BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def _ctype_dict(param_dict): """ Returns ctype arrays for keys and values(converted to strings) in a dictionary """ assert(isinstance(param_dict, dict)), \ "unexpected type for param_dict: " + str(type(param_dict)) c_keys = c_array(ctypes.c_char_p, [c_str(k) for k in param_dict.keys()]) ...
def _updater_wrapper(updater): """A wrapper for the user-defined handle.""" def updater_handle(key, lhs_handle, rhs_handle, _): """ ctypes function """ lhs = _ndarray_cls(NDArrayHandle(lhs_handle)) rhs = _ndarray_cls(NDArrayHandle(rhs_handle)) updater(key, lhs, rhs) return up...
def create(name='local'): """Creates a new KVStore. For single machine training, there are two commonly used types: ``local``: Copies all gradients to CPU memory and updates weights there. ``device``: Aggregates gradients and updates weights on GPUs. With this setting, the KVStore also attempts t...
def init(self, key, value): """ Initializes a single or a sequence of key-value pairs into the store. For each key, one must `init` it before calling `push` or `pull`. When multiple workers invoke `init` for the same key, only the value supplied by worker with rank `0` is used. This fun...
def push(self, key, value, priority=0): """ Pushes a single or a sequence of key-value pairs into the store. This function returns immediately after adding an operator to the engine. The actual operation is executed asynchronously. If there are consecutive pushes to the same key, there ...
def pull(self, key, out=None, priority=0, ignore_sparse=True): """ Pulls a single value or a sequence of values from the store. This function returns immediately after adding an operator to the engine. Subsequent attempts to read from the `out` variable will be blocked until the pull op...
def row_sparse_pull(self, key, out=None, priority=0, row_ids=None): """ Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \ from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull \ is invoked just once and the result is broadcast t...
def set_gradient_compression(self, compression_params): """ Specifies type of low-bit quantization for gradient compression \ and additional arguments depending on the type of compression being used. 2bit Gradient Compression takes a positive float `threshold`. The technique works by t...
def set_optimizer(self, optimizer): """ Registers an optimizer with the kvstore. When using a single machine, this function updates the local optimizer. If using multiple machines and this operation is invoked from a worker node, it will serialized the optimizer with pickle and send it ...
def type(self): """ Returns the type of this kvstore. Returns ------- type : str the string type """ kv_type = ctypes.c_char_p() check_call(_LIB.MXKVStoreGetType(self.handle, ctypes.byref(kv_type))) return py_str(kv_type.value)
def rank(self): """ Returns the rank of this worker node. Returns ------- rank : int The rank of this node, which is in range [0, num_workers()) """ rank = ctypes.c_int() check_call(_LIB.MXKVStoreGetRank(self.handle, ctypes.byref(rank))) retur...
def num_workers(self): """Returns the number of worker nodes. Returns ------- size :int The number of worker nodes. """ size = ctypes.c_int() check_call(_LIB.MXKVStoreGetGroupSize(self.handle, ctypes.byref(size))) return size.value
def save_optimizer_states(self, fname, dump_optimizer=False): """Saves the optimizer (updater) state to a file. This is often used when checkpointing the model during training. Parameters ---------- fname : str Path to the output states file. dump_optimizer :...
def load_optimizer_states(self, fname): """Loads the optimizer (updater) state from the file. Parameters ---------- fname : str Path to input states file. """ assert self._updater is not None, "Cannot load states for distributed training" self._update...
def _set_updater(self, updater): """Sets a push updater into the store. This function only changes the local store. When running on multiple machines one must use `set_optimizer`. Parameters ---------- updater : function The updater function. Exampl...
def _send_command_to_servers(self, head, body): """Sends a command to all server nodes. Sending command to a server node will cause that server node to invoke ``KVStoreServer.controller`` to execute the command. This function returns after the command has been executed on all server ...
def add(self, module, kwargs): """Add a module to the chain. Parameters ---------- module : BaseModule The new module to add. kwargs : ``keywords`` All the keyword arguments are saved as meta information for the added module. The currently...
def get_params(self): """Gets current parameters. Returns ------- (arg_params, aux_params) A pair of dictionaries each mapping parameter names to NDArray values. This is a merged dictionary of all the parameters in the modules. """ assert self.bin...
def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None, allow_missing=False, force_init=False, allow_extra=False): """Initializes parameters. Parameters ---------- initializer : Initializer arg_params : dict Default ``No...
def bind(self, data_shapes, label_shapes=None, for_training=True, inputs_need_grad=False, force_rebind=False, shared_module=None, grad_req='write'): """Binds the symbols to construct executors. This is necessary before one can perform computation with the module. Param...
def init_optimizer(self, kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), force_init=False): """Installs and initializes optimizers. Parameters ---------- kvstore : str or KVStore Default `'local'`. ...
def forward(self, data_batch, is_train=None): """Forward computation. Parameters ---------- data_batch : DataBatch is_train : bool Default is ``None``, in which case `is_train` is take as ``self.for_training``. """ assert self.binded and self.params_i...
def backward(self, out_grads=None): """Backward computation.""" assert self.binded and self.params_initialized for i_layer, module in reversed(list(zip(range(len(self._modules)), self._modules))): module.backward(out_grads=out_grads) if i_layer == 0: brea...
def update(self): """Updates parameters according to installed optimizer and the gradient computed in the previous forward-backward cycle. """ assert self.binded and self.params_initialized and self.optimizer_initialized for module in self._modules: module.update()
