BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def _load_all(self, anno_file, shuffle): """ initialize all entries given annotation json file Parameters: ---------- anno_file: str annotation json file shuffle: bool whether to shuffle image list """ image_set_index = [] ...
def init_params(self, initializer=mx.init.Uniform(0.01), kwargs): """Initializes the parameters and auxiliary states. """ self._module.init_params(initializer=initializer, kwargs)
def forward(self, data_batch, is_train=None, carry_state=True): """Forward computation. States from previous forward computation are carried to the current iteration if `carry_state` is set to `True`. """ # propagate states from the previous iteration if carry_state: ...
def update(self, max_norm=None): """Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch. Gradients are clipped by their global norm if `max_norm` is set. Parameters ---------- max_norm: float, optional...
def _clip_by_global_norm(self, max_norm): """Clips gradient norm. The norm is computed over all gradients together, as if they were concatenated into a single vector. Gradients are modified in-place. The method is first used in `[ICML2013] On the difficulty of training recurren...
def visual(title, X, name): """Image visualization and preservation :param title: title :param X: images to visualized :param name: saved picture`s name :return: """ assert len(X.shape) == 4 X = X.transpose((0, 2, 3, 1)) X = np.clip((X - np.min(X))*(255.0/(np.max(X) - np.min(X))), 0,...
def transformer(data, label): """Get the translation of images""" # resize to 64x64 data = mx.image.imresize(data, 64, 64) # transpose from (64, 64, 3) to (3, 64, 64) data = mx.nd.transpose(data, (2, 0, 1)) # normalize to [-1, 1] data = data.astype(np.float32)/128 - 1 # if image is greys...
def get_dataset(dataset_name): """Load the dataset and split it to train/valid data :param dataset_name: string Returns: train_data: int array training dataset val_data: int array valid dataset """ # mnist if dataset == "mnist": train_data = gluon.data.DataLoade...
def get_netG(): """Get net G""" # build the generator netG = nn.Sequential() with netG.name_scope(): # input is Z, going into a convolution netG.add(nn.Conv2DTranspose(ngf * 8, 4, 1, 0, use_bias=False)) netG.add(nn.BatchNorm()) netG.add(nn.Activation('relu')) # st...
def get_netD(): """Get the netD""" # build the discriminator netD = nn.Sequential() with netD.name_scope(): # input is (nc) x 64 x 64 netD.add(nn.Conv2D(ndf, 4, 2, 1, use_bias=False)) netD.add(nn.LeakyReLU(0.2)) # state size. (ndf) x 32 x 32 netD.add(nn.Conv2D(ndf...
def get_configurations(netG, netD): """Get configurations for net""" # loss loss = gluon.loss.SoftmaxCrossEntropyLoss() # initialize the generator and the discriminator netG.initialize(mx.init.Normal(0.02), ctx=ctx) netD.initialize(mx.init.Normal(0.02), ctx=ctx) # trainer for the generator...
def main(): """Entry point to dcgan""" print("\|------- new changes!!!!!!!!!") # to get the dataset and net configuration train_data, val_data = get_dataset(dataset) netG = get_netG() netD = get_netD() loss, trainerG, trainerD = get_configurations(netG, netD) # set labels real_label ...
def getLogger(name=None, filename=None, filemode=None, level=WARNING): """Gets a customized logger. .. note:: `getLogger` is deprecated. Use `get_logger` instead. """ warnings.warn("getLogger is deprecated, Use get_logger instead.", DeprecationWarning, stacklevel=2) return get_lo...
def get_logger(name=None, filename=None, filemode=None, level=WARNING): """Gets a customized logger. Parameters ---------- name: str, optional Name of the logger. filename: str, optional The filename to which the logger's output will be sent. filemode: str, optional The ...
def transformer(data, label): """ data preparation """ data = mx.image.imresize(data, IMAGE_SIZE, IMAGE_SIZE) data = mx.nd.transpose(data, (2, 0, 1)) data = data.astype(np.float32) / 128.0 - 1 return data, label
def get_training_data(batch_size): """ helper function to get dataloader""" return gluon.data.DataLoader( CIFAR10(train=True, transform=transformer), batch_size=batch_size, shuffle=True, last_batch='discard')
def get_resnet(version, num_layers, pretrained=False, ctx=cpu(), root=os.path.join(base.data_dir(), 'models'), **kwargs): r"""ResNet V1 model from `"Deep Residual Learning for Image Recognition" <http://arxiv.org/abs/1512.03385>`_ paper. ResNet V2 model from `"Identity Mappings in Deep Residu...
def _random_helper(random, sampler, params, shape, dtype, kwargs): """Helper function for random generators.""" if isinstance(params[0], Symbol): for i in params[1:]: assert isinstance(i, Symbol), \ "Distribution parameters must all have the same type, but got " \ ...
def poisson(lam=1, shape=_Null, dtype=_Null, *kwargs): """Draw random samples from a Poisson distribution. Samples are distributed according to a Poisson distribution parametrized by lambda* (rate). Samples will always be returned as a floating point data type. Parameters ---------- lam : fl...
def generalized_negative_binomial(mu=1, alpha=1, shape=_Null, dtype=_Null, *kwargs): """Draw random samples from a generalized negative binomial distribution. Samples are distributed according to a generalized negative binomial distribution parametrized by mu* (mean) and alpha (dispersion). alpha...
def multinomial(data, shape=_Null, get_prob=True, dtype='int32', **kwargs): """Concurrent sampling from multiple multinomial distributions. .. note:: The input distribution must be normalized, i.e. `data` must sum to 1 along its last dimension. Parameters ---------- data : Symbol ...
def get_symbol_train(num_classes=20, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """ Single-shot multi-box detection with VGG 16 layers ConvNet This is a modified version, with fc6/fc7 layers replaced by conv layers And the network is slightly smaller than origina...
def get_symbol(num_classes=20, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """ Single-shot multi-box detection with VGG 16 layers ConvNet This is a modified version, with fc6/fc7 layers replaced by conv layers And the network is slightly smaller than original VGG 16 net...
def load(prefix, epoch, load_optimizer_states=False, **kwargs): """Creates a model from previously saved checkpoint. Parameters ---------- prefix : str path prefix of saved model files. You should have "prefix-symbol.json", "prefix-xxxx.params", and o...
def save_checkpoint(self, prefix, epoch, save_optimizer_states=False): """Saves current progress to checkpoint. Use `mx.callback.module_checkpoint` as `epoch_end_callback` to save during training. Parameters ---------- prefix : str The file prefix to checkpoint to. ...
