BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def read_caffe_mean(caffe_mean_file): """ Reads caffe formatted mean file :param caffe_mean_file: path to caffe mean file, presumably with 'binaryproto' suffix :return: mean image, converted from BGR to RGB format """ import caffe_parser import numpy as np mean_blob = caffe_parser.caffe...
def get_distance(F, x): """Helper function for margin-based loss. Return a distance matrix given a matrix.""" n = x.shape[0] square = F.sum(x ** 2.0, axis=1, keepdims=True) distance_square = square + square.transpose() - (2.0 * F.dot(x, x.transpose())) # Adding identity to make sqrt work. retu...
def cross_entropy_loss(inputs, labels, rescale_loss=1): """ cross entropy loss with a mask """ criterion = mx.gluon.loss.SoftmaxCrossEntropyLoss(weight=rescale_loss) loss = criterion(inputs, labels) mask = S.var('mask') loss = loss * S.reshape(mask, shape=(-1,)) return S.make_loss(loss.mean())
def rnn(bptt, vocab_size, num_embed, nhid, num_layers, dropout, num_proj, batch_size): """ word embedding + LSTM Projected """ state_names = [] data = S.var('data') weight = S.var("encoder_weight", stype='row_sparse') embed = S.sparse.Embedding(data=data, weight=weight, input_dim=vocab_size, ...
def sampled_softmax(num_classes, num_samples, in_dim, inputs, weight, bias, sampled_values, remove_accidental_hits=True): """ Sampled softmax via importance sampling. This under-estimates the full softmax and is only used for training. """ # inputs = (n, in_dim) ...
def generate_samples(label, num_splits, sampler): """ Split labels into `num_splits` and generate candidates based on log-uniform distribution. """ def listify(x): return x if isinstance(x, list) else [x] label_splits = listify(label.split(num_splits, axis=0)) prob_samples = [] p...
def build_iters(data_dir, max_records, q, horizon, splits, batch_size): """ Load & generate training examples from multivariate time series data :return: data iters & variables required to define network architecture """ # Read in data as numpy array df = pd.read_csv(os.path.join(data_dir, "elec...
def get_model(name, **kwargs): """Returns a pre-defined model by name Parameters ---------- name : str Name of the model. pretrained : bool Whether to load the pretrained weights for model. classes : int Number of classes for the output layer. ctx : Context, default ...
def _new_alloc_handle(stype, shape, ctx, delay_alloc, dtype, aux_types, aux_shapes=None): """Return a new handle with specified storage type, shape, dtype and context. Empty handle is only used to hold results Returns ------- handle A new empty ndarray handle """ hdl = NDArrayHandl...
def _prepare_src_array(source_array, dtype): """Prepare `source_array` so that it can be used to construct NDArray. `source_array` is converted to a `np.ndarray` if it's neither an `NDArray` \ nor an `np.ndarray`. """ if not isinstance(source_array, NDArray) and not isinstance(source_array, np.ndarr...
def _prepare_default_dtype(src_array, dtype): """Prepare the value of dtype if `dtype` is None. If `src_array` is an NDArray, numpy.ndarray or scipy.sparse.csr.csr_matrix, return src_array.dtype. float32 is returned otherwise.""" if dtype is None: if isinstance(src_array, (NDArray, np.ndarray)): ...
def _check_shape(s1, s2): """check s1 == s2 if both are not None""" if s1 and s2 and s1 != s2: raise ValueError("Shape mismatch detected. " + str(s1) + " v.s. " + str(s2))
def csr_matrix(arg1, shape=None, ctx=None, dtype=None): """Creates a `CSRNDArray`, an 2D array with compressed sparse row (CSR) format. The CSRNDArray can be instantiated in several ways: - csr_matrix(D): to construct a CSRNDArray with a dense 2D array ``D`` - D (array_like) - A...
def _csr_matrix_from_definition(data, indices, indptr, shape=None, ctx=None, dtype=None, indices_type=None, indptr_type=None): """Create a `CSRNDArray` based on data, indices and indptr""" # pylint: disable= no-member, protected-access storage_type = 'csr' # context c...
def row_sparse_array(arg1, shape=None, ctx=None, dtype=None): """Creates a `RowSparseNDArray`, a multidimensional row sparse array with a set of \ tensor slices at given indices. The RowSparseNDArray can be instantiated in several ways: - row_sparse_array(D): to construct a RowSparseNDArray wi...
def _row_sparse_ndarray_from_definition(data, indices, shape=None, ctx=None, dtype=None, indices_type=None): """Create a `RowSparseNDArray` based on data and indices""" storage_type = 'row_sparse' # context ctx = current_context() if ctx is None else ctx # typ...
def add(lhs, rhs): """Returns element-wise sum of the input arrays with broadcasting. Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and ``mx.nd.broadcast_plus(lhs, rhs)`` when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape, this is equivalent to ``mx.nd.elemwise_add(...
def subtract(lhs, rhs): """Returns element-wise difference of the input arrays with broadcasting. Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and ``mx.nd.broadcast_minus(lhs, rhs)`` when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape, this is equivalent to ``mx.nd....
def multiply(lhs, rhs): """Returns element-wise product of the input arrays with broadcasting. Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)`` when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape, this is equivalent to ``mx.nd.elemwise_mul(lhs, rhs)`` .....
def divide(lhs, rhs): """Returns element-wise division of the input arrays with broadcasting. Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)`` when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape, this is equivalent to ``mx.nd.elemwise_div(lhs, rhs)`` .. note:: ...
def zeros(stype, shape, ctx=None, dtype=None, **kwargs): """Return a new array of given shape and type, filled with zeros. Parameters ---------- stype: string The storage type of the empty array, such as 'row_sparse', 'csr', etc shape : int or tuple of int The shape of the empty arr...
def empty(stype, shape, ctx=None, dtype=None): """Returns a new array of given shape and type, without initializing entries. Parameters ---------- stype: string The storage type of the empty array, such as 'row_sparse', 'csr', etc shape : int or tuple of int The shape of the empty a...
def array(source_array, ctx=None, dtype=None): """Creates a sparse array from any object exposing the array interface. Parameters ---------- source_array : RowSparseNDArray, CSRNDArray or scipy.sparse.csr.csr_matrix The source sparse array ctx : Context, optional The default context...
