
data_types[k] = static_cast<mace::DataType>(data_types_int[k]);
}
net_def.mutable_output_info().resize({{ net.output_info | length }});
for (int i = 0; i < {{ net.output_info | length }}; ++i) {
net_def.mutable_output_info()[i].set_data_type(data_types[i]);
net_def.mutable_output_info()[i].set_dims(dims[i]);
}
}
{% endif %}
void CreateOperators(std::vector<mace::OperatorDef> &ops) {
MACE_LATENCY_LOGGER(1, "Create operators");
ops.resize({{ net.op|length }});
{% for i in range(net.op|length) %}
mace::{{tag}}::CreateOperator{{i}}(ops[{{i}}]);
{% endfor %}
}
void CreateTensors(std::vector<mace::ConstTensor> &tensors,
const unsigned char *model_data) {
MACE_LATENCY_LOGGER(1, "Create tensors");
tensors.reserve({{ net.tensors|length }});
{% for tensor in tensors %}
mace::{{tag}}::CreateTensor{{tensor.id}}(tensors, model_data);
{% endfor %}
}
{% if net.mem_arena.mem_block|length != 0 %}
void CreateMemoryArena(mace::MemoryArena &mem_arena) {
std::vector<mace::MemoryBlock> &mem_block = mem_arena.mutable_mem_block();
mem_block.reserve({{ net.mem_arena.mem_block|length }});
{% for mem_blk in net.mem_arena.mem_block %}
mem_block.emplace_back(mace::MemoryBlock({{ mem_blk.mem_id }},
{{mem_blk.x}},
{{mem_blk.y}}));
{% endfor %}
}
{% endif %}
} // namespace
namespace {{tag}} {
NetDef CreateNet(const unsigned char *model_data) {
MACE_LATENCY_LOGGER(1, "Create net {{ net.name }}");
NetDef net_def;
net_def.set_name("{{ net.name}}");
net_def.set_version("{{ net.version }}");
{% if net.arg|length != 0 %}
CreateNetArg(net_def);
{% endif %}
CreateOperators(net_def.mutable_op());
CreateTensors(net_def.mutable_tensors(), model_data);
{% if net.mem_arena.mem_block|length != 0 %}
CreateMemoryArena(net_def.mutable_mem_arena());
{% endif %}
{% if net.output_info | length > 0 %}
CreateOutputInfo(net_def);
{% endif %}
return net_def;
}
const std::string ModelChecksum() {
return {{ model_pb_checksum|tojson }};
}
} // namespace {{tag}}
} // namespace mace
mace/python/tools/model_header.template 0 → 100644
浏览文件 @ fa4a9bf5
//
// Copyright (c) 2017 XiaoMi All rights reserved.
// Generated by the mace converter. DO NOT EDIT!
//
#include <string>
#include "mace/public/mace.h"
namespace mace {
namespace {{tag}} {
extern const unsigned char *LoadModelData(const char *model_data_file);
extern void UnloadModelData(const unsigned char *model_data);
extern NetDef CreateNet(const unsigned char *model_data);
extern const std::string ModelChecksum();
} // namespace {{ tag }}
} // namespace mace
mace/python/tools/opencl_codegen.py 0 → 100644
浏览文件 @ fa4a9bf5
import argparse
import os
import sys
import numpy as np
import jinja2
# python mace/python/tools/opencl_codegen.py \
# --cl_binary_dirs=${CL_BIN_DIR} --output_path=${CL_HEADER_PATH}
FLAGS = None
def generate_cpp_source():
maps = {}
cl_binary_dir_arr = FLAGS.cl_binary_dirs.split(",")
for cl_binary_dir in cl_binary_dir_arr:
if not os.path.exists(cl_binary_dir):
print("Input cl_binary_dir " + cl_binary_dir + " doesn't exist!")
for file_name in os.listdir(cl_binary_dir):
file_path = os.path.join(cl_binary_dir, file_name)
if file_path[-4:] == ".bin":
# read binary
f = open(file_path, "rb")
binary_array = np.fromfile(f, dtype=np.uint8)
f.close()
maps[file_name[:-4]] = []
for ele in binary_array:
maps[file_name[:-4]].append(hex(ele))
env = jinja2.Environment(loader=jinja2.FileSystemLoader(sys.path[0]))
return env.get_template('str2vec_maps.cc.tmpl').render(
maps = maps,
data_type = 'unsigned char',
variable_name = 'kCompiledProgramMap'
)
def main(unused_args):
cpp_cl_binary_source = generate_cpp_source()
if os.path.isfile(FLAGS.output_path):
os.remove(FLAGS.output_path)
w_file = open(FLAGS.output_path, "w")
w_file.write(cpp_cl_binary_source)
w_file.close()
def parse_args():
"""Parses command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
"--cl_binary_dirs",
type=str,
default="cl_bin0/,cl_bin1/,cl_bin2/",
help="The cl binaries directories.")
parser.add_argument(
"--output_path",
type=str,
default="./mace/examples/codegen/opencl/opencl_compiled_program.cc",
help="The path of generated C++ header file which contains cl binaries.")
return parser.parse_known_args()
if __name__ == '__main__':
FLAGS, unparsed = parse_args()
main(unused_args=[sys.argv[0]] + unparsed)
mace/python/tools/operator.template 0 → 100644
浏览文件 @ fa4a9bf5
//
// Copyright (c) 2017 XiaoMi All rights reserved.
// Generated by the mace converter. DO NOT EDIT!
//
#include <vector>
#include <string>
#include "mace/public/mace.h"
#include "mace/utils/env_time.h"
#include "mace/utils/logging.h"
namespace mace {
namespace {
void UpdateOp(mace::OperatorDef &op,
const std::string &name,
const std::string &type,
const std::vector<std::string> &inputs,
const std::vector<std::string> &outputs,
const std::vector<mace::DataType> &output_types,
uint32_t node_id,
const std::vector<int> &mem_ids) {
op.set_name(name);
op.set_type(type);
op.set_input(inputs);
op.set_output(outputs);
op.set_output_type(output_types);
op.set_node_id(node_id);
op.set_mem_id(mem_ids);
}
} // namespace
} // namespace mace
namespace mace {
namespace {{tag}} {
{% for i in range(start, end) %}
void CreateOperator{{i}}(mace::OperatorDef &op) {
MACE_LATENCY_LOGGER(2, "Create operator {{ net.op[i].name }}");
mace::Argument *arg = nullptr;
{% for arg in net.op[i].arg %}
arg = op.add_arg();
arg->set_name({{ arg.name|tojson }});
{%- if arg.HasField('f') %}
arg->set_f({{ arg.f }});
{%- endif %}
{%- if arg.HasField('i') %}
arg->set_i({{ arg.i }});
{%- endif %}
{%- if arg.HasField('s') %}
arg->set_s({{ arg.s|tojson }});
{%- endif %}
{% if arg.floats|length != 0 %}
arg->set_floats({ {{ arg.floats|join(', ') }} });
{% endif %}
{% if arg.ints|length != 0 %}
arg->set_ints({ {{ arg.ints|join(', ') }} });
{% endif %}
{% if arg.strings|length != 0 %}
arg->set_strings({ {{ arg.strings|stringfy() }} });
{% endif %}
{% endfor %}
{% for shape in net.op[i].output_shape %}
{% if shape.dims | length > 0 %}
op.add_output_shape(mace::OutputShape({ {{ shape.dims|join(', ') }} }));
{% endif %}
{% endfor %}
std::vector<int> output_types_int({ {{ net.op[i].output_type | join(', ') }} });
std::vector<mace::DataType> output_types({{ net.op[i].output_type | length }});
for (int k = 0; k < {{ net.op[i].output_type | length }}; ++k) {
output_types[k] = static_cast<mace::DataType>(output_types_int[k]);
}
UpdateOp(op, {{ net.op[i].name|tojson }}, {{ net.op[i].type|tojson}},
{ {{ net.op[i].input|stringfy }} },
{ {{ net.op[i].output|stringfy }} },
output_types,
{{ net.op[i].node_id }},
{ {{ net.op[i].mem_id | join(', ') }} });
{% if runtime == 'dsp' %}
op.set_padding({{ net.op[i].padding }});
{% if net.op[i].node_input | length > 0 %}
std::vector<int> input_node_ids({ {{ net.op[i].node_input | map(attribute='node_id') | join(', ') }} });
std::vector<int> input_output_ports({ {{ net.op[i].node_input | map(attribute='output_port') | join(', ')}} });
for (size_t i = 0; i < {{ net.op[i].node_input | length }}; ++i) {
mace::NodeInput input(input_node_ids[i], input_output_ports[i]);
op.add_node_input(input);
}
{% endif %}
{% if net.op[i].out_max_byte_size | length > 0 %}
std::vector<int> out_max_byte_sizes {{ net.op[i].out_max_byte_size | replace('[', '{') | replace(']', '}') }};
for (size_t i = 0; i < {{ net.op[i].out_max_byte_size | length }}; ++i) {
