def init_model(cfg, device, id_gpu=None):
model = models.resnet18(pretrained=True)
dims_out = (128, 256, 512)
model = ModelOuts4Resnet(model, dims_out)
cfg.SAVE_FILE_NAME = cfg.SAVE_FILE_NAME + '_res18'
if cfg.MODE_TRAIN == 1 or cfg.MODE_TRAIN == 2 or cfg.MODE_TRAIN == 5:
'''
1. 输出4层 共享输出
2. 输出4层 使用新FPN 共享
'''
cfg.STRIDES = [8, 16, 32, 64] # 特有参数下采样步距
cfg.SCALE_THRESHOLDS = [0, 49, 98, 196, 10000000000.0
] # 用于确保每一个特图预测相应大小的框,且一个GT只在一个层进行匹配
elif cfg.MODE_TRAIN == 3 or cfg.MODE_TRAIN == 4:
''' 输出4层 使用新FPN 共享 '''
# model = models.resnet50(pretrained=True)
# dims_out = (512, 1024, 2048)
# model = ModelOuts4Resnet(model, dims_out)
# cfg.SAVE_FILE_NAME = cfg.SAVE_FILE_NAME + '_res50'
cfg.STRIDES = [8, 16, 32, 64, 128] # size需要512 128的倍数
cfg.SCALE_THRESHOLDS = [0, 64, 128, 256, 512, 10000000000.0]
else:
raise Exception('cfg.MODE_TRAIN 出错 cfg.MODE_TRAIN=%s' %
(cfg.MODE_TRAIN))
model = Fcos(model, cfg, device)
if cfg.IS_LOCK_BACKBONE_WEIGHT:
for name, param in model.backbone.named_parameters():
param.requires_grad_(False)
model, is_mgpu = model_device_init(model, device, id_gpu, cfg)
# ------------------------自定义backbone完成-------------------------------
pg = model.parameters()
optimizer = optim.Adam(pg, lr=cfg.LR0)
# 两次不上升，降低一半
# lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, factor=0.5, verbose=True)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [50, 80, 100],
0.1)
start_epoch = load_weight(cfg.FILE_FIT_WEIGHT,
model,
optimizer,
lr_scheduler,
device,
is_mgpu=is_mgpu)
model.cfg = cfg
return model, optimizer, lr_scheduler, start_epoch
def init_model(cfg, device, id_gpu=None):
model = models.resnet18(pretrained=True)
dims_out = (128, 256, 512)
model = ModelOuts4Resnet(model, dims_out)
cfg.SAVE_FILE_NAME = cfg.SAVE_FILE_NAME + '_res18'
# model = models.resnet50(pretrained=True)
# dims_out = (512, 1024, 2048)
# model = ModelOuts4Resnet(model, dims_out)
# cfg.SAVE_FILE_NAME = cfg.SAVE_FILE_NAME + '_res50'
cfg.STRIDES = [8, 16, 32, 64] # 特有参数下采样步距
assert cfg.IMAGE_SIZE[0] % cfg.STRIDES[
-1] == 0, 'cfg.IMAGE_SIZE=%s 与 cfg.STRIDES=%s 不能匹配(整除)' % (
cfg.IMAGE_SIZE, cfg.STRIDES)
cfg.SCALE_THRESHOLDS = [0, 49, 98, 196,
10000000000.0] # 用于确保每一个特图预测相应大小的框,且一个GT只在一个层进行匹配
model = KFcos(model, cfg)
if cfg.IS_LOCK_BACKBONE_WEIGHT:
for name, param in model.backbone.named_parameters():
param.requires_grad_(False)
model, is_mgpu = model_device_init(model, device, id_gpu, cfg)
# ------------------------自定义backbone完成-------------------------------
pg = model.parameters()
# optimizer = optim.Adam(pg, lr=cfg.LR0)
optimizer = optim.SGD(pg, lr=cfg.LR0, momentum=0.9, weight_decay=5e-6)
# 两次不上升，降低一半
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=10,
factor=0.1,
verbose=True)
# lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [50, 80, 100], 0.1)
start_epoch = load_weight(cfg.FILE_FIT_WEIGHT,
model,
optimizer,
lr_scheduler,
device,
is_mgpu=is_mgpu)
model.cfg = cfg
return model, optimizer, lr_scheduler, start_epoch
def init_model(cfg, device, id_gpu=None):
# model = darknet53(pretrained=True)
# model = ModelOuts4DarkNet53(model)
# cfg.SAVE_FILE_NAME = cfg.SAVE_FILE_NAME + '_dark53'
# model = darknet19(pretrained=True)
# model = ModelOuts4DarkNet19(model)
# cfg.SAVE_FILE_NAME = cfg.SAVE_FILE_NAME + '_dark19'
model = models.resnet18(pretrained=True)
