Source code for mxnet.gluon.block

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# coding: utf-8
# pylint: disable= arguments-differ, too-many-lines
"""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 ..base import mx_real_t
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

    @staticmethod
    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:
                if not hasattr(_name.NameManager._current, "value"):
                    _name.NameManager._current.value = _name.NameManager()
                prefix = _name.NameManager._current.value.get(None, hint) + '_'
            if params is None:
                params = ParameterDict(prefix)
            else:
                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)
        else:
            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)
        self._name_scope.__enter__()
        return self

    def __exit__(self, ptype, value, trace):
        if self._block._empty_prefix:
            return
        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)
        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:]

    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)
        ret.append(res)
    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. You can also manually register child blocks with :py:meth:`register_child`. 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): children = set(self._children.values()) def _find_unregistered_block_in_container(data): # Find whether a nested container structure contains Blocks if isinstance(data, (list, tuple)): for ele in data: if _find_unregistered_block_in_container(ele): return True return False elif isinstance(data, dict): for _, v in data.items(): if _find_unregistered_block_in_container(v): return True return False elif isinstance(data, Block): return not data in children 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_unregistered_block_in_container(v): warnings.warn('"{name}" is an unregistered 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 parameters in ['conv1_weight', 'conv1_bias', 'fc_weight', 'fc_bias']:: model.collect_params('conv1_weight|conv1_bias|fc_weight|fc_bias') or collect all parameters whose names end 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_parameters(self, filename): """Save parameters to file. Saved parameters can only be loaded with `load_parameters`. Note that this method only saves parameters, not model structure. If you want to save model structures, please use :py:meth:`HybridBlock.export`. Parameters ---------- filename : str Path to file. References ---------- `Saving and Loading Gluon Models `_ """ params = self._collect_params_with_prefix() arg_dict = {key : val._reduce() for key, val in params.items()} ndarray.save(filename, arg_dict)
[docs] def save_params(self, filename): """[Deprecated] Please use save_parameters. Note that if you want load from SymbolBlock later, please use export instead. Save parameters to file. filename : str Path to file. """ warnings.warn("save_params is deprecated. Please use save_parameters. " "Note that if you want load from SymbolBlock later, please " "use export instead. For details, see " "https://mxnet.incubator.apache.org/tutorials/gluon/save_lo" "ad_params.html") try: self.collect_params().save(filename, strip_prefix=self.prefix) except ValueError as e: raise ValueError('%s\nsave_params is deprecated. Using ' \ 'save_parameters may resolve this error.'%e.message)
[docs] def load_parameters(self, filename, ctx=None, allow_missing=False, ignore_extra=False): """Load parameters from file previously saved by `save_parameters`. Parameters ---------- filename : str Path to parameter file. ctx : Context or list of Context, default cpu() Context(s) to 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. References ---------- `Saving and Loading Gluon Models `_ """ 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()))) if name in params: params[name]._load_init(loaded[name], ctx)
[docs] def load_params(self, filename, ctx=None, allow_missing=False, ignore_extra=False): """[Deprecated] Please use load_parameters. Load parameters from file. filename : str Path to parameter file. ctx : Context or list of Context, default cpu() Context(s) to 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. """ warnings.warn("load_params is deprecated. Please use load_parameters.") self.load_parameters(filename, ctx, allow_missing, ignore_extra)
[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. static_alloc : bool, default False Statically allocate memory to improve speed. Memory usage may increase. static_shape : bool, default False Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower. """ 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() seen = set() 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(block.name) 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 summary[m_key]['shared'] = 0 for p in block.params.values(): params += p.data().size summary[m_key]['trainable'] += 0 if p.grad_req == 'null' else p.data().size if p in seen: summary[m_key]['shared'] += p.data().size else: seen.add(p) 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 summary['Input']['shared'] = 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 shared_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'] shared_params += summary[layer]['shared'] print('='*80) print('Parameters in forward computation graph, duplicate included') print(' Total params: ' + str(total_params)) print(' Trainable params: ' + str(trainable_params)) print(' Non-trainable params: ' + str(total_params - trainable_params)) print('Shared params in forward computation graph: ' + str(shared_params)) print('Unique parameters in model: ' + str(total_params - shared_params)) print('-'*80) finally: for h in hooks: h.detach()
