Source code for mxnet.gluon.block

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# coding: utf-8
# pylint: disable= arguments-differ
"""Base container class for all neural network models."""
__all__ = ['Block', 'HybridBlock', 'SymbolBlock']

import threading
import copy
import warnings
import re
from collections import OrderedDict

from .. import symbol, ndarray, initializer
from ..symbol import Symbol
from ..ndarray import NDArray
from .. import name as _name
from .parameter import Parameter, ParameterDict, DeferredInitializationError
from .utils import _indent, _brief_print_list, HookHandle

class _BlockScope(object):
    """Scope for collecting child `Block` s."""
    _current = threading.local()

    def __init__(self, block):
        self._block = block
        self._counter = {}
        self._old_scope = None
        self._name_scope = None

    def create(prefix, params, hint):
        """Creates prefix and params for new `Block`."""
        current = getattr(_BlockScope._current, "value", None)
        if current is None:
            if prefix is None:
                prefix = _name.NameManager._current.value.get(None, hint) + '_'
            if params is None:
                params = ParameterDict(prefix)
                params = ParameterDict(params.prefix, params)
            return prefix, params

        if prefix is None:
            count = current._counter.get(hint, 0)
            prefix = '%s%d_'%(hint, count)
            current._counter[hint] = count + 1
        if params is None:
            parent = current._block.params
            params = ParameterDict(parent.prefix+prefix, parent._shared)
            params = ParameterDict(params.prefix, params)
        return current._block.prefix+prefix, params

    def __enter__(self):
        if self._block._empty_prefix:
            return self
        self._old_scope = getattr(_BlockScope._current, "value", None)
        _BlockScope._current.value = self
        self._name_scope = _name.Prefix(self._block.prefix)
        return self

    def __exit__(self, ptype, value, trace):
        if self._block._empty_prefix:
        self._name_scope.__exit__(ptype, value, trace)
        self._name_scope = None
        _BlockScope._current.value = self._old_scope

def _flatten(args, inout_str):
    if isinstance(args, NDArray):
        return [args], int(0)
    if isinstance(args, Symbol):
        length = len(args.list_outputs())
        length = length if length > 1 else 0
        return [args], int(length)

    assert isinstance(args, (list, tuple)), \
        "HybridBlock %s must be (nested) list of Symbol or NDArray, " \
        "but got %s of type %s"%(inout_str, str(args), str(type(args)))
    flat = []
    fmts = []
    for i in args:
        arg, fmt = _flatten(i, inout_str)
    return flat, fmts

def _regroup(args, fmt):
    if isinstance(fmt, int):
        if fmt == 0:
            return args[0], args[1:]
        return args[:fmt], args[fmt:]

    assert isinstance(args, (list, tuple)), \
        "HybridBlock output must be (nested) list of Symbol or NDArray, " \
        "but got %s of type %s"%(str(args), str(type(args)))
    ret = []
    for i in fmt:
        res, args = _regroup(args, i)
    return ret, args

