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support CASIA-E dataset

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darkliang
2023-04-09 17:15:42 +08:00
parent 786aded8af
commit e69fb6f439
8 changed files with 1449 additions and 9 deletions
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configs/gaitbase/gaitbase_casiae.yaml
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data_cfg:
dataset_name: CASIA-E
dataset_root: your_path
dataset_partition: ./datasets/CASIA-E/CASIA-E.json
num_workers: 1
remove_no_gallery: false # Remove probe if no gallery for it
test_dataset_name: CASIA-E
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: 60000
save_name: GaitBase
#eval_func: GREW_submission
sampler:
batch_shuffle: false
batch_size: 16
sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered
frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
metric: euc # cos
transform:
- type: BaseSilCuttingTransform
loss_cfg:
- loss_term_weight: 1.0
margin: 0.2
type: TripletLoss
log_prefix: triplet
- loss_term_weight: 1.0
scale: 16
type: CrossEntropyLoss
log_prefix: softmax
log_accuracy: true
model_cfg:
model: Baseline
backbone_cfg:
type: ResNet9
block: BasicBlock
channels: # Layers configuration for automatically model construction
- 64
- 128
- 256
- 512
layers:
- 1
- 1
- 1
- 1
strides:
- 1
- 2
- 2
- 1
maxpool: false
SeparateFCs:
in_channels: 512
out_channels: 256
parts_num: 16
SeparateBNNecks:
class_num: 200
in_channels: 256
parts_num: 16
bin_num:
- 16
optimizer_cfg:
lr: 0.1
momentum: 0.9
solver: SGD
weight_decay: 0.0005
scheduler_cfg:
gamma: 0.1
milestones: # Learning Rate Reduction at each milestones
- 20000
- 40000
- 50000
scheduler: MultiStepLR
trainer_cfg:
enable_float16: true # half_percesion float for memory reduction and speedup
fix_BN: false
with_test: false
log_iter: 100
restore_ckpt_strict: true
restore_hint: 0
save_iter: 20000
save_name: GaitBase
sync_BN: true
total_iter: 60000
sampler:
batch_shuffle: true
batch_size:
- 8 # TripletSampler, batch_size[0] indicates Number of Identity
- 32 # batch_size[1] indicates Samples sequqnce for each Identity
frames_num_fixed: 30 # fixed frames number for training
frames_num_max: 40 # max frames number for unfixed training
frames_num_min: 20 # min frames number for unfixed traing
sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
type: TripletSampler
transform:
- type: BaseSilCuttingTransform
configs/gaitpart/gaitpart_casiae.yaml
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data_cfg:
dataset_name: CASIA-E
dataset_root: your_path
dataset_partition: ./datasets/CASIA-E/CASIA-E.json
num_workers: 4
remove_no_gallery: false
test_dataset_name: CASIA-E
evaluator_cfg:
enable_float16: false
restore_ckpt_strict: true
restore_hint: 120000
save_name: GaitPart
sampler:
batch_size: 4
sample_type: all_ordered
type: InferenceSampler
metric: euc # cos
loss_cfg:
loss_term_weight: 1.0
margin: 0.2
type: TripletLoss
log_prefix: triplet
model_cfg:
model: GaitPart
backbone_cfg:
in_channels: 1
layers_cfg:
- BC-32
- BC-32
- M
- BC-64
- BC-64
- M
- FC-128-3
- FC-128-3
- FC-256-3
- FC-256-3
type: Plain
SeparateFCs:
in_channels: 256
out_channels: 256
parts_num: 16
bin_num:
- 16
optimizer_cfg:
lr: 0.0001
momentum: 0.9
solver: Adam
weight_decay: 0.0
scheduler_cfg:
gamma: 0.1
milestones:
- 100000
scheduler: MultiStepLR
trainer_cfg:
enable_float16: true
fix_BN: false
log_iter: 100
with_test: false
restore_ckpt_strict: true
restore_hint: 0
save_iter: 120000
save_name: GaitPart
sync_BN: false
total_iter: 120000
sampler:
batch_shuffle: false
batch_size:
- 8
- 32
frames_num_fixed: 30
frames_num_max: 50
frames_num_min: 25
frames_skip_num: 10
sample_type: fixed_ordered
type: TripletSampler
configs/gaitset/gaitset_casiae.yaml
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data_cfg:
dataset_name: CASIA-E
dataset_root: your_path
dataset_partition: ./datasets/CASIA-E/CASIA-E.json
num_workers: 1
remove_no_gallery: false
test_dataset_name: CASIA-E
evaluator_cfg:
enable_float16: false
restore_ckpt_strict: true
restore_hint: 60000
save_name: GaitSet
sampler:
batch_size: 16
sample_type: all_ordered
type: InferenceSampler
metric: euc # cos
loss_cfg:
loss_term_weight: 1.0
margin: 0.2
type: TripletLoss
log_prefix: triplet
model_cfg:
model: GaitSet
in_channels:
- 1
- 64
- 128
- 256
SeparateFCs:
in_channels: 256
out_channels: 256
parts_num: 62
bin_num:
- 16
- 8
- 4
- 2
- 1
optimizer_cfg:
lr: 0.1
momentum: 0.9
solver: SGD
weight_decay: 0.0005
scheduler_cfg:
gamma: 0.1
milestones:
- 20000
- 40000
- 50000
scheduler: MultiStepLR
trainer_cfg:
enable_float16: true
log_iter: 100
with_test: false
restore_ckpt_strict: true
restore_hint: 0
save_iter: 60000
save_name: GaitSet
sync_BN: false
total_iter: 60000
sampler:
batch_shuffle: false
batch_size:
- 8
- 32
frames_num_fixed: 30
frames_num_max: 50
frames_num_min: 25
sample_type: fixed_unordered
type: TripletSampler
datasets/CASIA-E/CASIA-E.json
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datasets/CASIA-E/README.md
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# CASIA-E
Application URL: https://www.scidb.cn/en/detail?dataSetId=57be0e918db743279baf44a38d013a06
- Original
```
test615-1014.zip
train001-500.zip
val501-614.zip
```
- Run `python datasets/CASIA-E/extractor.py --input_path CASIA-E/ --output_path CASIA-E-processed/ -n 8 -s 64`. \
`n` is number of workers. `s` is the target image size.
- Processed
```
CASIA-E-processed
forTrain # raw images
001 (subject)
H (height)
scene1 (scene)
bg (walking condition)
000 (view)
1 (sequence number)
xxx.jpg (images)
......
......
......
......
......
......
......
opengait # pickle file
001 (subject)
H_scene1_bg_1 (type)
000 (view)
000.pkl (contains all frames)
......
......
......
```
## Evaluation
Compared with the settings in the original paper, we only used 200 people for training, and the rest were used as the test set, and the division of gallery and probe is more practical and difficult.
For specific experimental settings, please refer to configs/gaitbase/gaitbase_casiae.yaml.
For the specific division of the probe and gallery, please refer to opengait/evaluation/evaluator.py.
datasets/CASIA-E/extractor.py
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import argparse
import os
from pathlib import Path
import tqdm
import cv2
import tarfile
import zipfile
from functools import partial
import numpy as np
import pickle
import multiprocessing as mp
def make_pkl_for_one_person(id_, output_path, img_size=64):
if id_.split(".")[-1] != "tar" or not os.path.exists(os.path.join(output_path, id_)):
return
with tarfile.TarFile(os.path.join(output_path, id_)) as f:
f.extractall(output_path)
os.remove(os.path.join(output_path, id_))
id_path = id_.split(".")[0]
input_path = os.path.join(output_path, "forTrain", id_path)
base_pkl_path = os.path.join(output_path, "opengait", id_path)
if not os.path.isdir(input_path):
print("Path not found: "+input_path)
for height in sorted(os.listdir(input_path)):
height_path = os.path.join(input_path, height)
for scene in sorted(os.listdir(height_path)):
scene_path = os.path.join(height_path, scene)
for type_ in sorted(os.listdir(scene_path)):
type_path = os.path.join(scene_path, type_)
for view in sorted(os.listdir(type_path)):
view_path = os.path.join(type_path, view)
for num in sorted(os.listdir(view_path)):
num_path = os.path.join(view_path, num)
imgs = []
for file_ in sorted(os.listdir(num_path)):
img = cv2.imread(os.path.join(
num_path, file_), cv2.IMREAD_GRAYSCALE)
if img_size != img.shape[0]:
img = cv2.resize(
img, dsize=(img_size, img_size))
imgs.append(img)
if len(imgs) > 5:
pkl_path = os.path.join(
base_pkl_path, f"{height}-{scene}-{type_}-{num}", view)
os.makedirs(pkl_path, exist_ok=True)
pickle.dump(np.asarray(imgs), open(
os.path.join(pkl_path, f"{view}.pkl"), "wb"))
else:
print("No enough imgs: "+num_path)
def extractall(base_path: Path, output_path: Path, workers=1, img_size=64) -> None:
"""Extract all archives in base_path to output_path.