op.add_out_max_byte_size(out_max_byte_sizes[i]);
}
{% endif %}
{% endif %}
}
{% endfor %}
} // namespace {{tag}}
} // namespace mace
mace/python/tools/source_converter_lib.py 0 → 100644
浏览文件 @ fa4a9bf5
import os
import uuid
import numpy as np
import hashlib
from lib.proto import mace_pb2
from jinja2 import Environment, FileSystemLoader
GENERATED_NAME = set()
def generate_obfuscated_name(namespace, name):
md5 = hashlib.md5()
md5.update(namespace)
md5.update(name)
md5_digest = md5.hexdigest()
name = md5_digest[:8]
while name in GENERATED_NAME:
name = md5_digest
assert name not in GENERATED_NAME
GENERATED_NAME.add(name)
return name
def generate_tensor_map(tensors):
tensor_map = {}
for t in tensors:
if not tensor_map.has_key(t.name):
tensor_map[t.name] = generate_obfuscated_name("tensor", t.name)
return tensor_map
def generate_in_out_map(ops, tensor_map):
in_out_map = {}
for op in ops:
op.name = generate_obfuscated_name("op", op.name)
for input_name in op.input:
if not in_out_map.has_key(input_name):
if tensor_map.has_key(input_name):
in_out_map[input_name] = tensor_map[input_name]
else:
in_out_map[input_name] = generate_obfuscated_name("in", input_name)
for output_name in op.output:
if not in_out_map.has_key(output_name):
if tensor_map.has_key(output_name):
in_out_map[output_name] = tensor_map[output_name]
else:
in_out_map[output_name] = generate_obfuscated_name("out", output_name)
return in_out_map
def obfuscate_name(net_def):
input_node = "mace_input_node"
output_node = "mace_output_node"
tensor_map = generate_tensor_map(net_def.tensors)
in_out_map = generate_in_out_map(net_def.op, tensor_map)
for t in net_def.tensors:
if input_node not in t.name and output_node not in t.name:
t.name = tensor_map[t.name]
for op in net_def.op:
for i in range(len(op.input)):
if input_node not in op.input[i]:
op.input[i] = in_out_map[op.input[i]]
for i in range(len(op.output)):
if output_node not in op.output[i]:
op.output[i] = in_out_map[op.output[i]]
def rename_tensor(net_def):
tensor_map = {}
for t in net_def.tensors:
if not tensor_map.has_key(t.name):
tensor_map[t.name] = "_" + t.name[:-2].replace("/", "_")
t.name = tensor_map[t.name]
for op in net_def.op:
for i in range(len(op.input)):
if tensor_map.has_key(op.input[i]):
op.input[i] = tensor_map[op.input[i]]
for i in range(len(op.output)):
if tensor_map.has_key(op.output[i]):
op.output[i] = tensor_map[op.output[i]]
class TensorInfo:
def __init__(self, id, t, runtime):
self.id = id
self.data_type = mace_pb2.DataType.Name(t.data_type)
if t.data_type == mace_pb2.DT_FLOAT:
if runtime == 'gpu':
self.data_type = mace_pb2.DT_HALF
self.data = bytearray(np.array(t.float_data).astype(np.float16).tobytes())
else:
self.data_type = mace_pb2.DT_FLOAT
self.data = bytearray(np.array(t.float_data).astype(np.float32).tobytes())
elif t.data_type == mace_pb2.DT_INT32:
self.data = bytearray(np.array(t.int32_data).astype(np.int32).tobytes())
elif t.data_type == mace_pb2.DT_UINT8:
self.data = bytearray(np.array(t.int32_data).astype(np.uint8).tolist())
def stringfy(value):
return ', '.join('"{0}"'.format(w) for w in value)
def convert_to_source(net_def, mode_pb_checksum, template_dir, obfuscate, model_tag, output, runtime, embed_model_data):
if obfuscate:
obfuscate_name(net_def)
else:
rename_tensor(net_def)
# Capture our current directory
print template_dir
# Create the jinja2 environment.
j2_env = Environment(loader=FileSystemLoader(template_dir), trim_blocks=True)
j2_env.filters['stringfy'] = stringfy
output_dir = os.path.dirname(output) + '/'
# generate tensor source files
template_name = 'tensor_source.template'
model_data = []
offset = 0
counter = 0
for t in net_def.tensors:
tensor_info = TensorInfo(counter, t, runtime)
# align
if tensor_info.data_type != 'DT_UINT8' and offset % 4 != 0:
padding = 4 - offset % 4
model_data.extend(bytearray([0] * padding))
offset += padding
source = j2_env.get_template(template_name).render(
tensor_info = tensor_info,
tensor = t,
tag = model_tag,
runtime = runtime,
offset = offset,
)
model_data.extend(tensor_info.data)
offset += len(tensor_info.data)
with open(output_dir + 'tensor' + str(counter) + '.cc', "wb") as f:
f.write(source)
counter += 1
# generate tensor data
template_name = 'tensor_data.template'
source = j2_env.get_template(template_name).render(
tag = model_tag,
embed_model_data = embed_model_data,
model_data_size = offset,
model_data = model_data
)
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")
f.write(bytearray(model_data))
f.close()
# generate op source files
template_name = 'operator.template'
counter = 0
op_size = len(net_def.op)
for start in range(0, op_size, 10):
source = j2_env.get_template(template_name).render(
start = start,
end = min(start+10, op_size),
net = net_def,
tag = model_tag,
runtime = runtime,
)
with open(output_dir + 'op' + str(counter) + '.cc', "wb") as f:
f.write(source)
counter += 1
# generate model source files
template_name = 'model.template'
tensors = [TensorInfo(i, net_def.tensors[i], runtime) for i in range(len(net_def.tensors))]
source = j2_env.get_template(template_name).render(
tensors = tensors,
net = net_def,
tag = model_tag,
runtime = runtime,
model_pb_checksum = mode_pb_checksum
)
with open(output, "wb") as f:
f.write(source)
# generate model header file
template_name = 'model_header.template'
source = j2_env.get_template(template_name).render(
tag = model_tag,
)
with open(output_dir + model_tag + '.h', "wb") as f:
f.write(source)
mace/python/tools/tensor_data.template 0 → 100644
浏览文件 @ fa4a9bf5
//
// Copyright (c) 2017 XiaoMi All rights reserved.
// Generated by the mace converter. DO NOT EDIT!
//
#include <vector>
#include <string>
#include "mace/public/mace.h"
#include "mace/utils/env_time.h"
#include "mace/utils/logging.h"
{% if not embed_model_data %}
#include <errno.h>
#include <fcntl.h>
#include <string.h>
#include <sys/mman.h>
#include <unistd.h>
{% endif %}
namespace mace {
namespace {{tag}} {
{% if embed_model_data %}
alignas(4) const unsigned char model_data[{{ model_data_size }}] = {
{% for d in model_data %}{{"0x%02X, " % d }}{%endfor%}
};
{% endif %}
const unsigned char *LoadModelData(const char *model_data_file) {
{% if embed_model_data %}
return model_data;
{% else %}
int fd = open(model_data_file, O_RDONLY);
MACE_CHECK(fd >= 0, "Failed to open model data file ",
model_data_file, ", error code: ", errno);
const unsigned char *model_data =
static_cast<const unsigned char *>(mmap(nullptr, {{ model_data_size }},
PROT_READ, MAP_PRIVATE, fd, 0));
MACE_CHECK(model_data != MAP_FAILED, "Failed to map model data file ",
model_data_file, ", error code: ", errno);
int ret = close(fd);
MACE_CHECK(ret == 0, "Failed to close model data file ",
model_data_file, ", error code: ", errno);
return model_data;
{% endif %}
}
void UnloadModelData(const unsigned char *model_data) {
{% if not embed_model_data %}
int ret = munmap(const_cast<unsigned char *>(model_data),
{{ model_data_size }});
MACE_CHECK(ret == 0, "Failed to unmap model data file, error code: ", errno);
{% endif %}
}
} // namespace {{tag}}
} // namespace mace
mace/python/tools/tensor_source.template 0 → 100644
浏览文件 @ fa4a9bf5
//
// Copyright (c) 2017 XiaoMi All rights reserved.
// Generated by the mace converter. DO NOT EDIT!
