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import logging
from copy import deepcopy
from typing import Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.nn import Identity
from siclib.geometry.camera import SimpleRadial
from siclib.geometry.gravity import Gravity
from siclib.models.base_model import BaseModel
from siclib.models.utils.metrics import dist_error, pitch_error, roll_error, vfov_error
from siclib.models.utils.modules import _DenseBlock, _Transition
from siclib.utils.conversions import deg2rad, pitch2rho, rho2pitch
logger = logging.getLogger(__name__)
# flake8: noqa
# mypy: ignore-errors
def get_centers_and_edges(min: float, max: float, num_bins: int) -> Tuple[np.ndarray, torch.Tensor]:
centers = torch.linspace(min, max + ((max - min) / (num_bins - 1)), num_bins + 1).float()
edges = centers.detach() - ((centers.detach()[1] - centers[0]) / 2.0)
return centers, edges
class DeepCalib(BaseModel):
default_conf = {
"name": "densenet",
"model": "densenet161",
"loss": "NLL",
"num_bins": 256,
"freeze_batch_normalization": False,
"model": "densenet161",
"pretrained": True, # whether to use ImageNet weights
"heads": ["roll", "rho", "vfov", "k1_hat"],
"flip": [], # keys of predictions to flip the sign of
"rpf_scales": [1, 1, 1],
"bounds": {
"roll": [-45, 45],
"rho": [-1, 1],
"vfov": [20, 105],
"k1_hat": [-0.7, 0.7],
},
"use_softamax": False,
}
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
strict_conf = False
required_data_keys = ["image", "image_size"]
def _init(self, conf):
self.is_classification = True if self.conf.loss in ["NLL"] else False
self.num_bins = conf.num_bins
self.roll_centers, self.roll_edges = get_centers_and_edges(
deg2rad(conf.bounds.roll[0]), deg2rad(conf.bounds.roll[1]), self.num_bins
)
self.rho_centers, self.rho_edges = get_centers_and_edges(
conf.bounds.rho[0], conf.bounds.rho[1], self.num_bins
)
self.fov_centers, self.fov_edges = get_centers_and_edges(
deg2rad(conf.bounds.vfov[0]), deg2rad(conf.bounds.vfov[1]), self.num_bins
)
self.k1_hat_centers, self.k1_hat_edges = get_centers_and_edges(
conf.bounds.k1_hat[0], conf.bounds.k1_hat[1], self.num_bins
)
Model = getattr(torchvision.models, conf.model)
weights = "DEFAULT" if self.conf.pretrained else None
self.model = Model(weights=weights)
layers = []
# 2208 for 161 layers. 1024 for 121
num_features = self.model.classifier.in_features
head_layers = 3
layers.append(_Transition(num_features, num_features // 2))
num_features = num_features // 2
growth_rate = 32
layers.append(
_DenseBlock(
num_layers=head_layers,
num_input_features=num_features,
growth_rate=growth_rate,
bn_size=4,
drop_rate=0,
)
)
layers.append(nn.BatchNorm2d(num_features + head_layers * growth_rate))
layers.append(nn.ReLU())
layers.append(nn.AdaptiveAvgPool2d((1, 1)))
layers.append(nn.Flatten())
layers.append(nn.Linear(num_features + head_layers * growth_rate, 512))
layers.append(nn.ReLU())
self.model.classifier = Identity()
self.model.features.norm5 = Identity()
if self.is_classification:
layers.append(nn.Linear(512, self.num_bins))
layers.append(nn.LogSoftmax(dim=1))
else:
layers.append(nn.Linear(512, 1))
layers.append(nn.Tanh())
self.roll_head = nn.Sequential(*deepcopy(layers))
self.rho_head = nn.Sequential(*deepcopy(layers))
self.vfov_head = nn.Sequential(*deepcopy(layers))
self.k1_hat_head = nn.Sequential(*deepcopy(layers))
def bins_to_val(self, centers, pred):
if centers.device != pred.device:
centers = centers.to(pred.device)
if not self.conf.use_softamax:
return centers[pred.argmax(1)]
beta = 1e-0
pred_softmax = F.softmax(pred / beta, dim=1)
weighted_centers = centers[:-1].unsqueeze(0) * pred_softmax
val = weighted_centers.sum(dim=1)
return val
def _forward(self, data):
image = data["image"]
mean, std = image.new_tensor(self.mean), image.new_tensor(self.std)
image = (image - mean[:, None, None]) / std[:, None, None]
shared_features = self.model.features(image)
pred = {}
if "roll" in self.conf.heads:
pred["roll"] = self.roll_head(shared_features)
if "rho" in self.conf.heads:
pred["rho"] = self.rho_head(shared_features)
if "vfov" in self.conf.heads:
pred["vfov"] = self.vfov_head(shared_features)
if "vfov" in self.conf.flip:
pred["vfov"] = pred["vfov"] * -1
if "k1_hat" in self.conf.heads:
pred["k1_hat"] = self.k1_hat_head(shared_features)
size = data["image_size"]
w, h = size[:, 0], size[:, 1]
if self.is_classification:
parameters = {
