BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def hard_reset(self): """Ignore roll over data and set to start.""" if self.shuffle: self._shuffle_data() self.cursor = -self.batch_size self._cache_data = None self._cache_label = None
def reset(self): """Resets the iterator to the beginning of the data.""" if self.shuffle: self._shuffle_data() # the range below indicate the last batch if self.last_batch_handle == 'roll_over' and \ self.num_data - self.batch_size < self.cursor < self.num_data: ...
def iter_next(self): """Increments the coursor by batch_size for next batch and check current cursor if it exceed the number of data points.""" self.cursor += self.batch_size return self.cursor < self.num_data
def next(self): """Returns the next batch of data.""" if not self.iter_next(): raise StopIteration data = self.getdata() label = self.getlabel() # iter should stop when last batch is not complete if data[0].shape[0] != self.batch_size: # in this case, ...
def _getdata(self, data_source, start=None, end=None): """Load data from underlying arrays.""" assert start is not None or end is not None, 'should at least specify start or end' start = start if start is not None else 0 if end is None: end = data_source[0][1].shape[0] if dat...
def _concat(self, first_data, second_data): """Helper function to concat two NDArrays.""" assert len(first_data) == len( second_data), 'data source should contain the same size' if first_data and second_data: return [ concat( first_data...
def _batchify(self, data_source): """Load data from underlying arrays, internal use only.""" assert self.cursor < self.num_data, 'DataIter needs reset.' # first batch of next epoch with 'roll_over' if self.last_batch_handle == 'roll_over' and \ -self.batch_size < self.cursor ...
def getpad(self): """Get pad value of DataBatch.""" if self.last_batch_handle == 'pad' and \ self.cursor + self.batch_size > self.num_data: return self.cursor + self.batch_size - self.num_data # check the first batch elif self.last_batch_handle == 'roll_over' and \...
def _shuffle_data(self): """Shuffle the data.""" # shuffle index np.random.shuffle(self.idx) # get the data by corresponding index self.data = _getdata_by_idx(self.data, self.idx) self.label = _getdata_by_idx(self.label, self.idx)
def _quantize_params(qsym, params, th_dict): """Given a quantized symbol and a dict of params that have not been quantized, generate quantized params. Currently only supports quantizing the arg_params with names of `weight` or `bias`, not aux_params. If `qsym` contains symbols that are excluded from bei...
def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_dtype='int8'): """Given a symbol object representing a neural network of data type FP32, quantize it into a INT8 network. Parameters ---------- sym : Symbol FP32 neural network symbol. excluded_sym_names : l...
def _calibrate_quantized_sym(qsym, th_dict): """Given a dictionary containing the thresholds for quantizing the layers, set the thresholds into the quantized symbol as the params of requantize operators. """ if th_dict is None or len(th_dict) == 0: return qsym num_layer_outputs = len(th_dict...
def _collect_layer_output_min_max(mod, data, include_layer=None, max_num_examples=None, logger=None): """Collect min and max values from layer outputs and save them in a dictionary mapped by layer names. """ collector = _LayerOutputMinMaxCollector(include_layer=include_...
def _collect_layer_outputs(mod, data, include_layer=None, max_num_examples=None, logger=None): """Collect layer outputs and save them in a dictionary mapped by layer names.""" collector = _LayerOutputCollector(include_layer=include_layer, logger=logger) num_examples = _collect_layer_statistics(mod, data, co...
def _smooth_distribution(p, eps=0.0001): """Given a discrete distribution (may have not been normalized to 1), smooth it by replacing zeros with eps multiplied by a scaling factor and taking the corresponding amount off the non-zero values. Ref: http://web.engr.illinois.edu/~hanj/cs412/bk3/KL-divergence...
def _get_optimal_threshold(arr, quantized_dtype, num_bins=8001, num_quantized_bins=255): """Given a dataset, find the optimal threshold for quantizing it. The reference distribution is `q`, and the candidate distribution is `p`. `q` is a truncated version of the original distribution. Ref: http://on-de...
def _get_optimal_thresholds(nd_dict, quantized_dtype, num_bins=8001, num_quantized_bins=255, logger=None): """Given a ndarray dict, find the optimal threshold for quantizing each value of the key.""" if stats is None: raise ImportError('scipy.stats is required for running entropy mode of calculating' ...
def _load_sym(sym, logger=logging): """Given a str as a path the symbol .json file or a symbol, returns a Symbol object.""" if isinstance(sym, str): # sym is a symbol file path cur_path = os.path.dirname(os.path.realpath(__file__)) symbol_file_path = os.path.join(cur_path, sym) logger.i...
def _load_params(params, logger=logging): """Given a str as a path to the .params file or a pair of params, returns two dictionaries representing arg_params and aux_params. """ if isinstance(params, str): cur_path = os.path.dirname(os.path.realpath(__file__)) param_file_path = os.path.jo...
def quantize_model(sym, arg_params, aux_params, data_names=('data',), label_names=('softmax_label',), ctx=cpu(), excluded_sym_names=None, calib_mode='entropy', calib_data=None, num_calib_examples=None, calib_layer=None, quantized_dtype='int8', ...
def collect(self, name, arr): """Callback function for collecting layer output NDArrays.""" name = py_str(name) if self.include_layer is not None and not self.include_layer(name): return handle = ctypes.cast(arr, NDArrayHandle) arr = NDArray(handle, writable=False).co...
def collect(self, name, arr): """Callback function for collecting min and max values from an NDArray.""" name = py_str(name) if self.include_layer is not None and not self.include_layer(name): return handle = ctypes.cast(arr, NDArrayHandle) arr = NDArray(handle, writa...
def encoder(nef, z_dim, batch_size, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12): '''The encoder is a CNN which takes 32x32 image as input generates the 100 dimensional shape embedding as a sample from normal distribution using predicted meand and variance ''' BatchNorm = mx.sym.BatchNorm da...
def generator(ngf, nc, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12, z_dim=100, activation='sigmoid'): '''The genrator is a CNN which takes 100 dimensional embedding as input and reconstructs the input image given to the encoder ''' BatchNorm = mx.sym.BatchNorm rand = mx.sym.Variable('rand') ...