def _reset_bind(self): """Internal function to reset binded state.""" self.binded = False self._exec_group = None self._data_shapes = None self._label_shapes = None
def get_params(self): """Gets current parameters. Returns ------- `(arg_params, aux_params)` A pair of dictionaries each mapping parameter names to NDArray values. """ assert self.binded and self.params_initialized if self._params_dirty: ...
def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None, allow_missing=False, force_init=False, allow_extra=False): """Initializes the parameters and auxiliary states. Parameters ---------- initializer : Initializer Called to ini...
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True, allow_extra=False): """Assigns parameter and aux state values. Parameters ---------- arg_params : dict Dictionary of name to `NDArray`. aux_params : dict ...
def bind(self, data_shapes, label_shapes=None, for_training=True, inputs_need_grad=False, force_rebind=False, shared_module=None, grad_req='write'): """Binds the symbols to construct executors. This is necessary before one can perform computation with the module. Param...
def reshape(self, data_shapes, label_shapes=None): """Reshapes the module for new input shapes. Parameters ---------- data_shapes : list of (str, tuple) Typically is ``data_iter.provide_data``. label_shapes : list of (str, tuple) Typically is ``data_iter....
def init_optimizer(self, kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), force_init=False): """Installs and initializes optimizers. Parameters ---------- kvstore : str or KVStore Default `'local'`. optimizer : str...
def borrow_optimizer(self, shared_module): """Borrows optimizer from a shared module. Used in bucketing, where exactly the same optimizer (esp. kvstore) is used. Parameters ---------- shared_module : Module """ assert shared_module.optimizer_initialized s...
def forward(self, data_batch, is_train=None): """Forward computation. It supports data batches with different shapes, such as different batch sizes or different image sizes. If reshaping of data batch relates to modification of symbol or module, such as changing image layout ordering or ...
def backward(self, out_grads=None): """Backward computation. See Also ---------- :meth:`BaseModule.backward`. Parameters ---------- out_grads : NDArray or list of NDArray, optional Gradient on the outputs to be propagated back. This param...
def update(self): """Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch. When KVStore is used to update parameters for multi-device or multi-machine training, a copy of the parameters are stored in KVStore. Note that...
def get_outputs(self, merge_multi_context=True): """Gets outputs of the previous forward computation. If ``merge_multi_context`` is ``True``, it is like ``[out1, out2]``. Otherwise, it is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output elements are `NDArray`. W...
def get_input_grads(self, merge_multi_context=True): """Gets the gradients with respect to the inputs of the module. If ``merge_multi_context`` is ``True``, it is like ``[grad1, grad2]``. Otherwise, it is like ``[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]``. All the output elem...
def get_states(self, merge_multi_context=True): """Gets states from all devices. If `merge_multi_context` is ``True``, it is like ``[out1, out2]``. Otherwise, it is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output elements are `NDArray`. Parameters ...
def update_metric(self, eval_metric, labels, pre_sliced=False): """Evaluates and accumulates evaluation metric on outputs of the last forward computation. See Also ---------- :meth:`BaseModule.update_metric`. Parameters ---------- eval_metric : EvalMetric ...
def _sync_params_from_devices(self): """Synchronizes parameters from devices to CPU. This function should be called after calling `update` that updates the parameters on the devices, before one can read the latest parameters from ``self._arg_params`` and ``self._aux_params``. For row_sp...
def save_optimizer_states(self, fname): """Saves optimizer (updater) state to a file. Parameters ---------- fname : str Path to output states file. """ assert self.optimizer_initialized if self._update_on_kvstore: self._kvstore.save_optim...
def load_optimizer_states(self, fname): """Loads optimizer (updater) state from a file. Parameters ---------- fname : str Path to input states file. """ assert self.optimizer_initialized if self._update_on_kvstore: self._kvstore.load_opti...
def prepare(self, data_batch, sparse_row_id_fn=None): '''Prepares the module for processing a data batch. Usually involves switching bucket and reshaping. For modules that contain `row_sparse` parameters in KVStore, it prepares the `row_sparse` parameters based on the sparse_row_id_fn. ...
def _random_helper(random, sampler, params, shape, dtype, ctx, out, kwargs): """Helper function for random generators.""" if isinstance(params[0], NDArray): for i in params[1:]: assert isinstance(i, NDArray), \ "Distribution parameters must all have the same type, but got " \...
def uniform(low=0, high=1, shape=_Null, dtype=_Null, ctx=None, out=None, *kwargs): """Draw random samples from a uniform distribution. Samples are uniformly distributed over the half-open interval [low, high)* (includes low, but excludes high). Parameters ---------- low : float or NDArra...
def normal(loc=0, scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, *kwargs): """Draw random samples from a normal (Gaussian) distribution. Samples are distributed according to a normal distribution parametrized by loc* (mean) and scale (standard deviation). Parameters ---------- loc...
def randn(shape, kwargs): """Draw random samples from a normal (Gaussian) distribution. Samples are distributed according to a normal distribution parametrized by loc* (mean) and scale (standard deviation). Parameters ---------- loc : float or NDArray Mean (centre) of the distri...
def exponential(scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs): r"""Draw samples from an exponential distribution. Its probability density function is .. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}), for x > 0 and 0 elsewhere. \beta is the scale parameter, w...
def gamma(alpha=1, beta=1, shape=_Null, dtype=_Null, ctx=None, out=None, *kwargs): """Draw random samples from a gamma distribution. Samples are distributed according to a gamma distribution parametrized by alpha* (shape) and beta (scale). Parameters ---------- alpha : float or NDArray, op...