//
#include <vector>
#include <string>
#include "mace/public/mace.h"
#include "mace/utils/env_time.h"
#include "mace/utils/logging.h"
namespace mace {
namespace {{tag}} {
void CreateTensor{{tensor_info.id}}(std::vector<mace::ConstTensor> &tensors,
const unsigned char *model_data) {
MACE_LATENCY_LOGGER(2, "Create tensor {{ tensor.name }}");
tensors.emplace_back(mace::ConstTensor(
{{ tensor.name|tojson }}, model_data + {{ offset }},
{ {{ tensor.dims|join(', ') }} }, {{ tensor_info.data_type }}, {{ tensor.node_id }}));
}
} // namespace {{tag}}
} // namespace mace
mace/python/tools/tf_converter_lib.py 0 → 100644
浏览文件 @ fa4a9bf5
from lib.proto import mace_pb2
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
# TODO: support NCHW formt, now only support NHWC.
padding_mode = {
'VALID': 0,
'SAME': 1,
'FULL': 2
}
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,
}
data_type_map = {
'DT_HALF' : mace_pb2.DT_HALF,
'DT_FLOAT': mace_pb2.DT_FLOAT
}
activation_name_map = {
'Relu' : 'RELU',
'Sigmoid' : 'SIGMOID',
'Tanh' : 'TANH',
'Relu6' : 'RELUX'
}
BATCH_NORM_ORDER = ["Add", "Rsqrt", "Mul", "Mul", "Mul", "Sub", "Add"]
MACE_INPUT_NODE_NAME = "mace_input_node"
MACE_OUTPUT_NODE_NAME = "mace_output_node"
OPENCL_IMAGE_MAX_SIZE = 16384
def get_input_tensor(op, index):
input_tensor = op.inputs[index]
if input_tensor.op.type == 'Reshape':
input_tensor = get_input_tensor(input_tensor.op, 0)
return input_tensor
class TFConverter(object):
def __init__(self, tf_ops, net_def, dt, device, winograd):
self.net_def = net_def
self.tf_ops = tf_ops
self.dt = dt
self.device = device
self.winograd = winograd
self.tf_graph = {}
self.tf_parents = {}
self.resolved_ops = {}
self.unused_tensor = set()
self.transpose_filter_tensor = {}
self.reshape_tensor = {}
self.ops = {}
for op in tf_ops:
self.ops[op.name] = op
for op in tf_ops:
self.resolved_ops[op.name] = 0
for input in op.inputs:
input_name = input.name[:-2]
if input_name not in self.tf_graph:
self.tf_graph[input_name] = []
self.tf_graph[input_name].append(op)
if op.name not in self.tf_parents:
self.tf_parents[op.name] = []
self.tf_parents[op.name].append(self.ops[input_name])
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, names, is_single):
for name in names:
if is_single:
new_input_name = MACE_INPUT_NODE_NAME + ":0"
else:
new_input_name = MACE_INPUT_NODE_NAME + '_' + name + ":0"
op_def = self.net_def.op.add()
op_def.name = name
op_def.type = 'BufferToImage'
op_def.input.extend([new_input_name])
op_def.output.extend([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, names, is_single):
for name in names:
if is_single:
output_name = MACE_OUTPUT_NODE_NAME + ":0"
else:
output_name = MACE_OUTPUT_NODE_NAME + '_' + 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']
@staticmethod
def add_output_shape(outputs, op):
output_shapes = []
for output in outputs:
if output.shape.num_elements() is not None:
output_shape = mace_pb2.OutputShape()
output_shape.dims.extend(output.shape.as_list())
output_shapes.append(output_shape)
op.output_shape.extend(output_shapes)
def add_tensor(self, name, shape, tf_dt, value):
tensor = self.net_def.tensors.add()
tensor.name = name
shape = list(shape)
tensor.dims.extend(shape)
if tf_dt == tf.float32:
tensor.data_type = mace_pb2.DT_FLOAT
tensor.float_data.extend(value.flat)
elif tf_dt == tf.int32:
tensor.data_type = mace_pb2.DT_INT32
tensor.int32_data.extend(value.flat)
else:
raise Exception("Not supported tensor type: " + tf_dt.name)
def convert_reshape(self, op):
input_tensor = get_input_tensor(op, 0)
shape_tensor = get_input_tensor(op, 1)
shape_value = shape_tensor.eval().astype(np.int32)
self.unused_tensor.add(shape_tensor.name)
self.reshape_tensor[input_tensor.name] = shape_value
self.resolved_ops[op.name] = 1
def convert_tensor(self, op):
output_name = op.outputs[0].name
if output_name not in self.unused_tensor:
tensor = self.net_def.tensors.add()
tf_tensor = op.outputs[0].eval()
if output_name in self.transpose_filter_tensor:
tf_tensor = tf_tensor.transpose(self.transpose_filter_tensor[output_name])
if output_name in self.reshape_tensor:
tf_tensor = tf_tensor.reshape(self.reshape_tensor[output_name])
tensor.name = op.outputs[0].name
shape = list(tf_tensor.shape)
tensor.dims.extend(shape)
tf_dt = op.get_attr('dtype')
if tf_dt == tf.float32:
tensor.data_type = mace_pb2.DT_FLOAT
tensor.float_data.extend(tf_tensor.astype(np.float32).flat)
elif tf_dt == tf.int32:
tensor.data_type = mace_pb2.DT_INT32
tensor.int32_data.extend(tf_tensor.astype(np.int32).flat)
else:
raise Exception("Not supported tensor type: " + tf_dt.name)
self.resolved_ops[op.name] = 1
def check_winograd_conv(self, op):
filter_shape = get_input_tensor(op, 1).shape.as_list()
strides = op.get_attr('strides')[1:3]
output_shape = op.outputs[0].shape.as_list()
if len(output_shape) == 0 or output_shape[0] is None:
return False
width = output_shape[0] * ((output_shape[1] + 1)/2) * ((output_shape[2]+1)/2)
return self.winograd and op.type != 'DepthwiseConv2dNative' and self.device == 'gpu' and \
filter_shape[0] == 3 and (filter_shape[0] == filter_shape[1]) and \
(strides[0] == 1) and (strides[0] == strides[1]) and \
(16 * filter_shape[2] < OPENCL_IMAGE_MAX_SIZE) and \
(16 * filter_shape[3] < OPENCL_IMAGE_MAX_SIZE) and \
(width < OPENCL_IMAGE_MAX_SIZE)
def convert_winograd_conv(self, op):
filter_tensor = get_input_tensor(op, 1)
filter_shape = filter_tensor.shape.as_list()
output_shape = op.outputs[0].shape.as_list()
self.transpose_filter_tensor[filter_tensor.name] = (3, 2, 0, 1)
filter_name = self.add_buffer_to_image(op.inputs[1].name, "WINOGRAD_FILTER")
# Input transform
wt_op = mace_pb2.OperatorDef()
arg = wt_op.arg.add()
arg.name = 'T'
arg.i = self.dt
padding_arg = wt_op.arg.add()
padding_arg.name = 'padding'
padding_arg.i = padding_mode[op.get_attr('padding')]
wt_op.name = op.name + '_input_transform'
wt_op.type = 'WinogradTransform'
wt_op.input.extend([op.inputs[0].name])
wt_output_name = wt_op.name + ":0"
wt_op.output.extend([wt_output_name])
wt_output_shape = mace_pb2.OutputShape()
wt_output_width = output_shape[0] * ((output_shape[1] + 1)/2) * ((output_shape[2]+1)/2)
wt_output_shape.dims.extend([16, filter_shape[2], wt_output_width, 1])
wt_op.output_shape.extend([wt_output_shape])
# MatMul
matmul_op = mace_pb2.OperatorDef()
arg = matmul_op.arg.add()
arg.name = 'T'
arg.i = self.dt
matmul_op.name = op.name + '_matmul'
matmul_op.type = 'MatMul'
matmul_op.input.extend([filter_name, wt_output_name])
matmul_output_name = matmul_op.name + ":0"
matmul_op.output.extend([matmul_output_name])
matmul_output_shape = mace_pb2.OutputShape()
matmul_output_shape.dims.extend([16, filter_shape[3], wt_output_width, 1])
matmul_op.output_shape.extend([matmul_output_shape])
# Inverse transform
iwt_op = mace_pb2.OperatorDef()
arg = iwt_op.arg.add()
arg.name = 'T'
arg.i = self.dt
batch_arg = iwt_op.arg.add()
batch_arg.name = 'batch'
batch_arg.i = output_shape[0]
height_arg = iwt_op.arg.add()
height_arg.name = 'height'
height_arg.i = output_shape[1]
width_arg = iwt_op.arg.add()
width_arg.name = 'width'
width_arg.i = output_shape[2]
iwt_op.name = op.name + '_inverse_transform'
iwt_op.type = 'WinogradInverseTransform'
iwt_op.input.extend([matmul_output_name])
final_op = op
self.resolved_ops[op.name] = 1
if len(self.tf_graph[op.name]) == 1 and self.tf_graph[op.name][0].type == 'BiasAdd' :
bias_add_op = self.tf_graph[op.name][0]
output_name = self.add_buffer_to_image(get_input_tensor(bias_add_op, 1).name, "ARGUMENT")
iwt_op.input.extend([output_name])
