My_Flow

SSD+Mobilenet v3 Object Detection 수행.

• https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API 에 다운로드 URL 있음.
• weight파일은 http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz 에서 다운로드
• SSD + Mobilenet v3 backbone은 opencv dnn 모듈이 아니라 dnn_DetectionModel() 함수로 생성 가능하며, 이를 사용하기 위해서는 Opencv의 버전을 Upgrade해야함.

!mkdir ./pretrained
!wget -O ./pretrained/ssd_mobilenet_v3_large_coco_2020_01_14.tar.gz http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v3_large_coco_2020_01_14.tar.gz
!wget -O ./pretrained/ssd_config_02.pbtxt https://gist.githubusercontent.com/dkurt/54a8e8b51beb3bd3f770b79e56927bd7/raw/2a20064a9d33b893dd95d2567da126d0ecd03e85/ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt

!!tar -xvf ./pretrained/ssd_mobilenet*.tar.gz -C ./pretrained

import cv2
print(cv2.__version__)
# 4.1.2

opencv의 버전을 Upgrade한 후 dnn_DetectionModel() 사용.

• https://github.com/opencv/opencv/pull/16760
• dnn_DetectionModel()은 dnn_Model 객체 반환
• 해당 SSD 모델은 image pixel값을 -1 ~ 1 사이로 정규화하고 image size는 320, 320으로 설정.

!pip install opencv-python==4.5.2.54

import cv2

cv_net_m = cv2.dnn_DetectionModel('/content/pretrained/ssd_mobilenet_v3_large_coco_2020_01_14/frozen_inference_graph.pb',
                                      '/content/pretrained/ssd_config_02.pbtxt')
cv_net_m.setInputSize(320, 320)
cv_net_m.setInputScale(1.0 / 127.5)
cv_net_m.setInputMean((127.5, 127.5, 127.5))
cv_net_m.setInputSwapRB(True)

dnn_Model 객체의 detect() 메소드는 입력 이미지를 받아서 특정 confidence threshold 이상의 모든 object inference 결과를 반환.

• class id값, confidence score값, bbox 좌표값이 arrary로 반환됨.
• bbox 좌표값의 경우 0~1사이 값이 아니라 정수형의 위치값이 반환됨. 단 xmin, ymin, width, height 형태로 반환되므로 유의 필요.

img = cv2.imread('/content/data/beatles01.jpg')
draw_img = img.copy()

classes, confidences, boxes = cv_net_m.detect(img, confThreshold=0.5)

classes, confidences, boxes

(array([[1],
        [1],
        [1],
        [1],
        [3],
        [3],
        [3],
        [3],
        [3],
        [3],
        [1]], dtype=int32), array([[0.7795709 ],
        [0.7573837 ],
        [0.75332576],
        [0.71246046],
        [0.6756758 ],
        [0.6396257 ],
        [0.5794208 ],
        [0.5773531 ],
        [0.553491  ],
        [0.5314793 ],
        [0.50632125]], dtype=float32), array([[ 48, 258, 154, 291],
        [213, 252, 158, 298],
        [386, 266, 167, 300],
        [560, 251, 153, 322],
        [496, 226,  80,  67],
        [451, 227,  28,  21],
        [472, 226,  43,  35],
        [375, 219,  17,  17],
        [415, 220,  18,  17],
        [314, 227,  38,  23],
        [258, 259, 101, 268]], dtype=int32))

classes.shape, confidences.shape, boxes.shape
# ((11, 1), (11, 1), (11, 4))

labels_to_names = {1:'person',2:'bicycle',3:'car',4:'motorcycle',5:'airplane',6:'bus',7:'train',8:'truck',9:'boat',10:'traffic light',
                    11:'fire hydrant',12:'street sign',13:'stop sign',14:'parking meter',15:'bench',16:'bird',17:'cat',18:'dog',19:'horse',20:'sheep',
                    21:'cow',22:'elephant',23:'bear',24:'zebra',25:'giraffe',26:'hat',27:'backpack',28:'umbrella',29:'shoe',30:'eye glasses',
                    31:'handbag',32:'tie',33:'suitcase',34:'frisbee',35:'skis',36:'snowboard',37:'sports ball',38:'kite',39:'baseball bat',40:'baseball glove',
                    41:'skateboard',42:'surfboard',43:'tennis racket',44:'bottle',45:'plate',46:'wine glass',47:'cup',48:'fork',49:'knife',50:'spoon',
                    51:'bowl',52:'banana',53:'apple',54:'sandwich',55:'orange',56:'broccoli',57:'carrot',58:'hot dog',59:'pizza',60:'donut',
                    61:'cake',62:'chair',63:'couch',64:'potted plant',65:'bed',66:'mirror',67:'dining table',68:'window',69:'desk',70:'toilet',
                    71:'door',72:'tv',73:'laptop',74:'mouse',75:'remote',76:'keyboard',77:'cell phone',78:'microwave',79:'oven',80:'toaster',
                    81:'sink',82:'refrigerator',83:'blender',84:'book',85:'clock',86:'vase',87:'scissors',88:'teddy bear',89:'hair drier',90:'toothbrush',
                    91:'hair brush'}

import matplotlib.pyplot as plt

green_color=(0, 255, 0)
red_color=(0, 0, 255)

for class_id, confidence_score, box in zip(classes.flatten(), confidences.flatten(), boxes):
    if confidence_score > 0.5:
      caption = "{}: {:.4f}".format(labels_to_names[class_id], confidence_score)
      # box 반환 좌표값은 정수형 위치 좌표임. xmin, ymin, width, height임에 유의 
      cv2.rectangle(draw_img, (box[0], box[1]), (box[0]+box[2], box[1]+box[3]), color=green_color, thickness=2)
      cv2.putText(draw_img, caption, (box[0], box[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.6, red_color, 2)
      print(caption, class_id, box)  

draw_img = cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(12, 12))
plt.imshow(draw_img)

person: 0.7796 1 [ 48 258 154 291]
person: 0.7574 1 [213 252 158 298]
person: 0.7533 1 [386 266 167 300]
person: 0.7125 1 [560 251 153 322]
car: 0.6757 3 [496 226  80  67]
car: 0.6396 3 [451 227  28  21]
car: 0.5794 3 [472 226  43  35]
car: 0.5774 3 [375 219  17  17]
car: 0.5535 3 [415 220  18  17]
car: 0.5315 3 [314 227  38  23]
person: 0.5063 1 [258 259 101 268]
<matplotlib.image.AxesImage at 0x7fbc190c4950>

단일 이미지의 object detection을 함수로 생성

import time 
def get_detected_img_renew(cv_net, img_array, score_threshold, is_print=True):
    
  draw_img = img_array.copy()
   
  start = time.time()

  classes, confidences, boxes = cv_net.detect(img_array, confThreshold=0.5)
  
  green_color=(0, 255, 0)
  red_color=(0, 0, 255)

  # detected 된 object들을 iteration 하면서 정보 추출
  for class_id, confidence_score, box in zip(classes.flatten(), confidences.flatten(), boxes):
    if confidence_score > 0.5:
      caption = "{}: {:.4f}".format(labels_to_names[class_id], confidence_score)
      cv2.rectangle(draw_img, (box[0], box[1]), (box[0]+box[2], box[1]+box[3]), color=green_color, thickness=2)
      cv2.putText(draw_img, caption, (box[0], box[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.6, red_color, 2)
      print(caption)
  
  if is_print:
      print('Detection 수행시간:',round(time.time() - start, 2),"초")

  return draw_img

dnn_Model을 만드는 함수 생성

## dnn_Model을 만드는 함수 생성. 
def get_cv_detection_model(pretrained_path, config_path):
  cv_net = cv2.dnn_DetectionModel(pretrained_path, config_path)
  cv_net.setInputSize(320, 320)
  cv_net.setInputScale(1.0 / 127.5)
  cv_net.setInputMean((127.5, 127.5, 127.5))
  cv_net.setInputSwapRB(True)

  return cv_net

cv_net_m = get_cv_detection_model('/content/pretrained/ssd_mobilenet_v3_large_coco_2020_01_14/frozen_inference_graph.pb',
                       '/content/pretrained/ssd_config_02.pbtxt')

img = cv2.imread('./data/beatles01.jpg')

# Object Detetion 수행 후 시각화 
draw_img = get_detected_img_renew(cv_net_m, img, score_threshold=0.5,  is_print=True)

img_rgb = cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB)

plt.figure(figsize=(12, 12))
plt.imshow(img_rgb)

# person: 0.7796
person: 0.7574
person: 0.7533
person: 0.7125
car: 0.6757
car: 0.6396
car: 0.5794
car: 0.5774
car: 0.5535
car: 0.5315
person: 0.5063
Detection 수행시간: 0.17 초

!wget -O ./data/baseball01.jpg https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/baseball01.jpg

img = cv2.imread('./data/baseball01.jpg')

# Object Detetion 수행 후 시각화 
draw_img = get_detected_img_renew(cv_net_m, img, score_threshold=0.5,  is_print=True)

img_rgb = cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB)

plt.figure(figsize=(12, 12))
plt.imshow(img_rgb)


person: 0.8579
person: 0.8535
person: 0.8509
baseball glove: 0.6942
sports ball: 0.5895
baseball bat: 0.5015
Detection 수행시간: 0.12 초

Video Inferece 수행.

!wget -O ./data/Jonh_Wick_small.mp4 https://github.com/chulminkw/DLCV/blob/master/data/video/John_Wick_small.mp4?raw=true

def do_detected_video_renew(cv_net, input_path, output_path, score_threshold, is_print):
    
    cap = cv2.VideoCapture(input_path)

    codec = cv2.VideoWriter_fourcc(*'XVID')

    vid_size = (round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
    vid_fps = cap.get(cv2.CAP_PROP_FPS)

    vid_writer = cv2.VideoWriter(output_path, codec, vid_fps, vid_size) 

    frame_cnt = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    print('총 Frame 갯수:', frame_cnt, )

    green_color=(0, 255, 0)
    red_color=(0, 0, 255)
    while True:
        hasFrame, img_frame = cap.read()
        if not hasFrame:
            print('더 이상 처리할 frame이 없습니다.')
            break
        
        returned_frame = get_detected_img_renew(cv_net, img_frame, score_threshold=score_threshold, is_print=True)
        vid_writer.write(returned_frame)
    # end of while loop

    vid_writer.release()
    cap.release()

do_detected_video_renew(cv_net_m, '/content/data/Jonh_Wick_small.mp4', './data/John_Wick_small_m3.mp4', 0.2, False)

Detection 수행시간: 0.12 초
person: 0.7723
car: 0.7415
car: 0.6630
person: 0.6345
person: 0.5755
person: 0.5379
person: 0.5283
car: 0.5281
person: 0.5165
car: 0.5078
person: 0.5013
Detection 수행시간: 0.11 초
person: 0.7713
car: 0.7474
car: 0.6665
person: 0.6326
person: 0.5710
person: 0.5382
person: 0.5300
car: 0.5279
person: 0.5178
person: 0.5146
car: 0.5085
Detection 수행시간: 0.11 초
person: 0.7318
car: 0.7291
car: 0.7254
person: 0.7085
car: 0.6152
person: 0.5985
car: 0.5417
person: 0.5402
Detection 수행시간: 0.11 초
person: 0.7599
car: 0.7229
car: 0.7176
person: 0.6913
person: 0.6340
car: 0.5954
car: 0.5493
car: 0.5431
bicycle: 0.5312
person: 0.5130
Detection 수행시간: 0.11 초
person: 0.7522
car: 0.7364
car: 0.7213
car: 0.6430
person: 0.6416
person: 0.6216
car: 0.5797
bicycle: 0.5395
person: 0.5202
car: 0.5138
bicycle: 0.5034
Detection 수행시간: 0.12 초
더 이상 처리할 frame이 없습니다.

OpenCV DNN을 이용하여 SSD 기반 Object Detection 수행

• Tensorflow 에서 Pretrained 된 모델 파일을 OpenCV에서 로드하여 이미지와 영상에 대한 Object Detection 수행.
• SSD+Inception과 SSD+MobileNet v3 를 모두 테스트
• CPU기반 환경에서 SSD의 Inference 속도 주시.

입력 이미지로 사용될 이미지 다운로드

!mkdir /content/data
!wget -O ./data/beatles01.jpg https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/beatles01.jpg

Tensorflow에서 Pretrained 된 Inference모델(Frozen graph)와 환경파일을 다운로드 받은 후 이를 이용해 OpenCV에서 Inference 모델 생성

• https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API 에 다운로드 URL 있음.
• pretrained 모델은 http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz 에서 다운로드 후 압축 해제
• pretrained 모델을 위한 환경 파일은 https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/ssd_inception_v2_coco_2017_11_17.pbtxt 에서 다운로드
• download된 모델 파일과 config 파일을 인자로 하여 inference 모델을 DNN에서 로딩함.

