diff --git a/src/mednet/libs/common/data/datamodule.py b/src/mednet/libs/common/data/datamodule.py
index c8ffcffed7ec2c5fc7265d6a509f355e5e25f37c..d51105571332a5728bacefe10446b437a56d4587 100644
--- a/src/mednet/libs/common/data/datamodule.py
+++ b/src/mednet/libs/common/data/datamodule.py
@@ -30,7 +30,7 @@ from .typing import (
logger = logging.getLogger(__name__)
-def _sample_size_bytes(dataset: Sample):
+def _sample_size_bytes(dataset: Dataset):
"""Recurse into the first sample of a dataset and figures out its total occupance in bytes.
Parameters
@@ -54,7 +54,7 @@ def _sample_size_bytes(dataset: Sample):
"""
logger.info(f"{list(t.shape)}@{t.dtype}")
- return int(t.element_size() * torch.prod(torch.tensor(t.shape)))
+ return int(t.element_size() * t.shape.numel())
def _dict_size_bytes(d):
"""Return a dictionary size in bytes.
diff --git a/src/mednet/libs/segmentation/config/models/m2unet.py b/src/mednet/libs/segmentation/config/models/m2unet.py
index ccfae59b323b2e9c0042248c24c4029b6eb597d2..b7de862491a0112f18ce56c80be483506d5ba246 100644
--- a/src/mednet/libs/segmentation/config/models/m2unet.py
+++ b/src/mednet/libs/segmentation/config/models/m2unet.py
@@ -18,7 +18,7 @@ References: [SANDLER-2018]_, [RONNEBERGER-2015]_
from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.engine.adabound import AdaBound
from mednet.libs.segmentation.models.losses import SoftJaccardBCELogitsLoss
-from mednet.libs.segmentation.models.m2unet import M2UNET
+from mednet.libs.segmentation.models.m2unet import M2Unet
lr = 0.001
alpha = 0.7
@@ -32,7 +32,7 @@ amsbound = False
resize_transform = ResizeMaxSide(512)
-model = M2UNET(
+model = M2Unet(
loss_type=SoftJaccardBCELogitsLoss,
loss_arguments=dict(alpha=alpha),
optimizer_type=AdaBound,
diff --git a/src/mednet/libs/segmentation/models/losses.py b/src/mednet/libs/segmentation/models/losses.py
index 2a344dd461fb4294bab6cb83f0edfa60789486b3..e36ca1602003553ad4612d54fcc145afd5746a63 100644
--- a/src/mednet/libs/segmentation/models/losses.py
+++ b/src/mednet/libs/segmentation/models/losses.py
@@ -18,21 +18,16 @@ class WeightedBCELogitsLoss(torch.nn.Module):
def __init__(self):
super().__init__()
- def forward(
- self, tensor: torch.Tensor, target: torch.Tensor, mask: torch.Tensor
- ) -> torch.Tensor:
+ def forward(self, input_: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""Forward pass.
Parameters
----------
- tensor
+ input_
Value produced by the model to be evaluated, with the shape ``[n, c,
h, w]``.
target
Ground-truth information with the shape ``[n, c, h, w]``.
- mask
- Mask to be use for specifying the region of interest where to
- compute the loss, with the shape ``[n, c, h, w]``.
Returns
-------
@@ -41,15 +36,12 @@ class WeightedBCELogitsLoss(torch.nn.Module):
# calculates the proportion of negatives to the total number of pixels
# available in the masked region
- valid = mask > 0.5
- num_pos = target[valid].sum()
- num_neg = valid.sum() - num_pos
- pos_weight = num_neg / num_pos
+ num_pos = target.sum()
return torch.nn.functional.binary_cross_entropy_with_logits(
- tensor[valid],
- target[valid],
+ input_,
+ target,
reduction="mean",
- pos_weight=pos_weight,
+ pos_weight=(input_.shape.numel() - num_pos) / num_pos,
)
@@ -75,21 +67,16 @@ class SoftJaccardBCELogitsLoss(torch.nn.Module):
super().__init__()
self.alpha = alpha
- def forward(
- self, tensor: torch.Tensor, target: torch.Tensor, mask: torch.Tensor
- ) -> torch.Tensor:
+ def forward(self, input_: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""Forward pass.
Parameters
----------
- tensor
+ input_
Value produced by the model to be evaluated, with the shape ``[n, c,
h, w]``.
target
Ground-truth information with the shape ``[n, c, h, w]``.
- mask
- Mask to be use for specifying the region of interest where to
- compute the loss, with the shape ``[n, c, h, w]``.