Args:
base_path (Path): Path to the directory containing the archives.
output_path (Path): Path to the directory to extract the archives to.
"""
os.makedirs(output_path, exist_ok=True)
print("Unzipping train set...")
with open(os.path.join(base_path, 'train001-500.zip'), 'rb') as f:
z = zipfile.ZipFile(f)
z.extractall(output_path)
print("Unzipping validation set...")
with open(os.path.join(base_path, 'val501-614.zip'), 'rb') as f:
z = zipfile.ZipFile(f)
z.extractall(output_path)
print("Unzipping test set...")
with open(os.path.join(base_path, 'test615-1014.zip'), 'rb') as f:
z = zipfile.ZipFile(f)
z.extractall(output_path)
print("Extracting tar file...")
os.makedirs(os.path.join(output_path,"forTrain"))
os.makedirs(os.path.join(output_path,"opengait"))
ids = os.listdir(os.path.join(output_path))
progress = tqdm.tqdm(total=len(ids), desc='Pretreating', unit='id')
with mp.Pool(workers) as pool:
for _ in pool.imap_unordered(partial(make_pkl_for_one_person, output_path=output_path, img_size=img_size), ids):
progress.update(1)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='CASIA-E extractor')
parser.add_argument('-b', '--input_path', type=str,
required=True, help='Base path to CASIA-E zip files')
parser.add_argument('-o', '--output_path', type=str,
required=True, help='Output path for extracted files. The pickle files are generated in ${output_path}/opengait/')
parser.add_argument('-s', '--img_size', default=64,
type=int, help='Image resizing size. Default 64')
parser.add_argument('-n', '--num_workers',
type=int, default=1, help='Number of workers')
args = parser.parse_args()
extractall(Path(args.input_path), Path(args.output_path),
args.num_workers, args.img_size)
docs/1.model_zoo.md
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@@ -56,6 +56,14 @@
| [DeepGaitV2-P3D](https://arxiv.org/pdf/2303.03301.pdf) | 74.4 | - | 64x44 | - | - |
| [SwinGait(Transformer-based)](https://arxiv.org/pdf/2303.03301.pdf) | 75.0 | - | 64x44 | - | - |
## [CASIA-E](https://www.scidb.cn/en/detail?dataSetId=57be0e918db743279baf44a38d013a06)
| Model | `Rank@1.NM` | `Rank@1.BG` | `Rank@1.CL` | Input size| Configuration |
| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------: | :---------:| :----: | :-------------: | :--------------------------------------------------------------: |
| GaitSet | 82.54 | 75.26 | 62.53 | 64x44 | configs/gaitset/gaitset_casiae.yaml
| GaitPart | 82.92 | 74.36 | 60.48 | 64x44 | configs/gaitpart/gaitpart_casiae.yaml
| GaitBase | 91.59 | 86.65 | 74.73 | 64x44 | configs/gaitbase/gaitbase_casiae.yaml
------------------------------------------
The results in the parentheses are mentioned in the papers.
