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ucf-crime_s3r_i3d.score
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ucf-crime_s3r_i3d.score
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**********
!!python/object:anomaly.apis.opts.S3RArgumentParser
descr:
- S3R
- video
- anomaly
- detection
version: vad-ws-0.2
lr: 0.001
quantize_size: 32
model_name: s3r
checkpoint_path: !!python/object/apply:pathlib.PosixPath
- checkpoint
dictionary_path: !!python/object/apply:pathlib.PosixPath
- dictionary
feature_size: 2048
evaluate_min_step: 10
report_k: 10
max_epoch: 15000
backbone: i3d
evaluate_freq: 1
resume: null
gpus: 1
seed: -1
inference: false
plot_freq: 10
workers: 0
dropout: 0.7
log_path: !!python/object/apply:pathlib.PosixPath
- logs
root_path: !!python/object/apply:pathlib.PosixPath
- data
debug: false
dataset: ucf-crime
batch_size: 32
PyTorch version: 1.6.0
Is debug build: No
CUDA used to build PyTorch: 10.1
OS: Ubuntu 18.04.6 LTS
GCC version: (Ubuntu 5.5.0-12ubuntu1) 5.5.0 20171010
CMake version: version 3.16.3
Python version: 3.6
Is CUDA available: Yes
CUDA runtime version: 10.1.243
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 2080 Ti
GPU 1: NVIDIA GeForce RTX 2080 Ti
Nvidia driver version: 470.103.01
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
Versions of relevant libraries:
[pip3] numpy==1.19.2
[pip3] torch==1.6.0
[conda] blas 1.0 mkl
[conda] cudatoolkit 10.1.243 h6bb024c_0
[conda] mkl 2020.2 256
[conda] mkl-service 2.3.0 py36he8ac12f_0
[conda] mkl_fft 1.3.0 py36h54f3939_0
[conda] mkl_random 1.1.1 py36h0573a6f_0
[conda] numpy 1.19.2 py36h54aff64_0
[conda] numpy-base 1.19.2 py36hfa32c7d_0
[conda] pytorch 1.6.0 py3.6_cuda10.1.243_cudnn7.6.3_0 pytorch
**********
==========
S3R(
(video_embedding): Sequential(
(0): Aggregate(
(conv_1): Sequential(
(0): Conv1d(2048, 512, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(8, 512, eps=1e-05, affine=True)
(2): ReLU()
)
(conv_2): Sequential(
(0): Conv1d(2048, 512, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(8, 512, eps=1e-05, affine=True)
(2): ReLU()
)
(conv_3): Sequential(
(0): Conv1d(2048, 512, kernel_size=(3,), stride=(1,), padding=(4,), dilation=(4,))
(1): GroupNorm(8, 512, eps=1e-05, affine=True)
(2): ReLU()
)
(conv_4): Sequential(
(0): Conv1d(2048, 512, kernel_size=(1,), stride=(1,), bias=False)
(1): ReLU()
)
(conv_5): Sequential(
(0): Conv1d(2048, 2048, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
(1): GroupNorm(8, 2048, eps=1e-05, affine=True)
(2): ReLU()
)
(non_local): NonLocalBlock1D(
(value): Conv1d(512, 256, kernel_size=(1,), stride=(1,))
(alter): Sequential(
(0): Conv1d(256, 512, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(query): Conv1d(512, 256, kernel_size=(1,), stride=(1,))
(key): Conv1d(512, 256, kernel_size=(1,), stride=(1,))
)
)
(1): Dropout(p=0.7, inplace=False)
)
(macro_embedding): Sequential(
(0): Aggregate(