[docs]class Block(object): """Base class for all neural network layers and models. Your models should subclass this class. :py:class:`Block` can be nested recursively in a tree structure. You can create and assign child :py:class:`Block` as regular attributes:: from mxnet.gluon import Block, nn from mxnet import ndarray as F class Model(Block): def __init__(self, **kwargs): super(Model, self).__init__(**kwargs) # use name_scope to give child Blocks appropriate names. with self.name_scope(): self.dense0 = nn.Dense(20) self.dense1 = nn.Dense(20) def forward(self, x): x = F.relu(self.dense0(x)) return F.relu(self.dense1(x)) model = Model() model.initialize(ctx=mx.cpu(0)) model(F.zeros((10, 10), ctx=mx.cpu(0))) Child :py:class:`Block` assigned this way will be registered and :py:meth:`collect_params` will collect their Parameters recursively. Parameters ---------- prefix : str Prefix acts like a name space. All children blocks created in parent block's :py:meth:`name_scope` will have parent block's prefix in their name. Please refer to `naming tutorial `_ for more info on prefix and naming. params : ParameterDict or None :py:class:`ParameterDict` for sharing weights with the new :py:class:`Block`. For example, if you want ``dense1`` to share ``dense0``'s weights, you can do:: dense0 = nn.Dense(20) dense1 = nn.Dense(20, params=dense0.collect_params()) """ def __init__(self, prefix=None, params=None): self._empty_prefix = prefix == '' self._prefix, self._params = _BlockScope.create(prefix, params, self._alias()) self._name = self._prefix[:-1] if self._prefix.endswith('_') else self._prefix self._scope = _BlockScope(self) self._children = OrderedDict() self._reg_params = {} self._forward_hooks = OrderedDict() self._forward_pre_hooks = OrderedDict() def __repr__(self): s = '{name}(\n{modstr}\n)' modstr = '\n'.join([' ({key}): {block}'.format(key=key, block=_indent(block.__repr__(), 2)) for key, block in self.__dict__.items() if isinstance(block, Block)]) return s.format(name=self.__class__.__name__, modstr=modstr)
[docs] def __setattr__(self, name, value): """Registers parameters.""" if hasattr(self, name): existing = getattr(self, name) if isinstance(existing, (Parameter, Block)) and not isinstance(value, type(existing)): raise TypeError('Changing attribute type for {name} from {type1} to {type2}' \ 'is not allowed.'.format( name=name, type1=type(existing), type2=type(value))) if isinstance(value, Block): self.register_child(value, name) elif isinstance(value, Parameter): assert name not in self._reg_params, \ "Overriding Parameter attribute %s is not allowed. " \ "If you want to share parameters between blocks, please set " \ "'params' at Block construction instead." self._reg_params[name] = value super(Block, self).__setattr__(name, value)
def _check_container_with_block(self): def _find_block_in_container(data): # Find whether a nested container structure contains Blocks if isinstance(data, (list, tuple)): for ele in data: if _find_block_in_container(ele): return True return False elif isinstance(data, dict): for _, v in data.items(): if _find_block_in_container(v): return True return False elif isinstance(data, Block): return True else: return False for k, v in self.__dict__.items(): if isinstance(v, (list, tuple, dict)) and not (k.startswith('__') or k == '_children'): if _find_block_in_container(v): warnings.warn('"{name}" is a container with Blocks. ' 'Note that Blocks inside the list, tuple or dict will not be ' 'registered automatically. Make sure to register them using ' 'register_child() or switching to ' 'nn.Sequential/nn.HybridSequential instead. ' .format(name=self.__class__.__name__ + "." + k), stacklevel=3) def _alias(self): return self.__class__.__name__.lower() @property def prefix(self): """Prefix of this :py:class:`Block`.""" return self._prefix @property def name(self): """Name of this :py:class:`Block`, without '_' in the end.""" return self._name
[docs] def name_scope(self): """Returns a name space object managing a child :py:class:`Block` and parameter names. Should be used within a ``with`` statement:: with self.name_scope(): self.dense = nn.Dense(20) Please refer to `naming tutorial `_ for more info on prefix and naming. """ return self._scope
@property def params(self): """Returns this :py:class:`Block`'s parameter dictionary (does not include its children's parameters).""" return self._params
