BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def print(self): """Prints the C representation of this AF.""" cond_var = None if self.supported: cond_var = conditional_var('{}{}'.format(self.func, self.fn_num)) print_conditional_if(cond_var) print(' AF', end='') else: print(' //', en...
def parse_port_pin(name_str): """Parses a string and returns a (port, gpio_bit) tuple.""" if len(name_str) < 3: raise ValueError("Expecting pin name to be at least 3 characters") if name_str[:2] != 'GP': raise ValueError("Expecting pin name to start with GP") if not name_str[2:].isdigit(...
def run_node(cls, node, # type: NodeProto inputs, # type: Any device='CPU', # type: Text outputs_info=None, # type: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]] **kwargs # type: Dict[Text, Any] ): # ty...
def load_external_data_for_tensor(tensor, base_dir): # type: (TensorProto, Text) -> None """ Load data from an external file for tensor. @params tensor: a TensorProto object. base_dir: directory that contains the external data. """ if tensor.HasField("raw_data"): # already loaded ...
def load_external_data_for_model(model, base_dir): # type: (ModelProto, Text) -> None """ Loads external tensors into model @params model: ModelProto to load external data to base_dir: directory that contains external data """ for tensor in _get_all_tensors(model): if uses_external...
def convert_model_to_external_data(model, all_tensors_to_one_file=True, location=None): # type: (ModelProto, bool, Optional[Text]) -> None """ call to set all tensors as external data. save_model saves all the tensors data as external data after calling this function. @params model: ModelProto to be...
def convert_model_from_external_data(model): # type: (ModelProto) -> None """ call to set all tensors data as embedded data. save_model saves all the tensors data as embedded data after calling this function. @params model: ModelProto to be converted. """ for tensor in _get_all_tensors(model): ...
def save_external_data(tensor, base_path): # type: (TensorProto, Text) -> None """ Write tensor data to an external file according to information in the `external_data` field. @params tensor: Tensor object to be serialized base_path: System path of a folder where tensor data is to be stored ""...
def _get_attribute_tensors(onnx_model_proto): # type: (ModelProto) -> Iterable[TensorProto] """Create an iterator of tensors from node attributes of an ONNX model.""" for node in onnx_model_proto.graph.node: for attribute in node.attribute: if attribute.HasField("t"): yield ...
def remove_external_data_field(tensor, field_key): # type: (TensorProto, Text) -> None """ Remove a field from a Tensor's external_data key-value store. Modifies tensor object in place. @params tensor: Tensor object from which value will be removed field_key: The key of the field to be remove...
def write_external_data_tensors(model, filepath): # type: (ModelProto, Text) -> ModelProto """ Write external data of all tensors to files on disk. Note: This function also strips basepath information from all tensors' external_data fields. @params model: Model object which is the source of tenso...
def _import(self, path, name): # type: (Text, Text) -> Text """Imports a stdlib path and returns a handle to it eg. self._import("typing", "Optional") -> "Optional" """ imp = path.replace('/', '.') self.imports[imp].add(name) return name
def _import_message(self, type_name): # type: (d.FieldDescriptorProto) -> Text """Import a referenced message and return a handle""" name = cast(Text, type_name) if name[0] == '.' and name[1].isupper() and name[2].islower(): # Message defined in this file return ...
def run(self): """Run command.""" onnx_script = os.path.realpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "tools/mypy-onnx.py")) returncode = subprocess.call([sys.executable, onnx_script]) sys.exit(returncode)
def make_node( op_type, # type: Text inputs, # type: Sequence[Text] outputs, # type: Sequence[Text] name=None, # type: Optional[Text] doc_string=None, # type: Optional[Text] domain=None, # type: Optional[Text] **kwargs # type: Any ): # type: (...) -> NodeP...
def make_operatorsetid( domain, # type: Text version, # type: int ): # type: (...) -> OperatorSetIdProto """Construct an OperatorSetIdProto. Arguments: domain (string): The domain of the operator set id version (integer): Version of operator set id """ operatorsetid =...
def _to_bytes_or_false(val): # type: (Union[Text, bytes]) -> Union[bytes, bool] """An internal graph to convert the input to a bytes or to False. The criteria for conversion is as follows and should be python 2 and 3 compatible: - If val is py2 str or py3 bytes: return bytes - If val is py2 unicod...
def make_attribute( key, # type: Text value, # type: Any doc_string=None # type: Optional[Text] ): # type: (...) -> AttributeProto """Makes an AttributeProto based on the value type.""" attr = AttributeProto() attr.name = key if doc_string: attr.doc_string = doc_strin...
def make_tensor_value_info( name, # type: Text elem_type, # type: int shape, # type: Optional[Sequence[Union[Text, int]]] doc_string="", # type: Text shape_denotation=None, # type: Optional[List[Text]] ): # type: (...) -> ValueInfoProto """Makes a ValueInfoProto based o...
def strip_doc_string(proto): # type: (google.protobuf.message.Message) -> None """ Empties `doc_string` field on any nested protobuf messages """ assert isinstance(proto, google.protobuf.message.Message) for descriptor in proto.DESCRIPTOR.fields: if descriptor.name == 'doc_string': ...
def to_array(tensor): # type: (TensorProto) -> np.ndarray[Any] """Converts a tensor def object to a numpy array. Inputs: tensor: a TensorProto object. Returns: arr: the converted array. """ if tensor.HasField("segment"): raise ValueError( "Currently not supporti...
def from_array(arr, name=None): # type: (np.ndarray[Any], Optional[Text]) -> TensorProto """Converts a numpy array to a tensor def. Inputs: arr: a numpy array. name: (optional) the name of the tensor. Returns: tensor_def: the converted tensor def. """ tensor = TensorProto()...
def _serialize(proto): # type: (Union[bytes, google.protobuf.message.Message]) -> bytes ''' Serialize a in-memory proto to bytes @params proto is a in-memory proto, such as a ModelProto, TensorProto, etc @return Serialized proto in bytes ''' if isinstance(proto, bytes): return...
