Source code for torchvision.models.feature_extraction

from typing import Dict, Callable, List, Union, Optional, Tuple
from collections import OrderedDict
import warnings
import re
from copy import deepcopy
from itertools import chain

import torch
from torch import nn
from torch import fx
from torch.fx.graph_module import _copy_attr

__all__ = ['create_feature_extractor', 'get_graph_node_names']

class LeafModuleAwareTracer(fx.Tracer):
    An fx.Tracer that allows the user to specify a set of leaf modules, ie.
    modules that are not to be traced through. The resulting graph ends up
    having single nodes referencing calls to the leaf modules' forward methods.
    def __init__(self, *args, **kwargs):
        self.leaf_modules = {}
        if 'leaf_modules' in kwargs:
            leaf_modules = kwargs.pop('leaf_modules')
            self.leaf_modules = leaf_modules
        super(LeafModuleAwareTracer, self).__init__(*args, **kwargs)

    def is_leaf_module(self, m: nn.Module, module_qualname: str) -> bool:
        if isinstance(m, tuple(self.leaf_modules)):
            return True
        return super().is_leaf_module(m, module_qualname)

class NodePathTracer(LeafModuleAwareTracer):
    NodePathTracer is an FX tracer that, for each operation, also records the
    name of the Node from which the operation originated. A node name here is
    a `.` seperated path walking the hierarchy from top level module down to
    leaf operation or leaf module. The name of the top level module is not
    included as part of the node name. For example, if we trace a module whose
    forward method applies a ReLU module, the name for that node will simply
    be 'relu'.

    Some notes on the specifics:
        - Nodes are recorded to `self.node_to_qualname` which is a dictionary
          mapping a given Node object to its node name.
        - Nodes are recorded in the order which they are executed during
        - When a duplicate node name is encountered, a suffix of the form
          _{int} is added. The counter starts from 1.
    def __init__(self, *args, **kwargs):
        super(NodePathTracer, self).__init__(*args, **kwargs)
        # Track the qualified name of the Node being traced
        self.current_module_qualname = ''
        # A map from FX Node to the qualified name\#
        # NOTE: This is loosely like the "qualified name" mentioned in the
        # torch.fx docs but adapted
        # for the purposes of the torchvision feature extractor
        self.node_to_qualname = OrderedDict()

    def call_module(self, m: torch.nn.Module, forward: Callable, args, kwargs):
        Override of `fx.Tracer.call_module`
        This override:
        1) Stores away the qualified name of the caller for restoration later
        2) Adds the qualified name of the caller to
           `current_module_qualname` for retrieval by `create_proxy`
        3) Once a leaf module is reached, calls `create_proxy`
        4) Restores the caller's qualified name into current_module_qualname
        old_qualname = self.current_module_qualname
            module_qualname = self.path_of_module(m)
            self.current_module_qualname = module_qualname
            if not self.is_leaf_module(m, module_qualname):
                out = forward(*args, **kwargs)
                return out
            return self.create_proxy('call_module', module_qualname, args, kwargs)
            self.current_module_qualname = old_qualname

    def create_proxy(self, kind: str, target: fx.node.Target, args, kwargs,
                     name=None, type_expr=None, *_) -> fx.proxy.Proxy:
        Override of `Tracer.create_proxy`. This override intercepts the recording
        of every operation and stores away the current traced module's qualified
        name in `node_to_qualname`
        proxy = super().create_proxy(kind, target, args, kwargs, name, type_expr)
        self.node_to_qualname[proxy.node] = self._get_node_qualname(
            self.current_module_qualname, proxy.node)
        return proxy

    def _get_node_qualname(
            self, module_qualname: str, node: fx.node.Node) -> str:
        node_qualname = module_qualname

        if node.op != 'call_module':
            # In this case module_qualname from torch.fx doesn't go all the
            # way to the leaf function/op so we need to append it
            if len(node_qualname) > 0:
                # Only append '.' if we are deeper than the top level module
                node_qualname += '.'
            node_qualname += str(node)

        # Now we need to add an _{index} postfix on any repeated node names
        # For modules we do this from scratch
        # But for anything else, torch.fx already has a globally scoped
        # _{index} postfix. But we want it locally (relative to direct parent)
        # scoped. So first we need to undo the torch.fx postfix
        if re.match(r'.+_[0-9]+$', node_qualname) is not None:
            node_qualname = node_qualname.rsplit('_', 1)[0]

