File size: 2,233 Bytes
54d2b91 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | """Video-level transforms. Apply per-clip, consistent across frames."""
from __future__ import annotations
import random
import torch
import torch.nn.functional as F
_IMAGENET_MEAN = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
_IMAGENET_STD = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
class VideoTransform:
"""All ops act on a (T, 3, H, W) float tensor in [0,1]."""
def __init__(self, aug_cfg, training: bool):
self.training = training
self.flip_p = aug_cfg.horizontal_flip if training else 0.0
self.cj_b = aug_cfg.color_jitter.brightness if training else 0.0
self.cj_c = aug_cfg.color_jitter.contrast if training else 0.0
self.cj_s = aug_cfg.color_jitter.saturation if training else 0.0
self.crop_scale = tuple(aug_cfg.random_crop_scale) if training else (1.0, 1.0)
def __call__(self, video: torch.Tensor) -> torch.Tensor:
T, C, H, W = video.shape
# horizontal flip (consistent)
if random.random() < self.flip_p:
video = torch.flip(video, dims=[-1])
# random crop then resize
sc = random.uniform(*self.crop_scale)
if sc < 1.0:
ch, cw = int(H * sc), int(W * sc)
top = random.randint(0, H - ch)
left = random.randint(0, W - cw)
video = video[..., top:top + ch, left:left + cw]
video = F.interpolate(video, size=(H, W), mode="bilinear", align_corners=False)
# color jitter (consistent)
if self.cj_b > 0:
video = video * (1.0 + (random.random() * 2 - 1) * self.cj_b)
if self.cj_c > 0:
mean = video.mean(dim=[-1, -2], keepdim=True)
video = mean + (video - mean) * (1.0 + (random.random() * 2 - 1) * self.cj_c)
if self.cj_s > 0:
gray = video.mean(dim=1, keepdim=True)
video = gray + (video - gray) * (1.0 + (random.random() * 2 - 1) * self.cj_s)
video = video.clamp(0, 1)
# imagenet normalize
video = (video - _IMAGENET_MEAN.to(video)) / _IMAGENET_STD.to(video)
return video
def build_video_transform(aug_cfg, training: bool) -> VideoTransform:
return VideoTransform(aug_cfg, training=training)
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