def model(model_name: str,
num_classes: int,
img_size: int,
pretrained: bool = True) -> nn.Module:
"""Creates the efficientdet model specified by `model_name`.
The model implementation is by Ross Wightman, original repo
[here](https://github.com/rwightman/efficientdet-pytorch).
# Arguments
model_name: Specifies the model to create. For pretrained models, check
[this](https://github.com/rwightman/efficientdet-pytorch#models) table.
num_classes: Number of classes of your dataset (including background).
img_size: Image size that will be fed to the model. Must be squared and
divisible by 64.
pretrained: If True, use a pretrained backbone (on COCO).
# Returns
A PyTorch model.
"""
config = get_efficientdet_config(model_name=model_name)
net = EfficientDet(config, pretrained_backbone=False)
if pretrained:
if not config.url:
raise RuntimeError(f"No pretrained weights for {model_name}")
state_dict = torch.hub.load_state_dict_from_url(
config.url, map_location=torch.device("cpu"))
net.load_state_dict(state_dict)
config.num_classes = num_classes
config.image_size = img_size
net.class_net = HeadNet(
config,
num_outputs=num_classes,
norm_kwargs=dict(eps=0.001, momentum=0.01),
)
# TODO: Break down param groups for backbone
def param_groups_fn(model: nn.Module) -> List[List[nn.Parameter]]:
unwrapped = unwrap_bench(model)
layers = [
unwrapped.backbone,
unwrapped.fpn,
nn.Sequential(unwrapped.class_net, unwrapped.box_net),
]
param_groups = [list(layer.parameters()) for layer in layers]
check_all_model_params_in_groups2(model, param_groups)
return param_groups
model_bench = DetBenchTrain(net, config)
model_bench.param_groups = MethodType(param_groups_fn, model_bench)
return model_bench
def model(model_name: str,
num_classes: int,
img_size: int,
pretrained: bool = True) -> nn.Module:
""" Creates the model specific by model_name
Args:
model_name (str): Specifies the model to create, available options are: TODO
num_classes (int): Number of classes of your dataset (including background)
pretrained (int): If True, use a pretrained backbone (on COCO)
Returns:
nn.Module: The requested model
"""
config = get_efficientdet_config(model_name=model_name)
net = EfficientDet(config, pretrained_backbone=False)
if pretrained:
if not config.url:
raise RuntimeError(f"No pretrained weights for {model_name}")
state_dict = torch.hub.load_state_dict_from_url(
config.url, map_location=torch.device("cpu"))
net.load_state_dict(state_dict)
config.num_classes = num_classes
config.image_size = img_size
net.class_net = HeadNet(
config,
num_outputs=num_classes,
norm_kwargs=dict(eps=0.001, momentum=0.01),
)
# TODO: Break down param groups for backbone
def param_groups_fn(model: nn.Module) -> List[List[nn.Parameter]]:
unwrapped = unwrap_bench(model)
layers = [
unwrapped.backbone,
unwrapped.fpn,
nn.Sequential(unwrapped.class_net, unwrapped.box_net),
]
param_groups = [list(layer.parameters()) for layer in layers]
check_all_model_params_in_groups2(model, param_groups)
return param_groups
model_bench = DetBenchTrain(net, config)
model_bench.param_groups = MethodType(param_groups_fn, model_bench)
return model_bench