/
layer_handlers.py
187 lines (154 loc) · 6.04 KB
/
layer_handlers.py
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from collections import Sequence
import numpy as np
import torch
from attr import attrs
from rfa_toolbox.encodings.pytorch.domain import LayerInfoHandler
from rfa_toolbox.graphs import LayerDefinition
def obtain_module_with_resolvable_string(
resolvable: str, model: torch.nn.Module
) -> torch.nn.Module:
"""Attempts to find the module inside a PyTorch-model based on a
resolvable-string extracted from
a JIT-compiled version of the same model.
Args:
resolvable: the resolvable string
model: the PyTorch-model instance in which the layer can be found.
Returns:
PyTorch-Module extracted from the model.
Raises:
ValueError if the module cannot be extracted from the module.
"""
current = model
for elem in resolvable.split("."):
if elem.isnumeric():
current = current[int(elem)]
else:
current = getattr(current, elem, None)
if current is None:
raise ValueError(f"Cannot resolve '{current}.{elem}' from {resolvable}")
return current
@attrs(auto_attribs=True, frozen=True, slots=True)
class Conv2d(LayerInfoHandler):
"""This handler explicitly operated on the torch.nn.Conv2d-Layer."""
def can_handle(self, name: str) -> bool:
if "Conv2d" in name.split(".")[-1]:
return True
else:
return False
def __call__(
self, model: torch.nn.Module, resolvable_string: str, name: str
) -> LayerDefinition:
conv_layer = obtain_module_with_resolvable_string(resolvable_string, model)
kernel_size = (
conv_layer.kernel_size
# if isinstance(conv_layer.kernel_size, int)
# else conv_layer.kernel_size[0]
)
stride_size = (
conv_layer.stride
# if isinstance(conv_layer.stride, int)
# else conv_layer.stride[0]
)
filters = conv_layer.out_channels
if not isinstance(kernel_size, Sequence) and not isinstance(
kernel_size, np.ndarray
):
kernel_size_name = f"{kernel_size}x{kernel_size}"
else:
kernel_size_name = "x".join([str(k) for k in kernel_size])
final_name = f"{name} {kernel_size_name} / {stride_size}"
return LayerDefinition(
name=final_name, # f"{name} {kernel_size}x{kernel_size}",
kernel_size=kernel_size,
stride_size=stride_size,
filters=filters,
)
@attrs(auto_attribs=True, frozen=True, slots=True)
class AnyConv(Conv2d):
"""Extract layer information in convolutional layers."""
def can_handle(self, name: str) -> bool:
if "Conv" in name.split(".")[-1]:
return True
else:
return False
@attrs(auto_attribs=True, frozen=True, slots=True)
class AnyPool(Conv2d):
"""Extract layer information in any pooling layer that is not adaptive."""
def can_handle(self, name: str) -> bool:
working_name = name.split(".")[-1]
return "Pool" in working_name and "Adaptive" not in working_name
def __call__(
self, model: torch.nn.Module, resolvable_string: str, name: str
) -> LayerDefinition:
conv_layer = obtain_module_with_resolvable_string(resolvable_string, model)
kernel_size = conv_layer.kernel_size
stride_size = conv_layer.stride
if not isinstance(kernel_size, Sequence) and not isinstance(
kernel_size, np.ndarray
):
kernel_size_name = f"{kernel_size}x{kernel_size}"
else:
kernel_size_name = "x".join([str(k) for k in kernel_size])
final_name = f"{name} {kernel_size_name} / {stride_size}"
return LayerDefinition(
name=final_name, # f"{name} {kernel_size}x{kernel_size}",
kernel_size=kernel_size,
stride_size=stride_size,
)
@attrs(auto_attribs=True, frozen=True, slots=True)
class AnyAdaptivePool(Conv2d):
"""Extract information from adaptive pooling layers."""
def can_handle(self, name: str) -> bool:
return "Pool" in name and "adaptive" in name
def __call__(
self, model: torch.nn.Module, resolvable_string: str, name: str
) -> LayerDefinition:
kernel_size = None
stride_size = 1
return LayerDefinition(
name=name, kernel_size=kernel_size, stride_size=stride_size
)
@attrs(auto_attribs=True, frozen=True, slots=True)
class LinearHandler(LayerInfoHandler):
"""Extracts information from linear (fully connected) layers."""
def can_handle(self, name: str) -> bool:
return "Linear" in name
def __call__(
self, model: torch.nn.Module, resolvable_string: str, name: str
) -> LayerDefinition:
kernel_size = None
stride_size = 1
features = obtain_module_with_resolvable_string(
resolvable_string, model
).out_features
return LayerDefinition(
name="Fully Connected",
kernel_size=kernel_size,
stride_size=stride_size,
units=features,
)
@attrs(auto_attribs=True, frozen=True, slots=True)
class AnyHandler(LayerInfoHandler):
"""This handler is a catch-all handler, which transform
any layer into an EnrichedNetworkNode.
However, this Handler will assume a kernel-size of 1 and a
stride-size of 1 and will not attempt to extract
any information on the number of filters or units.
Therefore, it is mostly meant as a "last-resort"-handler
for layers that are not handleable by any other handler.
"""
def can_handle(self, name: str) -> bool:
return True
def __call__(
self, model: torch.nn.Module, resolvable_string: str, name: str
) -> LayerDefinition:
kernel_size = 1
stride_size = 1
if "(" in resolvable_string and ")" in name:
# print(result)
result = name.split("(")[-1].replace(")", "")
else:
result = f"{name.split('.')[-1]}"
return LayerDefinition(
name=result, kernel_size=kernel_size, stride_size=stride_size
)