def __init__(self, cfg='yolov3.yaml', ch=3, nc=None): # model, input channels, number of classes
super(Model, self).__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name
with open(cfg) as f:
self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict
# Define model
if nc and nc != self.yaml['nc']:
logger.info('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc))
self.yaml['nc'] = nc # override yaml value
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist, ch_out
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 128 # 2x min stride
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
m.anchors /= m.stride.view(-1, 1, 1)
check_anchor_order(m)
self.stride = m.stride
self._initialize_biases() # only run once
# print('Strides: %s' % m.stride.tolist())
# Init weights, biases
initialize_weights(self)
self.info()
logger.info('')
def __init__(self,
nc,
anchors=None,
ch=(256, 512, 1024),
stride=[8., 16., 32.]): # detection layer
super(YOLOHead, self).__init__()
if anchors is None:
anchors = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119],
[116, 90, 156, 198, 373, 326]]
else:
anchors = anchors
self.nc = nc # number of classes
self.no = nc + 5 # number of outputs per anchor
self.nl = len(anchors) # number of detection layers
self.na = len(anchors[0]) // 2 # number of anchors
self.grid = [torch.zeros(1)] * self.nl # init grid
a = torch.tensor(anchors).float().view(self.nl, -1, 2)
self.register_buffer('anchors', a) # shape(nl,na,2)
self.register_buffer('anchor_grid',
a.clone().view(self.nl, 1, -1, 1, 1,
2)) # shape(nl,1,na,1,1,2)
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1)
for x in ch) # output conv
self.stride = torch.tensor(stride)
self.anchors /= self.stride.view(-1, 1, 1)
check_anchor_order(self)
def __init__(self,
cfg='yolov5s.yaml',
ch=3,
nc=None,
anchors=None): # model, input channels, number of classes
super(Model, self).__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name
with open(cfg) as f:
self.yaml = yaml.load(
f, Loader=yaml.SafeLoader) # yaml파일 내용을 딕셔너리로 읽어들임
# Define model
ch = self.yaml['ch'] = self.yaml.get(
'ch', ch) # input channels:3 을 self.yaml 딕셔너리에 추가
if nc and nc != self.yaml['nc']: # num_classes가 다르면 오버라이드
logger.info(
f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}"
) # 예상대로 진행되는지에 대한 확인
self.yaml['nc'] = nc # override yaml value
if anchors:
logger.info(
f'Overriding model.yaml anchors with anchors={anchors}')
self.yaml['anchors'] = round(
anchors) # 새로 입력된 앵커박스가 있다면 yaml dict에 override
self.model, self.save = parse_model(deepcopy(self.yaml),
ch=[ch]) # model, savelist
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 256 # 2x min stride
m.stride = torch.tensor([
s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))
]) # forward / 8, 16, 32
m.anchors /= m.stride.view(-1, 1, 1) # 각 스케일에 대한 비율로 anchor box 조정
check_anchor_order(m)
self.stride = m.stride # 8, 16, 32
self._initialize_biases() # only run once
# print('Strides: %s' % m.stride.tolist())
# Init weights, biases
initialize_weights(self)
self.info()
logger.info('')
def __init__(self,
cfg='yolov5s.yaml',
ch=3,
nc=None,
anchors=None): # model, input channels, number of classes
super(Model, self).__init__()
if isinstance(cfg, dict): # &&& looking for parameter file
self.yaml = cfg # model dict
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name
with open(cfg) as f:
self.yaml = yaml.safe_load(f) # model dict
# Define model
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
if nc and nc != self.yaml[
'nc']: ## &&& loading number of classes and anchors
logger.info(
f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
self.yaml['nc'] = nc # override yaml value
if anchors:
logger.info(
f'Overriding model.yaml anchors with anchors={anchors}')
self.yaml['anchors'] = round(anchors) # override yaml value
self.model, self.save = parse_model(deepcopy(
self.yaml), ch=[ch]) # model, savelist &&& here it loads model
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
# logger.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
# Build strides, anchors
m = self.model[
-1] # Detect() # &&&& stripped last layer, what is Detect???? its class defined above
if isinstance(m, Detect):
s = 256 # 2x min stride &&&& what is x below and size , what is stride https://discuss.pytorch.org/t/pytorch-tensor-stride-how-it-works/90537
m.stride = torch.tensor([
s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))
]) # forward &&&& m.stride = tensor([ 8., 16., 32.])