Returns
-------
@@ -97,15 +84,14 @@ class SoftJaccardBCELogitsLoss(torch.nn.Module):
"""
eps = 1e-8
- valid = mask > 0.5
- probabilities = torch.sigmoid(tensor[valid])
- intersection = (probabilities * target[valid]).sum()
- sums = probabilities.sum() + target[valid].sum()
+ probabilities = torch.sigmoid(input_)
+ intersection = (probabilities * target).sum()
+ sums = probabilities.sum() + target.sum()
j = intersection / (sums - intersection + eps)
# this implements the support for looking just into the RoI
h = torch.nn.functional.binary_cross_entropy_with_logits(
- tensor[valid], target[valid], reduction="mean"
+ input_, target, reduction="mean"
)
return (self.alpha * h) + ((1 - self.alpha) * (1 - j))
@@ -118,21 +104,16 @@ class MultiWeightedBCELogitsLoss(WeightedBCELogitsLoss):
def __init__(self):
super().__init__()
- def forward(
- self, tensor: torch.Tensor, target: torch.Tensor, mask: torch.Tensor
- ) -> torch.Tensor:
+ def forward(self, input_: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""Forward pass.
Parameters
----------
- tensor
+ input_
Value produced by the model to be evaluated, with the shape ``[L,
n, c, h, w]``.
target
Ground-truth information with the shape ``[n, c, h, w]``.
- mask
- Mask to be use for specifying the region of interest where to
- compute the loss, with the shape ``[n, c, h, w]``.
Returns
-------
@@ -140,18 +121,13 @@ class MultiWeightedBCELogitsLoss(WeightedBCELogitsLoss):
"""
return torch.cat(
- [
- super(MultiWeightedBCELogitsLoss, self)
- .forward(i, target, mask)
- .unsqueeze(0)
- for i in tensor
- ]
+ [super().forward(i, target).unsqueeze(0) for i in input_]
).mean()
class MultiSoftJaccardBCELogitsLoss(SoftJaccardBCELogitsLoss):
- """Implements Equation 3 in [IGLOVIKOV-2018]_ for the multi-output
- networks such as HED or Little W-Net.
+ """Implement Equation 3 in [IGLOVIKOV-2018]_ for the multi-output networks
+ such as HED or Little W-Net.
Parameters
----------
@@ -162,21 +138,16 @@ class MultiSoftJaccardBCELogitsLoss(SoftJaccardBCELogitsLoss):
def __init__(self, alpha: float = 0.7):
super().__init__(alpha=alpha)
- def forward(
- self, tensor: torch.Tensor, target: torch.Tensor, mask: torch.Tensor
- ) -> torch.Tensor:
+ def forward(self, input_: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""Forward pass.
Parameters
----------
- tensor
+ input_
Value produced by the model to be evaluated, with the shape ``[L,
n, c, h, w]``.
target
Ground-truth information with the shape ``[n, c, h, w]``.
- mask
- Mask to be use for specifying the region of interest where to
- compute the loss, with the shape ``[n, c, h, w]``.
Returns
-------
@@ -184,88 +155,5 @@ class MultiSoftJaccardBCELogitsLoss(SoftJaccardBCELogitsLoss):
"""
return torch.cat(
- [
- super(MultiSoftJaccardBCELogitsLoss, self)
- .forward(i, target, mask)
- .unsqueeze(0)
- for i in tensor
- ]
+ [super().forward(i, target).unsqueeze(0) for i in input_]
).mean()
-
-
-# class MixJacLoss(torch.nn.Module):
-# """Implements Mix Jaccard Loss.
-
-# Parameters
-# ----------
-# lambda_u
-# Determines the weighting of SoftJaccard and BCE.
-# jacalpha
-# Determines the weighting of J and H.
-# size_average
-# By default, the losses are averaged over each loss element in the
-# batch. Note that for some losses, there are multiple elements per
-# sample. If the field `size_average` is set to ``False``, the losses
-# are instead summed for each minibatch. Ignored when `reduce` is
-# ``False``. Default: ``True``.
-# reduce
-# By default, the losses are averaged or summed over observations for
-# each minibatch depending on `size_average`. When `reduce` is
-# ``False``, returns a loss per batch element instead and ignores
-# `size_average`. Default: ``True``.
-# reduction
-# Specifies the reduction to apply to the output: ``'none'`` |
-# ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
-# ``'mean'``: the sum of the output will be divided by the number of
-# elements in the output, ``'sum'``: the output will be summed. Note:
-# `size_average` and `reduce` are in the process of being deprecated,
-# and in the meantime, specifying either of those two args will
-# override `reduction`. Default: ``'mean'``.
