Ross Wightman
3 years ago
commit
809c7bb1ec
@ -0,0 +1,73 @@
|
||||
""" 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())
|
||||
Loading…
Reference in new issue