def get_outputs(self, merge_multi_context=True): """Gets outputs from a previous forward computation. Parameters ---------- merge_multi_context : bool Default is ``True``. In the case when data-parallelism is used, the outputs will be collected from multiple devi...
def get_input_grads(self, merge_multi_context=True): """Gets the gradients with respect to the inputs of the module. Parameters ---------- merge_multi_context : bool Default is ``True``. In the case when data-parallelism is used, the outputs will be collected fro...
def update_metric(self, eval_metric, labels, pre_sliced=False): """Evaluates and accumulates evaluation metric on outputs of the last forward computation. Parameters ---------- eval_metric : EvalMetric labels : list of NDArray Typically ``data_batch.label``. ...
def install_monitor(self, mon): """Installs monitor on all executors.""" assert self.binded for module in self._modules: module.install_monitor(mon)
def get_iterator(data_shape, use_caffe_data): """Generate the iterator of mnist dataset""" def get_iterator_impl_mnist(args, kv): """return train and val iterators for mnist""" # download data get_mnist_ubyte() flat = False if len(data_shape) != 1 else True train = mx.io...
def predict(prediction_dir='./Test'): """The function is used to run predictions on the audio files in the directory `pred_directory`. Parameters ---------- net: The model that has been trained. prediction_dir: string, default ./Test The directory that contains the audio files on wh...
def _proc_loop(proc_id, alive, queue, fn): """Thread loop for generating data Parameters ---------- proc_id: int Process id alive: multiprocessing.Value variable for signaling whether process should continue or not queue: multiprocessing.Queue ...
def _init_proc(self): """Start processes if not already started""" if not self.proc: self.proc = [ mp.Process(target=self._proc_loop, args=(i, self.alive, self.queue, self.fn)) for i in range(self.num_proc) ] self.alive.value = True ...
def reset(self): """Resets the generator by stopping all processes""" self.alive.value = False qsize = 0 try: while True: self.queue.get(timeout=0.1) qsize += 1 except QEmptyExcept: pass print("Queue size on reset: {...
def with_metaclass(meta, *bases): """Create a base class with a metaclass.""" # This requires a bit of explanation: the basic idea is to make a dummy # metaclass for one level of class instantiation that replaces itself with # the actual metaclass. class metaclass(type): def __new__(cls, na...
def _load_lib(): """Load library by searching possible path.""" lib_path = libinfo.find_lib_path() lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL) # DMatrix functions lib.MXGetLastError.restype = ctypes.c_char_p return lib
def c_array(ctype, values): """Create ctypes array from a Python array. Parameters ---------- ctype : ctypes data type Data type of the array we want to convert to, such as mx_float. values : tuple or list Data content. Returns ------- out : ctypes array Create...
def c_handle_array(objs): """Create ctypes const void ** from a list of MXNet objects with handles. Parameters ---------- objs : list of NDArray/Symbol. MXNet objects. Returns ------- (ctypes.c_void_p * len(objs)) A void ** pointer that can be passed to C API. """ a...
def ctypes2numpy_shared(cptr, shape): """Convert a ctypes pointer to a numpy array. The resulting NumPy array shares the memory with the pointer. Parameters ---------- cptr : ctypes.POINTER(mx_float) pointer to the memory region shape : tuple Shape of target `NDArray`. Re...
def build_param_doc(arg_names, arg_types, arg_descs, remove_dup=True): """Build argument docs in python style. arg_names : list of str Argument names. arg_types : list of str Argument type information. arg_descs : list of str Argument description information. remove_dup :...
def add_fileline_to_docstring(module, incursive=True): """Append the definition position to each function contained in module. Examples -------- # Put the following codes at the end of a file add_fileline_to_docstring(__name__) """ def _add_fileline(obj): """Add fileinto to a objec...
def _init_op_module(root_namespace, module_name, make_op_func): """ Registers op functions created by `make_op_func` under `root_namespace.module_name.[submodule_name]`, where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`. Parameters ---------- root_namespace : str Top level ...
def _generate_op_module_signature(root_namespace, module_name, op_code_gen_func): """ Generate op functions created by `op_code_gen_func` and write to the source file of `root_namespace.module_name.[submodule_name]`, where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`. Parameters -------...
def set_np_compat(active): """ Turns on/off NumPy compatibility. NumPy-compatibility is turned off by default in backend. Parameters ---------- active : bool Indicates whether to turn on/off NumPy compatibility. Returns ------- A bool value indicating the previous state of ...
def is_np_compat(): """ Checks whether the NumPy compatibility is currently turned on. NumPy-compatibility is turned off by default in backend. Returns ------- A bool value indicating whether the NumPy compatibility is currently on. """ curr = ctypes.c_bool() check_call(_LIB.MXI...
def use_np_compat(func): """Wraps a function with an activated NumPy-compatibility scope. This ensures that the execution of the function is guaranteed with NumPy compatible semantics, such as zero-dim and zero size tensors. Example:: import mxnet as mx @mx.use_np_compat def sca...
def rse(label, pred): """computes the root relative squared error (condensed using standard deviation formula)""" numerator = np.sqrt(np.mean(np.square(label - pred), axis = None)) denominator = np.std(label, axis = None) return numerator / denominator
def rae(label, pred): """computes the relative absolute error (condensed using standard deviation formula)""" numerator = np.mean(np.abs(label - pred), axis=None) denominator = np.mean(np.abs(label - np.mean(label, axis=None)), axis=None) return numerator / denominator
def corr(label, pred): """computes the empirical correlation coefficient""" numerator1 = label - np.mean(label, axis=0) numerator2 = pred - np.mean(pred, axis = 0) numerator = np.mean(numerator1 * numerator2, axis=0) denominator = np.std(label, axis=0) * np.std(pred, axis=0) return np.mean(numer...
def get_custom_metrics(): """ :return: mxnet metric object """ _rse = mx.metric.create(rse) _rae = mx.metric.create(rae) _corr = mx.metric.create(corr) return mx.metric.create([_rae, _rse, _corr])