def negative_binomial(k=1, p=1, shape=_Null, dtype=_Null, ctx=None, out=None, *kwargs): """Draw random samples from a negative binomial distribution. Samples are distributed according to a negative binomial distribution parametrized by k* (limit of unsuccessful experiments) and p ...
def multinomial(data, shape=_Null, get_prob=False, out=None, dtype='int32', **kwargs): """Concurrent sampling from multiple multinomial distributions. .. note:: The input distribution must be normalized, i.e. `data` must sum to 1 along its last dimension. Parameters ---------- data :...
def randint(low, high, shape=_Null, dtype=_Null, ctx=None, out=None, *kwargs): """Draw random samples from a discrete uniform distribution. Samples are uniformly distributed over the half-open interval [low, high)* (includes low, but excludes high). Parameters ---------- low : int, requi...
def preprocess_uci_adult(data_name): """Some tricks of feature engineering are adapted from tensorflow's wide and deep tutorial. """ csv_columns = [ "age", "workclass", "fnlwgt", "education", "education_num", "marital_status", "occupation", "relationship", "race", "gender", "capi...
def _init_params(self): """Initialize parameters in the KVStore. Parameters with incomplete initialization are ignored. """ assert self._kv_initialized, "Cannot initialize parameters in KVStore " \ "when KVStore is not initialized." params_t...
def _reset_kvstore(self): """Reset kvstore.""" if self._kvstore and 'dist' in self._kvstore.type: raise RuntimeError("Cannot reset distributed KVStore.") self._kv_initialized = False self._kvstore = None self._distributed = None self._update_on_kvstore = None ...
def _init_kvstore(self): """Create kvstore.""" config = self._kvstore_params # configure kvstore, update_on_kvstore and self._distributed on three cases: if self._contains_sparse_weight: # If weight is sparse, kvstore must be present and the weight must be updated on kvstore....
def set_learning_rate(self, lr): """Sets a new learning rate of the optimizer. Parameters ---------- lr : float The new learning rate of the optimizer. """ if not isinstance(self._optimizer, opt.Optimizer): raise UserWarning("Optimizer has to be d...
def _row_sparse_pull(self, parameter, out, row_id, full_idx=False): """Internal method to invoke pull operations on KVStore. If `full_idx` is set to True, `kv.pull` is preferred instead of `kv.row_sparse_pull`. """ # initialize kv and params if not already if not self._kv_initial...
def step(self, batch_size, ignore_stale_grad=False): """Makes one step of parameter update. Should be called after `autograd.backward()` and outside of `record()` scope. For normal parameter updates, `step()` should be used, which internally calls `allreduce_grads()` and then `update()`...
def allreduce_grads(self): """For each parameter, reduce the gradients from different contexts. Should be called after `autograd.backward()`, outside of `record()` scope, and before `trainer.update()`. For normal parameter updates, `step()` should be used, which internally calls ...
def update(self, batch_size, ignore_stale_grad=False): """Makes one step of parameter update. Should be called after `autograd.backward()` and outside of `record()` scope, and after `trainer.update()`. For normal parameter updates, `step()` should be used, which internally calls ...
def save_states(self, fname): """Saves trainer states (e.g. optimizer, momentum) to a file. Parameters ---------- fname : str Path to output states file. Note ---- `optimizer.param_dict`, which contains Parameter information (such as `lr_mul...
def load_states(self, fname): """Loads trainer states (e.g. optimizer, momentum) from a file. Parameters ---------- fname : str Path to input states file. Note ---- `optimizer.param_dict`, which contains Parameter information (such as `lr_mul...
def estimate_density(DATA_PATH, feature_size): """sample 10 times of a size of 1000 for estimating the density of the sparse dataset""" if not os.path.exists(DATA_PATH): raise Exception("Data is not there!") density = [] P = 0.01 for _ in range(10): num_non_zero = 0 num_sampl...
def exec_cmd(cmd, role, taskid, pass_env): """Execute the command line command.""" if cmd[0].find('/') == -1 and os.path.exists(cmd[0]) and os.name != 'nt': cmd[0] = './' + cmd[0] cmd = ' '.join(cmd) env = os.environ.copy() for k, v in pass_env.items(): env[k] = str(v) env['DMLC...
def submit(args): gpus = args.gpus.strip().split(',') """Submit function of local jobs.""" def mthread_submit(nworker, nserver, envs): """ customized submit script, that submit nslave jobs, each must contain args as parameter note this can be a lambda function containing additional p...
def ctc_label(p): """Iterates through p, identifying non-zero and non-repeating values, and returns them in a list Parameters ---------- p: list of int Returns ------- list of int """ ret = [] p1 = [0] + p for i, _ in enumerate(p): ...
def _remove_blank(l): """ Removes trailing zeros in the list of integers and returns a new list of integers""" ret = [] for i, _ in enumerate(l): if l[i] == 0: break ret.append(l[i]) return ret
def _lcs(p, l): """ Calculates the Longest Common Subsequence between p and l (both list of int) and returns its length""" # Dynamic Programming Finding LCS if len(p) == 0: return 0 P = np.array(list(p)).reshape((1, len(p))) L = np.array(list(l)).reshape((len(l), 1)) ...
def accuracy(self, label, pred): """ Simple accuracy measure: number of 100% accurate predictions divided by total number """ hit = 0. total = 0. batch_size = label.shape[0] for i in range(batch_size): l = self._remove_blank(label[i]) p = [] fo...
def accuracy_lcs(self, label, pred): """ Longest Common Subsequence accuracy measure: calculate accuracy of each prediction as LCS/length""" hit = 0. total = 0. batch_size = label.shape[0] for i in range(batch_size): l = self._remove_blank(label[i]) p = []...
def get_movielens_iter(filename, batch_size): """Not particularly fast code to parse the text file and load into NDArrays. return two data iters, one for train, the other for validation. """ logging.info("Preparing data iterators for " + filename + " ... ") user = [] item = [] score = [] ...
def imdecode(str_img, flag=1): """Decode image from str buffer. Wrapper for cv2.imdecode that uses mx.nd.NDArray Parameters ---------- str_img : str str buffer read from image file flag : int same as flag for cv2.imdecode Returns ------- img : NDArray decoded...
def resize(src, size, interpolation=cv2.INTER_LINEAR): """Decode image from str buffer. Wrapper for cv2.imresize that uses mx.nd.NDArray Parameters ---------- src : NDArray image in (width, height, channels) size : tuple target size in (width, height) interpolation : int ...