final_op = bias_add_op
self.resolved_ops[bias_add_op.name] = 1
if len(self.tf_graph[final_op.name]) == 1 \
and self.tf_graph[final_op.name][0].type in activation_name_map:
activation_op = self.tf_graph[final_op.name][0]
fused_act_arg = iwt_op.arg.add()
fused_act_arg.name = 'activation'
fused_act_arg.s = activation_name_map[activation_op.type]
if activation_op.type == 'Relu6':
max_limit_arg = iwt_op.arg.add()
max_limit_arg.name = 'max_limit'
max_limit_arg.f = 6
final_op = activation_op
self.resolved_ops[activation_op.name] = 1
iwt_op.output.extend([output.name for output in final_op.outputs])
self.add_output_shape(final_op.outputs, iwt_op)
self.net_def.op.extend([wt_op, matmul_op, iwt_op])
def convert_conv2d(self, op):
op_def = mace_pb2.OperatorDef()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
if op.type == 'DepthwiseConv2dNative':
op_def.type = 'DepthwiseConv2d'
else:
op_def.type = op.type
self.transpose_filter_tensor[get_input_tensor(op, 1).name] = (0, 1, 3, 2)
if self.device == 'gpu':
op_def.input.extend([op.inputs[0].name])
buffer_type = "DW_CONV2D_FILTER" if op_def.type == 'DepthwiseConv2d' else "CONV2D_FILTER"
output_name = self.add_buffer_to_image(get_input_tensor(op, 1).name, buffer_type)
op_def.input.extend([output_name])
else:
op_def.input.extend([get_input_tensor(op, i).name for i in range(len(op.inputs))])
padding_arg = op_def.arg.add()
padding_arg.name = 'padding'
padding_arg.i = padding_mode[op.get_attr('padding')]
strides_arg = op_def.arg.add()
strides_arg.name = 'strides'
strides_arg.ints.extend(op.get_attr('strides')[1:3])
data_format_arg = op_def.arg.add()
data_format_arg.name = 'data_format'
data_format_arg.s = 'NHWC'
final_op = op
self.resolved_ops[op.name] = 1
if len(self.tf_graph.get(op.name, [])) == 1 and self.tf_graph[op.name][0].type == 'BiasAdd':
bias_add_op = self.tf_graph[op.name][0]
if self.device == 'gpu':
output_name = self.add_buffer_to_image(get_input_tensor(bias_add_op, 1).name, "ARGUMENT")
op_def.input.extend([output_name])
else:
op_def.input.extend([get_input_tensor(bias_add_op, 1).name])
final_op = bias_add_op
self.resolved_ops[bias_add_op.name] = 1
if len(self.tf_graph.get(final_op.name, [])) == 1 \
and self.tf_graph[final_op.name][0].type in activation_name_map:
activation_op = self.tf_graph[final_op.name][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]
if activation_op.type == 'Relu6':
max_limit_arg = op_def.arg.add()
max_limit_arg.name = 'max_limit'
max_limit_arg.f = 6
final_op = activation_op
self.resolved_ops[activation_op.name] = 1
op_def.output.extend([output.name for output in final_op.outputs])
self.add_output_shape(final_op.outputs, op_def)
self.net_def.op.extend([op_def])
def convert_fused_batchnorm(self, op):
op_def = mace_pb2.OperatorDef()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.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 = 'FoldedBatchNorm'
gamma_tensor = get_input_tensor(op, 1)
for i in range(1, 5):
input_tensor = get_input_tensor(op, i)
assert input_tensor.shape == gamma_tensor.shape
self.unused_tensor.add(input_tensor.name)
gamma_value = get_input_tensor(op, 1).eval().astype(np.float32)
beta_value = get_input_tensor(op, 2).eval().astype(np.float32)
mean_value = get_input_tensor(op, 3).eval().astype(np.float32)
var_value = get_input_tensor(op, 4).eval().astype(np.float32)
epsilon_value = op.get_attr('epsilon')
scale_value = (
(1.0 / np.vectorize(math.sqrt)(var_value + epsilon_value)) *
gamma_value)
offset_value = (-mean_value * scale_value) + beta_value
idx = gamma_tensor.name.rfind('/')
name_prefix = gamma_tensor.name[:idx] + '/'
input_names = [name_prefix+'scale:0', name_prefix+'offset:0']
self.add_tensor(input_names[0], gamma_value.shape,
gamma_tensor.dtype, scale_value)
self.add_tensor(input_names[1], gamma_value.shape,
gamma_tensor.dtype, offset_value)
op_def.input.extend([op.inputs[0].name])
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[op.name] = 1
final_op = op
if len(self.tf_graph[op.name]) == 1 \
and self.tf_graph[op.name][0].type in activation_name_map:
activation_op = self.tf_graph[op.name][0]
fused_act_arg = op_def.arg.add()
fused_act_arg.name = 'activation'
fused_act_arg.s = activation_name_map[activation_op.type]
if activation_op.type == 'Relu6':
max_limit_arg = op_def.arg.add()
max_limit_arg.name = 'max_limit'
max_limit_arg.f = 6
final_op = activation_op
self.resolved_ops[activation_op.name] = 1
op_def.output.extend([final_op.outputs[0].name])
self.add_output_shape([final_op.outputs[0]], op_def)
self.net_def.op.extend([op_def])
def convert_batchnorm(self, op):
bn_ops = []
bn_ops.append(op)
for i in range(1, 3):
if len(self.tf_graph[bn_ops[i-1].name]) == 1 \
and self.tf_graph[bn_ops[i-1].name][0].type == BATCH_NORM_ORDER[i]:
bn_ops.append(self.tf_graph[bn_ops[i-1].name][0])
else:
raise Exception('Invalid BatchNorm Op')
if len(self.tf_graph[bn_ops[2].name]) == 2 \
and self.tf_graph[bn_ops[2].name][0].type == BATCH_NORM_ORDER[3] \
and self.tf_graph[bn_ops[2].name][1].type == BATCH_NORM_ORDER[4]:
bn_ops.append(self.tf_graph[bn_ops[2].name][0])
bn_ops.append(self.tf_graph[bn_ops[2].name][1])
else:
raise Exception('Invalid BatchNorm Op')
bn_ops.append(self.tf_graph[bn_ops[4].name][0])
bn_ops.append(self.tf_graph[bn_ops[3].name][0])
op_def = mace_pb2.OperatorDef()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
input_name = get_input_tensor(bn_ops[3], 0).name
gamma = get_input_tensor(bn_ops[2], 1).name
beta = get_input_tensor(bn_ops[5], 0).name
mean = get_input_tensor(bn_ops[4], 0).name
variance = get_input_tensor(bn_ops[0], 0).name
op_def.name = op.name[:-4] # remove /add
op_def.type = 'BatchNorm'
if self.device == 'gpu':
op_def.input.extend([input_name])
for tensor_name in [gamma, beta, mean, variance]:
output_name = self.add_buffer_to_image(tensor_name, "ARGUMENT")
op_def.input.extend([output_name])
else:
op_def.input.extend([input_name, gamma, beta, mean, variance])
op_def.output.extend([output.name for output in bn_ops[6].outputs])
self.add_output_shape(bn_ops[6].outputs, op_def)
epsilon_arg = op_def.arg.add()
epsilon_arg.name = 'epsilon'
epsilon_arg.f = get_input_tensor(op, 1).eval().astype(np.float)
data_format_arg = op_def.arg.add()
data_format_arg.name = 'data_format'
data_format_arg.s = 'NHWC'
self.unused_tensor.add(get_input_tensor(op, 1).name)
self.net_def.op.extend([op_def])
for i in range(0, 7):
self.resolved_ops[bn_ops[i].name] = 1
def convert_pooling(self, op):
op_def = self.net_def.op.add()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
op_def.type = 'Pooling'
op_def.input.extend([input.name for input in op.inputs])
op_def.output.extend([output.name for output in op.outputs])
self.add_output_shape(op.outputs, op_def)
pooling_type_arg = op_def.arg.add()
pooling_type_arg.name = 'pooling_type'
pooling_type_arg.i = pooling_type_mode[op.type]
padding_arg = op_def.arg.add()
padding_arg.name = 'padding'
padding_arg.i = padding_mode[op.get_attr('padding')]
strides_arg = op_def.arg.add()
strides_arg.name = 'strides'
strides_arg.ints.extend(op.get_attr('strides')[1:3])
kernels_arg = op_def.arg.add()
kernels_arg.name = 'kernels'
kernels_arg.ints.extend(op.get_attr('ksize')[1:3])
data_format_arg = op_def.arg.add()
data_format_arg.name = 'data_format'
data_format_arg.s = 'NHWC'
self.resolved_ops[op.name] = 1
def convert_global_avg_pooling(self, op):
op_def = self.net_def.op.add()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
op_def.type = 'Pooling'
op_def.input.extend([op.inputs[0].name])