def discriminator1(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12): '''First part of the discriminator which takes a 32x32 image as input and output a convolutional feature map, this is required to calculate the layer loss''' BatchNorm = mx.sym.BatchNorm data = mx.sym.Variable('data') d1 ...
def discriminator2(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12): '''Second part of the discriminator which takes a 256x8x8 feature map as input and generates the loss based on whether the input image was a real one or fake one''' BatchNorm = mx.sym.BatchNorm data = mx.sym.Variable('data') ...
def GaussianLogDensity(x, mu, log_var, name='GaussianLogDensity', EPSILON = 1e-6): '''GaussianLogDensity loss calculation for layer wise loss ''' c = mx.sym.ones_like(log_var)2.0 3.1416 c = mx.symbol.log(c) var = mx.sym.exp(log_var) x_mu2 = mx.symbol.square(x - mu) # [Issue] not sure the di...
def DiscriminatorLayerLoss(): '''Calculate the discriminator layer loss ''' data = mx.sym.Variable('data') label = mx.sym.Variable('label') data = mx.sym.Flatten(data) label = mx.sym.Flatten(label) label = mx.sym.BlockGrad(label) zeros = mx.sym.zeros_like(data) output = -Gaussi...
def KLDivergenceLoss(): '''KLDivergenceLoss loss ''' data = mx.sym.Variable('data') mu1, lv1 = mx.sym.split(data, num_outputs=2, axis=0) mu2 = mx.sym.zeros_like(mu1) lv2 = mx.sym.zeros_like(lv1) v1 = mx.sym.exp(lv1) v2 = mx.sym.exp(lv2) mu_diff_sq = mx.sym.square(mu1 - mu2) di...
def get_data(path, activation): '''Get the dataset ''' data = [] image_names = [] for filename in os.listdir(path): img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE) image_names.append(filename) if img is not None: data.append(img) data = np...
def fill_buf(buf, i, img, shape): '''fill the ith grid of the buffer matrix with the values from the img buf : buffer matrix i : serial of the image in the 2D grid img : image data shape : ( height width depth ) of image''' # grid height is a multiple of individual image height m = buf.shap...
def visual(title, X, activation): '''create a grid of images and save it as a final image title : grid image name X : array of images ''' assert len(X.shape) == 4 X = X.transpose((0, 2, 3, 1)) if activation == 'sigmoid': X = np.clip((X)*(255.0), 0, 255).astype(np.uint8) elif act...
def train(dataset, nef, ndf, ngf, nc, batch_size, Z, lr, beta1, epsilon, ctx, check_point, g_dl_weight, output_path, checkpoint_path, data_path, activation,num_epoch, save_after_every, visualize_after_every, show_after_every): '''adversarial training of the VAE ''' #encoder z_mu, z_lv, z = encoder(nef,...
def create_and_validate_dir(data_dir): '''Creates/Validates dir ''' if data_dir != "": if not os.path.exists(data_dir): try: logging.info('create directory %s', data_dir) os.makedirs(data_dir) except OSError as exc: if exc.errno...
def parse_args(): '''Parse args ''' parser = argparse.ArgumentParser(description='Train and Test an Adversarial Variatiional Encoder') parser.add_argument('--train', help='train the network', action='store_true') parser.add_argument('--test', help='test the network', action='store_true') parser...
def get_rmse_log(net, X_train, y_train): """Gets root mse between the logarithms of the prediction and the truth.""" num_train = X_train.shape[0] clipped_preds = nd.clip(net(X_train), 1, float('inf')) return np.sqrt(2 * nd.sum(square_loss( nd.log(clipped_preds), nd.log(y_train))).asscalar() / nu...
def get_net(): """Gets a neural network. Better results are obtained with modifications.""" net = gluon.nn.Sequential() with net.name_scope(): net.add(gluon.nn.Dense(50, activation="relu")) net.add(gluon.nn.Dense(1)) net.initialize() return net
def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate, weight_decay, batch_size): """Trains the model.""" dataset_train = gluon.data.ArrayDataset(X_train, y_train) data_iter_train = gluon.data.DataLoader(dataset_train, batch_size, shuffle...
def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train, learning_rate, weight_decay, batch_size): """Conducts k-fold cross validation for the model.""" assert k > 1 fold_size = X_train.shape[0] // k train_loss_sum = 0.0 test_loss_sum = 0.0 for test_idx in range...
def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate, weight_decay, batch_size): """Trains the model and predicts on the test data set.""" net = get_net() _ = train(net, X_train, y_train, epochs, verbose_epoch, learning_rate, weight_decay, batch_size) preds =...
def capsnet(batch_size, n_class, num_routing, recon_loss_weight): """Create CapsNet""" # data.shape = [batch_size, 1, 28, 28] data = mx.sym.Variable('data') input_shape = (1, 28, 28) # Conv2D layer # net.shape = [batch_size, 256, 20, 20] conv1 = mx.sym.Convolution(data=data, ...
def do_training(num_epoch, optimizer, kvstore, learning_rate, model_prefix, decay): """Perform CapsNet training""" summary_writer = SummaryWriter(args.tblog_dir) lr_scheduler = SimpleLRScheduler(learning_rate) optimizer_params = {'lr_scheduler': lr_scheduler} module.init_params() module.init_opt...
def _shuffle(data, idx): """Shuffle the data.""" shuffle_data = [] for idx_k, idx_v in data: shuffle_data.append((idx_k, mx.ndarray.array(idx_v.asnumpy()[idx], idx_v.context))) return shuffle_data
def update(self, labels, preds): """Update the hyper-parameters and loss of CapsNet""" batch_sum_metric = 0 batch_num_inst = 0 for label, pred_outcaps in zip(labels[0], preds[0]): label_np = int(label.asnumpy()) pred_label = int(np.argmax(pred_outcaps.asnumpy())) ...
def reset(self): """Reset class MNISTCustomIter(mx.io.NDArrayIter):""" # shuffle data if self.is_train: np.random.shuffle(self.idx) self.data = _shuffle(self.data, self.idx) self.label = _shuffle(self.label, self.idx) if self.last_batch_handle == 'rol...
def next(self): """Generate next of iterator""" if self.iter_next(): if self.is_train: data_raw_list = self.getdata() data_shifted = [] for data_raw in data_raw_list[0]: data_shifted.append(random_shift(data_raw.asnumpy(), 0...
def get(self, attr): """ Get the attribute dict given the attribute set by the symbol. Parameters ---------- attr : dict of string to string The attribute passed in by user during symbol creation. Returns ------- attr : dict of string to stri...
def _create_sparse_kvstore(kvstore): """Create kvstore assuming some parameters' storage types are row_sparse. Parameters ---------- kvstore : KVStore or str The kvstore. Returns ------- kvstore : KVStore update_on_kvstore : bool. Always True. """ # always update on kvs...
def _create_kvstore(kvstore, num_device, arg_params): """Create kvstore This function select and create a proper kvstore if given the kvstore type. Parameters ---------- kvstore : KVStore or str The kvstore. num_device : int The number of devices arg_params : dict of str to ...