def _aux_type(self, i): """Data-type of the array's ith aux data. Returns ------- numpy.dtype This BaseSparseNDArray's aux data type. """ aux_type = ctypes.c_int() check_call(_LIB.MXNDArrayGetAuxType(self.handle, i, ctypes.byref(aux_type))) re...
def _aux_types(self): """The data types of the aux data for the BaseSparseNDArray. """ aux_types = [] num_aux = self._num_aux for i in range(num_aux): aux_types.append(self._aux_type(i)) return aux_types
def astype(self, dtype, copy=True): """Return a copy of the array after casting to a specified type. Parameters ---------- dtype : numpy.dtype or str The type of the returned array. copy : bool Default `True`. By default, astype always returns a newly ...
def check_format(self, full_check=True): """Check whether the NDArray format is valid. Parameters ---------- full_check : bool, optional If `True`, rigorous check, O(N) operations. Otherwise basic check, O(1) operations (default True). """ check_c...
def _data(self): """A deep copy NDArray of the data array associated with the BaseSparseNDArray. This function blocks. Do not use it in performance critical code. """ self.wait_to_read() hdl = NDArrayHandle() check_call(_LIB.MXNDArrayGetDataNDArray(self.handle, ctypes.by...
def _aux_data(self, i): """ Get a deep copy NDArray of the i-th aux data array associated with the BaseSparseNDArray. This function blocks. Do not use it in performance critical code. """ self.wait_to_read() hdl = NDArrayHandle() check_call(_LIB.MXNDArrayGetAuxND...
def asscipy(self): """Returns a ``scipy.sparse.csr.csr_matrix`` object with value copied from this array Examples -------- >>> x = mx.nd.sparse.zeros('csr', (2,3)) >>> y = x.asscipy() >>> type(y) <type 'scipy.sparse.csr.csr_matrix'> >>> y <2x3 spa...
def tostype(self, stype): """Return a copy of the array with chosen storage type. Returns ------- NDArray or RowSparseNDArray A copy of the array with the chosen storage stype """ # pylint: disable= no-member, protected-access if stype == 'csr': ...
def copyto(self, other): """Copies the value of this array to another array. If ``other`` is a ``NDArray`` or ``RowSparseNDArray`` object, then ``other.shape`` and ``self.shape`` should be the same. This function copies the value from ``self`` to ``other``. If ``other`` is a co...
def export_model(sym, params, input_shape, input_type=np.float32, onnx_file_path='model.onnx', verbose=False): """Exports the MXNet model file, passed as a parameter, into ONNX model. Accepts both symbol,parameter objects as well as json and params filepaths as input. Operator support and c...
def bench_dot(lhs_row_dim, lhs_col_dim, rhs_col_dim, density, rhs_density, dot_func, trans_lhs, lhs_stype, rhs_stype, only_storage, distribution="uniform"): """ Benchmarking both storage and dot """ lhs_nd = rand_ndarray((lhs_row_dim, lhs_col_dim), lhs_stype, density, distributio...
def convert_mean(binaryproto_fname, output=None): """Convert caffe mean Parameters ---------- binaryproto_fname : str Filename of the mean output : str, optional Save the mean into mxnet's format Returns ------- NDArray Mean in ndarray """ mean_blob = ca...
def get_densenet(num_layers, pretrained=False, ctx=cpu(), root=os.path.join(base.data_dir(), 'models'), **kwargs): r"""Densenet-BC model from the `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ paper. Parameters ---------- num_layers : int ...
def load_module(sym_filepath, params_filepath): """Loads the MXNet model file and returns MXNet symbol and params (weights). Parameters ---------- json_path : str Path to the json file params_path : str Path to the params file Returns ------- sym : MXNet symbol ...
def import_module(module_name): """Helper function to import module""" import sys, os import importlib sys.path.append(os.path.dirname(__file__)) return importlib.import_module(module_name)
def get_symbol_train(network, num_classes, from_layers, num_filters, strides, pads, sizes, ratios, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network symbol for training SSD Parameters ------...
def get_symbol(network, num_classes, from_layers, num_filters, sizes, ratios, strides, pads, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network for testing SSD Parameters ---------- network : str ...
def _get_grad(net, image, class_id=None, conv_layer_name=None, image_grad=False): """This is an internal helper function that can be used for either of these but not both at the same time: 1. Record the output and gradient of output of an intermediate convolutional layer. 2. Record the gradients of the ...
def get_conv_out_grad(net, image, class_id=None, conv_layer_name=None): """Get the output and gradients of output of a convolutional layer. Parameters: ---------- net: Block Network to use for visualization. image: NDArray Preprocessed image to use for visualization. class_id: i...
def get_image_grad(net, image, class_id=None): """Get the gradients of the image. Parameters: ---------- net: Block Network to use for visualization. image: NDArray Preprocessed image to use for visualization. class_id: int Category ID this image belongs to. If not provi...
def grad_to_image(gradient): """Convert gradients of image obtained using `get_image_grad` into image. This shows parts of the image that is most strongly activating the output neurons.""" gradient = gradient - gradient.min() gradient /= gradient.max() gradient = np.uint8(gradient * 255).transpo...
def get_cam(imggrad, conv_out): """Compute CAM. Refer section 3 of https://arxiv.org/abs/1610.02391 for details""" weights = np.mean(imggrad, axis=(1, 2)) cam = np.ones(conv_out.shape[1:], dtype=np.float32) for i, w in enumerate(weights): cam += w * conv_out[i, :, :] cam = cv2.resize(cam, (i...
def get_img_heatmap(orig_img, activation_map): """Draw a heatmap on top of the original image using intensities from activation_map""" heatmap = cv2.applyColorMap(activation_map, cv2.COLORMAP_COOL) heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB) img_heatmap = np.float32(heatmap) + np.float32(orig_img...
def to_grayscale(cv2im): """Convert gradients to grayscale. This gives a saliency map.""" # How strongly does each position activate the output grayscale_im = np.sum(np.abs(cv2im), axis=0) # Normalize between min and 99th percentile im_max = np.percentile(grayscale_im, 99) im_min = np.min(grays...
