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proposal_methods.py
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import numpy as np
import torch
import utils.wsad_utils as utils
from scipy.signal import savgol_filter
import pdb
import pandas as pd
import options
import matplotlib.pyplot as plt
import torch.nn.functional as F
args = options.parser.parse_args()
def filter_segments(segment_predict, vn):
ambilist = './Thumos14reduced-Annotations/Ambiguous_test.txt'
try:
ambilist = list(open(ambilist, "r"))
ambilist = [a.strip("\n").split(" ") for a in ambilist]
except:
ambilist = []
ind = np.zeros(np.shape(segment_predict)[0])
for i in range(np.shape(segment_predict)[0]):
#s[j], e[j], np.max(seg)+0.7*c_s[c],c]
for a in ambilist:
if a[0] == vn:
gt = range(
int(round(float(a[2]) * 25 / 16)), int(round(float(a[3]) * 25 / 16))
)
pd = range(int(segment_predict[i][0]), int(segment_predict[i][1]))
IoU = float(len(set(gt).intersection(set(pd)))) / float(
len(set(gt).union(set(pd)))
)
if IoU > 0:
ind[i] = 1
s = [
segment_predict[i, :]
for i in range(np.shape(segment_predict)[0])
if ind[i] == 0
]
return np.array(s)
def smooth(v, order=2,lens=200):
l = min(lens, len(v))
l = l - (1 - l % 2)
if len(v) <= order:
return v
return savgol_filter(v, l, order)
def get_topk_mean(x, k, axis=0):
return np.mean(np.sort(x, axis=axis)[-int(k):, :], axis=0)
def get_cls_score(element_cls, dim=-2, rat=20, ind=None):
topk_val, _ = torch.topk(element_cls,
k=max(1, int(element_cls.shape[-2] // rat)),
dim=-2)
instance_logits = torch.mean(topk_val, dim=-2)
pred_vid_score = torch.softmax(
instance_logits, dim=-1)[..., :-1].squeeze().data.cpu().numpy()
return pred_vid_score
def _get_vid_score(pred):
# pred : (n, class)
if args is None:
k = 8
topk_mean = self.get_topk_mean(pred, k)
# ind = topk_mean > -50
return pred, topk_mean
win_size = int(args.topk)
split_list = [i*win_size for i in range(1, int(pred.shape[0]//win_size))]
splits = np.split(pred, split_list, axis=0)
tops = []
#select the avg over topk2 segments in each window
for each_split in splits:
top_mean = get_topk_mean(each_split, args.topk2)
tops.append(top_mean)
tops = np.array(tops)
c_s = np.max(tops, axis=0)
return pred, c_s
def __vector_minmax_norm(vector, min_val=None, max_val=None):
if min_val is None or max_val is None:
max_val = np.max(vector)
min_val = np.min(vector)
delta = max_val - min_val
# delta[delta <= 0] = 1
ret = (vector - min_val) / delta
return ret
@torch.no_grad()
def multiple_threshold_hamnet(vid_name,data_dict,labels):
elem = data_dict['cas']
element_atn=data_dict['attn']
element_logits = elem * element_atn
pred_vid_score = get_cls_score(element_logits, rat=10)
score_np = pred_vid_score.copy()
cas_supp = element_logits[..., :-1]
cas_supp_atn = element_atn
pred = np.where(pred_vid_score >= 0.2)[0]
# NOTE: threshold
act_thresh = np.linspace(0.1,0.9,10)
act_thresh_cas = np.linspace(0.1,0.9,10)
prediction = None
if len(pred) == 0:
pred = np.array([np.argmax(pred_vid_score)])
cas_pred = cas_supp[0].cpu().numpy()[:, pred]
num_segments = cas_pred.shape[0]
cas_pred = np.reshape(cas_pred, (num_segments, -1, 1))
cas_supp_softmax = F.softmax(cas_supp,dim=2)
cas_supp_pred = cas_supp_softmax[0].cpu().numpy()[:, pred]
cas_supp_pred = np.reshape(cas_supp_pred, (num_segments, -1, 1))
cas_pred_atn = cas_supp_atn[0].cpu().numpy()[:, [0]]
cas_pred_atn = np.reshape(cas_pred_atn, (num_segments, -1, 1))
proposal_dict = {}
for i in range(len(act_thresh)):
cas_temp = cas_pred.copy()
cas_temp_atn = cas_pred_atn.copy()
seg_list = []
for c in range(len(pred)):
pos = np.where(cas_temp_atn[:, 0, 0] > act_thresh[i])
seg_list.append(pos)
proposals = utils.get_proposal_oic_2(seg_list,
cas_temp,
pred_vid_score,
pred,
args.scale,
num_segments,
args.feature_fps,
num_segments,
gamma=args.gamma_oic)
for j in range(len(proposals)):
try:
class_id = proposals[j][0][0]
if class_id not in proposal_dict.keys():
proposal_dict[class_id] = []
proposal_dict[class_id] += proposals[j]
except IndexError:
print('index error')
final_proposals = []
for class_id in proposal_dict.keys():
final_proposals.append(
utils.soft_nms(proposal_dict[class_id], 0.7, sigma=0.3))
video_lst, t_start_lst, t_end_lst = [], [], []
label_lst, score_lst = [], []
#[c_pred[i], c_score, t_start, t_end]
segment_predict = []
for i in range(len(final_proposals)):
for j in range(len(final_proposals[i])):
[c_pred, c_score, t_start, t_end] = final_proposals[i][j]
segment_predict.append([t_start, t_end,c_score,c_pred])
segment_predict = np.array(segment_predict)
segment_predict = filter_segments(segment_predict, vid_name.decode())
video_lst, t_start_lst, t_end_lst = [], [], []
label_lst, score_lst = [], []
for i in range(np.shape(segment_predict)[0]):
video_lst.append(vid_name.decode())
t_start_lst.append(segment_predict[i, 0])
t_end_lst.append(segment_predict[i, 1])
score_lst.append(segment_predict[i, 2])
label_lst.append(segment_predict[i, 3])
prediction = pd.DataFrame(
{
"video-id": video_lst,
"t-start": t_start_lst,
"t-end": t_end_lst,
"label": label_lst,
"score": score_lst,
}
)
return prediction