[docs] def collect_params(self, select=None): """Returns a :py:class:`ParameterDict` containing this :py:class:`Block` and all of its children's Parameters(default), also can returns the select :py:class:`ParameterDict` which match some given regular expressions. For example, collect the specified parameter in ['conv1_weight', 'conv1_bias', 'fc_weight', 'fc_bias']:: model.collect_params('conv1_weight|conv1_bias|fc_weight|fc_bias') or collect all paramters which their name ends with 'weight' or 'bias', this can be done using regular expressions:: model.collect_params('.*weight|.*bias') Parameters ---------- select : str regular expressions Returns ------- The selected :py:class:`ParameterDict` """ # We need to check here because blocks inside containers are not supported. self._check_container_with_block() ret = ParameterDict(self._params.prefix) if not select: ret.update(self.params) else: pattern = re.compile(select) ret.update({name:value for name, value in self.params.items() if pattern.match(name)}) for cld in self._children.values(): ret.update(cld.collect_params(select=select)) return ret
def _collect_params_with_prefix(self, prefix=''): if prefix: prefix += '.' ret = {prefix + key : val for key, val in self._reg_params.items()} for name, child in self._children.items(): ret.update(child._collect_params_with_prefix(prefix + name)) return ret
[docs] def save_params(self, filename): """Save parameters to file. filename : str Path to file. """ params = self._collect_params_with_prefix() arg_dict = {key : val._reduce() for key, val in params.items()}, arg_dict)
[docs] def load_params(self, filename, ctx=None, allow_missing=False, ignore_extra=False): """Load parameters from file. filename : str Path to parameter file. ctx : Context or list of Context, default cpu() Context(s) initialize loaded parameters on. allow_missing : bool, default False Whether to silently skip loading parameters not represents in the file. ignore_extra : bool, default False Whether to silently ignore parameters from the file that are not present in this Block. """ loaded = ndarray.load(filename) params = self._collect_params_with_prefix() if not loaded and not params: return if not any('.' in i for i in loaded.keys()): # legacy loading del loaded self.collect_params().load( filename, ctx, allow_missing, ignore_extra, self.prefix) return if not allow_missing: for name in params.keys(): assert name in loaded, \ "Parameter '%s' is missing in file '%s', which contains parameters: %s. " \ "Set allow_missing=True to ignore missing parameters."%( name, filename, _brief_print_list(loaded.keys())) for name in loaded: if not ignore_extra and name not in params: raise ValueError( "Parameter '%s' loaded from file '%s' is not present in ParameterDict, " \ "which contains parameters %s. Set ignore_extra=True to ignore. "%( name, filename, _brief_print_list(self._params.keys()))) params[name]._load_init(loaded[name], ctx)
[docs] def register_child(self, block, name=None): """Registers block as a child of self. :py:class:`Block` s assigned to self as attributes will be registered automatically.""" if name is None: name = str(len(self._children)) self._children[name] = block
[docs] def register_forward_pre_hook(self, hook): r"""Registers a forward pre-hook on the block. The hook function is called immediately before :func:`forward`. It should not modify the input or output. Parameters ---------- hook : callable The forward hook function of form `hook(block, input) -> None`. Returns ------- :class:`mxnet.gluon.utils.HookHandle` """ handle = HookHandle() handle.attach(self._forward_pre_hooks, hook) return handle
[docs] def register_forward_hook(self, hook): r"""Registers a forward hook on the block. The hook function is called immediately after :func:`forward`. It should not modify the input or output. Parameters ---------- hook : callable The forward hook function of form `hook(block, input, output) -> None`. Returns ------- :class:`mxnet.gluon.utils.HookHandle` """ handle = HookHandle() handle.attach(self._forward_hooks, hook) return handle
[docs] def apply(self, fn): r"""Applies ``fn`` recursively to every child block as well as self. Parameters ---------- fn : callable Function to be applied to each submodule, of form `fn(block)`. Returns ------- this block """ for cld in self._children.values(): cld.apply(fn) fn(self) return self
[docs] def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False, force_reinit=False): """Initializes :py:class:`Parameter` s of this :py:class:`Block` and its children. Equivalent to ``block.collect_params().initialize(...)`` Parameters ---------- init : Initializer Global default Initializer to be used when :py:meth:`Parameter.init` is ``None``. Otherwise, :py:meth:`Parameter.init` takes precedence. ctx : Context or list of Context Keeps a copy of Parameters on one or many context(s). verbose : bool, default False Whether to verbosely print out details on initialization. force_reinit : bool, default False Whether to force re-initialization if parameter is already initialized. """ self.collect_params().initialize(init, ctx, verbose, force_reinit)
