def test_video_anomaly_detection(self):
# type: () -> None
"""
Actually performs tests.
"""
c, t, h, w = self.dataset.raw_shape
# Load the checkpoint
self.model.load_w(self.checkpoint)
# Prepare a table to show results
vad_table = self.empty_table
# Set up container for novelty scores from all test videos
global_llk = []
global_rec = []
global_ns = []
global_y = []
# Get accumulators
results_accumulator_llk = ResultsAccumulator(time_steps=t)
results_accumulator_rec = ResultsAccumulator(time_steps=t)
# Start iteration over test videos
for cl_idx, video_id in enumerate(self.dataset.test_videos):
# Run the test
self.dataset.test(video_id)
loader = DataLoader(self.dataset,
collate_fn=self.dataset.collate_fn)
# Build score containers
sample_llk = np.zeros(shape=(len(loader) + t - 1, ))
sample_rec = np.zeros(shape=(len(loader) + t - 1, ))
sample_y = self.dataset.load_test_sequence_gt(video_id)
for i, (x, y) in tqdm(enumerate(loader),
desc=f'Computing scores for {self.dataset}'):
# x = x.to('cuda')
x = x.to('cpu')
x_r, z, z_dist = self.model(x)
self.loss(x, x_r, z, z_dist)
# Feed results accumulators
results_accumulator_llk.push(self.loss.autoregression_loss)
results_accumulator_rec.push(self.loss.reconstruction_loss)
sample_llk[i] = results_accumulator_llk.get_next()
sample_rec[i] = results_accumulator_rec.get_next()
# Get last results
while results_accumulator_llk.results_left != 0:
index = (-results_accumulator_llk.results_left)
sample_llk[index] = results_accumulator_llk.get_next()
sample_rec[index] = results_accumulator_rec.get_next()
min_llk, max_llk, min_rec, max_rec = self.compute_normalizing_coefficients(
sample_llk, sample_rec)
# Compute the normalized scores and novelty score
sample_llk = normalize(sample_llk, min_llk, max_llk)
sample_rec = normalize(sample_rec, min_rec, max_rec)
sample_ns = novelty_score(sample_llk, sample_rec)
# Update global scores (used for global metrics)
global_llk.append(sample_llk)
global_rec.append(sample_rec)
global_ns.append(sample_ns)
global_y.append(sample_y)
try:
# Compute AUROC for this video
this_video_metrics = [
roc_auc_score(sample_y, sample_llk), # likelihood metric
roc_auc_score(sample_y,
sample_rec), # reconstruction metric
roc_auc_score(sample_y, sample_ns) # novelty score
]
vad_table.add_row([video_id] + this_video_metrics)
except ValueError:
# This happens for sequences in which all frames are abnormal
# Skipping this row in the table (the sequence will still count for global metrics)
continue
# Compute global AUROC and print table
global_llk = np.concatenate(global_llk)
global_rec = np.concatenate(global_rec)
global_ns = np.concatenate(global_ns)
global_y = np.concatenate(global_y)
global_metrics = [
roc_auc_score(global_y, global_llk), # likelihood metric
roc_auc_score(global_y, global_rec), # reconstruction metric
roc_auc_score(global_y, global_ns) # novelty score
]
vad_table.add_row(['avg'] + list(global_metrics))
print(vad_table)
# Save table
with open(self.output_file, mode='w') as f:
f.write(str(vad_table))
def test_one_class_classification(self):
# type: () -> None
"""
Actually performs tests.
