目标检测 YOLOv5 - 模型的样子
2024-05-27 16:59:22
目标检测 YOLOv5 - 模型的样子
flyfish
文章目录
- 目标检测 YOLOv5 - 模型的样子
- 开始加载模型文件
- 模型的层
- 模型的属性
- 模块的名称以及模块本身
- 模型的权重
- 模型权重的名字和权重
- 完整的model
- 完整模型图片展示
版本: YOLOv5:v5
以yolov5s.pt为例查看模型的样子
开始加载模型文件
yolov5s.pt
def attempt_load(weights, map_location=None):
模型内容如下
ckpt: {'epoch': -1,'best_fitness': array([0.37287]),'training_results': None,'model': Model((model): Sequential((0): Focus((conv): Conv((conv): Conv2d(12, 32, kernel_size = (3, 3), stride = (1, 1), padding = (1, 1), bias = False)(bn): BatchNorm2d(32, eps = 0.001, momentum = 0.03, affine = True, track_running_stats = True)(act): SiLU())) ......(24): Detect((m): ModuleList((0): Conv2d(128, 255, kernel_size = (1, 1), stride = (1, 1))(1): Conv2d(256, 255, kernel_size = (1, 1), stride = (1, 1))(2): Conv2d(512, 255, kernel_size = (1, 1), stride = (1, 1)))))),'optimizer': None,'wandb_id': None
}
模型的层
具体哪一层可以通过model.model[下标]的形式获取。负数表示倒着获取
倒数第一层
model.model[-1]
Detect((m): ModuleList((0): Conv2d(128, 255, kernel_size=(1, 1), stride=(1, 1))(1): Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1))(2): Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1)))
)
倒数第二层
model.model[-2]
C3((cv1): Conv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU(inplace=True))(cv2): Conv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU(inplace=True))(cv3): Conv((conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))(act): SiLU(inplace=True))(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU(inplace=True))(cv2): Conv((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU(inplace=True))))
)
模型的属性
for name,value in vars(model).items():print('%s=%s'%(name,value))
例如获取类别名称model.names
可以获取的属性包括以下内容
training = False
_parameters = OrderedDict()
_buffers = OrderedDict()
_non_persistent_buffers_set = set()
_backward_hooks = OrderedDict()
_forward_hooks = OrderedDict()
_forward_pre_hooks = OrderedDict()
_state_dict_hooks = OrderedDict()
_load_state_dict_pre_hooks = OrderedDict()
_modules = OrderedDict([('model', Sequential((0): Focus((conv): Conv((conv): Conv2d(12, 32, kernel_size = (3, 3), stride = (1, 1), padding = (1, 1))(act): SiLU(inplace = True)))
......(24): Detect((m): ModuleList((0): Conv2d(128, 255, kernel_size = (1, 1), stride = (1, 1))(1): Conv2d(256, 255, kernel_size = (1, 1), stride = (1, 1))(2): Conv2d(512, 255, kernel_size = (1, 1), stride = (1, 1))))
))])
yaml = {'nc': 80,'depth_multiple': 0.33,'width_multiple': 0.5,'anchors': [[10, 13, 16, 30, 33, 23],[30, 61, 62, 45, 59, 119],[116, 90, 156, 198, 373, 326]],'backbone': [[-1, 1, 'Focus', [64, 3]],[-1, 1, 'Conv', [128, 3, 2]],[-1, 3, 'C3', [128]],[-1, 1, 'Conv', [256, 3, 2]],[-1, 9, 'C3', [256]],[-1, 1, 'Conv', [512, 3, 2]],[-1, 9, 'C3', [512]],[-1, 1, 'Conv', [1024, 3, 2]],[-1, 1, 'SPP', [1024, [5, 9, 13]]],[-1, 3, 'C3', [1024, False]]],'head': [[-1, 1, 'Conv', [512, 1, 1]],[-1, 1, 'nn.Upsample', ['None', 2, 'nearest']],[[-1, 6], 1, 'Concat', [1]],[-1, 3, 'C3', [512, False]],[-1, 1, 'Conv', [256, 1, 1]],[-1, 1, 'nn.Upsample', ['None', 2, 'nearest']],[[-1, 4], 1, 'Concat', [1]],[-1, 3, 'C3', [256, False]],[-1, 1, 'Conv', [256, 3, 2]],[[-1, 14], 1, 'Concat', [1]],[-1, 3, 'C3', [512, False]],[-1, 1, 'Conv', [512, 3, 2]],[[-1, 10], 1, 'Concat', [1]],[-1, 3, 'C3', [1024, False]],[[17, 20, 23], 1, 'Detect', ['nc', 'anchors']]],'ch': 3
}
save = [4, 6, 10, 14, 17, 20, 23]
names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign','parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella','handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard','surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange','broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
stride = tensor([8., 16., 32.])
