BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def _check_classes(self): """ check input imdbs, make sure they have same classes """ try: self.classes = self.imdbs[0].classes self.num_classes = len(self.classes) except AttributeError: # fine, if no classes is provided pass ...
def _load_image_set_index(self, shuffle): """ get total number of images, init indices Parameters ---------- shuffle : bool whether to shuffle the initial indices """ self.num_images = 0 for db in self.imdbs: self.num_images += db....
def _locate_index(self, index): """ given index, find out sub-db and sub-index Parameters ---------- index : int index of a specific image Returns ---------- a tuple (sub-db, sub-index) """ assert index >= 0 and index < self.n...
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 Non...
def module_checkpoint(mod, prefix, period=1, save_optimizer_states=False): """Callback to checkpoint Module to prefix every epoch. Parameters ---------- mod : subclass of BaseModule The module to checkpoint. prefix : str The file prefix for this checkpoint. period : int ...
def do_checkpoint(prefix, period=1): """A callback that saves a model checkpoint every few epochs. Each checkpoint is made up of a couple of binary files: a model description file and a parameters (weights and biases) file. The model description file is named `prefix`--symbol.json and the parameters fil...
def log_train_metric(period, auto_reset=False): """Callback to log the training evaluation result every period. Parameters ---------- period : int The number of batch to log the training evaluation metric. auto_reset : bool Reset the metric after each log. Returns ------- ...
def install(self, exe): """install callback to executor. Supports installing to multiple exes. Parameters ---------- exe : mx.executor.Executor The Executor (returned by symbol.bind) to install to. """ exe.set_monitor_callback(self.stat_helper, self.m...
def tic(self): """Start collecting stats for current batch. Call before calling forward.""" if self.step % self.interval == 0: for exe in self.exes: for array in exe.arg_arrays: array.wait_to_read() for array in exe.aux_arrays: ...
def toc(self): """End collecting for current batch and return results. Call after computation of current batch. Returns ------- res : list of """ if not self.activated: return [] for exe in self.exes: for array in exe.arg_arrays: ...
def toc_print(self): """End collecting and print results.""" res = self.toc() for n, k, v in res: logging.info('Batch: {:7d} {:30s} {:s}'.format(n, k, v))
def make_data_iter_plan(self): "make a random data iteration plan" # truncate each bucket into multiple of batch-size bucket_n_batches = [] for i in range(len(self.data)): bucket_n_batches.append(np.floor((self.data[i]) / self.batch_size)) self.data[i] = self.data...
def expand(x, pending, stage): """ Expand the pending files in the current stage. Parameters ---------- x: str The file to expand. pending : str The list of pending files to expand. stage: str The current stage for file expansion, used for matching the prefix of f...
def get_imagenet_iterator(root, batch_size, num_workers, data_shape=224, dtype='float32'): """Dataset loader with preprocessing.""" train_dir = os.path.join(root, 'train') train_transform, val_transform = get_imagenet_transforms(data_shape, dtype) logging.info("Loading image folder %s, this may take a b...
def create(embedding_name, **kwargs): """Creates an instance of token embedding. Creates a token embedding instance by loading embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText. To get all the valid `embedding_name` and `pretrained_file_n...
def get_pretrained_file_names(embedding_name=None): """Get valid token embedding names and their pre-trained file names. To load token embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText, one should use `mxnet.contrib.text.embedding.create(...
def _load_embedding(self, pretrained_file_path, elem_delim, init_unknown_vec, encoding='utf8'): """Load embedding vectors from the pre-trained token embedding file. For every unknown token, if its representation `self.unknown_token` is encountered in the pre-trained token embedding file, index...
def _set_idx_to_vec_by_embeddings(self, token_embeddings, vocab_len, vocab_idx_to_token): """Sets the mapping between token indices and token embedding vectors. Parameters ---------- token_embeddings : instance or list `mxnet.contrib.text.embedding._TokenEmbedding` One or m...
def get_vecs_by_tokens(self, tokens, lower_case_backup=False): """Look up embedding vectors of tokens. Parameters ---------- tokens : str or list of strs A token or a list of tokens. lower_case_backup : bool, default False If False, each token in the ori...
def update_token_vectors(self, tokens, new_vectors): """Updates embedding vectors for tokens. Parameters ---------- tokens : str or a list of strs A token or a list of tokens whose embedding vector are to be updated. new_vectors : mxnet.ndarray.NDArray A...
def _check_pretrained_file_names(cls, pretrained_file_name): """Checks if a pre-trained token embedding file name is valid. Parameters ---------- pretrained_file_name : str The pre-trained token embedding file. """ embedding_name = cls.__name__.lower() ...
def calc_grad(exe, exe_grads, params, X, Y, label_name=None, outgrad_f=None): """Calculate gradient""" exe.copy_params_from(params) exe.arg_dict['data'][:] = X if outgrad_f is None: exe.arg_dict[label_name][:] = Y exe.forward(is_train=True) exe.backward() else: exe.fo...
def step_HMC(exe, exe_params, exe_grads, label_key, noise_precision, prior_precision, L=10, eps=1E-6): """Generate the implementation of step HMC""" init_params = {k: v.copyto(v.context) for k, v in exe_params.items()} end_params = {k: v.copyto(v.context) for k, v in exe_params.items()} init_momentums =...
def HMC(sym, data_inputs, X, Y, X_test, Y_test, sample_num, initializer=None, noise_precision=1 / 9.0, prior_precision=0.1, learning_rate=1E-6, L=10, dev=mx.gpu()): """Generate the implementation of HMC""" label_key = list(set(data_inputs.keys()) - set(['data']))[0] exe, exe_params, exe_grad...
def SGD(sym, data_inputs, X, Y, X_test, Y_test, total_iter_num, lr=None, lr_scheduler=None, prior_precision=1, out_grad_f=None, initializer=None, minibatch_size=100, dev=mx.gpu()): """Generate the implementation of SGD""" if out_grad_f is None: label_key = list(se...