def _get_input(proto): """Get input size """ layer = caffe_parser.get_layers(proto) if len(proto.input_dim) > 0: input_dim = proto.input_dim elif len(proto.input_shape) > 0: input_dim = proto.input_shape[0].dim elif layer[0].type == "Input": input_dim = layer[0].input_par...
def _convert_conv_param(param): """ Convert convolution layer parameter from Caffe to MXNet """ param_string = "num_filter=%d" % param.num_output pad_w = 0 pad_h = 0 if isinstance(param.pad, int): pad = param.pad param_string += ", pad=(%d, %d)" % (pad, pad) else: ...
def _convert_pooling_param(param): """Convert the pooling layer parameter """ param_string = "pooling_convention='full', " if param.global_pooling: param_string += "global_pool=True, kernel=(1,1)" else: param_string += "pad=(%d,%d), kernel=(%d,%d), stride=(%d,%d)" % ( par...
def _parse_proto(prototxt_fname): """Parse Caffe prototxt into symbol string """ proto = caffe_parser.read_prototxt(prototxt_fname) # process data layer input_name, input_dim, layers = _get_input(proto) # only support single input, so always use `data` as the input data mapping = {input_nam...
def convert_symbol(prototxt_fname): """Convert caffe model definition into Symbol Parameters ---------- prototxt_fname : str Filename of the prototxt file Returns ------- Symbol Converted Symbol tuple Input shape """ sym, output_name, input_dim = _parse_...
def train_episode(agent, envs, preprocessors, t_max, render): """Complete an episode's worth of training for each environment.""" num_envs = len(envs) # Buffers to hold trajectories, e.g. `env_xs[i]` will hold the observations # for environment `i`. env_xs, env_as = _2d_list(num_envs), _2d_list(num...
def parse_caffemodel(file_path): """ parses the trained .caffemodel file filepath: /path/to/trained-model.caffemodel returns: layers """ f = open(file_path, 'rb') contents = f.read() net_param = caffe_pb2.NetParameter() net_param.ParseFromString(contents) layers = find_layers...
def featurize(self, audio_clip, overwrite=False, save_feature_as_csvfile=False): """ For a given audio clip, calculate the log of its Fourier Transform Params: audio_clip(str): Path to the audio clip """ return spectrogram_from_file( audio_clip, step=self.step, wi...
def load_metadata_from_desc_file(self, desc_file, partition='train', max_duration=16.0,): """ Read metadata from the description file (possibly takes long, depending on the filesize) Params: desc_file (str): Path to a JSON-line file that cont...
def prepare_minibatch(self, audio_paths, texts, overwrite=False, is_bi_graphemes=False, seq_length=-1, save_feature_as_csvfile=False): """ Featurize a minibatch of audio, zero pad them and return a dictionary Params: audio_paths (list(str)): List of paths to audio f...
def sample_normalize(self, k_samples=1000, overwrite=False): """ Estimate the mean and std of the features from the training set Params: k_samples (int): Use this number of samples for estimation """ log = logUtil.getlogger() log.info("Calculating mean and std from sa...
def gru(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0., is_batchnorm=False, gamma=None, beta=None, name=None): """ GRU Cell symbol Reference: * Chung, Junyoung, et al. "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint arXiv:1412.3...
def save_image(data, epoch, image_size, batch_size, output_dir, padding=2): """ save image """ data = data.asnumpy().transpose((0, 2, 3, 1)) datanp = np.clip( (data - np.min(data))*(255.0/(np.max(data) - np.min(data))), 0, 255).astype(np.uint8) x_dim = min(8, batch_size) y_dim = int(math.cei...
def list_image(root, recursive, exts): """Traverses the root of directory that contains images and generates image list iterator. Parameters ---------- root: string recursive: bool exts: string Returns ------- image iterator that contains all the image under the specified path ...
def write_list(path_out, image_list): """Hepler function to write image list into the file. The format is as below, integer_image_index \t float_label_index \t path_to_image Note that the blank between number and tab is only used for readability. Parameters ---------- path_out: string im...
def make_list(args): """Generates .lst file. Parameters ---------- args: object that contains all the arguments """ image_list = list_image(args.root, args.recursive, args.exts) image_list = list(image_list) if args.shuffle is True: random.seed(100) random.shuffle(image_l...
def read_list(path_in): """Reads the .lst file and generates corresponding iterator. Parameters ---------- path_in: string Returns ------- item iterator that contains information in .lst file """ with open(path_in) as fin: while True: line = fin.readline() ...
def image_encode(args, i, item, q_out): """Reads, preprocesses, packs the image and put it back in output queue. Parameters ---------- args: object i: int item: list q_out: queue """ fullpath = os.path.join(args.root, item[1]) if len(item) > 3 and args.pack_label: header...
def read_worker(args, q_in, q_out): """Function that will be spawned to fetch the image from the input queue and put it back to output queue. Parameters ---------- args: object q_in: queue q_out: queue """ while True: deq = q_in.get() if deq is None: break...
def write_worker(q_out, fname, working_dir): """Function that will be spawned to fetch processed image from the output queue and write to the .rec file. Parameters ---------- q_out: queue fname: string working_dir: string """ pre_time = time.time() count = 0 fname = os.path.b...
def parse_args(): """Defines all arguments. Returns ------- args object that contains all the params """ parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='Create an image list or \ make a record database by reading from...
def transform(data, target_wd, target_ht, is_train, box): """Crop and normnalize an image nd array.""" if box is not None: x, y, w, h = box data = data[y:min(y+h, data.shape[0]), x:min(x+w, data.shape[1])] # Resize to target_wd * target_ht. data = mx.image.imresize(data, target_wd, targ...
def cub200_iterator(data_path, batch_k, batch_size, data_shape): """Return training and testing iterator for the CUB200-2011 dataset.""" return (CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=True), CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=False))
def get_image(self, img, is_train): """Load and transform an image.""" img_arr = mx.image.imread(img) img_arr = transform(img_arr, 256, 256, is_train, self.boxes[img]) return img_arr
