Travis CI, test suite

wangyoucao · Aug 13, 2019 · 82416af · 82416af
1 parent 6eefb77
commit 82416af
Show file tree

Hide file tree

Showing 6 changed files with 129 additions and 3 deletions.
diff --git a/.travis.yml b/.travis.yml
@@ -17,10 +17,13 @@ script:
   - wget -P data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/inception_v3_weights_tf_dim_ordering_tf_kernels.h5
   - wget -P data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/resnet50_weights_tf_dim_ordering_tf_kernels.h5
   - wget -P data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/squeezenet_weights_tf_dim_ordering_tf_kernels.h5
+  - wget -P data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/models-v3/idenprof_densenet-0.763500.h5
+  - wget -P data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/models-v3/idenprof_full_resnet_ex-001_acc-0.119792.h5
+  - wget -P data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/models-v3/idenprof_resnet.h5
   - wget -P data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/resnet50_coco_best_v2.0.1.h5
   - wget -P data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/yolo.h5
   - wget -P data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/yolo-tiny.h5
-  - pytest -v
+  - travis_wait 30 pytest -v
 
 
 

diff --git a/test/test_custom_recognition.py b/test/test_custom_recognition.py
@@ -2,6 +2,7 @@
 import os
 import pytest
 from os.path import dirname
+import keras
 
 
 main_folder = os.getcwd()
@@ -19,6 +20,12 @@ def images_to_image_array():
 @pytest.mark.resnet
 @pytest.mark.recognition_custom
 def test_custom_recognition_model_resnet():
+
+    try:
+        keras.backend.clear_session()
+    except:
+        None
+
     predictor = CustomImagePrediction()
     predictor.setModelTypeAsResNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_resnet.h5"))
@@ -35,6 +42,10 @@ def test_custom_recognition_model_resnet():
 @pytest.mark.resnet
 @pytest.mark.recognition_custom
 def test_custom_recognition_full_model_resnet():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = CustomImagePrediction()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_full_resnet_ex-001_acc-0.119792.h5"))
     predictor.setJsonPath(model_json=os.path.join(main_folder, "data-json", "idenprof.json"))
@@ -50,6 +61,10 @@ def test_custom_recognition_full_model_resnet():
 @pytest.mark.densenet
 @pytest.mark.recognition_custom
 def test_custom_recognition_model_densenet():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = CustomImagePrediction()
     predictor.setModelTypeAsDenseNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_densenet-0.763500.h5"))
@@ -69,6 +84,10 @@ def test_custom_recognition_model_densenet():
 @pytest.mark.recognition_custom
 @pytest.mark.recognition_multi
 def test_custom_recognition_model_resnet_multi():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = CustomImagePrediction()
     predictor.setModelTypeAsResNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_resnet.h5"))
@@ -91,6 +110,10 @@ def test_custom_recognition_model_resnet_multi():
 @pytest.mark.recognition_custom
 @pytest.mark.recognition_multi
 def test_custom_recognition_full_model_resnet_multi():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = CustomImagePrediction()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_full_resnet_ex-001_acc-0.119792.h5"))
     predictor.setJsonPath(model_json=os.path.join(main_folder, "data-json", "idenprof.json"))
@@ -112,6 +135,10 @@ def test_custom_recognition_full_model_resnet_multi():
 @pytest.mark.recognition_custom
 @pytest.mark.recognition_multi
 def test_custom_recognition_model_densenet_multi():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = CustomImagePrediction()
     predictor.setModelTypeAsDenseNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_densenet-0.763500.h5"))

diff --git a/test/test_image_recognition.py b/test/test_image_recognition.py
@@ -3,15 +3,18 @@
 import cv2
 import pytest
 from os.path import dirname
-# wget /data-models/ model_file.h5
+import keras
 
 main_folder = os.getcwd()
 
 @pytest.mark.squeezenet
 @pytest.mark.recognition
 def test_recognition_model_squeezenet():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
 