def SGLD(sym, X, Y, X_test, Y_test, total_iter_num, data_inputs=None, learning_rate=None, lr_scheduler=None, prior_precision=1, out_grad_f=None, initializer=None, minibatch_size=100, thin_interval=100, burn_in_iter_num=1000, task='classification', dev=mx.gp...
def DistilledSGLD(teacher_sym, student_sym, teacher_data_inputs, student_data_inputs, X, Y, X_test, Y_test, total_iter_num, teacher_learning_rate, student_learning_rate, teacher_lr_scheduler=None, student_lr_scheduler=None, studen...
def get_platforms(path: str = get_dockerfiles_path()) -> List[str]: """Get a list of architectures given our dockerfiles""" dockerfiles = glob.glob(os.path.join(path, "Dockerfile.")) dockerfiles = list(filter(lambda x: x[-1] != '~', dockerfiles)) files = list(map(lambda x: re.sub(r"Dockerfile.(.)", r"...
def get_docker_tag(platform: str, registry: str) -> str: """:return: docker tag to be used for the container""" platform = platform if any(x in platform for x in ['build.', 'publish.']) else 'build.{}'.format(platform) if not registry: registry = "mxnet_local" return "{0}/{1}".format(registry, p...
def build_docker(platform: str, docker_binary: str, registry: str, num_retries: int, no_cache: bool) -> str: """ Build a container for the given platform :param platform: Platform :param docker_binary: docker binary to use (docker/nvidia-docker) :param registry: Dockerhub registry name :param nu...
def _get_local_image_id(docker_binary, docker_tag): """ Get the image id of the local docker layer with the passed tag :param docker_tag: docker tag :return: Image id as string or None if tag does not exist """ cmd = [docker_binary, "images", "-q", docker_tag] image_id_b = check_output(cmd) ...
def default_ccache_dir() -> str: """:return: ccache directory for the current platform""" # Share ccache across containers if 'CCACHE_DIR' in os.environ: ccache_dir = os.path.realpath(os.environ['CCACHE_DIR']) try: os.makedirs(ccache_dir, exist_ok=True) return ccache_...
def container_run(platform: str, nvidia_runtime: bool, docker_registry: str, shared_memory_size: str, local_ccache_dir: str, command: List[str], cleanup: Cleanup, environment: Dict[str, str], ...
def load_docker_cache(tag, docker_registry) -> None: """Imports tagged container from the given docker registry""" if docker_registry: # noinspection PyBroadException try: import docker_cache logging.info('Docker cache download is enabled from registry %s', docker_registr...
def _load_general(data, targets, major_axis): """Load a list of arrays into a list of arrays specified by slices.""" for d_src, d_targets, axis in zip(data, targets, major_axis): # pylint: disable=too-many-nested-blocks if isinstance(d_targets, nd.NDArray): d_src.copyto(d_targets) el...
def _load_data(batch, targets, major_axis): """Load data into sliced arrays.""" if isinstance(batch, list): new_batch = [] for i in range(len(targets)): new_batch.append([b.data[i] for b in batch]) new_targets = [[dst for _, dst in d_target] for d_target in targets] _...
def _merge_multi_context(outputs, major_axis): """Merge outputs that lives on multiple context into one, so that they look like living on one context. """ rets = [] for tensors, axis in zip(outputs, major_axis): if axis >= 0: # pylint: disable=no-member,protected-access ...
def _prepare_group2ctxs(group2ctxs, ctx_len): """Prepare the group2contexts, will duplicate the context if some ctx_group map to only one context. """ if group2ctxs is None: return [None] * ctx_len elif isinstance(group2ctxs, list): assert(len(group2ctxs) == ctx_len), "length of grou...
def decide_slices(self, data_shapes): """Decide the slices for each context according to the workload. Parameters ---------- data_shapes : list list of (name, shape) specifying the shapes for the input data or label. """ assert len(data_shapes) > 0 ma...
def _collect_arrays(self): """Collect internal arrays from executors.""" # convenient data structures self.data_arrays = [[(self.slices[i], e.arg_dict[name]) for i, e in enumerate(self.execs)] for name, _ in self.data_shapes] self.state_arrays = [[e.arg_dict[...
def bind_exec(self, data_shapes, label_shapes, shared_group=None, reshape=False): """Bind executors on their respective devices. Parameters ---------- data_shapes : list label_shapes : list shared_group : DataParallelExecutorGroup reshape : bool """ ...
def reshape(self, data_shapes, label_shapes): """Reshape executors. Parameters ---------- data_shapes : list label_shapes : list """ if data_shapes == self.data_shapes and label_shapes == self.label_shapes: return if self._default_execs is Non...
def set_params(self, arg_params, aux_params, allow_extra=False): """Assign, i.e. copy parameters to all the executors. Parameters ---------- arg_params : dict A dictionary of name to `NDArray` parameter mapping. aux_params : dict A dictionary of name to `...
def get_params(self, arg_params, aux_params): """ Copy data from each executor to `arg_params` and `aux_params`. Parameters ---------- arg_params : list of NDArray Target parameter arrays. aux_params : list of NDArray Target aux arrays. Notes ...
def forward(self, data_batch, is_train=None): """Split `data_batch` according to workload and run forward on each devices. Parameters ---------- data_batch : DataBatch Or could be any object implementing similar interface. is_train : bool The hint for the...
def get_output_shapes(self): """Get the shapes of the outputs.""" outputs = self.execs[0].outputs shapes = [out.shape for out in outputs] concat_shapes = [] for key, the_shape, axis in zip(self.symbol.list_outputs(), shapes, self.output_layouts): the_shape = list(the...
def get_outputs(self, merge_multi_context=True, begin=0, end=None): """Get outputs of the previous forward computation. If begin or end is specified, return [begin, end)-th outputs, otherwise return all outputs. Parameters ---------- merge_multi_context : bool ...
def set_states(self, states=None, value=None): """Set value for states. Only one of states & value can be specified. Parameters ---------- states : list of list of NDArrays source states arrays formatted like [[state1_dev1, state1_dev2], [state2_dev1, state2_dev2...
def get_input_grads(self, merge_multi_context=True): """Get the gradients with respect to the inputs of the module. Parameters ---------- merge_multi_context : bool Defaults to ``True``. In the case when data-parallelism is used, the outputs will be collected fro...
def backward(self, out_grads=None): """Run backward on all devices. A backward should be called after a call to the forward function. Backward cannot be called unless ``self.for_training`` is ``True``. Parameters ---------- out_grads : NDArray or list of NDArray, optiona...