def check_label_shapes(labels, preds, wrap=False, shape=False): """Helper function for checking shape of label and prediction Parameters ---------- labels : list of `NDArray` The labels of the data. preds : list of `NDArray` Predicted values. wrap : boolean If True, wr...
def create(metric, args, *kwargs): """Creates evaluation metric from metric names or instances of EvalMetric or a custom metric function. Parameters ---------- metric : str or callable Specifies the metric to create. This argument must be one of the below: - Name of a met...
def np(numpy_feval, name=None, allow_extra_outputs=False): """Creates a custom evaluation metric that receives its inputs as numpy arrays. Parameters ---------- numpy_feval : callable(label, pred) Custom evaluation function that receives labels and predictions for a minibatch as numpy a...
def get_config(self): """Save configurations of metric. Can be recreated from configs with metric.create(``**config``) """ config = self._kwargs.copy() config.update({ 'metric': self.__class__.__name__, 'name': self.name, 'output_names': self.o...
def update_dict(self, label, pred): """Update the internal evaluation with named label and pred Parameters ---------- labels : OrderedDict of str -> NDArray name to array mapping for labels. preds : OrderedDict of str -> NDArray name to array mapping of ...
def reset(self): """Resets the internal evaluation result to initial state.""" self.num_inst = 0 self.sum_metric = 0.0 self.global_num_inst = 0 self.global_sum_metric = 0.0
def get(self): """Gets the current evaluation result. Returns ------- names : list of str Name of the metrics. values : list of float Value of the evaluations. """ if self.num_inst == 0: return (self.name, float('nan')) e...
def get_global(self): """Gets the current global evaluation result. Returns ------- names : list of str Name of the metrics. values : list of float Value of the evaluations. """ if self._has_global_stats: if self.global_num_inst ...
def get_name_value(self): """Returns zipped name and value pairs. Returns ------- list of tuples A (name, value) tuple list. """ name, value = self.get() if not isinstance(name, list): name = [name] if not isinstance(value, list): ...
def get_global_name_value(self): """Returns zipped name and value pairs for global results. Returns ------- list of tuples A (name, value) tuple list. """ if self._has_global_stats: name, value = self.get_global() if not isinstance(nam...
def update_binary_stats(self, label, pred): """ Update various binary classification counts for a single (label, pred) pair. Parameters ---------- label : `NDArray` The labels of the data. pred : `NDArray` Predicted values. """ ...
def matthewscc(self, use_global=False): """ Calculate the Matthew's Correlation Coefficent """ if use_global: if not self.global_total_examples: return 0. true_pos = float(self.global_true_positives) false_pos = float(self.global_false...
def transform(self, fn, lazy=True): """Returns a new dataset with each sample transformed by the transformer function `fn`. Parameters ---------- fn : callable A transformer function that takes a sample as input and returns the transformed sample. ...
def transform_first(self, fn, lazy=True): """Returns a new dataset with the first element of each sample transformed by the transformer function `fn`. This is useful, for example, when you only want to transform data while keeping label as is. Parameters ---------- ...
def forward_ocr(self, img_): """Forward the image through the LSTM network model Parameters ---------- img_: int of array Returns ---------- label_list: string of list """ img_ = cv2.resize(img_, (80, 30)) img_ = img_.transpose(1, 0) ...
def read_prototxt(fname): """Return a caffe_pb2.NetParameter object that defined in a prototxt file """ proto = caffe_pb2.NetParameter() with open(fname, 'r') as f: text_format.Merge(str(f.read()), proto) return proto
def get_layers(proto): """Returns layers in a caffe_pb2.NetParameter object """ if len(proto.layer): return proto.layer elif len(proto.layers): return proto.layers else: raise ValueError('Invalid proto file.')
def read_caffemodel(prototxt_fname, caffemodel_fname): """Return a caffe_pb2.NetParameter object that defined in a binary caffemodel file """ if use_caffe: caffe.set_mode_cpu() net = caffe.Net(prototxt_fname, caffemodel_fname, caffe.TEST) layer_names = net._layer_names la...
def layer_iter(layers, layer_names): """Iterate over all layers""" if use_caffe: for layer_idx, layer in enumerate(layers): layer_name = re.sub('[-/]', '_', layer_names[layer_idx]) layer_type = layer.type layer_blobs = layer.blobs yield (layer_name, layer_...
def set_config(**kwargs): """Set up the configure of profiler (only accepts keyword arguments). Parameters ---------- filename : string, output file for profile data profile_all : boolean, all profile types enabled profile_symbolic : boolean, whether to profile symbolic ...
def profiler_set_config(mode='symbolic', filename='profile.json'): """Set up the configure of profiler (Deprecated). Parameters ---------- mode : string, optional Indicates whether to enable the profiler, can be 'symbolic', or 'all'. Defaults to `symbolic`. filename : string, option...
def set_state(state='stop', profile_process='worker'): """Set up the profiler state to 'run' or 'stop'. Parameters ---------- state : string, optional Indicates whether to run the profiler, can be 'stop' or 'run'. Default is `stop`. profile_process : string whether to profil...
def dump(finished=True, profile_process='worker'): """Dump profile and stop profiler. Use this to save profile in advance in case your program cannot exit normally. Parameters ---------- finished : boolean Indicates whether to stop statistic output (dumping) after this dump. Default...
def dumps(reset=False): """Return a printable string of aggregate profile stats. Parameters ---------- reset: boolean Indicates whether to clean aggeregate statistical data collected up to this point """ debug_str = ctypes.c_char_p() do_reset = 1 if reset is True else 0 check_ca...
def pause(profile_process='worker'): """Pause profiling. Parameters ---------- profile_process : string whether to profile kvstore `server` or `worker`. server can only be profiled when kvstore is of type dist. if this is not passed, defaults to `worker` """ profile_proc...
def resume(profile_process='worker'): """ Resume paused profiling. Parameters ---------- profile_process : string whether to profile kvstore `server` or `worker`. server can only be profiled when kvstore is of type dist. if this is not passed, defaults to `worker` """ ...