def sample_train_batch(self): """Sample a training batch (data and label).""" batch = [] labels = [] num_groups = self.batch_size // self.batch_k # For CUB200, we use the first 100 classes for training. sampled_classes = np.random.choice(100, num_groups, replace=False) ...
def next(self): """Return a batch.""" if self.is_train: data, labels = self.sample_train_batch() else: if self.test_count * self.batch_size < len(self.test_image_files): data, labels = self.get_test_batch() self.test_count += 1 ...
def load_mnist(training_num=50000): """Load mnist dataset""" data_path = os.path.join(os.path.dirname(os.path.realpath('__file__')), 'mnist.npz') if not os.path.isfile(data_path): from six.moves import urllib origin = ( 'https://github.com/sxjscience/mxnet/raw/master/example/baye...
def feature_list(): """ Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc Returns ------- list List of :class:`.Feature` objects """ lib_features_c_array = ctypes.POINTER(Feature)() lib_features_size = ctypes.c_size_t() ...
def is_enabled(self, feature_name): """ Check for a particular feature by name Parameters ---------- feature_name: str The name of a valid feature as string for example 'CUDA' Returns ------- Boolean True if it's enabled, False if...
def cache_path(self): """ make a directory to store all caches Returns: --------- cache path """ cache_path = os.path.join(os.path.dirname(__file__), '..', 'cache') if not os.path.exists(cache_path): os.mkdir(cache_path) return cac...
def _load_image_set_index(self, shuffle): """ find out which indexes correspond to given image set (train or val) Parameters: ---------- shuffle : boolean whether to shuffle the image list Returns: ---------- entire list of images specified in...
def image_path_from_index(self, index): """ given image index, find out full path Parameters: ---------- index: int index of a specific image Returns: ---------- full path of this image """ assert self.image_set_index is not No...
def _label_path_from_index(self, index): """ given image index, find out annotation path Parameters: ---------- index: int index of a specific image Returns: ---------- full path of annotation file """ label_file = os.path.joi...
def _load_image_labels(self): """ preprocess all ground-truths Returns: ---------- labels packed in [num_images x max_num_objects x 5] tensor """ temp = [] # load ground-truth from xml annotations for idx in self.image_set_index: labe...
def evaluate_detections(self, detections): """ top level evaluations Parameters: ---------- detections: list result list, each entry is a matrix of detections Returns: ---------- None """ # make all these folders for results...
def get_result_file_template(self): """ this is a template VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt Returns: ---------- a string template """ res_file_folder = os.path.join(self.devkit_path, 'results', 'VOC' + self.year, 'Main')...
def write_pascal_results(self, all_boxes): """ write results files in pascal devkit path Parameters: ---------- all_boxes: list boxes to be processed [bbox, confidence] Returns: ---------- None """ for cls_ind, cls in enumerate(...
def do_python_eval(self): """ python evaluation wrapper Returns: ---------- None """ annopath = os.path.join(self.data_path, 'Annotations', '{:s}.xml') imageset_file = os.path.join(self.data_path, 'ImageSets', 'Main', self.image_set + '.txt') cach...
def _get_imsize(self, im_name): """ get image size info Returns: ---------- tuple of (height, width) """ img = cv2.imread(im_name) return (img.shape[0], img.shape[1])
def add_fit_args(parser): """ parser : argparse.ArgumentParser return a parser added with args required by fit """ train = parser.add_argument_group('Training', 'model training') train.add_argument('--network', type=str, help='the neural network to use') train.add_argu...
def fit(args, network, data_loader, **kwargs): """ train a model args : argparse returns network : the symbol definition of the nerual network data_loader : function that returns the train and val data iterators """ # kvstore kv = mx.kvstore.create(args.kv_store) if args.gc_type != '...
def CreateMultiRandCropAugmenter(min_object_covered=0.1, aspect_ratio_range=(0.75, 1.33), area_range=(0.05, 1.0), min_eject_coverage=0.3, max_attempts=50, skip_prob=0): """Helper function to create multiple random crop augmenters. Parameters ...
def CreateDetAugmenter(data_shape, resize=0, rand_crop=0, rand_pad=0, rand_gray=0, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0, pca_noise=0, hue=0, inter_method=2, min_object_covered=0.1, aspect_ratio_range=(0.75, 1....
def dumps(self): """Override default.""" return [self.__class__.__name__.lower(), [x.dumps() for x in self.aug_list]]
def _calculate_areas(self, label): """Calculate areas for multiple labels""" heights = np.maximum(0, label[:, 3] - label[:, 1]) widths = np.maximum(0, label[:, 2] - label[:, 0]) return heights * widths
def _intersect(self, label, xmin, ymin, xmax, ymax): """Calculate intersect areas, normalized.""" left = np.maximum(label[:, 0], xmin) right = np.minimum(label[:, 2], xmax) top = np.maximum(label[:, 1], ymin) bot = np.minimum(label[:, 3], ymax) invalid = np.where(np.logic...
def _check_satisfy_constraints(self, label, xmin, ymin, xmax, ymax, width, height): """Check if constrains are satisfied""" if (xmax - xmin) * (ymax - ymin) < 2: return False # only 1 pixel x1 = float(xmin) / width y1 = float(ymin) / height x2 = float(xmax) / width ...
def _update_labels(self, label, crop_box, height, width): """Convert labels according to crop box""" xmin = float(crop_box[0]) / width ymin = float(crop_box[1]) / height w = float(crop_box[2]) / width h = float(crop_box[3]) / height out = label.copy() out[:, (1, 3...
def _random_crop_proposal(self, label, height, width): """Propose cropping areas""" from math import sqrt if not self.enabled or height <= 0 or width <= 0: return () min_area = self.area_range[0] * height * width max_area = self.area_range[1] * height * width ...
def _update_labels(self, label, pad_box, height, width): """Update label according to padding region""" out = label.copy() out[:, (1, 3)] = (out[:, (1, 3)] * width + pad_box[0]) / pad_box[2] out[:, (2, 4)] = (out[:, (2, 4)] * height + pad_box[1]) / pad_box[3] return out
def _random_pad_proposal(self, label, height, width): """Generate random padding region""" from math import sqrt if not self.enabled or height <= 0 or width <= 0: return () min_area = self.area_range[0] * height * width max_area = self.area_range[1] * height * width ...