def update_metric(self, eval_metric, labels, pre_sliced): """Accumulate the performance according to `eval_metric` on all devices by comparing outputs from [begin, end) to labels. By default use all outputs. Parameters ---------- eval_metric : EvalMetric The ...
def _bind_ith_exec(self, i, data_shapes, label_shapes, shared_group): """Internal utility function to bind the i-th executor. This function utilizes simple_bind python interface. """ shared_exec = None if shared_group is None else shared_group.execs[i] context = self.contexts[i] ...
def _sliced_shape(self, shapes, i, major_axis): """Get the sliced shapes for the i-th executor. Parameters ---------- shapes : list of (str, tuple) The original (name, shape) pairs. i : int Which executor we are dealing with. """ sliced_sh...
def parse_class_names(args): """ parse # classes and class_names if applicable """ num_class = args.num_class if len(args.class_names) > 0: if os.path.isfile(args.class_names): # try to open it to read class names with open(args.class_names, 'r') as f: class_n...
def _has_instance(data, dtype): """Return True if ``data`` has instance of ``dtype``. This function is called after _init_data. ``data`` is a list of (str, NDArray)""" for item in data: _, arr = item if isinstance(arr, dtype): return True return False
def _getdata_by_idx(data, idx): """Shuffle the data.""" shuffle_data = [] for k, v in data: if (isinstance(v, h5py.Dataset) if h5py else False): shuffle_data.append((k, v)) elif isinstance(v, CSRNDArray): shuffle_data.append((k, sparse_array(v.asscipy()[idx], v.conte...
def get_mobilenet(multiplier, pretrained=False, ctx=cpu(), root=os.path.join(base.data_dir(), 'models'), **kwargs): r"""MobileNet model from the `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" <https://arxiv.org/abs/1704.04861>`_ paper. Parameters...
def get(self, name, hint): """Get the canonical name for a symbol. This is the default implementation. If the user specifies a name, the user-specified name will be used. When user does not specify a name, we automatically generate a name based on the hint string. ...
def draw(self, true_classes): """Draw samples from log uniform distribution and returns sampled candidates, expected count for true classes and sampled classes.""" range_max = self.range_max num_sampled = self.num_sampled ctx = true_classes.context log_range = math.log(ra...
def get_inception_score(images, splits=10): """ Inception_score function. The images will be divided into 'splits' parts, and calculate each inception_score separately, then return the mean and std of inception_scores of these parts. :param images: Images(num x c x w x h) that needs to calcu...
def load_param(params, ctx=None): """same as mx.model.load_checkpoint, but do not load symnet and will convert context""" if ctx is None: ctx = mx.cpu() save_dict = mx.nd.load(params) arg_params = {} aux_params = {} for k, v in save_dict.items(): tp, name = k.split(':', 1) ...
def rnn_unroll(cell, length, inputs=None, begin_state=None, input_prefix='', layout='NTC'): """Deprecated. Please use cell.unroll instead""" warnings.warn('rnn_unroll is deprecated. Please call cell.unroll directly.') return cell.unroll(length=length, inputs=inputs, begin_state=begin_state, ...
def save_rnn_checkpoint(cells, prefix, epoch, symbol, arg_params, aux_params): """Save checkpoint for model using RNN cells. Unpacks weight before saving. Parameters ---------- cells : mxnet.rnn.RNNCell or list of RNNCells The RNN cells used by this symbol. prefix : str Prefix o...
def load_rnn_checkpoint(cells, prefix, epoch): """Load model checkpoint from file. Pack weights after loading. Parameters ---------- cells : mxnet.rnn.RNNCell or list of RNNCells The RNN cells used by this symbol. prefix : str Prefix of model name. epoch : int Epoch ...
def do_rnn_checkpoint(cells, prefix, period=1): """Make a callback to checkpoint Module to prefix every epoch. unpacks weights used by cells before saving. Parameters ---------- cells : mxnet.rnn.RNNCell or list of RNNCells The RNN cells used by this symbol. prefix : str The fil...
def hybridize(self, active=True, kwargs): """Activates or deactivates `HybridBlock` s recursively. Has no effect on non-hybrid children. Parameters ---------- active : bool, default True Whether to turn hybrid on or off. kwargs : string Additi...
def read_img(path): """ Reads image specified by path into numpy.ndarray""" img = cv2.resize(cv2.imread(path, 0), (80, 30)).astype(np.float32) / 255 img = np.expand_dims(img.transpose(1, 0), 0) return img
def lstm_init_states(batch_size): """ Returns a tuple of names and zero arrays for LSTM init states""" hp = Hyperparams() init_shapes = lstm.init_states(batch_size=batch_size, num_lstm_layer=hp.num_lstm_layer, num_hidden=hp.num_hidden) init_names = [s[0] for s in init_shapes] init_arrays = [mx.nd.ze...
def load_module(prefix, epoch, data_names, data_shapes): """Loads the model from checkpoint specified by prefix and epoch, binds it to an executor, and sets its parameters and returns a mx.mod.Module """ sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) # We don't need CTC loss ...
def main(): """Program entry point""" parser = argparse.ArgumentParser() parser.add_argument("path", help="Path to the CAPTCHA image file") parser.add_argument("--prefix", help="Checkpoint prefix [Default 'ocr']", default='ocr') parser.add_argument("--epoch", help="Checkpoint epoch [Default 100]", t...
def bbox_flip(bbox, width, flip_x=False): """ invalid value in bbox_transform if this wrong (no overlap), note index 0 and 2 also note need to save before assignment :param bbox: [n][x1, y1, x2, y2] :param width: cv2 (height, width, channel) :param flip_x: will flip x1 and x2 :return: flippe...
def bbox_overlaps(boxes, query_boxes): """ determine overlaps between boxes and query_boxes :param boxes: n * 4 bounding boxes :param query_boxes: k * 4 bounding boxes :return: overlaps: n * k overlaps """ n_ = boxes.shape[0] k_ = query_boxes.shape[0] overlaps = np.zeros((n_, k_), dt...
def clip_boxes(boxes, im_shape): """ Clip boxes to image boundaries. :param boxes: [N, 4* num_classes] :param im_shape: tuple of 2 :return: [N, 4* num_classes] """ # x1 >= 0 boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0) # y1 >= 0 boxes[:, 1::4] = np....