def _deserialize(s, proto): # type: (bytes, _Proto) -> _Proto ''' Parse bytes into a in-memory proto @params s is bytes containing serialized proto proto is a in-memory proto object @return The proto instance filled in by s ''' if not isinstance(s, bytes): raise ValueError...
def load_model(f, format=None, load_external_data=True): # type: (Union[IO[bytes], Text], Optional[Any], bool) -> ModelProto ''' Loads a serialized ModelProto into memory @params f can be a file-like object (has "read" function) or a string containing a file name format is for future use @ret...
def load_tensor(f, format=None): # type: (Union[IO[bytes], Text], Optional[Any]) -> TensorProto ''' Loads a serialized TensorProto into memory @params f can be a file-like object (has "read" function) or a string containing a file name format is for future use @return Loaded in-memory Ten...
def save_model(proto, f, format=None): # type: (Union[ModelProto, bytes], Union[IO[bytes], Text], Optional[Any]) -> None ''' Saves the ModelProto to the specified path. @params proto should be a in-memory ModelProto f can be a file-like object (has "write" function) or a string containing a file n...
def polish_model(model): # type: (ModelProto) -> ModelProto ''' This function combines several useful utility functions together. ''' onnx.checker.check_model(model) onnx.helper.strip_doc_string(model) model = onnx.shape_inference.infer_shapes(model) model = onnx.optimizer.optimize(mode...
def dynamic_unroll(cell, inputs, begin_state, drop_inputs=0, drop_outputs=0, layout='TNC', valid_length=None): """Unrolls an RNN cell across time steps. Currently, 'TNC' is a preferred layout. unroll on the input of this layout runs much faster. Parameters ---------- cell : ...
def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None, valid_length=None): """Unrolls an RNN cell across time steps. Parameters ---------- length : int Number of steps to unroll. inputs : Symbol, list of Symbol, or None ...
def _fix_attribute_names(attrs, change_map): """ Change attribute names as per values in change_map dictionary. Parameters ---------- :param attrs : dict Dict of operator attributes :param change_map : dict Dict of onnx attribute name to mxnet attribute names. Returns ------- :retur...
def _remove_attributes(attrs, remove_list): """ Removes attributes in the remove list from the input attribute dict :param attrs : Dict of operator attributes :param remove_list : list of attributes to be removed :return new_attr : Dict of operator attributes without the listed attributes. """ ...
def _add_extra_attributes(attrs, extra_attr_map): """ :param attrs: Current Attribute list :param extraAttrMap: Additional attributes to be added :return: new_attr """ for attr in extra_attr_map: if attr not in attrs: attrs[attr] = extra_attr_map[attr] return attrs
def _pad_sequence_fix(attr, kernel_dim=None): """Changing onnx's pads sequence to match with mxnet's pad_width mxnet: (x1_begin, x1_end, ... , xn_begin, xn_end) onnx: (x1_begin, x2_begin, ... , xn_end, xn_end)""" new_attr = () if len(attr) % 2 == 0: for index in range(int(len(attr) / 2)): ...
def _fix_pooling(pool_type, inputs, new_attr): """onnx pooling operator supports asymmetrical padding Adding pad operator before pooling in mxnet to work with onnx""" stride = new_attr.get('stride') kernel = new_attr.get('kernel') padding = new_attr.get('pad') p_value = new_attr.get('p_value') ...
def _fix_bias(op_name, attrs, num_inputs): """A workaround for 'use_bias' attribute since onnx don't provide this attribute, we have to check the number of inputs to decide it.""" if num_inputs == 3: attrs['no_bias'] = False elif num_inputs == 2: attrs['no_bias'] = True else: ...
def _fix_broadcast(op_name, inputs, broadcast_axis, proto_obj): """A workaround to reshape bias term to (1, num_channel).""" if int(len(proto_obj._params)) > 0: assert len(list(inputs)) == 2 input0_shape = get_input_shape(inputs[0], proto_obj) #creating reshape shape reshape_sha...
def _fix_channels(op_name, attrs, inputs, proto_obj): """A workaround for getting 'channels' or 'units' since onnx don't provide these attributes. We check the shape of weights provided to get the number. """ weight_name = inputs[1].name if not weight_name in proto_obj._params: raise ValueEr...
def _fix_gemm(op_name, inputs, old_attr, proto_obj): """Using FullyConnected operator in place of linalg_gemm to perform same operation""" op_sym = getattr(symbol, op_name, None) alpha = float(old_attr.get('alpha', 1.0)) beta = float(old_attr.get('beta', 1.0)) trans_a = int(old_attr.get('transA', 0)...
def get_input_shape(sym, proto_obj): """Helper function to obtain the shape of an array""" arg_params = proto_obj.arg_dict aux_params = proto_obj.aux_dict model_input_shape = [data[1] for data in proto_obj.model_metadata.get('input_tensor_data')] data_names = [data[0] for data in proto_obj.model_...
def imresize(src, w, h, args, *kwargs): r"""Resize image with OpenCV. .. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imresize` to work. Parameters ---------- src : NDArray source image w : int, required ...
def imdecode(buf, args, *kwargs): """Decode an image to an NDArray. .. note:: `imdecode` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imdecode` to work. Parameters ---------- buf : str/bytes/bytearray or numpy.ndarray Binary image dat...
def scale_down(src_size, size): """Scales down crop size if it's larger than image size. If width/height of the crop is larger than the width/height of the image, sets the width/height to the width/height of the image. Parameters ---------- src_size : tuple of int Size of the image in ...
def copyMakeBorder(src, top, bot, left, right, args, *kwargs): """Pad image border with OpenCV. Parameters ---------- src : NDArray source image top : int, required Top margin. bot : int, required Bottom margin. left : int, required Left margin. right :...
def _get_interp_method(interp, sizes=()): """Get the interpolation method for resize functions. The major purpose of this function is to wrap a random interp method selection and a auto-estimation method. Parameters ---------- interp : int interpolation method for all resizing operation...
def resize_short(src, size, interp=2): """Resizes shorter edge to size. .. note:: `resize_short` uses OpenCV (not the CV2 Python library). MXNet must have been built with OpenCV for `resize_short` to work. Resizes the original image by setting the shorter edge to size and setting the longer edg...
def fixed_crop(src, x0, y0, w, h, size=None, interp=2): """Crop src at fixed location, and (optionally) resize it to size. Parameters ---------- src : NDArray Input image x0 : int Left boundary of the cropping area y0 : int Top boundary of the cropping area w : int ...