def _initialize_kvstore(kvstore, param_arrays, arg_params, param_names, update_on_kvstore): """Initialize kvstore""" for idx, param_on_devs in enumerate(param_arrays): name = param_names[idx] kvstore.init(name, arg_params[name]) if update_on_kvstore: kvstore.pull(name, param...
def _update_params_on_kvstore_nccl(param_arrays, grad_arrays, kvstore, param_names): """Perform update of param_arrays from grad_arrays on NCCL kvstore.""" valid_indices = [index for index, grad_list in enumerate(grad_arrays) if grad_list[0] is not None] valid_grad_arrays = [grad_arrays...
def _update_params_on_kvstore(param_arrays, grad_arrays, kvstore, param_names): """Perform update of param_arrays from grad_arrays on kvstore.""" for index, pair in enumerate(zip(param_arrays, grad_arrays)): arg_list, grad_list = pair if grad_list[0] is None: continue name = ...
def _update_params(param_arrays, grad_arrays, updater, num_device, kvstore=None, param_names=None): """Perform update of param_arrays from grad_arrays not on kvstore.""" updates = [[] for _ in range(num_device)] for i, pair in enumerate(zip(param_arrays, grad_arrays)): arg_list, g...
def _multiple_callbacks(callbacks, args, kwargs): """Sends args and kwargs to any configured callbacks. This handles the cases where the 'callbacks' variable is ``None``, a single function, or a list. """ if isinstance(callbacks, list): for cb in callbacks: cb(args, **kwargs)...
def _train_multi_device(symbol, ctx, arg_names, param_names, aux_names, arg_params, aux_params, begin_epoch, end_epoch, epoch_size, optimizer, kvstore, update_on_kvstore, train_data, eval_data=None, eval_metric=None, ...
def save_checkpoint(prefix, epoch, symbol, arg_params, aux_params): """Checkpoint the model data into file. Parameters ---------- prefix : str Prefix of model name. epoch : int The epoch number of the model. symbol : Symbol The input Symbol. arg_params : dict of str ...
def load_checkpoint(prefix, epoch): """Load model checkpoint from file. Parameters ---------- prefix : str Prefix of model name. epoch : int Epoch number of model we would like to load. Returns ------- symbol : Symbol The symbol configuration of computation netw...
def _check_arguments(self): """verify the argument of the default symbol and user provided parameters""" if self.argument_checked: return assert(self.symbol is not None) self.argument_checked = True # check if symbol contain duplicated names. _check_argument...
def _init_params(self, inputs, overwrite=False): """Initialize weight parameters and auxiliary states.""" inputs = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in inputs] input_shapes = {item.name: item.shape for item in inputs} arg_shapes, _, aux_shapes = self.symbol.infer_shap...
def _init_predictor(self, input_shapes, type_dict=None): """Initialize the predictor module for running prediction.""" shapes = {name: self.arg_params[name].shape for name in self.arg_params} shapes.update(dict(input_shapes)) if self._pred_exec is not None: arg_shapes, _, _ =...
def _init_iter(self, X, y, is_train): """Initialize the iterator given input.""" if isinstance(X, (np.ndarray, nd.NDArray)): if y is None: if is_train: raise ValueError('y must be specified when X is numpy.ndarray') else: ...
def _init_eval_iter(self, eval_data): """Initialize the iterator given eval_data.""" if eval_data is None: return eval_data if isinstance(eval_data, (tuple, list)) and len(eval_data) == 2: if eval_data[0] is not None: if eval_data[1] is None and isinstance...
def predict(self, X, num_batch=None, return_data=False, reset=True): """Run the prediction, always only use one device. Parameters ---------- X : mxnet.DataIter num_batch : int or None The number of batch to run. Go though all batches if ``None``. Returns ...
def score(self, X, eval_metric='acc', num_batch=None, batch_end_callback=None, reset=True): """Run the model given an input and calculate the score as assessed by an evaluation metric. Parameters ---------- X : mxnet.DataIter eval_metric : metric.metric The m...
def fit(self, X, y=None, eval_data=None, eval_metric='acc', epoch_end_callback=None, batch_end_callback=None, kvstore='local', logger=None, work_load_list=None, monitor=None, eval_end_callback=LogValidationMetricsCallback(), eval_batch_end_callback=None): """Fit the model. ...
def save(self, prefix, epoch=None): """Checkpoint the model checkpoint into file. You can also use `pickle` to do the job if you only work on Python. The advantage of `load` and `save` (as compared to `pickle`) is that the resulting file can be loaded from other MXNet language bindings. ...
def load(prefix, epoch, ctx=None, **kwargs): """Load model checkpoint from file. Parameters ---------- prefix : str Prefix of model name. epoch : int epoch number of model we would like to load. ctx : Context or list of Context, optional ...
def create(symbol, X, y=None, ctx=None, num_epoch=None, epoch_size=None, optimizer='sgd', initializer=Uniform(0.01), eval_data=None, eval_metric='acc', epoch_end_callback=None, batch_end_callback=None, kvstore='local', logger=None, work_load_list=None, ...
def build_save_containers(platforms, registry, load_cache) -> int: """ Entry point to build and upload all built dockerimages in parallel :param platforms: List of platforms :param registry: Docker registry name :param load_cache: Load cache before building :return: 1 if error occurred, 0 otherw...
def _build_save_container(platform, registry, load_cache) -> Optional[str]: """ Build image for passed platform and upload the cache to the specified S3 bucket :param platform: Platform :param registry: Docker registry name :param load_cache: Load cache before building :return: Platform if faile...
def _upload_image(registry, docker_tag, image_id) -> None: """ Upload the passed image by id, tag it with docker tag and upload to S3 bucket :param registry: Docker registry name :param docker_tag: Docker tag :param image_id: Image id :return: None """ # We don't have to retag the image ...
def _login_dockerhub(): """ Login to the Docker Hub account :return: None """ dockerhub_credentials = _get_dockerhub_credentials() logging.info('Logging in to DockerHub') # We use password-stdin instead of --password to avoid leaking passwords in case of an error. # This method will pro...
def load_docker_cache(registry, docker_tag) -> None: """ Load the precompiled docker cache from the registry :param registry: Docker registry name :param docker_tag: Docker tag to load :return: None """ # We don't have to retag the image since it's already in the right format if not regi...
def delete_local_docker_cache(docker_tag): """ Delete the local docker cache for the entire docker image chain :param docker_tag: Docker tag :return: None """ history_cmd = ['docker', 'history', '-q', docker_tag] try: image_ids_b = subprocess.check_output(history_cmd) image_...