def set_value(self, value): """Set counter value. Parameters ---------- value : int Value for the counter """ check_call(_LIB.MXProfileSetCounter(self.handle, int(value)))
def increment(self, delta=1): """Increment counter value. Parameters ---------- value_change : int Amount by which to add to the counter """ check_call(_LIB.MXProfileAdjustCounter(self.handle, int(delta)))
def decrement(self, delta=1): """Decrement counter value. Parameters ---------- value_change : int Amount by which to subtract from the counter """ check_call(_LIB.MXProfileAdjustCounter(self.handle, -int(delta)))
def mark(self, scope='process'): """Set up the profiler state to record operator. Parameters ---------- scope : string, optional Indicates what scope the marker should refer to. Can be 'global', 'process', thread', task', and 'marker' Default is `proc...
def get_kernel(self, name, signature): r"""Get CUDA kernel from compiled module. Parameters ---------- name : str String name of the kernel. signature : str Function signature for the kernel. For example, if a kernel is declared as:: ...
def launch(self, args, ctx, grid_dims, block_dims, shared_mem=0): """Launch cuda kernel. Parameters ---------- args : tuple of NDArray or numbers List of arguments for kernel. NDArrays are expected for pointer types (e.g. `float`, `double`) while numbers are ex...
def reset(self): """Clear the internal statistics to initial state.""" 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 self.records = dic...
def update(self, labels, preds): """ Update internal records. This function now only update internal buffer, sum_metric and num_inst are updated in _update() function instead when get() is called to return results. Params: ---------- labels: mx.nd.array (n * 6) o...
def _update(self): """ update num_inst and sum_metric """ aps = [] for k, v in self.records.items(): recall, prec = self._recall_prec(v, self.counts[k]) ap = self._average_precision(recall, prec) aps.append(ap) if self.num is not None and k < (self...
def _recall_prec(self, record, count): """ get recall and precision from internal records """ record = np.delete(record, np.where(record[:, 1].astype(int) == 0)[0], axis=0) sorted_records = record[record[:,0].argsort()[::-1]] tp = np.cumsum(sorted_records[:, 1].astype(int) == 1) ...
def _average_precision(self, rec, prec): """ calculate average precision Params: ---------- rec : numpy.array cumulated recall prec : numpy.array cumulated precision Returns: ---------- ap as float """ # app...
def _insert(self, key, records, count): """ Insert records according to key """ if key not in self.records: assert key not in self.counts self.records[key] = records self.counts[key] = count else: self.records[key] = np.vstack((self.records[key], r...
def _average_precision(self, rec, prec): """ calculate average precision, override the default one, special 11-point metric Params: ---------- rec : numpy.array cumulated recall prec : numpy.array cumulated precision Returns: ...
def get_fine_tune_model(symbol, arg_params, num_classes, layer_name, dtype='float32'): """ symbol: the pre-trained network symbol arg_params: the argument parameters of the pre-trained model num_classes: the number of classes for the fine-tune datasets layer_name: the layer name before the last full...
def _list_images(self, root): """ Description : generate list for lip images """ self.labels = [] self.items = [] valid_unseen_sub_idx = [1, 2, 20, 22] skip_sub_idx = [21] if self._mode == 'train': sub_idx = ['s' + str(i) for i in range(1, 35...
def align_generation(self, file_nm, padding=75): """ Description : Align to lip position """ align = Align(self._align_root + '/' + file_nm + '.align') return nd.array(align.sentence(padding))
def set_verbosity(self, verbose=False, print_func=None): """Switch on/off verbose mode Parameters ---------- verbose : bool switch on/off verbose mode print_func : function A function that computes statistics of initialized arrays. Takes an `N...
def _verbose_print(self, desc, init, arr): """Internal verbose print function Parameters ---------- desc : InitDesc or str name of the array init : str initializer pattern arr : NDArray initialized array """ if self._ve...
def _legacy_init(self, name, arr): """Legacy initialization method. Parameters ---------- name : str Name of corresponding NDArray. arr : NDArray NDArray to be initialized. """ warnings.warn( "\033[91mCalling initializer with ...
def save_imglist(self, fname=None, root=None, shuffle=False): """ save imglist to disk Parameters: ---------- fname : str saved filename """ def progress_bar(count, total, suffix=''): import sys bar_len = 24 filled_...
def _load_class_names(self, filename, dirname): """ load class names from text file Parameters: ---------- filename: str file stores class names dirname: str file directory """ full_path = osp.join(dirname, filename) classe...
def read_data(label, image): """ download and read data into numpy """ base_url = 'http://yann.lecun.com/exdb/mnist/' with gzip.open(download_file(base_url+label, os.path.join('data',label))) as flbl: magic, num = struct.unpack(">II", flbl.read(8)) label = np.fromstring(flbl.read(), ...
def get_mnist_iter(args, kv): """ create data iterator with NDArrayIter """ (train_lbl, train_img) = read_data( 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz') (val_lbl, val_img) = read_data( 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz') train = mx...
def make_file_extension_assertion(extension): """Function factory for file extension argparse assertion Args: extension (string): the file extension to assert Returns: string: the supplied extension, if assertion is successful. """ def file_extension_assertion(file_...
def get_palette(num_colors=256): """generates the colormap for visualizing the segmentation mask Args: num_colors (int): the number of colors to generate in the output palette Returns: string: the supplied extension, if assertion is successful. """ p...
def get_data(img_path): """get the (1, 3, h, w) np.array data for the supplied image Args: img_path (string): the input image path Returns: np.array: image data in a (1, 3, h, w) shape """ mean = np.array([123.68, 116.779, 103.939]) ...
def main(): """Module main execution""" # Initialization variables - update to change your model and execution context model_prefix = "FCN8s_VGG16" epoch = 19 # By default, MXNet will run on the CPU. Change to ctx = mx.gpu() to run on GPU. ctx = mx.cpu() fcnxs, fcnxs_args, fcnxs_auxs = mx....

