Merge a863278 into d9ad712

ivadomed · Aug 17, 2020 · 5a4fd51 · 5a4fd51
2 parents d9ad712 + a863278
commit 5a4fd51
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Showing 23 changed files with 588 additions and 65 deletions.
diff --git a/.coveragerc b/.coveragerc
@@ -0,0 +1,2 @@
+[run]
+omit = setup.py
diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml
@@ -3,15 +3,22 @@
 
 name: Run tests
 
-on: [push, pull_request]
+on:
+  # Trigger the workflow on push but only for the master branch
+  push:
+    branches:
+      - master
+  pull_request:
 
 jobs:
-  build:
+  test:
 
     runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: [3.6, 3.7, 3.8]
+        test-name:
+          - integration-test
 
     steps:
     - uses: actions/checkout@v2
@@ -22,7 +29,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install flake8 pytest
+        pip install flake8 pytest coverage coveralls pytest-cov
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
         pip install -e .
     - name: Lint with flake8
@@ -32,5 +39,23 @@ jobs:
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
+      env: 
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        COVERALLS_FLAG_NAME: ${{ matrix.test-name }}
+        COVERALLS_PARALLEL: true
       run: |
-        pytest
+         ivadomed_download_data -d data_testing -o testing_data
+         pytest --cov=./ --cov-report term-missing
+         coveralls
+
+  coveralls:
+    needs: test
+    runs-on: ubuntu-latest
+    container: python:3-slim
+    steps:
+    - name: Finished
+      run: |
+        pip3 install --upgrade coveralls
+        coveralls --finish
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/README.md b/README.md
@@ -2,7 +2,7 @@
 
 Integrated framework for medical image analysis with deep learning.
 
-[![Coverage Status](https://coveralls.io/repos/github/neuropoly/ivadomed/badge.svg?branch=master)](https://coveralls.io/github/neuropoly/ivadomed?branch=master)
+[![Coverage Status](https://coveralls.io/repos/github/ivadomed/ivadomed/badge.svg?branch=master)](https://coveralls.io/github/ivadomed/ivadomed?branch=master)
 ![](https://github.com/neuropoly/ivadomed/workflows/Python%20package/badge.svg)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE.md)
 

diff --git a/ivadomed/loader/loader.py b/ivadomed/loader/loader.py
@@ -517,7 +517,7 @@ class MRI3DSubVolumeSegmentationDataset(Dataset):
             space.
     """
 
-    def __init__(self, filename_pairs, transform=None, length=(64, 64, 64), stride=(0, 0, 0), slice_axis=0,
+    def __init__(self, filename_pairs, transform=None, length=(64, 64, 64), stride=(0, 0, 0),  slice_axis=0, task="segmentation",
                  soft_gt=False):
         self.filename_pairs = filename_pairs
         self.handlers = []
@@ -527,6 +527,7 @@ def __init__(self, filename_pairs, transform=None, length=(64, 64, 64), stride=(
         self.prepro_transforms, self.transform = transform
         self.slice_axis = slice_axis
         self.has_bounding_box = True
+        self.task = task
         self.soft_gt = soft_gt
 
         self._load_filenames()
@@ -673,7 +674,7 @@ class Bids3DDataset(MRI3DSubVolumeSegmentationDataset):
     """
     def __init__(self, root_dir, subject_lst, target_suffix, model_params, contrast_params, slice_axis=2,
                  cache=True, transform=None, metadata_choice=False, roi_params=None,
-                 multichannel=False, object_detection_params=None, soft_gt=False):
+                 multichannel=False, object_detection_params=None, task="segmentation", soft_gt=False):
         dataset = BidsDataset(root_dir,
                               subject_lst=subject_lst,
                               target_suffix=target_suffix,
@@ -686,7 +687,7 @@ def __init__(self, root_dir, subject_lst, target_suffix, model_params, contrast_
                               object_detection_params=object_detection_params)
 
         super().__init__(dataset.filename_pairs, length=model_params["length_3D"], stride=model_params["stride_3D"],
-                         transform=transform, slice_axis=slice_axis, soft_gt=soft_gt)
+                         transform=transform, slice_axis=slice_axis, task=task, soft_gt=soft_gt)
 
 
 class BidsDataset(MRI2DSegmentationDataset):

diff --git a/ivadomed/training.py b/ivadomed/training.py
@@ -310,7 +310,7 @@ def train(model_params, dataset_train, dataset_val, training_params, log_directo
     if thr_increment:
         print('\nRunning threshold analysis to find optimal threshold')
         # Choice of optimisation metric
-        metric = "recall_specificity" if model_params["name"] in imed_utils.imed_loader.CLASSIFIER_LIST else "dice"
+        metric = "recall_specificity" if imed_utils.get_task(model_params["name"]) == "classification" else "dice"
         # Run analysis
         optimal_thr = threshold_analysis(model=model,
                                          val_loader=val_loader,

