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# Copyright (C) 2018 Intel Corporation
#
# SPDX-License-Identifier: Apache-2.0
#
#distutils: language=c++
from cython.operator cimport dereference as deref
from .cimport ie_api_impl_defs as C
from .ie_api_impl_defs cimport Blob, TensorDesc, SizeVector, Precision
from libcpp.string cimport string
from libcpp.vector cimport vector
from libcpp.map cimport map
from libcpp.memory cimport unique_ptr
from libc.stdint cimport int64_t
import os
import numpy as np
from copy import deepcopy
cdef extern from "<utility>" namespace "std" nogil:
cdef unique_ptr[C.IEExecNetwork] move(unique_ptr[C.IEExecNetwork])
cdef string to_std_string(str py_string):
return py_string.encode()
cdef to_py_string(const string & std_string):
return bytes(std_string).decode()
cdef dict_to_c_map(py_dict):
cdef map[string, string] c_map
for k, v in py_dict.items():
if type(k) != str or type(v) != str:
raise TypeError("Only string keys and values are allowed!")
c_map[k.encode()] = v.encode()
return c_map
supported_precisions = ["FP32", "FP16", "Q78", "I32", "I16", "I8", "U32", "U16"]
supported_layouts = ["NCHW", "NHWC", "OIHW", "C", "CHW", "HW", "NC", "CN", "BLOCKED"]
known_plugins = ['CPU', 'GPU', 'FPGA', 'MYRIAD', 'HETERO']
def get_version():
return C.get_version().decode()
cdef class IENetLayer:
@property
def name(self):
return self.impl.name.decode()
@property
def type(self):
return self.impl.type.decode()
@property
def precision(self):
return self.impl.precision.decode()
@property
def affinity(self):
return self.impl.affinity.decode()
@property
def weights(self):
cdef map[string, Blob.Ptr] c_weights_map
c_weights_map = self.impl.getWeights()
weights_map = {}
cdef BlobBuffer weights_buffer
for weights in c_weights_map:
weights_buffer = BlobBuffer()
weights_buffer.reset(weights.second)
weights_map[weights.first.decode()] = weights_buffer.to_numpy()
return weights_map
@property
def params(self):
return {k.decode(): v.decode() for k, v in self.impl.params}
@affinity.setter
def affinity(self, target_affinity):
self.impl.setAffinity(target_affinity.encode())
@params.setter
def params(self, params_map):
self.impl.setParams(dict_to_c_map(params_map))
@precision.setter
def precision(self, precision: str):
self.impl.setPrecision(precision.upper().encode())
cdef class InputInfo:
@property
def precision(self):
return self.impl.precision.decode()
@property
def layout(self):
return self.impl.layout.decode()
@property
def shape(self):
return self.impl.dims
@precision.setter
def precision(self, precision):
if precision.upper() not in supported_precisions:
raise AttributeError(
"Unsupported precision {}! List of supported precisions: {}".format(precision, supported_precisions))
self.impl.setPrecision(precision.encode())
@layout.setter
def layout(self, layout):
if layout.upper() not in supported_layouts:
raise AttributeError(
"Unsupported layout {}! List of supported layouts: {}".format(layout, supported_layouts))
self.impl.setLayout(layout.encode())
cdef class OutputInfo:
@property
def precision(self):
return self.impl.precision.decode()
@property
def layout(self):
return self.impl.layout.decode()
@property
def shape(self):
return self.impl.dims
@precision.setter
def precision(self, precision):
if precision.upper() not in supported_precisions:
raise AttributeError(
"Unsupported precision {}! List of supported precisions: {}".format(precision, supported_precisions))
self.impl.setPrecision(precision.encode())
# @layout.setter
# def layout(self, layout):
# self.impl.setLayout(layout.encode())
cdef class ExecutableNetwork:
def __init__(self):
self._requests = []
def infer(self, inputs=None):
current_request = self.requests[0]
current_request.infer(inputs)
return deepcopy(current_request.outputs)
def start_async(self, request_id, inputs=None):
if request_id not in list(range(len(self.requests))):
raise ValueError("Incorrect request_id specified!")