"""
# Prepare a table to show results
oc_table = self.empty_table
# Set up container for metrics from all classes
all_metrics = []
# Start iteration over classes
for cl_idx, cl in enumerate(self.dataset.test_classes):
# Load the checkpoint
self.model.load_w(join(self.checkpoints_dir, f'{cl}.pkl'))
# First we need a run on validation, to compute
# normalizing coefficient of the Novelty Score (Eq.9)
min_llk, max_llk, min_rec, max_rec = self.compute_normalizing_coefficients(
cl)
# Run the actual test
self.dataset.test(cl)
loader = DataLoader(self.dataset)
sample_llk = np.zeros(shape=(len(loader), ))
sample_rec = np.zeros(shape=(len(loader), ))
sample_y = np.zeros(shape=(len(loader), ))
for i, (x, y) in tqdm(enumerate(loader),
desc=f'Computing scores for {self.dataset}'):
x = x.to(device)
x_r, z, z_dist = self.model(x)
self.loss(x, x_r, z, z_dist)
sample_llk[i] = -self.loss.autoregression_loss
sample_rec[i] = -self.loss.reconstruction_loss
sample_y[i] = y.item()
# Normalize scores
sample_llk = normalize(sample_llk, min_llk, max_llk)
sample_rec = normalize(sample_rec, min_rec, max_rec)
# Compute the normalized novelty score
sample_ns = novelty_score(sample_llk, sample_rec)
# Compute AUROC for this class
this_class_metrics = [
roc_auc_score(sample_y, sample_llk), # likelihood metric
roc_auc_score(sample_y, sample_rec), # reconstruction metric
roc_auc_score(sample_y, sample_ns) # novelty score
]
oc_table.add_row([cl_idx] + this_class_metrics)
all_metrics.append(this_class_metrics)
# Compute average AUROC and print table
all_metrics = np.array(all_metrics)
avg_metrics = np.mean(all_metrics, axis=0)
oc_table.add_row(['avg'] + list(avg_metrics))
print(oc_table)
# Save table
with open(self.output_file, mode='w') as f:
f.write(str(oc_table))
def test_video_anomaly_detection(self):
# type: () -> None
"""
Actually performs tests.
"""
c, t, h, w = self.dataset.raw_shape
# Load the checkpoint
self.model.load_w(self.checkpoint)
# Prepare a table to show results
vad_table = self.empty_table
# Set up container for novelty scores from all test videos
global_llk = []
global_rec = []
global_ns = []
global_y = []
# Get accumulators，干嘛的？答：get frame-level scores from clip-level scores
results_accumulator_llk = ResultsAccumulator(time_steps=t)
results_accumulator_rec = ResultsAccumulator(time_steps=t)
cnt_step = 0 # global_step
with SummaryWriter(log_dir="summary/test_{0}".format(
Config.output_file.split('.')[0]),
comment="{}".format(Config.dataset_name)) as writer:
# Start iteration over test videos
for cl_idx, video_id in enumerate(self.dataset.test_videos):
# test_videos 的内容是：TestXXX(XXX：001~012) 这些目录名，每个目录名保存有一个
# 视频的所有帧，所以代表一个视频，即 video_id
# Run the test
self.dataset.test(video_id) # 设置好cur_video_frames【其实是整个视频的全部clips】，
# cur_video_gt，cur_len【其实是clips number】
loader = DataLoader(self.dataset,
num_workers=Config.num_workers,
shuffle=Config.shuffle,
batch_size=Config.batch_size,
collate_fn=self.dataset.collate_fn) # 临时构建loader
# 因为是 inference，所以没有 batch_size (或者说==1)
# collate_fn：实际作用是：TODO
# Build score containers
sample_llk = np.zeros(shape=(len(loader) + t - 1,))
sample_rec = np.zeros(shape=(len(loader) + t - 1,))
# print("len(loader): ", len(loader)) # len(self.batch_sampler)
# 因为loader会把所有Dataset的所有item都做登记，而len(dataset) ==
# num_frames - t + 1，即所有的clips (带overlap的)，要恢复就是：
# len(loader) + t - 1
# print("len(loader) + t - 1: ", len(loader) + t - 1)
sample_y = self.dataset.load_test_sequence_gt(video_id) # (n_frames,)