!mkdir ./pretrained

!wget -O ./pretrained/ssd_inception_v2_coco_2017_11_17.tar.gz http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz 
!wget -O ./pretrained/ssd_config_01.pbtxt  https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/dnn/ssd_inception_v2_coco_2017_11_17.pbtxt

!tar -xvf ./pretrained/ssd_inception*.tar.gz -C ./pretrained

!pwd
!ls -lia ./pretrained/ssd_inception*

# /content
# 6160390 -rw-r--r-- 1 root   root 278126337 Nov 18  2017 ./pretrained/ssd_inception_v2_coco_2017_11_17.tar.gz
# 
# ./pretrained/ssd_inception_v2_coco_2017_11_17:
# total 201120
# 6160392 drwxr-x--- 3 275875 5000      4096 Nov 18  2017 .
# 6160389 drwxr-xr-x 3 root   root      4096 Oct 20 14:02 ..
# 6170292 -rw-r----- 1 275875 5000        77 Nov 18  2017 checkpoint
# 6170280 -rw-r----- 1 275875 5000 101987441 Nov 18  2017 frozen_inference_graph.pb
# 6170281 -rw-r----- 1 275875 5000 100214372 Nov 18  2017 model.ckpt.data-00000-of-00001
# 6160393 -rw-r----- 1 275875 5000     18073 Nov 18  2017 model.ckpt.index
# 6170279 -rw-r----- 1 275875 5000   3701354 Nov 18  2017 model.ckpt.meta
# 6170282 drwxr-x--- 3 275875 5000      4096 Nov 18  2017 saved_model

dnn에서 readNetFromTensorflow()로 tensorflow inference 모델을 로딩

import cv2

cv_net = cv2.dnn.readNetFromTensorflow('/content/pretrained/ssd_inception_v2_coco_2017_11_17/frozen_inference_graph.pb',
                                      '/content/pretrained/ssd_config_01.pbtxt')

coco 데이터 세트의 클래스id별 클래스명 지정.

labels_to_names = {1:'person',2:'bicycle',3:'car',4:'motorcycle',5:'airplane',6:'bus',7:'train',8:'truck',9:'boat',10:'traffic light',
                    11:'fire hydrant',12:'street sign',13:'stop sign',14:'parking meter',15:'bench',16:'bird',17:'cat',18:'dog',19:'horse',20:'sheep',
                    21:'cow',22:'elephant',23:'bear',24:'zebra',25:'giraffe',26:'hat',27:'backpack',28:'umbrella',29:'shoe',30:'eye glasses',
                    31:'handbag',32:'tie',33:'suitcase',34:'frisbee',35:'skis',36:'snowboard',37:'sports ball',38:'kite',39:'baseball bat',40:'baseball glove',
                    41:'skateboard',42:'surfboard',43:'tennis racket',44:'bottle',45:'plate',46:'wine glass',47:'cup',48:'fork',49:'knife',50:'spoon',
                    51:'bowl',52:'banana',53:'apple',54:'sandwich',55:'orange',56:'broccoli',57:'carrot',58:'hot dog',59:'pizza',60:'donut',
                    61:'cake',62:'chair',63:'couch',64:'potted plant',65:'bed',66:'mirror',67:'dining table',68:'window',69:'desk',70:'toilet',
                    71:'door',72:'tv',73:'laptop',74:'mouse',75:'remote',76:'keyboard',77:'cell phone',78:'microwave',79:'oven',80:'toaster',
                    81:'sink',82:'refrigerator',83:'blender',84:'book',85:'clock',86:'vase',87:'scissors',88:'teddy bear',89:'hair drier',90:'toothbrush',
                    91:'hair brush'}

이미지를 preprocessing 수행하여 Network에 입력하고 Object Detection 수행 후 결과를 이미지에 시각화

import matplotlib.pyplot as plt
import cv2

img = cv2.imread('/content/data/beatles01.jpg')

# 원본 이미지 (633, 806)를 네트웍에 입력시에는 (300, 300)로 resize 함. 
# 이후 결과가 출력되면 resize된 이미지 기반으로 bounding box 위치가 예측 되므로 이를 다시 원복하기 위해 원본 이미지 shape정보 필요
rows = img.shape[0]
cols = img.shape[1]
# cv2의 rectangle()은 인자로 들어온 이미지 배열에 직접 사각형을 업데이트 하므로 그림 표현을 위한 별도의 이미지 배열 생성. 
draw_img = img.copy()

# 원본 이미지 배열을 사이즈 (300, 300)으로, BGR을 RGB로 변환하여 배열 입력
cv_net.setInput(cv2.dnn.blobFromImage(img,  size=(300, 300), swapRB=True, crop=False))
# Object Detection 수행하여 결과를 cv_out으로 반환 
cv_out = cv_net.forward()
print(cv_out.shape)

# bounding box의 테두리와 caption 글자색 지정
green_color=(0, 255, 0)
red_color=(0, 0, 255)

# detected 된 object들을 iteration 하면서 정보 추출
for detection in cv_out[0,0,:,:]:
    score = float(detection[2])
    class_id = int(detection[1])
    # detected된 object들의 score가 0.4 이상만 추출
    if score > 0.4:
        # detected된 object들은 image 크기가 (300, 300)으로 scale된 기준으로 예측되었으므로 다시 원본 이미지 비율로 계산
        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows
        # labels_to_names 딕셔너리로 class_id값을 클래스명으로 변경. opencv에서는 class_id + 1로 매핑해야함.
        caption = "{}: {:.4f}".format(labels_to_names[class_id], score)
        
        #cv2.rectangle()은 인자로 들어온 draw_img에 사각형을 그림. 위치 인자는 반드시 정수형.
        cv2.rectangle(draw_img, (int(left), int(top)), (int(right), int(bottom)), color=green_color, thickness=2)
        cv2.putText(draw_img, caption, (int(left), int(top - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, red_color, 2)
        print(caption, class_id)

img_rgb = cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB)

plt.figure(figsize=(12, 12))
plt.imshow(img_rgb)

(1, 1, 100, 7)
person: 0.9696 1
person: 0.9660 1
person: 0.8916 1
person: 0.6298 1
car: 0.8609 3
car: 0.7223 3
car: 0.7184 3
car: 0.7095 3
car: 0.5949 3
car: 0.5511 3
<matplotlib.image.AxesImage at 0x7fd72d12b4d0>

단일 이미지의 object detection을 함수로 생성

import time

def get_detected_img(cv_net, img_array, score_threshold, is_print=True):
    
    rows = img_array.shape[0]
    cols = img_array.shape[1]
    
    draw_img = img_array.copy()
    
    cv_net.setInput(cv2.dnn.blobFromImage(img_array, size=(300, 300), swapRB=True, crop=False))
    
    start = time.time()
    cv_out = cv_net.forward()
    
    green_color=(0, 255, 0)
    red_color=(0, 0, 255)

    # detected 된 object들을 iteration 하면서 정보 추출
    for detection in cv_out[0,0,:,:]:
        score = float(detection[2])
        class_id = int(detection[1])
        # detected된 object들의 score가 0.4 이상만 추출
        if score > score_threshold:
            # detected된 object들은 image 크기가 (300, 300)으로 scale된 기준으로 예측되었으므로 다시 원본 이미지 비율로 계산
            left = detection[3] * cols
            top = detection[4] * rows
            right = detection[5] * cols
            bottom = detection[6] * rows
            # labels_to_names 딕셔너리로 class_id값을 클래스명으로 변경. opencv에서는 class_id + 1로 매핑해야함.
            caption = "{}: {:.4f}".format(labels_to_names[class_id], score)

            #cv2.rectangle()은 인자로 들어온 draw_img에 사각형을 그림. 위치 인자는 반드시 정수형.
            cv2.rectangle(draw_img, (int(left), int(top)), (int(right), int(bottom)), color=green_color, thickness=2)
            cv2.putText(draw_img, caption, (int(left), int(top - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, red_color, 2)
    if is_print:
        print('Detection 수행시간:',round(time.time() - start, 2),"초")

    return draw_img

# image 로드 
img = cv2.imread('/content/data/beatles01.jpg')

#coco dataset 클래스명 매핑

# Object Detetion 수행 후 시각화 
draw_img = get_detected_img(cv_net, img, score_threshold=0.4, is_print=True)

img_rgb = cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB)

plt.figure(figsize=(12, 12))
plt.imshow(img_rgb)

# Detection 수행시간: 0.47 초

!wget -O ./data/baseball01.jpg https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/baseball01.jpg

img = cv2.imread('/content/data/baseball01.jpg')

#coco dataset 클래스명 매핑

# Object Detetion 수행 후 시각화 
draw_img = get_detected_img(cv_net, img, score_threshold=0.4, is_print=True)

img_rgb = cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB)

plt.figure(figsize=(12, 12))
plt.imshow(img_rgb)

# Detection 수행시간: 0.38 초

Video Object Detection 수행

!wget -O ./data/Jonh_Wick_small.mp4 https://github.com/chulminkw/DLCV/blob/master/data/video/John_Wick_small.mp4?raw=true

VideoCapture와 VideoWriter 설정하고 Video Detection용 전용 함수 생성

• VideoCapture를 이용하여 Video를 frame별로 capture 할 수 있도록 설정
• VideoCapture의 속성을 이용하여 Video Frame의 크기 및 FPS 설정.
• VideoWriter를 위한 인코딩 코덱 설정 및 영상 write를 위한 설정 총 Frame 별로 iteration 하면서 Object Detection 수행. 개별 frame별로 단일 이미지 Object Detection과 유사

def do_detected_video(cv_net, input_path, output_path, score_threshold, is_print):
    
    cap = cv2.VideoCapture(input_path)

    codec = cv2.VideoWriter_fourcc(*'XVID')

    vid_size = (round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
    vid_fps = cap.get(cv2.CAP_PROP_FPS)

    vid_writer = cv2.VideoWriter(output_path, codec, vid_fps, vid_size) 

    frame_cnt = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    print('총 Frame 갯수:', frame_cnt, )

    green_color=(0, 255, 0)
    red_color=(0, 0, 255)
    while True:
        hasFrame, img_frame = cap.read()
        if not hasFrame:
            print('더 이상 처리할 frame이 없습니다.')
            break
        
        returned_frame = get_detected_img(cv_net, img_frame, score_threshold=score_threshold, is_print=True)
        vid_writer.write(returned_frame)
    # end of while loop

    vid_writer.release()
    cap.release()

do_detected_video(cv_net, '/content/data/Jonh_Wick_small.mp4', './data/John_Wick_small_incept.mp4', 0.2, False)
총 Frame 갯수: 58
Detection 수행시간: 0.38 초
Detection 수행시간: 0.4 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.39 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.39 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.4 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.35 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.39 초
Detection 수행시간: 0.39 초
Detection 수행시간: 0.39 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.4 초
Detection 수행시간: 0.39 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.38 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.36 초
Detection 수행시간: 0.37 초
Detection 수행시간: 0.37 초
더 이상 처리할 frame이 없습니다.

* tensorflow는 VGG대신 Resnet, inception을 씀, 혹은 mobilenet을 써서 detection 시간 줄이기도 함

# 각 다른 scale의 feature map에서 classifier를 구성하고 각 classifiers를 최종적으로 종합하는 구조임.

- object detecting 할 수 있는 Default box정보를 전달함

   => faster RCNN의 anchor box와 동일함

# anchor box 정보를 학습하는 방법

- 각각 feature map에서 3x3 cnn연산을 함

=> (4 : 개별 anchor box가 채워야하는 정보(class 개수 pascal 20개), 좌표 3개 ) x (4 : anchor box 개수) // * faster rcnn은 9개

# NMS

- 결과적으로 8732가 모였는데 너무 많으니 non-maximum suppression으로 가장 유력한 것만 남김

# Anchor box를 활용해서 convolution predictor for detection

(512, 38, 38) 으로 3x3 연산을 하는데 20여개의 class + bg 1개 object 확률(cs), 4개 좌표

# SSD의 multi scale Feature Map과 Anchor box 적용

8x8 feature map

- 4개의 anchorbox 중에 ground box와 매칭되는게 2개

- 매칭기준은 iou가 몇 이상인가

- 큰 개는 anchor box 내에 안들어와서 매칭이 안됨

4x4 feature map

- feature map이 추상화되어 scale이 작아지니 anchorbox가 매칭할 수 있게 됨.

feature map이 줄어드는 과정에서 anchor box와 ground box의 간격을 줄여나가는 계산을 함.

SSD Training

 n : matched anchorbox 개수, 

Matching 전략 

- bounding box와 겹치는 IOU가 0.5dltkddls anchor box 들의 classification과 bounding box Regression을 최적화 학습 수행.

위치 offset을 줄이도록 계속 weight를 갱신하여 줄여 냄.

# performance

- Data augmentation의 영향이 큼

- one-stage detector는 작은 object에 대한 성능이 떨어짐 => feature pyramid, retinanet로 개선

 augmentation을 하고나서 작은 데이터에 대한 성능이 높아졌다.

