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Running
on
Zero
File size: 5,546 Bytes
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import torch
import torch.nn as nn
from .utils import FNS, REGRESSION_DICT, masked_mean, masked_quantile
class Regression(nn.Module):
def __init__(
self,
weight: float,
gamma: float,
fn: str,
input_fn: str,
output_fn: str,
alpha: float = 1.0,
dims: tuple[int] = (-1,),
quantile: float = 0.0,
**kwargs,
):
super().__init__()
self.name = self.__class__.__name__
self.output_fn = FNS[output_fn]
self.input_fn = FNS[input_fn]
self.fn = REGRESSION_DICT[fn]
self.weight = weight
self.gamma = gamma
self.alpha = alpha
self.dims = dims
self.quantile = quantile
@torch.autocast(device_type="cuda", enabled=False, dtype=torch.float32)
def forward(
self,
input: torch.Tensor,
target: torch.Tensor,
mask: torch.Tensor | None = None,
**kwargs,
) -> torch.Tensor:
if mask is not None: # usually it is just repeated
mask = mask[:, 0]
input = self.input_fn(input.float())
target = self.input_fn(target.float())
error = self.fn(input - target, gamma=self.gamma, alpha=self.alpha).mean(dim=1)
if self.quantile > 0.0:
mask_quantile = error < masked_quantile(
error, mask, dims=self.dims, q=1 - self.quantile
).view(-1, *((1,) * len(self.dims)))
mask = mask & mask_quantile if mask is not None else mask_quantile
mean_error = masked_mean(data=error, mask=mask, dim=self.dims).squeeze(
self.dims
)
mean_error = self.output_fn(mean_error)
return mean_error
@classmethod
def build(cls, config):
obj = cls(
weight=config["weight"],
fn=config["fn"],
gamma=config["gamma"],
alpha=config.get("alpha", 1.0),
output_fn=config["output_fn"],
input_fn=config["input_fn"],
dims=config.get("dims", (-1,)),
quantile=config.get("quantile", 0.0),
)
return obj
class PolarRegression(nn.Module):
def __init__(
self,
weight: float,
gamma: float,
fn: str,
input_fn: str,
output_fn: str,
alpha: float = 1.0,
dims: list[int] = [-1, -2],
polar_weight: float = 1.0,
polar_asym: float = 0.5,
**kwargs,
):
super().__init__()
self.name = self.__class__.__name__
self.output_fn = FNS[output_fn]
self.input_fn = FNS[input_fn]
self.fn = REGRESSION_DICT[fn]
self.weight = weight
self.gamma = gamma
self.alpha = alpha
self.dims = dims
self.polar_weight = polar_weight
self.polar_asym = polar_asym
@torch.autocast(device_type="cuda", enabled=False, dtype=torch.float32)
def forward(
self,
input: torch.Tensor,
target: torch.Tensor,
mask: torch.Tensor | None = None,
**kwargs,
) -> torch.Tensor:
if mask is not None: # usually it is just repeated
mask = mask.squeeze(1)
input = self.input_fn(input.float())
target = self.input_fn(target.float())
input = input / torch.norm(input, dim=1, keepdim=True).clamp(min=1e-5)
target = target / torch.norm(target, dim=1, keepdim=True).clamp(min=1e-5)
x_target, y_target, z_target = target.unbind(dim=1)
z_clipped = z_target.clip(min=-0.99999, max=0.99999)
x_clipped = x_target.abs().clip(min=1e-5) * (2 * (x_target > 0).float() - 1)
polar_target = torch.arccos(z_clipped)
azimuth_target = torch.atan2(y_target, x_clipped)
x_input, y_input, z_input = input.unbind(dim=1)
z_clipped = z_input.clip(min=-0.99999, max=0.99999)
x_clipped = x_input.abs().clip(min=1e-5) * (2 * (x_input > 0).float() - 1)
polar_input = torch.arccos(z_clipped)
azimuth_input = torch.atan2(y_input, x_clipped)
polar_error = self.fn(
polar_input - polar_target, gamma=self.gamma, alpha=self.alpha
)
azimuth_error = self.fn(
azimuth_input - azimuth_target, gamma=self.gamma, alpha=self.alpha
)
quantile_weight = torch.ones_like(polar_input)
quantile_weight[
(polar_target > polar_input) & (polar_target > torch.pi / 2)
] = (2 * self.polar_asym)
quantile_weight[
(polar_target <= polar_input) & (polar_target > torch.pi / 2)
] = 2 * (1 - self.polar_asym)
mean_polar_error = masked_mean(
data=polar_error * quantile_weight, mask=mask, dim=self.dims
).squeeze(self.dims)
mean_azimuth_error = masked_mean(
data=azimuth_error, mask=mask, dim=self.dims
).squeeze(self.dims)
mean_error = (self.polar_weight * mean_polar_error + mean_azimuth_error) / (
1 + self.polar_weight
)
mean_error = self.output_fn(mean_error)
return mean_error
@classmethod
def build(cls, config):
obj = cls(
weight=config["weight"],
fn=config["fn"],
gamma=config["gamma"],
alpha=config.get("alpha", 1.0),
output_fn=config["output_fn"],
input_fn=config["input_fn"],
dims=config.get("dims", (-1,)),
polar_weight=config["polar_weight"],
polar_asym=config["polar_asym"],
)
return obj
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