# print("len(sample_y): ", len(sample_y))
# 事实证明：(len(loader) + t - 1) == len(sample_y), len(loader) =
#
for i, (x, y) in tqdm(enumerate(loader), desc=f'Computing scores for {self.dataset}'):
#
cnt_step = cnt_step + 1
x = x.to(device)
x_r, z, z_dist = self.model(x)
ttloss = self.loss(x, x_r, z, z_dist) # 记住，self.loss其实一个 object，这里
# 被执行了 forwrd()，所以等于修改了 object (即 self.loss被修改了)
total_loss = self.loss.total_loss
reconstruction_loss = self.loss.reconstruction_loss
autoregression_loss = self.loss.autoregression_loss
# write all loss
# if cnt_step % Config.plot_every == 0:
# writer.add_scalars("test_loss",
# {'total_loss': total_loss,
# 'reconstruction_loss': reconstruction_loss,
# 'autoregression_loss': autoregression_loss
# },
# cnt_step)
# Feed results accumulators: 模仿一个队列，队尾进，队头出
# 我的办法：通过设置断点，进去看results_accumulator_llk是怎么工作的？
# 因为 batch_szie == 1, 所以push了 it(==num_clips==num_frames-t+1)次，
# 所以还有 (t - 1) 帧没有计算 loss，留到 下面的 while
results_accumulator_llk.push(self.loss.autoregression_loss)
results_accumulator_rec.push(self.loss.reconstruction_loss)
sample_llk[i] = results_accumulator_llk.get_next()
sample_rec[i] = results_accumulator_rec.get_next()
# Get last results
# 计算剩下的 (t-1)帧各自的 loss
while results_accumulator_llk.results_left != 0:
index = (- results_accumulator_llk.results_left)
sample_llk[index] = results_accumulator_llk.get_next()
sample_rec[index] = results_accumulator_rec.get_next()
min_llk, max_llk, min_rec, max_rec = self.compute_normalizing_coefficients(sample_llk, sample_rec)
# Compute the normalized scores and novelty score
sample_llk = normalize(sample_llk, min_llk, max_llk)
sample_rec = normalize(sample_rec, min_rec, max_rec)
sample_ns = novelty_score(sample_llk, sample_rec)
# 绘制 score-map
# print("len of sample_ns:", len(sample_ns))
fig_novelty_score = plt.figure()
plt.title('novelty_score of {}'.format(video_id))
plt.plot(range(len(sample_ns)), sample_ns, color='green',
label='novelty_score')
plt.xlabel('frames')
plt.ylabel('novelty_score')
writer.add_figure('Novelty Score', fig_novelty_score, global_step=cl_idx)
# Update global scores (used for global metrics)
global_llk.append(sample_llk)
global_rec.append(sample_rec)
global_ns.append(sample_ns)
global_y.append(sample_y)
try:
# Compute AUROC for this video
this_video_metrics = [
roc_auc_score(sample_y, sample_llk), # likelihood metric
roc_auc_score(sample_y, sample_rec), # reconstruction metric
roc_auc_score(sample_y, sample_ns) # novelty score
]
vad_table.add_row([video_id] + this_video_metrics)
except ValueError:
# This happens for sequences in which all frames are abnormal
# Skipping this row in the table (the sequence will still count for global metrics)
continue
# Compute global AUROC and print table
global_llk = np.concatenate(global_llk)
global_rec = np.concatenate(global_rec)
global_ns = np.concatenate(global_ns)
global_y = np.concatenate(global_y)
global_metrics = [
roc_auc_score(global_y, global_llk), # likelihood metric
roc_auc_score(global_y, global_rec), # reconstruction metric
roc_auc_score(global_y, global_ns) # novelty score
]
vad_table.add_row(['avg'] + list(global_metrics))
print(vad_table)
# Save table
with open(self.output_file, mode='w') as f:
f.write(str(vad_table))
def test_joint_for_paramsopt(self, model, loss_fn, val_dataloader, metrics,
params):
# type: () -> None
"""
Actually performs tests.