- 큰 화면, 선명도 좋은 이미지 등

Object detection

직사각형 안에 무엇이 있는지 찾는 것

Multi-Labeled Classification + Bounding Box Regression(Localization)

여러 물체에 대해 어떤 물체인지 분류하는 Classification문제와 그 물체가 어디 있는지

박스(Bounding box)를 통해 위치 정보를 나타내는 Localization문제 둘 다를 해내야 하는 분야

Object dection paper flow

R-CNN은 object detection 문제에서 CNN을 활용한 최초의 논문이다

Localization

객체라고 판단되는 곳에 직사각형(bounding box)를 그려주는 작업

Segmentation

모든 픽셀의 label을 예측한다

1. Semantic segmentation

- 모든 픽셀을 미리 지정된 개수의 class로 분류하는 것

- 동일한 객체들끼리는 한 번에 masking을 수행한다

2. Instance segmentation

- sematic segmentation와 유사하지만 같은 class이더라도 다른 것으로 분류한다

- 동일한 객체들이여도 개별로 masking을 수행한다

Naive approach

물체가 존재할 수 있는 모든 크기의 영역에 대해 sliding window 방식으로 이미지를 모두 탐색하면서 classificaion을 수행하는 방식
탐색해야 할 영역이 많기 때문에 연산 시간이 오래걸리고 비효율적인 방법이다

Sliding window

일정 크기의 Window를 이미지 위에서 조금씩 옮기면서 값을 비교하는 알고리즘

Region proposals

sliding window 방식의 비효율성을 개선하기 위해 입력 영상에서 '물체가 있을 법한' 영역을 빠른 속도로 찾아내는 알고리즘

Selective search

객체의 주변 간의 색감, 질감 차이, 다른 물체에 둘러 쌓여 있는지 여부 등을 파악해서

다양한 전략으로 물체의 위치를 파악할 수 있도록 하는 알고리즘

Bounding box들을 Random하게 많이 생성을 하고 이들을 조금씩 Merge해나가면서 물체를 인식해 나간다

!pip install selective-search
!pip install selectivesearch
!pip install -U opencv-contrib-python

import selective_search
import selectivesearch

import skimage
from skimage import data
import matplotlib.pyplot as plt
import matplotlib.patches as pts

dir(selective_search)
# ['__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 'box_filter',
 'core',
 'measure',
 'selective_search',
 'structure',
 'util']

plt.imshow(data.astronaut())

plt.imshow(data.rocket())

im = data.rocket()

ss = selective_search.selective_search(im) # 좌표값을 list로 찾아준다 

len(ss)
# 489

boxes = selective_search.box_filter(ss, topN=30)

fig, ax = plt.subplots(1,1)
ax.imshow(im)
for b in boxes:
  box = pts.Rectangle((b[0],b[1]), b[2]-b[0], b[3]-b[1], fill=None, edgecolor='red') # xy(원점), width, height
  ax.add_patch(box)

ss2 = selective_search.selective_search(im, mode='fast')

boxes = selective_search.box_filter(ss2, topN=30)

fig, ax = plt.subplots(1,1)
ax.imshow(im)
for b in boxes:
  box = pts.Rectangle((b[0],b[1]), b[2]-b[0], b[3]-b[1], fill=None, edgecolor='red') 
  ax.add_patch(box)

im2 = data.astronaut()

ss = selective_search.selective_search(im2)
boxes = selective_search.box_filter(ss, topN=20)

fig, ax = plt.subplots(1,1)
ax.imshow(im2)
for b in boxes:
  box = pts.Rectangle((b[0],b[1]), b[2]-b[0], b[3]-b[1], fill=None, edgecolor='red') # xy(원점), width, height
  ax.add_patch(box)

sss = selectivesearch.selective_search(im, min_size=500) # numpy로 찾아준다 

sss[1] # 영역 
[{'labels': [0.0], 'rect': (0, 0, 18, 7), 'size': 121},
 {'labels': [1.0], 'rect': (19, 0, 9, 8), 'size': 65},
 {'labels': [2.0], 'rect': (16, 0, 32, 15), 'size': 207},
 {'labels': [3.0], 'rect': (45, 0, 14, 35), 'size': 223},
 {'labels': [4.0], 'rect': (57, 0, 34, 10), 'size': 300},
 {'labels': [5.0], 'rect': (88, 0, 29, 65), 'size': 209},
 {'labels': [6.0], 'rect': (89, 0, 49, 7), 'size': 217},
 {'labels': [7.0], 'rect': (133, 0, 42, 15), 'size': 242},
 {'labels': [8.0], 'rect': (152, 0, 68, 8), 'size': 292},
 {'labels': [9.0], 'rect': (214, 0, 22, 7), 'size': 96},
 {'labels': [10.0], 'rect': (232, 0, 32, 8), 'size': 189},
 {'labels': [11.0], 'rect': (265, 0, 20, 9), 'size': 114},
 {'labels': [12.0], 'rect': (269, 0, 26, 5), 'size': 118},
 {'labels': [13.0], 'rect': (291, 0, 22, 15), 'size': 218},
 {'labels': [14.0], 'rect': (307, 0, 20, 15), 'size': 145},
 {'labels': [15.0], 'rect': (312, 0, 18, 6), 'size': 81},
 {'labels': [16.0], 'rect': (325, 0, 28, 9), 'size': 115},
 {'labels': [17.0], 'rect': (339, 0, 19, 7), 'size': 93},
 {'labels': [18.0], 'rect': (358, 0, 25, 5), 'size': 92},
 {'labels': [19.0], 'rect': (384, 0, 11, 5), 'size': 55},
 {'labels': [20.0], 'rect': (384, 0, 18, 12), 'size': 126},
 {'labels': [21.0], 'rect': (398, 0, 27, 8), 'size': 163},
 {'labels': [22.0], 'rect': (402, 0, 31, 15), 'size': 191},
 {'labels': [23.0], 'rect': (429, 0, 15, 9), 'size': 142},
 {'labels': [24.0], 'rect': (432, 0, 19, 15), 'size': 136},
 {'labels': [25.0], 'rect': (447, 0, 22, 15), 'size': 161},
 {'labels': [26.0], 'rect': (456, 0, 26, 10), 'size': 206},
 {'labels': [27.0], 'rect': (472, 0, 16, 7), 'size': 72},
 {'labels': [28.0], 'rect': (487, 0, 16, 17), 'size': 139},
 {'labels': [29.0], 'rect': (493, 0, 37, 28), 'size': 503},
 {'labels': [30.0], 'rect': (529, 0, 16, 24), 'size': 234},
 {'labels': [31.0], 'rect': (546, 0, 12, 11), 'size': 118},
 {'labels': [32.0], 'rect': (539, 0, 21, 61), 'size': 209},
 {'labels': [33.0], 'rect': (560, 0, 11, 10), 'size': 97},
 {'labels': [34.0], 'rect': (567, 0, 14, 26), 'size': 107},
 {'labels': [35.0], 'rect': (579, 0, 19, 17), 'size': 225},
 {'labels': [36.0], 'rect': (591, 0, 31, 20), 'size': 232},
 {'labels': [37.0], 'rect': (613, 0, 18, 10), 'size': 94},
 {'labels': [38.0], 'rect': (626, 0, 13, 13), 'size': 124},
 {'labels': [39.0], 'rect': (10, 2, 14, 9), 'size': 70},
 {'labels': [40.0], 'rect': (114, 2, 21, 9), 'size': 131},
 {'labels': [41.0], 'rect': (221, 2, 16, 10), 'size': 107},
 {'labels': [42.0], 'rect': (354, 2, 43, 14), 'size': 224},
 {'labels': [43.0], 'rect': (90, 3, 13, 12), 'size': 95},
 {'labels': [44.0], 'rect': (199, 3, 36, 23), 'size': 365},
 {'labels': [45.0], 'rect': (320, 3, 17, 9), 'size': 90},
 {'labels': [46.0], 'rect': (160, 4, 39, 11), 'size': 125},
 {'labels': [47.0], 'rect': (280, 4, 19, 11), 'size': 122},
 {'labels': [48.0], 'rect': (336, 4, 23, 11), 'size': 132},
 {'labels': [49.0], 'rect': (0, 5, 51, 18), 'size': 243},
 {'labels': [50.0], 'rect': (99, 5, 14, 6), 'size': 71},
 {'labels': [51.0], 'rect': (208, 5, 14, 10), 'size': 75},
 {'labels': [52.0], 'rect': (236, 5, 33, 7), 'size': 124},
 {'labels': [53.0], 'rect': (479, 5, 25, 18), 'size': 184},
 {'labels': [54.0], 'rect': (496, 5, 23, 17), 'size': 171},
 {'labels': [55.0], 'rect': (89, 6, 22, 22), 'size': 280},
 {'labels': [56.0], 'rect': (104, 6, 44, 17), 'size': 402},
 {'labels': [57.0], 'rect': (169, 6, 29, 8), 'size': 170},
 {'labels': [58.0], 'rect': (600, 6, 21, 40), 'size': 270},
 {'labels': [59.0], 'rect': (0, 7, 15, 11), 'size': 129},
 {'labels': [60.0], 'rect': (358, 7, 15, 5), 'size': 60},
 {'labels': [61.0], 'rect': (549, 7, 9, 16), 'size': 67},
 {'labels': [62.0], 'rect': (574, 7, 26, 27), 'size': 140},
 {'labels': [63.0], 'rect': (146, 8, 22, 14), 'size': 165},
 {'labels': [64.0], 'rect': (160, 8, 35, 12), 'size': 163},
 {'labels': [65.0], 'rect': (238, 8, 47, 9), 'size': 241},
 {'labels': [66.0], 'rect': (470, 8, 15, 12), 'size': 81},
 {'labels': [67.0], 'rect': (527, 8, 9, 13), 'size': 69},
 {'labels': [68.0], 'rect': (576, 8, 13, 20), 'size': 139},
 {'labels': [69.0], 'rect': (62, 9, 25, 45), 'size': 226},
 {'labels': [70.0], 'rect': (540, 9, 12, 6), 'size': 62},
 {'labels': [71.0], 'rect': (560, 9, 11, 46), 'size': 175},
 {'labels': [72.0], 'rect': (611, 9, 20, 15), 'size': 104},
 {'labels': [73.0], 'rect': (622, 9, 17, 14), 'size': 149},
 {'labels': [74.0], 'rect': (31, 10, 19, 20), 'size': 134},
 {'labels': [75.0], 'rect': (53, 10, 15, 30), 'size': 195},
 {'labels': [76.0], 'rect': (342, 10, 44, 13), 'size': 351},
 {'labels': [77.0], 'rect': (394, 10, 38, 6), 'size': 102},
 {'labels': [78.0], 'rect': (454, 11, 24, 10), 'size': 131},
 {'labels': [79.0], 'rect': (62, 12, 22, 21), 'size': 273},
 {'labels': [80.0], 'rect': (313, 12, 30, 11), 'size': 232},
 {'labels': [81.0], 'rect': (544, 12, 14, 19), 'size': 175},
 {'labels': [82.0], 'rect': (231, 13, 16, 7), 'size': 76},
 {'labels': [83.0], 'rect': (236, 13, 25, 5), 'size': 57},
 {'labels': [84.0], 'rect': (249, 13, 31, 6), 'size': 75},
 {'labels': [85.0], 'rect': (282, 13, 30, 15), 'size': 226},
 {'labels': [86.0], 'rect': (408, 13, 20, 13), 'size': 127},
 {'labels': [87.0], 'rect': (440, 13, 17, 12), 'size': 97},
 {'labels': [88.0], 'rect': (156, 14, 56, 13), 'size': 233},
 {'labels': [89.0], 'rect': (296, 14, 17, 9), 'size': 122},
 {'labels': [90.0], 'rect': (392, 14, 16, 9), 'size': 101},
 {'labels': [91.0], 'rect': (588, 14, 9, 13), 'size': 76},
 {'labels': [92.0], 'rect': (429, 15, 10, 8), 'size': 56},
 {'labels': [93.0], 'rect': (450, 15, 34, 14), 'size': 223},
 {'labels': [94.0], 'rect': (0, 16, 23, 12), 'size': 141},
 {'labels': [95.0], 'rect': (247, 16, 16, 8), 'size': 63},
 {'labels': [96.0], 'rect': (339, 16, 18, 7), 'size': 100},
 {'labels': [97.0], 'rect': (384, 16, 17, 12), 'size': 83},
 {'labels': [98.0], 'rect': (515, 16, 12, 28), 'size': 146},
 {'labels': [99.0], 'rect': (561, 16, 11, 27), 'size': 201},
 {'labels': [100.0], 'rect': (575, 16, 8, 11), 'size': 58},
 {'labels': [101.0], 'rect': (621, 16, 18, 15), 'size': 148},
 {'labels': [102.0], 'rect': (32, 17, 16, 27), 'size': 182},
 {'labels': [103.0], 'rect': (606, 17, 13, 11), 'size': 91},
 {'labels': [104.0], 'rect': (48, 18, 8, 45), 'size': 202},
 {'labels': [105.0], 'rect': (106, 18, 21, 8), 'size': 113},
 {'labels': [106.0], 'rect': (262, 18, 26, 9), 'size': 168},
 {'labels': [107.0], 'rect': (400, 18, 15, 9), 'size': 95},
 {'labels': [108.0], 'rect': (432, 18, 13, 8), 'size': 65},
 {'labels': [109.0], 'rect': (493, 18, 23, 17), 'size': 190},
 {'labels': [110.0], 'rect': (536, 18, 7, 13), 'size': 78},
 {'labels': [111.0], 'rect': (5, 19, 34, 12), 'size': 182},
 {'labels': [112.0], 'rect': (59, 19, 12, 20), 'size': 139},
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fig, ax = plt.subplots(1,1)
ax.imshow(im2)
for b in sss[1]:
  box = pts.Rectangle((b['rect'][0],b['rect'][1]), b['rect'][2]-b['rect'][0], b['rect'][3]-b['rect'][1], fill=None, edgecolor='red') # xy(원점), width, height
  ax.add_patch(box)

import cv2 # 속도가 빠르고 다른 라이브러리와 연동하기가 좋다 

ssss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation() # monkey patch / opencv-contrib-python 모듈을 설치하면서 모듈이 확장되었다 

ssss.setBaseImage(im)

ssss.switchToSelectiveSearchFast() # 빠르게 찾는다 
# ssss.switchToSelectiveSearchQuality() # 좀 더 느리게 찾지만 비교적 정확하게 찾음 

rect = ssss.process()

rect
# array([[594, 388,  46,  39],
#        [433, 191,  48, 236],
#        [590, 194,  32,  28],
#        ...,
#        [  0,   0, 196, 427],
#        [169,   0, 471, 427],
#        [182,   0, 458, 427]], dtype=int32)

cv2.setUseOptimized(True)

