updated tests (#485)

tests/conftest.py

@@ -21,6 +21,7 @@ from fastai.vision.data import ImageList, imagenet_stats
 from typing import List, Tuple
 from tempfile import TemporaryDirectory
 from utils_cv.common.data import unzip_url
+from utils_cv.common.gpu import db_num_workers
 from utils_cv.classification.data import Urls as ic_urls
 from utils_cv.detection.data import Urls as od_urls
 from utils_cv.detection.bbox import DetectionBbox, AnnotationBbox
@@ -278,7 +279,7 @@ def tiny_ic_databunch(tmp_session):
         .split_by_rand_pct(valid_pct=0.1, seed=20)
         .label_from_folder()
         .transform(size=50)
-        .databunch(bs=16)
+        .databunch(bs=16, num_workers = db_num_workers())
         .normalize(imagenet_stats)
     )
 
@@ -350,7 +351,7 @@ def testing_databunch(tmp_session):
         .split_by_rand_pct(valid_pct=0.2, seed=20)
         .label_from_folder()
         .transform(size=300)
-        .databunch(bs=16)
+        .databunch(bs=16, num_workers = db_num_workers())
         .normalize(imagenet_stats)
     )
 

tests/integration/detection/test_integration_detection.py

@@ -57,6 +57,31 @@ def test_02_notebook_run(detection_notebooks):
         assert d > 0.15
 
 
+@pytest.mark.notebooks
+@pytest.mark.linuxgpu
+def test_03_notebook_run(detection_notebooks):
+    epochs = 5
+    notebook_path = detection_notebooks["03"]
+    pm.execute_notebook(
+        notebook_path,
+        OUTPUT_NOTEBOOK,
+        parameters=dict(
+            PM_VERSION=pm.__version__,
+            EPOCHS=epochs,
+        ),
+        kernel_name=KERNEL_NAME,
+    )
+
+    nb_output = sb.read_notebook(OUTPUT_NOTEBOOK)
+    training_losses = nb_output.scraps["training_losses"].data
+    assert len(training_losses) == epochs
+    assert training_losses[-1] < 1.5
+    assert training_losses[0] > 1.5*training_losses[-1]
+    assert len(nb_output.scraps["keypoints"].data) == len(
+        nb_output.scraps["bboxes"].data
+    )
+
+
 @pytest.mark.notebooks
 @pytest.mark.linuxgpu
 def test_12_notebook_run(detection_notebooks):

tests/integration/similarity/test_integration_similarity_notebooks.py

@@ -52,7 +52,8 @@ def test_11_notebook_run(similarity_notebooks, tiny_ic_data_path):
             # Speed up testing since otherwise would take ~12 minutes on V100
             DATA_PATHS=[tiny_ic_data_path],
             REPS=1,
-            IM_SIZES=[60, 100],
+            IM_SIZES=[60, 70],
+            LEARNING_RATES=[1e-3, 1e-4]
         ),
         kernel_name=KERNEL_NAME,
     )

tests/unit/classification/test_classification_model.py

@@ -118,7 +118,6 @@ def test_train_metrics_recorder(tiny_ic_databunch):
     def test_callback(learn):
         tmr = TrainMetricsRecorder(learn)
         learn.callbacks.append(tmr)
-        learn.unfreeze()
         learn.fit(epochs, lr)
         return tmr
 

tests/unit/common/test_common_misc.py

@@ -6,6 +6,7 @@ from pathlib import Path
 from PIL import ImageFont
 
 from fastai.vision import ImageList
+from utils_cv.common.gpu import db_num_workers
 from utils_cv.common.misc import copy_files, set_random_seed, get_font
 
 
@@ -17,7 +18,7 @@ def test_set_random_seed(tiny_ic_data_path):
         .split_by_rand_pct()
         .label_from_folder()
         .transform()
-        .databunch(bs=5)
+        .databunch(bs=5, num_workers = db_num_workers())
         .normalize()
     )
     first_batch = first_data.one_batch()
@@ -28,7 +29,7 @@ def test_set_random_seed(tiny_ic_data_path):
         .split_by_rand_pct()
         .label_from_folder()
         .transform()
-        .databunch(bs=5)
+        .databunch(bs=5, num_workers = db_num_workers())
         .normalize()
     )
     second_batch = second_data.one_batch()