def bbox_transform(ex_rois, gt_rois, box_stds): """ compute bounding box regression targets from ex_rois to gt_rois :param ex_rois: [N, 4] :param gt_rois: [N, 4] :return: [N, 4] """ assert ex_rois.shape[0] == gt_rois.shape[0], 'inconsistent rois number' ex_widths = ex_rois[:, 2] - ex_ro...
def bbox_pred(boxes, box_deltas, box_stds): """ Transform the set of class-agnostic boxes into class-specific boxes by applying the predicted offsets (box_deltas) :param boxes: !important [N 4] :param box_deltas: [N, 4 * num_classes] :return: [N 4 * num_classes] """ if boxes.shape[0] == ...
def nms(dets, thresh): """ greedily select boxes with high confidence and overlap with current maximum <= thresh rule out overlap >= thresh :param dets: [[x1, y1, x2, y2 score]] :param thresh: retain overlap < thresh :return: indexes to keep """ x1 = dets[:, 0] y1 = dets[:, 1] x2...
def im_detect(rois, scores, bbox_deltas, im_info, bbox_stds, nms_thresh, conf_thresh): """rois (nroi, 4), scores (nrois, nclasses), bbox_deltas (nrois, 4 * nclasses), im_info (3)""" rois = rois.asnumpy() scores = scores.asnumpy() bbox_deltas = bbox_deltas.asnumpy() im_info = im_info.a...
def c_str(string): """"Convert a python string to C string.""" if not isinstance(string, str): string = string.decode('ascii') return ctypes.c_char_p(string.encode('utf-8'))
def _find_lib_path(): """Find mxnet library.""" curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__))) amalgamation_lib_path = os.path.join(curr_path, '../../lib/libmxnet_predict.so') if os.path.exists(amalgamation_lib_path) and os.path.isfile(amalgamation_lib_path): lib_path...
def _load_lib(): """Load libary by searching possible path.""" lib_path = _find_lib_path() lib = ctypes.cdll.LoadLibrary(lib_path[0]) # DMatrix functions lib.MXGetLastError.restype = ctypes.c_char_p return lib
def load_ndarray_file(nd_bytes): """Load ndarray file and return as list of numpy array. Parameters ---------- nd_bytes : str or bytes The internal ndarray bytes Returns ------- out : dict of str to numpy array or list of numpy array The output list or dict, depending on wh...
def forward(self, kwargs): """Perform forward to get the output. Parameters ---------- kwargs Keyword arguments of input variable name to data. Examples -------- >>> predictor.forward(data=mydata) >>> out = predictor.get_output(0) ...
def reshape(self, input_shapes): """Change the input shape of the predictor. Parameters ---------- input_shapes : dict of str to tuple The new shape of input data. Examples -------- >>> predictor.reshape({'data':data_shape_tuple}) """ ...
def get_output(self, index): """Get the index-th output. Parameters ---------- index : int The index of output. Returns ------- out : numpy array. The output array. """ pdata = ctypes.POINTER(mx_uint)() ndim = mx_u...
def begin_episode(self, max_episode_step=DEFAULT_MAX_EPISODE_STEP): """ Begin an episode of a game instance. We can play the game for a maximum of `max_episode_step` and after that, we are forced to restart """ if self.episode_step > self.max_episode_step or self.ale.game...
def reset(self): """Reset before re-using the cell for another graph.""" self._init_counter = -1 self._counter = -1 for cell in self._children.values(): cell.reset()
def begin_state(self, batch_size=0, func=ndarray.zeros, **kwargs): """Initial state for this cell. Parameters ---------- func : callable, default symbol.zeros Function for creating initial state. For Symbol API, func can be `symbol.zeros`, `symbol.uniform`, ...
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 _get_activation(self, F, inputs, activation, **kwargs): """Get activation function. Convert if is string""" func = {'tanh': F.tanh, 'relu': F.relu, 'sigmoid': F.sigmoid, 'softsign': F.softsign}.get(activation) if func: return func(i...
def forward(self, inputs, states): """Unrolls the recurrent cell for one time step. Parameters ---------- inputs : sym.Variable Input symbol, 2D, of shape (batch_size * num_units). states : list of sym.Variable RNN state from previous step or the output o...
def _check_input_names(symbol, names, typename, throw): """Check that all input names are in symbol's arguments.""" args = symbol.list_arguments() for name in names: if name in args: continue candidates = [arg for arg in args if not arg.endswith('_weight') a...
def _check_names_match(data_names, data_shapes, name, throw): """Check that input names matches input data descriptors.""" actual = [x[0] for x in data_shapes] if sorted(data_names) != sorted(actual): msg = "Data provided by %s_shapes don't match names specified by %s_names (%s vs. %s)"%( ...
def _parse_data_desc(data_names, label_names, data_shapes, label_shapes): """parse data_attrs into DataDesc format and check that names match""" data_shapes = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in data_shapes] _check_names_match(data_names, data_shapes, 'data', True) if label_shapes i...
def forward_backward(self, data_batch): """A convenient function that calls both ``forward`` and ``backward``.""" self.forward(data_batch, is_train=True) self.backward()
def score(self, eval_data, eval_metric, num_batch=None, batch_end_callback=None, score_end_callback=None, reset=True, epoch=0, sparse_row_id_fn=None): """Runs prediction on ``eval_data`` and evaluates the performance according to the given ``eval_metric``. Checkout `...
def iter_predict(self, eval_data, num_batch=None, reset=True, sparse_row_id_fn=None): """Iterates over predictions. Examples -------- >>> for pred, i_batch, batch in module.iter_predict(eval_data): ... # pred is a list of outputs from the module ... # i_batch is ...
def predict(self, eval_data, num_batch=None, merge_batches=True, reset=True, always_output_list=False, sparse_row_id_fn=None): """Runs prediction and collects the outputs. When `merge_batches` is ``True`` (by default), the return value will be a list ``[out1, out2, out3]``, wher...
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True, allow_extra=False): """Assigns parameter and aux state values. Parameters ---------- arg_params : dict Dictionary of name to value (`NDArray`) mapping. aux_params : ...
def save_params(self, fname): """Saves model parameters to file. Parameters ---------- fname : str Path to output param file. Examples -------- >>> # An example of saving module parameters. >>> mod.save_params('myfile') """ ar...
def load_params(self, fname): """Loads model parameters from file. Parameters ---------- fname : str Path to input param file. Examples -------- >>> # An example of loading module parameters. >>> mod.load_params('myfile') """ ...