R-CNN

1. 이미지를 input data로 넣는다

2. 2000개의 bounding box를 selective search 알고리즘을 통해 추출한다

3. Flatten하기 위해 2000개의 bounding box를 같은 크기로 만든다 (warping)

- warping: 휘어지게 하다

- 왜곡하다의 의미로 보정이나 찌그러진 이미지를 정규화 하기위한 처리 방법

4. warped image를 CNN 모델에 넣는다 5. 마지막으로 SVM을 활용해 분류 한다

Domain-specific fine-tuning

기존에 AlexNet은 1000가지 이미지를 분류하는 모델이었다

그런데 R-CNN에서 해결하고자 하는 목표는 20가지 이미지를 분류해야 한다

따라서 Catastrophic forgetting을 막기 위해서 fine-tuning을 할 필요가 있다

AlexNet은 맨 끝에 있는 layer가 1000가지를 분류하기 때문에 마지막 layer를 변형해야 한다

우선 가장 먼저 feature selection하는 부분을 frozen 즉, trainable=False를 한다

그리고 초기 가중치를 그대로 사용한다(새로 학습할 이미지가 기존 이미지와 유사하기 때문에 가능하다)

그 다음 learning rate를 줄인후 PASCAL VOC dataset으로 학습 시킨다

그리고 나서 Fine tuning작업을 거쳐야 한다

frozen된 것을 풀고나서 learning rate를 확 줄이면서 재학습을 시킨다

R-CNN의 문제점

1. AlexNet의 구조를 사용했기 때문에 입력 이미지의 크기를 강제하므로 crop이나 wrapping으로 인한 이미지 손실이나 변형으로 성능 저하가 될 수 있다

2. 2000개의 bounding box에 대해 순차적으로 CNN을 수행하기 때문에 실행 시간이 길어진다

3. Region proposal이나 SVM때문에 GPU 사용에 적합하지 않다

4, 3개의 다른 모델을 학습시켜야 하기 때문에 전체적인 pipeline을 학습시키기 어려운 문제가 있다

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

SPPNet
Global average pooling에 영향을 받았다

R-CNN에서 2000개의 bounding box가 FC layer로 들어가기 위해서 wrapping하는 작업을 거치는데

이때 이미지의 크기를 고정된 크기로 자르거나 비율을 조정하기 때문에 본래의 이미지랑 달라지는 문제가 있었다

이러한 문제점을 해결하기 위해 입력 이미지의 크기에 관계없이 convolution layer를 통과 시키고

FC layer에 통과하기 전에 feature map들을 동일한 크기로 조절해주는 pooling을 적용하자는 아이디어를 활용하게 된다

Convolution layer들을 거쳐 추출된 feature map을 인풋으로 받는다

그리고 이를 미리 정해져 있는 영역으로 나누어 준다

위의 예시에서는 미리 4x4, 2x2, 1x1 세 가지 영역을 제공하며, 각각을 하나의 피라미드라고 부른다

여기서 피라미드의 한 칸을 bin이라고 한다

예를 들어 입력이 64 x 64 x 256 크기의 피쳐 맵이 들어온다고 했을 때, 4x4의 피라미드의 bin의 크기는 16x16이 된다

각 bin에서 max pooling을 수행하고 그 결과를 쭉 이어 붙인다

입력 feature map의 채널 크기를 k, bin의 개수를 m이라고 했을 때 최종 결과는 k*m차원 벡터이다

위의 예시는 k=256, m=(16+4+1)=21 입력 이미지의 크기와 상관없이 미리 설정한 bin의 수와 cnn의 채널값에 의해 출력이 결정되기 때문에 항상 동일한 크기의 결과를 반환하게 된다

Image(Object) Classification

이미지 분류 흐름

비용 절감 vs 성능 향상

┌─ 사람이 만든 알고리즘 (Rule 기반)

└─ ML

      ┌─ 전통적인 ML

      └─ Deep Learning

            └─ 크기와 위치가 같은 상황에서만 성능이 좋음 (가정이 필요)

                  ┌─ FN

                  └─ CNN

                        └─ Capacity가 크면 클 수록 성능이 좋다

                              (kernel, filter, layer, perceptron 개수 등)

                              └─ 이미지 데이터 개수가 아주 많아야 한다

                                    └─ Augmentation (데이터 뻥튀기)

                                          └─ 데이터 양의 한계가 있을 때

                                                └─ Transfer learing

                                                      ┌─ Feature Extraction

                                                      └─ Fine Tuning

Image data 저장 방법

- Directory -> 대부분 이 방법을 사용

- HDF -> 연구용

- LMDB

데이터를 불러오는 방법

ImageDataGenerator + tf.data

ImageDataGenerator를 활용하여 저장, 전처리, augmentation을 사용할 수 있지만 속도가 느리기 때문에 ImageDataGenerator와 tf.data를 섞어 사용하면 훨씬 효율적이다

import tensorflow as tf 

idg = tf.keras.preprocessing.image.ImageDataGenerator() # 옵션을 사용해서 저장, augmentation을 사용할 수 있는 장점이 있다 
didg = idg.flow_from_directory('flower_photos/')
# Found 3670 images belonging to 5 classes.

next(didg)[0].dtype, next(didg)[0].shape
# (dtype('float32'), (32, 256, 256, 3))

next(didg)[1].dtype, next(didg)[1].shape
# (dtype('float32'), (32, 5))

# 인자에는 callable이 들어가야 한다 
train = tf.data.Dataset.from_generator(lambda: didg, output_types=(tf.float32, tf.float32), output_shapes=((None,256,256,3),(None,5))) 

train
# <FlatMapDataset shapes: ((None, 256, 256, 3), (None, 5)), types: (tf.float32, tf.float32)>

for i in train.take(1):
  print(i)
  (<tf.Tensor: shape=(32, 256, 256, 3), dtype=float32, numpy=
array([[[[124., 118.,  94.],
         [125., 123., 102.],
         [112., 106.,  92.],
         ...,
         [ 10.,  17.,   9.],
         [  8.,  18.,   9.],
         [  7.,  19.,   9.]],

        [[111., 106.,  84.],
         [122., 121., 100.],
         [115., 112.,  95.],
         ...,
         [ 10.,  17.,   9.],
         [  8.,  18.,   9.],
         [  8.,  18.,   9.]],

        [[108., 103.,  84.],
         [124., 123., 103.],
         [118., 116.,  95.],
         ...,
         [ 10.,  17.,   9.],
         [ 10.,  17.,   9.],
         [ 11.,  16.,   9.]],

        ...,

        [[163., 127., 129.],
         [157., 118., 119.],
         [ 26.,  37.,  20.],
         ...,
         [  6.,  11.,   5.],
         [  6.,  11.,   5.],
         [  5.,  10.,   4.]],

        [[ 94., 117.,  99.],
         [ 48.,  50.,  39.],
         [ 24.,  37.,  20.],
         ...,
         [  6.,  11.,   5.],
         [  6.,  11.,   5.],
         [  6.,  11.,   4.]],

        [[ 36.,  48.,  34.],
         [ 21.,  27.,  15.],
         [ 25.,  36.,  20.],
         ...,
         [  6.,  11.,   5.],
         [  9.,  12.,   5.],
         [  9.,  12.,   5.]]],


       [[[ 68.,  61.,  53.],
         [ 67.,  58.,  51.],
         [ 73.,  63.,  54.],
         ...,
         [ 92., 101.,  56.],
         [ 72.,  95.,  41.],
         [ 67.,  94.,  41.]],

        [[ 64.,  55.,  48.],
         [ 64.,  55.,  46.],
         [ 75.,  62.,  54.],
         ...,
         [ 95., 100.,  59.],
         [ 72.,  88.,  41.],
         [ 68.,  87.,  41.]],

        [[ 63.,  53.,  44.],
         [ 66.,  56.,  47.],
         [ 79.,  64.,  57.],
         ...,
         [103., 103.,  67.],
         [ 76.,  82.,  44.],
         [ 74.,  82.,  45.]],

        ...,

        [[133., 126.,  80.],
         [132., 128.,  81.],
         [138., 133.,  93.],
         ...,
         [ 99.,  99.,  91.],
         [ 92.,  89.,  80.],
         [ 84.,  80.,  71.]],

        [[132., 126.,  78.],
         [131., 127.,  79.],
         [134., 130.,  85.],
         ...,
         [104., 105., 100.],
         [ 92.,  92.,  84.],
         [ 85.,  82.,  73.]],

        [[131., 125.,  75.],
         [129., 126.,  75.],
         [131., 127.,  80.],
         ...,
         [114., 114., 112.],
         [102., 102.,  94.],
         [ 94.,  94.,  84.]]],


       [[[177., 196., 104.],
         [135., 159., 123.],
         [135., 159., 123.],
         ...,
         [ 19.,  43., 151.],
         [ 19.,  43., 151.],
         [ 19.,  43., 151.]],

        [[181., 202.,  99.],
         [139., 158., 112.],
         [139., 158., 112.],
         ...,
         [ 20.,  47., 150.],
         [ 20.,  47., 150.],
         [ 20.,  47., 150.]],

        [[165., 183.,  65.],
         [190., 210.,  97.],
         [190., 210.,  97.],
         ...,
         [ 20.,  48., 148.],
         [ 20.,  48., 148.],
         [ 20.,  48., 148.]],

        ...,

        [[201., 194.,  44.],
         [205., 200.,  48.],
         [205., 200.,  48.],
         ...,
         [101.,  97., 112.],
         [101.,  97., 112.],
         [120., 111., 138.]],

        [[196., 191.,  27.],
         [186., 181.,  15.],
         [186., 181.,  15.],
         ...,
         [ 80.,  80.,  82.],
         [ 80.,  80.,  82.],
         [ 53.,  52.,  50.]],

        [[201., 192.,  39.],
         [205., 196.,  39.],
         [205., 196.,  39.],
         ...,
         [125., 127., 114.],
         [125., 127., 114.],
         [ 87.,  89.,  68.]]],


       ...,


       [[[152., 178.,  68.],
         [147., 172.,  44.],
         [148., 174.,  51.],
         ...,
         [ 47.,  86.,   5.],
         [ 55.,  80.,  22.],
         [ 14.,  28.,   3.]],

        [[159., 178.,  52.],
         [155., 184.,  56.],
         [147., 182.,  62.],
         ...,
         [ 49.,  84.,   4.],
         [ 13.,  29.,   0.],
         [ 37.,  43.,  31.]],

        [[166., 203.,  37.],
         [158., 192.,  43.],
         [120., 153.,  20.],
         ...,
         [ 53.,  79.,  32.],
         [ 37.,  53.,   8.],
         [ 20.,  29.,   0.]],

        ...,

        [[106., 140., 105.],
         [ 83.,  96.,  68.],
         [ 33.,  21.,   9.],
         ...,
         [ 59.,  69.,  35.],
         [ 45.,  77.,  12.],
         [ 41.,  67.,  80.]],

        [[171., 208., 157.],
         [ 74., 107.,  50.],
         [  5.,   3.,  16.],
         ...,
         [ 63.,  71.,  34.],
         [ 30.,  58.,   7.],
         [ 36.,  64.,  68.]],

        [[191., 209., 171.],
         [104., 142.,  65.],
         [  2.,   4.,  25.],
         ...,
         [ 57.,  76.,  31.],
         [ 39.,  57.,  19.],
         [ 47.,  61.,  48.]]],


       [[[255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.],
         ...,
         [255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.]],

        [[255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.],
         ...,
         [255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.]],

        [[255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.],
         ...,
         [255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.]],

        ...,

        [[255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.],
         ...,
         [255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.]],

        [[255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.],
         ...,
         [255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.]],

        [[255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.],
         ...,
         [255., 255., 255.],
         [255., 255., 255.],
         [255., 255., 255.]]],


       [[[ 92., 131., 164.],
         [ 91., 130., 163.],
         [ 91., 130., 163.],
         ...,
         [ 74., 110., 146.],
         [ 74., 110., 146.],
         [ 74., 110., 146.]],

        [[ 92., 131., 164.],
         [ 91., 130., 163.],
         [ 91., 130., 163.],
         ...,
         [ 74., 110., 146.],
         [ 74., 110., 146.],
         [ 74., 110., 146.]],

        [[ 94., 130., 164.],
         [ 94., 130., 164.],
         [ 94., 130., 164.],
         ...,
         [ 74., 110., 146.],
         [ 74., 110., 146.],
         [ 73., 109., 145.]],

        ...,

        [[ 94., 139., 181.],
         [ 94., 137., 180.],
         [ 94., 137., 180.],
         ...,
         [ 79., 107.,  92.],
         [ 79., 107.,  92.],
         [ 77., 107.,  83.]],

        [[ 99., 138., 179.],
         [ 99., 138., 179.],
         [ 99., 138., 179.],
         ...,
         [ 81., 111., 109.],
         [ 81., 111., 109.],
         [ 79., 110., 104.]],

