def __init__(self, cfg):
super().__init__(cfg)
# Get teacher model config
model_ts = []
for i in range(len(cfg.KD.MODEL_CONFIG)):
cfg_t = get_cfg()
cfg_t.merge_from_file(cfg.KD.MODEL_CONFIG[i])
cfg_t.defrost()
cfg_t.MODEL.META_ARCHITECTURE = "Baseline"
# Change syncBN to BN due to no DDP wrapper
if cfg_t.MODEL.BACKBONE.NORM == "syncBN":
cfg_t.MODEL.BACKBONE.NORM = "BN"
if cfg_t.MODEL.HEADS.NORM == "syncBN":
cfg_t.MODEL.HEADS.NORM = "BN"
model_t = build_model(cfg_t)
# No gradients for teacher model
for param in model_t.parameters():
param.requires_grad_(False)
logger.info("Loading teacher model weights ...")
Checkpointer(model_t).load(cfg.KD.MODEL_WEIGHTS[i])
model_ts.append(model_t)
self.ema_enabled = cfg.KD.EMA.ENABLED
self.ema_momentum = cfg.KD.EMA.MOMENTUM
if self.ema_enabled:
cfg_self = cfg.clone()
cfg_self.defrost()
cfg_self.MODEL.META_ARCHITECTURE = "Baseline"
if cfg_self.MODEL.BACKBONE.NORM == "syncBN":
cfg_self.MODEL.BACKBONE.NORM = "BN"
if cfg_self.MODEL.HEADS.NORM == "syncBN":
cfg_self.MODEL.HEADS.NORM = "BN"
model_self = build_model(cfg_self)
# No gradients for self model
for param in model_self.parameters():
param.requires_grad_(False)
if cfg_self.MODEL.WEIGHTS != '':
logger.info("Loading self distillation model weights ...")
Checkpointer(model_self).load(cfg_self.MODEL.WEIGHTS)
else:
# Make sure the initial state is same
for param_q, param_k in zip(self.parameters(), model_self.parameters()):
param_k.data.copy_(param_q.data)
model_ts.insert(0, model_self)
# Not register teacher model as `nn.Module`, this is
# make sure teacher model weights not saved
self.model_ts = model_ts
def __init__(self, cfg_):
self.cfg = cfg_.clone() # cfg can be modified by model
self.cfg.defrost()
self.cfg.MODEL.BACKBONE.PRETRAIN = False
self.model = build_model(self.cfg)
self.model.eval()
Checkpointer(self.model).load(cfg_.MODEL.WEIGHTS)
def __init__(self, cfg):
self.cfg = cfg.clone() # cfg can be modified by model
self.model = build_model(self.cfg)
self.model.eval()
# self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])
checkpointer = Checkpointer(self.model)
checkpointer.load(cfg.MODEL.WEIGHTS)
def build_model(cls, cfg):
"""
Returns:
torch.nn.Module:
It now calls :func:`detectron2.modeling.build_model`.
Overwrite it if you'd like a different model.
"""
model = build_model(cfg)
logger = logging.getLogger(__name__)
logger.info("Model:\n{}".format(model))
return model
def __init__(self, cfg, device='cpu'):
self.cfg = cfg.clone() # cfg can be modified by model
self.cfg.defrost()
self.cfg.MODEL.BACKBONE.PRETRAIN = False
self.device = device
self.model = build_model(self.cfg)
self.model.to(device)
self.model.eval()
checkpointer = Checkpointer(self.model)
checkpointer.load(cfg.MODEL.WEIGHTS)
def build_model(cls, cfg, show_model=True):
"""
Returns:
torch.nn.Module:
It now calls :func:`fastreid.modeling.build_model`.
Overwrite it if you'd like a different model.