-    print("TOP FOLDER: ", main_folder)
     predictor = ImagePrediction()
     predictor.setModelTypeAsSqueezeNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "squeezenet_weights_tf_dim_ordering_tf_kernels.h5"))
@@ -26,6 +29,10 @@ def test_recognition_model_squeezenet():
 @pytest.mark.resnet
 @pytest.mark.recognition
 def test_recognition_model_resnet():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = ImagePrediction()
     predictor.setModelTypeAsResNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "resnet50_weights_tf_dim_ordering_tf_kernels.h5"))
@@ -40,6 +47,10 @@ def test_recognition_model_resnet():
 @pytest.mark.inceptionv3
 @pytest.mark.recognition
 def test_recognition_model_inceptionv3():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = ImagePrediction()
     predictor.setModelTypeAsInceptionV3()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "inception_v3_weights_tf_dim_ordering_tf_kernels.h5"))
@@ -54,6 +65,10 @@ def test_recognition_model_inceptionv3():
 @pytest.mark.densenet
 @pytest.mark.recognition
 def test_recognition_model_densenet():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = ImagePrediction()
     predictor.setModelTypeAsDenseNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "DenseNet-BC-121-32.h5"))
@@ -70,6 +85,10 @@ def test_recognition_model_densenet():
 @pytest.mark.recognition
 def test_recognition_model_squeezenet_array_input():
 
+    try:
+        keras.backend.clear_session()
+    except:
+        None
 
     predictor = ImagePrediction()
     predictor.setModelTypeAsSqueezeNet()
@@ -86,6 +105,10 @@ def test_recognition_model_squeezenet_array_input():
 @pytest.mark.resnet
 @pytest.mark.recognition
 def test_recognition_model_resnet_array_input():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = ImagePrediction()
     predictor.setModelTypeAsResNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "resnet50_weights_tf_dim_ordering_tf_kernels.h5"))
@@ -101,6 +124,10 @@ def test_recognition_model_resnet_array_input():
 @pytest.mark.inceptionv3
 @pytest.mark.recognition
 def test_recognition_model_inceptionv3_array_input():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = ImagePrediction()
     predictor.setModelTypeAsInceptionV3()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "inception_v3_weights_tf_dim_ordering_tf_kernels.h5"))
@@ -116,6 +143,10 @@ def test_recognition_model_inceptionv3_array_input():
 @pytest.mark.densenet
 @pytest.mark.recognition
 def test_recognition_model_densenet_array_input():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = ImagePrediction()
     predictor.setModelTypeAsDenseNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "DenseNet-BC-121-32.h5"))

diff --git a/test/test_image_recognition_multi_images.py b/test/test_image_recognition_multi_images.py
@@ -2,6 +2,7 @@
 import os
 import pytest
 from os.path import dirname
+import keras
 
 
 main_folder = os.getcwd()
@@ -19,6 +20,11 @@ def images_to_image_array():
 @pytest.mark.recognition_multi
 def test_recognition_model_squeezenet():
 
+    try:
+        keras.backend.clear_session()
+    except:
+        None
+
     predictor = ImagePrediction()
     predictor.setModelTypeAsSqueezeNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "squeezenet_weights_tf_dim_ordering_tf_kernels.h5"))
@@ -42,6 +48,10 @@ def test_recognition_model_squeezenet():
 @pytest.mark.recognition
 @pytest.mark.recognition_multi
 def test_recognition_model_resnet():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = ImagePrediction()
     predictor.setModelTypeAsResNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "resnet50_weights_tf_dim_ordering_tf_kernels.h5"))
@@ -63,6 +73,10 @@ def test_recognition_model_resnet():
 @pytest.mark.recognition
 @pytest.mark.recognition_multi
 def test_recognition_model_inceptionv3():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = ImagePrediction()
     predictor.setModelTypeAsInceptionV3()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "inception_v3_weights_tf_dim_ordering_tf_kernels.h5"))
@@ -84,6 +98,10 @@ def test_recognition_model_inceptionv3():
 @pytest.mark.recognition
 @pytest.mark.recognition_multi
 def test_recognition_model_densenet():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     predictor = ImagePrediction()
     predictor.setModelTypeAsDenseNet()
     predictor.setModelPath(os.path.join(main_folder, "data-models", "DenseNet-BC-121-32.h5"))

diff --git a/test/test_object_detection.py b/test/test_object_detection.py
@@ -5,6 +5,7 @@
 from os.path import dirname
 import shutil
 from numpy import ndarray
+import keras
 
 main_folder = os.getcwd()
 