        [[105., 140., 178.],
         [108., 142., 179.],
         [108., 142., 179.],
         ...,
         [ 84., 114., 124.],
         [ 84., 114., 124.],
         [ 80., 110., 118.]]]], dtype=float32)>, <tf.Tensor: shape=(32, 5), dtype=float32, numpy=
array([[0., 1., 0., 0., 0.],
       [0., 0., 0., 1., 0.],
       [0., 1., 0., 0., 0.],
       [1., 0., 0., 0., 0.],
       [0., 0., 0., 0., 1.],
       [0., 1., 0., 0., 0.],
       [0., 0., 0., 1., 0.],
       [1., 0., 0., 0., 0.],
       [0., 0., 0., 0., 1.],
       [0., 0., 1., 0., 0.],
       [0., 0., 0., 1., 0.],
       [1., 0., 0., 0., 0.],
       [0., 0., 0., 0., 1.],
       [0., 0., 0., 1., 0.],
       [0., 0., 0., 0., 1.],
       [0., 0., 0., 1., 0.],
       [0., 0., 0., 0., 1.],
       [0., 0., 0., 0., 1.],
       [0., 0., 0., 1., 0.],
       [1., 0., 0., 0., 0.],
       [0., 0., 1., 0., 0.],
       [0., 1., 0., 0., 0.],
       [0., 0., 0., 0., 1.],
       [0., 0., 0., 0., 1.],
       [1., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0.],
       [0., 0., 0., 1., 0.],
       [1., 0., 0., 0., 0.],
       [0., 0., 1., 0., 0.],
       [0., 0., 0., 0., 1.],
       [0., 0., 0., 0., 1.],
       [1., 0., 0., 0., 0.]], dtype=float32)>)

전처리 방식

1. inside model

- 학습과 전처리를 동시에 하기 때문에 시간이 많이든다

2. outside model

- 모델 밖에서 전처리를 할 경우 번거롭고 실수가 발생할 가능성이 높다

tf.keras.layers.experimental.preprocessing(전처리 layer)를 사용할 경우 모델 안에서, 밖에서 유동적으로 사용 가능하다

=> 레이어는 함수이기 때문에 map과 함께 쓸수 있다

경량화 모델

m1 = tf.keras.applications.MobileNet()

m1.summary()
Model: "mobilenet_1.00_224"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
conv1 (Conv2D)               (None, 112, 112, 32)      864       
_________________________________________________________________
conv1_bn (BatchNormalization (None, 112, 112, 32)      128       
_________________________________________________________________
conv1_relu (ReLU)            (None, 112, 112, 32)      0         
_________________________________________________________________
conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       
_________________________________________________________________
conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       
_________________________________________________________________
conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         
_________________________________________________________________
conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      
_________________________________________________________________
conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       
_________________________________________________________________
conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         
_________________________________________________________________
conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         
_________________________________________________________________
conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       
_________________________________________________________________
conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       
_________________________________________________________________
conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         
_________________________________________________________________
conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      
_________________________________________________________________
conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       
_________________________________________________________________
conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         
_________________________________________________________________
conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      
_________________________________________________________________
conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       
_________________________________________________________________
conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         
_________________________________________________________________
conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     
_________________________________________________________________
conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       
_________________________________________________________________
conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         
_________________________________________________________________
conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         
_________________________________________________________________
conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      
_________________________________________________________________
conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       
_________________________________________________________________
conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         
_________________________________________________________________
conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     
_________________________________________________________________
conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      
_________________________________________________________________
conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         
_________________________________________________________________
conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      
_________________________________________________________________
conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      
_________________________________________________________________
conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         
_________________________________________________________________
conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     
_________________________________________________________________
conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      
_________________________________________________________________
conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         
_________________________________________________________________
conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         
_________________________________________________________________
conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      
_________________________________________________________________
conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      
_________________________________________________________________
conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         
_________________________________________________________________
conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    
_________________________________________________________________
conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      
_________________________________________________________________
conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    
_________________________________________________________________
conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      
_________________________________________________________________
conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    
_________________________________________________________________
conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      
_________________________________________________________________
conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    
_________________________________________________________________
conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      
_________________________________________________________________
conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    
_________________________________________________________________
conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      
_________________________________________________________________
conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    
_________________________________________________________________
conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         
_________________________________________________________________
conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      
_________________________________________________________________
conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      
_________________________________________________________________
conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         
_________________________________________________________________
conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    
_________________________________________________________________
conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      
_________________________________________________________________
conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         
_________________________________________________________________
conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      
_________________________________________________________________
conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      
_________________________________________________________________
conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         
_________________________________________________________________
conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   
_________________________________________________________________
conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      
_________________________________________________________________
conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         
_________________________________________________________________
global_average_pooling2d (Gl (None, 1024)              0         
_________________________________________________________________
reshape_1 (Reshape)          (None, 1, 1, 1024)        0         
_________________________________________________________________
dropout (Dropout)            (None, 1, 1, 1024)        0         
_________________________________________________________________
conv_preds (Conv2D)          (None, 1, 1, 1000)        1025000   
_________________________________________________________________
reshape_2 (Reshape)          (None, 1000)              0         
_________________________________________________________________
predictions (Activation)     (None, 1000)              0         
=================================================================
Total params: 4,253,864
Trainable params: 4,231,976
Non-trainable params: 21,888
_________________________________________________________________

tf.keras.utils.plot_model(m1,show_layer_names=True)

Convolution 4가지

1. Convolution

2. Grouped convolution

- 연산량을 줄이기 위해서 convolution연산을 분할 한다

3. 1x1 convolution

- 차원을 축소하고 non-linearity 특성을 활용하기 위해 elementwise 연산을 하는 convolution 방법

4. Depth-wise convolution

- 채널별로 나누어 convolution 연산을 한다

- 같은 depth끼리 연산한다

5. Depth-wise seperable convolution - Depthwise convolution + 1x1 convolution

Depth-wise convolution

Depth wise convolution은 각 채널별로 연산하는 방식으로

Standard convolution이 각 채널만의 spatial feature를 추출하는것이 불가능하기 때문에 고안해낸 방법이다

각 채널별로 연산하기 때문에 한번에 연산하는 파라미터의 수를 줄일 수 있는 장점이 있다

depth wise convolution은 일반 convolution보다 성능은 안좋지만 경량화에 목적이 있기 때문에 성능을 어느정도 감수해야 한다

가능하다면 성능을 유지하면서 경량화 하는 것을 최대 목표로 삼아야 한다

하지만 depth를 맞춰야 한다는 단점이 있다

Depth-wise seperable convolution

Depthwise convolution + 1x1 convolution

m2 = tf.keras.applications.MobileNetV2() # ResNet 방식을 따른다

tf.keras.utils.plot_model(m2 ,show_layer_names=True)

x = tf.keras.layers.BatchNormalization()
x.build((None,2))
x(tf.constant([[1. ,2.]]))

# <tf.Tensor: shape=(1, 2), dtype=float32, numpy=array([[0.99950033, 1.9990007 ]], dtype=float32)>

x.weights # gamma, beta는 학습을 통해서 값을 찾는다 
# [<tf.Variable 'gamma:0' shape=(2,) dtype=float32, numpy=array([1., 1.], dtype=float32)>,
#  <tf.Variable 'beta:0' shape=(2,) dtype=float32, numpy=array([0., 0.], dtype=float32)>,
#  <tf.Variable 'moving_mean:0' shape=(2,) dtype=float32, numpy=array([0., 0.], dtype=float32)>,
#  <tf.Variable 'moving_variance:0' shape=(2,) dtype=float32, numpy=array([1., 1.], dtype=float32)>]

x.trainable_weights
# [<tf.Variable 'gamma:0' shape=(2,) dtype=float32, numpy=array([1., 1.], dtype=float32)>,
#  <tf.Variable 'beta:0' shape=(2,) dtype=float32, numpy=array([0., 0.], dtype=float32)>]

x.non_trainable_weights # 단순 계산 
# [<tf.Variable 'moving_mean:0' shape=(2,) dtype=float32, numpy=array([0., 0.], dtype=float32)>,
#  <tf.Variable 'moving_variance:0' shape=(2,) dtype=float32, numpy=array([1., 1.], dtype=float32)>]

input_ = tf.keras.Input((28,28,1))
x = tf.keras.layers.MaxPool2D(2,2)(input_)
model = tf.keras.models.Model(input_,x)

model.summary() # pooling은 연산만 하기 때문에 파라미터가 없다 / 학습과 관련이 없다 
Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 14, 14, 1)         0         
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
_________________________________________________________________

data_augmentation = tf.keras.Sequential(
    [
        tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal"),
        tf.keras.layers.experimental.preprocessing.RandomRotation(0.1),
    ]
)

preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input

IMG_SHAPE = IMG_SIZE + (3,)
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
                                               include_top=False,
                                               weights='imagenet')

global_average_layer = tf.keras.layers.GlobalAveragePooling2D()

prediction_layer = tf.keras.layers.Dense(1)

inputs = tf.keras.Input(shape=(160,160,3)) # 배치 크기만큼 입력을 받는다 
x = data_augmentation(inputs) # augmentation 사용 
x = preprocess_input(x)  # 전처리 
x = base_model(x, training=False) # training = False이기 때문에 내부적으로 평균과 표준편차를 구해서 배치마다 다른 평균과 표준편차를 사용할 수 있게 된다 
x = global_average_layer(x) 
x = tf.keras.layers.Dropout(0.2)(x)
outputs = prediction_layer(x)   # prediction_layer만 backpropagation을 하고 이전 layer에서는 단순 계산만 한다 
model = tf.keras.Model(inputs, outputs)

base_learning_rate = 0.0001
model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=base_learning_rate),
              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=['accuracy'])
              

model.summary()
Model: "model_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_7 (InputLayer)         [(None, 160, 160, 3)]     0         
_________________________________________________________________
sequential_2 (Sequential)    (None, 160, 160, 3)       0         
_________________________________________________________________
tf.math.truediv_2 (TFOpLambd (None, 160, 160, 3)       0         
_________________________________________________________________
tf.math.subtract_2 (TFOpLamb (None, 160, 160, 3)       0         
_________________________________________________________________
mobilenetv2_1.00_160 (Functi (None, 5, 5, 1280)        2257984   
_________________________________________________________________
global_average_pooling2d_2 ( (None, 1280)              0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 1280)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 1281      
=================================================================
Total params: 2,259,265
Trainable params: 2,225,153
Non-trainable params: 34,112
_________________________________________________________________

import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf

from tensorflow.keras.preprocessing import image_dataset_from_directory

BATCH_SIZE = 32
IMG_SIZE = (160, 160)
initial_epochs = 10

train_dataset = image_dataset_from_directory('cats_and_dogs_filtered/train',
                                             shuffle=True,
                                             batch_size=BATCH_SIZE,
                                             image_size=IMG_SIZE)

validation_dataset = image_dataset_from_directory('cats_and_dogs_filtered/validation',
                                                  shuffle=True,
                                                  batch_size=BATCH_SIZE,
                                                  image_size=IMG_SIZE)
                                                  
# Found 2000 files belonging to 2 classes.
# Found 1000 files belonging to 2 classes.

image_batch, label_batch = next(iter(train_dataset))
feature_batch = base_model(image_batch)
print(feature_batch.shape)
# (32, 5, 5, 1280)

history = model.fit(train_dataset,
                    epochs=initial_epochs,
                    validation_data=validation_dataset)

Epoch 1/10
63/63 [==============================] - 252s 4s/step - loss: 0.3724 - accuracy: 0.8405 - val_loss: 0.1214 - val_accuracy: 0.9730
Epoch 2/10
63/63 [==============================] - 162s 3s/step - loss: 0.1438 - accuracy: 0.9430 - val_loss: 0.0879 - val_accuracy: 0.9520
Epoch 3/10
63/63 [==============================] - 161s 3s/step - loss: 0.1267 - accuracy: 0.9525 - val_loss: 0.0871 - val_accuracy: 0.9580
Epoch 4/10
63/63 [==============================] - 161s 3s/step - loss: 0.0929 - accuracy: 0.9590 - val_loss: 0.0485 - val_accuracy: 0.9770
Epoch 5/10
63/63 [==============================] - 162s 3s/step - loss: 0.0856 - accuracy: 0.9660 - val_loss: 0.3747 - val_accuracy: 0.9060
Epoch 6/10
63/63 [==============================] - 160s 3s/step - loss: 0.0874 - accuracy: 0.9655 - val_loss: 0.0987 - val_accuracy: 0.9780
Epoch 7/10
63/63 [==============================] - 160s 3s/step - loss: 0.0817 - accuracy: 0.9755 - val_loss: 0.0573 - val_accuracy: 0.9760
Epoch 8/10
63/63 [==============================] - 160s 3s/step - loss: 0.0556 - accuracy: 0.9830 - val_loss: 0.0556 - val_accuracy: 0.9810
Epoch 9/10
63/63 [==============================] - 160s 3s/step - loss: 0.0603 - accuracy: 0.9765 - val_loss: 0.0524 - val_accuracy: 0.9810
Epoch 10/10
63/63 [==============================] - 160s 3s/step - loss: 0.0663 - accuracy: 0.9815 - val_loss: 0.0380 - val_accuracy: 0.9840

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.ylabel('Accuracy')
plt.ylim([min(plt.ylim()),1])
plt.title('Training and Validation Accuracy')

plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.ylabel('Cross Entropy')
plt.ylim([0,1.0])
plt.title('Training and Validation Loss')
plt.xlabel('epoch')
plt.show()

Tensorflow에서는 학습할 때 validation dataset을 포함하면

평가시 dropout과 batch normalization을 하지 않고 하기 때문에 validation loss가 더 낮게 나오는 경우가 많이 발생한다

(이런 경우는 underfitting 문제가 아닐 수 있다)

Fine Tuning 고급 테크닉

Fine tuning

기존에 학습된 모델을 기반으로 아키텍쳐를 새로운 목적에 맞게 변형하고 이미 학습된 모델 Weights로 부터 학습을 업데이트하는 방법을 말한다.

