def create(name, pretrained, channels, classes):
"""Creates a specified YOLOv5 model
Arguments:
name (str): name of model, i.e. 'yolov5s'
pretrained (bool): load pretrained weights into the model
channels (int): number of input channels
classes (int): number of model classes
Returns:
pytorch model
"""
config = os.path.join(os.path.dirname(__file__), 'models', '%s.yaml' % name) # model.yaml path
try:
model = Model(config, channels, classes)
if pretrained:
ckpt = '%s.pt' % name # checkpoint filename
attempt_download(ckpt) # download if not found locally
state_dict = torch.load(ckpt, map_location=torch.device('cpu'))['model'].float().state_dict() # to FP32
state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter
model.load_state_dict(state_dict, strict=False) # load
m = NMS()
m.f = -1 # from
m.i = model.model[-1].i + 1 # index
model.model.add_module(name='%s' % m.i, module=m) # add NMS
model.eval()
return model
except Exception as e:
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
s = 'Cache maybe be out of date, deleting cache and retrying may solve this. See %s for help.' % help_url
raise Exception(s) from e
def add_nms(self): # fuse model Conv2d() + BatchNorm2d() layers
if type(self.model[-1]) is not NMS: # if missing NMS
print('Adding NMS module... ')
m = NMS() # module
m.f = -1 # from
m.i = self.model[-1].i + 1 # index
self.model.add_module(name='%s' % m.i, module=m) # add
return self
def nms(self, mode=True): # add or remove NMS module
present = type(self.model[-1]) is NMS # last layer is NMS
if mode and not present:
print('Adding NMS... ')
m = NMS() # module
m.f = -1 # from
m.i = self.model[-1].i + 1 # index
self.model.add_module(name='%s' % m.i, module=m) # add
self.eval()
elif not mode and present:
print('Removing NMS... ')
self.model = self.model[:-1] # remove
return self
