From ffad08889edf2c1fc25584103636b856f3bb85de Mon Sep 17 00:00:00 2001
From: Yvan Pannatier <yvan.pannatier@idiap.ch>
Date: Wed, 19 Jun 2024 14:37:35 +0200
Subject: [PATCH] Fix qa.
---
src/mednet/config/data/visceral/datamodule.py | 17 ++++---
src/mednet/config/data/visceral/default.json | 2 +-
src/mednet/config/data/visceral/default.py | 4 +-
src/mednet/config/models/cnn3d.py | 3 +-
src/mednet/models/cnn3d.py | 9 ++--
src/mednet/models/pasa3d.py | 50 ++++++++++++-------
tests/test_visceral.py | 4 +-
7 files changed, 50 insertions(+), 39 deletions(-)
diff --git a/src/mednet/config/data/visceral/datamodule.py b/src/mednet/config/data/visceral/datamodule.py
index c3142015..023962cf 100644
--- a/src/mednet/config/data/visceral/datamodule.py
+++ b/src/mednet/config/data/visceral/datamodule.py
@@ -1,18 +1,20 @@
# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
-""" VISCERAL dataset for 3D organ classification (only lungs and bladders).
+"""VISCERAL dataset for 3D organ classification (only lungs and bladders).
Loaded samples are not full scans but 16x16x16 volumes of organs.
-Database reference:
+Database reference:
"""
+
import os
import pathlib
+
import torchio as tio
from ....data.datamodule import CachingDataModule
-from ....data.typing import RawDataLoader as _BaseRawDataLoader
from ....data.split import make_split
+from ....data.typing import RawDataLoader as _BaseRawDataLoader
from ....data.typing import Sample
from ....utils.rc import load_rc
@@ -20,6 +22,7 @@ CONFIGURATION_KEY_DATADIR = "datadir." + (__name__.rsplit(".", 2)[-2])
"""Key to search for in the configuration file for the root directory of this
database."""
+
class RawDataLoader(_BaseRawDataLoader):
"""A specialized raw-data-loader for the VISCERAL dataset."""
@@ -51,12 +54,11 @@ class RawDataLoader(_BaseRawDataLoader):
"""
clamp = tio.Clamp(out_min=-1000, out_max=2000)
rescale = tio.RescaleIntensity(percentiles=(0.5, 99.5))
- preprocess = tio.Compose([clamp,rescale,])
+ preprocess = tio.Compose([clamp, rescale])
image = tio.ScalarImage(self.datadir / sample[0])
image = preprocess(image)
tensor = image.data
- return tensor, dict(label=sample[1], name=sample[0])
-
+ return tensor, dict(label=sample[1], name=sample[0])
def label(self, sample: tuple[str, int]) -> int:
"""Load a single image sample label from the disk.
@@ -77,8 +79,6 @@ class RawDataLoader(_BaseRawDataLoader):
return sample[1]
-
-
class DataModule(CachingDataModule):
"""VISCERAL DataModule for 3D organ binary classification.
@@ -107,6 +107,7 @@ class DataModule(CachingDataModule):
split_filename
Name of the .json file containing the split to load.
"""
+
def __init__(self, split_filename: str):
super().__init__(
make_split(__package__, split_filename),
diff --git a/src/mednet/config/data/visceral/default.json b/src/mednet/config/data/visceral/default.json
index f0d1c302..6bd8d5bd 100644
--- a/src/mednet/config/data/visceral/default.json
+++ b/src/mednet/config/data/visceral/default.json
@@ -350,4 +350,4 @@
["16/10000194_1_237_117.nii.gz",0],
["16/10000059_1_1302_117.nii.gz",1]
]
-}
\ No newline at end of file
+}
diff --git a/src/mednet/config/data/visceral/default.py b/src/mednet/config/data/visceral/default.py
index 385d00c0..46942bb7 100644
--- a/src/mednet/config/data/visceral/default.py
+++ b/src/mednet/config/data/visceral/default.py
@@ -1,9 +1,9 @@
# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
-""" VISCERAL dataset for 3D organ classification.