1. 어느정도 성능이 확보된 모델을 만든다

2. 일부 Freezon (trainable=False)시키고 느린 learning rate을 통해 나머지를 재학습 시킨다

3. 새 학습 데이터가 기존 학습 데이터와 큰 차이가 나면 안된다

4. 새로 학습할 레이어 초기화 하면 기존 능력이 없어지기 때문에 성능이 확보된 모델 가중치를 그대로 사용한다

최상위 층 고정 해제하기

# Fine tuning이기 때문에 이미 학습된 모델로 사용한다 

base_model.trainable = True # 학습할 수 있도록 우선 만들고 

fine_tune_at = 100

for layer in base_model.layers[:fine_tune_at]: # 맨 마지막 층을 제외하고 고정시킨다 
  layer.trainable = False                      # 맨 마지막 층은 결과를 내는 층 (개, 고양이 분류)

Fine tuning 하는 간단한 방식

1. 가능한 마지막 층 부터 재학습을 한다 (마지막 층들은 전문화된 특징을 분류한다)

1. learning rate 바꾸기

2. epoch 수 줄이기

Catastrophic forgetting

- 다른 종류의 데이터를 학습하면 이전에 학습했던 데이터에 대한 성능이 현저하게 떨어지는 문제

- 이 현상은 이전 학습 dataset과 새로운 학습 dataset 사이에 연관성이 있더라도 이전 dataset에 대한 정보를 대량으로 손실하는 문제가 발생한다

Semantic shift

- 기존에 학습된 모델에 새로운 형태의 데이터가 추가 학습 되었을 때 가중치가 변하면서 의미가 변해버리는 현상

Incremental learning

전혀 다른 특징을 가진 데이터 셋을 학습하여 내가 가진 모델에서 전혀 다른 특징을 가진 데이터를 분류할 수 있는 모델로 능력을 키우는 것

Transfer learning

적은 데이터를 갖고 있더라도 복잡한 문제 상황을 해결할 수 있는 방법
이미 학습된 모델을 통해 적은 데이터를 학습시켜 최적의 성능을 낼수 있는 방법

Layer의 앞부분에서는 간단한 특징들(선, 면)만 탐지할 수 있지만, layer들의 조합으로 인해 점차 복잡한 특징을 탐지해낸다

결국 layer의 뒷부분으로 갈수록 학습한 데이터에 맞춰 특징을 탐지할 수 있도록 변환된다

데이터가 유사하면 유사할 수록 앞단의 layer부분에서는 비슷한 특징을 추출한다

예를 들어서 고양이를 학습한 모델을 활용하여 개를 분류하는 모델로 전이학습을 한다고 한다면 앞부분의 layer는 학습을 시키지 않고 뒷부분만 새로운 데이터인 '개' 데이터로 학습을 시켜 새로운 모델을 만들어 낸다

유사한 데이터를 전이학습하고자 할 때는 특징을 추출하는 Convolutional layer부분은 그대로 사용하고

Fully connected 부분만 변형하여 사용한다

Transfer learning 두 가지 특징

1. Feature extraction

- 비슷한 이미지를 학습한 모델의 convolutional layer만 활용하는 방식

- Fully connected layer는 새로 학습할 데이터에 맞게 새로운 추출기를 사용한다

- 예시) 개와 고양이 분류 모델

2. Fine tuning

- 범용적인 이미지를 학습한 모델을 그대로 사용하되 Fully connected layer 부분을 미세하게 조정하여 사용하는 방법

- 예시) ImageNet 데이터를 학습한 모델을 활용할 때

Fine tuning

import tensorflow as tf 
import numpy as np

vgg = tf.keras.applications.VGG16(include_top=True)

tf.keras.utils.plot_model(vgg, rankdir='BT')

vgg.summary()
Model: "vgg16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              102764544 
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
predictions (Dense)          (None, 1000)              4097000   
=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________

vgg.input, vgg.output, vgg.layers
# (<KerasTensor: shape=(None, 224, 224, 3) dtype=float32 (created by layer 'input_3')>,
 <KerasTensor: shape=(None, 1000) dtype=float32 (created by layer 'predictions')>,
 [<keras.engine.input_layer.InputLayer at 0x7f580e71d090>,
  <keras.layers.convolutional.Conv2D at 0x7f580f887390>,
  <keras.layers.convolutional.Conv2D at 0x7f580e71f110>,
  <keras.layers.pooling.MaxPooling2D at 0x7f580e78f710>,
  <keras.layers.convolutional.Conv2D at 0x7f580f830d10>,
  <keras.layers.convolutional.Conv2D at 0x7f580e788110>,
  <keras.layers.pooling.MaxPooling2D at 0x7f580e7938d0>,
  <keras.layers.convolutional.Conv2D at 0x7f580f821ed0>,
  <keras.layers.convolutional.Conv2D at 0x7f580e771b50>,
  <keras.layers.convolutional.Conv2D at 0x7f580e77a110>,
  <keras.layers.pooling.MaxPooling2D at 0x7f580e76be10>,
  <keras.layers.convolutional.Conv2D at 0x7f580f82ec10>,
  <keras.layers.convolutional.Conv2D at 0x7f580e75a310>,
  <keras.layers.convolutional.Conv2D at 0x7f580e76bbd0>,
  <keras.layers.pooling.MaxPooling2D at 0x7f580e79bcd0>,
  <keras.layers.convolutional.Conv2D at 0x7f580e7cbf50>,
  <keras.layers.convolutional.Conv2D at 0x7f580e79ca90>,
  <keras.layers.convolutional.Conv2D at 0x7f580f8a3250>,
  <keras.layers.pooling.MaxPooling2D at 0x7f5880044250>,
  <keras.layers.core.Flatten at 0x7f5880040b90>,
  <keras.layers.core.Dense at 0x7f5880040890>,
  <keras.layers.core.Dense at 0x7f580e7d8c10>,
  <keras.layers.core.Dense at 0x7f588002ee50>])

#vgg.layers.pop() # 맨 마지막 layer 삭제 # 내가 직접 지정하려고
# <keras.layers.core.Dense at 0x7f580f8308d0>

vgg.layers.append(tf.keras.layers.Dense(5)) # 마지막 layer에 추가

vgg.layers
# [<keras.engine.input_layer.InputLayer at 0x7f5880019110>,
#  <keras.layers.convolutional.Conv2D at 0x7f588003ed90>,
#  <keras.layers.convolutional.Conv2D at 0x7f580f898810>,
#  <keras.layers.pooling.MaxPooling2D at 0x7f580f8ce050>,
#  <keras.layers.convolutional.Conv2D at 0x7f580e7df8d0>,
#  <keras.layers.convolutional.Conv2D at 0x7f580f8c8ad0>,
#  <keras.layers.pooling.MaxPooling2D at 0x7f588003ef10>,
#  <keras.layers.convolutional.Conv2D at 0x7f580e7e7450>,
#  <keras.layers.convolutional.Conv2D at 0x7f588005f410>,
#  <keras.layers.convolutional.Conv2D at 0x7f58d9d08790>,
#  <keras.layers.pooling.MaxPooling2D at 0x7f580e80b0d0>,
#  <keras.layers.convolutional.Conv2D at 0x7f580e7e7750>,
#  <keras.layers.convolutional.Conv2D at 0x7f580f843ed0>,
#  <keras.layers.convolutional.Conv2D at 0x7f580f841350>,
#  <keras.layers.pooling.MaxPooling2D at 0x7f580f83f050>,
#  <keras.layers.convolutional.Conv2D at 0x7f580f83dfd0>,
#  <keras.layers.convolutional.Conv2D at 0x7f580f8381d0>,
#  <keras.layers.convolutional.Conv2D at 0x7f580f83db10>,
#  <keras.layers.pooling.MaxPooling2D at 0x7f580f8349d0>,
#  <keras.layers.core.Flatten at 0x7f580f82e950>,
#  <keras.layers.core.Dense at 0x7f580f82e850>,
#  <keras.layers.core.Dense at 0x7f580f82e190>,
#  <keras.layers.core.Dense at 0x7f580f8308d0>]

mylayer = vgg.layers

vgg.layers[1].trainable
# True

vgg.trainable = False # weight를 학습하지 마세요

mylayer = []
for i in vgg.layers[:-1]:
  i.trainable = False 
  mylayer.append(i)

mylayer.append(tf.keras.layers.Dense(5))

model = tf.keras.models.Sequential(mylayer)

model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              102764544 
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
dense_2 (Dense)              (None, 5)                 20485     
=================================================================
Total params: 134,281,029
Trainable params: 20,485
Non-trainable params: 134,260,544
_________________________________________________________________

성능은 어느정도 원하는 기대치에 도달했으나 모델을 사용하는 것이 현실적으로 쉽지 않았다

정확도가 높으면 메모리 사용량이 많아지고, 연산량이 많아져서 실제로 사용하기에는 적합하지 않았다

그래서 2017년 이후 모델들은 경량화에 집중하기 시작했다

google = tf.keras.applications.InceptionV3()

tf.keras.utils.plot_model(google) 
# GoogLeNet은 복잡하기 때문에 transfer learing으로 사용하기에 부적합한 모델이다 / 
# Fine tuning하기 쉽지 않음

Sequential 방식

vgg = tf.keras.applications.VGG16(include_top=True)
# fine tuning 관점 / # input은 224,224 형태 데이터를 받아야 한다 

vgg.trainable = False # 모든 layer를 학습하지 않도록 설정 

mylayer = vgg.layers

mylayer.pop()
# <keras.layers.core.Dense at 0x7f580e1e7f90>

model = tf.keras.models.Sequential(mylayer + [tf.keras.layers.Dense(5)])
model.summary() # Flatten을 사용하면 데이터 크기가 맞아야 한다 
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              102764544 
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
dense_3 (Dense)              (None, 5)                 20485     
=================================================================
Total params: 134,281,029
Trainable params: 20,485
Non-trainable params: 134,260,544
_________________________________________________________________

Model 방식

input_ = vgg.input
x = vgg.layers[1](input_)
x = vgg.layers[2](x)
x = vgg.layers[3](x) 

import numpy as np

im = tf.keras.preprocessing.image.load_img('people.jpg')
im2 = np.resize(np.array(im), (224,224,3)) # 입력 크기를 맞추기 위해서 크기를 조절한다 

im2.shape
# (224, 224, 3)

model(im2[np.newaxis])
# <tf.Tensor: shape=(1, 5), dtype=float32, numpy=
# array([[ 0.60317594, -0.7978455 ,  2.3856583 , -0.96538734, -0.4498136 ]],
#       dtype=float32)>

Feature extraction

Feature extraction에서는 FC부분이 없기 때문에 FC로 넘겨주기 위한 데이터가 1개인 1차원으로 변경해야 한다

# Hyperparameter로써 접근해야 한다 / 상황에 맞게 선택하여 사용

1. Flatten

2. Global average pooling

vgg2 = tf.keras.applications.VGG16(include_top=False) 
# include_top = False로 지정하면 분류 층이 포함되지 않은 네트워크를 로드하므로 특징 추출에 이상적이다

vgg2.summary() # 크기 맞출 필요 없다 
Model: "vgg16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_8 (InputLayer)         [(None, None, None, 3)]   0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, None, None, 128)   73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, None, None, 128)   147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, None, None, 128)   0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, None, None, 256)   295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, None, None, 256)   0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, None, None, 512)   0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, None, None, 512)   0         
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________

vgg2.trainable = False 
vgg2.layers[0] = tf.keras.layers.InputLayer((224,224,3))
layers = vgg2.layers+[tf.keras.layers.GlobalAvgPool2D()] # flatten과 달리 입력 크기와 상관없이 결과 낼수 있는 모델을 만들 수 있다 
layers = layers + [tf.keras.layers.Dense(5)]
model2 = tf.keras.models.Sequential(layers) 

model2(np.array(im)[np.newaxis])
# <tf.Tensor: shape=(1, 5), dtype=float32, numpy=
# array([[-1.9419088,  6.326975 , -2.2549076, -3.0776834,  1.4872327]],
#       dtype=float32)>

model2(im2[np.newaxis])
# <tf.Tensor: shape=(1, 5), dtype=float32, numpy=
# array([[ 2.4478257 ,  1.1684433 , -8.583718  , -0.92841846,  6.4567146 ]],
#       dtype=float32)>

model2.summary()

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, None, None, 128)   73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, None, None, 128)   147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, None, None, 128)   0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, None, None, 256)   295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, None, None, 256)   0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, None, None, 512)   0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, None, None, 512)   0         
_________________________________________________________________
global_average_pooling2d (Gl (None, 512)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 5)                 2565      
=================================================================
Total params: 14,717,253
Trainable params: 2,565
Non-trainable params: 14,714,688
_________________________________________________________________

vgg2 = tf.keras.applications.VGG16(include_top=False, input_shape=(224,224,3)) 
vgg2.trainable = False 
layers = vgg2.layers+[tf.keras.layers.GlobalAvgPool2D()] 
layers = layers + [tf.keras.layers.Dense(5)]
model2 = tf.keras.models.Sequential(layers)

model2.summary()
Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
global_average_pooling2d_1 ( (None, 512)               0         
_________________________________________________________________
dense_5 (Dense)              (None, 5)                 2565      
=================================================================
Total params: 14,717,253
Trainable params: 2,565
Non-trainable params: 14,714,688
_________________________________________________________________

Transfer learning + preprocessing layer

im = tf.keras.preprocessing.image.load_img('people.jpg')
im2 = np.resize(np.array(im), (224,224,3)) 