"""
c, t, h, w = val_dataloader.raw_shape
# set model to evaluation mode
model.eval()
# summary for current eval loop
# summ = []
metrics = {}
# Load the checkpoint
# self.model.load_w(self.checkpoint)
# self.ckpt = torch.load(self.checkpoint)
# self.model.load_state_dict(self.ckpt['net_dict'])
# self.R = self.ckpt['R']
# self.c = self.ckpt['c']
# Prepare a table to show results
vad_table = self.empty_table
# Set up container for novelty scores from all test videos
global_llk = []
global_rec = []
global_ns = []
global_y = []
# Get accumulators，干嘛的？答：get frame-level scores from clip-level scores
results_accumulator_llk = ResultsAccumulator(time_steps=t)
results_accumulator_rec = ResultsAccumulator(time_steps=t)
cnt_step = 0 # global_step
with SummaryWriter(
log_dir="summary/test_deepSVDD/test_{0}_lr={1}_lam_rec={2}_"
"lam_svdd={3}_code_length={4}".format(self.params.dataset_name,
self.params.LR,
self.params.lam_rec,
self.params.lam_svdd,
self.params.code_length),
comment="{}".format(self.params.dataset_name)) as writer:
# Start iteration over test videos
for cl_idx, video_id in enumerate(val_dataloader.test_videos):
# test_videos 的内容是：TestXXX(XXX：001~012) 这些目录名，每个目录名保存有一个
# 视频的所有帧，所以代表一个视频，即 video_id
# Run the test
val_dataloader.test(
video_id) # 设置好cur_video_frames【其实是整个视频的全部clips】，
# cur_video_gt，cur_len【其实是clips number】
loader = DataLoader(
val_dataloader,
num_workers=1,
shuffle=False,
batch_size=1,
collate_fn=val_dataloader.collate_fn) # 临时构建loader
# 因为是 inference，所以没有 batch_size (或者说==1)
# collate_fn：实际作用是：TODO
# Build score containers
sample_llk = np.zeros(shape=(len(loader) + t - 1, ))
sample_rec = np.zeros(shape=(len(loader) + t - 1, ))
# print("len(loader): ", len(loader)) # len(self.batch_sampler)
# 因为loader会把所有Dataset的所有item都做登记，而len(dataset) ==
# num_frames - t + 1，即所有的clips (带overlap的)，要恢复就是：
# len(loader) + t - 1
# print("len(loader) + t - 1: ", len(loader) + t - 1)
sample_y = val_dataloader.load_test_sequence_gt(
video_id) # (n_frames,)
# print("len(sample_y): ", len(sample_y))
# 事实证明：(len(loader) + t - 1) == len(sample_y), len(loader) =
#
for i, (x, y) in tqdm(
enumerate(loader),
desc=f'Computing scores for {self.params.dataset_name}'
):
#
cnt_step = cnt_step + 1
x = x.to(self.device)
x_r, z = model(x)
z = z.view(-1, 690, 2 * (self.params.code_length))
# print("in 327 line, z.size: ", z.size())
ttloss = loss_fn(x, x_r, z) # 记住，self.loss其实一个 object，这里
# 被执行了 forwrd()，所以等于修改了 object (即 self.loss被修改了)
total_loss = loss_fn.total_loss
reconstruction_loss = loss_fn.reconstruction_loss
deepSVDD_loss = loss_fn.deepSVDD_loss
# write all loss
# if cnt_step % Config.plot_every == 0:
# writer.add_scalars("test_loss",
# {'total_loss': total_loss,
# 'reconstruction_loss': reconstruction_loss,
# 'autoregression_loss': autoregression_loss
# },
# cnt_step)
# Feed results accumulators: 模仿一个队列，队尾进，队头出
# 我的办法：通过设置断点，进去看results_accumulator_llk是怎么工作的？
# 因为 batch_szie == 1, 所以push了 it(==num_clips==num_frames-t+1)次，
# 所以还有 (t - 1) 帧没有计算 loss，留到 下面的 while
results_accumulator_llk.push(loss_fn.deepSVDD_loss)
results_accumulator_rec.push(loss_fn.reconstruction_loss)
sample_llk[i] = results_accumulator_llk.get_next()
sample_rec[i] = results_accumulator_rec.get_next()
# Get last results
# 计算剩下的 (t-1)帧各自的 loss
while results_accumulator_llk.results_left != 0:
index = (-results_accumulator_llk.results_left)
sample_llk[index] = results_accumulator_llk.get_next()
sample_rec[index] = results_accumulator_rec.get_next()
min_llk, max_llk, min_rec, max_rec = self.compute_normalizing_coefficients(
sample_llk, sample_rec)
# Compute the normalized scores and novelty score
sample_llk = normalize(sample_llk, min_llk, max_llk)
sample_rec = normalize(sample_rec, min_rec, max_rec)