op_def.output.extend([output.name for output in op.outputs])
self.add_output_shape(op.outputs, op_def)
pooling_type_arg = op_def.arg.add()
pooling_type_arg.name = 'pooling_type'
pooling_type_arg.i = pooling_type_mode['AvgPool']
padding_arg = op_def.arg.add()
padding_arg.name = 'padding'
padding_arg.i = padding_mode['VALID']
strides_arg = op_def.arg.add()
strides_arg.name = 'strides'
strides_arg.ints.extend([1, 1])
kernels_arg = op_def.arg.add()
kernels_arg.name = 'kernels'
kernels_arg.ints.extend(op.inputs[0].shape.as_list()[1:3])
data_format_arg = op_def.arg.add()
data_format_arg.name = 'data_format'
data_format_arg.s = 'NHWC'
self.resolved_ops[op.name] = 1
def convert_activation(self, op):
op_def = self.net_def.op.add()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
op_def.type = 'Activation'
activation_arg = op_def.arg.add()
activation_arg.name = 'activation'
activation_arg.s = activation_name_map[op.type]
op_def.input.extend([input.name for input in op.inputs])
op_def.output.extend([output.name for output in op.outputs])
self.add_output_shape(op.outputs, op_def)
self.resolved_ops[op.name] = 1
def convert_relu6(self, op):
op_def = self.net_def.op.add()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
op_def.type = 'Activation'
op_def.input.extend([input.name for input in op.inputs])
op_def.output.extend([output.name for output in op.outputs])
self.add_output_shape(op.outputs, op_def)
activation_arg = op_def.arg.add()
activation_arg.name = 'activation'
activation_arg.s = "RELUX"
max_limit_arg = op_def.arg.add()
max_limit_arg.name = 'max_limit'
max_limit_arg.f = 6
self.resolved_ops[op.name] = 1
def convert_add(self, op):
op_def = self.net_def.op.add()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
op_def.type = "AddN"
op_def.input.extend([input.name for input in op.inputs])
op_def.output.extend([output.name for output in op.outputs])
self.add_output_shape(op.outputs, op_def)
self.resolved_ops[op.name] = 1
def convert_concat(self, op):
op_def = self.net_def.op.add()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
op_def.type = "Concat"
op_def.input.extend([input.name for input in op.inputs[:-1]])
op_def.output.extend([output.name for output in op.outputs])
axis_arg = op_def.arg.add()
axis_arg.name = 'axis'
axis_arg.i = get_input_tensor(op, len(op.inputs) - 1).eval().astype(np.int32)
self.add_output_shape(op.outputs, op_def)
self.resolved_ops[op.name] = 1
self.unused_tensor.add(get_input_tensor(op, len(op.inputs) - 1).name)
def convert_resize_bilinear(self, op):
op_def = self.net_def.op.add()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
op_def.type = "ResizeBilinear"
op_def.input.extend([op.inputs[0].name])
op_def.output.extend([output.name for output in op.outputs])
size_arg = op_def.arg.add()
size_arg.name = 'size'
size_arg.ints.extend(get_input_tensor(op, 1).eval().astype(np.int32).flat)
size_arg = op_def.arg.add()
size_arg.name = 'align_corners'
size_arg.i = op.get_attr('align_corners')
self.add_output_shape(op.outputs, op_def)
self.resolved_ops[op.name] = 1
self.unused_tensor.add(get_input_tensor(op, 1).name)
def convert_bias_add(self, op):
op_def = mace_pb2.OperatorDef()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
op_def.type = "BiasAdd"
op_def.input.extend([op.inputs[0].name])
if self.device == 'gpu':
output_name = self.add_buffer_to_image(get_input_tensor(op, 1).name, "ARGUMENT")
op_def.input.extend([output_name])
else:
op_def.input.extend([get_input_tensor(op, 1).name])
op_def.output.extend([output.name for output in op.outputs])
self.add_output_shape(op.outputs, op_def)
self.net_def.op.extend([op_def])
self.resolved_ops[op.name] = 1
def convert_space_to_batch(self, op, b2s):
op_def = self.net_def.op.add()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
op_def.type = op.type
op_def.input.extend([op.inputs[0].name])
op_def.output.extend([output.name for output in op.outputs])
size_arg = op_def.arg.add()
size_arg.name = 'block_shape'
size_arg.ints.extend(get_input_tensor(op, 1).eval().astype(np.int32).flat)
size_arg = op_def.arg.add()
if b2s:
size_arg.name = 'crops'
else:
size_arg.name = 'paddings'
size_arg.ints.extend(get_input_tensor(op, 2).eval().astype(np.int32).flat)
self.add_output_shape(op.outputs, op_def)
self.resolved_ops[op.name] = 1
self.unused_tensor.add(get_input_tensor(op, 1).name)
self.unused_tensor.add(get_input_tensor(op, 2).name)
def is_atrous_conv2d(self, op):
return op.type == 'SpaceToBatchND' and\
len(self.tf_graph[op.name]) == 1 and self.tf_graph[op.name][0].type == 'Conv2D'
def convert_atrous_conv2d(self, op):
op_def = mace_pb2.OperatorDef()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
conv_op = self.tf_graph[op.name][0]
op_def.name = conv_op.name
op_def.type = conv_op.type
self.transpose_filter_tensor[get_input_tensor(conv_op, 1).name] = (0, 1, 3, 2)
if self.device == 'gpu':
op_def.input.extend([op.inputs[0].name])
output_name = self.add_buffer_to_image(get_input_tensor(conv_op, 1).name, "CONV2D_FILTER")
op_def.input.extend([output_name])
else:
op_def.input.extend([get_input_tensor(op, 0).name])
op_def.input.extend([get_input_tensor(conv_op, 1).name])
dilation_arg = op_def.arg.add()
dilation_arg.name = 'dilations'
dilation_arg.ints.extend(get_input_tensor(op, 1).eval().astype(np.int32).flat)
padding_arg = op_def.arg.add()
padding_arg.name = 'padding'
padding_values = get_input_tensor(op, 2).eval().astype(np.int32).flat
if len(padding_values) > 0 and padding_values[0] > 0:
padding_arg.i = padding_mode['SAME']
else:
padding_arg.i = padding_mode['VALID']
self.unused_tensor.add(get_input_tensor(op, 1).name)
self.unused_tensor.add(get_input_tensor(op, 2).name)
strides_arg = op_def.arg.add()
strides_arg.name = 'strides'
strides_arg.ints.extend([1, 1])
data_format_arg = op_def.arg.add()
data_format_arg.name = 'data_format'
data_format_arg.s = 'NHWC'
final_op = conv_op
self.resolved_ops[op.name] = 1
self.resolved_ops[conv_op.name] = 1
if len(self.tf_graph[final_op.name]) == 1 and self.tf_graph[final_op.name][0].type == 'BiasAdd' :
bias_add_op = self.tf_graph[final_op.name][0]
if self.device == 'gpu':
output_name = self.add_buffer_to_image(get_input_tensor(bias_add_op, 1).name, "ARGUMENT")
op_def.input.extend([output_name])
else:
op_def.input.extend([get_input_tensor(bias_add_op, 1).name])
final_op = bias_add_op
self.resolved_ops[bias_add_op.name] = 1
if len(self.tf_graph[final_op.name]) == 1 \
and self.tf_graph[final_op.name][0].type == 'BatchToSpaceND':
final_op = self.tf_graph[final_op.name][0]
self.resolved_ops[final_op.name] = 1
self.unused_tensor.add(get_input_tensor(final_op, 1).name)
self.unused_tensor.add(get_input_tensor(final_op, 2).name)
else:
raise Exception('Convert atrous conv error: no BatchToSpaceND op')
if len(self.tf_graph[final_op.name]) == 1 \
and self.tf_graph[final_op.name][0].type == 'Relu':
relu_op = self.tf_graph[final_op.name][0]
op_def.type = "FusedConv2D"
fused_relu_arg = op_def.arg.add()
fused_relu_arg.name = 'activation'
fused_relu_arg.s = "RELU"
final_op = relu_op
self.resolved_ops[relu_op.name] = 1
op_def.output.extend([output.name for output in final_op.outputs])
self.add_output_shape(final_op.outputs, op_def)
self.net_def.op.extend([op_def])
def is_softmax(self, op):
return op.type == 'Softmax' and \
len(self.tf_parents[op.name]) == 1 and self.tf_parents[op.name][0].type == 'Reshape' and \
len(self.tf_graph[op.name]) == 1 and self.tf_graph[op.name][0].type == 'Reshape'
def convert_softmax(self, softmax_op):
