mednet.models.segment.unet¶
UNet network architecture, from [RFB15].
Classes
|
UNet head module. |
|
UNet network architecture, from [RFB15]. |
- class mednet.models.segment.unet.UNetHead(in_channels_list, pixel_shuffle=False)[source]¶
Bases:
Module
UNet head module.
- Parameters:
- class mednet.models.segment.unet.Unet(loss_type=<class 'mednet.models.losses.SoftJaccardAndBCEWithLogitsLoss'>, loss_arguments=None, optimizer_type=<class 'torch.optim.adam.Adam'>, optimizer_arguments=None, scheduler_type=None, scheduler_arguments=None, model_transforms=None, augmentation_transforms=None, pretrained=False)[source]¶
Bases:
Model
UNet network architecture, from [RFB15].
- Parameters:
The loss to be used for training and evaluation.
Warning
The loss should be set to always return batch averages (as opposed to the batch sum), as our logging system expects it so.
loss_arguments (
dict
[str
,Any
] |None
) – Arguments to the loss.optimizer_type (
type
[Optimizer
]) – The type of optimizer to use for training.optimizer_arguments (
dict
[str
,Any
] |None
) – Arguments to the optimizer afterparams
.scheduler_type (
type
[LRScheduler
] |None
) – The type of scheduler to use for training.scheduler_arguments (
dict
[str
,Any
] |None
) – Arguments to the scheduler afterparams
.model_transforms (
Optional
[Sequence
[Callable
[[Tensor
],Tensor
]]]) – An optional sequence of torch modules containing transforms to be applied on the input before it is fed into the network.augmentation_transforms (
Optional
[Sequence
[Callable
[[Tensor
],Tensor
]]]) – An optional sequence of torch modules containing transforms to be applied on the input before it is fed into the network.pretrained (
bool
) – If True, will use VGG16 pretrained weights.
- forward(x)[source]¶
Same as
torch.nn.Module.forward()
.- Parameters:
*args – Whatever you decide to pass into the forward method.
**kwargs – Keyword arguments are also possible.
- Returns:
Your model’s output