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import math
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.nn.functional as F
import numpy as np
from einops import rearrange
from torch.utils.data import Sampler
# -- # Common Functions
class InputPadder:
""" Pads images such that dimensions are divisible by ds """
def __init__(self, dims, mode='leftend', ds=32):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // ds) + 1) * ds - self.ht) % ds
pad_wd = (((self.wd // ds) + 1) * ds - self.wd) % ds
if mode == 'leftend':
self._pad = [0, pad_wd, 0, pad_ht]
else:
self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, pad_ht // 2, pad_ht - pad_ht // 2]
self.mode = mode
def pad(self, *inputs):
return [F.pad(x, self._pad, mode='replicate') for x in inputs]
def unpad(self, x):
ht, wd = x.shape[-2:]
c = [self._pad[2], ht - self._pad[3], self._pad[0], wd - self._pad[1]]
return x[..., c[0]:c[1], c[2]:c[3]]
def coords_gridN(batch, ht, wd, device):
coords = torch.meshgrid(
(
torch.linspace(-1 + 1 / ht, 1 - 1 / ht, ht, device=device),
torch.linspace(-1 + 1 / wd, 1 - 1 / wd, wd, device=device),
)
)
coords = torch.stack((coords[1], coords[0]), dim=0)[
None
].repeat(batch, 1, 1, 1)
return coords
def to_cuda(batch):
for key, value in batch.items():
if isinstance(value, torch.Tensor):
batch[key] = value.cuda()
return batch
def rename_ckpt(ckpt):
renamed_ckpt = dict()
for k in ckpt.keys():
if 'module.' in k:
renamed_ckpt[k.replace('module.', '')] = torch.clone(ckpt[k])
else:
renamed_ckpt[k] = torch.clone(ckpt[k])
return renamed_ckpt
def resample_rgb(rgb, scaleM, batch, ht, wd, device):
coords = coords_gridN(batch, ht, wd, device)
x, y = torch.split(coords, 1, dim=1)
x = (x + 1) / 2 * wd
y = (y + 1) / 2 * ht
scaleM = scaleM.squeeze()
x = x * scaleM[0, 0] + scaleM[0, 2]
y = y * scaleM[1, 1] + scaleM[1, 2]
_, _, orgh, orgw = rgb.shape
x = x / orgw * 2 - 1.0
y = y / orgh * 2 - 1.0
coords = torch.stack([x.squeeze(1), y.squeeze(1)], dim=3)
rgb_resized = torch.nn.functional.grid_sample(rgb, coords, mode='bilinear', align_corners=True)
return rgb_resized
def intrinsic2incidence(K, b, h, w, device):
coords = coords_gridN(b, h, w, device)
x, y = torch.split(coords, 1, dim=1)
x = (x + 1) / 2.0 * w
y = (y + 1) / 2.0 * h
pts3d = torch.cat([x, y, torch.ones_like(x)], dim=1)
pts3d = rearrange(pts3d, 'b d h w -> b h w d')
pts3d = pts3d.unsqueeze(dim=4)
K_ex = K.view([b, 1, 1, 3, 3])
pts3d = torch.linalg.inv(K_ex) @ pts3d
pts3d = torch.nn.functional.normalize(pts3d, dim=3)
return pts3d
def apply_augmentation(rgb, K, seed=None, augscale=2.0, no_change_prob=0.0):
_, h, w = rgb.shape
if seed is not None:
np.random.seed(seed)
if np.random.uniform(0, 1) < no_change_prob:
extension_rx, extension_ry = 1.0, 1.0
else:
extension_rx, extension_ry = np.random.uniform(1, augscale), np.random.uniform(1, augscale)
hs, ws = int(np.ceil(h * extension_ry)), int(np.ceil(w * extension_rx))
stx = float(np.random.randint(0, int(ws - w + 1), 1).item() + 0.5)
edx = float(stx + w - 1)
sty = float(np.random.randint(0, int(hs - h + 1), 1).item() + 0.5)
edy = float(sty + h - 1)
stx = stx / ws * w
edx = edx / ws * w
sty = sty / hs * h
edy = edy / hs * h
ptslt, ptslt_ = np.array([stx, sty, 1]), np.array([0.5, 0.5, 1])
ptsrt, ptsrt_ = np.array([edx, sty, 1]), np.array([w-0.5, 0.5, 1])
ptslb, ptslb_ = np.array([stx, edy, 1]), np.array([0.5, h-0.5, 1])
ptsrb, ptsrb_ = np.array([edx, edy, 1]), np.array([w-0.5, h-0.5, 1])
pts1 = np.stack([ptslt, ptsrt, ptslb, ptsrb], axis=1)
pts2 = np.stack([ptslt_, ptsrt_, ptslb_, ptsrb_], axis=1)
T_num = pts1 @ pts2.T @ np.linalg.inv(pts2 @ pts2.T)
T = np.eye(3)
T[0, 0] = T_num[0, 0]
T[0, 2] = T_num[0, 2]
T[1, 1] = T_num[1, 1]
T[1, 2] = T_num[1, 2]
T = torch.from_numpy(T).float()
K_trans = torch.inverse(T) @ K
b = 1
_, h, w = rgb.shape
device = rgb.device
rgb_trans = resample_rgb(rgb.unsqueeze(0), T, b, h, w, device).squeeze(0)
return rgb_trans, K_trans, T
class IncidenceLoss(nn.Module):
def __init__(self, loss='cosine'):
super(IncidenceLoss, self).__init__()
self.loss = loss
self.smoothl1 = torch.nn.SmoothL1Loss(beta=0.2)
def forward(self, incidence, K):
b, _, h, w = incidence.shape
device = incidence.device
incidence_gt = intrinsic2incidence(K, b, h, w, device)
incidence_gt = incidence_gt.squeeze(4)
incidence_gt = rearrange(incidence_gt, 'b h w d -> b d h w')
if self.loss == 'cosine':
loss = 1 - torch.cosine_similarity(incidence, incidence_gt, dim=1)
elif self.loss == 'absolute':
loss = self.smoothl1(incidence, incidence_gt)
loss = loss.mean()
return loss
class DistributedSamplerNoEvenlyDivisible(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
process can pass a DistributedSampler instance as a DataLoader sampler,
and load a subset of the original dataset that is exclusive to it.
.. note::
Dataset is assumed to be of constant size.
Arguments:
dataset: Dataset used for sampling.
num_replicas (optional): Number of processes participating in
distributed training.
rank (optional): Rank of the current process within num_replicas.
shuffle (optional): If true (default), sampler will shuffle the indices
"""
def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
num_samples = int(math.floor(len(self.dataset) * 1.0 / self.num_replicas))
rest = len(self.dataset) - num_samples * self.num_replicas
if self.rank < rest:
num_samples += 1
self.num_samples = num_samples
self.total_size = len(dataset)
self.shuffle = shuffle
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
if self.shuffle:
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = list(range(len(self.dataset)))
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
self.num_samples = len(indices)
return iter(indices)
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch |