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# -*- coding: utf-8 -*-
import os
import time
import numpy as np
import warnings
import random
from omegaconf.listconfig import ListConfig
from webdataset import pipelinefilter
import torch
import torchvision.transforms.functional as TVF
from torchvision.transforms import InterpolationMode
from torchvision.transforms.transforms import _interpolation_modes_from_int
from typing import Sequence
from michelangelo.utils import instantiate_from_config
def _uid_buffer_pick(buf_dict, rng):
uid_keys = list(buf_dict.keys())
selected_uid = rng.choice(uid_keys)
buf = buf_dict[selected_uid]
k = rng.randint(0, len(buf) - 1)
sample = buf[k]
buf[k] = buf[-1]
buf.pop()
if len(buf) == 0:
del buf_dict[selected_uid]
return sample
def _add_to_buf_dict(buf_dict, sample):
key = sample["__key__"]
uid, uid_sample_id = key.split("_")
if uid not in buf_dict:
buf_dict[uid] = []
buf_dict[uid].append(sample)
return buf_dict
def _uid_shuffle(data, bufsize=1000, initial=100, rng=None, handler=None):
"""Shuffle the data in the stream.
This uses a buffer of size `bufsize`. Shuffling at
startup is less random; this is traded off against
yielding samples quickly.
data: iterator
bufsize: buffer size for shuffling
returns: iterator
rng: either random module or random.Random instance
"""
if rng is None:
rng = random.Random(int((os.getpid() + time.time()) * 1e9))
initial = min(initial, bufsize)
buf_dict = dict()
current_samples = 0
for sample in data:
_add_to_buf_dict(buf_dict, sample)
current_samples += 1
if current_samples < bufsize:
try:
_add_to_buf_dict(buf_dict, next(data)) # skipcq: PYL-R1708
current_samples += 1
except StopIteration:
pass
if current_samples >= initial:
current_samples -= 1
yield _uid_buffer_pick(buf_dict, rng)
while current_samples > 0:
current_samples -= 1
yield _uid_buffer_pick(buf_dict, rng)
uid_shuffle = pipelinefilter(_uid_shuffle)
class RandomSample(object):
def __init__(self,
num_volume_samples: int = 1024,
num_near_samples: int = 1024):
super().__init__()
self.num_volume_samples = num_volume_samples
self.num_near_samples = num_near_samples
def __call__(self, sample):
rng = np.random.default_rng()
# 1. sample surface input
total_surface = sample["surface"]
ind = rng.choice(total_surface.shape[0], replace=False)
surface = total_surface[ind]
# 2. sample volume/near geometric points
vol_points = sample["vol_points"]
vol_label = sample["vol_label"]
near_points = sample["near_points"]
near_label = sample["near_label"]
ind = rng.choice(vol_points.shape[0], self.num_volume_samples, replace=False)
vol_points = vol_points[ind]
vol_label = vol_label[ind]
vol_points_labels = np.concatenate([vol_points, vol_label[:, np.newaxis]], axis=1)
ind = rng.choice(near_points.shape[0], self.num_near_samples, replace=False)
near_points = near_points[ind]
near_label = near_label[ind]
near_points_labels = np.concatenate([near_points, near_label[:, np.newaxis]], axis=1)
# concat sampled volume and near points
geo_points = np.concatenate([vol_points_labels, near_points_labels], axis=0)
sample = {
"surface": surface,
"geo_points": geo_points
}
return sample
class SplitRandomSample(object):
def __init__(self,
use_surface_sample: bool = False,
num_surface_samples: int = 4096,
num_volume_samples: int = 1024,
num_near_samples: int = 1024):
super().__init__()
self.use_surface_sample = use_surface_sample
self.num_surface_samples = num_surface_samples
self.num_volume_samples = num_volume_samples
self.num_near_samples = num_near_samples
def __call__(self, sample):
rng = np.random.default_rng()
# 1. sample surface input
surface = sample["surface"]
if self.use_surface_sample:
replace = surface.shape[0] < self.num_surface_samples
ind = rng.choice(surface.shape[0], self.num_surface_samples, replace=replace)
surface = surface[ind]
# 2. sample volume/near geometric points
vol_points = sample["vol_points"]
vol_label = sample["vol_label"]
near_points = sample["near_points"]
near_label = sample["near_label"]
ind = rng.choice(vol_points.shape[0], self.num_volume_samples, replace=False)
vol_points = vol_points[ind]
vol_label = vol_label[ind]
vol_points_labels = np.concatenate([vol_points, vol_label[:, np.newaxis]], axis=1)
ind = rng.choice(near_points.shape[0], self.num_near_samples, replace=False)
near_points = near_points[ind]
near_label = near_label[ind]
near_points_labels = np.concatenate([near_points, near_label[:, np.newaxis]], axis=1)
# concat sampled volume and near points
geo_points = np.concatenate([vol_points_labels, near_points_labels], axis=0)
sample = {
"surface": surface,
"geo_points": geo_points
}
return sample
class FeatureSelection(object):
VALID_SURFACE_FEATURE_DIMS = {
"none": [0, 1, 2], # xyz
"watertight_normal": [0, 1, 2, 3, 4, 5], # xyz, normal
"normal": [0, 1, 2, 6, 7, 8]
}
def __init__(self, surface_feature_type: str):
self.surface_feature_type = surface_feature_type
self.surface_dims = self.VALID_SURFACE_FEATURE_DIMS[surface_feature_type]
def __call__(self, sample):
sample["surface"] = sample["surface"][:, self.surface_dims]
return sample