sample_ns = novelty_score(sample_llk, sample_rec)
# # 绘制 score-map
# # print("len of sample_ns:", len(sample_ns))
# fig_novelty_score = plt.figure()
# plt.title('novelty_score of {}'.format(video_id))
# plt.plot(range(len(sample_ns)), sample_ns, color='green',
# label='novelty_score')
# plt.xlabel('frames')
# plt.ylabel('novelty_score')
# writer.add_figure('Novelty Score', fig_novelty_score, global_step=cl_idx)
# Update global scores (used for global metrics)
global_llk.append(sample_llk)
global_rec.append(sample_rec)
global_ns.append(sample_ns)
global_y.append(sample_y)
try:
# Compute AUROC for this video
this_video_metrics = [
roc_auc_score(sample_y,
sample_llk), # likelihood metric
roc_auc_score(sample_y,
sample_rec), # reconstruction metric
roc_auc_score(sample_y, sample_ns) # novelty score
]
vad_table.add_row([video_id] + this_video_metrics)
except ValueError:
# This happens for sequences in which all frames are abnormal
# Skipping this row in the table (the sequence will still count for global metrics)
continue
# Compute global AUROC and print table
global_llk = np.concatenate(global_llk)
global_rec = np.concatenate(global_rec)
global_ns = np.concatenate(global_ns)
global_y = np.concatenate(global_y)
global_metrics = [
roc_auc_score(global_y, global_llk), # likelihood metric
roc_auc_score(global_y, global_rec), # reconstruction metric
roc_auc_score(global_y, global_ns) # novelty score
]
vad_table.add_row(['avg'] + list(global_metrics))
print(vad_table)
# # Save table
# with open(self.output_file, mode='w') as f:
# f.write(str(vad_table))
# #
# # 查看下网络
# # model_input = torch.rand([1380, 1, 8, 32, 32])
# # writer.add_graph(self.model, input_to_model=model_input)
# print("ag_auc: ", list(global_metrics)[2])
metrics['auc'] = list(global_metrics)[2]
return metrics # 返回 avg_auc
def test_one_class_classification(self):
# type: () -> None
"""
Actually performs tests.
"""
# Prepare a table to show results
oc_table = self.empty_table
# Set up container for metrics from all classes
all_metrics = []
# Start iteration over classes
for cl_idx, cl in enumerate(self.dataset.test_classes):
if(cl_idx == 2):
lala = True
print('cl_idx ' +str(cl_idx))
print('cl '+ str(cl))
# Load the checkpoint
self.model.load_w(join(self.checkpoints_dir, f'{cl}.pkl'))
# First we need a run on validation, to compute
# normalizing coefficient of the Novelty Score (Eq.9)
min_llk, max_llk, min_rec, max_rec = self.compute_normalizing_coefficients(cl)
# Run the actual test
self.dataset.test(cl)
loader = DataLoader(self.dataset)
sample_llk = np.zeros(shape=(len(loader),))
sample_rec = np.zeros(shape=(len(loader),))
sample_y = np.zeros(shape=(len(loader),))
print("Length of the dataset = " + str(len(self.dataset)))
labels = np.zeros((len(self.dataset)))
for i, (x, y) in tqdm(enumerate(loader), desc=f'Computing scores for {self.dataset}'):
labels[i] = y.item()
print(labels)
print(np.where(labels==0))
labels_inliers = np.zeros((len(np.where(labels==1)[0])))
labels_outliers = np.zeros((len(np.where(labels==0)[0])))
zs_in = np.empty((64,len(labels_inliers)))
zs_out = np.empty((64, len(labels_outliers)))
print("number of inliers = " + str(len(labels_inliers)))
print("number of outliers = " + str(len(labels_outliers)))
count_in = 0
count_out = 0
for i, (x, y) in tqdm(enumerate(loader), desc=f'Computing scores for {self.dataset}'):
x = x.to('cuda')
x_r, z, z_dist = self.model(x) # z_dist has shape torch.Size([1, 100, 64])
print(i)
# print("y.item() = " + str(y.item()))
if(y.item() == 1):
zs_in[:,count_in] = z.cpu().numpy()
count_in += 1
else:
zs_out[:,count_out] = z.cpu().numpy()
count_out += 1