"""
model = build_model(cfg)
if show_model:
logger = logging.getLogger('fastreid')
logger.info("Model:\n{}".format(model))
return model
def __init__(self, cfg):
self.cfg = cfg.clone() # cfg can be modified by model
model = build_model(self.cfg)
self.model = DataParallel(model)
self.model.cuda()
self.model.eval()
checkpointer = Checkpointer(self.model)
checkpointer.load(cfg.MODEL.WEIGHTS)
num_channels = len(cfg.MODEL.PIXEL_MEAN)
self.mean = torch.tensor(cfg.MODEL.PIXEL_MEAN).view(
1, num_channels, 1, 1)
self.std = torch.tensor(cfg.MODEL.PIXEL_STD).view(
1, num_channels, 1, 1)
def build_model(cls,
cfg,
load_model=True,
show_model=True,
use_dsbn=False):
cfg = cfg.clone() # cfg can be modified by model
cfg.defrost()
cfg.MODEL.DEVICE = "cpu"
model = build_model(cfg)
logger = logging.getLogger('fastreid')
if load_model:
pretrain_path = cfg.MODEL.PRETRAIN_PATH
try:
state_dict = torch.load(
pretrain_path, map_location=torch.device("cpu"))['model']
for layer in cfg.MODEL.IGNORE_LAYERS:
if layer in state_dict.keys():
del state_dict[layer]
logger.info(f"Loading pretrained model from {pretrain_path}")
except FileNotFoundError as e:
logger.info(
f"{pretrain_path} is not found! Please check this path.")
raise e
except KeyError as e:
logger.info(
"State dict keys error! Please check the state dict.")
raise e
incompatible = model.load_state_dict(state_dict, strict=False)
if incompatible.missing_keys:
logger.info(
get_missing_parameters_message(incompatible.missing_keys))
if incompatible.unexpected_keys:
logger.info(
get_unexpected_parameters_message(
incompatible.unexpected_keys))
if use_dsbn:
logger.info("==> Convert BN to Domain Specific BN")
convert_dsbn(model)
if show_model:
logger.info("Model:\n{}".format(model))
model.to(torch.device("cuda"))
return model
def __init__(self, cfg):
super(Distiller, self).__init__(cfg)
# Get teacher model config
cfg_t = get_cfg()
cfg_t.merge_from_file(cfg.KD.MODEL_CONFIG)
model_t = build_model(cfg_t)
logger.info("Teacher model:\n{}".format(model_t))
# No gradients for teacher model
for param in model_t.parameters():
param.requires_grad_(False)
logger.info("Loading teacher model weights ...")
Checkpointer(model_t).load(cfg.KD.MODEL_WEIGHTS)
# Not register teacher model as `nn.Module`, this is
# make sure teacher model weights not saved
self.model_t = [model_t.backbone, model_t.heads]
def __init__(self, cfg):
super().__init__(cfg)
# Get teacher model config
model_ts = []
for i in range(len(cfg.KD.MODEL_CONFIG)):
cfg_t = get_cfg()
cfg_t.merge_from_file(cfg.KD.MODEL_CONFIG[i])
model_t = build_model(cfg_t)
# No gradients for teacher model
for param in model_t.parameters():
param.requires_grad_(False)
logger.info("Loading teacher model weights ...")
Checkpointer(model_t).load(cfg.KD.MODEL_WEIGHTS[i])
model_ts.append(model_t)
# Not register teacher model as `nn.Module`, this is
# make sure teacher model weights not saved
self.model_ts = model_ts
default=[],
nargs=argparse.REMAINDER,
)
return parser
if __name__ == '__main__':
args = get_parser().parse_args()
cfg = setup_cfg(args)
cfg.defrost()
cfg.MODEL.BACKBONE.PRETRAIN = False
if cfg.MODEL.HEADS.POOL_LAYER == 'FastGlobalAvgPool':
cfg.MODEL.HEADS.POOL_LAYER = 'GlobalAvgPool'
cfg.MODEL.BACKBONE.WITH_NL = False
model = build_model(cfg)
Checkpointer(model).load(cfg.MODEL.WEIGHTS)
model.eval()
logger.info(model)
inputs = torch.randn(1, 3, cfg.INPUT.SIZE_TEST[0],
cfg.INPUT.SIZE_TEST[1]).to(
torch.device(cfg.MODEL.DEVICE))
PathManager.mkdirs(args.output)
pytorch_to_caffe.trans_net(model, inputs, args.name)
pytorch_to_caffe.save_prototxt(f"{args.output}/{args.name}.prototxt")
pytorch_to_caffe.save_caffemodel(f"{args.output}/{args.name}.caffemodel")
logger.info(f"Export caffe model in {args.output} sucessfully!")
def build_model_teacher(cls, cfg) -> nn.Module:
cfg_t = update_model_teacher_config(cfg)
model_t = build_model(cfg_t)
return model_t