op_def = self.net_def.op.add()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
# deal with first Reshape op
parent_reshape_op = self.tf_parents[softmax_op.name][0]
self.unused_tensor.add(get_input_tensor(parent_reshape_op, 1).name)
self.resolved_ops[parent_reshape_op.name] = 1
# FIXME: hardcode for inception_v3
# remove squeeze if exist
squeeze_op = self.tf_parents[parent_reshape_op.name][0]
if squeeze_op.type == 'Squeeze':
op_def.input.extend([squeeze_op.inputs[0].name])
self.resolved_ops[squeeze_op.name] = 1
# remove shape if exist
children_ops = self.tf_graph[squeeze_op.name]
print children_ops
if len(children_ops) > 1 and children_ops[0].type == 'Shape':
self.unused_tensor.add(get_input_tensor(children_ops[1], 0).name)
self.resolved_ops[children_ops[1].name] = 1
else:
op_def.input.extend([parent_reshape_op.inputs[0].name])
# deal with Softmax op
op_def.name = softmax_op.name
op_def.type = softmax_op.type
self.resolved_ops[softmax_op.name] = 1
# deal with last Reshape op
reshape_op = self.tf_graph[softmax_op.name][0]
self.unused_tensor.add(get_input_tensor(reshape_op, 1).name)
if reshape_op.outputs[0].shape.ndims == 2:
shape = reshape_op.outputs[0].shape
from tensorflow.python.framework.tensor_shape import as_shape
reshape_op.outputs[0]._shape = as_shape([1, 1, shape[0], shape[1]])
op_def.output.extend([output.name for output in reshape_op.outputs])
self.add_output_shape(reshape_op.outputs, op_def)
self.resolved_ops[reshape_op.name] = 1
def convert_normal_op(self, op):
op_def = self.net_def.op.add()
arg = op_def.arg.add()
arg.name = 'T'
arg.i = self.dt
op_def.name = op.name
op_def.type = op.type
op_def.input.extend([input.name for input in op.inputs])
op_def.output.extend([output.name for output in op.outputs])
self.add_output_shape(op.outputs, op_def)
self.resolved_ops[op.name] = 1
def replace_in_out_name(self, input_names, output_names, is_single):
in_names = set([input_name + ":0" for input_name in input_names])
out_names = set([output_name + ":0" for output_name in output_names])
if is_single:
for op in self.net_def.op:
if len(op.input) > 0 and op.input[0] in in_names:
op.input[0] = MACE_INPUT_NODE_NAME + ':0'
if len(op.output) > 0 and op.output[0] in out_names:
op.output[0] = MACE_OUTPUT_NODE_NAME + ':0'
else:
for op in self.net_def.op:
if len(op.input) > 0 and op.input[0] in in_names:
op.input[0] = MACE_INPUT_NODE_NAME + '_' + op.input[0]
if len(op.output) > 0 and op.output[0] in out_names:
op.output[0] = MACE_OUTPUT_NODE_NAME + '_' + op.output[0]
def convert(self, input_nodes, output_nodes):
is_single = len(input_nodes) == 1 and len(output_nodes) == 1
if self.device == 'gpu':
self.add_input_transform(input_nodes, is_single)
for op in self.tf_ops:
if self.resolved_ops[op.name] == 1:
continue
if op.type in ['Placeholder', 'Identity']:
self.resolved_ops[op.name] = 1
pass
elif op.type == 'Const':
pass
elif op.type == 'Reshape':
self.convert_reshape(op)
elif self.is_atrous_conv2d(op):
self.convert_atrous_conv2d(op)
elif op.type == 'Conv2D' or op.type == 'DepthwiseConv2dNative':
if self.check_winograd_conv(op):
self.convert_winograd_conv(op)
else:
self.convert_conv2d(op)
elif op.type == 'FusedBatchNorm':
self.convert_fused_batchnorm(op)
elif op.type == 'Add' and op.name.endswith('batchnorm/add'):
self.convert_batchnorm(op)
elif op.type == 'AvgPool' or op.type == 'MaxPool':
self.convert_pooling(op)
elif op.type == 'Relu6':
self.convert_relu6(op)
elif op.type == 'Add':
self.convert_add(op)
elif op.type == 'ConcatV2':
self.convert_concat(op)
elif op.type == 'ResizeBilinear':
self.convert_resize_bilinear(op)
elif op.type == 'BiasAdd':
self.convert_bias_add(op)
elif op.type == 'SpaceToBatchND':
self.convert_space_to_batch(op, False)
elif op.type == 'BatchToSpaceND':
self.convert_space_to_batch(op, True)
elif self.is_softmax(op):
self.convert_softmax(op)
elif op.type in ['Relu', 'Sigmoid', 'Tanh']:
self.convert_activation(op)
# FIXME: hardcode for inception_v3
elif op.type in ['Squeeze', 'Shape']:
self.resolved_ops[op.name] = 1
elif op.type == 'Mean':
# Global avg pooling
reduce_dims = op.inputs[1].eval()
if reduce_dims[0] == 1 and reduce_dims[1] == 2:
self.convert_global_avg_pooling(op)
self.unused_tensor.add(op.inputs[1].name)
else:
raise Exception('Unknown Op: %s, type: %s' % (op.name, op.type))
#elif op.type in ['']:
# self.convert_normal_op(op)
else:
raise Exception('Unknown Op: %s, type: %s' % (op.name, op.type))
for op in self.tf_ops:
if self.resolved_ops[op.name] == 1:
continue
elif op.type == 'Const':
self.convert_tensor(op)
else:
raise Exception('Unknown Op: %s, type: %s' % (op.name, op.type))
if self.device == 'gpu':
self.add_output_transform(output_nodes, is_single)
if self.device == 'cpu':
self.replace_in_out_name(input_nodes, output_nodes, is_single)
for key in self.resolved_ops:
if self.resolved_ops[key] != 1:
print 'Unresolve Op: %s' % key
class Optimizer:
def __init__(self, net_def, device):
self.net_def = net_def
self.device = device
self.mace_graph = {}
self.tensor_map = {}
for op in net_def.op:
for input_name in op.input:
if input_name not in self.mace_graph:
self.mace_graph[input_name] = []
self.mace_graph[input_name].append(op)
for tensor in net_def.tensors:
self.tensor_map[tensor.name] = tensor
def get_buffer_tensor_name(self, name):
if self.device == 'gpu':
return name[:-6] + name[-2:]
else:
return name
def fold_batch_norm(self):
unused_tensors = set()
new_tensors = []
new_net = mace_pb2.NetDef()
resolved_ops = set()
for op in self.net_def.op:
if op.name in resolved_ops:
pass
elif op.type == 'DepthwiseConv2d' and len(op.output) == 1 \
and self.mace_graph[op.output[0]][0].type == 'FoldedBatchNorm':
depthwise_conv2d_op = op
folded_bn_op = self.mace_graph[op.output[0]][0]
weight_buffer_name = self.get_buffer_tensor_name(depthwise_conv2d_op.input[1])
weight_tensor = self.tensor_map[weight_buffer_name]
scale_buffer_name = self.get_buffer_tensor_name(folded_bn_op.input[1])
offset_buffer_name = self.get_buffer_tensor_name(folded_bn_op.input[2])
scale_tensor = self.tensor_map[scale_buffer_name]
weight_shape = weight_tensor.dims
idx = 0
for i in range(weight_shape[0]):
for j in range(weight_shape[1]):
for ic in range(weight_shape[2]):
for oc in range(weight_shape[3]):
weight_tensor.float_data[idx] *= scale_tensor.float_data[ic * weight_shape[3] + oc]
idx += 1
new_tensors.append(weight_tensor)
unused_tensors.add(weight_tensor.name)
unused_tensors.add(scale_tensor.name)
if self.device == 'gpu':
scale_b2i_op = self.mace_graph[scale_buffer_name][0]
offset_b2i_op = self.mace_graph[offset_buffer_name][0]
resolved_ops.add(scale_b2i_op.name)
resolved_ops.add(offset_b2i_op.name)
new_net.op.extend([offset_b2i_op])
resolved_ops.add(depthwise_conv2d_op.name)
resolved_ops.add(folded_bn_op.name)
offset_tensor_name = folded_bn_op.input[2]
depthwise_conv2d_op.input.extend([offset_tensor_name])
for arg in folded_bn_op.arg:
if arg.name == 'activation':
act_arg = depthwise_conv2d_op.arg.add()
act_arg.name = arg.name
act_arg.s = arg.s
elif arg.name == 'max_limit':
act_arg = depthwise_conv2d_op.arg.add()
act_arg.name = arg.name
act_arg.f = arg.f
depthwise_conv2d_op.output[0] = folded_bn_op.output[0]
new_net.op.extend([depthwise_conv2d_op])
else:
new_net.op.extend([op])
for tensor in self.net_def.tensors:
if tensor.name in unused_tensors:
pass
else:
new_net.tensors.extend([tensor])
for tensor in new_tensors:
new_net.tensors.extend([tensor])
return new_net
def optimize(self):
new_net = self.fold_batch_norm()
return new_net
def add_shape_info(input_graph_def, input_nodes, input_shapes):
