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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from numpy import random
class GridMask(nn.Module):
def __init__(self, ratio=0.5, prob=0.7):
super(GridMask, self).__init__()
self.ratio = ratio
self.prob = prob
def forward(self, x):
if np.random.rand() > self.prob or not self.training:
return x
n, c, h, w = x.size()
x = x.view(-1, h, w)
hh = int(1.5 * h)
ww = int(1.5 * w)
d = np.random.randint(2, h)
l = min(max(int(d * self.ratio + 0.5), 1), d - 1)
mask = np.ones((hh, ww), np.uint8)
st_h = np.random.randint(d)
st_w = np.random.randint(d)
for i in range(hh // d):
s = d*i + st_h
t = min(s + l, hh)
mask[s:t, :] = 0
for i in range(ww // d):
s = d*i + st_w
t = min(s + l, ww)
mask[:, s:t] = 0
mask = mask[(hh-h)//2:(hh-h)//2+h, (ww-w)//2:(ww-w)//2+w]
mask = torch.tensor(mask, dtype=x.dtype, device=x.device)
mask = 1 - mask
mask = mask.expand_as(x)
x = x * mask
return x.view(n, c, h, w)
def rotation_3d_in_axis(points, angles):
assert points.shape[-1] == 3
assert angles.shape[-1] == 1
angles = angles[..., 0]
n_points = points.shape[-2]
input_dims = angles.shape
if len(input_dims) > 1:
points = points.reshape(-1, n_points, 3)
angles = angles.reshape(-1)
rot_sin = torch.sin(angles)
rot_cos = torch.cos(angles)
ones = torch.ones_like(rot_cos)
zeros = torch.zeros_like(rot_cos)
if VERSION.name == 'v0.17.1':
rot_mat_T = torch.stack([
rot_cos, -rot_sin, zeros,
rot_sin, rot_cos, zeros,
zeros, zeros, ones,
]).transpose(0, 1).reshape(-1, 3, 3)
else:
rot_mat_T = torch.stack([
rot_cos, rot_sin, zeros,
-rot_sin, rot_cos, zeros,
zeros, zeros, ones,
]).transpose(0, 1).reshape(-1, 3, 3)
points = torch.bmm(points, rot_mat_T)
if len(input_dims) > 1:
points = points.reshape(*input_dims, n_points, 3)
return points
def inverse_sigmoid(x, eps=1e-5):
"""Inverse function of sigmoid.
Args:
x (Tensor): The tensor to do the
inverse.
eps (float): EPS avoid numerical
overflow. Defaults 1e-5.
Returns:
Tensor: The x has passed the inverse
function of sigmoid, has same
shape with input.
"""
x = x.clamp(min=0, max=1)
x1 = x.clamp(min=eps)
x2 = (1 - x).clamp(min=eps)
return torch.log(x1 / x2)
def pad_multiple(inputs, img_metas, size_divisor=32):
_, _, img_h, img_w = inputs.shape
pad_h = 0 if img_h % size_divisor == 0 else size_divisor - (img_h % size_divisor)
pad_w = 0 if img_w % size_divisor == 0 else size_divisor - (img_w % size_divisor)
B = len(img_metas)
N = len(img_metas[0]['ori_shape'])
for b in range(B):
img_metas[b]['img_shape'] = [(img_h + pad_h, img_w + pad_w, 3) for _ in range(N)]
img_metas[b]['pad_shape'] = [(img_h + pad_h, img_w + pad_w, 3) for _ in range(N)]
if pad_h == 0 and pad_w == 0:
return inputs
else:
return F.pad(inputs, [0, pad_w, 0, pad_h], value=0)
def rgb_to_hsv(image: torch.Tensor, eps: float = 1e-8) -> torch.Tensor:
r"""Convert an image from RGB to HSV.
.. image:: _static/img/rgb_to_hsv.png
The image data is assumed to be in the range of (0, 1).
Args:
image: RGB Image to be converted to HSV with shape of :math:`(*, 3, H, W)`.
eps: scalar to enforce numarical stability.
Returns:
HSV version of the image with shape of :math:`(*, 3, H, W)`.
The H channel values are in the range 0..2pi. S and V are in the range 0..1.
.. note::
See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
color_conversions.html>`__.