def copyMakeBorder(src, top, bot, left, right, border_type=cv2.BORDER_CONSTANT, value=0): """Pad image border Wrapper for cv2.copyMakeBorder that uses mx.nd.NDArray Parameters ---------- src : NDArray Image in (width, height, channels). Others are the same with cv2.copyMakeBorder ...
def fixed_crop(src, x0, y0, w, h, size=None, interpolation=cv2.INTER_CUBIC): """Crop src at fixed location, and (optionally) resize it to size""" out = mx.nd.crop(src, begin=(y0, x0, 0), end=(y0+h, x0+w, int(src.shape[2]))) if size is not None and (w, h) != size: out = resize(out, size, interpolatio...
def random_crop(src, size): """Randomly crop src with size. Upsample result if src is smaller than size""" h, w, _ = src.shape new_w, new_h = scale_down((w, h), size) x0 = random.randint(0, w - new_w) y0 = random.randint(0, h - new_h) out = fixed_crop(src, x0, y0, new_w, new_h, size) retur...
def random_size_crop(src, size, min_area=0.25, ratio=(3.0/4.0, 4.0/3.0)): """Randomly crop src with size. Randomize area and aspect ratio""" h, w, _ = src.shape area = wh for _ in range(10): new_area = random.uniform(min_area, 1.0) area new_ratio = random.uniform(*ratio) new_w...
def next(self): """Move iterator position forward""" batch = mx.nd.zeros((self.batch_size, self.size[1], self.size[0], 3)) i = self.cur for i in range(self.cur, min(len(self.list), self.cur+self.batch_size)): str_img = open(self.root+self.list[i]+'.jpg').read() im...
def check_label_shapes(labels, preds, shape=0): """Check to see if the two arrays are the same size.""" if shape == 0: label_shape, pred_shape = len(labels), len(preds) else: label_shape, pred_shape = labels.shape, preds.shape if label_shape != pred_shape: raise ValueError("Sha...
def import_to_gluon(model_file, ctx): """ Imports the ONNX model files, passed as a parameter, into Gluon SymbolBlock object. Parameters ---------- model_file : str ONNX model file name ctx : Context or list of Context Loads the model into one or many context(s). Returns ...
def get_model(model, ctx, opt): """Model initialization.""" kwargs = {'ctx': ctx, 'pretrained': opt.use_pretrained, 'classes': classes} if model.startswith('resnet'): kwargs['thumbnail'] = opt.use_thumbnail elif model.startswith('vgg'): kwargs['batch_norm'] = opt.batch_norm net = mo...
def get_data_iters(dataset, batch_size, opt): """get dataset iterators""" if dataset == 'mnist': train_data, val_data = get_mnist_iterator(batch_size, (1, 28, 28), num_parts=kv.num_workers, part_index=kv.rank) elif dataset == 'cifar10': train...
def update_learning_rate(lr, trainer, epoch, ratio, steps): """Set the learning rate to the initial value decayed by ratio every N epochs.""" new_lr = lr * (ratio ** int(np.sum(np.array(steps) < epoch))) trainer.set_learning_rate(new_lr) return trainer
def seed(seed_state, ctx="all"): """Seeds the random number generators in MXNet. This affects the behavior of modules in MXNet that uses random number generators, like the dropout operator and `NDArray`'s random sampling operators. Parameters ---------- seed_state : int The random numb...
def random_uniform(attrs, inputs, proto_obj): """Draw random samples from a uniform distribtuion.""" try: from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE except ImportError: raise ImportError("Onnx and protobuf need to be installed. " "Instructions to install - http...
def random_normal(attrs, inputs, proto_obj): """Draw random samples from a Gaussian distribution.""" try: from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE except ImportError: raise ImportError("Onnx and protobuf need to be installed. " "Instructions to install - http...
def add(attrs, inputs, proto_obj): """Adding two tensors""" new_attr = {} if 'broadcast' in attrs and attrs['broadcast'] == 1: broadcast_axis = attrs['axis'] op_value = translation_utils._fix_broadcast('broadcast_add', inputs, broadcast_ax...
def mean(attrs, inputs, proto_obj): """Mean of all the input tensors.""" concat_input = [symbol.expand_dims(op_input, axis=0) for op_input in inputs] concat_sym = symbol.concat(*concat_input, dim=0) mean_sym = symbol.mean(concat_sym, axis=0) return mean_sym, attrs, inputs
def argmax(attrs, inputs, proto_obj): """Returns indices of the maximum values along an axis""" axis = attrs.get('axis', 0) keepdims = attrs.get('keepdims', 1) argmax_op = symbol.argmax(inputs[0], axis=axis, keepdims=keepdims) # onnx argmax operator always expects int64 as output type cast_attrs...
def argmin(attrs, inputs, proto_obj): """Returns indices of the minimum values along an axis.""" axis = attrs.get('axis', 0) keepdims = attrs.get('keepdims', 1) argmin_op = symbol.argmin(inputs[0], axis=axis, keepdims=keepdims) # onnx argmax operator always expects int64 as output type cast_attr...
def maximum(attrs, inputs, proto_obj): """ Elementwise maximum of arrays. MXNet maximum compares only two symbols at a time. ONNX can send more than two to compare. Breaking into multiple mxnet ops to compare two symbols at a time """ if len(inputs) > 1: mxnet_op = symbol.maximum(inp...
def minimum(attrs, inputs, proto_obj): """Elementwise minimum of arrays.""" # MXNet minimum compares only two symbols at a time. # ONNX can send more than two to compare. # Breaking into multiple mxnet ops to compare two symbols at a time if len(inputs) > 1: mxnet_op = symbol.minimum(inputs[...
def concat(attrs, inputs, proto_obj): """ Joins input arrays along a given axis. """ new_attrs = translation_utils._fix_attribute_names(attrs, {'axis': 'dim'}) return 'concat', new_attrs, inputs
def pad(attrs, inputs, proto_obj): """ Add padding to input tensor""" new_attrs = translation_utils._fix_attribute_names(attrs, {'pads' : 'pad_width', 'value' : 'constant_value' }) n...
def batch_norm(attrs, inputs, proto_obj): """Batch normalization.""" new_attrs = translation_utils._fix_attribute_names(attrs, {'epsilon': 'eps', 'is_test': 'fix_gamma'}) new_attrs = translation_utils._remove_attributes(new_attrs, ...
def instance_norm(attrs, inputs, proto_obj): """Instance Normalization.""" new_attrs = translation_utils._fix_attribute_names(attrs, {'epsilon' : 'eps'}) new_attrs['eps'] = attrs.get('epsilon', 1e-5) return 'InstanceNorm', new_attrs, inputs
def leaky_relu(attrs, inputs, proto_obj): """Leaky Relu function""" if 'alpha' in attrs: new_attrs = translation_utils._fix_attribute_names(attrs, {'alpha' : 'slope'}) else: new_attrs = translation_utils._add_extra_attributes(attrs, {'slope': 0.01}) return 'LeakyReLU', new_attrs, inputs
def _elu(attrs, inputs, proto_obj): """Elu function""" if 'alpha' in attrs: new_attrs = translation_utils._fix_attribute_names(attrs, {'alpha' : 'slope'}) else: new_attrs = translation_utils._add_extra_attributes(attrs, {'slope': 1.0}) new_attrs = translation_utils._add_extra_attributes(...