nc = 80
hyp = {'lr0': 0.01,'lrf': 0.2,'momentum': 0.937,'weight_decay': 0.0005,'warmup_epochs': 3.0,'warmup_momentum': 0.8,'warmup_bias_lr': 0.1,'box': 0.05,'cls': 0.3,'cls_pw': 1.0,'obj': 0.7,'obj_pw': 1.0,'iou_t': 0.2,'anchor_t': 4.0,'fl_gamma': 0.0,'hsv_h': 0.015,'hsv_s': 0.7,'hsv_v': 0.4,'degrees': 0.0,'translate': 0.1,'scale': 0.9,'shear': 0.0,'perspective': 0.0,'flipud': 0.0,'fliplr': 0.5,'mosaic': 1.0,'mixup': 0.01
}
gr = 1.0
class_weights = tensor([1.20752e-02, ......, 2.12184e+00, 6.56878e-01, 1.56888e+01, 1.59711e+00
], dtype = torch.float64)
_is_full_backward_hook = None
模块的名称以及模块本身
代码
for name, module in model.named_modules():print(name)
这里只输出名字
model
model.0
model.0.conv
model.0.conv.conv
model.0.conv.act
model.1
model.1.conv
model.1.act
......
model.22
model.23
model.23.cv1
model.23.cv1.conv
model.23.cv1.act
model.23.cv2
model.23.cv2.conv
model.23.cv2.act
model.23.cv3
model.23.cv3.conv
model.23.cv3.act
model.23.m
model.23.m.0
model.23.m.0.cv1
model.23.m.0.cv1.conv
model.23.m.0.cv1.act
model.23.m.0.cv2
model.23.m.0.cv2.conv
model.23.m.0.cv2.act
model.24
model.24.m
model.24.m.0
model.24.m.1
model.24.m.2
模型的权重
代码
list(model.parameters())[0]
内容
Parameter containing:
tensor([[[[-2.73007e-01, -1.44108e+00, -1.18709e+00],[-3.13122e-01, -9.47540e-01, -7.23963e-01],[ 4.31564e-01, 2.05478e+00, 1.50800e+00]],......[[ 8.07673e-01, 1.51624e+00, 2.86977e-01],[-1.81044e+00, -1.48112e+00, -6.51210e-01],[-5.19720e-01, -1.99577e-01, 1.39392e-01]]]])
模型权重的名字和权重
代码
list(model.named_parameters())
model.0.conv.conv.weight是名字
('model.0.conv.conv.weight', Parameter containing:
tensor([[[[-2.73007e-01, -1.44108e+00, -1.18709e+00],[-3.13122e-01, -9.47540e-01, -7.23963e-01],[ 4.31564e-01, 2.05478e+00, 1.50800e+00]],......[[ 8.07673e-01, 1.51624e+00, 2.86977e-01],[-1.81044e+00, -1.48112e+00, -6.51210e-01],[-5.19720e-01, -1.99577e-01, 1.39392e-01]]]]))
完整的model
Model((model): Sequential((0): Focus((conv): Conv((conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))(1): Conv((conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))(act): SiLU())(2): C3((cv1): Conv((conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv3): Conv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))))(3): Conv((conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))(act): SiLU())(4): C3((cv1): Conv((conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv3): Conv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))(1): Bottleneck((cv1): Conv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))(2): Bottleneck((cv1): Conv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))))(5): Conv((conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))(act): SiLU())(6): C3((cv1): Conv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv3): Conv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))(1): Bottleneck((cv1): Conv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))(2): Bottleneck((cv1): Conv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))))(7): Conv((conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))(act): SiLU())(8): SPP((cv1): Conv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(m): ModuleList((0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)(1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)(2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False)))(9): C3((cv1): Conv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv3): Conv((conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))))(10): Conv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(11): Upsample(scale_factor=2.0, mode=nearest)(12): Concat()(13): C3((cv1): Conv((conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv3): Conv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))))(14): Conv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(15): Upsample(scale_factor=2.0, mode=nearest)(16): Concat()(17): C3((cv1): Conv((conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv3): Conv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))))(18): Conv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))(act): SiLU())(19): Concat()(20): C3((cv1): Conv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv3): Conv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))))(21): Conv((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))(act): SiLU())(22): Concat()(23): C3((cv1): Conv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv3): Conv((conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))(act): SiLU())(cv2): Conv((conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(act): SiLU()))))(24): Detect((m): ModuleList((0): Conv2d(128, 255, kernel_size=(1, 1), stride=(1, 1))(1): Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1))(2): Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1)))))
)
完整模型图片展示
图片较大，可下载看
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