Example:
>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_hsv(input) # 2x3x4x5
"""
if not isinstance(image, torch.Tensor):
raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}")
if len(image.shape) < 3 or image.shape[-3] != 3:
raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}")
image = image / 255.0
max_rgb, argmax_rgb = image.max(-3)
min_rgb, argmin_rgb = image.min(-3)
deltac = max_rgb - min_rgb
v = max_rgb
s = deltac / (max_rgb + eps)
deltac = torch.where(deltac == 0, torch.ones_like(deltac), deltac)
rc, gc, bc = torch.unbind((max_rgb.unsqueeze(-3) - image), dim=-3)
h1 = bc - gc
h2 = (rc - bc) + 2.0 * deltac
h3 = (gc - rc) + 4.0 * deltac
h = torch.stack((h1, h2, h3), dim=-3) / deltac.unsqueeze(-3)
h = torch.gather(h, dim=-3, index=argmax_rgb.unsqueeze(-3)).squeeze(-3)
h = (h / 6.0) % 1.0
h = h * 360.0
v = v * 255.0
return torch.stack((h, s, v), dim=-3)
def hsv_to_rgb(image: torch.Tensor) -> torch.Tensor:
r"""Convert an image from HSV to RGB.
The H channel values are assumed to be in the range 0..2pi. S and V are in the range 0..1.
Args:
image: HSV Image to be converted to HSV with shape of :math:`(*, 3, H, W)`.
Returns:
RGB version of the image with shape of :math:`(*, 3, H, W)`.
Example:
>>> input = torch.rand(2, 3, 4, 5)
>>> output = hsv_to_rgb(input) # 2x3x4x5
"""
if not isinstance(image, torch.Tensor):
raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}")
if len(image.shape) < 3 or image.shape[-3] != 3:
raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}")
h: torch.Tensor = image[..., 0, :, :] / 360.0
s: torch.Tensor = image[..., 1, :, :]
v: torch.Tensor = image[..., 2, :, :] / 255.0
hi: torch.Tensor = torch.floor(h * 6) % 6
f: torch.Tensor = ((h * 6) % 6) - hi
one: torch.Tensor = torch.tensor(1.0, device=image.device, dtype=image.dtype)
p: torch.Tensor = v * (one - s)
q: torch.Tensor = v * (one - f * s)
t: torch.Tensor = v * (one - (one - f) * s)
hi = hi.long()
indices: torch.Tensor = torch.stack([hi, hi + 6, hi + 12], dim=-3)
out = torch.stack((v, q, p, p, t, v, t, v, v, q, p, p, p, p, t, v, v, q), dim=-3)
out = torch.gather(out, -3, indices)
out = out * 255.0
return out
class GpuPhotoMetricDistortion:
"""Apply photometric distortion to image sequentially, every transformation
is applied with a probability of 0.5. The position of random contrast is in
second or second to last.
1. random brightness
2. random contrast (mode 0)
3. convert color from BGR to HSV
4. random saturation
5. random hue
6. convert color from HSV to BGR
7. random contrast (mode 1)
8. randomly swap channels
Args:
brightness_delta (int): delta of brightness.
contrast_range (tuple): range of contrast.
saturation_range (tuple): range of saturation.
hue_delta (int): delta of hue.
"""
def __init__(self,
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18):
self.brightness_delta = brightness_delta
self.contrast_lower, self.contrast_upper = contrast_range
self.saturation_lower, self.saturation_upper = saturation_range
self.hue_delta = hue_delta
def __call__(self, imgs):
"""Call function to perform photometric distortion on images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Result dict with images distorted.
"""
imgs = imgs[:, [2, 1, 0], :, :] # BGR to RGB
contrast_modes = []
for _ in range(imgs.shape[0]):
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
contrast_modes.append(random.randint(2))
for idx in range(imgs.shape[0]):
# random brightness
if random.randint(2):
delta = random.uniform(-self.brightness_delta, self.brightness_delta)
imgs[idx] += delta
if contrast_modes[idx] == 0:
if random.randint(2):
alpha = random.uniform(self.contrast_lower, self.contrast_upper)
imgs[idx] *= alpha
# convert color from BGR to HSV
imgs = rgb_to_hsv(imgs)
for idx in range(imgs.shape[0]):
# random saturation
if random.randint(2):
imgs[idx, 1] *= random.uniform(self.saturation_lower, self.saturation_upper)
# random hue
if random.randint(2):
imgs[idx, 0] += random.uniform(-self.hue_delta, self.hue_delta)
imgs[:, 0][imgs[:, 0] > 360] -= 360
imgs[:, 0][imgs[:, 0] < 0] += 360
# convert color from HSV to BGR
imgs = hsv_to_rgb(imgs)
for idx in range(imgs.shape[0]):
# random contrast
if contrast_modes[idx] == 1:
if random.randint(2):
alpha = random.uniform(self.contrast_lower, self.contrast_upper)
imgs[idx] *= alpha
# randomly swap channels
if random.randint(2):
imgs[idx] = imgs[idx, random.permutation(3)]
imgs = imgs[:, [2, 1, 0], :, :] # RGB to BGR
return imgs
class DumpConfig:
def __init__(self):
self.enabled = False
self.out_dir = tempfile.mkdtemp()
self.stage_count = 0
self.frame_count = 0
DUMP = DumpConfig()
# for backward compatibility
class Version:
def __init__(self):
self.name = 'v1.0.0'
VERSION = Version()
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