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TinyYOLOv3 Finetuning #613

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TinyYOLOv3 Finetuning #613

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b08fcbe
added tinyyolov3 training
Wheest Dec 6, 2020
1f7dd5a
updated TinyYOLOv3
Wheest Dec 6, 2020
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118 changes: 97 additions & 21 deletions imageai/Detection/Custom/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,10 @@
import numpy as np
import json
from imageai.Detection.Custom.voc import parse_voc_annotation
from imageai.Detection.Custom.yolo import create_yolov3_model, dummy_loss
from imageai.Detection.YOLOv3.models import yolo_main
from imageai.Detection.Custom.yolo import create_yolov3_model, create_tinyyolov3_model, dummy_loss
from imageai.Detection.YOLOv3.models import yolo_main, tiny_yolo_main
from imageai.Detection.Custom.generator import BatchGenerator
from imageai.Detection.Custom.tinyyolov3_generator import TinyYOLOv3BatchGenerator
from imageai.Detection.Custom.utils.utils import normalize, evaluate, makedirs
from keras.callbacks import ReduceLROnPlateau
from keras.optimizers import Adam
Expand Down Expand Up @@ -74,6 +75,14 @@ def setModelTypeAsYOLOv3(self):
"""
self.__model_type = "yolov3"

def setModelTypeAsTinyYOLOv3(self):
"""
'setModelTypeAsTinyYOLOv3()' is used to set the model type to the TinyYOLOv3 model
for the training instance object .
:return:
"""
self.__model_type = "tinyyolov3"

def setDataDirectory(self, data_directory):

"""
Expand Down Expand Up @@ -166,9 +175,16 @@ def setTrainConfig(self, object_names_array, batch_size=4, num_experiments=100,
:return:
"""

if self.__model_type == "tinyyolov3":
num_anchors = 6
elif self.__model_type == "yolov3":
num_anchors = 9
else:
raise ValueError(f'Unsupported model type: {self.__model_type}')
self.__model_anchors, self.__inference_anchors = generateAnchors(self.__train_annotations_folder,
self.__train_images_folder,
self.__train_cache_file, self.__model_labels)
self.__train_cache_file, self.__model_labels,
num_anchors)

self.__model_labels = sorted(object_names_array)
self.__num_objects = len(object_names_array)
Expand Down Expand Up @@ -220,6 +236,8 @@ def trainModel(self):
###############################
# Create the generators
###############################
if self.__model_type == "tinyyolov3":
BatchGenerator = TinyYOLOv3BatchGenerator
train_generator = BatchGenerator(
instances=train_ints,
anchors=self.__model_anchors,
Expand Down Expand Up @@ -527,12 +545,47 @@ def _create_model(
):
if len(multi_gpu) > 1:
with tf.device('/cpu:0'):
if self.__model_type == "yolov3":
template_model, infer_model = create_yolov3_model(
nb_class=nb_class,
anchors=anchors,
max_box_per_image=max_box_per_image,
max_grid=max_grid,
batch_size=batch_size // len(multi_gpu),
warmup_batches=warmup_batches,
ignore_thresh=ignore_thresh,
grid_scales=grid_scales,
obj_scale=obj_scale,
noobj_scale=noobj_scale,
xywh_scale=xywh_scale,
class_scale=class_scale
)
elif self.__model_type == "tinyyolov3":
template_model, infer_model = create_tinyyolov3_model(
nb_class=nb_class,
anchors=anchors,
max_box_per_image=max_box_per_image,
max_grid=max_grid,
batch_size=batch_size // len(multi_gpu),
warmup_batches=warmup_batches,
ignore_thresh=ignore_thresh,
grid_scales=grid_scales,
obj_scale=obj_scale,
noobj_scale=noobj_scale,
xywh_scale=xywh_scale,
class_scale=class_scale
)
else:
raise ValueError(f'Unsupported model type: {self.__model_type}')
else:
print('Hello world\n\n\n\n\n', self.__model_type)
if self.__model_type == "yolov3":
template_model, infer_model = create_yolov3_model(
nb_class=nb_class,
anchors=anchors,
max_box_per_image=max_box_per_image,
max_grid=max_grid,
batch_size=batch_size // len(multi_gpu),
batch_size=batch_size,
warmup_batches=warmup_batches,
ignore_thresh=ignore_thresh,
grid_scales=grid_scales,
Expand All @@ -541,21 +594,23 @@ def _create_model(
xywh_scale=xywh_scale,
class_scale=class_scale
)
else:
template_model, infer_model = create_yolov3_model(
nb_class=nb_class,
anchors=anchors,
max_box_per_image=max_box_per_image,
max_grid=max_grid,
batch_size=batch_size,
warmup_batches=warmup_batches,
ignore_thresh=ignore_thresh,
grid_scales=grid_scales,
obj_scale=obj_scale,
noobj_scale=noobj_scale,
xywh_scale=xywh_scale,
class_scale=class_scale
)
elif self.__model_type == "tinyyolov3":
template_model, infer_model = create_tinyyolov3_model(
nb_class=nb_class,
anchors=anchors,
max_box_per_image=max_box_per_image,
max_grid=max_grid,
batch_size=batch_size,
warmup_batches=warmup_batches,
ignore_thresh=ignore_thresh,
grid_scales=grid_scales,
obj_scale=obj_scale,
noobj_scale=noobj_scale,
xywh_scale=xywh_scale,
class_scale=class_scale
)
else:
raise ValueError(f'Unsupported model type: {self.__model_type}')