        # ... and now we add on our own postfix
        for existing_qualname in reversed(self.node_to_qualname.values()):
            # Check to see if existing_qualname is of the form
            # {node_qualname} or {node_qualname}_{int}
            if re.match(rf'{node_qualname}(_[0-9]+)?$',
                        existing_qualname) is not None:
                postfix = existing_qualname.replace(node_qualname, '')
                if len(postfix):
                    # existing_qualname is of the form {node_qualname}_{int}
                    next_index = int(postfix[1:]) + 1
                    # existing_qualname is of the form {node_qualname}
                    next_index = 1
                node_qualname += f'_{next_index}'

        return node_qualname

def _is_subseq(x, y):
    """Check if y is a subseqence of x
    iter_x = iter(x)
    return all(any(x_item == y_item for x_item in iter_x) for y_item in y)

def _warn_graph_differences(
        train_tracer: NodePathTracer, eval_tracer: NodePathTracer):
    Utility function for warning the user if there are differences between
    the train graph nodes and the eval graph nodes.
    train_nodes = list(train_tracer.node_to_qualname.values())
    eval_nodes = list(eval_tracer.node_to_qualname.values())

    if len(train_nodes) == len(eval_nodes) and all(
            t == e for t, e in zip(train_nodes, eval_nodes)):

    suggestion_msg = (
        "When choosing nodes for feature extraction, you may need to specify "
        "output nodes for train and eval mode separately.")

    if _is_subseq(train_nodes, eval_nodes):
        msg = ("NOTE: The nodes obtained by tracing the model in eval mode "
               "are a subsequence of those obtained in train mode. ")
    elif _is_subseq(eval_nodes, train_nodes):
        msg = ("NOTE: The nodes obtained by tracing the model in train mode "
               "are a subsequence of those obtained in eval mode. ")
        msg = ("The nodes obtained by tracing the model in train mode "
               "are different to those obtained in eval mode. ")
    warnings.warn(msg + suggestion_msg)