(conv_1): Sequential(
(0): Conv1d(2048, 512, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(8, 512, eps=1e-05, affine=True)
(2): ReLU()
)
(conv_2): Sequential(
(0): Conv1d(2048, 512, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(8, 512, eps=1e-05, affine=True)
(2): ReLU()
)
(conv_3): Sequential(
(0): Conv1d(2048, 512, kernel_size=(3,), stride=(1,), padding=(4,), dilation=(4,))
(1): GroupNorm(8, 512, eps=1e-05, affine=True)
(2): ReLU()
)
(conv_4): Sequential(
(0): Conv1d(2048, 512, kernel_size=(1,), stride=(1,), bias=False)
(1): ReLU()
)
(conv_5): Sequential(
(0): Conv1d(2048, 2048, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
(1): GroupNorm(8, 2048, eps=1e-05, affine=True)
(2): ReLU()
)
(non_local): NonLocalBlock1D(
(value): Conv1d(512, 256, kernel_size=(1,), stride=(1,))
(alter): Sequential(
(0): Conv1d(256, 512, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(query): Conv1d(512, 256, kernel_size=(1,), stride=(1,))
(key): Conv1d(512, 256, kernel_size=(1,), stride=(1,))
)
)
(1): Dropout(p=0.7, inplace=False)
)
(en_normal): enNormal(
(en_normal_module): enNormalModule(
(query_embedding): Linear(in_features=2048, out_features=512, bias=True)
(cache_embedding): Linear(in_features=2048, out_features=512, bias=True)
(value_embedding): Linear(in_features=2048, out_features=2048, bias=True)
)
)
(de_normal): deNormal(
(channel_attention): ChannelAttention(
(channel_gate): ChannelGate(
(mlp): Sequential(
(0): Flatten()
(1): Linear(in_features=2048, out_features=128, bias=True)
(2): ReLU()
(3): Linear(in_features=128, out_features=2048, bias=True)
)
)
)
)
(video_projection): Sequential(
(0): Conv1d(2048, 2048, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(8, 2048, eps=1e-05, affine=True)
(2): ReLU()
)
(macro_projection): Sequential(
(0): Conv1d(2048, 2048, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(8, 2048, eps=1e-05, affine=True)
(2): ReLU()
)
(video_classifier): Sequential(
(0): Linear(in_features=2048, out_features=512, bias=True)
(1): ReLU()
(2): Dropout(p=0.7, inplace=False)
(3): Linear(in_features=512, out_features=128, bias=True)
(4): ReLU()
(5): Dropout(p=0.7, inplace=False)
(6): Linear(in_features=128, out_features=1, bias=True)
(7): Sigmoid()
)
(macro_classifier): GlobalStatistics(
(flat): Flatten()
(mlp): Sequential(
(0): Linear(in_features=2048, out_features=512, bias=True)
(1): ReLU()
(2): Dropout(p=0.7, inplace=False)
(3): Linear(in_features=512, out_features=128, bias=True)
(4): ReLU()
(5): Dropout(p=0.7, inplace=False)
(6): Linear(in_features=128, out_features=1, bias=True)
)
)
(drop_out): Dropout(p=0.7, inplace=False)
)
==========
[1m[35mVideo Anomaly Detection[0m
- dataset: [4m[1m[37mucf-crime[0m
- version: vad-ws-0.2
- description: [1m[32mS3R video anomaly detection[0m
- initial AUC score: 43.402 %
- initial learning rate: 0.0010
+-------------------------------------------------------------------------------------------------------+
