Python module.Module方法代碼示例

# 需要導入模塊: from mxnet.module import module [as 別名]
# 或者: from mxnet.module.module import Module [as 別名]
def forward(self, data_batch, is_train=None):
        assert self.binded and self.params_initialized

        # get current_shapes
        if self._curr_module.label_shapes is not None:
            current_shapes = dict(self._curr_module.data_shapes + self._curr_module.label_shapes)
        else:
            current_shapes = dict(self._curr_module.data_shapes)

        # get input_shapes
        if data_batch.provide_label is not None:
            input_shapes = dict(data_batch.provide_data + data_batch.provide_label)
        else:
            input_shapes = dict(data_batch.provide_data)

        # decide if shape changed
        shape_changed = False
        for k, v in current_shapes.items():
            if v != input_shapes[k]:
                shape_changed = True

        if shape_changed:
            module = Module(self._symbol, self._data_names, self._label_names,
                            logger=self.logger, context=self._context,
                            work_load_list=self._work_load_list,
                            fixed_param_names=self._fixed_param_names)
            module.bind(data_batch.provide_data, data_batch.provide_label, self._curr_module.for_training,
                        self._curr_module.inputs_need_grad, force_rebind=False,
                        shared_module=self._curr_module)
            self._curr_module = module

        self._curr_module.forward(data_batch, is_train=is_train) 

# 需要導入模塊: from mxnet.module import module [as 別名]
# 或者: from mxnet.module.module import Module [as 別名]
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
        self._optimizer = shared_module._optimizer
        self._kvstore = shared_module._kvstore
        self._update_on_kvstore = shared_module._update_on_kvstore
        self._updater = shared_module._updater
        self.optimizer_initialized = True 

# 需要導入模塊: from mxnet.module import module [as 別名]
# 或者: from mxnet.module.module import Module [as 別名]
def __init__(self, symbol, data_names, label_names,
                 logger=logging, context=ctx.cpu(), work_load_list=None,
                 asymbol = None,
                 args = None):
        super(ParallModule, self).__init__(logger=logger)
        self._symbol = symbol
        self._asymbol = asymbol
        self._data_names = data_names
        self._label_names = label_names
        self._context = context
        self._work_load_list = work_load_list
        self._num_classes = config.num_classes
        self._batch_size = args.batch_size
        self._verbose = args.verbose
        self._emb_size = config.emb_size
        self._local_class_start = args.local_class_start
        self._iter = 0

        self._curr_module = None

        self._num_workers = config.num_workers
        self._num_ctx = len(self._context)
        self._ctx_num_classes = args.ctx_num_classes
        self._nd_cache = {}
        self._ctx_cpu = mx.cpu()
        self._ctx_single_gpu = self._context[-1]
        self._fixed_param_names = None
        self._curr_module = Module(self._symbol, self._data_names, self._label_names, logger=self.logger,
                        context=self._context, work_load_list=self._work_load_list,
                        fixed_param_names=self._fixed_param_names)
        self._arcface_modules = []
        self._ctx_class_start = []
        for i in range(len(self._context)):

          args._ctxid = i
          _module = Module(self._asymbol(args), self._data_names, self._label_names, logger=self.logger,
                          context=mx.gpu(i), work_load_list=self._work_load_list,
                          fixed_param_names=self._fixed_param_names)
          self._arcface_modules.append(_module)
          _c = args.local_class_start + i*args.ctx_num_classes
          self._ctx_class_start.append(_c)
        self._usekv = False
        if self._usekv:
          self._distkv = mx.kvstore.create('dist_sync')
          self._kvinit = {} 

# 需要導入模塊: from mxnet.module import module [as 別名]
# 或者: from mxnet.module.module import Module [as 別名]
def bind(self, data_shapes, label_shapes=None, for_training=True,
             inputs_need_grad=False, force_rebind=False, shared_module=None, grad_req='write'):
        # in case we already initialized params, keep it
        if self.params_initialized:
            arg_params, aux_params = self.get_params()

        # force rebinding is typically used when one want to switch from
        # training to prediction phase.
        if force_rebind:
            self._reset_bind()

        if self.binded:
            self.logger.warning('Already binded, ignoring bind()')
            return

        assert shared_module is None, 'shared_module for MutableModule is not supported'

        self.for_training = for_training
        self.inputs_need_grad = inputs_need_grad
        self.binded = True

        max_shapes_dict = dict()
        if self._max_data_shapes is not None:
            max_shapes_dict.update(dict(self._max_data_shapes))
        if self._max_label_shapes is not None:
            max_shapes_dict.update(dict(self._max_label_shapes))

        max_data_shapes = list()
        for name, shape in data_shapes:
            if name in max_shapes_dict:
                max_data_shapes.append((name, max_shapes_dict[name]))
            else:
                max_data_shapes.append((name, shape))

        max_label_shapes = list()
        if label_shapes is not None:
            for name, shape in label_shapes:
                if name in max_shapes_dict:
                    max_label_shapes.append((name, max_shapes_dict[name]))
                else:
                    max_label_shapes.append((name, shape))

        if len(max_label_shapes) == 0:
            max_label_shapes = None

        module = Module(self._symbol, self._data_names, self._label_names, logger=self.logger,
                        context=self._context, work_load_list=self._work_load_list,
                        fixed_param_names=self._fixed_param_names)
        module.bind(max_data_shapes, max_label_shapes, for_training, inputs_need_grad,
                    force_rebind=False, shared_module=None)
        self._curr_module = module

        # copy back saved params, if already initialized
        if self.params_initialized:
            self.set_params(arg_params, aux_params) 

# 需要導入模塊: from mxnet.module import module [as 別名]
# 或者: from mxnet.module.module import Module [as 別名]
def bind(self, data_shapes, label_shapes=None, for_training=True,
             inputs_need_grad=False, force_rebind=False, shared_module=None):
        # in case we already initialized params, keep it
        if self.params_initialized:
            arg_params, aux_params = self.get_params()

        # force rebinding is typically used when one want to switch from
        # training to prediction phase.
        if force_rebind:
            self._reset_bind()

        if self.binded:
            self.logger.warning('Already binded, ignoring bind()')
            return

        assert shared_module is None, 'shared_module for MutableModule is not supported'

        self.for_training = for_training
        self.inputs_need_grad = inputs_need_grad
        self.binded = True

        max_shapes_dict = dict()
        if self._max_data_shapes is not None:
            max_shapes_dict.update(dict(self._max_data_shapes))
        if self._max_label_shapes is not None:
            max_shapes_dict.update(dict(self._max_label_shapes))

        max_data_shapes = list()
        for name, shape in data_shapes:
            if name in max_shapes_dict:
                max_data_shapes.append((name, max_shapes_dict[name]))
            else:
                max_data_shapes.append((name, shape))

        max_label_shapes = list()
        if label_shapes is not None:
            for name, shape in label_shapes:
                if name in max_shapes_dict:
                    max_label_shapes.append((name, max_shapes_dict[name]))
                else:
                    max_label_shapes.append((name, shape))

        if len(max_label_shapes) == 0:
            max_label_shapes = None

        module = Module(self._symbol, self._data_names, self._label_names, logger=self.logger,
                        context=self._context, work_load_list=self._work_load_list,
                        fixed_param_names=self._fixed_param_names)
        module.bind(max_data_shapes, max_label_shapes, for_training, inputs_need_grad,
                    force_rebind=False, shared_module=None)
        self._curr_module = module

        # copy back saved params, if already initialized
        if self.params_initialized:
            self.set_params(arg_params, aux_params)