def _check_classes(self): """ check input imdbs, make sure they have same classes """ try: self.classes = self.imdbs[0].classes self.num_classes = len(self.classes) except AttributeError: # fine, if no classes is provided pass ...

def _load_image_set_index(self, shuffle): """ get total number of images, init indices Parameters ---------- shuffle : bool whether to shuffle the initial indices """ self.num_images = 0 for db in self.imdbs: self.num_images += db....

def _locate_index(self, index): """ given index, find out sub-db and sub-index Parameters ---------- index : int index of a specific image Returns ---------- a tuple (sub-db, sub-index) """ assert index >= 0 and index < self.n...

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 Non...

def module_checkpoint(mod, prefix, period=1, save_optimizer_states=False): """Callback to checkpoint Module to prefix every epoch. Parameters ---------- mod : subclass of BaseModule The module to checkpoint. prefix : str The file prefix for this checkpoint. period : int ...

def do_checkpoint(prefix, period=1): """A callback that saves a model checkpoint every few epochs. Each checkpoint is made up of a couple of binary files: a model description file and a parameters (weights and biases) file. The model description file is named `prefix`--symbol.json and the parameters fil...

def log_train_metric(period, auto_reset=False): """Callback to log the training evaluation result every period. Parameters ---------- period : int The number of batch to log the training evaluation metric. auto_reset : bool Reset the metric after each log. Returns ------- ...

def install(self, exe): """install callback to executor. Supports installing to multiple exes. Parameters ---------- exe : mx.executor.Executor The Executor (returned by symbol.bind) to install to. """ exe.set_monitor_callback(self.stat_helper, self.m...

def tic(self): """Start collecting stats for current batch. Call before calling forward.""" if self.step % self.interval == 0: for exe in self.exes: for array in exe.arg_arrays: array.wait_to_read() for array in exe.aux_arrays: ...

def toc(self): """End collecting for current batch and return results. Call after computation of current batch. Returns ------- res : list of """ if not self.activated: return [] for exe in self.exes: for array in exe.arg_arrays: ...

def toc_print(self): """End collecting and print results.""" res = self.toc() for n, k, v in res: logging.info('Batch: {:7d} {:30s} {:s}'.format(n, k, v))

def make_data_iter_plan(self): "make a random data iteration plan" # truncate each bucket into multiple of batch-size bucket_n_batches = [] for i in range(len(self.data)): bucket_n_batches.append(np.floor((self.data[i]) / self.batch_size)) self.data[i] = self.data...

def expand(x, pending, stage): """ Expand the pending files in the current stage. Parameters ---------- x: str The file to expand. pending : str The list of pending files to expand. stage: str The current stage for file expansion, used for matching the prefix of f...

def get_imagenet_iterator(root, batch_size, num_workers, data_shape=224, dtype='float32'): """Dataset loader with preprocessing.""" train_dir = os.path.join(root, 'train') train_transform, val_transform = get_imagenet_transforms(data_shape, dtype) logging.info("Loading image folder %s, this may take a b...

def create(embedding_name, **kwargs): """Creates an instance of token embedding. Creates a token embedding instance by loading embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText. To get all the valid `embedding_name` and `pretrained_file_n...

def get_pretrained_file_names(embedding_name=None): """Get valid token embedding names and their pre-trained file names. To load token embedding vectors from an externally hosted pre-trained token embedding file, such as those of GloVe and FastText, one should use `mxnet.contrib.text.embedding.create(...

def _load_embedding(self, pretrained_file_path, elem_delim, init_unknown_vec, encoding='utf8'): """Load embedding vectors from the pre-trained token embedding file. For every unknown token, if its representation `self.unknown_token` is encountered in the pre-trained token embedding file, index...

def _set_idx_to_vec_by_embeddings(self, token_embeddings, vocab_len, vocab_idx_to_token): """Sets the mapping between token indices and token embedding vectors. Parameters ---------- token_embeddings : instance or list `mxnet.contrib.text.embedding._TokenEmbedding` One or m...

def get_vecs_by_tokens(self, tokens, lower_case_backup=False): """Look up embedding vectors of tokens. Parameters ---------- tokens : str or list of strs A token or a list of tokens. lower_case_backup : bool, default False If False, each token in the ori...

def update_token_vectors(self, tokens, new_vectors): """Updates embedding vectors for tokens. Parameters ---------- tokens : str or a list of strs A token or a list of tokens whose embedding vector are to be updated. new_vectors : mxnet.ndarray.NDArray A...

def _check_pretrained_file_names(cls, pretrained_file_name): """Checks if a pre-trained token embedding file name is valid. Parameters ---------- pretrained_file_name : str The pre-trained token embedding file. """ embedding_name = cls.__name__.lower() ...

def calc_grad(exe, exe_grads, params, X, Y, label_name=None, outgrad_f=None): """Calculate gradient""" exe.copy_params_from(params) exe.arg_dict['data'][:] = X if outgrad_f is None: exe.arg_dict[label_name][:] = Y exe.forward(is_train=True) exe.backward() else: exe.fo...

def step_HMC(exe, exe_params, exe_grads, label_key, noise_precision, prior_precision, L=10, eps=1E-6): """Generate the implementation of step HMC""" init_params = {k: v.copyto(v.context) for k, v in exe_params.items()} end_params = {k: v.copyto(v.context) for k, v in exe_params.items()} init_momentums =...

def HMC(sym, data_inputs, X, Y, X_test, Y_test, sample_num, initializer=None, noise_precision=1 / 9.0, prior_precision=0.1, learning_rate=1E-6, L=10, dev=mx.gpu()): """Generate the implementation of HMC""" label_key = list(set(data_inputs.keys()) - set(['data']))[0] exe, exe_params, exe_grad...

def SGD(sym, data_inputs, X, Y, X_test, Y_test, total_iter_num, lr=None, lr_scheduler=None, prior_precision=1, out_grad_f=None, initializer=None, minibatch_size=100, dev=mx.gpu()): """Generate the implementation of SGD""" if out_grad_f is None: label_key = list(se...

def SGLD(sym, X, Y, X_test, Y_test, total_iter_num, data_inputs=None, learning_rate=None, lr_scheduler=None, prior_precision=1, out_grad_f=None, initializer=None, minibatch_size=100, thin_interval=100, burn_in_iter_num=1000, task='classification', dev=mx.gp...