[docs]class HybridBlock(Block): """`HybridBlock` supports forwarding with both Symbol and NDArray. `HybridBlock` is similar to `Block`, with a few differences:: import mxnet as mx from mxnet.gluon import HybridBlock, nn class Model(HybridBlock): 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 hybrid_forward(self, F, x): x = F.relu(self.dense0(x)) return F.relu(self.dense1(x)) model = Model() model.initialize(ctx=mx.cpu(0)) model.hybridize() model(mx.nd.zeros((10, 10), ctx=mx.cpu(0))) 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`, `NDArray` indexing (`x[i]`) 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`. Please see references for detailed tutorial. References ---------- `Hybrid - Faster training and easy deployment `_ """ 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): data, out = self._get_graph(*args) data_names = {data.name : i for i, data in enumerate(data)} params = self.collect_params() input_names = out.list_inputs() param_names = set(params.keys()) expected_names = set(input_names) for name in expected_names: assert name in param_names or name in data_names, \ "Unknown input to HybridBlock: %s"%name used_data_names = [i for i in data_names if i in expected_names] if len(used_data_names) != len(data_names): unused = ', '.join(['%d-th'%i for name, i in data_names.items() 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 = [i for i in param_names if i in expected_names] if len(used_param_names) != len(param_names): unused = ', '.join(list(param_names - set(used_param_names))) warnings.warn("Parameter %s is not used by any computation. " "Is this intended?"%unused, stacklevel=4) data_indices = [] param_indices = [] self._cached_op_args = [] for i, name in enumerate(input_names): if name in data_names: data_indices.append(i) self._cached_op_args.append((True, data_names[name])) else: param_indices.append(i) self._cached_op_args.append((False, params[name])) flags = [('data_indices', data_indices), ('param_indices', param_indices)] + \ self._flags self._cached_op = ndarray.CachedOp(out, flags) 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" try: cargs = [args[i] if is_arg else i.data() for is_arg, i in self._cached_op_args] except DeferredInitializationError: self._deferred_infer_shape(*args) cargs = [] for is_arg, i in self._cached_op_args: if is_arg: cargs.append(args[i]) else: i._finish_deferred_init() cargs.append(i.data()) out = self._cached_op(*cargs) 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 = list(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)( **{i.name: 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[i.name])
[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 `SymbolBlock.imports`, `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] sym.save('%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() ndarray.save('%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: j.data(ctx) 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: j.data(ctx) 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 = gluon.model_zoo.vision.alexnet(pretrained=True, 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)) """ @staticmethod
[docs] def imports(symbol_file, input_names, param_file=None, ctx=None): """Import model previously saved by `HybridBlock.export` or `Module.save_checkpoint` as a SymbolBlock for use in Gluon. Parameters ---------- symbol_file : str Path to symbol file. input_names : list of str List of input variable names param_file : str, optional Path to parameter file. ctx : Context, default None The context to initialize SymbolBlock on. Returns ------- SymbolBlock SymbolBlock loaded from symbol and parameter files. Examples -------- >>> net1 = gluon.model_zoo.vision.resnet18_v1( ... prefix='resnet', pretrained=True) >>> net1.hybridize() >>> x = mx.nd.random.normal(shape=(1, 3, 32, 32)) >>> out1 = net1(x) >>> net1.export('net1', epoch=1) >>> >>> net2 = gluon.SymbolBlock.imports( ... 'net1-symbol.json', ['data'], 'net1-0001.params') >>> out2 = net2(x) """ sym = symbol.load(symbol_file) if isinstance(input_names, str): input_names = [input_names] inputs = [symbol.var(i) for i in input_names] ret = SymbolBlock(sym, inputs) if param_file is not None: ret.collect_params().load(param_file, ctx=ctx) return ret
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(i.name) # check if any symbol is row_sparse row_sparse_storage = ndarray.ndarray._STORAGE_TYPE_STR_TO_ID['row_sparse'] for i in out: for j in i.get_internals(): assert(j.attr("__storage_type__") != str(row_sparse_storage)), \ "SymbolBlock doesn't support Parameter '%s' because its storage " \ "type is 'row_sparse'." % j.name # Infer type of parameters. Without this, every parameter will be created with # default type i.e., fp32 arg_params = out.list_arguments() aux_params = out.list_auxiliary_states() arg_types, aux_types = _infer_param_types(syms, out, arg_params, aux_params) for i, arg in enumerate(arg_params): if arg not in input_names: self.params.get(arg, allow_deferred_init=True, dtype=arg_types[i]) for i, aux in enumerate(aux_params): if aux not in input_names: self.params.get(aux, grad_req='null', allow_deferred_init=True, dtype=aux_types[i]) 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(**{k.name: 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 cast(self, dtype): self._clear_cached_op() super(SymbolBlock, self).cast(dtype) def hybrid_forward(self, F, x, *args, **kwargs): raise NotImplementedError
def _infer_param_types(in_params, out_params, arg_params, aux_params, default_dtype=mx_real_t): """Utility function that helps in inferring DType of args and auxs params from given input param. Parameters ---------- in_params: List of Symbol List of input symbol variables. out_params: Symbol Output symbol variable. arg_params: List of Str List of names of argument parametrs. aux_params: List of Str List of names of auxiliary parameters. default_dtype: numpy.dtype or str, default 'float32' Default data type for arg_params and aux_params, if unable to infer the type. Returns ------- arg_types: List of numpy.dtype List of arg_params type. Order is same as arg_params. Defaults to 'float32', if unable to infer type. aux_types: List of numpy.dtype List of aux_params type. Order is same as aux_params. Defaults to 'float32', if unable to infer type. """ arg_types = None aux_types = None # Get Input symbol details. This will be used to infer types of # other parameters. input_sym_names = [in_param.name for in_param in in_params] # Try to infer input types. If not successful, we will set default dtype. # If successful, we will try to infer other params in the graph. input_sym_arg_types = [] can_infer_input_type = True for in_param in in_params: input_sym_arg_type = in_param.infer_type()[0] if not input_sym_arg_type or len(input_sym_arg_type) < 1: can_infer_input_type = False break else: input_sym_arg_types.append(in_param.infer_type()[0][0]) # Try to infer types of other parameters. if can_infer_input_type: params = {k:v for k, v in zip(input_sym_names, input_sym_arg_types)} arg_types, _, aux_types = out_params.infer_type(**params) if arg_types is None or len(arg_types) != len(arg_params): arg_types = [] for _ in arg_params: arg_types.append(default_dtype) if aux_types is None or len(aux_types) != len(aux_params): aux_types = [] for _ in aux_params: aux_types.append(default_dtype) return (arg_types, aux_types)