def _ctype_dict(param_dict): """ Returns ctype arrays for keys and values(converted to strings) in a dictionary """ assert(isinstance(param_dict, dict)), \ "unexpected type for param_dict: " + str(type(param_dict)) c_keys = c_array(ctypes.c_char_p, [c_str(k) for k in param_dict.keys()]) ...

def _updater_wrapper(updater): """A wrapper for the user-defined handle.""" def updater_handle(key, lhs_handle, rhs_handle, _): """ ctypes function """ lhs = _ndarray_cls(NDArrayHandle(lhs_handle)) rhs = _ndarray_cls(NDArrayHandle(rhs_handle)) updater(key, lhs, rhs) return up...

def create(name='local'): """Creates a new KVStore. For single machine training, there are two commonly used types: ``local``: Copies all gradients to CPU memory and updates weights there. ``device``: Aggregates gradients and updates weights on GPUs. With this setting, the KVStore also attempts t...

def init(self, key, value): """ Initializes a single or a sequence of key-value pairs into the store. For each key, one must `init` it before calling `push` or `pull`. When multiple workers invoke `init` for the same key, only the value supplied by worker with rank `0` is used. This fun...

def push(self, key, value, priority=0): """ Pushes a single or a sequence of key-value pairs into the store. This function returns immediately after adding an operator to the engine. The actual operation is executed asynchronously. If there are consecutive pushes to the same key, there ...

def pull(self, key, out=None, priority=0, ignore_sparse=True): """ Pulls a single value or a sequence of values from the store. This function returns immediately after adding an operator to the engine. Subsequent attempts to read from the `out` variable will be blocked until the pull op...

def row_sparse_pull(self, key, out=None, priority=0, row_ids=None): """ Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \ from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull \ is invoked just once and the result is broadcast t...

def set_gradient_compression(self, compression_params): """ Specifies type of low-bit quantization for gradient compression \ and additional arguments depending on the type of compression being used. 2bit Gradient Compression takes a positive float `threshold`. The technique works by t...

def set_optimizer(self, optimizer): """ Registers an optimizer with the kvstore. When using a single machine, this function updates the local optimizer. If using multiple machines and this operation is invoked from a worker node, it will serialized the optimizer with pickle and send it ...

def type(self): """ Returns the type of this kvstore. Returns ------- type : str the string type """ kv_type = ctypes.c_char_p() check_call(_LIB.MXKVStoreGetType(self.handle, ctypes.byref(kv_type))) return py_str(kv_type.value)

def rank(self): """ Returns the rank of this worker node. Returns ------- rank : int The rank of this node, which is in range [0, num_workers()) """ rank = ctypes.c_int() check_call(_LIB.MXKVStoreGetRank(self.handle, ctypes.byref(rank))) retur...

def num_workers(self): """Returns the number of worker nodes. Returns ------- size :int The number of worker nodes. """ size = ctypes.c_int() check_call(_LIB.MXKVStoreGetGroupSize(self.handle, ctypes.byref(size))) return size.value

def save_optimizer_states(self, fname, dump_optimizer=False): """Saves the optimizer (updater) state to a file. This is often used when checkpointing the model during training. Parameters ---------- fname : str Path to the output states file. dump_optimizer :...

def load_optimizer_states(self, fname): """Loads the optimizer (updater) state from the file. Parameters ---------- fname : str Path to input states file. """ assert self._updater is not None, "Cannot load states for distributed training" self._update...

def _set_updater(self, updater): """Sets a push updater into the store. This function only changes the local store. When running on multiple machines one must use `set_optimizer`. Parameters ---------- updater : function The updater function. Exampl...

def _send_command_to_servers(self, head, body): """Sends a command to all server nodes. Sending command to a server node will cause that server node to invoke ``KVStoreServer.controller`` to execute the command. This function returns after the command has been executed on all server ...

def add(self, module, **kwargs): """Add a module to the chain. Parameters ---------- module : BaseModule The new module to add. kwargs : ``**keywords`` All the keyword arguments are saved as meta information for the added module. The currently...

def get_params(self): """Gets current parameters. Returns ------- (arg_params, aux_params) A pair of dictionaries each mapping parameter names to NDArray values. This is a merged dictionary of all the parameters in the modules. """ assert self.bin...

def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None, allow_missing=False, force_init=False, allow_extra=False): """Initializes parameters. Parameters ---------- initializer : Initializer arg_params : dict Default ``No...

def bind(self, data_shapes, label_shapes=None, for_training=True, inputs_need_grad=False, force_rebind=False, shared_module=None, grad_req='write'): """Binds the symbols to construct executors. This is necessary before one can perform computation with the module. Param...

def init_optimizer(self, kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), force_init=False): """Installs and initializes optimizers. Parameters ---------- kvstore : str or KVStore Default `'local'`. ...

def forward(self, data_batch, is_train=None): """Forward computation. Parameters ---------- data_batch : DataBatch is_train : bool Default is ``None``, in which case `is_train` is take as ``self.for_training``. """ assert self.binded and self.params_i...

def backward(self, out_grads=None): """Backward computation.""" assert self.binded and self.params_initialized for i_layer, module in reversed(list(zip(range(len(self._modules)), self._modules))): module.backward(out_grads=out_grads) if i_layer == 0: brea...

def update(self): """Updates parameters according to installed optimizer and the gradient computed in the previous forward-backward cycle. """ assert self.binded and self.params_initialized and self.optimizer_initialized for module in self._modules: module.update()

def get_outputs(self, merge_multi_context=True): """Gets outputs from a previous forward computation. Parameters ---------- merge_multi_context : bool Default is ``True``. In the case when data-parallelism is used, the outputs will be collected from multiple devi...

def get_input_grads(self, merge_multi_context=True): """Gets the gradients with respect to the inputs of the module. Parameters ---------- merge_multi_context : bool Default is ``True``. In the case when data-parallelism is used, the outputs will be collected fro...