[docs] def hybridize(self, active=True, **kwargs): """Activates or deactivates :py:class:`HybridBlock` s recursively. Has no effect on non-hybrid children. Parameters ---------- active : bool, default True Whether to turn hybrid on or off. **kwargs : string Additional flags for hybridized operator. """ for cld in self._children.values(): cld.hybridize(active, **kwargs)
[docs] def cast(self, dtype): """Cast this Block to use another data type. Parameters ---------- dtype : str or numpy.dtype The new data type. """ for child in self._children.values(): child.cast(dtype) for _, param in self.params.items(): param.cast(dtype)
[docs] def __call__(self, *args): """Calls forward. Only accepts positional arguments.""" for hook in self._forward_pre_hooks.values(): hook(self, args) out = self.forward(*args) for hook in self._forward_hooks.values(): hook(self, args, out) return out
[docs] def forward(self, *args): """Overrides to implement forward computation using :py:class:`NDArray`. Only accepts positional arguments. Parameters ---------- *args : list of NDArray Input tensors. """ # pylint: disable= invalid-name raise NotImplementedError
[docs] def summary(self, *inputs): """Print the summary of the model's output and parameters. The network must have been initialized, and must not have been hybridized. Parameters ---------- inputs : object Any input that the model supports. For any tensor in the input, only :class:`mxnet.ndarray.NDArray` is supported. """ summary = OrderedDict() hooks = [] def _get_shape_str(args): def flatten(args): if not isinstance(args, (list, tuple)): return [args], int(0) flat = [] fmts = [] for i in args: arg, fmt = flatten(i) flat.extend(arg) fmts.append(fmt) return flat, fmts def regroup(args, fmt): if isinstance(fmt, int): if fmt == 0: return args[0], args[1:] return args[:fmt], args[fmt:] ret = [] for i in fmt: res, args = regroup(args, i) ret.append(res) return ret, args flat_args, fmts = flatten(args) flat_arg_shapes = [x.shape if isinstance(x, ndarray.NDArray) else x for x in flat_args] shapes = regroup(flat_arg_shapes, fmts)[0] if isinstance(shapes, list): shape_str = str(shapes)[1:-1] else: shape_str = str(shapes) return shape_str.replace('L', '') def _register_summary_hook(block): assert not isinstance(block, HybridBlock) or not block._active, \ '"{}" must not be hybridized to print summary.'.format( def _summary_hook(block, _, outputs): class_name = block.__class__.__name__ block_idx = len(summary) - 1 m_key = '%s-%i' % (class_name, block_idx+1) summary[m_key] = OrderedDict() summary[m_key]['output_shape'] = _get_shape_str(outputs) params = 0 summary[m_key]['trainable'] = 0 for p in block._reg_params.values(): params += summary[m_key]['trainable'] += 0 if p.grad_req == 'null' else summary[m_key]['n_params'] = params from .nn.basic_layers import Sequential, HybridSequential if not isinstance(block, (Sequential, HybridSequential)): hooks.append(block.register_forward_hook(_summary_hook)) summary['Input'] = OrderedDict() summary['Input']['output_shape'] = _get_shape_str(inputs) summary['Input']['n_params'] = 0 summary['Input']['trainable'] = 0 try: self.apply(_register_summary_hook) self(*inputs) line_format = '{:>20} {:>42} {:>15}' print('-'*80) print(line_format.format('Layer (type)', 'Output Shape', 'Param #')) print('='*80) total_params = 0 trainable_params = 0 for layer in summary: print(line_format.format(layer, str(summary[layer]['output_shape']), summary[layer]['n_params'])) total_params += summary[layer]['n_params'] trainable_params += summary[layer]['trainable'] print('='*80) print('Total params: ' + str(total_params)) print('Trainable params: ' + str(trainable_params)) print('Non-trainable params: ' + str(total_params - trainable_params)) print('-'*80) finally: for h in hooks: h.detach()
[docs]class HybridBlock(Block): """`HybridBlock` supports forwarding with both Symbol and NDArray. Forward computation in :py:class:`HybridBlock` must be static to work with :py:class:`Symbol` s, i.e. you cannot call :py:meth:`NDArray.asnumpy`, :py:attr:`NDArray.shape`, :py:attr:`NDArray.dtype`, etc on tensors. Also, you cannot use branching or loop logic that bases on non-constant expressions like random numbers or intermediate results, since they change the graph structure for each iteration. Before activating with :py:meth:`hybridize()`, :py:class:`HybridBlock` works just like normal :py:class:`Block`. After activation, :py:class:`HybridBlock` will create a symbolic graph representing the forward computation and cache it. On subsequent forwards, the cached graph will be used instead of :py:meth:`hybrid_forward`. Refer `Hybrid tutorial `_ to see the end-to-end usage. """ def __init__(self, prefix=None, params=None): super(HybridBlock, self).__init__(prefix=prefix, params=params) self._cached_graph = () self._cached_op = None self._out_format = None self._in_format = None self._active = False self._flags = {}