inputs_replaced_graph = graph_pb2.GraphDef()
for node in input_graph_def.node:
if node.name in input_nodes:
idx = input_nodes.index(node.name)
input_shape = input_shapes[idx]
placeholder_node = copy.deepcopy(node)
placeholder_node.attr.clear()
placeholder_node.attr['shape'].shape.dim.extend([
tensor_shape_pb2.TensorShapeProto.Dim(size=i) for i in input_shape
])
placeholder_node.attr['dtype'].CopyFrom(node.attr['dtype'])
inputs_replaced_graph.node.extend([placeholder_node])
else:
inputs_replaced_graph.node.extend([copy.deepcopy(node)])
return inputs_replaced_graph
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_nodes = [x for x in input_node.split(',')]
input_shapes = []
if input_shape != "":
input_shape_strs = [x for x in input_shape.split(':')]
for shape_str in input_shape_strs:
input_shapes.extend([[int(x) for x in shape_str.split(',')]])
output_nodes = [x for x in output_node.split(',')]
assert len(input_nodes) == len(input_shapes)
input_graph_def = add_shape_info(input_graph_def, input_nodes, input_shapes)
with tf.Session() as session:
with session.graph.as_default() as graph:
tf.import_graph_def(input_graph_def, name="")
ops = graph.get_operations()
converter = TFConverter(ops, net_def, dt, device, winograd)
converter.convert(input_nodes, output_nodes)
optimizer = Optimizer(net_def, device)
net_def = optimizer.optimize()
print "Model 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
mace/python/tools/tf_dsp_converter_lib.py 0 → 100644
浏览文件 @ fa4a9bf5
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
from lib.python.tools.convert_util import tf_dtype_2_mace_dtype
# converter --input ../libcv/quantized_model.pb --output quantized_model_dsp.pb \
# --runtime dsp --input_node input_node --output_node output_node
padding_mode = {
'NA': 0,
'SAME': 1,
'VALID': 2,
'MIRROR_REFLECT': 3,
'MIRROR_SYMMETRIC': 4,
'SAME_CAFFE': 5
}
def get_tensor_name_from_op(op_name, port):
return op_name + ':' + str(port)
def get_node_from_map(op_map, op_or_tensor_name):
op_name = op_or_tensor_name.split(':')[0]
return op_map[op_name]
def get_op_and_port_from_tensor(tensor_name):
op, port = tensor_name.split(':')
port = int(port)
return op, port
def max_elem_size(tensor):
if len(tensor.shape.as_list()) == 0:
return tensor.dtype.size
else:
return reduce(mul, tensor.shape.as_list()) * tensor.dtype.size
def find_dtype(tensor_dtype):
if tensor_dtype == tf.float32:
return mace_pb2.DT_FLOAT
elif tensor_dtype == tf.uint8 or tensor_dtype == tf.quint8:
return mace_pb2.DT_UINT8
elif tensor_dtype == tf.int32 or tensor_dtype == tf.qint32:
return mace_pb2.DT_INT32
else:
raise Exception('Unsupported data type: ', tensor_dtype)
def has_padding_and_strides(op):
return 'padding' in op.node_def.attr and 'strides' in op.node_def.attr
def is_node_flatten_reshape(op):
return op.type == 'Reshape' and len(op.outputs[0].shape) == 1
def get_input_tensor(op, index):
input_tensor = op.inputs[index]
if input_tensor.op.type == 'Reshape':
input_tensor = get_input_tensor(input_tensor.op, 0)
return input_tensor
def add_shape_const_node(net_def, op, values, name):
print ('Add const node: ', op.name + '/' + name)
tensor = net_def.tensors.add()
node_name = op.name + '/' + name
tensor.name = node_name + ':0'
tensor.data_type = mace_pb2.DT_INT32
tensor.dims.extend(values)
return tensor.name
def convert_op_outputs(mace_op_def, tf_op):
mace_op_def.output_type.extend([tf_dtype_2_mace_dtype(output.dtype)
for output in tf_op.outputs])
output_shapes = []
for output in tf_op.outputs:
output_shape = mace_pb2.OutputShape()
output_shape.dims.extend(output.shape.as_list())
output_shapes.append(output_shape)
mace_op_def.output_shape.extend(output_shapes)
def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
first_op = unresolved_ops[0]
print ('Op: ', first_op.name, first_op.type, first_op.outputs[0].shape)
if first_op.name in resolved_ops:
pass
elif first_op.type == 'Const':
print ('Add const node: ', first_op.name)
tf_tensor = first_op.outputs[0].eval()
tensor = net_def.tensors.add()
tensor.name = first_op.outputs[0].name
tensor.data_type = find_dtype(first_op.outputs[0].dtype)
shape = list(tf_tensor.shape)
if len(shape) > 0:
tensor.dims.extend(shape)
if first_op.outputs[0].dtype == tf.float32:
tensor.float_data.extend(tf_tensor.astype(float).flat)
elif first_op.outputs[0].dtype == tf.int32 or \
first_op.outputs[0].dtype == tf.int8 or \
first_op.outputs[0].dtype == tf.int16 or \
first_op.outputs[0].dtype == tf.quint8 or \
first_op.outputs[0].dtype == tf.quint16:
tensor.int32_data.extend(tf_tensor.astype(int).flat)
else:
op_def = net_def.op.add()
op_def.name = first_op.name
op_def.type = dsp_ops.map_nn_op(first_op.type)
op_def.padding = padding_mode['NA']
if len(first_op.outputs) > 0 and first_op.type == 'Dequantize' \
and len(first_op.outputs[0].consumers()) > 0 \
and (first_op.outputs[0].consumers()[0].type == 'SpaceToBatchND' \
or first_op.outputs[0].consumers()[0].type == 'BatchToSpaceND'):
input_tensor = first_op.inputs[0]
min_tensor = first_op.inputs[1]
max_tensor = first_op.inputs[2]
s2b_op = first_op.outputs[0].consumers()[0]
reshape_op = s2b_op.outputs[0].consumers()[0]
min_op = reshape_op.outputs[0].consumers()[0]
max_op = reshape_op.outputs[0].consumers()[1]
quantize_op = min_op.outputs[0].consumers()[0]
resolved_ops.add(s2b_op.name)
resolved_ops.add(reshape_op.name)
resolved_ops.add(min_op.name)
resolved_ops.add(max_op.name)
resolved_ops.add(quantize_op.name)
op_def.name = quantize_op.name
op_def.type = dsp_ops.map_nn_op('Quantized' + s2b_op.type)
op_def.input.append(input_tensor.name)
op_def.input.extend([t.name for t in s2b_op.inputs[1:]])
op_def.input.extend([min_tensor.name, max_tensor.name])
op_def.out_max_byte_size.extend([max_elem_size(out) for out in quantize_op.outputs])
convert_op_outputs(op_def, quantize_op)
elif len(first_op.outputs) > 0 and first_op.type == 'QuantizedReshape' \
and len(first_op.outputs[0].consumers()) > 0 \
and first_op.outputs[0].consumers()[0].type == 'Dequantize' \
and len(first_op.outputs[0].consumers()[0].outputs[0].consumers()) > 0 \
and first_op.outputs[0].consumers()[0].outputs[0].consumers()[0].type == 'Softmax':
input_tensor = first_op.inputs[0]
min_tensor = first_op.inputs[2]
max_tensor = first_op.inputs[3]
dequantize_op = first_op.outputs[0].consumers()[0]
softmax_op = dequantize_op.outputs[0].consumers()[0]
reshape_op = softmax_op.outputs[0].consumers()[0]
min_op = reshape_op.outputs[0].consumers()[0]
max_op = reshape_op.outputs[0].consumers()[1]
quantize_op = min_op.outputs[0].consumers()[0]
quantize_reshape_op = quantize_op.outputs[0].consumers()[0]
resolved_ops.add(dequantize_op.name)
resolved_ops.add(softmax_op.name)
resolved_ops.add(reshape_op.name)
resolved_ops.add(min_op.name)
resolved_ops.add(max_op.name)
resolved_ops.add(quantize_op.name)
resolved_ops.add(quantize_reshape_op.name)
op_def.name = quantize_reshape_op.name
op_def.type = dsp_ops.map_nn_op('QuantizedSoftmax')
op_def.input.extend([input_tensor.name, min_tensor.name, max_tensor.name])
op_def.out_max_byte_size.extend([max_elem_size(out) for out in quantize_reshape_op.outputs])
convert_op_outputs(op_def, quantize_reshape_op)
elif has_padding_and_strides(first_op):
op_def.padding = padding_mode[first_op.get_attr('padding')]
op_def.input.extend([t.name for t in first_op.inputs])
if 'ksize' in first_op.node_def.attr:
ksize = first_op.get_attr('ksize')
ksize_tensor = add_shape_const_node(net_def, first_op, ksize, 'ksize')
op_def.input.extend([ksize_tensor])
