if __name__ == '__main__':
from f_pytorch.tools_model.model_look import f_look_tw
''' 模型测试 '''
cfg = CFG()
cfg.NUMS_ANC = [3, 3, 3]
cfg.NUM_CLASSES = 3
cfg.IMAGE_SIZE = (320, 320)
cfg.STRIDES = [8, 16, 32, 64]
cfg.MODE_TRAIN = 3
model = models.resnet18(pretrained=True)
dims_out = (128, 256, 512)
model = ModelOuts4Resnet(model, dims_out)
# model = FcosNet_v1(model, cfg)
# model = FcosNet_v2(model, cfg, o_ceng=4, num_conv=3)
# model = FcosNet_v2(model, cfg, o_ceng=5, num_conv=3)
model = FcosNet_v3(model, cfg, o_ceng=5, num_conv=3)
model.train()
# cfg.STRIDES = [8, 16, 32, 64, 128]
# model = FcosNet(model, cfg, o_ceng=5)
print(model(torch.rand(2, 3, 416, 416)).shape)
# model.eval()
f_look_tw(model, input=(1, 3, 320, 320), name='fcos')
# from f_pytorch.tools_model.model_look import f_look_summary
# f_look_summary(model)
with torch.no_grad(): # 这个没用
ids_batch, p_boxes_ltrb, p_keypoints, p_labels, p_scores = self.preder(outs, x)
return ids_batch, p_boxes_ltrb, p_keypoints, p_labels, p_scores
if __name__ == '__main__':
model = darknet19(pretrained=True)
model = ModelOuts4DarkNet19(model)
# model = ModelOuts4DarkNet19(model)
# model = models.resnet18(pretrained=True)
# dims_out = (128, 256, 512)
# model = ModelOuts4Resnet(model, dims_out)
class CFG:
pass
cfg = CFG()
cfg.NUM_CLASSES = 20
cfg.NUM_ANC = 5
net = Yolo_v2_Net(backbone=model, cfg=cfg)
# x = torch.rand([5, 3, 416, 416])
# print(net(x).shape)
from f_pytorch.tools_model.model_look import f_look_tw
f_look_tw(net, input=(5, 3, 416, 416), name='Yolo_v2_Net')
outs, x)
return ids_batch, p_boxes_ltrb, p_keypoints, p_labels, p_scores
if __name__ == '__main__':
class CFG:
pass
from f_pytorch.tools_model.f_layer_get import ModelOuts4Resnet
from f_pytorch.tools_model.model_look import f_look_tw
cfg = CFG()
cfg.NUMS_ANC = [3, 3, 3]
cfg.NUM_CLASSES = 3
cfg.IMAGE_SIZE = (416, 416)
cfg.ANCS_SCALE = [[0.06, 0.06], [0.122, 0.098], [0.18, 0.19],
[0.382, 0.251], [0.262, 0.378], [0.408, 0.529],
[0.621, 0.415], [0.622, 0.704], [0.85, 0.61]]
cfg.FEATURE_MAP_STEPS = [8, 16, 32, 64, 128]
cfg.NUM_KEYPOINTS = 5
model = models.resnet18(pretrained=True)
dims_out = (128, 256, 512)
model = ModelOuts4Resnet(model, dims_out)
model = Retina_Net2(model, cfg)
f_look_tw(model, input=(1, 3, 416, 416), name='Retina_Net')
# f_look_summary(model, input=(3, 416, 416))
# model(torch.randn((2, 3, 416, 416)))
[0.07, 0.078],
[0.106, 0.052],
[0.054, 0.158],
[0.084, 0.114],
[0.146, 0.092],
[0.078, 0.21],
[0.118, 0.154],
[0.15, 0.204],
[0.106, 0.294],
[0.242, 0.13],
[0.212, 0.234],
[0.162, 0.382],
[0.422, 0.198],
[0.298, 0.288],
[0.23, 0.412],
[0.47, 0.31],
[0.34, 0.444],
[0.274, 0.62187],
[0.792, 0.268],
[0.57806, 0.42],
[0.432, 0.6],
[0.843, 0.49],
[0.64, 0.732],
[0.942, 0.662],
]
# net = SSD_Net(backbone=model, num_classes=cfg.NUM_CLASSES, cfg=cfg)
model = SSD(backbone=model, cfg=cfg, device=torch.device('cpu'))
from f_pytorch.tools_model.model_look import f_look_tw
f_look_tw(model, input=(1, 3, 300, 300), name='SSD_Net')
# 可以不要返回值
self.preder(outs, x, *args)
return
else:
ids_batch, p_boxes_ltrb, p_keypoints, p_labels, p_scores = self.preder(outs, x, *args)
return ids_batch, p_boxes_ltrb, p_keypoints, p_labels, p_scores
# if torch.jit.is_scripting(): # 这里是生产环境部署
if __name__ == '__main__':
from torchvision import models
from f_pytorch.tools_model.model_look import f_look_tw
# model = models.mobilenet_v2(pretrained=True)
# model = ModelOuts4Mobilenet_v2(model)
# dims_out = [256, 512, 1024]
class cfg:
pass
cfg.NUM_CLASSES = 3
model = models.resnet18(pretrained=True)
model = ModelOut4Resnet18(model)
model = CenterNet(backbone=model, cfg=cfg, )
model.eval()
# 输出是 tuple 要报错但可以生成
f_look_tw(model, input=(1, 3, 512, 512), name='CenterNet')
if targets is None:
raise ValueError("In training mode, targets should be passed")
loss_total, log_dict = self.losser(outs, targets, x)
'''------验证loss 待扩展------'''
return loss_total, log_dict
else:
with torch.no_grad(): # 这个没用
ids_batch, p_boxes_ltrb, p_keypoints, p_labels, p_scores = self.preder(
outs, x)
return ids_batch, p_boxes_ltrb, p_keypoints, p_labels, p_scores
if __name__ == '__main__':
from torchvision import models
from f_pytorch.tools_model.f_layer_get import ModelOut4Resnet18
model = models.resnet18(pretrained=True)
model = ModelOut4Resnet18(model)
class CFG:
pass
cfg = CFG()
cfg.NUM_CLASSES = 3
net = YOLOv1_Net(backbone=model, cfg=cfg)
from f_pytorch.tools_model.model_look import f_look_tw
f_look_tw(net, input=(5, 3, 416, 416), name='YOLOv1_Net')