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"""
Documentation on Hugging Face: https://huggingface.co/docs/transformers/en/custom_models
"""
from monai.inferers import sliding_window_inference
from monai.losses import DiceCELoss
from transformers import PreTrainedModel
from monai.networks.nets import SwinUNETR
from magdi_segmentation_models_3d.models.swinunetrv2.configuration_swinvunetr2 import (
SwinUNETRv2Config,
)
# @auto_docstring
class SwinUNETRv2PreTrainedModel(PreTrainedModel):
config_class = SwinUNETRv2Config
# @auto_docstring
class SwinUNETRv2Model(SwinUNETRv2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.model = SwinUNETR(
in_channels=config.in_channels,
out_channels=config.out_channels,
patch_size=config.patch_size,
depths=config.depths,
num_heads=config.num_heads,
window_size=config.window_size,
qkv_bias=config.qkv_bias,
mlp_ratio=config.mlp_ratio,
feature_size=config.feature_size,
norm_name=config.norm_name,
drop_rate=config.drop_rate,
attn_drop_rate=config.attn_drop_rate,
dropout_path_rate=config.dropout_path_rate,
normalize=config.normalize,
# norm_layer=config.norm_layer,
patch_norm=config.patch_norm,
use_checkpoint=config.use_checkpoint,
spatial_dims=config.spatial_dims,
downsample=config.downsample,
use_v2=True,
)
def forward(self, tensor):
return self.model(tensor)
# @auto_docstring
class SwinUNETRv2ForImageSegmentation(SwinUNETRv2PreTrainedModel):
config_class = SwinUNETRv2Config
def __init__(self, config):
super().__init__(config)
self.model = SwinUNETR(
in_channels=config.in_channels,
out_channels=config.out_channels,
patch_size=config.patch_size,
depths=config.depths,
num_heads=config.num_heads,
window_size=config.window_size,
qkv_bias=config.qkv_bias,
mlp_ratio=config.mlp_ratio,
feature_size=config.feature_size,
norm_name=config.norm_name,
drop_rate=config.drop_rate,
attn_drop_rate=config.attn_drop_rate,
dropout_path_rate=config.dropout_path_rate,
normalize=config.normalize,
# norm_layer=config.norm_layer,
patch_norm=config.patch_norm,
use_checkpoint=config.use_checkpoint,
spatial_dims=config.spatial_dims,
downsample=config.downsample,
use_v2=True,
)
def forward(self, tensor, train=False, roi_size=(128, 128, 128), sw_batch_size=1):
criterion = DiceCELoss(to_onehot_y=True, softmax=True)
image = tensor["image"]
annotations = tensor["annotations"]
if train:
logits = self.model(image)
loss = criterion(logits, annotations)
else:
logits = sliding_window_inference(
tensor["image"],
roi_size,
sw_batch_size,
self.model.forward,
)
loss = criterion(logits, annotations)
return {
"logits": logits,
"loss": loss,
}
# @auto_docstring
class SwinUNETRv2Backbone(SwinUNETRv2PreTrainedModel):
config_class = SwinUNETRv2Config
def __init__(self, config):
super().__init__(config)
self.swinViT = SwinUNETR(
in_channels=config.in_channels,
out_channels=config.out_channels,
patch_size=config.patch_size,
depths=config.depths,
num_heads=config.num_heads,
window_size=config.window_size,
qkv_bias=config.qkv_bias,
mlp_ratio=config.mlp_ratio,
feature_size=config.feature_size,
norm_name=config.norm_name,
drop_rate=config.drop_rate,
attn_drop_rate=config.attn_drop_rate,
dropout_path_rate=config.dropout_path_rate,
normalize=config.normalize,
# norm_layer=config.norm_layer,
patch_norm=config.patch_norm,
use_checkpoint=config.use_checkpoint,
spatial_dims=config.spatial_dims,
downsample=config.downsample,
use_v2=True,
).swinViT
def forward(self, tensor):
return self.model(tensor)
__all__ = [
"SwinUNETRv2ForImageSegmentation",
"SwinUNETRv2Model",
"SwinUNETRv2PreTrainedModel",
"SwinUNETRv2Backbone",
]
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