+"""VISCERAL dataset for 3D organ classification.
-Database reference:
+Database reference:
See :py:class:`mednet.config.data.visceral.datamodule.DataModule` for
technical details.
diff --git a/src/mednet/config/models/cnn3d.py b/src/mednet/config/models/cnn3d.py
index 790570ab..305b5452 100644
--- a/src/mednet/config/models/cnn3d.py
+++ b/src/mednet/config/models/cnn3d.py
@@ -1,8 +1,7 @@
# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
-"""Simple CNN for 3D organ classification, to be trained from scratch.
-"""
+"""Simple CNN for 3D organ classification, to be trained from scratch."""
from torch.nn import BCEWithLogitsLoss
from torch.optim import Adam
diff --git a/src/mednet/models/cnn3d.py b/src/mednet/models/cnn3d.py
index def0a916..b27c073c 100644
--- a/src/mednet/models/cnn3d.py
+++ b/src/mednet/models/cnn3d.py
@@ -88,10 +88,9 @@ class Conv3DNet(Model):
self.fc1 = nn.Linear(256, 64)
self.fc2 = nn.Linear(64, num_classes)
-
def forward(self, x):
- #x = self.normalizer(x) # type: ignore
-
+ # x = self.normalizer(x) # type: ignore
+
x = F.relu(self.batchnorm_1(self.conv3d_1(x)))
x = self.pool(x)
x = F.relu(self.batchnorm_2(self.conv3d_2(x)))
@@ -103,9 +102,7 @@ class Conv3DNet(Model):
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = self.dropout(x)
- x = self.fc2(x)
- return x
-
+ return self.fc2(x)
# x = F.log_softmax(x, dim=1) # 0 is batch size
diff --git a/src/mednet/models/pasa3d.py b/src/mednet/models/pasa3d.py
index 7fcb0e08..6b73d297 100644
--- a/src/mednet/models/pasa3d.py
+++ b/src/mednet/models/pasa3d.py
@@ -69,8 +69,7 @@ class Pasa3D(Model):
self.name = "pasa3D"
self.num_classes = num_classes
- self.model_transforms = [
- ]
+ self.model_transforms = []
# First convolution block
self.conv3d_1_1 = nn.Conv3d(in_channels=1, out_channels=4, kernel_size=3)
@@ -80,7 +79,6 @@ class Pasa3D(Model):
self.batch_norm_1_2 = nn.BatchNorm3d(16)
self.batch_norm_1_3 = nn.BatchNorm3d(16)
-
# Second convolution block
self.conv3d_2_1 = nn.Conv3d(in_channels=16, out_channels=24, kernel_size=3)
self.conv3d_2_2 = nn.Conv3d(in_channels=24, out_channels=32, kernel_size=3)
@@ -89,37 +87,52 @@ class Pasa3D(Model):
self.batch_norm_2_2 = nn.BatchNorm3d(32)
self.batch_norm_2_3 = nn.BatchNorm3d(32)
-
# Third convolution block
- self.conv3d_3_1 = nn.Conv3d(in_channels=32, out_channels=40, kernel_size=3, stride=1, padding=1)
- self.conv3d_3_2 = nn.Conv3d(in_channels=40, out_channels=48, kernel_size=3, stride=1, padding=1)
- self.conv3d_3_3 = nn.Conv3d(in_channels=32, out_channels=48, kernel_size=1, stride=1)
+ self.conv3d_3_1 = nn.Conv3d(
+ in_channels=32, out_channels=40, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_3_2 = nn.Conv3d(
+ in_channels=40, out_channels=48, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_3_3 = nn.Conv3d(
+ in_channels=32, out_channels=48, kernel_size=1, stride=1
+ )
self.batch_norm_3_1 = nn.BatchNorm3d(40)
self.batch_norm_3_2 = nn.BatchNorm3d(48)
self.batch_norm_3_3 = nn.BatchNorm3d(48)