def center_crop(src, size, interp=2): """Crops the image `src` to the given `size` by trimming on all four sides and preserving the center of the image. Upsamples if `src` is smaller than `size`. .. note:: This requires MXNet to be compiled with USE_OPENCV. Parameters ---------- src : NDAr...
def color_normalize(src, mean, std=None): """Normalize src with mean and std. Parameters ---------- src : NDArray Input image mean : NDArray RGB mean to be subtracted std : NDArray RGB standard deviation to be divided Returns ------- NDArray An `NDAr...
def random_size_crop(src, size, area, ratio, interp=2, **kwargs): """Randomly crop src with size. Randomize area and aspect ratio. Parameters ---------- src : NDArray Input image size : tuple of (int, int) Size of the crop formatted as (width, height). area : float in (0, 1] or ...
def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0, pca_noise=0, rand_gray=0, inter_method=2): """Creates an augmenter list. Parameters ---------- dat...
def dumps(self): """Saves the Augmenter to string Returns ------- str JSON formatted string that describes the Augmenter. """ return json.dumps([self.__class__.__name__.lower(), self._kwargs])
def dumps(self): """Override the default to avoid duplicate dump.""" return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]]
def reset(self): """Resets the iterator to the beginning of the data.""" if self.seq is not None and self.shuffle: random.shuffle(self.seq) if self.last_batch_handle != 'roll_over' or \ self._cache_data is None: if self.imgrec is not None: self...
def hard_reset(self): """Resets the iterator and ignore roll over data""" if self.seq is not None and self.shuffle: random.shuffle(self.seq) if self.imgrec is not None: self.imgrec.reset() self.cur = 0 self._allow_read = True self._cache_data = Non...
def next_sample(self): """Helper function for reading in next sample.""" if self._allow_read is False: raise StopIteration if self.seq is not None: if self.cur < self.num_image: idx = self.seq[self.cur] else: if self.last_batch_...
def _batchify(self, batch_data, batch_label, start=0): """Helper function for batchifying data""" i = start batch_size = self.batch_size try: while i < batch_size: label, s = self.next_sample() data = self.imdecode(s) try: ...
def imdecode(self, s): """Decodes a string or byte string to an NDArray. See mx.img.imdecode for more details.""" def locate(): """Locate the image file/index if decode fails.""" if self.seq is not None: idx = self.seq[(self.cur % self.num_image) - 1] ...
def read_image(self, fname): """Reads an input image `fname` and returns the decoded raw bytes. Examples -------- >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes. """ with open(os.path.join(self.path_root, fname), 'rb') as fin: img = fin.read()...
def facc(label, pred): """ evaluate accuracy """ pred = pred.ravel() label = label.ravel() return ((pred > 0.5) == label).mean()
def word_to_vector(word): """ Convert character vectors to integer vectors. """ vector = [] for char in list(word): vector.append(char2int(char)) return vector
def vector_to_word(vector): """ Convert integer vectors to character vectors. """ word = "" for vec in vector: word = word + int2char(vec) return word
def char_conv(out): """ Convert integer vectors to character vectors for batch. """ out_conv = list() for i in range(out.shape[0]): tmp_str = '' for j in range(out.shape[1]): if int(out[i][j]) >= 0: tmp_char = int2char(int(out[i][j])) if in...
def add_pooling_with_padding_types(builder, name, height, width, stride_height, stride_width, layer_type, padding_type, input_name, output_name, padding_top = 0, padding_bottom = 0, padding_left = 0, padding_right = 0, same_padding_asymmetry_mode = 'BOTTOM_RIGHT_HEAVY', exclude_pad_a...
def get_frames(root_path): """Get path to all the frame in view SAX and contain complete frames""" ret = [] for root, _, files in os.walk(root_path): root=root.replace('\\','/') files=[s for s in files if ".dcm" in s] if len(files) == 0 or not files[0].endswith(".dcm") or root.find("sax") ...
def write_data_csv(fname, frames, preproc): """Write data to csv file""" fdata = open(fname, "w") dr = Parallel()(delayed(get_data)(lst,preproc) for lst in frames) data,result = zip(*dr) for entry in data: fdata.write(','.join(entry)+'\r\n') print("All finished, %d slices in total" % len(data)) ...
def crop_resize(img, size): """crop center and resize""" if img.shape[0] < img.shape[1]: img = img.T # we crop image from center short_egde = min(img.shape[:2]) yy = int((img.shape[0] - short_egde) / 2) xx = int((img.shape[1] - short_egde) / 2) crop_img = img[yy : yy + short_egde, xx : xx + ...
def get_generator(): """ construct and return generator """ g_net = gluon.nn.Sequential() with g_net.name_scope(): g_net.add(gluon.nn.Conv2DTranspose( channels=512, kernel_size=4, strides=1, padding=0, use_bias=False)) g_net.add(gluon.nn.BatchNorm()) g_net.add(gluon.nn.L...
def get_descriptor(ctx): """ construct and return descriptor """ d_net = gluon.nn.Sequential() with d_net.name_scope(): d_net.add(SNConv2D(num_filter=64, kernel_size=4, strides=2, padding=1, in_channels=3, ctx=ctx)) d_net.add(gluon.nn.LeakyReLU(0.2)) d_net.add(SNConv2D(num_filter=1...
def _spectral_norm(self): """ spectral normalization """ w = self.params.get('weight').data(self.ctx) w_mat = nd.reshape(w, [w.shape[0], -1]) _u = self.u.data(self.ctx) _v = None for _ in range(POWER_ITERATION): _v = nd.L2Normalization(nd.dot(_u, w_mat)) ...
def conv_output_length(input_length, filter_size, border_mode, stride, dilation=1): """ Compute the length of the output sequence after 1D convolution along time. Note that this function is in line with the function used in Convolution1D class from Keras. Params: i...
def spectrogram(samples, fft_length=256, sample_rate=2, hop_length=128): """ Compute the spectrogram for a real signal. The parameters follow the naming convention of matplotlib.mlab.specgram Args: samples (1D array): input audio signal fft_length (int): number of elements in fft win...
def spectrogram_from_file(filename, step=10, window=20, max_freq=None, eps=1e-14, overwrite=False, save_feature_as_csvfile=False): """ Calculate the log of linear spectrogram from FFT energy Params: filename (str): Path to the audio file step (int): Step size in millise...