# load the pretrained weight if exists, otherwise load the backend weight only

Expand Down Expand Up @@ -639,6 +694,18 @@ def loadModel(self):
self.__model = yolo_main(Input(shape=(None, None, 3)), 3, len(self.__model_labels))

self.__model.load_weights(self.__model_path)
elif self.__model_type == "tinyyolov3":
detection_model_json = json.load(open(self.__detection_config_json_path))

self.__model_labels = detection_model_json["labels"]
self.__model_anchors = detection_model_json["anchors"]

self.__detection_utils = CustomDetectionUtils(labels=self.__model_labels)

self.__model = tiny_yolo_main(Input(shape=(None, None, 3)), 3, len(self.__model_labels))

self.__model.load_weights(self.__model_path)


def detectObjectsFromImage(self, input_image="", output_image_path="", input_type="file", output_type="file",
extract_detected_objects=False, minimum_percentage_probability=50, nms_treshold=0.4,
Expand Down Expand Up @@ -762,7 +829,7 @@ def detectObjectsFromImage(self, input_image="", output_image_path="", input_typ
# expand the image to batch
image = np.expand_dims(image, 0)

if self.__model_type == "yolov3":
if self.__model_type == "yolov3" or self.__model_type == "tinyyolov3":
if thread_safe == True:
with K.get_session().graph.as_default():
yolo_results = self.__model.predict(image)
Expand Down Expand Up @@ -901,6 +968,15 @@ def loadModel(self):

self.__detector = detector
self.__model_loaded = True
elif(self.__model_type == "tinyyolov3"):
detector = CustomObjectDetection()
detector.setModelTypeAsTinyYOLOv3()
detector.setModelPath(self.__model_path)
detector.setJsonPath(self.__detection_config_json_path)
detector.loadModel()

self.__detector = detector
self.__model_loaded = True


def detectObjectsFromVideo(self, input_file_path="", camera_input=None, output_file_path="", frames_per_second=20,
Expand Down Expand Up @@ -1015,7 +1091,7 @@ def detectObjectsFromVideo(self, input_file_path="", camera_input=None, output_f
video_frames_count = 0


if(self.__model_type == "yolov3"):
if(self.__model_type == "yolov3" or self.__model_type == "tinyyolov3"):



Expand Down
4 changes: 2 additions & 2 deletions imageai/Detection/Custom/gen_anchors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,10 +70,10 @@ def run_kmeans(ann_dims, anchor_num):
old_distances = distances.copy()


def generateAnchors(train_annotation_folder, train_image_folder, train_cache_file, model_labels):
def generateAnchors(train_annotation_folder, train_image_folder, train_cache_file, model_labels,
num_anchors=9):

print("Generating anchor boxes for training images and annotation...")
num_anchors = 9

train_imgs, train_labels = parse_voc_annotation(
train_annotation_folder,
Expand Down
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