# Fourth convolution block
- self.conv3d_4_1 = nn.Conv3d(in_channels=48, out_channels=56, kernel_size=3, stride=1, padding=1)
- self.conv3d_4_2 = nn.Conv3d(in_channels=56, out_channels=64, kernel_size=3, stride=1, padding=1)
- self.conv3d_4_3 = nn.Conv3d(in_channels=48, out_channels=64, kernel_size=1, stride=1)
+ self.conv3d_4_1 = nn.Conv3d(
+ in_channels=48, out_channels=56, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_4_2 = nn.Conv3d(
+ in_channels=56, out_channels=64, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_4_3 = nn.Conv3d(
+ in_channels=48, out_channels=64, kernel_size=1, stride=1
+ )
self.batch_norm_4_1 = nn.BatchNorm3d(56)
self.batch_norm_4_2 = nn.BatchNorm3d(64)
self.batch_norm_4_3 = nn.BatchNorm3d(64)
# Fifth convolution block
- self.conv3d_5_1 = nn.Conv3d(in_channels=64, out_channels=72, kernel_size=3, stride=1, padding=1)
- self.conv3d_5_2 = nn.Conv3d(in_channels=72, out_channels=80, kernel_size=3, stride=1, padding=1)
- self.conv3d_5_3 = nn.Conv3d(in_channels=64, out_channels=80, kernel_size=1, stride=1)
+ self.conv3d_5_1 = nn.Conv3d(
+ in_channels=64, out_channels=72, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_5_2 = nn.Conv3d(
+ in_channels=72, out_channels=80, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_5_3 = nn.Conv3d(
+ in_channels=64, out_channels=80, kernel_size=1, stride=1
+ )
self.batch_norm_5_1 = nn.BatchNorm3d(72)
self.batch_norm_5_2 = nn.BatchNorm3d(80)
self.batch_norm_5_3 = nn.BatchNorm3d(80)
-
self.pool = nn.MaxPool3d(kernel_size=3, stride=2)
self.global_avg_pool = nn.AdaptiveAvgPool3d(1)
- self.fc1 = nn.Linear(80, self.num_classes,)
-
+ self.fc1 = nn.Linear(80, self.num_classes)
def forward(self, x):
x = self.normalizer(x) # type: ignore
@@ -129,7 +142,7 @@ class Pasa3D(Model):
x = F.relu(self.batch_norm_1_1(self.conv3d_1_1(x)))
x = F.relu(self.batch_norm_1_2(self.conv3d_1_2(x)))
x = (x + F.relu(self.batch_norm_1_3(self.conv3d_1_3(_x)))) / 2
-
+
# Second convolution block
_x = x
x = F.relu(self.batch_norm_2_1(self.conv3d_2_1(x)))
@@ -156,8 +169,7 @@ class Pasa3D(Model):
x = torch.mean(x.view(x.size(0), x.size(1), -1), dim=2)
- x = self.fc1(x)
- return x
+ return self.fc1(x)
# x = F.log_softmax(x, dim=1) # 0 is batch size
diff --git a/tests/test_visceral.py b/tests/test_visceral.py
index fdd1b8ff..9743577e 100644
--- a/tests/test_visceral.py
+++ b/tests/test_visceral.py
@@ -6,11 +6,13 @@
import pytest
from click.testing import CliRunner
+
def id_function(val):
if isinstance(val, dict):
return str(val)
return repr(val)
+
@pytest.mark.parametrize(
"split,lenghts",
[
@@ -32,13 +34,13 @@ def test_protocol_consistency(
possible_labels=(0, 1),
)
+
@pytest.mark.skip_if_rc_var_not_set("datadir.visceral")
def test_database_check():
from mednet.scripts.database import check
runner = CliRunner()
result = runner.invoke(check, ["visceral"])
- assert(1 == 0), f"test"
assert (
result.exit_code == 0
), f"Exit code {result.exit_code} != 0 -- Output:\n{result.output}"
--
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