class AxisScaleTransform(object):
def __init__(self, interval=(0.75, 1.25), jitter=True, jitter_scale=0.005):
assert isinstance(interval, (tuple, list, ListConfig))
self.interval = interval
self.min_val = interval[0]
self.max_val = interval[1]
self.inter_size = interval[1] - interval[0]
self.jitter = jitter
self.jitter_scale = jitter_scale
def __call__(self, sample):
surface = sample["surface"][..., 0:3]
geo_points = sample["geo_points"][..., 0:3]
scaling = torch.rand(1, 3) * self.inter_size + self.min_val
# print(scaling)
surface = surface * scaling
geo_points = geo_points * scaling
scale = (1 / torch.abs(surface).max().item()) * 0.999999
surface *= scale
geo_points *= scale
if self.jitter:
surface += self.jitter_scale * torch.randn_like(surface)
surface.clamp_(min=-1.015, max=1.015)
sample["surface"][..., 0:3] = surface
sample["geo_points"][..., 0:3] = geo_points
return sample
class ToTensor(object):
def __init__(self, tensor_keys=("surface", "geo_points", "tex_points")):
self.tensor_keys = tensor_keys
def __call__(self, sample):
for key in self.tensor_keys:
if key not in sample:
continue
sample[key] = torch.tensor(sample[key], dtype=torch.float32)
return sample
class AxisScale(object):
def __init__(self, interval=(0.75, 1.25), jitter=True, jitter_scale=0.005):
assert isinstance(interval, (tuple, list, ListConfig))
self.interval = interval
self.jitter = jitter
self.jitter_scale = jitter_scale
def __call__(self, surface, *args):
scaling = torch.rand(1, 3) * 0.5 + 0.75
# print(scaling)
surface = surface * scaling
scale = (1 / torch.abs(surface).max().item()) * 0.999999
surface *= scale
args_outputs = []
for _arg in args:
_arg = _arg * scaling * scale
args_outputs.append(_arg)
if self.jitter:
surface += self.jitter_scale * torch.randn_like(surface)
surface.clamp_(min=-1, max=1)
if len(args) == 0:
return surface
else:
return surface, *args_outputs
class RandomResize(torch.nn.Module):
"""Apply randomly Resize with a given probability."""
def __init__(
self,
size,
resize_radio=(0.5, 1),
allow_resize_interpolations=(InterpolationMode.BICUBIC, InterpolationMode.BILINEAR, InterpolationMode.BILINEAR),
interpolation=InterpolationMode.BICUBIC,
max_size=None,
antialias=None,
):
super().__init__()
if not isinstance(size, (int, Sequence)):
raise TypeError(f"Size should be int or sequence. Got {type(size)}")
if isinstance(size, Sequence) and len(size) not in (1, 2):
raise ValueError("If size is a sequence, it should have 1 or 2 values")
self.size = size
self.max_size = max_size
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.interpolation = interpolation
self.antialias = antialias
self.resize_radio = resize_radio
self.allow_resize_interpolations = allow_resize_interpolations
def random_resize_params(self):
radio = torch.rand(1) * (self.resize_radio[1] - self.resize_radio[0]) + self.resize_radio[0]
if isinstance(self.size, int):
size = int(self.size * radio)
elif isinstance(self.size, Sequence):
size = list(self.size)
size = (int(size[0] * radio), int(size[1] * radio))
else:
raise RuntimeError()
interpolation = self.allow_resize_interpolations[
torch.randint(low=0, high=len(self.allow_resize_interpolations), size=(1,))
]
return size, interpolation
def forward(self, img):
size, interpolation = self.random_resize_params()
img = TVF.resize(img, size, interpolation, self.max_size, self.antialias)
img = TVF.resize(img, self.size, self.interpolation, self.max_size, self.antialias)
return img
def __repr__(self) -> str:
detail = f"(size={self.size}, interpolation={self.interpolation.value},"
detail += f"max_size={self.max_size}, antialias={self.antialias}), resize_radio={self.resize_radio}"
return f"{self.__class__.__name__}{detail}"
class Compose(object):
"""Composes several transforms together. This transform does not support torchscript.
Please, see the note below.
Args:
transforms (list of ``Transform`` objects): list of transforms to compose.
Example:
>>> transforms.Compose([
>>> transforms.CenterCrop(10),
>>> transforms.ToTensor(),
>>> ])
.. note::
In order to script the transformations, please use ``torch.nn.Sequential`` as below.
>>> transforms = torch.nn.Sequential(
>>> transforms.CenterCrop(10),
>>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
>>> )
>>> scripted_transforms = torch.jit.script(transforms)
Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
`lambda` functions or ``PIL.Image``.
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, *args):
for t in self.transforms:
args = t(*args)
return args
def __repr__(self):
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += '\n'
format_string += ' {0}'.format(t)
format_string += '\n)'
return format_string
def identity(*args, **kwargs):
if len(args) == 1:
return args[0]
else:
return args
def build_transforms(cfg):
if cfg is None:
return identity
transforms = []
for transform_name, cfg_instance in cfg.items():
transform_instance = instantiate_from_config(cfg_instance)
transforms.append(transform_instance)
print(f"Build transform: {transform_instance}")
transforms = Compose(transforms)
return transforms