def read_caffe_mean(caffe_mean_file): """ Reads caffe formatted mean file :param caffe_mean_file: path to caffe mean file, presumably with 'binaryproto' suffix :return: mean image, converted from BGR to RGB format """ import caffe_parser import numpy as np mean_blob = caffe_parser.caffe...

def get_distance(F, x): """Helper function for margin-based loss. Return a distance matrix given a matrix.""" n = x.shape[0] square = F.sum(x ** 2.0, axis=1, keepdims=True) distance_square = square + square.transpose() - (2.0 * F.dot(x, x.transpose())) # Adding identity to make sqrt work. retu...

def cross_entropy_loss(inputs, labels, rescale_loss=1): """ cross entropy loss with a mask """ criterion = mx.gluon.loss.SoftmaxCrossEntropyLoss(weight=rescale_loss) loss = criterion(inputs, labels) mask = S.var('mask') loss = loss * S.reshape(mask, shape=(-1,)) return S.make_loss(loss.mean())

def rnn(bptt, vocab_size, num_embed, nhid, num_layers, dropout, num_proj, batch_size): """ word embedding + LSTM Projected """ state_names = [] data = S.var('data') weight = S.var("encoder_weight", stype='row_sparse') embed = S.sparse.Embedding(data=data, weight=weight, input_dim=vocab_size, ...

def sampled_softmax(num_classes, num_samples, in_dim, inputs, weight, bias, sampled_values, remove_accidental_hits=True): """ Sampled softmax via importance sampling. This under-estimates the full softmax and is only used for training. """ # inputs = (n, in_dim) ...

def generate_samples(label, num_splits, sampler): """ Split labels into `num_splits` and generate candidates based on log-uniform distribution. """ def listify(x): return x if isinstance(x, list) else [x] label_splits = listify(label.split(num_splits, axis=0)) prob_samples = [] p...

def build_iters(data_dir, max_records, q, horizon, splits, batch_size): """ Load & generate training examples from multivariate time series data :return: data iters & variables required to define network architecture """ # Read in data as numpy array df = pd.read_csv(os.path.join(data_dir, "elec...

def get_model(name, **kwargs): """Returns a pre-defined model by name Parameters ---------- name : str Name of the model. pretrained : bool Whether to load the pretrained weights for model. classes : int Number of classes for the output layer. ctx : Context, default ...

def _new_alloc_handle(stype, shape, ctx, delay_alloc, dtype, aux_types, aux_shapes=None): """Return a new handle with specified storage type, shape, dtype and context. Empty handle is only used to hold results Returns ------- handle A new empty ndarray handle """ hdl = NDArrayHandl...

def _prepare_src_array(source_array, dtype): """Prepare `source_array` so that it can be used to construct NDArray. `source_array` is converted to a `np.ndarray` if it's neither an `NDArray` \ nor an `np.ndarray`. """ if not isinstance(source_array, NDArray) and not isinstance(source_array, np.ndarr...

def _prepare_default_dtype(src_array, dtype): """Prepare the value of dtype if `dtype` is None. If `src_array` is an NDArray, numpy.ndarray or scipy.sparse.csr.csr_matrix, return src_array.dtype. float32 is returned otherwise.""" if dtype is None: if isinstance(src_array, (NDArray, np.ndarray)): ...

def _check_shape(s1, s2): """check s1 == s2 if both are not None""" if s1 and s2 and s1 != s2: raise ValueError("Shape mismatch detected. " + str(s1) + " v.s. " + str(s2))

def csr_matrix(arg1, shape=None, ctx=None, dtype=None): """Creates a `CSRNDArray`, an 2D array with compressed sparse row (CSR) format. The CSRNDArray can be instantiated in several ways: - csr_matrix(D): to construct a CSRNDArray with a dense 2D array ``D`` - **D** (*array_like*) - A...

def _csr_matrix_from_definition(data, indices, indptr, shape=None, ctx=None, dtype=None, indices_type=None, indptr_type=None): """Create a `CSRNDArray` based on data, indices and indptr""" # pylint: disable= no-member, protected-access storage_type = 'csr' # context c...

def row_sparse_array(arg1, shape=None, ctx=None, dtype=None): """Creates a `RowSparseNDArray`, a multidimensional row sparse array with a set of \ tensor slices at given indices. The RowSparseNDArray can be instantiated in several ways: - row_sparse_array(D): to construct a RowSparseNDArray wi...

def _row_sparse_ndarray_from_definition(data, indices, shape=None, ctx=None, dtype=None, indices_type=None): """Create a `RowSparseNDArray` based on data and indices""" storage_type = 'row_sparse' # context ctx = current_context() if ctx is None else ctx # typ...

def add(lhs, rhs): """Returns element-wise sum of the input arrays with broadcasting. Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and ``mx.nd.broadcast_plus(lhs, rhs)`` when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape, this is equivalent to ``mx.nd.elemwise_add(...

def subtract(lhs, rhs): """Returns element-wise difference of the input arrays with broadcasting. Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and ``mx.nd.broadcast_minus(lhs, rhs)`` when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape, this is equivalent to ``mx.nd....

def multiply(lhs, rhs): """Returns element-wise product of the input arrays with broadcasting. Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)`` when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape, this is equivalent to ``mx.nd.elemwise_mul(lhs, rhs)`` .....

def divide(lhs, rhs): """Returns element-wise division of the input arrays with broadcasting. Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)`` when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape, this is equivalent to ``mx.nd.elemwise_div(lhs, rhs)`` .. note:: ...

def zeros(stype, shape, ctx=None, dtype=None, **kwargs): """Return a new array of given shape and type, filled with zeros. Parameters ---------- stype: string The storage type of the empty array, such as 'row_sparse', 'csr', etc shape : int or tuple of int The shape of the empty arr...

def empty(stype, shape, ctx=None, dtype=None): """Returns a new array of given shape and type, without initializing entries. Parameters ---------- stype: string The storage type of the empty array, such as 'row_sparse', 'csr', etc shape : int or tuple of int The shape of the empty a...