def main() -> int: """ Utility to create and publish the Docker cache to Docker Hub :return: """ # We need to be in the same directory than the script so the commands in the dockerfiles work as # expected. But the script can be invoked from a different path base = os.path.split(os.path.realp...
def get_chinese_text(): """Download the chinese_text dataset and unzip it""" if not os.path.isdir("data/"): os.system("mkdir data/") if (not os.path.exists('data/pos.txt')) or \ (not os.path.exists('data/neg')): os.system("wget -q https://raw.githubusercontent.com/dmlc/web-data/master...
def load_data_and_labels(): """Loads MR polarity data from files, splits the data into words and generates labels. Returns split sentences and labels. """ # download dataset get_chinese_text() # Load data from files positive_examples = list(codecs.open("./data/pos.txt", "r", "utf-8").readli...
def reset(self): """ override reset behavior """ if getattr(self, 'num', None) is None: self.num_inst = 0 self.sum_metric = 0.0 else: self.num_inst = [0] * self.num self.sum_metric = [0.0] * self.num
def reset_local(self): """ override reset behavior """ if getattr(self, 'num', None) is None: self.num_inst = 0 self.sum_metric = 0.0 else: self.num_inst = [0] * self.num self.sum_metric = [0.0] * self.num
def update(self, labels, preds): """ Implementation of updating metrics """ # get generated multi label from network cls_prob = preds[0].asnumpy() loc_loss = preds[1].asnumpy() cls_label = preds[2].asnumpy() valid_count = np.sum(cls_label >= 0) # o...
def get(self): """Get the current evaluation result. Override the default behavior Returns ------- name : str Name of the metric. value : float Value of the evaluation. """ if self.num is None: if self.num_inst == 0: ...
def dqn_sym_nips(action_num, data=None, name='dqn'): """Structure of the Deep Q Network in the NIPS 2013 workshop paper: Playing Atari with Deep Reinforcement Learning (https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) Parameters ---------- action_num : int data : mxnet.sym.Symbol, optional n...
def _monitor_callback_wrapper(callback): """A wrapper for the user-defined handle.""" def callback_handle(name, array, _): """ ctypes function """ callback(name, array) return callback_handle
def _get_dict(names, ndarrays): """Get the dictionary given name and ndarray pairs.""" nset = set() for nm in names: if nm in nset: raise ValueError('Duplicate names detected, %s' % str(names)) nset.add(nm) return dict(zip(names, ndarrays))
def _get_outputs(self): """List all the output NDArray. Returns ------- A list of ndarray bound to the heads of executor. """ out_size = mx_uint() handles = ctypes.POINTER(NDArrayHandle)() check_call(_LIB.MXExecutorOutputs(self.handle, ...
def forward(self, is_train=False, kwargs): """Calculate the outputs specified by the bound symbol. Parameters ---------- is_train: bool, optional Whether this forward is for evaluation purpose. If True, a backward call is expected to follow. kwargs ...
def backward(self, out_grads=None, is_train=True): """Do backward pass to get the gradient of arguments. Parameters ---------- out_grads : NDArray or list of NDArray or dict of str to NDArray, optional Gradient on the outputs to be propagated back. This parameter...
def set_monitor_callback(self, callback, monitor_all=False): """Install callback for monitor. Parameters ---------- callback : function Takes a string and an NDArrayHandle. monitor_all : bool, default False If true, monitor both input and output, otherwis...
def arg_dict(self): """Get dictionary representation of argument arrrays. Returns ------- arg_dict : dict of str to NDArray The dictionary that maps the names of arguments to NDArrays. Raises ------ ValueError : if there are duplicated names in the a...
def grad_dict(self): """Get dictionary representation of gradient arrays. Returns ------- grad_dict : dict of str to NDArray The dictionary that maps name of arguments to gradient arrays. """ if self._grad_dict is None: self._grad_dict = Executor....
def aux_dict(self): """Get dictionary representation of auxiliary states arrays. Returns ------- aux_dict : dict of str to NDArray The dictionary that maps name of auxiliary states to NDArrays. Raises ------ ValueError : if there are duplicated names...
def output_dict(self): """Get dictionary representation of output arrays. Returns ------- output_dict : dict of str to NDArray The dictionary that maps name of output names to NDArrays. Raises ------ ValueError : if there are duplicated names in the ...
def copy_params_from(self, arg_params, aux_params=None, allow_extra_params=False): """Copy parameters from arg_params, aux_params into executor's internal array. Parameters ---------- arg_params : dict of str to NDArray Parameters, dict of name to NDArray of arguments. ...
def reshape(self, partial_shaping=False, allow_up_sizing=False, **kwargs): """Return a new executor with the same symbol and shared memory, but different input/output shapes. For runtime reshaping, variable length sequences, etc. The returned executor shares state with the current one, ...
def debug_str(self): """Get a debug string about internal execution plan. Returns ------- debug_str : string Debug string of the executor. Examples -------- >>> a = mx.sym.Variable('a') >>> b = mx.sym.sin(a) >>> c = 2 * a + b ...
def parse_voc_rec(filename): """ parse pascal voc record into a dictionary :param filename: xml file path :return: list of dict """ import xml.etree.ElementTree as ET tree = ET.parse(filename) objects = [] for obj in tree.findall('object'): obj_dict = dict() obj_dict[...
def voc_eval(detpath, annopath, imageset_file, classname, cache_dir, ovthresh=0.5, use_07_metric=False): """ pascal voc evaluation :param detpath: detection results detpath.format(classname) :param annopath: annotations annopath.format(classname) :param imageset_file: text file containing list of im...
def register(op_name): """Register operators""" def wrapper(func): """Helper function to map functions""" try: import onnx as _ MXNetGraph.registry_[op_name] = func except ImportError: pass return func ...
def convert_layer(node, **kwargs): """Convert MXNet layer to ONNX""" op = str(node["op"]) if op not in MXNetGraph.registry_: raise AttributeError("No conversion function registered for op type %s yet." % op) convert_func = MXNetGraph.registry_[op] return convert_func(...
def split_params(sym, params): """Helper function to split params dictionary into args and aux params Parameters ---------- sym : :class:`~mxnet.symbol.Symbol` MXNet symbol object params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray` Dict of convert...

