refactor efficientnet backbone on yolo3, and use processbar for eval …

…display

README.md

@@ -11,7 +11,7 @@ A common YOLOv3/v2 object detection pipeline inherited from [keras-yolo3-Mobilen
 - [x] Darknet19
 - [x] MobilenetV1
 - [x] MobilenetV2
-- [x] EfficientNetB0
+- [x] EfficientNet
 - [x] VGG16
 - [x] Xception
 

common/backbones/efficientnet.py

@@ -606,8 +606,8 @@ def preprocess_input(x):
 
 if __name__ == '__main__':
     input_tensor = Input(shape=(None, None, 3), name='image_input')
-    #model = EfficientNetB0(include_top=False, input_shape=(416, 416, 3), weights='imagenet')
-    model = EfficientNetB0(include_top=True, input_tensor=input_tensor, weights='imagenet')
+    model = EfficientNetB1(include_top=False, input_shape=(416, 416, 3), weights='imagenet')
+    #model = EfficientNetB0(include_top=True, input_tensor=input_tensor, weights='imagenet')
     model.summary()
 
     import numpy as np

common/utils.py

@@ -117,7 +117,8 @@ def draw_boxes(image, boxes, classes, scores, class_names, colors, show_score=Tr
             label = '{} {:.2f}'.format(class_name, score)
         else:
             label = '{}'.format(class_name)
-        print(label, (xmin, ymin), (xmax, ymax))
+        #print(label, (xmin, ymin), (xmax, ymax))
+
         # if no color info, use black(0,0,0)
         if colors == None:
             color = (0,0,0)

eval.py

@@ -13,6 +13,7 @@
 from PIL import Image
 import operator
 import matplotlib.pyplot as plt
+from tqdm import tqdm
 
 from tensorflow.keras.models import load_model
 import tensorflow.keras.backend as K
@@ -278,6 +279,7 @@ def get_prediction_class_records(model, model_format, annotation_records, anchor
         session = model.createSession()
 
     pred_classes_records = {}
+    pbar = tqdm(total=len(annotation_records), desc='Eval model')
     for (image_name, gt_records) in annotation_records.items():
         image = Image.open(image_name)
         image_array = np.array(image, dtype='uint8')
@@ -297,7 +299,8 @@ def get_prediction_class_records(model, model_format, annotation_records, anchor
         else:
             raise ValueError('invalid model format')
 
-        print('Found {} boxes for {}'.format(len(pred_boxes), image_name))
+        #print('Found {} boxes for {}'.format(len(pred_boxes), image_name))
+        pbar.update(1)
 
         if save_result:
 
@@ -334,6 +337,7 @@ def get_prediction_class_records(model, model_format, annotation_records, anchor
     for pred_class_list in pred_classes_records.values():
         pred_class_list.sort(key=lambda ele: ele[2], reverse=True)
 
+    pbar.close()
     return pred_classes_records
 
 

requirements.txt

@@ -4,5 +4,6 @@ opencv-python
 tensorflow-gpu
 tensorflow-model-optimization
 matplotlib
+tqdm
 pillow
 mnn

tools/validate_yolo.py

@@ -250,7 +250,8 @@ def handle_prediction(prediction, image_file, image, image_shape, anchors, class
 
     print('Found {} boxes for {}'.format(len(boxes), image_file))
     for box, cls, score in zip(boxes, classes, scores):
-        print("Class: {}, Score: {}".format(class_names[cls], score))
+        xmin, ymin, xmax, ymax = box
+        print("Class: {}, Score: {}, Box: {},{}".format(class_names[cls], score, (xmin, ymin), (xmax, ymax)))
 
     colors = get_colors(class_names)
     image = draw_boxes(image, boxes, classes, scores, class_names, colors)

yolo3/model.py

@@ -16,7 +16,7 @@
 from yolo3.models.yolo3_vgg16 import yolo3_vgg16_body, tiny_yolo3_vgg16_body
 from yolo3.models.yolo3_xception import yolo3_xception_body, yolo3lite_xception_body, tiny_yolo3_xception_body, tiny_yolo3lite_xception_body, yolo3_spp_xception_body
 from yolo3.models.yolo3_nano import yolo3_nano_body
-from yolo3.models.yolo3_efficientnet import yolo3_efficientnetb0_body, tiny_yolo3_efficientnetb0_body, yolo3lite_efficientnetb0_body, yolo3lite_spp_efficientnetb0_body, tiny_yolo3lite_efficientnetb0_body
+from yolo3.models.yolo3_efficientnet import yolo3_efficientnet_body, tiny_yolo3_efficientnet_body, yolo3lite_efficientnet_body, yolo3lite_spp_efficientnet_body, tiny_yolo3lite_efficientnet_body
 from yolo3.loss import yolo3_loss
 from yolo3.postprocess import batched_yolo3_postprocess, batched_yolo3_prenms, Yolo3PostProcessLayer
 
@@ -40,9 +40,11 @@
     'yolo3_shufflenetv2_lite': [yolo3lite_shufflenetv2_body, 205, None],
     'yolo3_shufflenetv2_lite_spp': [yolo3lite_spp_shufflenetv2_body, 205, None],
 
-    'yolo3_efficientnetb0': [yolo3_efficientnetb0_body, 235, None],
-    'yolo3_efficientnetb0_lite': [yolo3lite_efficientnetb0_body, 235, None],
-    'yolo3_efficientnetb0_lite_spp': [yolo3lite_spp_efficientnetb0_body, 235, None],
+    # NOTE: backbone_length is for EfficientNetB0
+    # if change to other efficientnet level, you need to modify it
+    'yolo3_efficientnet': [yolo3_efficientnet_body, 235, None],
+    'yolo3_efficientnet_lite': [yolo3lite_efficientnet_body, 235, None],
+    'yolo3_efficientnet_lite_spp': [yolo3lite_spp_efficientnet_body, 235, None],
 
     'yolo3_darknet': [yolo3_body, 185, 'weights/darknet53.h5'],
     'yolo3_darknet_spp': [custom_yolo3_spp_body, 185, 'weights/yolov3-spp.h5'],
@@ -73,8 +75,10 @@
     'tiny_yolo3_shufflenetv2': [tiny_yolo3_shufflenetv2_body, 205, None],
     'tiny_yolo3_shufflenetv2_lite': [tiny_yolo3lite_shufflenetv2_body, 205, None],
 
-    'tiny_yolo3_efficientnetb0': [tiny_yolo3_efficientnetb0_body, 235, None],
-    'tiny_yolo3_efficientnetb0_lite': [tiny_yolo3lite_efficientnetb0_body, 235, None],
+    # NOTE: backbone_length is for EfficientNetB0
+    # if change to other efficientnet level, you need to modify it
+    'tiny_yolo3_efficientnet': [tiny_yolo3_efficientnet_body, 235, None],
+    'tiny_yolo3_efficientnet_lite': [tiny_yolo3lite_efficientnet_body, 235, None],
 
     'tiny_yolo3_darknet': [custom_tiny_yolo3_body, 20, 'weights/yolov3-tiny.h5'],
     #Doesn't have pretrained weights, so no need to return backbone length