def DistilledSGLD(teacher_sym, student_sym, teacher_data_inputs, student_data_inputs, X, Y, X_test, Y_test, total_iter_num, teacher_learning_rate, student_learning_rate, teacher_lr_scheduler=None, student_lr_scheduler=None, studen...

def get_platforms(path: str = get_dockerfiles_path()) -> List[str]: """Get a list of architectures given our dockerfiles""" dockerfiles = glob.glob(os.path.join(path, "Dockerfile.*")) dockerfiles = list(filter(lambda x: x[-1] != '~', dockerfiles)) files = list(map(lambda x: re.sub(r"Dockerfile.(.*)", r"...

def get_docker_tag(platform: str, registry: str) -> str: """:return: docker tag to be used for the container""" platform = platform if any(x in platform for x in ['build.', 'publish.']) else 'build.{}'.format(platform) if not registry: registry = "mxnet_local" return "{0}/{1}".format(registry, p...

def build_docker(platform: str, docker_binary: str, registry: str, num_retries: int, no_cache: bool) -> str: """ Build a container for the given platform :param platform: Platform :param docker_binary: docker binary to use (docker/nvidia-docker) :param registry: Dockerhub registry name :param nu...

def _get_local_image_id(docker_binary, docker_tag): """ Get the image id of the local docker layer with the passed tag :param docker_tag: docker tag :return: Image id as string or None if tag does not exist """ cmd = [docker_binary, "images", "-q", docker_tag] image_id_b = check_output(cmd) ...

def default_ccache_dir() -> str: """:return: ccache directory for the current platform""" # Share ccache across containers if 'CCACHE_DIR' in os.environ: ccache_dir = os.path.realpath(os.environ['CCACHE_DIR']) try: os.makedirs(ccache_dir, exist_ok=True) return ccache_...

def container_run(platform: str, nvidia_runtime: bool, docker_registry: str, shared_memory_size: str, local_ccache_dir: str, command: List[str], cleanup: Cleanup, environment: Dict[str, str], ...

def load_docker_cache(tag, docker_registry) -> None: """Imports tagged container from the given docker registry""" if docker_registry: # noinspection PyBroadException try: import docker_cache logging.info('Docker cache download is enabled from registry %s', docker_registr...

def _load_general(data, targets, major_axis): """Load a list of arrays into a list of arrays specified by slices.""" for d_src, d_targets, axis in zip(data, targets, major_axis): # pylint: disable=too-many-nested-blocks if isinstance(d_targets, nd.NDArray): d_src.copyto(d_targets) el...

def _load_data(batch, targets, major_axis): """Load data into sliced arrays.""" if isinstance(batch, list): new_batch = [] for i in range(len(targets)): new_batch.append([b.data[i] for b in batch]) new_targets = [[dst for _, dst in d_target] for d_target in targets] _...

def _merge_multi_context(outputs, major_axis): """Merge outputs that lives on multiple context into one, so that they look like living on one context. """ rets = [] for tensors, axis in zip(outputs, major_axis): if axis >= 0: # pylint: disable=no-member,protected-access ...

def _prepare_group2ctxs(group2ctxs, ctx_len): """Prepare the group2contexts, will duplicate the context if some ctx_group map to only one context. """ if group2ctxs is None: return [None] * ctx_len elif isinstance(group2ctxs, list): assert(len(group2ctxs) == ctx_len), "length of grou...

def decide_slices(self, data_shapes): """Decide the slices for each context according to the workload. Parameters ---------- data_shapes : list list of (name, shape) specifying the shapes for the input data or label. """ assert len(data_shapes) > 0 ma...

def _collect_arrays(self): """Collect internal arrays from executors.""" # convenient data structures self.data_arrays = [[(self.slices[i], e.arg_dict[name]) for i, e in enumerate(self.execs)] for name, _ in self.data_shapes] self.state_arrays = [[e.arg_dict[...

def bind_exec(self, data_shapes, label_shapes, shared_group=None, reshape=False): """Bind executors on their respective devices. Parameters ---------- data_shapes : list label_shapes : list shared_group : DataParallelExecutorGroup reshape : bool """ ...

def reshape(self, data_shapes, label_shapes): """Reshape executors. Parameters ---------- data_shapes : list label_shapes : list """ if data_shapes == self.data_shapes and label_shapes == self.label_shapes: return if self._default_execs is Non...

def set_params(self, arg_params, aux_params, allow_extra=False): """Assign, i.e. copy parameters to all the executors. Parameters ---------- arg_params : dict A dictionary of name to `NDArray` parameter mapping. aux_params : dict A dictionary of name to `...

def get_params(self, arg_params, aux_params): """ Copy data from each executor to `arg_params` and `aux_params`. Parameters ---------- arg_params : list of NDArray Target parameter arrays. aux_params : list of NDArray Target aux arrays. Notes ...

def forward(self, data_batch, is_train=None): """Split `data_batch` according to workload and run forward on each devices. Parameters ---------- data_batch : DataBatch Or could be any object implementing similar interface. is_train : bool The hint for the...

def get_output_shapes(self): """Get the shapes of the outputs.""" outputs = self.execs[0].outputs shapes = [out.shape for out in outputs] concat_shapes = [] for key, the_shape, axis in zip(self.symbol.list_outputs(), shapes, self.output_layouts): the_shape = list(the...

def get_outputs(self, merge_multi_context=True, begin=0, end=None): """Get outputs of the previous forward computation. If begin or end is specified, return [begin, end)-th outputs, otherwise return all outputs. Parameters ---------- merge_multi_context : bool ...

def set_states(self, states=None, value=None): """Set value for states. Only one of states & value can be specified. Parameters ---------- states : list of list of NDArrays source states arrays formatted like [[state1_dev1, state1_dev2], [state2_dev1, state2_dev2...