[docs] def __setattr__(self, name, value): """Registers parameters.""" super(HybridBlock, self).__setattr__(name, value) if isinstance(value, HybridBlock): self._clear_cached_op()
def _get_graph(self, *args): if not self._cached_graph: args, self._in_format = _flatten(args, "input") if len(args) > 1: inputs = [symbol.var('data%d'%i) for i in range(len(args))] else: inputs = [symbol.var('data')] grouped_inputs = _regroup(inputs, self._in_format)[0] params = {i: j.var() for i, j in self._reg_params.items()} with self.name_scope(): out = self.hybrid_forward(symbol, *grouped_inputs, **params) # pylint: disable=no-value-for-parameter out, self._out_format = _flatten(out, "output") self._cached_graph = inputs, symbol.Group(out) return self._cached_graph def _build_cache(self, *args): inputs, out = self._get_graph(*args) input_names = [ for i in inputs] params = self.collect_params() param_names = set(params.keys()) expected_names = set(out.list_inputs()) for name in expected_names: assert name in param_names or name in input_names, \ "Unknown input to HybridBlock: %s"%name used_input_names = [i for i in input_names if i in expected_names] if len(used_input_names) != len(input_names): unused = ', '.join(['%d-th'%i for i, name in enumerate(input_names) if name not in expected_names]) warnings.warn("The %s input to HybridBlock is not used by any " "computation. Is this intended?"%unused, stacklevel=4) used_param_names = set(i for i in param_names if i in expected_names) if len(used_param_names) != len(param_names): unused = ', '.join(list(param_names - used_param_names)) warnings.warn("Parameter %s is not used by any computation. " "Is this intended?"%unused, stacklevel=4) used_params = {k: params[k] for k in used_param_names} try: param_dict = {k: v.list_data() for k, v in used_params.items()} except DeferredInitializationError: self._deferred_infer_shape(*args) for i in used_params.values(): i._finish_deferred_init() param_dict = {k: v.list_data() for k, v in used_params.items()} self._cached_op = ndarray.CachedOp(out, self._flags, input_names, param_dict) def _deferred_infer_shape(self, *args): try: self.infer_shape(*args) except Exception as e: error_msg = "Deferred initialization failed because shape"\ " cannot be inferred. {}".format(e) raise ValueError(error_msg) def _call_cached_op(self, *args): if self._cached_op is None: self._build_cache(*args) args, fmt = _flatten(args, "input") assert fmt == self._in_format, "Invalid input format" out = self._cached_op(*args) if isinstance(out, NDArray): out = [out] return _regroup(out, self._out_format)[0] def _clear_cached_op(self): self._cached_graph = () self._cached_op = None def register_child(self, block, name=None): if not isinstance(block, HybridBlock): raise ValueError( "Children of HybridBlock must also be HybridBlock, " \ "but %s has type %s. If you are using Sequential, " \ "please try HybridSequential instead."%( str(block), str(type(block)))) super(HybridBlock, self).register_child(block, name) self._clear_cached_op() def hybridize(self, active=True, **kwargs): self._active = active self._flags = kwargs.items() self._clear_cached_op() if active and self._forward_hooks or self._forward_pre_hooks: warnings.warn('"{}" is being hybridized while still having forward hook/pre-hook. ' 'If "{}" is a child of HybridBlock, the hooks will not take effect.') super(HybridBlock, self).hybridize(active, **kwargs) def cast(self, dtype): self._clear_cached_op() super(HybridBlock, self).cast(dtype) def _infer_attrs(self, infer_fn, attr, *args): """Generic infer attributes.""" inputs, out = self._get_graph(*args) args, _ = _flatten(args, "input") with warnings.catch_warnings(record=True) as w: arg_attrs, _, aux_attrs = getattr(out, infer_fn)( **{ getattr(j, attr) for i, j in zip(inputs, args)}) if arg_attrs is None: raise ValueError(w[0].message) sdict = {i: j for i, j in zip(out.list_arguments(), arg_attrs)} sdict.update({name : attr for name, attr in \ zip(out.list_auxiliary_states(), aux_attrs)}) for i in self.collect_params().values(): setattr(i, attr, sdict[])
[docs] def infer_shape(self, *args): """Infers shape of Parameters from inputs.""" self._infer_attrs('infer_shape', 'shape', *args)
[docs] def infer_type(self, *args): """Infers data type of Parameters from inputs.""" self._infer_attrs('infer_type', 'dtype', *args)