current_request = self.requests[request_id]
current_request.async_infer(inputs)
return current_request
@property
def requests(self):
return self._requests
cdef class InferRequest:
def __init__(self):
self._inputs = {}
self._outputs = {}
cpdef BlobBuffer _get_input_buffer(self, const string & blob_name):
cdef BlobBuffer buffer = BlobBuffer()
buffer.reset(deref(self.impl).getInputBlob(blob_name))
return buffer
cpdef BlobBuffer _get_output_buffer(self, const string & blob_name):
cdef BlobBuffer buffer = BlobBuffer()
buffer.reset(deref(self.impl).getOutputBlob(blob_name))
return buffer
cpdef infer(self, inputs=None):
if inputs is not None:
self._fill_inputs(inputs)
deref(self.impl).infer()
cpdef async_infer(self, inputs=None):
if inputs is not None:
self._fill_inputs(inputs)
deref(self.impl).infer_async()
cpdef wait(self, timeout=None):
if timeout is None:
timeout = -1
return deref(self.impl).wait(<int64_t> timeout)
cpdef get_perf_counts(self):
cdef map[string, C.ProfileInfo] c_profile = deref(self.impl).getPerformanceCounts()
profile = {}
for l in c_profile:
info = l.second
# TODO: add execution index. Check if unsigned int is properly converted to int in python.
profile[l.first.decode()] = {"status": info.status.decode(), "exec_type": info.exec_type.decode(),
"layer_type": info.layer_type.decode(), "real_time": info.real_time,
"cpu_time": info.cpu_time}
return profile
@property
def inputs(self):
return self._inputs
@property
def outputs(self):
return self._outputs
def _fill_inputs(self, inputs):
for k, v in inputs.items():
self._inputs[k][:] = v
cdef class IENetwork:
@property
def name(self):
name = bytes(self.impl.name)
return name.decode()
@property
def inputs(self):
cdef map[string, C.InputInfo] c_inputs = self.impl.getInputs()
inputs = {}
cdef InputInfo in_info
for input in c_inputs:
in_info = InputInfo()
in_info.impl = input.second
inputs[input.first.decode()] = in_info
return inputs
@property
def outputs(self):
cdef map[string, C.OutputInfo] c_outputs = self.impl.getOutputs()
outputs = {}
cdef OutputInfo out_info
for out in c_outputs:
out_info = OutputInfo()
out_info.impl = out.second
outputs[out.first.decode()] = out_info
return outputs
@property
def batch_size(self):
return self.impl.batch_size
@batch_size.setter
def batch_size(self, batch: int):
if batch <= 0:
raise AttributeError("Invalid batch size {}! Batch size should be positive integer value".format(batch))
self.impl.setBatch(batch)
self.impl.batch_size = batch
@property
def layers(self):
cdef map[string, C.IENetLayer] c_layers = <map[string, C.IENetLayer]> self.impl.getLayers()
layers = {}
cdef IENetLayer net_l = IENetLayer()
for l in c_layers:
net_l = IENetLayer()
net_l.impl = l.second
layers[l.first.decode()] = net_l
return layers
@classmethod
def from_ir(cls, model: str, weights: str):
if not os.path.isfile(model):
raise Exception("Path to the model {} doesn't exists or it's a directory".format(model))
if not os.path.isfile(weights):
raise Exception("Path to the weights {} doesn't exists or it's a directory".format(weights))
net_reader = IENetReader()
return net_reader.read(model, weights)
# TODO: Use enum with precision type instead of srting parameter when python2 support will not be required.
def add_outputs(self, outputs, precision="FP32"):
if precision.upper() not in supported_precisions:
raise AttributeError(
"Unsupported precision {}! List of supported precisions: {}".format(precision, supported_precisions))
if not isinstance(outputs, list):
outputs = [outputs]
cdef vector[string] _outputs
for l in outputs:
_outputs.push_back(l.encode())
self.impl.addOutputs(_outputs, precision.upper().encode())
def reshape(self, input_shapes: dict):
cdef map[string, vector[size_t]] c_input_shapes;
cdef vector[size_t] c_shape
net_inputs = self.inputs
for input, shape in input_shapes.items():
if input not in net_inputs:
raise AttributeError("Specified {} layer not in network inputs {}! ".format(input, net_inputs))
for v in shape:
c_shape.push_back(v)
c_input_shapes[input.encode()] = c_shape
self.impl.reshape(c_input_shapes)
cdef class IEPlugin:
def __cinit__(self, device: str, plugin_dirs=None):
plugin_base = device.split(':')[0]
if plugin_base not in known_plugins:
raise ValueError("Unknown plugin: {}, expected one of: {}"
.format(plugin_base, ",".join(known_plugins)))
if plugin_dirs is None:
plugin_dirs = [""]
elif isinstance(plugin_dirs, str):
