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Add best.pt val and COCO pycocotools val (#98)

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Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2022-12-27 04:56:24 +01:00
committed by GitHub
parent a1808eeda4
commit 6f0ba81427
12 changed files with 159 additions and 115 deletions
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25
ultralytics/nn/tasks.py
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@ -78,7 +78,7 @@ class BaseModel(nn.Module):
class DetectionModel(BaseModel):
# YOLOv5 detection model
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, verbose=True): # model, input channels, number of classes
super().__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
@ -92,7 +92,7 @@ class DetectionModel(BaseModel):
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
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch], verbose=verbose) # model, savelist
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
self.inplace = self.yaml.get('inplace', True)
@ -108,8 +108,9 @@ class DetectionModel(BaseModel):
# Init weights, biases
initialize_weights(self)
self.info()
LOGGER.info('')
if verbose:
self.info()
LOGGER.info('')
def forward(self, x, augment=False, profile=False, visualize=False):
if augment:
@ -152,11 +153,12 @@ class DetectionModel(BaseModel):
y[-1] = y[-1][..., i:] # small
return y
def load(self, weights):
def load(self, weights, verbose=True):
csd = weights['model'].float().state_dict() # checkpoint state_dict as FP32
csd = intersect_state_dicts(csd, self.state_dict()) # intersect
self.load_state_dict(csd, strict=False) # load
LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights')
if verbose:
LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights')
class SegmentationModel(DetectionModel):
@ -260,13 +262,15 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=True):
return model
def parse_model(d, ch): # model_dict, input_channels(3)
def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
# Parse a YOLOv5 model.yaml dictionary
LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10} {'module':<45}{'arguments':<30}")
if verbose:
LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10} {'module':<45}{'arguments':<30}")
nc, gd, gw, act = d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
if act:
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
LOGGER.info(f"{colorstr('activation:')} {act}") # print
if verbose:
LOGGER.info(f"{colorstr('activation:')} {act}") # print
no = nc + 4 # number of outputs = classes + box
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
@ -304,7 +308,8 @@ def parse_model(d, ch): # model_dict, input_channels(3)
t = str(m)[8:-2].replace('__main__.', '') # module type
m.np = sum(x.numel() for x in m_.parameters()) # number params
m_.i, m_.f, m_.type = i, f, t # attach index, 'from' index, type
LOGGER.info(f'{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f} {t:<45}{str(args):<30}') # print
if verbose:
LOGGER.info(f'{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f} {t:<45}{str(args):<30}') # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
if i == 0: