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9e14838 | 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 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | # Utility functions for training process
import numpy as np
import torch
from matplotlib import pyplot as plt
from random import random
from scipy.special import softmax
from einops import rearrange
from statistics import mean
import cv2
import math
from typing import Dict
import json
import urllib
from torchvision.transforms import Compose, Lambda
from torchvision.transforms._transforms_video import (
CenterCropVideo,
NormalizeVideo,
)
from pytorchvideo.data.encoded_video import EncodedVideo
from pytorchvideo.transforms import (
ApplyTransformToKey,
ShortSideScale,
UniformTemporalSubsample,
UniformCropVideo
)
PLOTS_NAMES = ["space", "time", "combined"]
# Convert the preds into final video-level prediction
def check_correct(preds, labels, multiclass_labels = None, multiclass_errors = None, videos_ids = None):
preds = [np.asarray(torch.sigmoid(pred).detach().numpy()).round() for pred in preds]
correct = 0
positive_class = 0
negative_class = 0
videos_errors = []
for i in range(len(labels)):
pred = int(preds[i])
if labels[i] == pred:
correct += 1
if labels[i] != pred:
if multiclass_labels is not None and not math.isnan(multiclass_labels[i]):
multiclass_errors[multiclass_labels[i].item()][0] += 1
if videos_ids != None:
videos_errors.append(videos_ids[i])
if pred == 1:
positive_class += 1
else:
negative_class += 1
if multiclass_errors != None:
return correct, positive_class, negative_class, multiclass_errors, videos_errors
else:
return correct, positive_class, negative_class
def unix_time_millis(dt):
return dt.total_seconds() * 1000.0
def multiple_lists_mean(a):
return sum(a) / len(a)
# Aggregate space and time attention
def aggregate_attentions(attentions, heads, num_frames, frames_per_identity, scale_factor = 50000):
# Collapse attentions heads for each attention separated
aggregated_attentions = []
for attention in attentions:
attention = attention.squeeze(1)
attention = rearrange(attention, '(b h) t -> b h t', h = heads)
tokens_means = [torch.max(attention[:, :, i]).item() for i in range(attention.shape[2])]
aggregated_attentions.append(tokens_means)
# Combined space and time attention
tokens_means_combined = list(np.sum(np.asarray(aggregated_attentions), axis=0))
aggregated_attentions.append(tokens_means_combined)
# Softmax all the attentions
for i in range(len(aggregated_attentions)):
aggregated_attentions[i] = np.array_split(np.asarray(aggregated_attentions[i]), num_frames)
aggregated_attentions[i] = softmax([mean(values)*scale_factor for values in aggregated_attentions[i]])
identity_attentions = []
for index, identity_frames in enumerate(frames_per_identity):
if index == 0:
identity_attention = sum(aggregated_attentions[-1][:identity_frames-1])
else:
previous_identity_frames = frames_per_identity[index-1]
identity_attention = sum(aggregated_attentions[-1][previous_identity_frames-1:identity_frames-1])
identity_attentions.append(identity_attention)
return aggregated_attentions, identity_attentions
# Visualize the attention
def save_attention_plots(aggregated_attentions, identity_names, frames_per_identity, num_frames, video_id):
colors = np.random.rand(len(frames_per_identity), 4)
for index, tokens_means in enumerate(aggregated_attentions):
plt.bar([i+1 for i in range(num_frames)], tokens_means)
for i in range(len(frames_per_identity)):
plt.vlines(frames_per_identity[i], ymin=min(tokens_means), ymax=max(tokens_means), colors=colors[i], label = str(identity_names[i]))
plt.legend()
plt.savefig("outputs/tokens/" + video_id + "_" + PLOTS_NAMES[index] + ".jpg")
plt.clf()
def draw_border(img, pt1, pt2, color, thickness, r, d):
x1,y1 = pt1
x2,y2 = pt2
# Top left
cv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness)
cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness)
cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness)
# Top right
cv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness)
cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness)
cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness)
# Bottom left
cv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness)
cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness)
cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness)
# Bottom right
cv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness)
cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness)
cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness)
return img
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
SLOWFAST_ALPHA = 4
class PackPathway(torch.nn.Module):
"""
Transform for converting video frames as a list of tensors.
"""
def __init__(self):
super().__init__()
def forward(self, frames: torch.Tensor):
fast_pathway = frames
# Perform temporal sampling from the fast pathway.
slow_pathway = torch.index_select(
frames,
1,
torch.linspace(
0, frames.shape[1] - 1, frames.shape[1] // SLOWFAST_ALPHA
).long(),
)
frame_list = [slow_pathway, fast_pathway]
return frame_list
def slowfast_input_transform(videos, crop_size = 256, side_size = 256, num_frames = 32, sampling_rate = 2, frames_per_second = 30, mean = [0.45, 0.45, 0.45], std = [0.225, 0.225, 0.225]):
transform=Compose(
[
UniformTemporalSubsample(num_frames),
Lambda(lambda x: x/255.0),
NormalizeVideo(mean, std),
ShortSideScale(
size=side_size
),
CenterCropVideo(crop_size),
PackPathway()
]
)
transformed_videos = [[],[]]
for video in videos:
output = transform(video)
transformed_videos[0].append(output[0])
transformed_videos[1].append(output[1])
return transformed_videos |