[docs] def export(self, path, epoch=0): """Export HybridBlock to json format that can be loaded by `mxnet.mod.Module` or the C++ interface. .. note:: When there are only one input, it will have name `data`. When there Are more than one inputs, they will be named as `data0`, `data1`, etc. Parameters ---------- path : str Path to save model. Two files `path-symbol.json` and `path-xxxx.params` will be created, where xxxx is the 4 digits epoch number. epoch : int Epoch number of saved model. """ if not self._cached_graph: raise RuntimeError( "Please first call block.hybridize() and then run forward with " "this block at least once before calling export.") sym = self._cached_graph[1]'%s-symbol.json'%path) arg_names = set(sym.list_arguments()) aux_names = set(sym.list_auxiliary_states()) arg_dict = {} for name, param in self.collect_params().items(): if name in arg_names: arg_dict['arg:%s'%name] = param._reduce() else: assert name in aux_names arg_dict['aux:%s'%name] = param._reduce()'%s-%04d.params'%(path, epoch), arg_dict)
[docs] def forward(self, x, *args): """Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`.""" if isinstance(x, NDArray): with x.context as ctx: if self._active: return self._call_cached_op(x, *args) try: params = {i: for i, j in self._reg_params.items()} except DeferredInitializationError: self._deferred_infer_shape(x, *args) for _, i in self.params.items(): i._finish_deferred_init() params = {i: for i, j in self._reg_params.items()} return self.hybrid_forward(ndarray, x, *args, **params) assert isinstance(x, Symbol), \ "HybridBlock requires the first argument to forward be either " \ "Symbol or NDArray, but got %s"%type(x) params = {i: j.var() for i, j in self._reg_params.items()} with self.name_scope(): return self.hybrid_forward(symbol, x, *args, **params)
[docs] def hybrid_forward(self, F, x, *args, **kwargs): """Overrides to construct symbolic graph for this `Block`. Parameters ---------- x : Symbol or NDArray The first input tensor. *args : list of Symbol or list of NDArray Additional input tensors. """ # pylint: disable= invalid-name raise NotImplementedError
def _common_prefix(names): """Get the common prefix for all names""" if not names: return '' prefix = names[0] for name in names: i = 0 while i < len(prefix) and i < len(name) and prefix[i] == name[i]: i += 1 prefix = prefix[:i] return prefix
[docs]class SymbolBlock(HybridBlock): """Construct block from symbol. This is useful for using pre-trained models as feature extractors. For example, you may want to extract the output from fc2 layer in AlexNet. Parameters ---------- outputs : Symbol or list of Symbol The desired output for SymbolBlock. inputs : Symbol or list of Symbol The Variables in output's argument that should be used as inputs. params : ParameterDict Parameter dictionary for arguments and auxililary states of outputs that are not inputs. Examples -------- >>> # To extract the feature from fc1 and fc2 layers of AlexNet: >>> alexnet =, ctx=mx.cpu(), prefix='model_') >>> inputs = mx.sym.var('data') >>> out = alexnet(inputs) >>> internals = out.get_internals() >>> print(internals.list_outputs()) ['data', ..., 'model_dense0_relu_fwd_output', ..., 'model_dense1_relu_fwd_output', ...] >>> outputs = [internals['model_dense0_relu_fwd_output'], internals['model_dense1_relu_fwd_output']] >>> # Create SymbolBlock that shares parameters with alexnet >>> feat_model = gluon.SymbolBlock(outputs, inputs, params=alexnet.collect_params()) >>> x = mx.nd.random.normal(shape=(16, 3, 224, 224)) >>> print(feat_model(x)) """ def __init__(self, outputs, inputs, params=None): super(SymbolBlock, self).__init__(prefix=None, params=None) self._prefix = '' self._params = ParameterDict('', params) if isinstance(inputs, symbol.Symbol) and len(inputs.list_outputs()) == 1: inputs = [inputs] if isinstance(outputs, (list, tuple)) and len(outputs) == 1: outputs = outputs[0] syms, self._in_format = _flatten(inputs, "input") out, self._out_format = _flatten(outputs, "output") out = symbol.Group(out) input_names = set() for i in syms: assert len(i.get_internals().list_outputs()) == 1, \ "Input symbols must be variable, but %s is an output of operators"%str(i) input_names.add( for i in out.list_arguments(): if i not in input_names: self.params.get(i, allow_deferred_init=True) for i in out.list_auxiliary_states(): if i not in input_names: self.params.get(i, grad_req='null', allow_deferred_init=True) self._cached_graph = syms, out len_prefix = len(_common_prefix(list(self._params.keys()))) self._reg_params = {key[len_prefix:]: val for key, val in self._params.items()} def forward(self, x, *args): if isinstance(x, NDArray): with x.context: return self._call_cached_op(x, *args) assert isinstance(x, Symbol), \ "HybridBlock requires the first argument to forward be either " \ "Symbol or NDArray, but got %s"%type(x) args, in_fmt = _flatten([x] + list(args), "input") assert in_fmt == self._in_format, "Invalid input format" ret = copy.copy(self._cached_graph[1]) ret._compose(**{ v for k, v in zip(self._cached_graph[0], args)}) return _regroup(list(ret), self._out_format)[0] def _clear_cached_op(self): tmp = self._cached_graph super(SymbolBlock, self)._clear_cached_op() self._cached_graph = tmp def hybrid_forward(self, F, x, *args, **kwargs): raise NotImplementedError