diff --git a/testing/ivado_test.py b/testing/ivado_test.py
@@ -1,4 +1,18 @@
 #Add custom tests here
+# this test is run first so we will also do a little setup here
+
+import ivadomed.models as imed_models
+import torch
+import os
+
 
 def test_sample():
     assert 1 == 1
+
+
+def test_model_creation():
+    # creating basic model for test
+    model = imed_models.Unet()
+    torch.save(model, "testing_data/model_unet_test.pt")
+    assert os.path.isfile("testing_data/model_unet_test.pt")
+
diff --git a/testing/test_HeMIS.py b/testing/test_HeMIS.py
@@ -48,7 +48,7 @@ def test_HeMIS(p=0.0001):
 
     roi_params = {"suffix": "_seg-manual", "slice_filter_roi": None}
 
-    train_lst = ['sub-test001']
+    train_lst = ['sub-unf01']
     contrasts = ['T1w', 'T2w', 'T2star']
 
     print('[INFO]: Creating dataset ...\n')

diff --git a/testing/test_adaptative.py b/testing/test_adaptative.py
@@ -20,7 +20,7 @@
 
 def test_hdf5():
     print('[INFO]: Starting test ... \n')
-    train_lst = ['sub-test001']
+    train_lst = ['sub-unf01']
 
     training_transform_dict = {
         "Resample":

diff --git a/testing/test_bounding_box.py b/testing/test_bounding_box.py
@@ -14,7 +14,7 @@
 LOG_DIR = "log"
 
 
-@pytest.mark.parametrize('train_lst', [['sub-test001']])
+@pytest.mark.parametrize('train_lst', [['sub-unf01']])
 @pytest.mark.parametrize('target_lst', [["_lesion-manual"]])
 @pytest.mark.parametrize('config', [
     {
@@ -23,7 +23,9 @@
             "safety_factor": [1.0, 1.0, 1.0],
             "log_directory": LOG_DIR
         },
-        "transforms_params": {"NumpyToTensor": {}},
+        "transforms_params": {
+            "Resample": {"wspace": 0.75, "hspace": 0.75},
+            "NumpyToTensor": {}},
         "roi_params": {"suffix": "_seg-manual", "slice_filter_roi": 10},
         "contrast_params": {"contrast_lst": ['T2w'], "balance": {}},
         "multichannel": False,
@@ -95,3 +97,15 @@ def test_bounding_box(train_lst, target_lst, config):
             assert seg_pair['input'][0].shape[-2:] == (mx2 - mx1, my2 - my1)
 
     shutil.rmtree(LOG_DIR)
+
+
+# testing adjust bb size
+def test_adjust_bb_size():
+    test_coord = (0, 10, 0, 10, 0, 10)
+    imed_obj_detect.adjust_bb_size(test_coord, (2, 2, 2), True)
+
+
+# testing bb statistic
+def test_compute_bb_statistics():
+    imed_obj_detect.compute_bb_statistics("testing_data/bounding_box_dict.json")
+
diff --git a/testing/test_inference.py b/testing/test_inference.py
@@ -22,6 +22,7 @@
 PATH_BIDS = 'testing_data'
 PATH_OUT = 'tmp'
 
+
 @pytest.mark.parametrize('transforms_dict', [{
         "Resample":
             {
@@ -35,7 +36,7 @@
         "NumpyToTensor": {},
         "NormalizeInstance": {"applied_to": ["im"]}
     }])
-@pytest.mark.parametrize('test_lst', [['sub-test001']])
+@pytest.mark.parametrize('test_lst', [['sub-unf01']])
 @pytest.mark.parametrize('target_lst', [["_lesion-manual"], ["_seg-manual"]])
 @pytest.mark.parametrize('roi_params', [{"suffix": "_seg-manual", "slice_filter_roi": 10}])
 @pytest.mark.parametrize('testing_params', [{
@@ -55,7 +56,7 @@ def test_inference(transforms_dict, test_lst, target_lst, roi_params, testing_pa
         "data_list": test_lst,
         "dataset_type": "testing",
         "requires_undo": True,
-        "contrast_params": {"contrast_lst": ['T2w', 'T2star'], "balance": {}},
+        "contrast_params": {"contrast_lst": ['T2w'], "balance": {}},
         "bids_path": PATH_BIDS,
         "target_suffix": target_lst,
         "roi_params": roi_params,

diff --git a/testing/test_losses.py b/testing/test_losses.py
@@ -7,7 +7,8 @@
 import pytest
 import torch
 
-from ivadomed.losses import GeneralizedDiceLoss, MultiClassDiceLoss, TverskyLoss, FocalTverskyLoss, DiceLoss
+from ivadomed.losses import GeneralizedDiceLoss, MultiClassDiceLoss, TverskyLoss, FocalTverskyLoss, DiceLoss, \
+    AdapWingLoss, L2loss, LossCombination, FocalDiceLoss
 