-# """
-
-# def __init__(
-# self,
-# lambda_u: int = 100,
-# jacalpha=0.7,
-# size_average=None,
-# reduce=None,
-# reduction="mean",
-# ):
-# super().__init__(size_average, reduce, reduction)
-# self.lambda_u = lambda_u
-# self.labeled_loss = SoftJaccardBCELogitsLoss(alpha=jacalpha)
-# self.unlabeled_loss = torch.nn.BCEWithLogitsLoss()
-
-# def forward(
-# self,
-# tensor: torch.Tensor,
-# target: torch.Tensor,
-# unlabeled_tensor: torch.Tensor,
-# unlabeled_target: torch.Tensor,
-# ramp_up_factor: float,
-# ) -> tuple:
-# """Forward pass.
-
-# Parameters
-# ----------
-# tensor
-# Value produced by the model to be evaluated, with the shape ``[L,
-# n, c, h, w]``.
-# target
-# Ground-truth information with the shape ``[n, c, h, w]``.
-
-# unlabeled_tensor
-
-# unlabeled_target
-
-# ramp_up_factor
-
-# Returns
-# -------
-# list
-# """
-# ll = self.labeled_loss(tensor, target)
-# ul = self.unlabeled_loss(unlabeled_tensor, unlabeled_target)
-
-# loss = ll + self.lambda_u * ramp_up_factor * ul
-# return loss, ll, ul
diff --git a/src/mednet/libs/segmentation/models/lwnet.py b/src/mednet/libs/segmentation/models/lwnet.py
index 5cd491b04d392bbf8579bbca3525e4ea6f46bd45..b2c0ac2f803ea77c8f6d2e7527d0bd2fc68c2293 100644
--- a/src/mednet/libs/segmentation/models/lwnet.py
+++ b/src/mednet/libs/segmentation/models/lwnet.py
@@ -353,3 +353,7 @@ class LittleWNet(SegmentationModel):
x1 = self.unet1(xn)
x2 = self.unet2(torch.cat([xn, x1], dim=1))
return x1, x2
+
+ def predict_step(self, batch, batch_idx, dataloader_idx=0):
+ # prediction only returns the result of the last unet
+ return torch.sigmoid(self(batch[0]["image"])[1])
diff --git a/src/mednet/libs/segmentation/models/m2unet.py b/src/mednet/libs/segmentation/models/m2unet.py
index 8735956595b3737b7320a7f76ac26db37fe35c0b..5734a2a4e83252b3a2d956c850903b98de5ae9dd 100644
--- a/src/mednet/libs/segmentation/models/m2unet.py
+++ b/src/mednet/libs/segmentation/models/m2unet.py
@@ -122,8 +122,8 @@ class M2UNetHead(torch.nn.Module):
return self.decode1(decode2, x[1]) # 30, 3
-class M2UNET(SegmentationModel):
- """Implementation of the M2UNET model.
+class M2Unet(SegmentationModel):
+ """Implementation of the M2Unet model.
Parameters
----------
diff --git a/src/mednet/libs/segmentation/models/segmentation_model.py b/src/mednet/libs/segmentation/models/segmentation_model.py
index dae5cde663ad0aebef3302b8a68210efc396beac..8f3c5f5e2d1e79b147c3f7677e52738ba5f517e1 100644
--- a/src/mednet/libs/segmentation/models/segmentation_model.py
+++ b/src/mednet/libs/segmentation/models/segmentation_model.py
@@ -94,21 +94,20 @@ class SegmentationModel(Model):
self.normalizer = make_z_normalizer(dataloader)
def training_step(self, batch, _):
- images = self.augmentation_transforms(batch[0]["image"])
- ground_truths = self.augmentation_transforms(batch[0]["target"])
masks = self.augmentation_transforms(batch[0]["mask"])
+ images = self.augmentation_transforms(batch[0]["image"]) * masks
+ ground_truths = self.augmentation_transforms(batch[0]["target"]) * masks
outputs = self(images)
- return self._train_loss(outputs, ground_truths, masks)
+ return self._train_loss(outputs, ground_truths)
def validation_step(self, batch, batch_idx, dataloader_idx=0):
- images = batch[0]["image"]
- ground_truths = batch[0]["target"]
- masks = batch[0]["mask"]
+ images = batch[0]["image"] * batch[0]["mask"]
+ ground_truths = batch[0]["target"] * batch[0]["mask"]
outputs = self(images)
- return self._validation_loss(outputs, ground_truths, masks)
+ return self._validation_loss(outputs, ground_truths)
def predict_step(self, batch, batch_idx, dataloader_idx=0):
- output = self(batch[0]["image"])[1]
+ output = self(batch[0]["image"])
return torch.sigmoid(output)