# vgg는 채널별로 평균을 내서 빼준다 (Zero centered)
tf.keras.applications.vgg16.preprocess_input(im2) 

array([[[ 73.061    ,  72.221    ,  77.32     ],
        [ 75.061    ,  74.221    ,  79.32     ],
        [ 76.061    ,  75.221    ,  80.32     ],
        ...,
        [  4.060997 ,  26.221    ,  45.32     ],
        [  7.060997 ,  29.221    ,  48.32     ],
        [ 12.060997 ,  34.221    ,  53.32     ]],

       [[ 15.060997 ,  29.221    ,  43.32     ],
        [ 10.060997 ,  28.221    ,  44.32     ],
        [  7.060997 ,  26.221    ,  48.32     ],
        ...,
        [ 67.061    ,  84.221    , 102.32     ],
        [106.061    , 123.221    , 131.32     ],
        [ 56.060997 ,  75.221    ,  91.32     ]],

       [[ 41.060997 ,  61.221    ,  87.32     ],
        [ 43.060997 ,  63.221    ,  89.32     ],
        [ 45.060997 ,  65.221    ,  91.32     ],
        ...,
        [ 29.060997 ,  28.221    ,  33.32     ],
        [ 29.060997 ,  34.221    ,  44.32     ],
        [ 38.060997 ,  49.221    ,  64.32     ]],

       ...,

       [[  5.060997 ,  22.221    ,  40.32     ],
        [  6.060997 ,  23.221    ,  41.32     ],
        [  6.060997 ,  23.221    ,  41.32     ],
        ...,
        [-43.939003 , -49.779    , -53.68     ],
        [-37.939003 , -46.779    , -52.68     ],
        [-47.939003 , -58.779    , -64.68     ]],

       [[-46.939003 , -61.779    , -68.68     ],
        [-38.939003 , -53.779    , -60.68     ],
        [-39.939003 , -54.779    , -61.68     ],
        ...,
        [ 31.060997 ,  46.221    ,  63.32     ],
        [ 30.060997 ,  45.221    ,  62.32     ],
        [ 32.060997 ,  45.221    ,  62.32     ]],

       [[ 31.060997 ,  45.221    ,  64.32     ],
        [ 31.060997 ,  45.221    ,  64.32     ],
        [ 29.060997 ,  44.221    ,  61.32     ],
        ...,
        [-20.939003 , -25.779    ,  -3.6800003],
        [-11.939003 , -18.779    ,   3.3199997],
        [ -3.939003 , -10.778999 ,  11.32     ]]], dtype=float32)

pretrain_model = tf.keras.applications.VGG16(include_top=False, input_shape=(224,224,3))
pretrain_model.trainable = False

model = tf.keras.models.Sequential([
    tf.keras.layers.Lambda(lambda data: tf.keras.applications.vgg16.preprocess_input(
        tf.cast(data, tf.float32)), input_shape=(224,224,3) # data n개씩 처리 할 수 있도록 lambda layer를 사용한다  
    ), 
    pretrain_model,
    tf.keras.layers.GlobalAvgPool2D(),
    tf.keras.layers.Dense(5)
]) # 전처리를 하지 않는 실수를 방지할 수 있다

model.summary()
Model: "sequential_6"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lambda_2 (Lambda)            (None, 224, 224, 3)       0         
_________________________________________________________________
vgg16 (Functional)           (None, 7, 7, 512)         14714688  
_________________________________________________________________
global_average_pooling2d_2 ( (None, 512)               0         
_________________________________________________________________
dense_8 (Dense)              (None, 5)                 2565      
=================================================================
Total params: 14,717,253
Trainable params: 2,565
Non-trainable params: 14,714,688
_________________________________________________________________

Zero centered

Type Markdown and LaTeX: 𝛼2

x = tf.keras.layers.Dense(5)

x.weights, x.trainable_weights, x.non_trainable_weights
# ([], [], [])

x(tf.constant([[1,2,],[3,4,]]))
# <tf.Tensor: shape=(2, 5), dtype=float32, numpy=
# array([[-0.12723184,  1.5356824 , -0.45605123, -1.7108853 , -2.0955188 ],
#        [ 0.3099841 ,  2.8317063 , -0.42237318, -3.7084074 , -4.6590133 ]],
#       dtype=float32)>

x.weights, x.trainable_weights, x.non_trainable_weights

# ([<tf.Variable 'dense_14/kernel:0' shape=(2, 5) dtype=float32, numpy=
#   array([[ 0.56444776, -0.23965877,  0.4897293 , -0.28663665, -0.46797565],
#          [-0.3458398 ,  0.88767064, -0.47289026, -0.71212435, -0.81377155]],
#         dtype=float32)>,
#   <tf.Variable 'dense_14/bias:0' shape=(5,) dtype=float32, numpy=array([0., 0., 0., 0., 0.], dtype=float32)>],
#  [<tf.Variable 'dense_14/kernel:0' shape=(2, 5) dtype=float32, numpy=
#   array([[ 0.56444776, -0.23965877,  0.4897293 , -0.28663665, -0.46797565],
#          [-0.3458398 ,  0.88767064, -0.47289026, -0.71212435, -0.81377155]],
#         dtype=float32)>,
#   <tf.Variable 'dense_14/bias:0' shape=(5,) dtype=float32, numpy=array([0., 0., 0., 0., 0.], dtype=float32)>],
#  [])

y = tf.keras.layers.BatchNormalization()

y.built
# False

y.build((None,4))

y.built
# True

y.weights 
# [<tf.Variable 'gamma:0' shape=(4,) dtype=float32, numpy=array([1., 1., 1., 1.], dtype=float32)>,
#  <tf.Variable 'beta:0' shape=(4,) dtype=float32, numpy=array([0., 0., 0., 0.], dtype=float32)>,
#  <tf.Variable 'moving_mean:0' shape=(4,) dtype=float32, numpy=array([0., 0., 0., 0.], dtype=float32)>,
#  <tf.Variable 'moving_variance:0' shape=(4,) dtype=float32, numpy=array([1., 1., 1., 1.], dtype=float32)>]

# 이동평균과 표준편차는 학습을 통해 구하는 것이 아니기 때문에 trainable weight가 아니다
y.trainable_weights  
# [<tf.Variable 'gamma:0' shape=(4,) dtype=float32, numpy=array([1., 1., 1., 1.], dtype=float32)>,
#  <tf.Variable 'beta:0' shape=(4,) dtype=float32, numpy=array([0., 0., 0., 0.], dtype=float32)>]

y.trainable = False
y.trainable_weights
# []

y.non_trainable_weights
# [<tf.Variable 'gamma:0' shape=(4,) dtype=float32, numpy=array([1., 1., 1., 1.], dtype=float32)>,
#  <tf.Variable 'beta:0' shape=(4,) dtype=float32, numpy=array([0., 0., 0., 0.], dtype=float32)>,
#  <tf.Variable 'moving_mean:0' shape=(4,) dtype=float32, numpy=array([0., 0., 0., 0.], dtype=float32)>,
#  <tf.Variable 'moving_variance:0' shape=(4,) dtype=float32, numpy=array([1., 1., 1., 1.], dtype=float32)>]

model(im2[tf.newaxis], training=True)
# <tf.Tensor: shape=(1, 5), dtype=float32, numpy=
# array([[-1.6044631,  4.722974 ,  2.0047941, -2.9608102,  1.3918904]],
#       dtype=float32)>

Batch normalization할 때 layer.trainable = False를 설정하면 학습하지 않고 이동평균을 그대로 사용하게 된다

따라서 학습 상태를 유지하기 위해서 batch normalization layer를 포함하는 모델을 동결 해제 하면

기본 모델을 예측(호출)할 때 training = False를 전달해야 한다

Image pyramid

object를 인식하기 위해 window scale에 변화도 줘는 와중에

image scale을 조정하게 됨, image가 점차 작아지면 window scale은 상대적으로 커져서 같은 크기로 큰 object를 찾을 수 있음

sliding window가 작은 상태에서 보면 IOU가 너무 작은 상태로 나와서 의미가 없는 수치임.

feature map이 작아지면서 추상화됨. 추상화된 상태에서 detect 하면 큰 것도 찾을 수 있음

=> 그 결과를 합침

Anchor box를 활용한 object detection

feature map에서 활성화된 상태에서 anchor box로 1) feature map영역에서의 object 클래스 분류 2) gtbox위치를 예측할 수 있도록 수정된 좌표 등 정보 학습

 이상태에서 test data가 들어오면 예측

개별 anchor box별로 detectin 하려는 object 유형 softmax값, 수정 좌표값

two stage의 큰 차이는 Region Proposal로 영역 추천하는 단계가 있는가의 차이이다.

- selective search

- RPN

- anchor box

=> feature map을 object detector에게 보내서 예측 수행

하지만 최신모델인 faster RCNN도 수행속도가 7fpn이나 걸린다.

그래서 시간을 줄이기 위해 one step detector 연구가 시작됨

=> yolo가 등장하는데 4배의 시간단축 성공함. 하지만 mAP가 떨어짐

이후의 SSD는 당시 YOLO보다 수행속도도 빠르고 mAP도 높은 모델로 등장함.

그리고 YOLO는 독자적인 모델로 지속적으로 개선을 해냄. V5까지

RetinaNet은 수행속도는 조금 낮지만 정밀도에 집중한 모델

fps가 높을수록 빠른 수행속도, mAP가 높을수록 높은 정밀도

SSD Network 구조

VGG같은 CNN으로 결과로 나온 scale이 작은 feature map에서 추출한 anchor box에 기반한 detection model 임

주요구성요소로 1) MultiScale Feature Layer, 2) Default(Anchor) Box 등임

특히 Default Anchor box가 one step detector에 큰영향을 끼침

테스트 데이터 세트에 Inference 및 Evaluation 적용하기

• 런타임 다시 시작 수행 필요(버그?)
• tools/test.py 스크립트는 colab에서 오류 발생
• 테스트용 Dataset과 DataLoader생성하고 single_gpu_test()를 호출하여 inference 결과를 반환. batch_size를 1로 설정하지 않으면 single_gpu_test() 오류 발생.

### 아래는 런타임 다시 시작 후 실행

from mmcv import Config
from mmdet.datasets.builder import DATASETS
from mmdet.datasets.coco import CocoDataset
from mmdet.apis import set_random_seed

@DATASETS.register_module(force=True)
class BCCDDataset(CocoDataset):
  CLASSES = ('WBC', 'RBC', 'Platelets') 

config_file = '/content/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
checkpoint_file = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'

cfg = Config.fromfile(config_file)

# dataset에 대한 환경 파라미터 수정. 
cfg.dataset_type = 'BCCDDataset'
cfg.data_root = '/content/BCCD_Dataset/BCCD/'

# train, val, test dataset에 대한 type, data_root, ann_file, img_prefix 환경 파라미터 수정. 
cfg.data.train.type = 'BCCDDataset'
cfg.data.train.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.train.ann_file = 'train.json'
cfg.data.train.img_prefix = 'JPEGImages'

cfg.data.val.type = 'BCCDDataset'
cfg.data.val.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.val.ann_file = 'val.json'
cfg.data.val.img_prefix = 'JPEGImages'

cfg.data.test.type = 'BCCDDataset'
cfg.data.test.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.test.ann_file = 'test.json'
cfg.data.test.img_prefix = 'JPEGImages'

# class의 갯수 수정. 
cfg.model.roi_head.bbox_head.num_classes = 3
# pretrained 모델
cfg.load_from = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'

# 학습 weight 파일로 로그를 저장하기 위한 디렉토리 설정. 
cfg.work_dir = './tutorial_exps'

# 학습율 변경 환경 파라미터 설정. 
cfg.optimizer.lr = 0.02 / 8
cfg.lr_config.warmup = None
cfg.log_config.interval = 10

# CocoDataset의 경우 metric을 bbox로 설정해야 함.(mAP아님. bbox로 설정하면 mAP를 iou threshold를 0.5 ~ 0.95까지 변경하면서 측정)
cfg.evaluation.metric = 'bbox'
cfg.evaluation.interval = 12
cfg.checkpoint_config.interval = 12

# bug(?)로 인해 test용 dataset evaluation 시 1로 설정. data loader에서 GPU갯수별 Batch size 임. 
cfg.data.samples_per_gpu = 1

# 두번 config를 로드하면 lr_config의 policy가 사라지는 오류로 인하여 설정. 
cfg.lr_config.policy='step'
# Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
print(cfg.pretty_text)


#model = dict(
    type='FasterRCNN',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=3,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100)))
dataset_type = 'BCCDDataset'
data_root = '/content/BCCD_Dataset/BCCD/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=1,
    workers_per_gpu=2,
    train=dict(
        type='BCCDDataset',
        ann_file='train.json',
        img_prefix='JPEGImages',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        data_root='/content/BCCD_Dataset/BCCD/'),
    val=dict(
        type='BCCDDataset',
        ann_file='val.json',
        img_prefix='JPEGImages',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        data_root='/content/BCCD_Dataset/BCCD/'),
    test=dict(
        type='BCCDDataset',
        ann_file='test.json',
        img_prefix='JPEGImages',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        data_root='/content/BCCD_Dataset/BCCD/'))
evaluation = dict(interval=12, metric='bbox')
optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup=None,
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=12)
log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
resume_from = None
workflow = [('train', 1)]
work_dir = './tutorial_exps'
seed = 0
gpu_ids = range(0, 1)

cfg.dump('/content/tutorial_exps/bccd_faster_rcnn_conf.py')

!mkdir -p /content/show_test_output

# tools/test.py 는 colab에서 제대로 동작하지 않음. 
%cd /content/mmdetection
!python tools/test.py /content/tutorial_exps/bccd_faster_rcnn_conf.py /content/tutorial_exps/epoch_12.pth \
--eval 'bbox' \
--show-dir /content/show_test_output