def _load_all(self, anno_file, shuffle): """ initialize all entries given annotation json file Parameters: ---------- anno_file: str annotation json file shuffle: bool whether to shuffle image list """ image_set_index = [] ...

def init_params(self, initializer=mx.init.Uniform(0.01), **kwargs): """Initializes the parameters and auxiliary states. """ self._module.init_params(initializer=initializer, **kwargs)

def forward(self, data_batch, is_train=None, carry_state=True): """Forward computation. States from previous forward computation are carried to the current iteration if `carry_state` is set to `True`. """ # propagate states from the previous iteration if carry_state: ...

def update(self, max_norm=None): """Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch. Gradients are clipped by their global norm if `max_norm` is set. Parameters ---------- max_norm: float, optional...

def _clip_by_global_norm(self, max_norm): """Clips gradient norm. The norm is computed over all gradients together, as if they were concatenated into a single vector. Gradients are modified in-place. The method is first used in `[ICML2013] On the difficulty of training recurren...

def visual(title, X, name): """Image visualization and preservation :param title: title :param X: images to visualized :param name: saved picture`s name :return: """ assert len(X.shape) == 4 X = X.transpose((0, 2, 3, 1)) X = np.clip((X - np.min(X))*(255.0/(np.max(X) - np.min(X))), 0,...

def transformer(data, label): """Get the translation of images""" # resize to 64x64 data = mx.image.imresize(data, 64, 64) # transpose from (64, 64, 3) to (3, 64, 64) data = mx.nd.transpose(data, (2, 0, 1)) # normalize to [-1, 1] data = data.astype(np.float32)/128 - 1 # if image is greys...

def get_dataset(dataset_name): """Load the dataset and split it to train/valid data :param dataset_name: string Returns: train_data: int array training dataset val_data: int array valid dataset """ # mnist if dataset == "mnist": train_data = gluon.data.DataLoade...

def get_netG(): """Get net G""" # build the generator netG = nn.Sequential() with netG.name_scope(): # input is Z, going into a convolution netG.add(nn.Conv2DTranspose(ngf * 8, 4, 1, 0, use_bias=False)) netG.add(nn.BatchNorm()) netG.add(nn.Activation('relu')) # st...

def get_netD(): """Get the netD""" # build the discriminator netD = nn.Sequential() with netD.name_scope(): # input is (nc) x 64 x 64 netD.add(nn.Conv2D(ndf, 4, 2, 1, use_bias=False)) netD.add(nn.LeakyReLU(0.2)) # state size. (ndf) x 32 x 32 netD.add(nn.Conv2D(ndf...

def get_configurations(netG, netD): """Get configurations for net""" # loss loss = gluon.loss.SoftmaxCrossEntropyLoss() # initialize the generator and the discriminator netG.initialize(mx.init.Normal(0.02), ctx=ctx) netD.initialize(mx.init.Normal(0.02), ctx=ctx) # trainer for the generator...

def main(): """Entry point to dcgan""" print("|------- new changes!!!!!!!!!") # to get the dataset and net configuration train_data, val_data = get_dataset(dataset) netG = get_netG() netD = get_netD() loss, trainerG, trainerD = get_configurations(netG, netD) # set labels real_label ...

def getLogger(name=None, filename=None, filemode=None, level=WARNING): """Gets a customized logger. .. note:: `getLogger` is deprecated. Use `get_logger` instead. """ warnings.warn("getLogger is deprecated, Use get_logger instead.", DeprecationWarning, stacklevel=2) return get_lo...

def get_logger(name=None, filename=None, filemode=None, level=WARNING): """Gets a customized logger. Parameters ---------- name: str, optional Name of the logger. filename: str, optional The filename to which the logger's output will be sent. filemode: str, optional The ...

def transformer(data, label): """ data preparation """ data = mx.image.imresize(data, IMAGE_SIZE, IMAGE_SIZE) data = mx.nd.transpose(data, (2, 0, 1)) data = data.astype(np.float32) / 128.0 - 1 return data, label

def get_training_data(batch_size): """ helper function to get dataloader""" return gluon.data.DataLoader( CIFAR10(train=True, transform=transformer), batch_size=batch_size, shuffle=True, last_batch='discard')

def get_resnet(version, num_layers, pretrained=False, ctx=cpu(), root=os.path.join(base.data_dir(), 'models'), **kwargs): r"""ResNet V1 model from `"Deep Residual Learning for Image Recognition" <http://arxiv.org/abs/1512.03385>`_ paper. ResNet V2 model from `"Identity Mappings in Deep Residu...

def _random_helper(random, sampler, params, shape, dtype, kwargs): """Helper function for random generators.""" if isinstance(params[0], Symbol): for i in params[1:]: assert isinstance(i, Symbol), \ "Distribution parameters must all have the same type, but got " \ ...

def poisson(lam=1, shape=_Null, dtype=_Null, **kwargs): """Draw random samples from a Poisson distribution. Samples are distributed according to a Poisson distribution parametrized by *lambda* (rate). Samples will always be returned as a floating point data type. Parameters ---------- lam : fl...

def generalized_negative_binomial(mu=1, alpha=1, shape=_Null, dtype=_Null, **kwargs): """Draw random samples from a generalized negative binomial distribution. Samples are distributed according to a generalized negative binomial distribution parametrized by *mu* (mean) and *alpha* (dispersion). *alpha*...

def multinomial(data, shape=_Null, get_prob=True, dtype='int32', **kwargs): """Concurrent sampling from multiple multinomial distributions. .. note:: The input distribution must be normalized, i.e. `data` must sum to 1 along its last dimension. Parameters ---------- data : Symbol ...

def get_symbol_train(num_classes=20, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """ Single-shot multi-box detection with VGG 16 layers ConvNet This is a modified version, with fc6/fc7 layers replaced by conv layers And the network is slightly smaller than origina...

def get_symbol(num_classes=20, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """ Single-shot multi-box detection with VGG 16 layers ConvNet This is a modified version, with fc6/fc7 layers replaced by conv layers And the network is slightly smaller than original VGG 16 net...

