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""" PyTorch FX Based Feature Extraction Helpers
Using https://pytorch.org/vision/stable/feature_extraction.html
"""
from typing import Callable
from torch import nn
from .features import _get_feature_info
try:
from torchvision.models.feature_extraction import create_feature_extractor
has_fx_feature_extraction = True
except ImportError:
has_fx_feature_extraction = False
# Layers we went to treat as leaf modules
from .layers import Conv2dSame, ScaledStdConv2dSame, BatchNormAct2d, BlurPool2d, CondConv2d, StdConv2dSame, DropPath
from .layers.non_local_attn import BilinearAttnTransform
from .layers.pool2d_same import MaxPool2dSame, AvgPool2dSame
# NOTE: By default, any modules from timm.models.layers that we want to treat as leaf modules go here
# BUT modules from timm.models should use the registration mechanism below
_leaf_modules = {
BatchNormAct2d, # reason: flow control for jit scripting
BilinearAttnTransform, # reason: flow control t <= 1
BlurPool2d, # reason: TypeError: F.conv2d received Proxy in groups=x.shape[1]
# Reason: get_same_padding has a max which raises a control flow error
Conv2dSame, MaxPool2dSame, ScaledStdConv2dSame, StdConv2dSame, AvgPool2dSame,
CondConv2d, # reason: TypeError: F.conv2d received Proxy in groups=self.groups * B (because B = x.shape[0])
DropPath, # reason: TypeError: rand recieved Proxy in `size` argument
}
try:
from .layers import InplaceAbn
_leaf_modules.add(InplaceAbn)
except ImportError:
pass
def register_notrace_module(module: nn.Module):
"""
Any module not under timm.models.layers should get this decorator if we don't want to trace through it.
"""
_leaf_modules.add(module)
return module
# Functions we want to autowrap (treat them as leaves)
_autowrap_functions = set()
def register_notrace_function(func: Callable):
"""
Decorator for functions which ought not to be traced through
"""
_autowrap_functions.add(func)
return func
class FeatureGraphNet(nn.Module):
def __init__(self, model, out_indices, out_map=None):
super().__init__()
assert has_fx_feature_extraction, 'Please update to PyTorch 1.10+, torchvision 0.11+ for FX feature extraction'
self.feature_info = _get_feature_info(model, out_indices)
if out_map is not None:
assert len(out_map) == len(out_indices)
return_nodes = {info['module']: out_map[i] if out_map is not None else info['module']
for i, info in enumerate(self.feature_info) if i in out_indices}
self.graph_module = create_feature_extractor(
model, return_nodes,
tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
def forward(self, x):
return list(self.graph_module(x).values())
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