# print("INLIER")
# # # print(z_dist_sm.size())
# z_d = z.detach()
# z_d = z_d.view(len(z_d), -1).contiguous()
# idxs_of_bins = torch.clamp(torch.unsqueeze(z_d, dim=1) * 100, min=0,
# max=(100 - 1)).long()
# visualize_instance_on_tensorboard(writer,x,idxs_of_bins,z_d,z_dist,i,inlier=True)
# elif(y.item()==0):
# print("OUTLIER")
# z_d = z.detach()
# z_d = z_d.view(len(z_d), -1).contiguous()
# idxs_of_bins = torch.clamp(torch.unsqueeze(z_d, dim=1) * 100, min=0,
# max=(100 - 1)).long()
# visualize_instance_on_tensorboard(writer,x,idxs_of_bins,z_d,z_dist,i,inlier=False)
# self.loss(x, x_r, z, z_dist)
sample_llk[i] = - self.loss.autoregression_loss
sample_rec[i] = - self.loss.reconstruction_loss
# if(y.item()==1):
# writer.add_scalar('data/reconstruction_eror_in', sample_rec[i],i)
# writer.add_scalar('data/llk_error_in', sample_llk[i],i)
# else:
# writer.add_scalar('data/reconstruction_eror_out', sample_rec[i],i)
# writer.add_scalar('data/llk_error_out', sample_llk[i],i)
# writer.add_custom_scalars_multilinechart(['data/reconstruction_eror_in', 'data/reconstruction_eror_out'],title='reconstruction error')
# writer.add_custom_scalars_multilinechart(['data/llk_error_in', 'data/llk_error_out'], title='llk error')
sample_y[i] = y.item()
np.save('/data/Ponc/zs_2_in.npy',zs_in)
np.save('/data/Ponc/zs_2_out.npy',zs_out)
print("WRITING")
hist, x_axis, _ = plt.hist(zs[0,:], bins = 100)
x_axis = x_axis[:-1]
hist = hist/np.sum(hist)
ord_g = 4
M = generateMoments(hist, ord_g,1)
magic_q = comb(1+ord_g, 1)
print(magic_q)
q_eval = Q(M, x_axis)
plt.subplot(211)
plt.title("Gaussian Distr. mu=0.5, ss=0.1")
plt.plot(x_axis, hist)
plt.subplot(212)
plt.title("Q(x) with M"+str(ord_g))
plt.plot(x_axis, q_eval)
plt.plot(x_axis, magic_q*np.ones(len(x_axis)))
plt.show()
writer.close()
# Normalize scores
sample_llk = normalize(sample_llk, min_llk, max_llk)
sample_rec = normalize(sample_rec, min_rec, max_rec)
# Compute the normalized novelty score
sample_ns = novelty_score(sample_llk, sample_rec)
# Compute AUROC for this class
this_class_metrics = [
roc_auc_score(sample_y, sample_llk), # likelihood metric
roc_auc_score(sample_y, sample_rec), # reconstruction metric
roc_auc_score(sample_y, sample_ns) # novelty score
]
oc_table.add_row([cl_idx] + this_class_metrics)
all_metrics.append(this_class_metrics)
# Compute average AUROC and print table
all_metrics = np.array(all_metrics)
avg_metrics = np.mean(all_metrics, axis=0)
oc_table.add_row(['avg'] + list(avg_metrics))
print(oc_table)
# Save table
with open(self.output_file, mode='w') as f:
f.write(str(oc_table))
def test_video_anomaly_detection(self, model, flow=None, val=False):
_, t, _, _ = self.videoshape
test_loglikelihood = NLL().to(self.cuda)
test_loglikelihood.eval()
model.eval()
if flow is not None:
flow.eval()
vad_table = self.empty_table(True)
flow_input = Static_intensity(self.cuda)
else:
vad_table = self.empty_table(False)
global_rec = []
global_f = []
global_y = []
results_accumulator_rec = ResultsAccumulator(time_steps=t)
results_accumulator_nll = ResultsAccumulator(time_steps=t)
if val == True:
video_list = self.dataset.val_videos
else:
video_list = self.dataset.test_videos
for cl_idx, video_id in enumerate(video_list):
self.dataset.test(video_id)
loader = DataLoader(self.dataset,
num_workers=self.workers,
collate_fn=self.dataset.collate_fn)
sample_nll = np.zeros(shape=(len(loader) + t - 1, ))
sample_rec = np.zeros(shape=(len(loader) + t - 1, ))
sample_y = self.dataset.load_test_sequence_gt(video_id)
for i, input in tqdm(enumerate(loader)):
x = input[0]
x = x.to(self.cuda)
if flow is not None:
x_r, flow_z = model(x)
intensity = flow_input(input[0])
flows = torch.cat((flow_z[0], intensity), 1)
_, nll, _ = flow(flows.to(self.cuda))
# _, nll, _ = flow(flow_z[1].to(self.cuda))
else:
x_r, _ = model(x)
nll = torch.Tensor([0.])