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

def backward(self, out_grads=None): """Run backward on all devices. A backward should be called after a call to the forward function. Backward cannot be called unless ``self.for_training`` is ``True``. Parameters ---------- out_grads : NDArray or list of NDArray, optiona...

def update_metric(self, eval_metric, labels, pre_sliced): """Accumulate the performance according to `eval_metric` on all devices by comparing outputs from [begin, end) to labels. By default use all outputs. Parameters ---------- eval_metric : EvalMetric The ...

def _bind_ith_exec(self, i, data_shapes, label_shapes, shared_group): """Internal utility function to bind the i-th executor. This function utilizes simple_bind python interface. """ shared_exec = None if shared_group is None else shared_group.execs[i] context = self.contexts[i] ...

def _sliced_shape(self, shapes, i, major_axis): """Get the sliced shapes for the i-th executor. Parameters ---------- shapes : list of (str, tuple) The original (name, shape) pairs. i : int Which executor we are dealing with. """ sliced_sh...

def parse_class_names(args): """ parse # classes and class_names if applicable """ num_class = args.num_class if len(args.class_names) > 0: if os.path.isfile(args.class_names): # try to open it to read class names with open(args.class_names, 'r') as f: class_n...

def _has_instance(data, dtype): """Return True if ``data`` has instance of ``dtype``. This function is called after _init_data. ``data`` is a list of (str, NDArray)""" for item in data: _, arr = item if isinstance(arr, dtype): return True return False

def _getdata_by_idx(data, idx): """Shuffle the data.""" shuffle_data = [] for k, v in data: if (isinstance(v, h5py.Dataset) if h5py else False): shuffle_data.append((k, v)) elif isinstance(v, CSRNDArray): shuffle_data.append((k, sparse_array(v.asscipy()[idx], v.conte...

def get_mobilenet(multiplier, pretrained=False, ctx=cpu(), root=os.path.join(base.data_dir(), 'models'), **kwargs): r"""MobileNet model from the `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" <https://arxiv.org/abs/1704.04861>`_ paper. Parameters...

def get(self, name, hint): """Get the canonical name for a symbol. This is the default implementation. If the user specifies a name, the user-specified name will be used. When user does not specify a name, we automatically generate a name based on the hint string. ...

def draw(self, true_classes): """Draw samples from log uniform distribution and returns sampled candidates, expected count for true classes and sampled classes.""" range_max = self.range_max num_sampled = self.num_sampled ctx = true_classes.context log_range = math.log(ra...

def get_inception_score(images, splits=10): """ Inception_score function. The images will be divided into 'splits' parts, and calculate each inception_score separately, then return the mean and std of inception_scores of these parts. :param images: Images(num x c x w x h) that needs to calcu...

def load_param(params, ctx=None): """same as mx.model.load_checkpoint, but do not load symnet and will convert context""" if ctx is None: ctx = mx.cpu() save_dict = mx.nd.load(params) arg_params = {} aux_params = {} for k, v in save_dict.items(): tp, name = k.split(':', 1) ...

def rnn_unroll(cell, length, inputs=None, begin_state=None, input_prefix='', layout='NTC'): """Deprecated. Please use cell.unroll instead""" warnings.warn('rnn_unroll is deprecated. Please call cell.unroll directly.') return cell.unroll(length=length, inputs=inputs, begin_state=begin_state, ...

def save_rnn_checkpoint(cells, prefix, epoch, symbol, arg_params, aux_params): """Save checkpoint for model using RNN cells. Unpacks weight before saving. Parameters ---------- cells : mxnet.rnn.RNNCell or list of RNNCells The RNN cells used by this symbol. prefix : str Prefix o...

def load_rnn_checkpoint(cells, prefix, epoch): """Load model checkpoint from file. Pack weights after loading. Parameters ---------- cells : mxnet.rnn.RNNCell or list of RNNCells The RNN cells used by this symbol. prefix : str Prefix of model name. epoch : int Epoch ...

def do_rnn_checkpoint(cells, prefix, period=1): """Make a callback to checkpoint Module to prefix every epoch. unpacks weights used by cells before saving. Parameters ---------- cells : mxnet.rnn.RNNCell or list of RNNCells The RNN cells used by this symbol. prefix : str The fil...

def hybridize(self, active=True, **kwargs): """Activates or deactivates `HybridBlock` s recursively. Has no effect on non-hybrid children. Parameters ---------- active : bool, default True Whether to turn hybrid on or off. **kwargs : string Additi...

def read_img(path): """ Reads image specified by path into numpy.ndarray""" img = cv2.resize(cv2.imread(path, 0), (80, 30)).astype(np.float32) / 255 img = np.expand_dims(img.transpose(1, 0), 0) return img

def lstm_init_states(batch_size): """ Returns a tuple of names and zero arrays for LSTM init states""" hp = Hyperparams() init_shapes = lstm.init_states(batch_size=batch_size, num_lstm_layer=hp.num_lstm_layer, num_hidden=hp.num_hidden) init_names = [s[0] for s in init_shapes] init_arrays = [mx.nd.ze...

def load_module(prefix, epoch, data_names, data_shapes): """Loads the model from checkpoint specified by prefix and epoch, binds it to an executor, and sets its parameters and returns a mx.mod.Module """ sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) # We don't need CTC loss ...

def main(): """Program entry point""" parser = argparse.ArgumentParser() parser.add_argument("path", help="Path to the CAPTCHA image file") parser.add_argument("--prefix", help="Checkpoint prefix [Default 'ocr']", default='ocr') parser.add_argument("--epoch", help="Checkpoint epoch [Default 100]", t...

def bbox_flip(bbox, width, flip_x=False): """ invalid value in bbox_transform if this wrong (no overlap), note index 0 and 2 also note need to save before assignment :param bbox: [n][x1, y1, x2, y2] :param width: cv2 (height, width, channel) :param flip_x: will flip x1 and x2 :return: flippe...

def bbox_overlaps(boxes, query_boxes): """ determine overlaps between boxes and query_boxes :param boxes: n * 4 bounding boxes :param query_boxes: k * 4 bounding boxes :return: overlaps: n * k overlaps """ n_ = boxes.shape[0] k_ = query_boxes.shape[0] overlaps = np.zeros((n_, k_), dt...

def clip_boxes(boxes, im_shape): """ Clip boxes to image boundaries. :param boxes: [N, 4* num_classes] :param im_shape: tuple of 2 :return: [N, 4* num_classes] """ # x1 >= 0 boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0) # y1 >= 0 boxes[:, 1::4] = np....

def bbox_transform(ex_rois, gt_rois, box_stds): """ compute bounding box regression targets from ex_rois to gt_rois :param ex_rois: [N, 4] :param gt_rois: [N, 4] :return: [N, 4] """ assert ex_rois.shape[0] == gt_rois.shape[0], 'inconsistent rois number' ex_widths = ex_rois[:, 2] - ex_ro...

