You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
79 lines
3.1 KiB
79 lines
3.1 KiB
""" 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 import EvoNorm2dB0, EvoNorm2dB1, EvoNorm2dB2
|
|
from .layers import EvoNorm2dS0, EvoNorm2dS0a, EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a
|
|
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
|
|
EvoNorm2dB0, EvoNorm2dB1, EvoNorm2dB2, # to(dtype) use that causes tracing failure (on scripted models only?)
|
|
EvoNorm2dS0, EvoNorm2dS0a, EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a,
|
|
|
|
}
|
|
|
|
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())
|