def array(source_array, ctx=None, dtype=None): """Creates a sparse array from any object exposing the array interface. Parameters ---------- source_array : RowSparseNDArray, CSRNDArray or scipy.sparse.csr.csr_matrix The source sparse array ctx : Context, optional The default context...

def _aux_type(self, i): """Data-type of the array's ith aux data. Returns ------- numpy.dtype This BaseSparseNDArray's aux data type. """ aux_type = ctypes.c_int() check_call(_LIB.MXNDArrayGetAuxType(self.handle, i, ctypes.byref(aux_type))) re...

def _aux_types(self): """The data types of the aux data for the BaseSparseNDArray. """ aux_types = [] num_aux = self._num_aux for i in range(num_aux): aux_types.append(self._aux_type(i)) return aux_types

def astype(self, dtype, copy=True): """Return a copy of the array after casting to a specified type. Parameters ---------- dtype : numpy.dtype or str The type of the returned array. copy : bool Default `True`. By default, astype always returns a newly ...

def check_format(self, full_check=True): """Check whether the NDArray format is valid. Parameters ---------- full_check : bool, optional If `True`, rigorous check, O(N) operations. Otherwise basic check, O(1) operations (default True). """ check_c...

def _data(self): """A deep copy NDArray of the data array associated with the BaseSparseNDArray. This function blocks. Do not use it in performance critical code. """ self.wait_to_read() hdl = NDArrayHandle() check_call(_LIB.MXNDArrayGetDataNDArray(self.handle, ctypes.by...

def _aux_data(self, i): """ Get a deep copy NDArray of the i-th aux data array associated with the BaseSparseNDArray. This function blocks. Do not use it in performance critical code. """ self.wait_to_read() hdl = NDArrayHandle() check_call(_LIB.MXNDArrayGetAuxND...

def asscipy(self): """Returns a ``scipy.sparse.csr.csr_matrix`` object with value copied from this array Examples -------- >>> x = mx.nd.sparse.zeros('csr', (2,3)) >>> y = x.asscipy() >>> type(y) <type 'scipy.sparse.csr.csr_matrix'> >>> y <2x3 spa...

def tostype(self, stype): """Return a copy of the array with chosen storage type. Returns ------- NDArray or RowSparseNDArray A copy of the array with the chosen storage stype """ # pylint: disable= no-member, protected-access if stype == 'csr': ...

def copyto(self, other): """Copies the value of this array to another array. If ``other`` is a ``NDArray`` or ``RowSparseNDArray`` object, then ``other.shape`` and ``self.shape`` should be the same. This function copies the value from ``self`` to ``other``. If ``other`` is a co...

def export_model(sym, params, input_shape, input_type=np.float32, onnx_file_path='model.onnx', verbose=False): """Exports the MXNet model file, passed as a parameter, into ONNX model. Accepts both symbol,parameter objects as well as json and params filepaths as input. Operator support and c...

def bench_dot(lhs_row_dim, lhs_col_dim, rhs_col_dim, density, rhs_density, dot_func, trans_lhs, lhs_stype, rhs_stype, only_storage, distribution="uniform"): """ Benchmarking both storage and dot """ lhs_nd = rand_ndarray((lhs_row_dim, lhs_col_dim), lhs_stype, density, distributio...

def convert_mean(binaryproto_fname, output=None): """Convert caffe mean Parameters ---------- binaryproto_fname : str Filename of the mean output : str, optional Save the mean into mxnet's format Returns ------- NDArray Mean in ndarray """ mean_blob = ca...

def get_densenet(num_layers, pretrained=False, ctx=cpu(), root=os.path.join(base.data_dir(), 'models'), **kwargs): r"""Densenet-BC model from the `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ paper. Parameters ---------- num_layers : int ...

def load_module(sym_filepath, params_filepath): """Loads the MXNet model file and returns MXNet symbol and params (weights). Parameters ---------- json_path : str Path to the json file params_path : str Path to the params file Returns ------- sym : MXNet symbol ...

def import_module(module_name): """Helper function to import module""" import sys, os import importlib sys.path.append(os.path.dirname(__file__)) return importlib.import_module(module_name)

def get_symbol_train(network, num_classes, from_layers, num_filters, strides, pads, sizes, ratios, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network symbol for training SSD Parameters ------...

def get_symbol(network, num_classes, from_layers, num_filters, sizes, ratios, strides, pads, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network for testing SSD Parameters ---------- network : str ...

def _get_grad(net, image, class_id=None, conv_layer_name=None, image_grad=False): """This is an internal helper function that can be used for either of these but not both at the same time: 1. Record the output and gradient of output of an intermediate convolutional layer. 2. Record the gradients of the ...

def get_conv_out_grad(net, image, class_id=None, conv_layer_name=None): """Get the output and gradients of output of a convolutional layer. Parameters: ---------- net: Block Network to use for visualization. image: NDArray Preprocessed image to use for visualization. class_id: i...

def get_image_grad(net, image, class_id=None): """Get the gradients of the image. Parameters: ---------- net: Block Network to use for visualization. image: NDArray Preprocessed image to use for visualization. class_id: int Category ID this image belongs to. If not provi...

def grad_to_image(gradient): """Convert gradients of image obtained using `get_image_grad` into image. This shows parts of the image that is most strongly activating the output neurons.""" gradient = gradient - gradient.min() gradient /= gradient.max() gradient = np.uint8(gradient * 255).transpo...

def get_cam(imggrad, conv_out): """Compute CAM. Refer section 3 of https://arxiv.org/abs/1610.02391 for details""" weights = np.mean(imggrad, axis=(1, 2)) cam = np.ones(conv_out.shape[1:], dtype=np.float32) for i, w in enumerate(weights): cam += w * conv_out[i, :, :] cam = cv2.resize(cam, (i...

def get_img_heatmap(orig_img, activation_map): """Draw a heatmap on top of the original image using intensities from activation_map""" heatmap = cv2.applyColorMap(activation_map, cv2.COLORMAP_COOL) heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB) img_heatmap = np.float32(heatmap) + np.float32(orig_img...