def update_metric(self, eval_metric, labels, pre_sliced=False): """Evaluates and accumulates evaluation metric on outputs of the last forward computation. Parameters ---------- eval_metric : EvalMetric labels : list of NDArray Typically ``data_batch.label``. ...

def install_monitor(self, mon): """Installs monitor on all executors.""" assert self.binded for module in self._modules: module.install_monitor(mon)

def get_iterator(data_shape, use_caffe_data): """Generate the iterator of mnist dataset""" def get_iterator_impl_mnist(args, kv): """return train and val iterators for mnist""" # download data get_mnist_ubyte() flat = False if len(data_shape) != 1 else True train = mx.io...

def predict(prediction_dir='./Test'): """The function is used to run predictions on the audio files in the directory `pred_directory`. Parameters ---------- net: The model that has been trained. prediction_dir: string, default ./Test The directory that contains the audio files on wh...

def _proc_loop(proc_id, alive, queue, fn): """Thread loop for generating data Parameters ---------- proc_id: int Process id alive: multiprocessing.Value variable for signaling whether process should continue or not queue: multiprocessing.Queue ...

def _init_proc(self): """Start processes if not already started""" if not self.proc: self.proc = [ mp.Process(target=self._proc_loop, args=(i, self.alive, self.queue, self.fn)) for i in range(self.num_proc) ] self.alive.value = True ...

def reset(self): """Resets the generator by stopping all processes""" self.alive.value = False qsize = 0 try: while True: self.queue.get(timeout=0.1) qsize += 1 except QEmptyExcept: pass print("Queue size on reset: {...

def with_metaclass(meta, *bases): """Create a base class with a metaclass.""" # This requires a bit of explanation: the basic idea is to make a dummy # metaclass for one level of class instantiation that replaces itself with # the actual metaclass. class metaclass(type): def __new__(cls, na...

def _load_lib(): """Load library by searching possible path.""" lib_path = libinfo.find_lib_path() lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL) # DMatrix functions lib.MXGetLastError.restype = ctypes.c_char_p return lib

def c_array(ctype, values): """Create ctypes array from a Python array. Parameters ---------- ctype : ctypes data type Data type of the array we want to convert to, such as mx_float. values : tuple or list Data content. Returns ------- out : ctypes array Create...

def c_handle_array(objs): """Create ctypes const void ** from a list of MXNet objects with handles. Parameters ---------- objs : list of NDArray/Symbol. MXNet objects. Returns ------- (ctypes.c_void_p * len(objs)) A void ** pointer that can be passed to C API. """ a...

def ctypes2numpy_shared(cptr, shape): """Convert a ctypes pointer to a numpy array. The resulting NumPy array shares the memory with the pointer. Parameters ---------- cptr : ctypes.POINTER(mx_float) pointer to the memory region shape : tuple Shape of target `NDArray`. Re...

def build_param_doc(arg_names, arg_types, arg_descs, remove_dup=True): """Build argument docs in python style. arg_names : list of str Argument names. arg_types : list of str Argument type information. arg_descs : list of str Argument description information. remove_dup :...

def add_fileline_to_docstring(module, incursive=True): """Append the definition position to each function contained in module. Examples -------- # Put the following codes at the end of a file add_fileline_to_docstring(__name__) """ def _add_fileline(obj): """Add fileinto to a objec...

def _init_op_module(root_namespace, module_name, make_op_func): """ Registers op functions created by `make_op_func` under `root_namespace.module_name.[submodule_name]`, where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`. Parameters ---------- root_namespace : str Top level ...

def _generate_op_module_signature(root_namespace, module_name, op_code_gen_func): """ Generate op functions created by `op_code_gen_func` and write to the source file of `root_namespace.module_name.[submodule_name]`, where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`. Parameters -------...

def set_np_compat(active): """ Turns on/off NumPy compatibility. NumPy-compatibility is turned off by default in backend. Parameters ---------- active : bool Indicates whether to turn on/off NumPy compatibility. Returns ------- A bool value indicating the previous state of ...

def is_np_compat(): """ Checks whether the NumPy compatibility is currently turned on. NumPy-compatibility is turned off by default in backend. Returns ------- A bool value indicating whether the NumPy compatibility is currently on. """ curr = ctypes.c_bool() check_call(_LIB.MXI...

def use_np_compat(func): """Wraps a function with an activated NumPy-compatibility scope. This ensures that the execution of the function is guaranteed with NumPy compatible semantics, such as zero-dim and zero size tensors. Example:: import mxnet as mx @mx.use_np_compat def sca...

def rse(label, pred): """computes the root relative squared error (condensed using standard deviation formula)""" numerator = np.sqrt(np.mean(np.square(label - pred), axis = None)) denominator = np.std(label, axis = None) return numerator / denominator

def rae(label, pred): """computes the relative absolute error (condensed using standard deviation formula)""" numerator = np.mean(np.abs(label - pred), axis=None) denominator = np.mean(np.abs(label - np.mean(label, axis=None)), axis=None) return numerator / denominator

def corr(label, pred): """computes the empirical correlation coefficient""" numerator1 = label - np.mean(label, axis=0) numerator2 = pred - np.mean(pred, axis = 0) numerator = np.mean(numerator1 * numerator2, axis=0) denominator = np.std(label, axis=0) * np.std(pred, axis=0) return np.mean(numer...

def get_custom_metrics(): """ :return: mxnet metric object """ _rse = mx.metric.create(rse) _rae = mx.metric.create(rae) _corr = mx.metric.create(corr) return mx.metric.create([_rae, _rse, _corr])

def _get_input(proto): """Get input size """ layer = caffe_parser.get_layers(proto) if len(proto.input_dim) > 0: input_dim = proto.input_dim elif len(proto.input_shape) > 0: input_dim = proto.input_shape[0].dim elif layer[0].type == "Input": input_dim = layer[0].input_par...