| Step | AUC | Training loss | Elapsed time | Now |
---------------------------------------------------------------------------------------------------------
| 11 | 60.563 | 1.847 | 0:02:25.234683 | 2022-07-06 14:10:58 |
| 12 | 63.036 | 1.803 | 0:02:47.178872 | 2022-07-06 14:11:20 |
| 13 | 66.282 | 1.818 | 0:03:17.182761 | 2022-07-06 14:11:50 |
| 14 | 69.290 | 1.831 | 0:03:52.384378 | 2022-07-06 14:12:25 |
| 15 | 71.569 | 1.816 | 0:04:19.742447 | 2022-07-06 14:12:52 |
| 16 | 71.726 | 1.780 | 0:04:53.823076 | 2022-07-06 14:13:27 |
| 25 | 72.647 | 1.755 | 0:10:53.272501 | 2022-07-06 14:19:26 |
| 26 | 73.840 | 1.719 | 0:11:24.412741 | 2022-07-06 14:19:57 |
| 27 | 74.537 | 1.681 | 0:11:49.959312 | 2022-07-06 14:20:23 |
| 28 | 74.960 | 1.686 | 0:12:15.055509 | 2022-07-06 14:20:48 |
| 29 | 75.122 | 1.646 | 0:12:44.218784 | 2022-07-06 14:21:17 |
| 31 | 75.451 | 1.611 | 0:13:52.935771 | 2022-07-06 14:22:26 |
| 32 | 76.059 | 1.528 | 0:14:28.065080 | 2022-07-06 14:23:01 |
| 33 | 77.044 | 1.495 | 0:14:54.184786 | 2022-07-06 14:23:27 |
| 34 | 78.120 | 1.493 | 0:15:24.661538 | 2022-07-06 14:23:57 |
| 35 | 78.614 | 1.383 | 0:15:55.836888 | 2022-07-06 14:24:29 |
| 37 | 79.603 | 1.176 | 0:17:07.600838 | 2022-07-06 14:25:40 |
| 38 | 80.143 | 1.218 | 0:17:32.499405 | 2022-07-06 14:26:05 |
| 41 | 80.322 | 1.165 | 0:18:58.870996 | 2022-07-06 14:27:32 |
| 48 | 80.337 | 1.230 | 0:23:20.682554 | 2022-07-06 14:31:53 |
| 54 | 80.771 | 0.968 | 0:25:40.208024 | 2022-07-06 14:34:13 |
| 59 | 81.176 | 1.084 | 0:28:50.435038 | 2022-07-06 14:37:23 |
| 75 | 82.019 | 0.801 | 0:37:58.632291 | 2022-07-06 14:46:31 |
| 85 | 82.122 | 0.943 | 0:42:50.156467 | 2022-07-06 14:51:23 |
| 204 | 82.359 | 0.715 | 1:46:27.104988 | 2022-07-06 15:55:00 |
| 324 | 82.668 | 0.651 | 2:49:22.510431 | 2022-07-06 16:57:55 |
| 495 | 82.775 | 0.417 | 4:21:08.025886 | 2022-07-06 18:29:41 |
| 499 | 82.875 | 0.640 | 4:23:20.780447 | 2022-07-06 18:31:53 |
| 548 | 82.961 | 0.595 | 4:49:33.457842 | 2022-07-06 18:58:06 |
| 561 | 82.964 | 0.463 | 4:55:51.662075 | 2022-07-06 19:04:24 |
| 593 | 83.382 | 0.447 | 5:13:33.147303 | 2022-07-06 19:22:06 |
| 728 | 83.505 | 0.452 | 6:23:44.093006 | 2022-07-06 20:32:17 |
| 844 | 83.580 | 0.448 | 7:24:48.335081 | 2022-07-06 21:33:21 |
| 920 | 83.980 | 0.411 | 8:03:11.602768 | 2022-07-06 22:11:44 |
| 1100 | 84.036 | 0.511 | 9:36:58.865187 | 2022-07-06 23:45:32 |
| 1115 | 84.173 | 0.457 | 9:43:28.198562 | 2022-07-06 23:52:01 |
| 1306 | 84.224 | 0.344 | 11:22:52.388350 | 2022-07-07 01:31:25 |
| 1792 | 84.326 | 0.332 | 15:25:21.061279 | 2022-07-07 05:33:54 |
| 1799 | 84.504 | 0.383 | 15:29:07.151406 | 2022-07-07 05:37:40 |
| 2466 | 84.958 | 0.346 | 21:09:37.409583 | 2022-07-07 11:18:10 |
| 2930 | 85.328 | 0.373 | 1 day, 1:00:01.928991 | 2022-07-07 15:08:35 |
| 2935 | 85.989 | 0.553 | 1 day, 1:02:03.063862 | 2022-07-07 15:10:36 |