opengait/evaluation/evaluator.py
+19 -9
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@@ -70,12 +70,21 @@ def cross_view_gallery_evaluation(feature, label, seq_type, view, dataset, metri
def single_view_gallery_evaluation(feature, label, seq_type, view, dataset, metric):
probe_seq_dict = {'CASIA-B': {'NM': ['nm-05', 'nm-06'], 'BG': ['bg-01', 'bg-02'], 'CL': ['cl-01', 'cl-02']},
'OUMVLP': {'NM': ['00']}}
'OUMVLP': {'NM': ['00']},
'CASIA-E': {'NM': ['H-scene2-nm-1', 'H-scene2-nm-2', 'L-scene2-nm-1', 'L-scene2-nm-2', 'H-scene3-nm-1', 'H-scene3-nm-2', 'L-scene3-nm-1', 'L-scene3-nm-2', 'H-scene3_s-nm-1', 'H-scene3_s-nm-2', 'L-scene3_s-nm-1', 'L-scene3_s-nm-2',],
'BG': ['H-scene2-bg-1', 'H-scene2-bg-2', 'L-scene2-bg-1', 'L-scene2-bg-2', 'H-scene3-bg-1', 'H-scene3-bg-2', 'L-scene3-bg-1', 'L-scene3-bg-2', 'H-scene3_s-bg-1', 'H-scene3_s-bg-2', 'L-scene3_s-bg-1', 'L-scene3_s-bg-2'],
'CL': ['H-scene2-cl-1', 'H-scene2-cl-2', 'L-scene2-cl-1', 'L-scene2-cl-2', 'H-scene3-cl-1', 'H-scene3-cl-2', 'L-scene3-cl-1', 'L-scene3-cl-2', 'H-scene3_s-cl-1', 'H-scene3_s-cl-2', 'L-scene3_s-cl-1', 'L-scene3_s-cl-2']
}
}
gallery_seq_dict = {'CASIA-B': ['nm-01', 'nm-02', 'nm-03', 'nm-04'],
'OUMVLP': ['01']}
'OUMVLP': ['01'],
'CASIA-E': ['H-scene1-nm-1', 'H-scene1-nm-2', 'L-scene1-nm-1', 'L-scene1-nm-2']}
msg_mgr = get_msg_mgr()
acc = {}
view_list = sorted(np.unique(view))
if dataset == 'CASIA-E':
view_list.remove("270")
view_num = len(view_list)
num_rank = 1
for (type_, probe_seq) in probe_seq_dict[dataset].items():
@@ -92,8 +101,8 @@ def single_view_gallery_evaluation(feature, label, seq_type, view, dataset, metr
gallery_y = label[gseq_mask]
gallery_x = feature[gseq_mask, :]
dist = cuda_dist(probe_x, gallery_x, metric)
idx = dist.cpu().sort(1)[1].numpy()
acc[type_][v1, v2] = np.round(np.sum(np.cumsum(np.reshape(probe_y, [-1, 1]) == gallery_y[idx[:, 0:num_rank]], 1) > 0,
idx = dist.topk(num_rank, largest=False)[1].cpu().numpy()
acc[type_][v1, v2] = np.round(np.sum(np.cumsum(np.reshape(probe_y, [-1, 1]) == gallery_y[idx], 1) > 0,
0) * 100 / dist.shape[0], 2)
result_dict = {}
@@ -113,7 +122,7 @@ def evaluate_indoor_dataset(data, dataset, metric='euc', cross_view_gallery=Fals
label = np.array(label)
view = np.array(view)
if dataset not in ('CASIA-B', 'OUMVLP'):
if dataset not in ('CASIA-B', 'OUMVLP', 'CASIA-E'):
raise KeyError("DataSet %s hasn't been supported !" % dataset)
if cross_view_gallery:
return cross_view_gallery_evaluation(
@@ -145,7 +154,7 @@ def evaluate_real_scene(data, dataset, metric='euc'):
probe_y = label[pseq_mask]
dist = cuda_dist(probe_x, gallery_x, metric)
idx = dist.cpu().sort(1)[1].numpy()
idx = dist.topk(num_rank, largest=False)[1].cpu().numpy()
acc = np.round(np.sum(np.cumsum(np.reshape(probe_y, [-1, 1]) == gallery_y[idx[:, 0:num_rank]], 1) > 0,
0) * 100 / dist.shape[0], 2)
msg_mgr.log_info('==Rank-1==')
@@ -173,8 +182,9 @@ def GREW_submission(data, dataset, metric='euc'):
probe_x = feature[pseq_mask, :]
probe_y = view[pseq_mask]
num_rank = 20
dist = cuda_dist(probe_x, gallery_x, metric)
idx = dist.cpu().sort(1)[1].numpy()
idx = dist.topk(num_rank, largest=False)[1].cpu().numpy()
save_path = os.path.join(
"GREW_result/"+strftime('%Y-%m%d-%H%M%S', localtime())+".csv")
@@ -182,8 +192,8 @@ def GREW_submission(data, dataset, metric='euc'):
with open(save_path, "w") as f:
f.write("videoId,rank1,rank2,rank3,rank4,rank5,rank6,rank7,rank8,rank9,rank10,rank11,rank12,rank13,rank14,rank15,rank16,rank17,rank18,rank19,rank20\n")
for i in range(len(idx)):
r_format = [int(idx) for idx in gallery_y[idx[i, 0:20]]]
output_row = '{}'+',{}'*20+'\n'
r_format = [int(idx) for idx in gallery_y[idx[i, 0:num_rank]]]
output_row = '{}'+',{}'*num_rank+'\n'
f.write(output_row.format(probe_y[i], *r_format))
print("GREW result saved to {}/{}".format(os.getcwd(), save_path))
return