[docs]def get_graph_node_names( model: nn.Module, tracer_kwargs: Dict = {}, suppress_diff_warning: bool = False) -> Tuple[List[str], List[str]]: """ Dev utility to return node names in order of execution. See note on node names under :func:`create_feature_extractor`. Useful for seeing which node names are available for feature extraction. There are two reasons that node names can't easily be read directly from the code for a model: 1. Not all submodules are traced through. Modules from ``torch.nn`` all fall within this category. 2. Nodes representing the repeated application of the same operation or leaf module get a ``_{counter}`` postfix. The model is traced twice: once in train mode, and once in eval mode. Both sets of node names are returned. For more details on the node naming conventions used here, please see the :ref:`relevant subheading <about-node-names>` in the `documentation <>`_. Args: model (nn.Module): model for which we'd like to print node names tracer_kwargs (dict, optional): a dictionary of keywork arguments for ``NodePathTracer`` (they are eventually passed onto `torch.fx.Tracer <>`_). suppress_diff_warning (bool, optional): whether to suppress a warning when there are discrepancies between the train and eval version of the graph. Defaults to False. Returns: tuple(list, list): a list of node names from tracing the model in train mode, and another from tracing the model in eval mode. Examples:: >>> model = torchvision.models.resnet18() >>> train_nodes, eval_nodes = get_graph_node_names(model) """ is_training = train_tracer = NodePathTracer(**tracer_kwargs) train_tracer.trace(model.train()) eval_tracer = NodePathTracer(**tracer_kwargs) eval_tracer.trace(model.eval()) train_nodes = list(train_tracer.node_to_qualname.values()) eval_nodes = list(eval_tracer.node_to_qualname.values()) if not suppress_diff_warning: _warn_graph_differences(train_tracer, eval_tracer) # Restore training state model.train(is_training) return train_nodes, eval_nodes
class DualGraphModule(fx.GraphModule): """ A derivative of `fx.GraphModule`. Differs in the following ways: - Requires a train and eval version of the underlying graph - Copies submodules according to the nodes of both train and eval graphs. - Calling train(mode) switches between train graph and eval graph. """ def __init__(self, root: torch.nn.Module, train_graph: fx.Graph, eval_graph: fx.Graph, class_name: str = 'GraphModule'): """ Args: root (nn.Module): module from which the copied module hierarchy is built train_graph (fx.Graph): the graph that should be used in train mode eval_graph (fx.Graph): the graph that should be used in eval mode """ super(fx.GraphModule, self).__init__() self.__class__.__name__ = class_name self.train_graph = train_graph self.eval_graph = eval_graph # Copy all get_attr and call_module ops (indicated by BOTH train and # eval graphs) for node in chain(iter(train_graph.nodes), iter(eval_graph.nodes)): if node.op in ['get_attr', 'call_module']: assert isinstance(, str) _copy_attr(root, self, # train mode by default self.train() self.graph = train_graph # (borrowed from fx.GraphModule): # Store the Tracer class responsible for creating a Graph separately as part of the # GraphModule state, except when the Tracer is defined in a local namespace. # Locally defined Tracers are not pickleable. This is needed because torch.package will # serialize a GraphModule without retaining the Graph, and needs to use the correct Tracer # to re-create the Graph during deserialization. assert self.eval_graph._tracer_cls == self.train_graph._tracer_cls, \ "Train mode and eval mode should use the same tracer class" self._tracer_cls = None if self.graph._tracer_cls and '<locals>' not in self.graph._tracer_cls.__qualname__: self._tracer_cls = self.graph._tracer_cls def train(self, mode=True): """ Swap out the graph depending on the selected training mode. NOTE this should be safe when calling model.eval() because that just calls this with mode == False. """ # NOTE: Only set self.graph if the current graph is not the desired # one. This saves us from recompiling the graph where not necessary. if mode and not self.graph = self.train_graph elif not mode and self.graph = self.eval_graph return super().train(mode=mode)
[docs]def create_feature_extractor( model: nn.Module, return_nodes: Optional[Union[List[str], Dict[str, str]]] = None, train_return_nodes: Optional[Union[List[str], Dict[str, str]]] = None, eval_return_nodes: Optional[Union[List[str], Dict[str, str]]] = None, tracer_kwargs: Dict = {}, suppress_diff_warning: bool = False) -> fx.GraphModule: """ Creates a new graph module that returns intermediate nodes from a given model as dictionary with user specified keys as strings, and the requested outputs as values. This is achieved by re-writing the computation graph of the model via FX to return the desired nodes as outputs. All unused nodes are removed, together with their corresponding parameters. Desired output nodes must be specified as a ``.`` separated path walking the module hierarchy from top level module down to leaf operation or leaf module. For more details on the node naming conventions used here, please see the :ref:`relevant subheading <about-node-names>` in the `documentation <>`_. Not all models will be FX traceable, although with some massaging they can be made to cooperate. Here's a (not exhaustive) list of tips: - If you don't need to trace through a particular, problematic sub-module, turn it into a "leaf module" by passing a list of ``leaf_modules`` as one of the ``tracer_kwargs`` (see example below). It will not be traced through, but rather, the resulting graph will hold a reference to that module's forward method. - Likewise, you may turn functions into leaf functions by passing a list of ``autowrap_functions`` as one of the ``tracer_kwargs`` (see example below). - Some inbuilt Python functions can be problematic. For instance, ``int`` will raise an error during tracing. You may wrap them in your own function and then pass that in ``autowrap_functions`` as one of the ``tracer_kwargs``. For further information on FX see the `torch.fx documentation <>`_. Args: model (nn.Module): model on which we will extract the features return_nodes (list or dict, optional): either a ``List`` or a ``Dict`` containing the names (or partial names - see note above) of the nodes for which the activations will be returned. If it is a ``Dict``, the keys are the node names, and the values are the user-specified keys for the graph module's returned