def hard_reset(self): """Ignore roll over data and set to start.""" if self.shuffle: self._shuffle_data() self.cursor = -self.batch_size self._cache_data = None self._cache_label = None

def reset(self): """Resets the iterator to the beginning of the data.""" if self.shuffle: self._shuffle_data() # the range below indicate the last batch if self.last_batch_handle == 'roll_over' and \ self.num_data - self.batch_size < self.cursor < self.num_data: ...

def iter_next(self): """Increments the coursor by batch_size for next batch and check current cursor if it exceed the number of data points.""" self.cursor += self.batch_size return self.cursor < self.num_data

def next(self): """Returns the next batch of data.""" if not self.iter_next(): raise StopIteration data = self.getdata() label = self.getlabel() # iter should stop when last batch is not complete if data[0].shape[0] != self.batch_size: # in this case, ...

def _getdata(self, data_source, start=None, end=None): """Load data from underlying arrays.""" assert start is not None or end is not None, 'should at least specify start or end' start = start if start is not None else 0 if end is None: end = data_source[0][1].shape[0] if dat...

def _concat(self, first_data, second_data): """Helper function to concat two NDArrays.""" assert len(first_data) == len( second_data), 'data source should contain the same size' if first_data and second_data: return [ concat( first_data...

def _batchify(self, data_source): """Load data from underlying arrays, internal use only.""" assert self.cursor < self.num_data, 'DataIter needs reset.' # first batch of next epoch with 'roll_over' if self.last_batch_handle == 'roll_over' and \ -self.batch_size < self.cursor ...

def getpad(self): """Get pad value of DataBatch.""" if self.last_batch_handle == 'pad' and \ self.cursor + self.batch_size > self.num_data: return self.cursor + self.batch_size - self.num_data # check the first batch elif self.last_batch_handle == 'roll_over' and \...

def _shuffle_data(self): """Shuffle the data.""" # shuffle index np.random.shuffle(self.idx) # get the data by corresponding index self.data = _getdata_by_idx(self.data, self.idx) self.label = _getdata_by_idx(self.label, self.idx)

def _quantize_params(qsym, params, th_dict): """Given a quantized symbol and a dict of params that have not been quantized, generate quantized params. Currently only supports quantizing the arg_params with names of `weight` or `bias`, not aux_params. If `qsym` contains symbols that are excluded from bei...

def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_dtype='int8'): """Given a symbol object representing a neural network of data type FP32, quantize it into a INT8 network. Parameters ---------- sym : Symbol FP32 neural network symbol. excluded_sym_names : l...

def _calibrate_quantized_sym(qsym, th_dict): """Given a dictionary containing the thresholds for quantizing the layers, set the thresholds into the quantized symbol as the params of requantize operators. """ if th_dict is None or len(th_dict) == 0: return qsym num_layer_outputs = len(th_dict...

def _collect_layer_output_min_max(mod, data, include_layer=None, max_num_examples=None, logger=None): """Collect min and max values from layer outputs and save them in a dictionary mapped by layer names. """ collector = _LayerOutputMinMaxCollector(include_layer=include_...

def _collect_layer_outputs(mod, data, include_layer=None, max_num_examples=None, logger=None): """Collect layer outputs and save them in a dictionary mapped by layer names.""" collector = _LayerOutputCollector(include_layer=include_layer, logger=logger) num_examples = _collect_layer_statistics(mod, data, co...

def _smooth_distribution(p, eps=0.0001): """Given a discrete distribution (may have not been normalized to 1), smooth it by replacing zeros with eps multiplied by a scaling factor and taking the corresponding amount off the non-zero values. Ref: http://web.engr.illinois.edu/~hanj/cs412/bk3/KL-divergence...

def _get_optimal_threshold(arr, quantized_dtype, num_bins=8001, num_quantized_bins=255): """Given a dataset, find the optimal threshold for quantizing it. The reference distribution is `q`, and the candidate distribution is `p`. `q` is a truncated version of the original distribution. Ref: http://on-de...

def _get_optimal_thresholds(nd_dict, quantized_dtype, num_bins=8001, num_quantized_bins=255, logger=None): """Given a ndarray dict, find the optimal threshold for quantizing each value of the key.""" if stats is None: raise ImportError('scipy.stats is required for running entropy mode of calculating' ...

def _load_sym(sym, logger=logging): """Given a str as a path the symbol .json file or a symbol, returns a Symbol object.""" if isinstance(sym, str): # sym is a symbol file path cur_path = os.path.dirname(os.path.realpath(__file__)) symbol_file_path = os.path.join(cur_path, sym) logger.i...

def _load_params(params, logger=logging): """Given a str as a path to the .params file or a pair of params, returns two dictionaries representing arg_params and aux_params. """ if isinstance(params, str): cur_path = os.path.dirname(os.path.realpath(__file__)) param_file_path = os.path.jo...

def quantize_model(sym, arg_params, aux_params, data_names=('data',), label_names=('softmax_label',), ctx=cpu(), excluded_sym_names=None, calib_mode='entropy', calib_data=None, num_calib_examples=None, calib_layer=None, quantized_dtype='int8', ...

def collect(self, name, arr): """Callback function for collecting layer output NDArrays.""" name = py_str(name) if self.include_layer is not None and not self.include_layer(name): return handle = ctypes.cast(arr, NDArrayHandle) arr = NDArray(handle, writable=False).co...

def collect(self, name, arr): """Callback function for collecting min and max values from an NDArray.""" name = py_str(name) if self.include_layer is not None and not self.include_layer(name): return handle = ctypes.cast(arr, NDArrayHandle) arr = NDArray(handle, writa...

def encoder(nef, z_dim, batch_size, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12): '''The encoder is a CNN which takes 32x32 image as input generates the 100 dimensional shape embedding as a sample from normal distribution using predicted meand and variance ''' BatchNorm = mx.sym.BatchNorm da...

def generator(ngf, nc, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12, z_dim=100, activation='sigmoid'): '''The genrator is a CNN which takes 100 dimensional embedding as input and reconstructs the input image given to the encoder ''' BatchNorm = mx.sym.BatchNorm rand = mx.sym.Variable('rand') ...

def discriminator1(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12): '''First part of the discriminator which takes a 32x32 image as input and output a convolutional feature map, this is required to calculate the layer loss''' BatchNorm = mx.sym.BatchNorm data = mx.sym.Variable('data') d1 ...

def discriminator2(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12): '''Second part of the discriminator which takes a 256x8x8 feature map as input and generates the loss based on whether the input image was a real one or fake one''' BatchNorm = mx.sym.BatchNorm data = mx.sym.Variable('data') ...