plugin_dirs = [plugin_dirs]
# add package directory to plugin_dirs
lib_location = os.path.dirname(os.path.realpath(__file__))
plugin_dirs.append(lib_location)
cpdef string device_ = <string> device.encode()
cdef vector[string] dirs_
for d in plugin_dirs:
dirs_.push_back(<string> d.encode())
self.impl = C.IEPlugin(device_, dirs_)
cpdef ExecutableNetwork load(self, IENetwork network, int num_requests=1, config=None):
if num_requests <= 0:
raise ValueError(
"Incorrect number of requests specified: {}. Expected positive integer number.".format(num_requests))
cdef ExecutableNetwork exec_net = ExecutableNetwork()
cdef vector[string] inputs_list
cdef vector[string] outputs_list
cdef map[string, string] c_config
if config:
for k, v in config.items():
c_config[to_std_string(k)] = to_std_string(v)
exec_net.impl = move(self.impl.load(network.impl, num_requests, c_config))
requests = []
for i in range(deref(exec_net.impl).infer_requests.size()):
infer_request = InferRequest()
infer_request.impl = &(deref(exec_net.impl).infer_requests[i])
inputs_list = infer_request.impl.getInputsList()
outputs_list = infer_request.impl.getOutputsList()
for input_b in inputs_list:
input_s = input_b.decode()
infer_request._inputs[input_s] = infer_request._get_input_buffer(input_b).to_numpy()
for output_b in outputs_list:
output_s = output_b.decode()
infer_request._outputs[output_s] = infer_request._get_output_buffer(output_b).to_numpy()
# create blob buffers
requests.append(infer_request)
exec_net._requests = tuple(requests)
return exec_net
cpdef void set_initial_affinity(self, IENetwork net) except *:
if self.device.find("HETERO") == -1:
raise RuntimeError("set_initial_affinity method applicable only for HETERO device")
self.impl.setInitialAffinity(net.impl)
cpdef set get_supported_layers(self, IENetwork net):
return set([l.decode() for l in self.impl.queryNetwork(net.impl)])
@property
def device(self):
device_name = bytes(self.impl.device_name)
return to_py_string(device_name)
@property
def version(self):
version = bytes(self.impl.version)
return version.decode()
cpdef void add_cpu_extension(self, str extension_path) except *:
if self.device.find("CPU") == -1:
raise RuntimeError("add_cpu_extension method applicable only for CPU or HETERO devices")
cdef string extension_str = extension_path.encode()
self.impl.addCpuExtension(extension_str)
cpdef void set_config(self, config):
cdef map[string, string] c_config
for k, v in config.items():
c_config[to_std_string(k)] = to_std_string(v)
self.impl.setConfig(c_config)
cdef class IENetReader:
def read(self, model: str, weights: str) -> IENetwork:
cdef IENetwork net = IENetwork()
net.impl = self.impl.read(model.encode(), weights.encode())
return net
cdef class BlobBuffer:
"""Copy-less accessor for Inference Engine Blob"""
cdef reset(self, Blob.Ptr & ptr):
self.ptr = ptr
cdef TensorDesc desc = deref(ptr).getTensorDesc()
cdef SizeVector shape = desc.getDims()
cdef Py_ssize_t itemsize = deref(ptr).element_size()
self.strides.resize(shape.size())
self.shape.resize(shape.size())
total_stride = itemsize
# dims are in row major (C - style),
# thence strides are computed starting from latest dimension
for i in reversed(range(shape.size())):
self.strides[i] = total_stride
self.shape[i] = shape[i]
total_stride *= shape[i]
self.total_stride = total_stride
self.format = self._get_blob_format(desc)
self.item_size = itemsize
def __getbuffer__(self, Py_buffer *buffer, int flags):
buffer.buf = C.get_buffer[char](deref(self.ptr))
buffer.format = self.format
buffer.internal = NULL
buffer.itemsize = self.item_size
buffer.len = self.total_stride
buffer.ndim = self.shape.size()
buffer.obj = self
buffer.readonly = 0
buffer.shape = self.shape.data()
buffer.strides = self.strides.data()
buffer.suboffsets = NULL
cdef char*_get_blob_format(self, const TensorDesc & desc):
cdef Precision precision = desc.getPrecision()
name = bytes(precision.name()).decode()
# todo: half floats
precision_to_format = {
'FP32': 'f', # float
'FP16': 'h', # signed short
'Q78': 'h', # signed short
'I16': 'h', # signed short
'U8': 'B', # unsigned char
'I8': 'b', # signed char
'U16': 'H', # unsigned short
'I32': 'i' # signed int
}
if name not in precision_to_format:
raise ValueError("Unknown Blob precision: {}".format(name))
return precision_to_format[name].encode()
def to_numpy(self):
return np.asarray(self)
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