dims_out = (128, 256, 512)
model = ModelOuts4Resnet(model, dims_out)
cfg.SAVE_FILE_NAME = cfg.SAVE_FILE_NAME + '_res18'
# model = CSPDarknet_slim()
# model.dims_out = (128, 256, 512)
# cfg.SAVE_FILE_NAME = cfg.SAVE_FILE_NAME + '_csspds'
model = Yolo_v3(backbone=model, cfg=cfg)
# f_look_model(model, input=(1, 3, *cfg.IMAGE_SIZE))
if cfg.IS_LOCK_BACKBONE_WEIGHT:
for name, param in model.net.backbone.named_parameters():
param.requires_grad_(False)
model, is_mgpu = model_device_init(model, device, id_gpu, cfg)
# ------------------------模型完成-------------------------------
pg = model.parameters()
optimizer = optim.Adam(pg, cfg.LR0)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [50, 80, 100],
0.1)
start_epoch = load_weight(cfg.FILE_FIT_WEIGHT,
model,
optimizer,
lr_scheduler,
device,
is_mgpu=is_mgpu)
model.cfg = cfg
return model, optimizer, lr_scheduler, start_epoch
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
def init_model(cfg, device, id_gpu=None):
# model = models.densenet121(pretrained=True)
# ret_name_dict = {'denseblock2': 1, 'denseblock3': 2, 'denseblock4': 3}
# dims_out = [512, 1024, 1024]
# model = ModelOuts4Densenet121(model, 'features', ret_name_dict, dims_out)
# cfg.FEATURE_MAP_STEPS = [8, 16, 32] # 特图的步距 下采倍数
# cfg.SAVE_FILE_NAME = cfg.SAVE_FILE_NAME + 'densenet121'
# model = models.resnet50(pretrained=True)
# dims_out = (512, 1024, 2048)
# model = ModelOuts4Resnet(model, dims_out)
# cfg.SAVE_FILE_NAME = cfg.SAVE_FILE_NAME + '_res50'
# cfg.FEATURE_MAP_STEPS = [8, 16, 32] # 特图的步距 下采倍数
model = models.resnet18(pretrained=True)
dims_out = (128, 256, 512)
model = ModelOuts4Resnet(model, dims_out)
cfg.FEATURE_MAP_STEPS = [8, 16, 32, 64, 128]
# 每层特图5个anc
cfg.NUMS_ANC = [1, 1, 1, 1, 1]
cfg.ANCS_SCALE = [[0.078, 0.07775],
[0.174, 0.164],
[0.324, 0.336],
[0.578, 0.466],
[0.698, 0.674]]
# cfg.NUMS_ANC = [3, 3, 3, 3, 3]
# cfg.ANCS_SCALE = [[0.052, 0.046],
# [0.088, 0.092],
# [0.152, 0.106],
# [0.184, 0.186],
# [0.224, 0.32],
# [0.38, 0.242],
# [0.338, 0.424],
# [0.572, 0.32],
# [0.34, 0.624],
# [0.484, 0.527],
# [0.7, 0.44],
# [0.642, 0.656],
# [0.836, 0.573],
# [0.61, 0.882],
# [0.92, 0.74231]]
# cfg.LOSS_WEIGHT = [1, 1, 1, 1, 1] # conf_pos conf_neg cls loss_txty loss_twth
model = Retina2(model, cfg, device)
# model = Retina3(model, cfg, device)
# cfg.NUMS_ANC = [3, 3, 3]
# cfg.FEATURE_MAP_STEPS = [8, 16, 32]
# model = Retina(model, cfg, device)
if cfg.IS_LOCK_BACKBONE_WEIGHT:
for name, param in model.backbone.named_parameters():
param.requires_grad_(False)
model, is_mgpu = model_device_init(model, device, id_gpu, cfg)
# ------------------------自定义backbone完成-------------------------------
pg = model.parameters()
optimizer = optim.Adam(pg, lr=cfg.LR0)
# 两次不上升，降低一半
# lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, factor=0.5, verbose=True)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [50, 70, 100], 0.1)
start_epoch = load_weight(cfg.FILE_FIT_WEIGHT, model, optimizer, lr_scheduler, device, is_mgpu=is_mgpu)
model.cfg = cfg
return model, optimizer, lr_scheduler, start_epoch