@@ -16,6 +17,10 @@
 @pytest.mark.detection
 @pytest.mark.retinanet
 def test_object_detection_retinanet():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     detector = ObjectDetection()
     detector.setModelTypeAsRetinaNet()
     detector.setModelPath(os.path.join(main_folder, "data-models", "resnet50_coco_best_v2.0.1.h5"))
@@ -52,6 +57,10 @@ def test_object_detection_retinanet():
 @pytest.mark.detection
 @pytest.mark.yolov3
 def test_object_detection_yolov3():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     detector = ObjectDetection()
     detector.setModelTypeAsYOLOv3()
     detector.setModelPath(os.path.join(main_folder, "data-models", "yolo.h5"))
@@ -88,6 +97,10 @@ def test_object_detection_yolov3():
 @pytest.mark.detection
 @pytest.mark.tiny_yolov3
 def test_object_detection_tiny_yolov3():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
     detector = ObjectDetection()
     detector.setModelTypeAsTinyYOLOv3()
     detector.setModelPath(os.path.join(main_folder, "data-models", "yolo-tiny.h5"))
@@ -128,6 +141,10 @@ def test_object_detection_tiny_yolov3():
 @pytest.mark.retinanet
 @pytest.mark.array_io
 def test_object_detection_retinanet_array_io():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
 
     image_input_array = cv2.imread(image_input)
 
@@ -163,6 +180,10 @@ def test_object_detection_retinanet_array_io():
 @pytest.mark.yolov3
 @pytest.mark.array_io
 def test_object_detection_yolov3_array_io():
+    try:
+        keras.backend.clear_session()
+    except:
+        None
 
     image_input_array = cv2.imread(image_input)
 
@@ -197,6 +218,11 @@ def test_object_detection_yolov3_array_io():
 @pytest.mark.array_io
 def test_object_detection_tiny_yolov3_array_io():
 
+    try:
+        keras.backend.clear_session()
+    except:
+        None
+
     image_input_array = cv2.imread(image_input)
 
     detector = ObjectDetection()

diff --git a/test/test_video_object_detection.py b/test/test_video_object_detection.py
@@ -4,6 +4,7 @@
 import os
 from os.path import dirname
 from numpy import ndarray
+import keras
 
 main_folder = os.getcwd()
 video_file = os.path.join(main_folder, "data-videos", "traffic-micro.mp4")
@@ -15,6 +16,11 @@
 @pytest.mark.retinanet
 def test_video_detection_retinanet():
 
+    try:
+        keras.backend.clear_session()
+    except:
+        None
+
     detector = VideoObjectDetection()
     detector.setModelTypeAsRetinaNet()
     detector.setModelPath(model_path=os.path.join(main_folder, "data-models", "resnet50_coco_best_v2.0.1.h5"))
@@ -32,6 +38,11 @@ def test_video_detection_retinanet():
 @pytest.mark.yolov3
 def test_video_detection_yolov3():
 
+    try:
+        keras.backend.clear_session()
+    except:
+        None
+
     detector = VideoObjectDetection()
     detector.setModelTypeAsYOLOv3()
     detector.setModelPath(model_path=os.path.join(main_folder, "data-models", "yolo.h5"))
@@ -48,6 +59,11 @@ def test_video_detection_yolov3():
 @pytest.mark.tiny_yolov3
 def test_video_detection_tiny_yolov3():
 
+    try:
+        keras.backend.clear_session()
+    except:
+        None
+
     detector = VideoObjectDetection()
     detector.setModelTypeAsTinyYOLOv3()
     detector.setModelPath(model_path=os.path.join(main_folder, "data-models", "yolo-tiny.h5"))
@@ -67,6 +83,11 @@ def test_video_detection_tiny_yolov3():
 @pytest.mark.video_analysis
 def test_video_detection_retinanet_analysis():
 
+    try:
+        keras.backend.clear_session()
+    except:
+        None
+
     detector = VideoObjectDetection()
     detector.setModelTypeAsRetinaNet()
     detector.setModelPath(model_path=os.path.join(main_folder, "data-models", "resnet50_coco_best_v2.0.1.h5"))