def GaussianLogDensity(x, mu, log_var, name='GaussianLogDensity', EPSILON = 1e-6): '''GaussianLogDensity loss calculation for layer wise loss ''' c = mx.sym.ones_like(log_var)*2.0 * 3.1416 c = mx.symbol.log(c) var = mx.sym.exp(log_var) x_mu2 = mx.symbol.square(x - mu) # [Issue] not sure the di...

def DiscriminatorLayerLoss(): '''Calculate the discriminator layer loss ''' data = mx.sym.Variable('data') label = mx.sym.Variable('label') data = mx.sym.Flatten(data) label = mx.sym.Flatten(label) label = mx.sym.BlockGrad(label) zeros = mx.sym.zeros_like(data) output = -Gaussi...

def KLDivergenceLoss(): '''KLDivergenceLoss loss ''' data = mx.sym.Variable('data') mu1, lv1 = mx.sym.split(data, num_outputs=2, axis=0) mu2 = mx.sym.zeros_like(mu1) lv2 = mx.sym.zeros_like(lv1) v1 = mx.sym.exp(lv1) v2 = mx.sym.exp(lv2) mu_diff_sq = mx.sym.square(mu1 - mu2) di...

def get_data(path, activation): '''Get the dataset ''' data = [] image_names = [] for filename in os.listdir(path): img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE) image_names.append(filename) if img is not None: data.append(img) data = np...

def fill_buf(buf, i, img, shape): '''fill the ith grid of the buffer matrix with the values from the img buf : buffer matrix i : serial of the image in the 2D grid img : image data shape : ( height width depth ) of image''' # grid height is a multiple of individual image height m = buf.shap...

def visual(title, X, activation): '''create a grid of images and save it as a final image title : grid image name X : array of images ''' assert len(X.shape) == 4 X = X.transpose((0, 2, 3, 1)) if activation == 'sigmoid': X = np.clip((X)*(255.0), 0, 255).astype(np.uint8) elif act...

def train(dataset, nef, ndf, ngf, nc, batch_size, Z, lr, beta1, epsilon, ctx, check_point, g_dl_weight, output_path, checkpoint_path, data_path, activation,num_epoch, save_after_every, visualize_after_every, show_after_every): '''adversarial training of the VAE ''' #encoder z_mu, z_lv, z = encoder(nef,...

def create_and_validate_dir(data_dir): '''Creates/Validates dir ''' if data_dir != "": if not os.path.exists(data_dir): try: logging.info('create directory %s', data_dir) os.makedirs(data_dir) except OSError as exc: if exc.errno...

def parse_args(): '''Parse args ''' parser = argparse.ArgumentParser(description='Train and Test an Adversarial Variatiional Encoder') parser.add_argument('--train', help='train the network', action='store_true') parser.add_argument('--test', help='test the network', action='store_true') parser...

def get_rmse_log(net, X_train, y_train): """Gets root mse between the logarithms of the prediction and the truth.""" num_train = X_train.shape[0] clipped_preds = nd.clip(net(X_train), 1, float('inf')) return np.sqrt(2 * nd.sum(square_loss( nd.log(clipped_preds), nd.log(y_train))).asscalar() / nu...

def get_net(): """Gets a neural network. Better results are obtained with modifications.""" net = gluon.nn.Sequential() with net.name_scope(): net.add(gluon.nn.Dense(50, activation="relu")) net.add(gluon.nn.Dense(1)) net.initialize() return net

def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate, weight_decay, batch_size): """Trains the model.""" dataset_train = gluon.data.ArrayDataset(X_train, y_train) data_iter_train = gluon.data.DataLoader(dataset_train, batch_size, shuffle...

def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train, learning_rate, weight_decay, batch_size): """Conducts k-fold cross validation for the model.""" assert k > 1 fold_size = X_train.shape[0] // k train_loss_sum = 0.0 test_loss_sum = 0.0 for test_idx in range...

def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate, weight_decay, batch_size): """Trains the model and predicts on the test data set.""" net = get_net() _ = train(net, X_train, y_train, epochs, verbose_epoch, learning_rate, weight_decay, batch_size) preds =...

def capsnet(batch_size, n_class, num_routing, recon_loss_weight): """Create CapsNet""" # data.shape = [batch_size, 1, 28, 28] data = mx.sym.Variable('data') input_shape = (1, 28, 28) # Conv2D layer # net.shape = [batch_size, 256, 20, 20] conv1 = mx.sym.Convolution(data=data, ...

def do_training(num_epoch, optimizer, kvstore, learning_rate, model_prefix, decay): """Perform CapsNet training""" summary_writer = SummaryWriter(args.tblog_dir) lr_scheduler = SimpleLRScheduler(learning_rate) optimizer_params = {'lr_scheduler': lr_scheduler} module.init_params() module.init_opt...

def _shuffle(data, idx): """Shuffle the data.""" shuffle_data = [] for idx_k, idx_v in data: shuffle_data.append((idx_k, mx.ndarray.array(idx_v.asnumpy()[idx], idx_v.context))) return shuffle_data

def update(self, labels, preds): """Update the hyper-parameters and loss of CapsNet""" batch_sum_metric = 0 batch_num_inst = 0 for label, pred_outcaps in zip(labels[0], preds[0]): label_np = int(label.asnumpy()) pred_label = int(np.argmax(pred_outcaps.asnumpy())) ...

def reset(self): """Reset class MNISTCustomIter(mx.io.NDArrayIter):""" # shuffle data if self.is_train: np.random.shuffle(self.idx) self.data = _shuffle(self.data, self.idx) self.label = _shuffle(self.label, self.idx) if self.last_batch_handle == 'rol...

def next(self): """Generate next of iterator""" if self.iter_next(): if self.is_train: data_raw_list = self.getdata() data_shifted = [] for data_raw in data_raw_list[0]: data_shifted.append(random_shift(data_raw.asnumpy(), 0...

def get(self, attr): """ Get the attribute dict given the attribute set by the symbol. Parameters ---------- attr : dict of string to string The attribute passed in by user during symbol creation. Returns ------- attr : dict of string to stri...

def _create_sparse_kvstore(kvstore): """Create kvstore assuming some parameters' storage types are row_sparse. Parameters ---------- kvstore : KVStore or str The kvstore. Returns ------- kvstore : KVStore update_on_kvstore : bool. Always True. """ # always update on kvs...