def to_grayscale(cv2im): """Convert gradients to grayscale. This gives a saliency map.""" # How strongly does each position activate the output grayscale_im = np.sum(np.abs(cv2im), axis=0) # Normalize between min and 99th percentile im_max = np.percentile(grayscale_im, 99) im_min = np.min(grays...

def check_label_shapes(labels, preds, wrap=False, shape=False): """Helper function for checking shape of label and prediction Parameters ---------- labels : list of `NDArray` The labels of the data. preds : list of `NDArray` Predicted values. wrap : boolean If True, wr...

def create(metric, *args, **kwargs): """Creates evaluation metric from metric names or instances of EvalMetric or a custom metric function. Parameters ---------- metric : str or callable Specifies the metric to create. This argument must be one of the below: - Name of a met...

def np(numpy_feval, name=None, allow_extra_outputs=False): """Creates a custom evaluation metric that receives its inputs as numpy arrays. Parameters ---------- numpy_feval : callable(label, pred) Custom evaluation function that receives labels and predictions for a minibatch as numpy a...

def get_config(self): """Save configurations of metric. Can be recreated from configs with metric.create(``**config``) """ config = self._kwargs.copy() config.update({ 'metric': self.__class__.__name__, 'name': self.name, 'output_names': self.o...

def update_dict(self, label, pred): """Update the internal evaluation with named label and pred Parameters ---------- labels : OrderedDict of str -> NDArray name to array mapping for labels. preds : OrderedDict of str -> NDArray name to array mapping of ...

def reset(self): """Resets the internal evaluation result to initial state.""" self.num_inst = 0 self.sum_metric = 0.0 self.global_num_inst = 0 self.global_sum_metric = 0.0

def get(self): """Gets the current evaluation result. Returns ------- names : list of str Name of the metrics. values : list of float Value of the evaluations. """ if self.num_inst == 0: return (self.name, float('nan')) e...

def get_global(self): """Gets the current global evaluation result. Returns ------- names : list of str Name of the metrics. values : list of float Value of the evaluations. """ if self._has_global_stats: if self.global_num_inst ...

def get_name_value(self): """Returns zipped name and value pairs. Returns ------- list of tuples A (name, value) tuple list. """ name, value = self.get() if not isinstance(name, list): name = [name] if not isinstance(value, list): ...

def get_global_name_value(self): """Returns zipped name and value pairs for global results. Returns ------- list of tuples A (name, value) tuple list. """ if self._has_global_stats: name, value = self.get_global() if not isinstance(nam...

def update_binary_stats(self, label, pred): """ Update various binary classification counts for a single (label, pred) pair. Parameters ---------- label : `NDArray` The labels of the data. pred : `NDArray` Predicted values. """ ...

def matthewscc(self, use_global=False): """ Calculate the Matthew's Correlation Coefficent """ if use_global: if not self.global_total_examples: return 0. true_pos = float(self.global_true_positives) false_pos = float(self.global_false...

def transform(self, fn, lazy=True): """Returns a new dataset with each sample transformed by the transformer function `fn`. Parameters ---------- fn : callable A transformer function that takes a sample as input and returns the transformed sample. ...

def transform_first(self, fn, lazy=True): """Returns a new dataset with the first element of each sample transformed by the transformer function `fn`. This is useful, for example, when you only want to transform data while keeping label as is. Parameters ---------- ...

def forward_ocr(self, img_): """Forward the image through the LSTM network model Parameters ---------- img_: int of array Returns ---------- label_list: string of list """ img_ = cv2.resize(img_, (80, 30)) img_ = img_.transpose(1, 0) ...

def read_prototxt(fname): """Return a caffe_pb2.NetParameter object that defined in a prototxt file """ proto = caffe_pb2.NetParameter() with open(fname, 'r') as f: text_format.Merge(str(f.read()), proto) return proto

def get_layers(proto): """Returns layers in a caffe_pb2.NetParameter object """ if len(proto.layer): return proto.layer elif len(proto.layers): return proto.layers else: raise ValueError('Invalid proto file.')

def read_caffemodel(prototxt_fname, caffemodel_fname): """Return a caffe_pb2.NetParameter object that defined in a binary caffemodel file """ if use_caffe: caffe.set_mode_cpu() net = caffe.Net(prototxt_fname, caffemodel_fname, caffe.TEST) layer_names = net._layer_names la...

def layer_iter(layers, layer_names): """Iterate over all layers""" if use_caffe: for layer_idx, layer in enumerate(layers): layer_name = re.sub('[-/]', '_', layer_names[layer_idx]) layer_type = layer.type layer_blobs = layer.blobs yield (layer_name, layer_...

def set_config(**kwargs): """Set up the configure of profiler (only accepts keyword arguments). Parameters ---------- filename : string, output file for profile data profile_all : boolean, all profile types enabled profile_symbolic : boolean, whether to profile symbolic ...

def profiler_set_config(mode='symbolic', filename='profile.json'): """Set up the configure of profiler (Deprecated). Parameters ---------- mode : string, optional Indicates whether to enable the profiler, can be 'symbolic', or 'all'. Defaults to `symbolic`. filename : string, option...

def set_state(state='stop', profile_process='worker'): """Set up the profiler state to 'run' or 'stop'. Parameters ---------- state : string, optional Indicates whether to run the profiler, can be 'stop' or 'run'. Default is `stop`. profile_process : string whether to profil...

def dump(finished=True, profile_process='worker'): """Dump profile and stop profiler. Use this to save profile in advance in case your program cannot exit normally. Parameters ---------- finished : boolean Indicates whether to stop statistic output (dumping) after this dump. Default...

def dumps(reset=False): """Return a printable string of aggregate profile stats. Parameters ---------- reset: boolean Indicates whether to clean aggeregate statistical data collected up to this point """ debug_str = ctypes.c_char_p() do_reset = 1 if reset is True else 0 check_ca...

def pause(profile_process='worker'): """Pause profiling. Parameters ---------- profile_process : string whether to profile kvstore `server` or `worker`. server can only be profiled when kvstore is of type dist. if this is not passed, defaults to `worker` """ profile_proc...

