mednet.config.classify.models.densenet_pretrained¶
DenseNet, to be fine-tuned. Pre-trained on ImageNet.
This configuration contains a version of DenseNet (c.f. TorchVision’s page <alexnet_pytorch_>), modified for a variable number of outputs (defaults to 1).
N.B.: The output layer is always initialized from scratch.
# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
"""DenseNet_, to be fine-tuned. Pre-trained on ImageNet_.
This configuration contains a version of DenseNet_ (c.f. `TorchVision's
page <alexnet_pytorch_>`), modified for a variable number of outputs
(defaults to 1).
N.B.: The output layer is **always** initialized from scratch.
"""
import torch.nn
import torch.optim
import torchvision.transforms
import torchvision.transforms.v2
import mednet.models.classify.densenet
import mednet.models.transforms
model = mednet.models.classify.densenet.Densenet(
loss_type=torch.nn.BCEWithLogitsLoss,
optimizer_type=torch.optim.Adam,
optimizer_arguments=dict(lr=0.0001),
pretrained=True,
dropout=0.1,
model_transforms=[
mednet.models.transforms.SquareCenterPad(),
torchvision.transforms.v2.Resize(512, antialias=True),
torchvision.transforms.v2.RGB(),
],
)