def _create_kvstore(kvstore, num_device, arg_params): """Create kvstore This function select and create a proper kvstore if given the kvstore type. Parameters ---------- kvstore : KVStore or str The kvstore. num_device : int The number of devices arg_params : dict of str to ...

def _initialize_kvstore(kvstore, param_arrays, arg_params, param_names, update_on_kvstore): """Initialize kvstore""" for idx, param_on_devs in enumerate(param_arrays): name = param_names[idx] kvstore.init(name, arg_params[name]) if update_on_kvstore: kvstore.pull(name, param...

def _update_params_on_kvstore_nccl(param_arrays, grad_arrays, kvstore, param_names): """Perform update of param_arrays from grad_arrays on NCCL kvstore.""" valid_indices = [index for index, grad_list in enumerate(grad_arrays) if grad_list[0] is not None] valid_grad_arrays = [grad_arrays...

def _update_params_on_kvstore(param_arrays, grad_arrays, kvstore, param_names): """Perform update of param_arrays from grad_arrays on kvstore.""" for index, pair in enumerate(zip(param_arrays, grad_arrays)): arg_list, grad_list = pair if grad_list[0] is None: continue name = ...

def _update_params(param_arrays, grad_arrays, updater, num_device, kvstore=None, param_names=None): """Perform update of param_arrays from grad_arrays not on kvstore.""" updates = [[] for _ in range(num_device)] for i, pair in enumerate(zip(param_arrays, grad_arrays)): arg_list, g...

def _multiple_callbacks(callbacks, *args, **kwargs): """Sends args and kwargs to any configured callbacks. This handles the cases where the 'callbacks' variable is ``None``, a single function, or a list. """ if isinstance(callbacks, list): for cb in callbacks: cb(*args, **kwargs)...

def _train_multi_device(symbol, ctx, arg_names, param_names, aux_names, arg_params, aux_params, begin_epoch, end_epoch, epoch_size, optimizer, kvstore, update_on_kvstore, train_data, eval_data=None, eval_metric=None, ...

def save_checkpoint(prefix, epoch, symbol, arg_params, aux_params): """Checkpoint the model data into file. Parameters ---------- prefix : str Prefix of model name. epoch : int The epoch number of the model. symbol : Symbol The input Symbol. arg_params : dict of str ...

def load_checkpoint(prefix, epoch): """Load model checkpoint from file. Parameters ---------- prefix : str Prefix of model name. epoch : int Epoch number of model we would like to load. Returns ------- symbol : Symbol The symbol configuration of computation netw...

def _check_arguments(self): """verify the argument of the default symbol and user provided parameters""" if self.argument_checked: return assert(self.symbol is not None) self.argument_checked = True # check if symbol contain duplicated names. _check_argument...

def _init_params(self, inputs, overwrite=False): """Initialize weight parameters and auxiliary states.""" inputs = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in inputs] input_shapes = {item.name: item.shape for item in inputs} arg_shapes, _, aux_shapes = self.symbol.infer_shap...

def _init_predictor(self, input_shapes, type_dict=None): """Initialize the predictor module for running prediction.""" shapes = {name: self.arg_params[name].shape for name in self.arg_params} shapes.update(dict(input_shapes)) if self._pred_exec is not None: arg_shapes, _, _ =...

def _init_iter(self, X, y, is_train): """Initialize the iterator given input.""" if isinstance(X, (np.ndarray, nd.NDArray)): if y is None: if is_train: raise ValueError('y must be specified when X is numpy.ndarray') else: ...

def _init_eval_iter(self, eval_data): """Initialize the iterator given eval_data.""" if eval_data is None: return eval_data if isinstance(eval_data, (tuple, list)) and len(eval_data) == 2: if eval_data[0] is not None: if eval_data[1] is None and isinstance...

def predict(self, X, num_batch=None, return_data=False, reset=True): """Run the prediction, always only use one device. Parameters ---------- X : mxnet.DataIter num_batch : int or None The number of batch to run. Go though all batches if ``None``. Returns ...

def score(self, X, eval_metric='acc', num_batch=None, batch_end_callback=None, reset=True): """Run the model given an input and calculate the score as assessed by an evaluation metric. Parameters ---------- X : mxnet.DataIter eval_metric : metric.metric The m...

def fit(self, X, y=None, eval_data=None, eval_metric='acc', epoch_end_callback=None, batch_end_callback=None, kvstore='local', logger=None, work_load_list=None, monitor=None, eval_end_callback=LogValidationMetricsCallback(), eval_batch_end_callback=None): """Fit the model. ...

def save(self, prefix, epoch=None): """Checkpoint the model checkpoint into file. You can also use `pickle` to do the job if you only work on Python. The advantage of `load` and `save` (as compared to `pickle`) is that the resulting file can be loaded from other MXNet language bindings. ...

def load(prefix, epoch, ctx=None, **kwargs): """Load model checkpoint from file. Parameters ---------- prefix : str Prefix of model name. epoch : int epoch number of model we would like to load. ctx : Context or list of Context, optional ...

def create(symbol, X, y=None, ctx=None, num_epoch=None, epoch_size=None, optimizer='sgd', initializer=Uniform(0.01), eval_data=None, eval_metric='acc', epoch_end_callback=None, batch_end_callback=None, kvstore='local', logger=None, work_load_list=None, ...

def build_save_containers(platforms, registry, load_cache) -> int: """ Entry point to build and upload all built dockerimages in parallel :param platforms: List of platforms :param registry: Docker registry name :param load_cache: Load cache before building :return: 1 if error occurred, 0 otherw...

def _build_save_container(platform, registry, load_cache) -> Optional[str]: """ Build image for passed platform and upload the cache to the specified S3 bucket :param platform: Platform :param registry: Docker registry name :param load_cache: Load cache before building :return: Platform if faile...

def _upload_image(registry, docker_tag, image_id) -> None: """ Upload the passed image by id, tag it with docker tag and upload to S3 bucket :param registry: Docker registry name :param docker_tag: Docker tag :param image_id: Image id :return: None """ # We don't have to retag the image ...

def _login_dockerhub(): """ Login to the Docker Hub account :return: None """ dockerhub_credentials = _get_dockerhub_credentials() logging.info('Logging in to DockerHub') # We use password-stdin instead of --password to avoid leaking passwords in case of an error. # This method will pro...

def load_docker_cache(registry, docker_tag) -> None: """ Load the precompiled docker cache from the registry :param registry: Docker registry name :param docker_tag: Docker tag to load :return: None """ # We don't have to retag the image since it's already in the right format if not regi...