def sample(self, label): """ generate random cropping boxes according to parameters if satifactory crops generated, apply to ground-truth as well Parameters: ---------- label : numpy.array (n x 5 matrix) ground-truths Returns: ---------- ...
def _check_satisfy(self, rand_box, gt_boxes): """ check if overlap with any gt box is larger than threshold """ l, t, r, b = rand_box num_gt = gt_boxes.shape[0] ls = np.ones(num_gt) * l ts = np.ones(num_gt) * t rs = np.ones(num_gt) * r bs = np.ones...
def sample(self, label): """ generate random padding boxes according to parameters if satifactory padding generated, apply to ground-truth as well Parameters: ---------- label : numpy.array (n x 5 matrix) ground-truths Returns: ---------- ...
def measure_cost(repeat, scipy_trans_lhs, scipy_dns_lhs, func_name, args, *kwargs): """Measure time cost of running a function """ mx.nd.waitall() args_list = [] for arg in args: args_list.append(arg) start = time.time() if scipy_trans_lhs: args_list[0] = np.transpose(args_...
def info(self): """ Print information about the annotation file. :return: """ for key, value in self.dataset['info'].items(): print('{}: {}'.format(key, value))
def getCatIds(self, catNms=[], supNms=[], catIds=[]): """ filtering parameters. default skips that filter. :param catNms (str array) : get cats for given cat names :param supNms (str array) : get cats for given supercategory names :param catIds (int array) : get cats for given...
def loadAnns(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying anns :return: anns (object array) : loaded ann objects """ if type(ids) == list: return [self.anns[id] for id in ids] elif type(ids)...
def loadCats(self, ids=[]): """ Load cats with the specified ids. :param ids (int array) : integer ids specifying cats :return: cats (object array) : loaded cat objects """ if type(ids) == list: return [self.cats[id] for id in ids] elif type(ids)...
def loadImgs(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying img :return: imgs (object array) : loaded img objects """ if type(ids) == list: return [self.imgs[id] for id in ids] elif type(ids) ...
def showAnns(self, anns): """ Display the specified annotations. :param anns (array of object): annotations to display :return: None """ if len(anns) == 0: return 0 if 'segmentation' in anns[0] or 'keypoints' in anns[0]: datasetType = 'inst...
def download(self, tarDir = None, imgIds = [] ): ''' Download COCO images from mscoco.org server. :param tarDir (str): COCO results directory name imgIds (list): images to be downloaded :return: ''' if tarDir is None: print('Please specify targe...
def loadNumpyAnnotations(self, data): """ Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} :param data (numpy.ndarray) :return: annotations (python nested list) """ print('Converting ndarray to lists...') assert...
def annToRLE(self, ann): """ Convert annotation which can be polygons, uncompressed RLE to RLE. :return: binary mask (numpy 2D array) """ t = self.imgs[ann['image_id']] h, w = t['height'], t['width'] segm = ann['segmentation'] if type(segm) == list: ...
def save_model(): """Save cnn model Returns ---------- callback: A callback function that can be passed as epoch_end_callback to fit """ if not os.path.exists("checkpoint"): os.mkdir("checkpoint") return mx.callback.do_checkpoint("checkpoint/checkpoint", args.save_period)
def highway(data): """Construct highway net Parameters ---------- data: Returns ---------- Highway Networks """ _data = data high_weight = mx.sym.Variable('high_weight') high_bias = mx.sym.Variable('high_bias') high_fc = mx.sym.FullyConnected(data=data, weight=high_weight...
def train(symbol_data, train_iterator, valid_iterator, data_column_names, target_names): """Train cnn model Parameters ---------- symbol_data: symbol train_iterator: DataIter Train DataIter valid_iterator: DataIter Valid DataIter data_column_names: lis...
def default_batchify_fn(data): """Collate data into batch.""" if isinstance(data[0], nd.NDArray): return nd.stack(data) elif isinstance(data[0], tuple): data = zip(data) return [default_batchify_fn(i) for i in data] else: data = np.asarray(data) return nd.array(...
def default_mp_batchify_fn(data): """Collate data into batch. Use shared memory for stacking.""" if isinstance(data[0], nd.NDArray): out = nd.empty((len(data),) + data[0].shape, dtype=data[0].dtype, ctx=context.Context('cpu_shared', 0)) return nd.stack(*data, out=out) ...
def _as_in_context(data, ctx): """Move data into new context.""" if isinstance(data, nd.NDArray): return data.as_in_context(ctx) elif isinstance(data, (list, tuple)): return [_as_in_context(d, ctx) for d in data] return data
def worker_loop_v1(dataset, key_queue, data_queue, batchify_fn): """Worker loop for multiprocessing DataLoader.""" while True: idx, samples = key_queue.get() if idx is None: break batch = batchify_fn([dataset[i] for i in samples]) data_queue.put((idx, batch))
def fetcher_loop_v1(data_queue, data_buffer, pin_memory=False, pin_device_id=0, data_buffer_lock=None): """Fetcher loop for fetching data from queue and put in reorder dict.""" while True: idx, batch = data_queue.get() if idx is None: break if pin_memory: ...
def _worker_fn(samples, batchify_fn, dataset=None): """Function for processing data in worker process.""" # pylint: disable=unused-argument # it is required that each worker process has to fork a new MXIndexedRecordIO handle # preserving dataset as global variable can save tons of overhead and is safe i...
def send(self, obj): """Send object""" buf = io.BytesIO() ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(obj) self.send_bytes(buf.getvalue())
def _push_next(self): """Assign next batch workload to workers.""" r = next(self._iter, None) if r is None: return self._key_queue.put((self._sent_idx, r)) self._sent_idx += 1
def shutdown(self): """Shutdown internal workers by pushing terminate signals.""" if not self._shutdown: # send shutdown signal to the fetcher and join data queue first # Remark: loop_fetcher need to be joined prior to the workers. # otherwise, the the fet...
def _push_next(self): """Assign next batch workload to workers.""" r = next(self._iter, None) if r is None: return async_ret = self._worker_pool.apply_async( self._worker_fn, (r, self._batchify_fn, self._dataset)) self._data_buffer[self._sent_idx] = async_...
def _ctype_key_value(keys, vals): """ Returns ctype arrays for the key-value args, and the whether string keys are used. For internal use only. """ if isinstance(keys, (tuple, list)): assert(len(keys) == len(vals)) c_keys = [] c_vals = [] use_str_keys = None f...