if self.dataname == 'shanghaitech':
score = self.test_method(x, input[1], x_r)
else:
score = self.test_method(x, None, x_r)
results_accumulator_rec.push(
self.test_method.reconstruction_loss)
sample_rec[i] = results_accumulator_rec.get_next()
test_nll = test_loglikelihood(nll)
results_accumulator_nll.push(test_nll.item())
sample_nll[i] = results_accumulator_nll.get_next()
# Get last results
while results_accumulator_rec.results_left != 0:
index = (-results_accumulator_rec.results_left)
sample_rec[index] = results_accumulator_rec.get_next()
sample_nll[index] = results_accumulator_nll.get_next()
min_nll, max_nll, min_rec, max_rec = self.compute_normalizing_coefficients(
sample_nll, sample_rec)
# Compute the normalized scores and novelty score
sample_rec = normalize(sample_rec, min_rec, max_rec)
if flow is not None:
sample_nll = normalize(sample_nll, min_nll, max_nll)
sample_f = novelty_score(sample_nll, sample_rec, self.lamb)
global_f.append(sample_f)
global_rec.append(sample_rec)
global_y.append(sample_y)
try:
if flow is not None:
# Compute AUROC for this video
this_video_metrics = [
roc_auc_score(sample_y,
sample_rec), # reconstruction metric
roc_auc_score(sample_y, sample_f), # likelihood metric
]
else:
this_video_metrics = [roc_auc_score(sample_y, sample_rec)]
vad_table.add_row([video_id] + list(this_video_metrics))
except ValueError:
# This happens for sequences in which all frames are abnormal
# Skipping this row in the table (the sequence will still count for global metrics)
continue
# Compute global AUROC and print table
global_rec = np.concatenate(global_rec)
global_y = np.concatenate(global_y)
if flow is not None:
global_f = np.concatenate(global_f)
if flow is not None:
global_metrics = [
roc_auc_score(global_y, global_rec), # reconstruction metric
roc_auc_score(global_y, global_f), # likelihood metric
]
else:
global_metrics = [roc_auc_score(global_y, global_rec)]
vad_table.add_row(['avg'] + list(global_metrics))
print(vad_table)
# Save table
if val:
with open(self.output_file_val, mode='a+') as f:
f.write(str(vad_table))
f.write('\n')
return list(global_metrics)
else:
with open(self.output_file, mode='a+') as f:
f.write(str(vad_table))
f.write('\n')
return list(global_metrics)
def test_video_anomaly_detection_multiflow(self,
model,
flow1,
flow2,
val=False):
_, t, _, _ = self.videoshape
flow_input = Static_intensity(self.cuda)
test_loglikelihood = NLL().to(self.cuda)
test_loglikelihood.eval()
model.eval()
flow1.eval()
flow2.eval()
vad_table = PrettyTable()
vad_table.field_names = [
'VIDEO-ID', 'AUROC-Recon', 'AUROC-FlowS', 'AUROC-FlowD',
'AUROC-Total'
]
vad_table.float_format = '0.4'
global_rec = []
global_fa = []
global_fm = []
global_ns = []
global_y = []
results_accumulator_rec = ResultsAccumulator(time_steps=t)
results_accumulator_nll1 = ResultsAccumulator(time_steps=t)
results_accumulator_nll2 = ResultsAccumulator(time_steps=t)
if val == True:
video_list = self.dataset.val_videos
else:
video_list = self.dataset.test_videos
for cl_idx, video_id in enumerate(video_list):
self.dataset.test(video_id)
loader = DataLoader(self.dataset,
num_workers=self.workers,
collate_fn=self.dataset.collate_fn)
sample_nll1 = np.zeros(shape=(len(loader) + t - 1, ))
sample_nll2 = np.zeros(shape=(len(loader) + t - 1, ))
sample_rec = np.zeros(shape=(len(loader) + t - 1, ))
sample_y = self.dataset.load_test_sequence_gt(video_id)