def delete_local_docker_cache(docker_tag): """ Delete the local docker cache for the entire docker image chain :param docker_tag: Docker tag :return: None """ history_cmd = ['docker', 'history', '-q', docker_tag] try: image_ids_b = subprocess.check_output(history_cmd) image_...

def main() -> int: """ Utility to create and publish the Docker cache to Docker Hub :return: """ # We need to be in the same directory than the script so the commands in the dockerfiles work as # expected. But the script can be invoked from a different path base = os.path.split(os.path.realp...

def get_chinese_text(): """Download the chinese_text dataset and unzip it""" if not os.path.isdir("data/"): os.system("mkdir data/") if (not os.path.exists('data/pos.txt')) or \ (not os.path.exists('data/neg')): os.system("wget -q https://raw.githubusercontent.com/dmlc/web-data/master...

def load_data_and_labels(): """Loads MR polarity data from files, splits the data into words and generates labels. Returns split sentences and labels. """ # download dataset get_chinese_text() # Load data from files positive_examples = list(codecs.open("./data/pos.txt", "r", "utf-8").readli...

def reset(self): """ override reset behavior """ if getattr(self, 'num', None) is None: self.num_inst = 0 self.sum_metric = 0.0 else: self.num_inst = [0] * self.num self.sum_metric = [0.0] * self.num

def reset_local(self): """ override reset behavior """ if getattr(self, 'num', None) is None: self.num_inst = 0 self.sum_metric = 0.0 else: self.num_inst = [0] * self.num self.sum_metric = [0.0] * self.num

def update(self, labels, preds): """ Implementation of updating metrics """ # get generated multi label from network cls_prob = preds[0].asnumpy() loc_loss = preds[1].asnumpy() cls_label = preds[2].asnumpy() valid_count = np.sum(cls_label >= 0) # o...

def get(self): """Get the current evaluation result. Override the default behavior Returns ------- name : str Name of the metric. value : float Value of the evaluation. """ if self.num is None: if self.num_inst == 0: ...

def dqn_sym_nips(action_num, data=None, name='dqn'): """Structure of the Deep Q Network in the NIPS 2013 workshop paper: Playing Atari with Deep Reinforcement Learning (https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) Parameters ---------- action_num : int data : mxnet.sym.Symbol, optional n...

def _monitor_callback_wrapper(callback): """A wrapper for the user-defined handle.""" def callback_handle(name, array, _): """ ctypes function """ callback(name, array) return callback_handle

def _get_dict(names, ndarrays): """Get the dictionary given name and ndarray pairs.""" nset = set() for nm in names: if nm in nset: raise ValueError('Duplicate names detected, %s' % str(names)) nset.add(nm) return dict(zip(names, ndarrays))

def _get_outputs(self): """List all the output NDArray. Returns ------- A list of ndarray bound to the heads of executor. """ out_size = mx_uint() handles = ctypes.POINTER(NDArrayHandle)() check_call(_LIB.MXExecutorOutputs(self.handle, ...

def forward(self, is_train=False, **kwargs): """Calculate the outputs specified by the bound symbol. Parameters ---------- is_train: bool, optional Whether this forward is for evaluation purpose. If True, a backward call is expected to follow. **kwargs ...

def backward(self, out_grads=None, is_train=True): """Do backward pass to get the gradient of arguments. Parameters ---------- out_grads : NDArray or list of NDArray or dict of str to NDArray, optional Gradient on the outputs to be propagated back. This parameter...

def set_monitor_callback(self, callback, monitor_all=False): """Install callback for monitor. Parameters ---------- callback : function Takes a string and an NDArrayHandle. monitor_all : bool, default False If true, monitor both input and output, otherwis...

def arg_dict(self): """Get dictionary representation of argument arrrays. Returns ------- arg_dict : dict of str to NDArray The dictionary that maps the names of arguments to NDArrays. Raises ------ ValueError : if there are duplicated names in the a...

def grad_dict(self): """Get dictionary representation of gradient arrays. Returns ------- grad_dict : dict of str to NDArray The dictionary that maps name of arguments to gradient arrays. """ if self._grad_dict is None: self._grad_dict = Executor....

def aux_dict(self): """Get dictionary representation of auxiliary states arrays. Returns ------- aux_dict : dict of str to NDArray The dictionary that maps name of auxiliary states to NDArrays. Raises ------ ValueError : if there are duplicated names...

def output_dict(self): """Get dictionary representation of output arrays. Returns ------- output_dict : dict of str to NDArray The dictionary that maps name of output names to NDArrays. Raises ------ ValueError : if there are duplicated names in the ...

def copy_params_from(self, arg_params, aux_params=None, allow_extra_params=False): """Copy parameters from arg_params, aux_params into executor's internal array. Parameters ---------- arg_params : dict of str to NDArray Parameters, dict of name to NDArray of arguments. ...

def reshape(self, partial_shaping=False, allow_up_sizing=False, **kwargs): """Return a new executor with the same symbol and shared memory, but different input/output shapes. For runtime reshaping, variable length sequences, etc. The returned executor shares state with the current one, ...

def debug_str(self): """Get a debug string about internal execution plan. Returns ------- debug_str : string Debug string of the executor. Examples -------- >>> a = mx.sym.Variable('a') >>> b = mx.sym.sin(a) >>> c = 2 * a + b ...

def parse_voc_rec(filename): """ parse pascal voc record into a dictionary :param filename: xml file path :return: list of dict """ import xml.etree.ElementTree as ET tree = ET.parse(filename) objects = [] for obj in tree.findall('object'): obj_dict = dict() obj_dict[...

def voc_eval(detpath, annopath, imageset_file, classname, cache_dir, ovthresh=0.5, use_07_metric=False): """ pascal voc evaluation :param detpath: detection results detpath.format(classname) :param annopath: annotations annopath.format(classname) :param imageset_file: text file containing list of im...

def register(op_name): """Register operators""" def wrapper(func): """Helper function to map functions""" try: import onnx as _ MXNetGraph.registry_[op_name] = func except ImportError: pass return func ...

def convert_layer(node, **kwargs): """Convert MXNet layer to ONNX""" op = str(node["op"]) if op not in MXNetGraph.registry_: raise AttributeError("No conversion function registered for op type %s yet." % op) convert_func = MXNetGraph.registry_[op] return convert_func(...

def split_params(sym, params): """Helper function to split params dictionary into args and aux params Parameters ---------- sym : :class:`~mxnet.symbol.Symbol` MXNet symbol object params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray` Dict of convert...