"roll": self.bins_to_val(self.roll_centers, pred["roll"]),
"rho": self.bins_to_val(self.rho_centers, pred["rho"]),
"vfov": self.bins_to_val(self.fov_centers, pred["vfov"]),
"k1_hat": self.bins_to_val(self.k1_hat_centers, pred["k1_hat"]),
"width": w,
"height": h,
}
for k in self.conf.flip:
parameters[k] = parameters[k] * -1
for i, k in enumerate(["roll", "rho", "vfov"]):
parameters[k] = parameters[k] * self.conf.rpf_scales[i]
camera = SimpleRadial.from_dict(parameters)
roll, pitch = parameters["roll"], rho2pitch(parameters["rho"], camera.f[..., 1], h)
gravity = Gravity.from_rp(roll, pitch)
else: # regression
if "roll" in self.conf.heads:
pred["roll"] = pred["roll"] * deg2rad(45)
if "vfov" in self.conf.heads:
pred["vfov"] = (pred["vfov"] + 1) * deg2rad((105 - 20) / 2 + 20)
camera = SimpleRadial.from_dict(pred | {"width": w, "height": h})
gravity = Gravity.from_rp(pred["roll"], pred["pitch"])
return pred | {"camera": camera, "gravity": gravity}
def loss(self, pred, data):
loss = {"total": 0}
if self.conf.loss == "Huber":
loss_fn = nn.HuberLoss(reduction="none")
elif self.conf.loss == "L1":
loss_fn = nn.L1Loss(reduction="none")
elif self.conf.loss == "L2":
loss_fn = nn.MSELoss(reduction="none")
elif self.conf.loss == "NLL":
loss_fn = nn.NLLLoss(reduction="none")
gt_cam = data["camera"]
if "roll" in self.conf.heads:
# nbins softmax values if classification, else scalar value
gt_roll = data["gravity"].roll.float()
pred_roll = pred["roll"].float()
if gt_roll.device != self.roll_edges.device:
self.roll_edges = self.roll_edges.to(gt_roll.device)
self.roll_centers = self.roll_centers.to(gt_roll.device)
if self.is_classification:
gt_roll = (
torch.bucketize(gt_roll.contiguous(), self.roll_edges) - 1
) # converted to class
assert (gt_roll >= 0).all(), gt_roll
assert (gt_roll < self.num_bins).all(), gt_roll
else:
assert pred_roll.dim() == gt_roll.dim()
loss_roll = loss_fn(pred_roll, gt_roll)
loss["roll"] = loss_roll
loss["total"] += loss_roll
if "rho" in self.conf.heads:
gt_rho = pitch2rho(data["gravity"].pitch, gt_cam.f[..., 1], gt_cam.size[..., 1]).float()
pred_rho = pred["rho"].float()
if gt_rho.device != self.rho_edges.device:
self.rho_edges = self.rho_edges.to(gt_rho.device)
self.rho_centers = self.rho_centers.to(gt_rho.device)
if self.is_classification:
gt_rho = torch.bucketize(gt_rho.contiguous(), self.rho_edges) - 1
assert (gt_rho >= 0).all(), gt_rho
assert (gt_rho < self.num_bins).all(), gt_rho
else:
assert pred_rho.dim() == gt_rho.dim()
# print(f"Rho: {gt_rho.shape}, {pred_rho.shape}")
loss_rho = loss_fn(pred_rho, gt_rho)
loss["rho"] = loss_rho
loss["total"] += loss_rho
if "vfov" in self.conf.heads:
gt_vfov = gt_cam.vfov.float()
pred_vfov = pred["vfov"].float()
if gt_vfov.device != self.fov_edges.device:
self.fov_edges = self.fov_edges.to(gt_vfov.device)
self.fov_centers = self.fov_centers.to(gt_vfov.device)
if self.is_classification:
gt_vfov = torch.bucketize(gt_vfov.contiguous(), self.fov_edges) - 1
assert (gt_vfov >= 0).all(), gt_vfov
assert (gt_vfov < self.num_bins).all(), gt_vfov
else:
min_vfov = deg2rad(self.conf.bounds.vfov[0])
max_vfov = deg2rad(self.conf.bounds.vfov[1])
gt_vfov = (2 * (gt_vfov - min_vfov) / (max_vfov - min_vfov)) - 1
assert pred_vfov.dim() == gt_vfov.dim()
loss_vfov = loss_fn(pred_vfov, gt_vfov)
loss["vfov"] = loss_vfov
loss["total"] += loss_vfov
if "k1_hat" in self.conf.heads:
gt_k1_hat = data["camera"].k1_hat.float()
pred_k1_hat = pred["k1_hat"].float()
if gt_k1_hat.device != self.k1_hat_edges.device:
self.k1_hat_edges = self.k1_hat_edges.to(gt_k1_hat.device)
self.k1_hat_centers = self.k1_hat_centers.to(gt_k1_hat.device)
if self.is_classification:
gt_k1_hat = torch.bucketize(gt_k1_hat.contiguous(), self.k1_hat_edges) - 1
assert (gt_k1_hat >= 0).all(), gt_k1_hat
assert (gt_k1_hat < self.num_bins).all(), gt_k1_hat
else:
assert pred_k1_hat.dim() == gt_k1_hat.dim()
loss_k1_hat = loss_fn(pred_k1_hat, gt_k1_hat)
loss["k1_hat"] = loss_k1_hat
loss["total"] += loss_k1_hat
return loss, self.metrics(pred, data)
def metrics(self, pred, data):
pred_cam, gt_cam = pred["camera"], data["camera"]
pred_gravity, gt_gravity = pred["gravity"], data["gravity"]
return {
"roll_error": roll_error(pred_gravity, gt_gravity),
"pitch_error": pitch_error(pred_gravity, gt_gravity),
"vfov_error": vfov_error(pred_cam, gt_cam),
"k1_error": dist_error(pred_cam, gt_cam),
}
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