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2020.12.02
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compare with other methods
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luozhiwang committed Dec 2, 2020
1 parent 665a6ca commit 734ee61
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14 changes: 14 additions & 0 deletions README.md
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Expand Up @@ -157,6 +157,20 @@ export PYTHONPATH=$PWD
python3 test.py --weights 权重路径
```

## Comparision with other methods in github
github上也有一些其他作者实现的mobile-yolo系列，其中paddle和keras都是用mobilev1作为backbone，无法直接计算Flops。
darknet的Flops采用作者Readme中的BFlops。

|Model|resolution|COCO mAP|VOC mAP|Size|
|----|----|----|----|----|
|Ours|320|23.2|71.7|1x|
|Ours|640|27.5|74.4|4x|
|[caffe](https://github.com/eric612/MobileNet-YOLO)|352|-|71.5|x1.2|
|[paddle](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.5/docs/MODEL_ZOO_cn.md)|608|29.3|76.2|-|
|[darknet](https://github.com/dog-qiuqiu/MobileNet-Yolo)|352|-|70.7|x2.2|
|[keras](https://github.com/Adamdad/keras-YOLOv3-mobilenet)|320|-|74.56|-|
|[tensorflow](https://github.com/fsx950223/mobilenetv2-yolov3)|416|-|66.9|x1.7|

## Inference with TensorRT
1. python3 model/onnx_export.py --weights weight_path
2. 参考[https://github.com/Syencil/tensorRT](https://github.com/Syencil/tensorRT)
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4 changes: 3 additions & 1 deletion script/coco2darknet.py
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Expand Up @@ -469,13 +469,15 @@ def convert_ath_json(json_dir): # dir contains json annotations and images
print('Done. Output saved to %s' % Path(dir).absolute())


def convert_coco_json(json_dir='../coco/annotations/'):
def convert_coco_json(json_dir='/data/dataset/coco/annotations/'):
dir = make_folders(path='out/') # output directory
jsons = glob.glob(json_dir + '*.json')
coco80 = coco91_to_coco80_class()

# Import json
for json_file in sorted(jsons):
if 'instances' not in json_file:
continue
fn = 'out/labels/%s/' % Path(json_file).stem.replace('instances_', '') # folder name
os.mkdir(fn)
with open(json_file) as f:
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9 changes: 4 additions & 5 deletions test.py
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Expand Up @@ -87,10 +87,8 @@ def test(data,
collate_fn=dataset.collate_fn)

seen = 0
names = {0: 'aeroplane', 1: 'bicycle', 2: 'bird', 3: 'boat', 4: 'bottle', 5: 'bus',
6: 'car', 7: 'cat', 8: 'chair', 9: 'cow', 10: 'diningtable', 11: 'dog', 12: 'horse',
13: 'motorbike', 14: 'person', 15: 'pottedplant', 16: 'sheep', 17: 'sofa',
18: 'train', 19: 'tvmonitor'}
names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}

coco91class = coco80_to_coco91_class()
s = ('%20s' + '%12s' * 6) % ('Class', 'Images',
'Targets', 'P', 'R', '[email protected]', '[email protected]:.95')
Expand Down Expand Up @@ -365,7 +363,8 @@ def test(data,
opt.iou_thres,
opt.save_json,
opt.single_cls,
opt.augment)
opt.augment,
verbose=opt.verbose)

elif opt.task == 'study': # run over a range of settings and save/plot
for weights in ['yolov5s.pt', 'yolov5m.pt',
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58 changes: 30 additions & 28 deletions train.py
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Expand Up @@ -201,22 +201,22 @@ def get_hooks():
c1 = 256
c2 = 512
c3 = 1024
# S_Converter_1 = Converter(32, c1, act=True)
# S_Converter_2 = Converter(96, c2, act=True)
# S_Converter_3 = Converter(320, c3, act=True)
# S_Converter_1.to(device)
# S_Converter_2.to(device)
# S_Converter_3.to(device)
# S_Converter_1.train()
# S_Converter_2.train()
# S_Converter_3.train()

# T_Converter_1 = nn.ReLU6()
# T_Converter_2 = nn.ReLU6()
# T_Converter_3 = nn.ReLU6()
T_Converter_1 = Converter(c1, 32, act=True)