def print(self): """Prints the C representation of this AF.""" cond_var = None if self.supported: cond_var = conditional_var('{}{}'.format(self.func, self.fn_num)) print_conditional_if(cond_var) print(' AF', end='') else: print(' //', en...

def parse_port_pin(name_str): """Parses a string and returns a (port, gpio_bit) tuple.""" if len(name_str) < 3: raise ValueError("Expecting pin name to be at least 3 characters") if name_str[:2] != 'GP': raise ValueError("Expecting pin name to start with GP") if not name_str[2:].isdigit(...

def run_node(cls, node, # type: NodeProto inputs, # type: Any device='CPU', # type: Text outputs_info=None, # type: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]] **kwargs # type: Dict[Text, Any] ): # ty...

def load_external_data_for_tensor(tensor, base_dir): # type: (TensorProto, Text) -> None """ Load data from an external file for tensor. @params tensor: a TensorProto object. base_dir: directory that contains the external data. """ if tensor.HasField("raw_data"): # already loaded ...

def load_external_data_for_model(model, base_dir): # type: (ModelProto, Text) -> None """ Loads external tensors into model @params model: ModelProto to load external data to base_dir: directory that contains external data """ for tensor in _get_all_tensors(model): if uses_external...

def convert_model_to_external_data(model, all_tensors_to_one_file=True, location=None): # type: (ModelProto, bool, Optional[Text]) -> None """ call to set all tensors as external data. save_model saves all the tensors data as external data after calling this function. @params model: ModelProto to be...

def convert_model_from_external_data(model): # type: (ModelProto) -> None """ call to set all tensors data as embedded data. save_model saves all the tensors data as embedded data after calling this function. @params model: ModelProto to be converted. """ for tensor in _get_all_tensors(model): ...

def save_external_data(tensor, base_path): # type: (TensorProto, Text) -> None """ Write tensor data to an external file according to information in the `external_data` field. @params tensor: Tensor object to be serialized base_path: System path of a folder where tensor data is to be stored ""...

def _get_attribute_tensors(onnx_model_proto): # type: (ModelProto) -> Iterable[TensorProto] """Create an iterator of tensors from node attributes of an ONNX model.""" for node in onnx_model_proto.graph.node: for attribute in node.attribute: if attribute.HasField("t"): yield ...

def remove_external_data_field(tensor, field_key): # type: (TensorProto, Text) -> None """ Remove a field from a Tensor's external_data key-value store. Modifies tensor object in place. @params tensor: Tensor object from which value will be removed field_key: The key of the field to be remove...

def write_external_data_tensors(model, filepath): # type: (ModelProto, Text) -> ModelProto """ Write external data of all tensors to files on disk. Note: This function also strips basepath information from all tensors' external_data fields. @params model: Model object which is the source of tenso...

def _import(self, path, name): # type: (Text, Text) -> Text """Imports a stdlib path and returns a handle to it eg. self._import("typing", "Optional") -> "Optional" """ imp = path.replace('/', '.') self.imports[imp].add(name) return name

def _import_message(self, type_name): # type: (d.FieldDescriptorProto) -> Text """Import a referenced message and return a handle""" name = cast(Text, type_name) if name[0] == '.' and name[1].isupper() and name[2].islower(): # Message defined in this file return ...

def run(self): """Run command.""" onnx_script = os.path.realpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "tools/mypy-onnx.py")) returncode = subprocess.call([sys.executable, onnx_script]) sys.exit(returncode)

def make_node( op_type, # type: Text inputs, # type: Sequence[Text] outputs, # type: Sequence[Text] name=None, # type: Optional[Text] doc_string=None, # type: Optional[Text] domain=None, # type: Optional[Text] **kwargs # type: Any ): # type: (...) -> NodeP...

def make_operatorsetid( domain, # type: Text version, # type: int ): # type: (...) -> OperatorSetIdProto """Construct an OperatorSetIdProto. Arguments: domain (string): The domain of the operator set id version (integer): Version of operator set id """ operatorsetid =...

def _to_bytes_or_false(val): # type: (Union[Text, bytes]) -> Union[bytes, bool] """An internal graph to convert the input to a bytes or to False. The criteria for conversion is as follows and should be python 2 and 3 compatible: - If val is py2 str or py3 bytes: return bytes - If val is py2 unicod...

def make_attribute( key, # type: Text value, # type: Any doc_string=None # type: Optional[Text] ): # type: (...) -> AttributeProto """Makes an AttributeProto based on the value type.""" attr = AttributeProto() attr.name = key if doc_string: attr.doc_string = doc_strin...

def make_tensor_value_info( name, # type: Text elem_type, # type: int shape, # type: Optional[Sequence[Union[Text, int]]] doc_string="", # type: Text shape_denotation=None, # type: Optional[List[Text]] ): # type: (...) -> ValueInfoProto """Makes a ValueInfoProto based o...

def strip_doc_string(proto): # type: (google.protobuf.message.Message) -> None """ Empties `doc_string` field on any nested protobuf messages """ assert isinstance(proto, google.protobuf.message.Message) for descriptor in proto.DESCRIPTOR.fields: if descriptor.name == 'doc_string': ...

def to_array(tensor): # type: (TensorProto) -> np.ndarray[Any] """Converts a tensor def object to a numpy array. Inputs: tensor: a TensorProto object. Returns: arr: the converted array. """ if tensor.HasField("segment"): raise ValueError( "Currently not supporti...

def from_array(arr, name=None): # type: (np.ndarray[Any], Optional[Text]) -> TensorProto """Converts a numpy array to a tensor def. Inputs: arr: a numpy array. name: (optional) the name of the tensor. Returns: tensor_def: the converted tensor def. """ tensor = TensorProto()...

def _serialize(proto): # type: (Union[bytes, google.protobuf.message.Message]) -> bytes ''' Serialize a in-memory proto to bytes @params proto is a in-memory proto, such as a ModelProto, TensorProto, etc @return Serialized proto in bytes ''' if isinstance(proto, bytes): return...

def _deserialize(s, proto): # type: (bytes, _Proto) -> _Proto ''' Parse bytes into a in-memory proto @params s is bytes containing serialized proto proto is a in-memory proto object @return The proto instance filled in by s ''' if not isinstance(s, bytes): raise ValueError...