def _convert_conv_param(param): """ Convert convolution layer parameter from Caffe to MXNet """ param_string = "num_filter=%d" % param.num_output pad_w = 0 pad_h = 0 if isinstance(param.pad, int): pad = param.pad param_string += ", pad=(%d, %d)" % (pad, pad) else: ...

def _convert_pooling_param(param): """Convert the pooling layer parameter """ param_string = "pooling_convention='full', " if param.global_pooling: param_string += "global_pool=True, kernel=(1,1)" else: param_string += "pad=(%d,%d), kernel=(%d,%d), stride=(%d,%d)" % ( par...

def _parse_proto(prototxt_fname): """Parse Caffe prototxt into symbol string """ proto = caffe_parser.read_prototxt(prototxt_fname) # process data layer input_name, input_dim, layers = _get_input(proto) # only support single input, so always use `data` as the input data mapping = {input_nam...

def convert_symbol(prototxt_fname): """Convert caffe model definition into Symbol Parameters ---------- prototxt_fname : str Filename of the prototxt file Returns ------- Symbol Converted Symbol tuple Input shape """ sym, output_name, input_dim = _parse_...

def train_episode(agent, envs, preprocessors, t_max, render): """Complete an episode's worth of training for each environment.""" num_envs = len(envs) # Buffers to hold trajectories, e.g. `env_xs[i]` will hold the observations # for environment `i`. env_xs, env_as = _2d_list(num_envs), _2d_list(num...

def parse_caffemodel(file_path): """ parses the trained .caffemodel file filepath: /path/to/trained-model.caffemodel returns: layers """ f = open(file_path, 'rb') contents = f.read() net_param = caffe_pb2.NetParameter() net_param.ParseFromString(contents) layers = find_layers...

def featurize(self, audio_clip, overwrite=False, save_feature_as_csvfile=False): """ For a given audio clip, calculate the log of its Fourier Transform Params: audio_clip(str): Path to the audio clip """ return spectrogram_from_file( audio_clip, step=self.step, wi...

def load_metadata_from_desc_file(self, desc_file, partition='train', max_duration=16.0,): """ Read metadata from the description file (possibly takes long, depending on the filesize) Params: desc_file (str): Path to a JSON-line file that cont...

def prepare_minibatch(self, audio_paths, texts, overwrite=False, is_bi_graphemes=False, seq_length=-1, save_feature_as_csvfile=False): """ Featurize a minibatch of audio, zero pad them and return a dictionary Params: audio_paths (list(str)): List of paths to audio f...

def sample_normalize(self, k_samples=1000, overwrite=False): """ Estimate the mean and std of the features from the training set Params: k_samples (int): Use this number of samples for estimation """ log = logUtil.getlogger() log.info("Calculating mean and std from sa...

def gru(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0., is_batchnorm=False, gamma=None, beta=None, name=None): """ GRU Cell symbol Reference: * Chung, Junyoung, et al. "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint arXiv:1412.3...

def save_image(data, epoch, image_size, batch_size, output_dir, padding=2): """ save image """ data = data.asnumpy().transpose((0, 2, 3, 1)) datanp = np.clip( (data - np.min(data))*(255.0/(np.max(data) - np.min(data))), 0, 255).astype(np.uint8) x_dim = min(8, batch_size) y_dim = int(math.cei...

def list_image(root, recursive, exts): """Traverses the root of directory that contains images and generates image list iterator. Parameters ---------- root: string recursive: bool exts: string Returns ------- image iterator that contains all the image under the specified path ...

def write_list(path_out, image_list): """Hepler function to write image list into the file. The format is as below, integer_image_index \t float_label_index \t path_to_image Note that the blank between number and tab is only used for readability. Parameters ---------- path_out: string im...

def make_list(args): """Generates .lst file. Parameters ---------- args: object that contains all the arguments """ image_list = list_image(args.root, args.recursive, args.exts) image_list = list(image_list) if args.shuffle is True: random.seed(100) random.shuffle(image_l...

def read_list(path_in): """Reads the .lst file and generates corresponding iterator. Parameters ---------- path_in: string Returns ------- item iterator that contains information in .lst file """ with open(path_in) as fin: while True: line = fin.readline() ...

def image_encode(args, i, item, q_out): """Reads, preprocesses, packs the image and put it back in output queue. Parameters ---------- args: object i: int item: list q_out: queue """ fullpath = os.path.join(args.root, item[1]) if len(item) > 3 and args.pack_label: header...

def read_worker(args, q_in, q_out): """Function that will be spawned to fetch the image from the input queue and put it back to output queue. Parameters ---------- args: object q_in: queue q_out: queue """ while True: deq = q_in.get() if deq is None: break...

def write_worker(q_out, fname, working_dir): """Function that will be spawned to fetch processed image from the output queue and write to the .rec file. Parameters ---------- q_out: queue fname: string working_dir: string """ pre_time = time.time() count = 0 fname = os.path.b...

def parse_args(): """Defines all arguments. Returns ------- args object that contains all the params """ parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='Create an image list or \ make a record database by reading from...

def transform(data, target_wd, target_ht, is_train, box): """Crop and normnalize an image nd array.""" if box is not None: x, y, w, h = box data = data[y:min(y+h, data.shape[0]), x:min(x+w, data.shape[1])] # Resize to target_wd * target_ht. data = mx.image.imresize(data, target_wd, targ...

def cub200_iterator(data_path, batch_k, batch_size, data_shape): """Return training and testing iterator for the CUB200-2011 dataset.""" return (CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=True), CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=False))

def get_image(self, img, is_train): """Load and transform an image.""" img_arr = mx.image.imread(img) img_arr = transform(img_arr, 256, 256, is_train, self.boxes[img]) return img_arr

def sample_train_batch(self): """Sample a training batch (data and label).""" batch = [] labels = [] num_groups = self.batch_size // self.batch_k # For CUB200, we use the first 100 classes for training. sampled_classes = np.random.choice(100, num_groups, replace=False) ...