T_Converter_2 = Converter(c2, 96, act=True)
T_Converter_3 = Converter(c3, 320, act=True)
S_Converter_1 = Converter(32, c1, act=True)
S_Converter_2 = Converter(96, c2, act=True)
S_Converter_3 = Converter(320, c3, act=True)
S_Converter_1.to(device)
S_Converter_2.to(device)
S_Converter_3.to(device)
S_Converter_1.train()
S_Converter_2.train()
S_Converter_3.train()

T_Converter_1 = nn.ReLU6()
T_Converter_2 = nn.ReLU6()
T_Converter_3 = nn.ReLU6()
# T_Converter_1 = Converter(c1, 32, act=True)
# T_Converter_2 = Converter(c2, 96, act=True)
# T_Converter_3 = Converter(c3, 320, act=True)
T_Converter_1.to(device)
T_Converter_2.to(device)
T_Converter_3.to(device)
Expand Down Expand Up @@ -360,22 +360,24 @@ def get_hooks():
if opt.dist:
if opt.d_online:
t_pred = t_model(imgs)
for p in t_pred:
p = p.detach()
else:
with torch.no_grad():
t_pred = t_model(imgs)
if opt.d_feature:
# s_f1 = S_Converter_1(activation["s_f1"])
# s_f2 = S_Converter_2(activation["s_f2"])
# s_f3 = S_Converter_3(activation["s_f3"])
# s_f = [s_f1, s_f2, s_f3]
s_f1 = S_Converter_1(activation["s_f1"])
s_f2 = S_Converter_2(activation["s_f2"])
s_f3 = S_Converter_3(activation["s_f3"])
s_f = [s_f1, s_f2, s_f3]
s_f = (activation["s_f1"], activation["s_f2"], activation["s_f3"])
t_f1 = T_Converter_1(activation["t_f1"])
t_f2 = T_Converter_2(activation["t_f2"])
t_f3 = T_Converter_3(activation["t_f3"])
t_f = [t_f1, t_f2, t_f3]
# t_f = (activation["t_f1"], activation["t_f2"], activation["t_f3"])
# Loss
loss, loss_items = compute_loss(pred, targets.to(device), model)
loss, loss_items = compute_loss(pred, targets.to(device), model, None)

# Sparse Learning
if opt.sl > 0:
Expand All @@ -384,7 +386,7 @@ def get_hooks():
# distillation
if opt.dist:
if opt.d_online:
loss, _ = compute_loss(t_pred, targets.to(device), model, loss)
loss, _ = compute_loss(t_pred, targets.to(device), t_model, loss)
loss = compute_distillation_output_loss(pred, t_pred, model, loss)
if opt.d_feature:
loss = compute_distillation_feature_loss(s_f, t_f, model, loss)
Expand Down Expand Up @@ -524,12 +526,12 @@ def get_hooks():
parser.add_argument('--nw', type=int, default=None, help='num of worker')
# pruning
parser.add_argument('--sl', default=0, type=float, help='sparse learning')
parser.add_argument('--ft', action='store_true', help='fine-tune')
parser.add_argument('--ft', action='store_true', default=False, help='fine-tune')
# distillation
parser.add_argument('--dist', action='store_true', help='distillation')
parser.add_argument('--t_weights', type=str, help='teacher model for distillation')
parser.add_argument('--d_feature', action='store_true', help='if true, distill both feature and output layers')
parser.add_argument('--d_online', action='store_true', help='if true, using online-distillation')
parser.add_argument('--t_weights', type=str, default="", help='teacher model for distillation')
parser.add_argument('--d_feature', action='store_true', default=False, help='if true, distill both feature and output layers')
parser.add_argument('--d_online', action='store_true', default=False, help='if true, using online-distillation')
opt = parser.parse_args()

if opt.type == "mcocos":
Expand All @@ -546,7 +548,7 @@ def get_hooks():
opt.data = "data/coco.yaml"
opt.name = opt.type
opt.weights = "outputs/dmvocs/weights/best_dmvocs.pt"
opt.epochs = 50
opt.epochs = 10
opt.batch_size = 24
opt.multi_scale = False
opt.dist = True
Expand Down Expand Up @@ -737,7 +739,7 @@ def get_hooks():
opt.multi_scale = False
opt.dist = True
opt.d_online = True
opt.t_weights = "outputs/voc/weights/best_voc.pt"
opt.t_weights = "/data/checkpoints/yolov5/yolov5s.pt"
hyp["dist"] = 1

if opt.nw is None:
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