m.anchors /= m.stride.view(
-1, 1,
1) # &&& this perform division and scale down of anchor sizes
check_anchor_order(m)
self.stride = m.stride
self._initialize_biases() # only run once
# logger.info('Strides: %s' % m.stride.tolist())
# Init weights, biases
initialize_weights(self)
self.info()
logger.info('')
def __init__(self,
cfg='yolov5s.yaml',
ch=3,
nc=None,
anchors=None): # model, input channels, number of classes
super(Model, self).__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name
with open(cfg) as f:
self.yaml = yaml.safe_load(f) # model dict
# Define model
if ch == 3: # If using default num channels, check model config to make sure this is the right number of channels
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
if nc and nc != self.yaml['nc']:
logger.info(
f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
self.yaml['nc'] = nc # override yaml value
if anchors:
logger.info(
f'Overriding model.yaml anchors with anchors={anchors}')
self.yaml['anchors'] = round(anchors) # override yaml value
self.model, self.save = parse_model(deepcopy(self.yaml),
ch=[ch]) # model, savelist
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
self.inplace = self.yaml.get('inplace', True)
# logger.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 256 # 2x min stride
m.inplace = self.inplace
m.stride = torch.tensor([
s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))
]) # forward
m.anchors /= m.stride.view(-1, 1, 1)
check_anchor_order(m)
self.stride = m.stride
self._initialize_biases() # only run once
# logger.info('Strides: %s' % m.stride.tolist())
# Init weights, biases
initialize_weights(self)
self.info()
logger.info('')
def __init__(self,
cfg='EDSR.yaml',
ch=3,
nc=None,
anchors=None): # model, input channels, number of classes
super(Model, self).__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
else: # is *.yaml
import yaml
self.yaml_file = Path(cfg).name
with open(cfg) as f:
self.yaml = yaml.load(f, Loader=yaml.SafeLoader) # model dict
# Define model
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
if nc and nc != self.yaml['nc']:
logger.info(
f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
self.yaml['nc'] = nc # override yaml value
if anchors:
logger.info(
f'Overriding model.yaml anchors with anchors={anchors}')
self.yaml['anchors'] = round(anchors) # override yaml value
self.model, self.save = parse_model(deepcopy(self.yaml),
ch=[ch]) # model, savelist
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
print([x.shape for x in self.forward(torch.zeros(1, ch, 128, 128))])
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 256 # 2x min stride
m.stride = torch.tensor([
s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))
]) # forward
m.anchors /= m.stride.view(-1, 1,
1) # if SR_mixed model -> * scale
check_anchor_order(m)
self.stride = m.stride
self._initialize_biases() # only run once
# print('Strides: %s' % m.stride.tolist())
# Init weights, biases
initialize_weights(self)
self.info()
logger.info('')
def __init__(self,
cfg='yolov5s.yaml',
ch=3,
nc=None,
anchors=None): # model, input channels, number of classes
super().__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name
with open(cfg, encoding='ascii', errors='ignore') as f:
self.yaml = yaml.safe_load(f) # model dict
# Define model
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
if nc and nc != self.yaml['nc']:
LOGGER.info(
f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
self.yaml['nc'] = nc # override yaml value
if anchors:
LOGGER.info(
f'Overriding model.yaml anchors with anchors={anchors}')
self.yaml['anchors'] = round(anchors) # override yaml value
self.model, self.save = parse_model(deepcopy(self.yaml),
ch=[ch]) # model, savelist
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
self.inplace = self.yaml.get('inplace', True)
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 256 # 2x min stride
m.inplace = self.inplace
m.stride = torch.tensor([
s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))
]) # forward
check_anchor_order(m) # must be in pixel-space (not grid-space)
m.anchors /= m.stride.view(-1, 1, 1)
self.stride = m.stride
self._initialize_biases() # only run once
# Init weights, biases
initialize_weights(self)
self.info()
LOGGER.info('')
def __init__(self,
cfg='yolov5s.yaml',
ch=3,
nc=None,
anchors=None): # model, input channels, number of classes
super(Model, self).__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name
with open(cfg) as f:
self.yaml = yaml.load(f, Loader=yaml.SafeLoader) # model dict
# Define model
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
if nc and nc != self.yaml['nc']:
logger.info(
f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
self.yaml['nc'] = nc # override yaml value
if anchors:
logger.info(
f'Overriding model.yaml anchors with anchors={anchors}')
self.yaml['anchors'] = round(anchors) # override yaml value
self.model, self.save = parse_model(deepcopy(self.yaml),
ch=[ch]) # model, savelist
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
#upsampling's head
self.center = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=11, padding=5, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
)
self.decode1 = ResDecode(384 + 512, 256) #layer11
self.decode2 = ResDecode(256 + 256, 128) #layer8
self.decode3 = ResDecode(128 + 128, 64) #layer6
self.decode4 = ResDecode(64 + 64, 32) #layer3
self.decode5 = ResDecode(32, 16) #layer2
# self.logit = nn.Conv2d(16, 1, kernel_size=3, padding=1) #segmentation output
# fc[-1].bias.data.fill_(-2.19)
self.logit = nn.Sequential(
nn.Conv2d(16, 256, kernel_size=3, padding=1, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(256, 1, kernel_size=1, stride=1, padding=0, bias=True))
self.logit[-1].bias.data.fill_(-2.19)
#~upsampling's head
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 256 # 2x min stride
m.stride = torch.tensor([
s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))
]) # forward
m.anchors /= m.stride.view(-1, 1, 1)
check_anchor_order(m)
self.stride = m.stride
self._initialize_biases() # only run once
# print('Strides: %s' % m.stride.tolist())
# Init weights, biases
initialize_weights(self)
self.info()
logger.info('')