def next(self): """Return a batch.""" if self.is_train: data, labels = self.sample_train_batch() else: if self.test_count * self.batch_size < len(self.test_image_files): data, labels = self.get_test_batch() self.test_count += 1 ...

def load_mnist(training_num=50000): """Load mnist dataset""" data_path = os.path.join(os.path.dirname(os.path.realpath('__file__')), 'mnist.npz') if not os.path.isfile(data_path): from six.moves import urllib origin = ( 'https://github.com/sxjscience/mxnet/raw/master/example/baye...

def feature_list(): """ Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc Returns ------- list List of :class:`.Feature` objects """ lib_features_c_array = ctypes.POINTER(Feature)() lib_features_size = ctypes.c_size_t() ...

def is_enabled(self, feature_name): """ Check for a particular feature by name Parameters ---------- feature_name: str The name of a valid feature as string for example 'CUDA' Returns ------- Boolean True if it's enabled, False if...

def cache_path(self): """ make a directory to store all caches Returns: --------- cache path """ cache_path = os.path.join(os.path.dirname(__file__), '..', 'cache') if not os.path.exists(cache_path): os.mkdir(cache_path) return cac...

def _load_image_set_index(self, shuffle): """ find out which indexes correspond to given image set (train or val) Parameters: ---------- shuffle : boolean whether to shuffle the image list Returns: ---------- entire list of images specified in...

def image_path_from_index(self, index): """ given image index, find out full path Parameters: ---------- index: int index of a specific image Returns: ---------- full path of this image """ assert self.image_set_index is not No...

def _label_path_from_index(self, index): """ given image index, find out annotation path Parameters: ---------- index: int index of a specific image Returns: ---------- full path of annotation file """ label_file = os.path.joi...

def _load_image_labels(self): """ preprocess all ground-truths Returns: ---------- labels packed in [num_images x max_num_objects x 5] tensor """ temp = [] # load ground-truth from xml annotations for idx in self.image_set_index: labe...

def evaluate_detections(self, detections): """ top level evaluations Parameters: ---------- detections: list result list, each entry is a matrix of detections Returns: ---------- None """ # make all these folders for results...

def get_result_file_template(self): """ this is a template VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt Returns: ---------- a string template """ res_file_folder = os.path.join(self.devkit_path, 'results', 'VOC' + self.year, 'Main')...

def write_pascal_results(self, all_boxes): """ write results files in pascal devkit path Parameters: ---------- all_boxes: list boxes to be processed [bbox, confidence] Returns: ---------- None """ for cls_ind, cls in enumerate(...

def do_python_eval(self): """ python evaluation wrapper Returns: ---------- None """ annopath = os.path.join(self.data_path, 'Annotations', '{:s}.xml') imageset_file = os.path.join(self.data_path, 'ImageSets', 'Main', self.image_set + '.txt') cach...

def _get_imsize(self, im_name): """ get image size info Returns: ---------- tuple of (height, width) """ img = cv2.imread(im_name) return (img.shape[0], img.shape[1])

def add_fit_args(parser): """ parser : argparse.ArgumentParser return a parser added with args required by fit """ train = parser.add_argument_group('Training', 'model training') train.add_argument('--network', type=str, help='the neural network to use') train.add_argu...

def fit(args, network, data_loader, **kwargs): """ train a model args : argparse returns network : the symbol definition of the nerual network data_loader : function that returns the train and val data iterators """ # kvstore kv = mx.kvstore.create(args.kv_store) if args.gc_type != '...

def CreateMultiRandCropAugmenter(min_object_covered=0.1, aspect_ratio_range=(0.75, 1.33), area_range=(0.05, 1.0), min_eject_coverage=0.3, max_attempts=50, skip_prob=0): """Helper function to create multiple random crop augmenters. Parameters ...

def CreateDetAugmenter(data_shape, resize=0, rand_crop=0, rand_pad=0, rand_gray=0, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0, pca_noise=0, hue=0, inter_method=2, min_object_covered=0.1, aspect_ratio_range=(0.75, 1....

def dumps(self): """Override default.""" return [self.__class__.__name__.lower(), [x.dumps() for x in self.aug_list]]

def _calculate_areas(self, label): """Calculate areas for multiple labels""" heights = np.maximum(0, label[:, 3] - label[:, 1]) widths = np.maximum(0, label[:, 2] - label[:, 0]) return heights * widths

def _intersect(self, label, xmin, ymin, xmax, ymax): """Calculate intersect areas, normalized.""" left = np.maximum(label[:, 0], xmin) right = np.minimum(label[:, 2], xmax) top = np.maximum(label[:, 1], ymin) bot = np.minimum(label[:, 3], ymax) invalid = np.where(np.logic...

def _check_satisfy_constraints(self, label, xmin, ymin, xmax, ymax, width, height): """Check if constrains are satisfied""" if (xmax - xmin) * (ymax - ymin) < 2: return False # only 1 pixel x1 = float(xmin) / width y1 = float(ymin) / height x2 = float(xmax) / width ...

def _update_labels(self, label, crop_box, height, width): """Convert labels according to crop box""" xmin = float(crop_box[0]) / width ymin = float(crop_box[1]) / height w = float(crop_box[2]) / width h = float(crop_box[3]) / height out = label.copy() out[:, (1, 3...

def _random_crop_proposal(self, label, height, width): """Propose cropping areas""" from math import sqrt if not self.enabled or height <= 0 or width <= 0: return () min_area = self.area_range[0] * height * width max_area = self.area_range[1] * height * width ...

def _update_labels(self, label, pad_box, height, width): """Update label according to padding region""" out = label.copy() out[:, (1, 3)] = (out[:, (1, 3)] * width + pad_box[0]) / pad_box[2] out[:, (2, 4)] = (out[:, (2, 4)] * height + pad_box[1]) / pad_box[3] return out

def _random_pad_proposal(self, label, height, width): """Generate random padding region""" from math import sqrt if not self.enabled or height <= 0 or width <= 0: return () min_area = self.area_range[0] * height * width max_area = self.area_range[1] * height * width ...