 
 @pytest.mark.parametrize('params', [
@@ -207,3 +208,133 @@ def test_focaltverskyloss(params):
     input, target, expected_value, loss_fct = params
     loss = loss_fct.forward(input, target)
     assert isclose(loss.detach().cpu().numpy(), expected_value, rel_tol=1e-2)
+
+
+@pytest.mark.parametrize('params', [
+    (torch.tensor([[[[[0.0, 1.0], [0.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]]]]]),
+     torch.tensor([[[[[0.0, 1.0], [0.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]]]]]),
+     0.,
+     L2loss()),
+
+    (torch.tensor([[[[1.0, 0.0], [0.0, 1.0]]]]),
+     torch.tensor([[[[1.0, 0.0], [0.0, 1.0]]]]),
+     0.,
+     L2loss()),
+
+    (torch.tensor([[[[0.0, 0.0], [0.0, 0.0]]]]),
+     torch.tensor([[[[1.0, 0.0], [0.0, 1.0]]]]),
+     1.0,
+     L2loss()),
+
+    (torch.tensor([[[[0.5, 0.5], [0.5, 0.5]]]]),
+     torch.tensor([[[[0.5, 1.0], [1.0, 1.0]]]]),
+     0.375,
+     L2loss()),
+])
+def test_L2loss(params):
+    """
+    test L2 loss
+
+    Args:
+        params (tuple): containing input tensor, target tensor, expected value, loss function
+
+    """
+
+    input, target, expected_value, loss_fct = params
+    loss = loss_fct.forward(input, target)
+    assert isclose(loss.detach().cpu().numpy(), expected_value, rel_tol=1e-2)
+
+
+@pytest.mark.parametrize('params', [
+    (torch.tensor([[[[1.0, 1.0], [0.0, 0.0]]]]),
+     torch.tensor([[[[1.0, 1.0], [0.0, 0.0]]]]),
+     0.,
+     AdapWingLoss()),
+
+    (torch.tensor([[[[1.0, 1.0], [0.0, 0.0]]]]),
+     torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]]),
+     29.0147,
+     AdapWingLoss()),
+
+    (torch.tensor([[[[1.0, 1.0], [0.0, 0.0]]]]),
+     torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]]),
+     41.4496,
+     AdapWingLoss(omega=20)),
+
+    (torch.tensor([[[[0.5, 0.5], [0.5, 0.5]]]]),
+     torch.tensor([[[[0.5, 1.0], [1.0, 1.0]]]]),
+     8.2703,
+     AdapWingLoss(epsilon=2)),
+])
+def test_adapwingloss(params):
+    """
+    test AdapWingLoss
+
+    Args:
+        params (tuple): containing input tensor, target tensor, expected value, loss function
+
+    """
+
+    input, target, expected_value, loss_fct = params
+    loss = loss_fct.forward(input, target)
+    assert isclose(loss.detach().cpu().numpy(), expected_value, rel_tol=1e-2)
+
+
+@pytest.mark.parametrize('params', [
+    (torch.tensor([[[[1.0, 1.0], [0.0, 0.0]]]]),
+     torch.tensor([[[[1.0, 1.0], [0.0, 0.0]]]]),
+     -1.0,
+     LossCombination(["DiceLoss", "L2loss"], [None, None])),
+
+    (torch.tensor([[[[1.0, 1.0], [0.0, 0.0]]]]),
+     torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]]),
+     1.0,
+     LossCombination(["DiceLoss", "L2loss"], [None, None])),
+
+    (torch.tensor([[[[0.5, 0.5], [0.5, 0.5]]]]),
+     torch.tensor([[[[0.5, 1.0], [1.0, 1.0]]]]),
+     -0.3173,
+     LossCombination(["DiceLoss", "L2loss"], [None, None])),
+])
+def test_losscombination(params):
+    """
+    test LossCombination
+
+    Args:
+        params (tuple): containing input tensor, target tensor, expected value, loss function
+
+    """
+
+    input, target, expected_value, loss_fct = params
+    loss = loss_fct.forward(input, target)
+    assert isclose(loss.detach().cpu().numpy(), expected_value, rel_tol=1e-2)
+
+
+@pytest.mark.parametrize('params', [
+    (torch.tensor([[[[1.0, 1.0], [0.0, 0.0]]]]),
+     torch.tensor([[[[1.0, 1.0], [0.0, 0.0]]]]),
+     -16.1181,
+     FocalDiceLoss()),
+
+    (torch.tensor([[[[1.0, 1.0], [0.0, 0.0]]]]),
+     torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]]),
+     3.6897,
+     FocalDiceLoss()),
+
+    (torch.tensor([[[[0.5, 0.5], [0.5, 0.5]]]]),
+     torch.tensor([[[[0.5, 1.0], [1.0, 1.0]]]]),
+     -1.1619,
+     FocalDiceLoss()),
+])
+def test_focaldiceloss(params):
+    """
+    test focaldiceloss
+
+    Args:
+        params (tuple): containing input tensor, target tensor, expected value, loss function
+
+    """
+
+    input, target, expected_value, loss_fct = params
+    loss = loss_fct.forward(input, target)
+    assert isclose(loss.detach().cpu().numpy(), expected_value, rel_tol=1e-2)