def load_model(f, format=None, load_external_data=True): # type: (Union[IO[bytes], Text], Optional[Any], bool) -> ModelProto ''' Loads a serialized ModelProto into memory @params f can be a file-like object (has "read" function) or a string containing a file name format is for future use @ret...

def load_tensor(f, format=None): # type: (Union[IO[bytes], Text], Optional[Any]) -> TensorProto ''' Loads a serialized TensorProto into memory @params f can be a file-like object (has "read" function) or a string containing a file name format is for future use @return Loaded in-memory Ten...

def save_model(proto, f, format=None): # type: (Union[ModelProto, bytes], Union[IO[bytes], Text], Optional[Any]) -> None ''' Saves the ModelProto to the specified path. @params proto should be a in-memory ModelProto f can be a file-like object (has "write" function) or a string containing a file n...

def polish_model(model): # type: (ModelProto) -> ModelProto ''' This function combines several useful utility functions together. ''' onnx.checker.check_model(model) onnx.helper.strip_doc_string(model) model = onnx.shape_inference.infer_shapes(model) model = onnx.optimizer.optimize(mode...

def dynamic_unroll(cell, inputs, begin_state, drop_inputs=0, drop_outputs=0, layout='TNC', valid_length=None): """Unrolls an RNN cell across time steps. Currently, 'TNC' is a preferred layout. unroll on the input of this layout runs much faster. Parameters ---------- cell : ...

def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None, valid_length=None): """Unrolls an RNN cell across time steps. Parameters ---------- length : int Number of steps to unroll. inputs : Symbol, list of Symbol, or None ...

def _fix_attribute_names(attrs, change_map): """ Change attribute names as per values in change_map dictionary. Parameters ---------- :param attrs : dict Dict of operator attributes :param change_map : dict Dict of onnx attribute name to mxnet attribute names. Returns ------- :retur...

def _remove_attributes(attrs, remove_list): """ Removes attributes in the remove list from the input attribute dict :param attrs : Dict of operator attributes :param remove_list : list of attributes to be removed :return new_attr : Dict of operator attributes without the listed attributes. """ ...

def _add_extra_attributes(attrs, extra_attr_map): """ :param attrs: Current Attribute list :param extraAttrMap: Additional attributes to be added :return: new_attr """ for attr in extra_attr_map: if attr not in attrs: attrs[attr] = extra_attr_map[attr] return attrs

def _pad_sequence_fix(attr, kernel_dim=None): """Changing onnx's pads sequence to match with mxnet's pad_width mxnet: (x1_begin, x1_end, ... , xn_begin, xn_end) onnx: (x1_begin, x2_begin, ... , xn_end, xn_end)""" new_attr = () if len(attr) % 2 == 0: for index in range(int(len(attr) / 2)): ...

def _fix_pooling(pool_type, inputs, new_attr): """onnx pooling operator supports asymmetrical padding Adding pad operator before pooling in mxnet to work with onnx""" stride = new_attr.get('stride') kernel = new_attr.get('kernel') padding = new_attr.get('pad') p_value = new_attr.get('p_value') ...

def _fix_bias(op_name, attrs, num_inputs): """A workaround for 'use_bias' attribute since onnx don't provide this attribute, we have to check the number of inputs to decide it.""" if num_inputs == 3: attrs['no_bias'] = False elif num_inputs == 2: attrs['no_bias'] = True else: ...

def _fix_broadcast(op_name, inputs, broadcast_axis, proto_obj): """A workaround to reshape bias term to (1, num_channel).""" if int(len(proto_obj._params)) > 0: assert len(list(inputs)) == 2 input0_shape = get_input_shape(inputs[0], proto_obj) #creating reshape shape reshape_sha...

def _fix_channels(op_name, attrs, inputs, proto_obj): """A workaround for getting 'channels' or 'units' since onnx don't provide these attributes. We check the shape of weights provided to get the number. """ weight_name = inputs[1].name if not weight_name in proto_obj._params: raise ValueEr...

def _fix_gemm(op_name, inputs, old_attr, proto_obj): """Using FullyConnected operator in place of linalg_gemm to perform same operation""" op_sym = getattr(symbol, op_name, None) alpha = float(old_attr.get('alpha', 1.0)) beta = float(old_attr.get('beta', 1.0)) trans_a = int(old_attr.get('transA', 0)...

def get_input_shape(sym, proto_obj): """Helper function to obtain the shape of an array""" arg_params = proto_obj.arg_dict aux_params = proto_obj.aux_dict model_input_shape = [data[1] for data in proto_obj.model_metadata.get('input_tensor_data')] data_names = [data[0] for data in proto_obj.model_...

def imresize(src, w, h, *args, **kwargs): r"""Resize image with OpenCV. .. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imresize` to work. Parameters ---------- src : NDArray source image w : int, required ...

def imdecode(buf, *args, **kwargs): """Decode an image to an NDArray. .. note:: `imdecode` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imdecode` to work. Parameters ---------- buf : str/bytes/bytearray or numpy.ndarray Binary image dat...

def scale_down(src_size, size): """Scales down crop size if it's larger than image size. If width/height of the crop is larger than the width/height of the image, sets the width/height to the width/height of the image. Parameters ---------- src_size : tuple of int Size of the image in ...

def copyMakeBorder(src, top, bot, left, right, *args, **kwargs): """Pad image border with OpenCV. Parameters ---------- src : NDArray source image top : int, required Top margin. bot : int, required Bottom margin. left : int, required Left margin. right :...

def _get_interp_method(interp, sizes=()): """Get the interpolation method for resize functions. The major purpose of this function is to wrap a random interp method selection and a auto-estimation method. Parameters ---------- interp : int interpolation method for all resizing operation...

def resize_short(src, size, interp=2): """Resizes shorter edge to size. .. note:: `resize_short` uses OpenCV (not the CV2 Python library). MXNet must have been built with OpenCV for `resize_short` to work. Resizes the original image by setting the shorter edge to size and setting the longer edg...

def fixed_crop(src, x0, y0, w, h, size=None, interp=2): """Crop src at fixed location, and (optionally) resize it to size. Parameters ---------- src : NDArray Input image x0 : int Left boundary of the cropping area y0 : int Top boundary of the cropping area w : int ...

def center_crop(src, size, interp=2): """Crops the image `src` to the given `size` by trimming on all four sides and preserving the center of the image. Upsamples if `src` is smaller than `size`. .. note:: This requires MXNet to be compiled with USE_OPENCV. Parameters ---------- src : NDAr...