def load(prefix, epoch, load_optimizer_states=False, **kwargs): """Creates a model from previously saved checkpoint. Parameters ---------- prefix : str path prefix of saved model files. You should have "prefix-symbol.json", "prefix-xxxx.params", and o...

def save_checkpoint(self, prefix, epoch, save_optimizer_states=False): """Saves current progress to checkpoint. Use `mx.callback.module_checkpoint` as `epoch_end_callback` to save during training. Parameters ---------- prefix : str The file prefix to checkpoint to. ...

def _reset_bind(self): """Internal function to reset binded state.""" self.binded = False self._exec_group = None self._data_shapes = None self._label_shapes = None

def get_params(self): """Gets current parameters. Returns ------- `(arg_params, aux_params)` A pair of dictionaries each mapping parameter names to NDArray values. """ assert self.binded and self.params_initialized if self._params_dirty: ...

def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None, allow_missing=False, force_init=False, allow_extra=False): """Initializes the parameters and auxiliary states. Parameters ---------- initializer : Initializer Called to ini...

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

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

def reshape(self, data_shapes, label_shapes=None): """Reshapes the module for new input shapes. Parameters ---------- data_shapes : list of (str, tuple) Typically is ``data_iter.provide_data``. label_shapes : list of (str, tuple) Typically is ``data_iter....

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

def borrow_optimizer(self, shared_module): """Borrows optimizer from a shared module. Used in bucketing, where exactly the same optimizer (esp. kvstore) is used. Parameters ---------- shared_module : Module """ assert shared_module.optimizer_initialized s...

def forward(self, data_batch, is_train=None): """Forward computation. It supports data batches with different shapes, such as different batch sizes or different image sizes. If reshaping of data batch relates to modification of symbol or module, such as changing image layout ordering or ...

def backward(self, out_grads=None): """Backward computation. See Also ---------- :meth:`BaseModule.backward`. Parameters ---------- out_grads : NDArray or list of NDArray, optional Gradient on the outputs to be propagated back. This param...

def update(self): """Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch. When KVStore is used to update parameters for multi-device or multi-machine training, a copy of the parameters are stored in KVStore. Note that...

def get_outputs(self, merge_multi_context=True): """Gets outputs of the previous forward computation. If ``merge_multi_context`` is ``True``, it is like ``[out1, out2]``. Otherwise, it is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output elements are `NDArray`. W...

def get_input_grads(self, merge_multi_context=True): """Gets the gradients with respect to the inputs of the module. If ``merge_multi_context`` is ``True``, it is like ``[grad1, grad2]``. Otherwise, it is like ``[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]``. All the output elem...

def get_states(self, merge_multi_context=True): """Gets states from all devices. If `merge_multi_context` is ``True``, it is like ``[out1, out2]``. Otherwise, it is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output elements are `NDArray`. Parameters ...

def update_metric(self, eval_metric, labels, pre_sliced=False): """Evaluates and accumulates evaluation metric on outputs of the last forward computation. See Also ---------- :meth:`BaseModule.update_metric`. Parameters ---------- eval_metric : EvalMetric ...

def _sync_params_from_devices(self): """Synchronizes parameters from devices to CPU. This function should be called after calling `update` that updates the parameters on the devices, before one can read the latest parameters from ``self._arg_params`` and ``self._aux_params``. For row_sp...

def save_optimizer_states(self, fname): """Saves optimizer (updater) state to a file. Parameters ---------- fname : str Path to output states file. """ assert self.optimizer_initialized if self._update_on_kvstore: self._kvstore.save_optim...

def load_optimizer_states(self, fname): """Loads optimizer (updater) state from a file. Parameters ---------- fname : str Path to input states file. """ assert self.optimizer_initialized if self._update_on_kvstore: self._kvstore.load_opti...

def prepare(self, data_batch, sparse_row_id_fn=None): '''Prepares the module for processing a data batch. Usually involves switching bucket and reshaping. For modules that contain `row_sparse` parameters in KVStore, it prepares the `row_sparse` parameters based on the sparse_row_id_fn. ...

def _random_helper(random, sampler, params, shape, dtype, ctx, out, kwargs): """Helper function for random generators.""" if isinstance(params[0], NDArray): for i in params[1:]: assert isinstance(i, NDArray), \ "Distribution parameters must all have the same type, but got " \...

def uniform(low=0, high=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs): """Draw random samples from a uniform distribution. Samples are uniformly distributed over the half-open interval *[low, high)* (includes *low*, but excludes *high*). Parameters ---------- low : float or NDArra...

def normal(loc=0, scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs): """Draw random samples from a normal (Gaussian) distribution. Samples are distributed according to a normal distribution parametrized by *loc* (mean) and *scale* (standard deviation). Parameters ---------- loc...

def randn(*shape, **kwargs): """Draw random samples from a normal (Gaussian) distribution. Samples are distributed according to a normal distribution parametrized by *loc* (mean) and *scale* (standard deviation). Parameters ---------- loc : float or NDArray Mean (centre) of the distri...

def exponential(scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs): r"""Draw samples from an exponential distribution. Its probability density function is .. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}), for x > 0 and 0 elsewhere. \beta is the scale parameter, w...

def gamma(alpha=1, beta=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs): """Draw random samples from a gamma distribution. Samples are distributed according to a gamma distribution parametrized by *alpha* (shape) and *beta* (scale). Parameters ---------- alpha : float or NDArray, op...

def negative_binomial(k=1, p=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs): """Draw random samples from a negative binomial distribution. Samples are distributed according to a negative binomial distribution parametrized by *k* (limit of unsuccessful experiments) and *p* ...

def multinomial(data, shape=_Null, get_prob=False, out=None, dtype='int32', **kwargs): """Concurrent sampling from multiple multinomial distributions. .. note:: The input distribution must be normalized, i.e. `data` must sum to 1 along its last dimension. Parameters ---------- data :...

def randint(low, high, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs): """Draw random samples from a discrete uniform distribution. Samples are uniformly distributed over the half-open interval *[low, high)* (includes *low*, but excludes *high*). Parameters ---------- low : int, requi...

def preprocess_uci_adult(data_name): """Some tricks of feature engineering are adapted from tensorflow's wide and deep tutorial. """ csv_columns = [ "age", "workclass", "fnlwgt", "education", "education_num", "marital_status", "occupation", "relationship", "race", "gender", "capi...