for i, input in tqdm(enumerate(loader)):
x = input[0]
x = x.to(self.cuda)
intensity = flow_input(input[0])
x_r, flow_z = model(x)
flows = torch.cat((flow_z[0], intensity), 1)
_, nll1, _ = flow1(flows.to(self.cuda))
_, nll2, _ = flow2(flow_z[1].to(self.cuda))
if self.dataname == 'shanghaitech':
score = self.test_method(x, input[1], x_r)
else:
score = self.test_method(x, None, x_r)
results_accumulator_rec.push(
self.test_method.reconstruction_loss)
sample_rec[i] = results_accumulator_rec.get_next()
test_nll1 = test_loglikelihood(nll1)
results_accumulator_nll1.push(test_nll1.item())
sample_nll1[i] = results_accumulator_nll1.get_next()
test_nll2 = test_loglikelihood(nll2)
results_accumulator_nll2.push(test_nll2.item())
sample_nll2[i] = results_accumulator_nll2.get_next()
# Get last results
while results_accumulator_rec.results_left != 0:
index = (-results_accumulator_rec.results_left)
sample_rec[index] = results_accumulator_rec.get_next()
sample_nll1[index] = results_accumulator_nll1.get_next()
sample_nll2[index] = results_accumulator_nll2.get_next()
min_nll1, max_nll1, min_nll2, max_nll2 = self.compute_normalizing_coefficients(
sample_nll1, sample_nll2)
sample_nll = (
(max_nll1 - min_nll1) /
(max_nll1 - min_nll1 + max_nll2 - min_nll2)) * sample_nll1 + (
(max_nll2 - min_nll2) /
(max_nll1 - min_nll1 + max_nll2 - min_nll2)) * sample_nll2
# sample_nll = sample_nll1+sample_nll2
min_nll, max_nll, min_rec, max_rec = self.compute_normalizing_coefficients(
sample_nll, sample_rec)
# Compute the normalized scores and novelty score
sample_rec = normalize(sample_rec, min_rec, max_rec)
sample_nll1 = normalize(sample_nll1, min_nll1, max_nll1)
sample_nll2 = normalize(sample_nll2, min_nll2, max_nll2)
sample_nll = normalize(sample_nll, min_nll, max_nll)
sample_ns = novelty_score(sample_nll, sample_rec, self.lamb)
sample_fa = novelty_score(sample_nll1, sample_rec, self.lamb)
sample_fm = novelty_score(sample_nll2, sample_rec, self.lamb)
global_ns.append(sample_ns)
global_rec.append(sample_rec)
global_y.append(sample_y)
global_fa.append(sample_fa)
global_fm.append(sample_fm)
try:
this_video_metrics = [
roc_auc_score(sample_y,
sample_rec), # reconstruction metric
roc_auc_score(sample_y, sample_fa), # likelihood metric
roc_auc_score(sample_y, sample_fm), # likelihood metric
roc_auc_score(sample_y, sample_ns) # novelty score
]
vad_table.add_row([video_id] + list(this_video_metrics))
except ValueError:
# This happens for sequences in which all frames are abnormal
# Skipping this row in the table (the sequence will still count for global metrics)
continue
# Compute global AUROC and print table
global_ns = np.concatenate(global_ns)
global_fa = np.concatenate(global_fa)
global_fm = np.concatenate(global_fm)
global_rec = np.concatenate(global_rec)
global_y = np.concatenate(global_y)
global_metrics = [
roc_auc_score(global_y, global_rec), # reconstruction metric
roc_auc_score(global_y, global_fa), # likelihood metric
roc_auc_score(global_y, global_fm), # likelihood metric
roc_auc_score(global_y, global_ns), # novelty score
]
vad_table.add_row(['avg'] + list(global_metrics))
print(vad_table)
# Save table
if val:
with open(self.output_file_val, mode='a+') as f:
f.write(str(vad_table))
f.write('\n')
return list(global_metrics)
else:
with open(self.output_file, mode='a+') as f:
f.write(str(vad_table))
f.write('\n')
return list(global_metrics)