def color_normalize(src, mean, std=None): """Normalize src with mean and std. Parameters ---------- src : NDArray Input image mean : NDArray RGB mean to be subtracted std : NDArray RGB standard deviation to be divided Returns ------- NDArray An `NDAr...

def random_size_crop(src, size, area, ratio, interp=2, **kwargs): """Randomly crop src with size. Randomize area and aspect ratio. Parameters ---------- src : NDArray Input image size : tuple of (int, int) Size of the crop formatted as (width, height). area : float in (0, 1] or ...

def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0, pca_noise=0, rand_gray=0, inter_method=2): """Creates an augmenter list. Parameters ---------- dat...

def dumps(self): """Saves the Augmenter to string Returns ------- str JSON formatted string that describes the Augmenter. """ return json.dumps([self.__class__.__name__.lower(), self._kwargs])

def dumps(self): """Override the default to avoid duplicate dump.""" return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]]

def reset(self): """Resets the iterator to the beginning of the data.""" if self.seq is not None and self.shuffle: random.shuffle(self.seq) if self.last_batch_handle != 'roll_over' or \ self._cache_data is None: if self.imgrec is not None: self...

def hard_reset(self): """Resets the iterator and ignore roll over data""" if self.seq is not None and self.shuffle: random.shuffle(self.seq) if self.imgrec is not None: self.imgrec.reset() self.cur = 0 self._allow_read = True self._cache_data = Non...

def next_sample(self): """Helper function for reading in next sample.""" if self._allow_read is False: raise StopIteration if self.seq is not None: if self.cur < self.num_image: idx = self.seq[self.cur] else: if self.last_batch_...

def _batchify(self, batch_data, batch_label, start=0): """Helper function for batchifying data""" i = start batch_size = self.batch_size try: while i < batch_size: label, s = self.next_sample() data = self.imdecode(s) try: ...

def imdecode(self, s): """Decodes a string or byte string to an NDArray. See mx.img.imdecode for more details.""" def locate(): """Locate the image file/index if decode fails.""" if self.seq is not None: idx = self.seq[(self.cur % self.num_image) - 1] ...

def read_image(self, fname): """Reads an input image `fname` and returns the decoded raw bytes. Examples -------- >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes. """ with open(os.path.join(self.path_root, fname), 'rb') as fin: img = fin.read()...

def facc(label, pred): """ evaluate accuracy """ pred = pred.ravel() label = label.ravel() return ((pred > 0.5) == label).mean()

def word_to_vector(word): """ Convert character vectors to integer vectors. """ vector = [] for char in list(word): vector.append(char2int(char)) return vector

def vector_to_word(vector): """ Convert integer vectors to character vectors. """ word = "" for vec in vector: word = word + int2char(vec) return word

def char_conv(out): """ Convert integer vectors to character vectors for batch. """ out_conv = list() for i in range(out.shape[0]): tmp_str = '' for j in range(out.shape[1]): if int(out[i][j]) >= 0: tmp_char = int2char(int(out[i][j])) if in...

def add_pooling_with_padding_types(builder, name, height, width, stride_height, stride_width, layer_type, padding_type, input_name, output_name, padding_top = 0, padding_bottom = 0, padding_left = 0, padding_right = 0, same_padding_asymmetry_mode = 'BOTTOM_RIGHT_HEAVY', exclude_pad_a...

def get_frames(root_path): """Get path to all the frame in view SAX and contain complete frames""" ret = [] for root, _, files in os.walk(root_path): root=root.replace('\\','/') files=[s for s in files if ".dcm" in s] if len(files) == 0 or not files[0].endswith(".dcm") or root.find("sax") ...

def write_data_csv(fname, frames, preproc): """Write data to csv file""" fdata = open(fname, "w") dr = Parallel()(delayed(get_data)(lst,preproc) for lst in frames) data,result = zip(*dr) for entry in data: fdata.write(','.join(entry)+'\r\n') print("All finished, %d slices in total" % len(data)) ...

def crop_resize(img, size): """crop center and resize""" if img.shape[0] < img.shape[1]: img = img.T # we crop image from center short_egde = min(img.shape[:2]) yy = int((img.shape[0] - short_egde) / 2) xx = int((img.shape[1] - short_egde) / 2) crop_img = img[yy : yy + short_egde, xx : xx + ...

def get_generator(): """ construct and return generator """ g_net = gluon.nn.Sequential() with g_net.name_scope(): g_net.add(gluon.nn.Conv2DTranspose( channels=512, kernel_size=4, strides=1, padding=0, use_bias=False)) g_net.add(gluon.nn.BatchNorm()) g_net.add(gluon.nn.L...

def get_descriptor(ctx): """ construct and return descriptor """ d_net = gluon.nn.Sequential() with d_net.name_scope(): d_net.add(SNConv2D(num_filter=64, kernel_size=4, strides=2, padding=1, in_channels=3, ctx=ctx)) d_net.add(gluon.nn.LeakyReLU(0.2)) d_net.add(SNConv2D(num_filter=1...

def _spectral_norm(self): """ spectral normalization """ w = self.params.get('weight').data(self.ctx) w_mat = nd.reshape(w, [w.shape[0], -1]) _u = self.u.data(self.ctx) _v = None for _ in range(POWER_ITERATION): _v = nd.L2Normalization(nd.dot(_u, w_mat)) ...

def conv_output_length(input_length, filter_size, border_mode, stride, dilation=1): """ Compute the length of the output sequence after 1D convolution along time. Note that this function is in line with the function used in Convolution1D class from Keras. Params: i...

def spectrogram(samples, fft_length=256, sample_rate=2, hop_length=128): """ Compute the spectrogram for a real signal. The parameters follow the naming convention of matplotlib.mlab.specgram Args: samples (1D array): input audio signal fft_length (int): number of elements in fft win...

def spectrogram_from_file(filename, step=10, window=20, max_freq=None, eps=1e-14, overwrite=False, save_feature_as_csvfile=False): """ Calculate the log of linear spectrogram from FFT energy Params: filename (str): Path to the audio file step (int): Step size in millise...

def sample(self, label): """ generate random cropping boxes according to parameters if satifactory crops generated, apply to ground-truth as well Parameters: ---------- label : numpy.array (n x 5 matrix) ground-truths Returns: ---------- ...