def _init_params(self): """Initialize parameters in the KVStore. Parameters with incomplete initialization are ignored. """ assert self._kv_initialized, "Cannot initialize parameters in KVStore " \ "when KVStore is not initialized." params_t...

def _reset_kvstore(self): """Reset kvstore.""" if self._kvstore and 'dist' in self._kvstore.type: raise RuntimeError("Cannot reset distributed KVStore.") self._kv_initialized = False self._kvstore = None self._distributed = None self._update_on_kvstore = None ...

def _init_kvstore(self): """Create kvstore.""" config = self._kvstore_params # configure kvstore, update_on_kvstore and self._distributed on three cases: if self._contains_sparse_weight: # If weight is sparse, kvstore must be present and the weight must be updated on kvstore....

def set_learning_rate(self, lr): """Sets a new learning rate of the optimizer. Parameters ---------- lr : float The new learning rate of the optimizer. """ if not isinstance(self._optimizer, opt.Optimizer): raise UserWarning("Optimizer has to be d...

def _row_sparse_pull(self, parameter, out, row_id, full_idx=False): """Internal method to invoke pull operations on KVStore. If `full_idx` is set to True, `kv.pull` is preferred instead of `kv.row_sparse_pull`. """ # initialize kv and params if not already if not self._kv_initial...

def step(self, batch_size, ignore_stale_grad=False): """Makes one step of parameter update. Should be called after `autograd.backward()` and outside of `record()` scope. For normal parameter updates, `step()` should be used, which internally calls `allreduce_grads()` and then `update()`...

def allreduce_grads(self): """For each parameter, reduce the gradients from different contexts. Should be called after `autograd.backward()`, outside of `record()` scope, and before `trainer.update()`. For normal parameter updates, `step()` should be used, which internally calls ...

def update(self, batch_size, ignore_stale_grad=False): """Makes one step of parameter update. Should be called after `autograd.backward()` and outside of `record()` scope, and after `trainer.update()`. For normal parameter updates, `step()` should be used, which internally calls ...

def save_states(self, fname): """Saves trainer states (e.g. optimizer, momentum) to a file. Parameters ---------- fname : str Path to output states file. Note ---- `optimizer.param_dict`, which contains Parameter information (such as `lr_mul...

def load_states(self, fname): """Loads trainer states (e.g. optimizer, momentum) from a file. Parameters ---------- fname : str Path to input states file. Note ---- `optimizer.param_dict`, which contains Parameter information (such as `lr_mul...

def estimate_density(DATA_PATH, feature_size): """sample 10 times of a size of 1000 for estimating the density of the sparse dataset""" if not os.path.exists(DATA_PATH): raise Exception("Data is not there!") density = [] P = 0.01 for _ in range(10): num_non_zero = 0 num_sampl...

def exec_cmd(cmd, role, taskid, pass_env): """Execute the command line command.""" if cmd[0].find('/') == -1 and os.path.exists(cmd[0]) and os.name != 'nt': cmd[0] = './' + cmd[0] cmd = ' '.join(cmd) env = os.environ.copy() for k, v in pass_env.items(): env[k] = str(v) env['DMLC...

def submit(args): gpus = args.gpus.strip().split(',') """Submit function of local jobs.""" def mthread_submit(nworker, nserver, envs): """ customized submit script, that submit nslave jobs, each must contain args as parameter note this can be a lambda function containing additional p...

def ctc_label(p): """Iterates through p, identifying non-zero and non-repeating values, and returns them in a list Parameters ---------- p: list of int Returns ------- list of int """ ret = [] p1 = [0] + p for i, _ in enumerate(p): ...

def _remove_blank(l): """ Removes trailing zeros in the list of integers and returns a new list of integers""" ret = [] for i, _ in enumerate(l): if l[i] == 0: break ret.append(l[i]) return ret

def _lcs(p, l): """ Calculates the Longest Common Subsequence between p and l (both list of int) and returns its length""" # Dynamic Programming Finding LCS if len(p) == 0: return 0 P = np.array(list(p)).reshape((1, len(p))) L = np.array(list(l)).reshape((len(l), 1)) ...

def accuracy(self, label, pred): """ Simple accuracy measure: number of 100% accurate predictions divided by total number """ hit = 0. total = 0. batch_size = label.shape[0] for i in range(batch_size): l = self._remove_blank(label[i]) p = [] fo...

def accuracy_lcs(self, label, pred): """ Longest Common Subsequence accuracy measure: calculate accuracy of each prediction as LCS/length""" hit = 0. total = 0. batch_size = label.shape[0] for i in range(batch_size): l = self._remove_blank(label[i]) p = []...

def get_movielens_iter(filename, batch_size): """Not particularly fast code to parse the text file and load into NDArrays. return two data iters, one for train, the other for validation. """ logging.info("Preparing data iterators for " + filename + " ... ") user = [] item = [] score = [] ...

def imdecode(str_img, flag=1): """Decode image from str buffer. Wrapper for cv2.imdecode that uses mx.nd.NDArray Parameters ---------- str_img : str str buffer read from image file flag : int same as flag for cv2.imdecode Returns ------- img : NDArray decoded...

def resize(src, size, interpolation=cv2.INTER_LINEAR): """Decode image from str buffer. Wrapper for cv2.imresize that uses mx.nd.NDArray Parameters ---------- src : NDArray image in (width, height, channels) size : tuple target size in (width, height) interpolation : int ...

def copyMakeBorder(src, top, bot, left, right, border_type=cv2.BORDER_CONSTANT, value=0): """Pad image border Wrapper for cv2.copyMakeBorder that uses mx.nd.NDArray Parameters ---------- src : NDArray Image in (width, height, channels). Others are the same with cv2.copyMakeBorder ...

def fixed_crop(src, x0, y0, w, h, size=None, interpolation=cv2.INTER_CUBIC): """Crop src at fixed location, and (optionally) resize it to size""" out = mx.nd.crop(src, begin=(y0, x0, 0), end=(y0+h, x0+w, int(src.shape[2]))) if size is not None and (w, h) != size: out = resize(out, size, interpolatio...

def random_crop(src, size): """Randomly crop src with size. Upsample result if src is smaller than size""" h, w, _ = src.shape new_w, new_h = scale_down((w, h), size) x0 = random.randint(0, w - new_w) y0 = random.randint(0, h - new_h) out = fixed_crop(src, x0, y0, new_w, new_h, size) retur...