dictionary. If it is a ``List``, it is treated as a ``Dict`` mapping node specification strings directly to output names. In the case that ``train_return_nodes`` and ``eval_return_nodes`` are specified, this should not be specified. train_return_nodes (list or dict, optional): similar to ``return_nodes``. This can be used if the return nodes for train mode are different than those from eval mode. If this is specified, ``eval_return_nodes`` must also be specified, and ``return_nodes`` should not be specified. eval_return_nodes (list or dict, optional): similar to ``return_nodes``. This can be used if the return nodes for train mode are different than those from eval mode. If this is specified, ``train_return_nodes`` must also be specified, and `return_nodes` should not be specified. tracer_kwargs (dict, optional): a dictionary of keywork arguments for ``NodePathTracer`` (which passes them onto it's parent class `torch.fx.Tracer <>`_). suppress_diff_warning (bool, optional): whether to suppress a warning when there are discrepancies between the train and eval version of the graph. Defaults to False. Examples:: >>> # Feature extraction with resnet >>> model = torchvision.models.resnet18() >>> # extract layer1 and layer3, giving as names `feat1` and feat2` >>> model = create_feature_extractor( >>> model, {'layer1': 'feat1', 'layer3': 'feat2'}) >>> out = model(torch.rand(1, 3, 224, 224)) >>> print([(k, v.shape) for k, v in out.items()]) >>> [('feat1', torch.Size([1, 64, 56, 56])), >>> ('feat2', torch.Size([1, 256, 14, 14]))] >>> # Specifying leaf modules and leaf functions >>> def leaf_function(x): >>> # This would raise a TypeError if traced through >>> return int(x) >>> >>> class LeafModule(torch.nn.Module): >>> def forward(self, x): >>> # This would raise a TypeError if traced through >>> int(x.shape[0]) >>> return torch.nn.functional.relu(x + 4) >>> >>> class MyModule(torch.nn.Module): >>> def __init__(self): >>> super().__init__() >>> self.conv = torch.nn.Conv2d(3, 1, 3) >>> self.leaf_module = LeafModule() >>> >>> def forward(self, x): >>> leaf_function(x.shape[0]) >>> x = self.conv(x) >>> return self.leaf_module(x) >>> >>> model = create_feature_extractor( >>> MyModule(), return_nodes=['leaf_module'], >>> tracer_kwargs={'leaf_modules': [LeafModule], >>> 'autowrap_functions': [leaf_function]}) """ is_training = assert any(arg is not None for arg in [ return_nodes, train_return_nodes, eval_return_nodes]), ( "Either `return_nodes` or `train_return_nodes` and " "`eval_return_nodes` together, should be specified") assert not ((train_return_nodes is None) ^ (eval_return_nodes is None)), \ ("If any of `train_return_nodes` and `eval_return_nodes` are " "specified, then both should be specified") assert ((return_nodes is None) ^ (train_return_nodes is None)), \ ("If `train_return_nodes` and `eval_return_nodes` are specified, " "then both should be specified") # Put *_return_nodes into Dict[str, str] format def to_strdict(n) -> Dict[str, str]: if isinstance(n, list): return {str(i): str(i) for i in n} return {str(k): str(v) for k, v in n.items()} if train_return_nodes is None: return_nodes = to_strdict(return_nodes) train_return_nodes = deepcopy(return_nodes) eval_return_nodes = deepcopy(return_nodes) else: train_return_nodes = to_strdict(train_return_nodes) eval_return_nodes = to_strdict(eval_return_nodes) # Repeat the tracing and graph rewriting for train and eval mode tracers = {} graphs = {} mode_return_nodes: Dict[str, Dict[str, str]] = { 'train': train_return_nodes, 'eval': eval_return_nodes } for mode in ['train', 'eval']: if mode == 'train': model.train() elif mode == 'eval': model.eval() # Instantiate our NodePathTracer and use that to trace the model tracer = NodePathTracer(**tracer_kwargs) graph = tracer.trace(model) name = model.__class__.__name__ if isinstance( model, nn.Module) else model.__name__ graph_module = fx.GraphModule(tracer.root, graph, name) available_nodes = list(tracer.node_to_qualname.values()) # FIXME We don't know if we should expect this to happen assert len(set(available_nodes)) == len(available_nodes), \ "There are duplicate nodes! Please raise an issue" # Check that all outputs in return_nodes are present in the model for query in mode_return_nodes[mode].keys(): # To check if a query is available we need to check that at least # one of the available names starts with it up to a . if not any([re.match(rf'^{query}(\.|$)', n) is not None for n in available_nodes]): raise ValueError( f"node: '{query}' is not present in model. Hint: use " "`get_graph_node_names` to make sure the " "`return_nodes` you specified are present. It may even " "be that you need to specify `train_return_nodes` and " "`eval_return_nodes` separately.") # Remove existing output nodes (train mode) orig_output_nodes = [] for n in reversed(graph_module.graph.nodes): if n.op == 'output': orig_output_nodes.append(n) assert len(orig_output_nodes) for n in orig_output_nodes: graph_module.graph.erase_node(n) # Find nodes corresponding to return_nodes and make them into output_nodes nodes = [n for n in graph_module.graph.nodes] output_nodes = OrderedDict() for n in reversed(nodes): module_qualname = tracer.node_to_qualname.get(n) if module_qualname is None: # NOTE - Know cases where this happens: # - Node representing creation of a tensor constant - probably # not interesting as a return node # - When packing outputs into a named tuple like in InceptionV3 continue for query in mode_return_nodes[mode]: depth = query.count('.') if '.'.join(module_qualname.split('.')[:depth + 1]) == query: output_nodes[mode_return_nodes[mode][query]] = n mode_return_nodes[mode].pop(query) break output_nodes = OrderedDict(reversed(list(output_nodes.items()))) # And add them in the end of the graph with graph_module.graph.inserting_after(nodes[-1]): graph_module.graph.output(output_nodes) # Remove unused modules / parameters graph_module.graph.eliminate_dead_code() graph_module.recompile() # Keep track of the tracer and graph so we can choose the main one tracers[mode] = tracer graphs[mode] = graph # Warn user if there are any discrepancies between the graphs of the # train and eval modes if not suppress_diff_warning: _warn_graph_differences(tracers['train'], tracers['eval']) # Build the final graph module graph_module = DualGraphModule( model, graphs['train'], graphs['eval'], class_name=name) # Restore original training mode model.train(is_training) graph_module.train(is_training) return graph_module


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