def _check_satisfy(self, rand_box, gt_boxes): """ check if overlap with any gt box is larger than threshold """ l, t, r, b = rand_box num_gt = gt_boxes.shape[0] ls = np.ones(num_gt) * l ts = np.ones(num_gt) * t rs = np.ones(num_gt) * r bs = np.ones...

def sample(self, label): """ generate random padding boxes according to parameters if satifactory padding generated, apply to ground-truth as well Parameters: ---------- label : numpy.array (n x 5 matrix) ground-truths Returns: ---------- ...

def measure_cost(repeat, scipy_trans_lhs, scipy_dns_lhs, func_name, *args, **kwargs): """Measure time cost of running a function """ mx.nd.waitall() args_list = [] for arg in args: args_list.append(arg) start = time.time() if scipy_trans_lhs: args_list[0] = np.transpose(args_...

def info(self): """ Print information about the annotation file. :return: """ for key, value in self.dataset['info'].items(): print('{}: {}'.format(key, value))

def getCatIds(self, catNms=[], supNms=[], catIds=[]): """ filtering parameters. default skips that filter. :param catNms (str array) : get cats for given cat names :param supNms (str array) : get cats for given supercategory names :param catIds (int array) : get cats for given...

def loadAnns(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying anns :return: anns (object array) : loaded ann objects """ if type(ids) == list: return [self.anns[id] for id in ids] elif type(ids)...

def loadCats(self, ids=[]): """ Load cats with the specified ids. :param ids (int array) : integer ids specifying cats :return: cats (object array) : loaded cat objects """ if type(ids) == list: return [self.cats[id] for id in ids] elif type(ids)...

def loadImgs(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying img :return: imgs (object array) : loaded img objects """ if type(ids) == list: return [self.imgs[id] for id in ids] elif type(ids) ...

def showAnns(self, anns): """ Display the specified annotations. :param anns (array of object): annotations to display :return: None """ if len(anns) == 0: return 0 if 'segmentation' in anns[0] or 'keypoints' in anns[0]: datasetType = 'inst...

def download(self, tarDir = None, imgIds = [] ): ''' Download COCO images from mscoco.org server. :param tarDir (str): COCO results directory name imgIds (list): images to be downloaded :return: ''' if tarDir is None: print('Please specify targe...

def loadNumpyAnnotations(self, data): """ Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} :param data (numpy.ndarray) :return: annotations (python nested list) """ print('Converting ndarray to lists...') assert...

def annToRLE(self, ann): """ Convert annotation which can be polygons, uncompressed RLE to RLE. :return: binary mask (numpy 2D array) """ t = self.imgs[ann['image_id']] h, w = t['height'], t['width'] segm = ann['segmentation'] if type(segm) == list: ...

def save_model(): """Save cnn model Returns ---------- callback: A callback function that can be passed as epoch_end_callback to fit """ if not os.path.exists("checkpoint"): os.mkdir("checkpoint") return mx.callback.do_checkpoint("checkpoint/checkpoint", args.save_period)

def highway(data): """Construct highway net Parameters ---------- data: Returns ---------- Highway Networks """ _data = data high_weight = mx.sym.Variable('high_weight') high_bias = mx.sym.Variable('high_bias') high_fc = mx.sym.FullyConnected(data=data, weight=high_weight...

def train(symbol_data, train_iterator, valid_iterator, data_column_names, target_names): """Train cnn model Parameters ---------- symbol_data: symbol train_iterator: DataIter Train DataIter valid_iterator: DataIter Valid DataIter data_column_names: lis...

def default_batchify_fn(data): """Collate data into batch.""" if isinstance(data[0], nd.NDArray): return nd.stack(*data) elif isinstance(data[0], tuple): data = zip(*data) return [default_batchify_fn(i) for i in data] else: data = np.asarray(data) return nd.array(...

def default_mp_batchify_fn(data): """Collate data into batch. Use shared memory for stacking.""" if isinstance(data[0], nd.NDArray): out = nd.empty((len(data),) + data[0].shape, dtype=data[0].dtype, ctx=context.Context('cpu_shared', 0)) return nd.stack(*data, out=out) ...

def _as_in_context(data, ctx): """Move data into new context.""" if isinstance(data, nd.NDArray): return data.as_in_context(ctx) elif isinstance(data, (list, tuple)): return [_as_in_context(d, ctx) for d in data] return data

def worker_loop_v1(dataset, key_queue, data_queue, batchify_fn): """Worker loop for multiprocessing DataLoader.""" while True: idx, samples = key_queue.get() if idx is None: break batch = batchify_fn([dataset[i] for i in samples]) data_queue.put((idx, batch))

def fetcher_loop_v1(data_queue, data_buffer, pin_memory=False, pin_device_id=0, data_buffer_lock=None): """Fetcher loop for fetching data from queue and put in reorder dict.""" while True: idx, batch = data_queue.get() if idx is None: break if pin_memory: ...

def _worker_fn(samples, batchify_fn, dataset=None): """Function for processing data in worker process.""" # pylint: disable=unused-argument # it is required that each worker process has to fork a new MXIndexedRecordIO handle # preserving dataset as global variable can save tons of overhead and is safe i...

def send(self, obj): """Send object""" buf = io.BytesIO() ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(obj) self.send_bytes(buf.getvalue())

def _push_next(self): """Assign next batch workload to workers.""" r = next(self._iter, None) if r is None: return self._key_queue.put((self._sent_idx, r)) self._sent_idx += 1

def shutdown(self): """Shutdown internal workers by pushing terminate signals.""" if not self._shutdown: # send shutdown signal to the fetcher and join data queue first # Remark: loop_fetcher need to be joined prior to the workers. # otherwise, the the fet...

def _push_next(self): """Assign next batch workload to workers.""" r = next(self._iter, None) if r is None: return async_ret = self._worker_pool.apply_async( self._worker_fn, (r, self._batchify_fn, self._dataset)) self._data_buffer[self._sent_idx] = async_...

def _ctype_key_value(keys, vals): """ Returns ctype arrays for the key-value args, and the whether string keys are used. For internal use only. """ if isinstance(keys, (tuple, list)): assert(len(keys) == len(vals)) c_keys = [] c_vals = [] use_str_keys = None f...