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0122a25 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 | """Loss module maps loss function input keys and controls loss weight."""
from __future__ import annotations
from typing import TypedDict, Union
import torch
from torch import Tensor, nn
from typing_extensions import NotRequired
from mapdet3d.common.named_tuple import is_namedtuple
from mapdet3d.common.typing import LossesType
from mapdet3d.data.typing import DictData
from mapdet3d.engine.connectors import LossConnector
from mapdet3d.op.loss.base import Loss
NestedLossesType = Union[dict[str, "NestedLossesType"], LossesType]
class LossDefinition(TypedDict):
"""Loss definition.
Attributes:
loss (Loss | nn.Module): Loss function to use.
connector (LossConnector): Connector to use for the loss.
weight (float | dict[str, float], optional): Weight to use for the
loss.
name (str, optional): Name to use for the loss.
"""
loss: Loss | nn.Module
connector: LossConnector
weight: NotRequired[float | dict[str, float]]
name: NotRequired[str]
def _get_tensors_nested(
loss_dict: NestedLossesType, prefix: str = ""
) -> list[tuple[str, Tensor]]:
"""Get tensors from loss dict.
Args:
loss_dict (LossesType): Loss dict.
prefix (str, optional): Prefix to add to keys. Defaults to "".
Returns:
list[tuple[str, Tensor]]: List of tensors.
Raises:
ValueError: If loss dict contains non-tensor or dict values.
"""
named_tensors: list[tuple[str, Tensor]] = []
for key in loss_dict:
value = loss_dict[key]
if isinstance(value, Tensor):
named_tensors.append((prefix + key, value))
elif isinstance(value, dict):
named_tensors.extend(
_get_tensors_nested(value, prefix + key + ".")
)
else:
raise ValueError(
f"Loss dict must only contain tensors or dicts. "
f"Found {type(loss_dict[key])} at {prefix + key}."
)
return named_tensors
class LossModule(nn.Module):
"""Loss module maps input keys and combines losses with weights.
This loss combines multiple losses with weights. The loss values are
weighted by the corresponding weight and returned as a dictionary.
"""
def __init__(
self,
losses: list[LossDefinition] | LossDefinition,
exclude_attributes: list[str] | None = None,
) -> None:
"""Creates an instance of the class.
Each loss will be called with arguments matching the kwargs of the loss
function through its connector. By default, the weight is set to 1.0.
Args:
losses (list[LossDefinition]): List of loss definitions.
exclude_attributes (list[str] | None): List of attributes returned
by the losses that should be excluded from the total loss
computation. Use it to log metrics that should not be
optimised. Defaults to None.
Example:
>>> loss = LossModule(
>>> [
>>> {
>>> "loss": nn.MSELoss(),
>>> "weight": 0.7,
>>> "connector": LossConnector(
>>> {
>>> "input": pred_key("input"),
>>> "target": data_key("target"),
>>> }
>>> ),
>>> },
>>> {
>>> "loss": nn.L1Loss(),
>>> "weight": 0.3
>>> "connector": LossConnector(
>>> {
>>> "input": pred_key("input"),
>>> "target": data_key("target"),
>>> }
>>> ),
>>> },
>>> ]
>>> )
"""
super().__init__()
self.losses: list[LossDefinition] = []
if not isinstance(losses, list):
losses = [losses]
for loss in losses:
assert "loss" in loss, "Loss definition must contain a loss."
assert (
"connector" in loss
), "Loss definition must contain a connector."
if "name" not in loss:
loss["name"] = loss["loss"].__class__.__name__
if "weight" not in loss:
loss["weight"] = 1.0
self.losses.append(loss)
self.exclude_attributes = exclude_attributes
def forward(
self, output: DictData, batch: DictData
) -> tuple[Tensor, dict[str, float]]:
"""Forward of loss module.
This function will call all loss functions and return a dictionary
containing the loss values. The loss values are weighted by the
corresponding weight.
If two losses have the same name, the name will be appended with
two underscores.
Args:
output (DictData): Output of the model.
batch (DictData): Batch data.
Returns:
total_loss: The total loss value.
metrics: The metrics disctionary.
"""
loss_dict: LossesType = {}
for loss in self.losses:
loss_values_as_dict: LossesType = {}
name = loss["name"]
loss_value = loss["loss"](**loss["connector"](output, batch))
# Convert loss value to one level dict.
if isinstance(loss_value, Tensor):
# Loss returned a simple tensor
loss_values_as_dict[name] = loss_value
elif isinstance(loss_value, dict):
# Loss returned a dictionary.
for loss_name, loss_value in _get_tensors_nested(
loss_value, name + "."
):
loss_values_as_dict[loss_name] = loss_value
elif is_namedtuple(loss_value):
# Loss returned a named tuple.
for loss_name, loss_value in zip(
loss_value._fields, loss_value
):
loss_values_as_dict[name + "." + loss_name] = loss_value
# Assign values
for key, value in loss_values_as_dict.items():
if value is None:
continue
if isinstance(loss["weight"], dict):
loss_weight = loss["weight"].get(key, 1.0)
else:
loss_weight = loss["weight"]
while key in loss_dict:
key = "__" + key
loss_dict[key] = torch.mul(loss_weight, value)
# Convert loss_dict to total loss and metrics dictionary
metrics: dict[str, float] = {}
keep_loss_dict: LossesType = {}
for k, v in loss_dict.items():
metrics[k] = v.detach().cpu().item()
if (
self.exclude_attributes is None
or k not in self.exclude_attributes
):
keep_loss_dict[k] = v
total_loss: Tensor = sum(keep_loss_dict.values()) # type: ignore
metrics["loss"] = total_loss.detach().cpu().item()
return total_loss, metrics
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