def resume(profile_process='worker'): """ Resume paused profiling. Parameters ---------- profile_process : string whether to profile kvstore `server` or `worker`. server can only be profiled when kvstore is of type dist. if this is not passed, defaults to `worker` """ ...

def set_value(self, value): """Set counter value. Parameters ---------- value : int Value for the counter """ check_call(_LIB.MXProfileSetCounter(self.handle, int(value)))

def increment(self, delta=1): """Increment counter value. Parameters ---------- value_change : int Amount by which to add to the counter """ check_call(_LIB.MXProfileAdjustCounter(self.handle, int(delta)))

def decrement(self, delta=1): """Decrement counter value. Parameters ---------- value_change : int Amount by which to subtract from the counter """ check_call(_LIB.MXProfileAdjustCounter(self.handle, -int(delta)))

def mark(self, scope='process'): """Set up the profiler state to record operator. Parameters ---------- scope : string, optional Indicates what scope the marker should refer to. Can be 'global', 'process', thread', task', and 'marker' Default is `proc...

def get_kernel(self, name, signature): r"""Get CUDA kernel from compiled module. Parameters ---------- name : str String name of the kernel. signature : str Function signature for the kernel. For example, if a kernel is declared as:: ...

def launch(self, args, ctx, grid_dims, block_dims, shared_mem=0): """Launch cuda kernel. Parameters ---------- args : tuple of NDArray or numbers List of arguments for kernel. NDArrays are expected for pointer types (e.g. `float*`, `double*`) while numbers are ex...

def reset(self): """Clear the internal statistics to initial state.""" 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 self.records = dic...

def update(self, labels, preds): """ Update internal records. This function now only update internal buffer, sum_metric and num_inst are updated in _update() function instead when get() is called to return results. Params: ---------- labels: mx.nd.array (n * 6) o...

def _update(self): """ update num_inst and sum_metric """ aps = [] for k, v in self.records.items(): recall, prec = self._recall_prec(v, self.counts[k]) ap = self._average_precision(recall, prec) aps.append(ap) if self.num is not None and k < (self...

def _recall_prec(self, record, count): """ get recall and precision from internal records """ record = np.delete(record, np.where(record[:, 1].astype(int) == 0)[0], axis=0) sorted_records = record[record[:,0].argsort()[::-1]] tp = np.cumsum(sorted_records[:, 1].astype(int) == 1) ...

def _average_precision(self, rec, prec): """ calculate average precision Params: ---------- rec : numpy.array cumulated recall prec : numpy.array cumulated precision Returns: ---------- ap as float """ # app...

def _insert(self, key, records, count): """ Insert records according to key """ if key not in self.records: assert key not in self.counts self.records[key] = records self.counts[key] = count else: self.records[key] = np.vstack((self.records[key], r...

def _average_precision(self, rec, prec): """ calculate average precision, override the default one, special 11-point metric Params: ---------- rec : numpy.array cumulated recall prec : numpy.array cumulated precision Returns: ...

def get_fine_tune_model(symbol, arg_params, num_classes, layer_name, dtype='float32'): """ symbol: the pre-trained network symbol arg_params: the argument parameters of the pre-trained model num_classes: the number of classes for the fine-tune datasets layer_name: the layer name before the last full...

def _list_images(self, root): """ Description : generate list for lip images """ self.labels = [] self.items = [] valid_unseen_sub_idx = [1, 2, 20, 22] skip_sub_idx = [21] if self._mode == 'train': sub_idx = ['s' + str(i) for i in range(1, 35...

def align_generation(self, file_nm, padding=75): """ Description : Align to lip position """ align = Align(self._align_root + '/' + file_nm + '.align') return nd.array(align.sentence(padding))

def set_verbosity(self, verbose=False, print_func=None): """Switch on/off verbose mode Parameters ---------- verbose : bool switch on/off verbose mode print_func : function A function that computes statistics of initialized arrays. Takes an `N...

def _verbose_print(self, desc, init, arr): """Internal verbose print function Parameters ---------- desc : InitDesc or str name of the array init : str initializer pattern arr : NDArray initialized array """ if self._ve...

def _legacy_init(self, name, arr): """Legacy initialization method. Parameters ---------- name : str Name of corresponding NDArray. arr : NDArray NDArray to be initialized. """ warnings.warn( "\033[91mCalling initializer with ...

def save_imglist(self, fname=None, root=None, shuffle=False): """ save imglist to disk Parameters: ---------- fname : str saved filename """ def progress_bar(count, total, suffix=''): import sys bar_len = 24 filled_...

def _load_class_names(self, filename, dirname): """ load class names from text file Parameters: ---------- filename: str file stores class names dirname: str file directory """ full_path = osp.join(dirname, filename) classe...

def read_data(label, image): """ download and read data into numpy """ base_url = 'http://yann.lecun.com/exdb/mnist/' with gzip.open(download_file(base_url+label, os.path.join('data',label))) as flbl: magic, num = struct.unpack(">II", flbl.read(8)) label = np.fromstring(flbl.read(), ...

def get_mnist_iter(args, kv): """ create data iterator with NDArrayIter """ (train_lbl, train_img) = read_data( 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz') (val_lbl, val_img) = read_data( 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz') train = mx...

def make_file_extension_assertion(extension): """Function factory for file extension argparse assertion Args: extension (string): the file extension to assert Returns: string: the supplied extension, if assertion is successful. """ def file_extension_assertion(file_...

def get_palette(num_colors=256): """generates the colormap for visualizing the segmentation mask Args: num_colors (int): the number of colors to generate in the output palette Returns: string: the supplied extension, if assertion is successful. """ p...

def get_data(img_path): """get the (1, 3, h, w) np.array data for the supplied image Args: img_path (string): the input image path Returns: np.array: image data in a (1, 3, h, w) shape """ mean = np.array([123.68, 116.779, 103.939]) ...

def main(): """Module main execution""" # Initialization variables - update to change your model and execution context model_prefix = "FCN8s_VGG16" epoch = 19 # By default, MXNet will run on the CPU. Change to ctx = mx.gpu() to run on GPU. ctx = mx.cpu() fcnxs, fcnxs_args, fcnxs_auxs = mx....