def bbox_pred(boxes, box_deltas, box_stds): """ Transform the set of class-agnostic boxes into class-specific boxes by applying the predicted offsets (box_deltas) :param boxes: !important [N 4] :param box_deltas: [N, 4 * num_classes] :return: [N 4 * num_classes] """ if boxes.shape[0] == ...

def nms(dets, thresh): """ greedily select boxes with high confidence and overlap with current maximum <= thresh rule out overlap >= thresh :param dets: [[x1, y1, x2, y2 score]] :param thresh: retain overlap < thresh :return: indexes to keep """ x1 = dets[:, 0] y1 = dets[:, 1] x2...

def im_detect(rois, scores, bbox_deltas, im_info, bbox_stds, nms_thresh, conf_thresh): """rois (nroi, 4), scores (nrois, nclasses), bbox_deltas (nrois, 4 * nclasses), im_info (3)""" rois = rois.asnumpy() scores = scores.asnumpy() bbox_deltas = bbox_deltas.asnumpy() im_info = im_info.a...

def c_str(string): """"Convert a python string to C string.""" if not isinstance(string, str): string = string.decode('ascii') return ctypes.c_char_p(string.encode('utf-8'))

def _find_lib_path(): """Find mxnet library.""" curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__))) amalgamation_lib_path = os.path.join(curr_path, '../../lib/libmxnet_predict.so') if os.path.exists(amalgamation_lib_path) and os.path.isfile(amalgamation_lib_path): lib_path...

def _load_lib(): """Load libary by searching possible path.""" lib_path = _find_lib_path() lib = ctypes.cdll.LoadLibrary(lib_path[0]) # DMatrix functions lib.MXGetLastError.restype = ctypes.c_char_p return lib

def load_ndarray_file(nd_bytes): """Load ndarray file and return as list of numpy array. Parameters ---------- nd_bytes : str or bytes The internal ndarray bytes Returns ------- out : dict of str to numpy array or list of numpy array The output list or dict, depending on wh...

def forward(self, **kwargs): """Perform forward to get the output. Parameters ---------- **kwargs Keyword arguments of input variable name to data. Examples -------- >>> predictor.forward(data=mydata) >>> out = predictor.get_output(0) ...

def reshape(self, input_shapes): """Change the input shape of the predictor. Parameters ---------- input_shapes : dict of str to tuple The new shape of input data. Examples -------- >>> predictor.reshape({'data':data_shape_tuple}) """ ...

def get_output(self, index): """Get the index-th output. Parameters ---------- index : int The index of output. Returns ------- out : numpy array. The output array. """ pdata = ctypes.POINTER(mx_uint)() ndim = mx_u...

def begin_episode(self, max_episode_step=DEFAULT_MAX_EPISODE_STEP): """ Begin an episode of a game instance. We can play the game for a maximum of `max_episode_step` and after that, we are forced to restart """ if self.episode_step > self.max_episode_step or self.ale.game...

def reset(self): """Reset before re-using the cell for another graph.""" self._init_counter = -1 self._counter = -1 for cell in self._children.values(): cell.reset()

def begin_state(self, batch_size=0, func=ndarray.zeros, **kwargs): """Initial state for this cell. Parameters ---------- func : callable, default symbol.zeros Function for creating initial state. For Symbol API, func can be `symbol.zeros`, `symbol.uniform`, ...

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 _get_activation(self, F, inputs, activation, **kwargs): """Get activation function. Convert if is string""" func = {'tanh': F.tanh, 'relu': F.relu, 'sigmoid': F.sigmoid, 'softsign': F.softsign}.get(activation) if func: return func(i...

def forward(self, inputs, states): """Unrolls the recurrent cell for one time step. Parameters ---------- inputs : sym.Variable Input symbol, 2D, of shape (batch_size * num_units). states : list of sym.Variable RNN state from previous step or the output o...

def _check_input_names(symbol, names, typename, throw): """Check that all input names are in symbol's arguments.""" args = symbol.list_arguments() for name in names: if name in args: continue candidates = [arg for arg in args if not arg.endswith('_weight') a...

def _check_names_match(data_names, data_shapes, name, throw): """Check that input names matches input data descriptors.""" actual = [x[0] for x in data_shapes] if sorted(data_names) != sorted(actual): msg = "Data provided by %s_shapes don't match names specified by %s_names (%s vs. %s)"%( ...

def _parse_data_desc(data_names, label_names, data_shapes, label_shapes): """parse data_attrs into DataDesc format and check that names match""" data_shapes = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in data_shapes] _check_names_match(data_names, data_shapes, 'data', True) if label_shapes i...

def forward_backward(self, data_batch): """A convenient function that calls both ``forward`` and ``backward``.""" self.forward(data_batch, is_train=True) self.backward()

def score(self, eval_data, eval_metric, num_batch=None, batch_end_callback=None, score_end_callback=None, reset=True, epoch=0, sparse_row_id_fn=None): """Runs prediction on ``eval_data`` and evaluates the performance according to the given ``eval_metric``. Checkout `...

def iter_predict(self, eval_data, num_batch=None, reset=True, sparse_row_id_fn=None): """Iterates over predictions. Examples -------- >>> for pred, i_batch, batch in module.iter_predict(eval_data): ... # pred is a list of outputs from the module ... # i_batch is ...

def predict(self, eval_data, num_batch=None, merge_batches=True, reset=True, always_output_list=False, sparse_row_id_fn=None): """Runs prediction and collects the outputs. When `merge_batches` is ``True`` (by default), the return value will be a list ``[out1, out2, out3]``, wher...

def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True, allow_extra=False): """Assigns parameter and aux state values. Parameters ---------- arg_params : dict Dictionary of name to value (`NDArray`) mapping. aux_params : ...

def save_params(self, fname): """Saves model parameters to file. Parameters ---------- fname : str Path to output param file. Examples -------- >>> # An example of saving module parameters. >>> mod.save_params('myfile') """ ar...

def load_params(self, fname): """Loads model parameters from file. Parameters ---------- fname : str Path to input param file. Examples -------- >>> # An example of loading module parameters. >>> mod.load_params('myfile') """ ...