def random_size_crop(src, size, min_area=0.25, ratio=(3.0/4.0, 4.0/3.0)): """Randomly crop src with size. Randomize area and aspect ratio""" h, w, _ = src.shape area = w*h for _ in range(10): new_area = random.uniform(min_area, 1.0) * area new_ratio = random.uniform(*ratio) new_w...

def next(self): """Move iterator position forward""" batch = mx.nd.zeros((self.batch_size, self.size[1], self.size[0], 3)) i = self.cur for i in range(self.cur, min(len(self.list), self.cur+self.batch_size)): str_img = open(self.root+self.list[i]+'.jpg').read() im...

def check_label_shapes(labels, preds, shape=0): """Check to see if the two arrays are the same size.""" if shape == 0: label_shape, pred_shape = len(labels), len(preds) else: label_shape, pred_shape = labels.shape, preds.shape if label_shape != pred_shape: raise ValueError("Sha...

def import_to_gluon(model_file, ctx): """ Imports the ONNX model files, passed as a parameter, into Gluon SymbolBlock object. Parameters ---------- model_file : str ONNX model file name ctx : Context or list of Context Loads the model into one or many context(s). Returns ...

def get_model(model, ctx, opt): """Model initialization.""" kwargs = {'ctx': ctx, 'pretrained': opt.use_pretrained, 'classes': classes} if model.startswith('resnet'): kwargs['thumbnail'] = opt.use_thumbnail elif model.startswith('vgg'): kwargs['batch_norm'] = opt.batch_norm net = mo...

def get_data_iters(dataset, batch_size, opt): """get dataset iterators""" if dataset == 'mnist': train_data, val_data = get_mnist_iterator(batch_size, (1, 28, 28), num_parts=kv.num_workers, part_index=kv.rank) elif dataset == 'cifar10': train...

def update_learning_rate(lr, trainer, epoch, ratio, steps): """Set the learning rate to the initial value decayed by ratio every N epochs.""" new_lr = lr * (ratio ** int(np.sum(np.array(steps) < epoch))) trainer.set_learning_rate(new_lr) return trainer

def seed(seed_state, ctx="all"): """Seeds the random number generators in MXNet. This affects the behavior of modules in MXNet that uses random number generators, like the dropout operator and `NDArray`'s random sampling operators. Parameters ---------- seed_state : int The random numb...

def random_uniform(attrs, inputs, proto_obj): """Draw random samples from a uniform distribtuion.""" try: from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE except ImportError: raise ImportError("Onnx and protobuf need to be installed. " "Instructions to install - http...

def random_normal(attrs, inputs, proto_obj): """Draw random samples from a Gaussian distribution.""" try: from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE except ImportError: raise ImportError("Onnx and protobuf need to be installed. " "Instructions to install - http...

def add(attrs, inputs, proto_obj): """Adding two tensors""" new_attr = {} if 'broadcast' in attrs and attrs['broadcast'] == 1: broadcast_axis = attrs['axis'] op_value = translation_utils._fix_broadcast('broadcast_add', inputs, broadcast_ax...

def mean(attrs, inputs, proto_obj): """Mean of all the input tensors.""" concat_input = [symbol.expand_dims(op_input, axis=0) for op_input in inputs] concat_sym = symbol.concat(*concat_input, dim=0) mean_sym = symbol.mean(concat_sym, axis=0) return mean_sym, attrs, inputs

def argmax(attrs, inputs, proto_obj): """Returns indices of the maximum values along an axis""" axis = attrs.get('axis', 0) keepdims = attrs.get('keepdims', 1) argmax_op = symbol.argmax(inputs[0], axis=axis, keepdims=keepdims) # onnx argmax operator always expects int64 as output type cast_attrs...

def argmin(attrs, inputs, proto_obj): """Returns indices of the minimum values along an axis.""" axis = attrs.get('axis', 0) keepdims = attrs.get('keepdims', 1) argmin_op = symbol.argmin(inputs[0], axis=axis, keepdims=keepdims) # onnx argmax operator always expects int64 as output type cast_attr...

def maximum(attrs, inputs, proto_obj): """ Elementwise maximum of arrays. MXNet maximum compares only two symbols at a time. ONNX can send more than two to compare. Breaking into multiple mxnet ops to compare two symbols at a time """ if len(inputs) > 1: mxnet_op = symbol.maximum(inp...

def minimum(attrs, inputs, proto_obj): """Elementwise minimum of arrays.""" # MXNet minimum compares only two symbols at a time. # ONNX can send more than two to compare. # Breaking into multiple mxnet ops to compare two symbols at a time if len(inputs) > 1: mxnet_op = symbol.minimum(inputs[...

def concat(attrs, inputs, proto_obj): """ Joins input arrays along a given axis. """ new_attrs = translation_utils._fix_attribute_names(attrs, {'axis': 'dim'}) return 'concat', new_attrs, inputs

def pad(attrs, inputs, proto_obj): """ Add padding to input tensor""" new_attrs = translation_utils._fix_attribute_names(attrs, {'pads' : 'pad_width', 'value' : 'constant_value' }) n...

def batch_norm(attrs, inputs, proto_obj): """Batch normalization.""" new_attrs = translation_utils._fix_attribute_names(attrs, {'epsilon': 'eps', 'is_test': 'fix_gamma'}) new_attrs = translation_utils._remove_attributes(new_attrs, ...

def instance_norm(attrs, inputs, proto_obj): """Instance Normalization.""" new_attrs = translation_utils._fix_attribute_names(attrs, {'epsilon' : 'eps'}) new_attrs['eps'] = attrs.get('epsilon', 1e-5) return 'InstanceNorm', new_attrs, inputs

def leaky_relu(attrs, inputs, proto_obj): """Leaky Relu function""" if 'alpha' in attrs: new_attrs = translation_utils._fix_attribute_names(attrs, {'alpha' : 'slope'}) else: new_attrs = translation_utils._add_extra_attributes(attrs, {'slope': 0.01}) return 'LeakyReLU', new_attrs, inputs

def _elu(attrs, inputs, proto_obj): """Elu function""" if 'alpha' in attrs: new_attrs = translation_utils._fix_attribute_names(attrs, {'alpha' : 'slope'}) else: new_attrs = translation_utils._add_extra_attributes(attrs, {'slope': 1.0}) new_attrs = translation_utils._add_extra_attributes(...