strides = first_op.get_attr('strides')
strides_tensor = add_shape_const_node(net_def, first_op, strides, 'strides')
op_def.input.extend([strides_tensor])
op_def.out_max_byte_size.extend([max_elem_size(out) for out in first_op.outputs])
convert_op_outputs(op_def, first_op)
elif is_node_flatten_reshape(first_op):
op_def.type = 'Flatten'
op_def.input.extend([t.name for t in first_op.inputs])
op_def.out_max_byte_size.extend([max_elem_size(out) for out in first_op.outputs])
convert_op_outputs(op_def, first_op)
elif dsp_ops.has_op(first_op.type):
op_def.input.extend([t.name for t in first_op.inputs])
op_def.out_max_byte_size.extend([max_elem_size(out) for out in first_op.outputs])
convert_op_outputs(op_def, first_op)
else:
raise Exception('Unsupported op: ', first_op)
resolved_ops.add(first_op.name)
del unresolved_ops[0]
def add_output_node(net_def, output_node):
op_def = net_def.op.add()
op_def.name = '__output__'
op_def.type = 'OUTPUT'
op_def.input.extend([get_tensor_name_from_op(output_node, 0)])
def reverse_batch_to_space_and_biasadd(net_def):
tensor_map = {}
for tensor in net_def.tensors:
tensor_map[tensor.name] = tensor
op_map = {}
for op in net_def.op:
op_map[op.name] = op
consumers = {}
for op in net_def.op:
for ipt in op.input:
if ipt not in consumers:
consumers[ipt] = []
consumers[ipt].append(op)
new_ops = []
skip_ops = set()
visited_ops = set()
for op in net_def.op:
if op.name in visited_ops:
pass
# pattern: QConv -> RR -> R -> QB2S -> QBiasAdd -> RR -> R
success = False
if op.type == 'Requantize_32to8':
biasadd_requantize_op = op
biasadd_op = get_node_from_map(op_map, biasadd_requantize_op.input[0])
if biasadd_op.type == 'QuantizedBiasAdd_8p8to32':
b2s_op = get_node_from_map(op_map, biasadd_op.input[0])
if b2s_op.type == 'QuantizedBatchToSpaceND_8':
conv_requantize_op = get_node_from_map(op_map, b2s_op.input[0])
conv_op = get_node_from_map(op_map, conv_requantize_op.input[0])
if conv_op.type == 'QuantizedConv2d_8x8to32':
new_biasadd_op = mace_pb2.OperatorDef()
new_biasadd_op.CopyFrom(biasadd_op)
new_biasadd_op.input[0] = get_tensor_name_from_op(conv_requantize_op.name, 0)
new_biasadd_op.input[2] = get_tensor_name_from_op(conv_requantize_op.name, 1)
new_biasadd_op.input[3] = get_tensor_name_from_op(conv_requantize_op.name, 2)
new_biasadd_op.out_max_byte_size[0] = conv_requantize_op.out_max_byte_size[0] * 4
new_biasadd_requantize_op = mace_pb2.OperatorDef()
new_biasadd_requantize_op.CopyFrom(biasadd_requantize_op)
new_biasadd_requantize_op.out_max_byte_size[0] = new_biasadd_op.out_max_byte_size[0] / 4
new_b2s_op = mace_pb2.OperatorDef()
new_b2s_op.CopyFrom(b2s_op)
new_b2s_op.input[0] = get_tensor_name_from_op(biasadd_requantize_op.name, 0)
new_b2s_op.input[3] = get_tensor_name_from_op(biasadd_requantize_op.name, 1)
new_b2s_op.input[4] = get_tensor_name_from_op(biasadd_requantize_op.name, 2)
new_ops.extend([new_biasadd_op, new_biasadd_requantize_op, new_b2s_op])
skip_ops = skip_ops.union([biasadd_op.name, biasadd_requantize_op.name, b2s_op.name])
visited_ops.add(op.name)
follow_ops = consumers[get_tensor_name_from_op(biasadd_requantize_op.name, 0)]
for follow_op in follow_ops:
new_follow_op = mace_pb2.OperatorDef()
new_follow_op.CopyFrom(follow_op)
for i in xrange(len(follow_op.input)):
for k in xrange(3):
if new_follow_op.input[i] == get_tensor_name_from_op(biasadd_requantize_op.name, k):
new_follow_op.input[i] = get_tensor_name_from_op(b2s_op.name, k)
new_ops.append(new_follow_op)
skip_ops.add(follow_op.name)
visited_ops.add(follow_op.name)
visited_ops.add(op.name)
new_net_def = mace_pb2.NetDef()
new_net_def.tensors.extend(tensor_map.values())
new_net_def.op.extend([op for op in net_def.op if op.name not in skip_ops])
new_net_def.op.extend(new_ops)
return new_net_def
def add_node_id(net_def):
node_id_counter = 0
node_id_map = {}
for tensor in net_def.tensors:
tensor.node_id = node_id_counter
node_id_counter += 1
tensor_op, port = get_op_and_port_from_tensor(tensor.name)
node_id_map[tensor_op] = tensor.node_id
for op in net_def.op:
op.node_id = node_id_counter
node_id_counter += 1
node_id_map[op.name] = op.node_id
for ipt in op.input:
op_name, port = get_op_and_port_from_tensor(ipt)
node_id = node_id_map[op_name]
node_input = op.node_input.add()
node_input.node_id = node_id
node_input.output_port = int(port)
return net_def
def add_input_output_info(net_def, input_node, output_node, graph, dtype):
input_tensor = graph.get_tensor_by_name(get_tensor_name_from_op(input_node, 0))
output_tensor = graph.get_tensor_by_name(get_tensor_name_from_op(output_node, 0))
input_info = net_def.input_info.add()
input_info.dims.extend(input_tensor.shape.as_list())
input_info.data_type = dtype
if dtype == mace_pb2.DT_UINT8:
for i in xrange(2):
input_info = net_def.input_info.add()
input_info.dims.extend([1,1,1,1])
input_info.data_type = mace_pb2.DT_FLOAT
output_info = net_def.output_info.add()
output_info.dims.extend(output_tensor.shape.as_list())
output_info.data_type = dtype
if dtype == mace_pb2.DT_UINT8:
for i in xrange(2):
output_info = net_def.output_info.add()
output_info.dims.extend([1,1,1,1])
output_info.data_type = mace_pb2.DT_FLOAT
return net_def
def fuse_quantize(net_def, input_node, output_node):
tensor_map = {}
for tensor in net_def.tensors:
tensor_map[tensor.name] = tensor
op_map = {}
for op in net_def.op:
op_map[op.name] = op
consumers = {}
for op in net_def.op:
for ipt in op.input:
if ipt not in consumers:
consumers[ipt] = []
consumers[ipt].append(op)
skip_ops = set()
new_ops = []
skip_tensors = set()
# INPUT->Flatten->Minf, Maxf->Quantize
for op in net_def.op:
if op.type == 'INPUT':
input_op = op
flatten_op = None
quantize_op = None
for o in consumers[get_tensor_name_from_op(input_op.name, 0)]:
if o.type == 'Flatten':
flatten_op = o
elif o.type == 'Quantize':
quantize_op = o
if quantize_op is not None:
minf_op, maxf_op = consumers[get_tensor_name_from_op(flatten_op.name, 0)]
skip_ops = skip_ops.union([flatten_op.name, minf_op.name, maxf_op.name])
skip_tensors = skip_tensors.union([flatten_op.input[1], minf_op.input[1], maxf_op.input[1]])
quantize_op.type = 'AutoQuantize'
del quantize_op.input[1:]
new_net_def = mace_pb2.NetDef()
new_net_def.tensors.extend([tensor for tensor in net_def.tensors if tensor.name not in skip_tensors])
new_net_def.op.extend([op for op in net_def.op if op.name not in skip_ops])
new_net_def.op.extend(new_ops)
return new_net_def
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()
with tf.Session() as session:
with session.graph.as_default() as graph:
tf.import_graph_def(input_graph_def, name="")
ops = graph.get_operations()
dsp_ops = DspOps()
resolved_ops = set()
# convert const node
unresolved_ops = [op for op in ops if op.type == 'Const']
while len(unresolved_ops) > 0:
convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops)
# convert op node
unresolved_ops = [op for op in ops if op.type != 'Const']
while len(unresolved_ops) > 0:
convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops)
add_output_node(net_def, output_node)
net_def = reverse_batch_to_space_and_biasadd(net_def)
net_def = fuse_quantize(net_def, input_node, output_node)
sorted_net_def = graph_util.sort_mace_graph(net_def, '__output__')
net_def_with_node_id = add_node_id(sorted_net_def)
dtype = mace_pb2.DT_FLOAT
final_net_def = add_input_output_info(net_def_with_node_id, input_node, output_node, graph, dtype)
arg = final_net_def.arg.add()
arg.name = 'dsp_mode'
arg.i = dsp_mode
return final_net_def
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