# /content/mmdetection
# Traceback (most recent call last):
#   File "tools/test.py", line 238, in <module>
#     main()
#   File "tools/test.py", line 177, in main
#     dataset = build_dataset(cfg.data.test)
#   File "/usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/datasets/builder.py", line 78, in build_dataset
#     dataset = build_from_cfg(cfg, DATASETS, default_args)
#   File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 45, in build_from_cfg
#     f'{obj_type} is not in the {registry.name} registry')
# KeyError: 'BCCDDataset is not in the dataset registry'

테스트용 dataset와 dataloader를 별도로 설정하고 trained된 checkpoint 모델을 로딩하여 test 수행.

from mmdet.datasets import (build_dataloader, build_dataset,
                            replace_ImageToTensor)

# test용 Dataset과 DataLoader 생성. 
# build_dataset()호출 시 list로 감싸지 않는 것이 train용 dataset 생성시와 차이. 
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
        dataset,
        # 반드시 아래 samples_per_gpu 인자값은 1로 설정
        samples_per_gpu=cfg.data.samples_per_gpu,
        workers_per_gpu=cfg.data.workers_per_gpu,
        dist=False,
        shuffle=False)

# 반드시 아래 코드에서 'img' 키값이 tensor로 출력되어야 함. 
next(iter(data_loader))


# loading annotations into memory...
# Done (t=0.01s)
# creating index...
# index created!
# {'img': [tensor([[[[0.9474, 0.9474, 0.9474,  ..., 0.0000, 0.0000, 0.0000],
#             [0.9474, 0.9474, 0.9474,  ..., 0.0000, 0.0000, 0.0000],
#             [0.9646, 0.9646, 0.9646,  ..., 0.0000, 0.0000, 0.0000],
#             ...,
#             [1.1700, 1.1700, 1.1700,  ..., 0.0000, 0.0000, 0.0000],
#             [1.1700, 1.1700, 1.1700,  ..., 0.0000, 0.0000, 0.0000],
#             [1.1700, 1.1700, 1.1700,  ..., 0.0000, 0.0000, 0.0000]],
#   
#            [[1.2031, 1.2031, 1.2031,  ..., 0.0000, 0.0000, 0.0000],
#             [1.2031, 1.2031, 1.2031,  ..., 0.0000, 0.0000, 0.0000],
#             [1.2206, 1.2206, 1.2206,  ..., 0.0000, 0.0000, 0.0000],
#             ...,
#             [1.5007, 1.5007, 1.5007,  ..., 0.0000, 0.0000, 0.0000],
#             [1.5007, 1.5007, 1.5007,  ..., 0.0000, 0.0000, 0.0000],
#             [1.5007, 1.5007, 1.5007,  ..., 0.0000, 0.0000, 0.0000]],
#   
#            [[1.3851, 1.3851, 1.3851,  ..., 0.0000, 0.0000, 0.0000],
#             [1.3851, 1.3851, 1.3851,  ..., 0.0000, 0.0000, 0.0000],
#             [1.4025, 1.3851, 1.3677,  ..., 0.0000, 0.0000, 0.0000],
#             ...,
#             [1.5768, 1.5768, 1.5768,  ..., 0.0000, 0.0000, 0.0000],
#             [1.5768, 1.5768, 1.5768,  ..., 0.0000, 0.0000, 0.0000],
#             [1.5768, 1.5768, 1.5768,  ..., 0.0000, 0.0000, 0.0000]]]])],
#  'img_metas': [DataContainer([[{'filename': '/content/BCCD_Dataset/BCCD/JPEGImages/BloodImage_00007.jpg', 'ori_filename': 'BloodImage_00007.jpg', 'ori_shape': (480, 640, 3), 'img_shape': (800, 1067, 3), 'pad_shape': (800, 1088, 3), 'scale_factor': array([1.6671875, 1.6666666, 1.6671875, 1.6666666], dtype=float32), 'flip': False, 'flip_direction': None, 'img_norm_cfg': {'mean': array([123.675, 116.28 , 103.53 ], dtype=float32), 'std': array([58.395, 57.12 , 57.375], dtype=float32), 'to_rgb': True}}]])]}

from mmdet.apis import inference_detector, init_detector, show_result_pyplot

checkpoint_file = '/content/tutorial_exps/epoch_12.pth'

# checkpoint 저장된 model 파일을 이용하여 모델을 생성, 이때 Config는 위에서 update된 config 사용. 
model_ckpt = init_detector(cfg, checkpoint_file, device='cuda:0')

# /usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/builder.py:17: UserWarning: ``build_anchor_generator`` would be deprecated soon, please use ``build_prior_generator`` 
#   '``build_anchor_generator`` would be deprecated soon, please use '
# Use load_from_local loader

from mmdet.apis import multi_gpu_test, single_gpu_test
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmdet.apis import inference_detector, init_detector, show_result_pyplot

model_ckpt = MMDataParallel(model_ckpt, device_ids=[0])
# single_gpu_test() 를 호출하여 test데이터 세트의 interence 수행. 반드시 batch size는 1이 되어야 함. 
# 위에서 만든 /content/show_test_output 디렉토리에 interence 결과가 시각화된 이미지가 저장됨. 
outputs = single_gpu_test(model_ckpt, data_loader, True, '/content/show_test_output', 0.3)

batch 크기를 1보다 키우고 테스트 수행 하려면

• 만일 batch size를 1보다 크게 해서 설정하려면 cfg.data.samples_per_gpu 값을 1보다 크게 설정하고 single_gpu_test() 함수의 bug(?) 코드를 수정 필요.
• 다시 런타임 다시 시작을 실행 후 config 부터 재 설정후 아래 코드 수행 필요. 여기서는 수행하지 않는 걸로 스킵

'''
import os.path as osp
import pickle
import shutil
import tempfile
import time

import mmcv
import torch
import torch.distributed as dist
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info

def single_gpu_test_batch(model,
                    data_loader,
                    show=False,
                    out_dir=None,
                    show_score_thr=0.3):
    model.eval()
    results = []
    dataset = data_loader.dataset
    prog_bar = mmcv.ProgressBar(len(dataset))
    for i, data in enumerate(data_loader):
        with torch.no_grad():
            result = model(return_loss=False, rescale=True, **data)

        batch_size = len(result)
        if show or out_dir:
            if batch_size == 1 and isinstance(data['img'][0], torch.Tensor):
                img_tensor = data['img'][0]
            else:
                # 아래 코드를 주석 처리 필요. batch_size가 1보다 클때는 data['img'][0].data[0]가 아니라 data['img'][0].data 가 되어야 함. 
                #img_tensor = data['img'][0].data[0]
                # 아래는 버그(?) 수정 코드 
                img_tensor = data['img'][0].data
                print('img_tensor shape2:', img_tensor.shape)
            img_metas = data['img_metas'][0].data[0]
            imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
            assert len(imgs) == len(img_metas)

            for i, (img, img_meta) in enumerate(zip(imgs, img_metas)):
                h, w, _ = img_meta['img_shape']
                img_show = img[:h, :w, :]

                ori_h, ori_w = img_meta['ori_shape'][:-1]
                img_show = mmcv.imresize(img_show, (ori_w, ori_h))

                if out_dir:
                    out_file = osp.join(out_dir, img_meta['ori_filename'])
                else:
                    out_file = None

                model.module.show_result(
                    img_show,
                    result[i],
                    show=show,
                    out_file=out_file,
                    score_thr=show_score_thr)

        # encode mask results
        if isinstance(result[0], tuple):
            result = [(bbox_results, encode_mask_results(mask_results))
                      for bbox_results, mask_results in result]
        results.extend(result)

        for _ in range(batch_size):
            prog_bar.update()
    return results

from mmdet.apis import multi_gpu_test, single_gpu_test
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmdet.apis import inference_detector, init_detector, show_result_pyplot

model_ckpt = MMDataParallel(model_ckpt, device_ids=[0])
# single_gpu_test() 를 호출하여 test데이터 세트의 interence 수행. 반드시 batch size는 1이 되어야 함. 
outputs = single_gpu_test(model_ckpt, data_loader, True, '/content/tutorial_exps', 0.3)
'''

반환된 test용 데이터세트의 inference 적용 결과 확인 및 성능 evaluation 수행.

print('결과 outputs type:', type(outputs))
print('evalution 된 파일의 갯수:', len(outputs))
print('첫번째 evalutation 결과의 type:', type(outputs[0]))
print('첫번째 evaluation 결과의 CLASS 갯수:', len(outputs[0]))
print('첫번째 evaluation 결과의 CLASS ID 0의 type과 shape', type(outputs[0][0]), outputs[0][0].shape)
# 결과 outputs type: <class 'list'>
# evalution 된 파일의 갯수: 72
# 첫번째 evalutation 결과의 type: <class 'list'>
# 첫번째 evaluation 결과의 CLASS 갯수: 3
# 첫번째 evaluation 결과의 CLASS ID 0의 type과 shape <class 'numpy.ndarray'> (1, 5)

print(outputs)

# [[array([[200.52539   ,  92.739204  , 418.35672   , 284.60696   ,
          0.99753225]], dtype=float32), array([[1.7161997e+01, 2.9421707e+02, 1.3896806e+02, 3.9979282e+02,
        9.7877252e-01],
       [4.9222028e+02, 2.9748608e+02, 6.0555103e+02, 4.0151849e+02,
        9.5355266e-01],
       [5.3456342e+02, 3.6799695e+02, 6.4000000e+02, 4.7710013e+02,
        9.1202462e-01],
       [3.1212755e+01, 1.2918649e+02, 1.3549530e+02, 2.3172231e+02,
        8.3944076e-01],
       [1.6357803e+02, 2.6890323e+02, 2.6914523e+02, 3.8934702e+02,
        8.2909942e-01],
       [4.9686783e+02, 1.6922240e+02, 6.1225970e+02, 2.9487891e+02,
        7.9936832e-01],
       [3.2403314e+02, 3.0470923e+02, 4.5897992e+02, 4.3507742e+02,
        7.3746300e-01],
       [4.0063834e+02, 1.7505469e+02, 4.9044974e+02, 2.7783981e+02,
        3.9149642e-01],
       [1.7672256e+02, 4.6024315e+01, 2.9580939e+02, 1.4779662e+02,
        3.6525750e-01],
       [1.7738934e+01, 3.6607216e+01, 1.3155940e+02, 1.4271782e+02,
        2.8692830e-01],
       [5.1438306e+02, 3.7719646e+01, 6.2653558e+02, 1.4797528e+02,
        2.6563898e-01],
       [6.7035019e+01, 3.6262000e+02, 1.8478546e+02, 4.7663516e+02,
        2.5038856e-01],
       [2.6938425e+02, 3.3076352e+02, 3.4397900e+02, 4.1794055e+02,
        2.4135420e-01],
       [2.2859131e+02, 3.2165677e+02, 3.2059082e+02, 4.1296759e+02,
        2.2338705e-01],
       [3.0515436e+02, 3.3208936e+02, 3.7526111e+02, 4.2279739e+02,
        1.7713916e-01],
       [3.0313062e+02, 5.2947921e-01, 3.9315964e+02, 7.4024773e+01,
        1.6601905e-01],
       [8.6881599e+01, 6.5668274e+01, 1.8508673e+02, 1.7201787e+02,
        1.6582030e-01],
       [1.3413922e+02, 3.3622974e+02, 2.2196902e+02, 4.3628970e+02,
        1.3876209e-01],
       [1.9649214e+02, 2.9620236e+02, 2.8163809e+02, 3.9727570e+02,
        1.0988881e-01]], dtype=float32), array([[190.40208   ,   1.928508  , 249.41455   ,  53.596752  ,
          0.72563523]], dtype=float32)], [array([[ 95.058044  , 117.593155  , 304.42578   , 336.90408   ,
          0.99798656]], dtype=float32), array([[2.27881546e+01, 2.88934845e+02, 1.22559387e+02, 4.06159576e+02,
        9.83550668e-01],
       [3.60884521e+02, 1.93495972e+02, 4.61068817e+02, 2.75006439e+02,
        9.67219412e-01],
       [2.22220963e+02, 7.82205057e+00, 3.19117798e+02, 1.14660339e+02,
        9.57490087e-01],
       [4.60161316e+02, 2.35600983e+02, 5.84374268e+02, 3.41285736e+02,
        8.97422075e-01],
       [3.15736511e+02, 8.00962219e+01, 4.32226105e+02, 1.83316772e+02,
        8.77112269e-01],
       [1.86204315e+02, 3.35129059e+02, 2.97592682e+02, 4.37328430e+02,
        8.35789442e-01],
       [9.58452988e+01, 3.73430176e+02, 2.10823975e+02, 4.67960510e+02,
        7.68432021e-01],
       [3.93364136e+02, 2.86181610e+02, 4.95512543e+02, 3.93047699e+02,
        7.47329295e-01],
       [1.45720154e-01, 6.98454971e+01, 8.60034714e+01, 1.75958191e+02,
        4.26101834e-01],
       [1.12770584e+02, 2.56167769e+00, 2.14098450e+02, 8.29900665e+01,
        4.17314649e-01],
       [1.12388439e+01, 4.06329071e+02, 1.15801048e+02, 4.79824677e+02,
        3.94235700e-01],
       [3.41978821e+02, 5.11509132e+01, 4.38547333e+02, 1.42716858e+02,
        3.29336613e-01],
       [4.61112091e+02, 1.39268600e+02, 5.53948914e+02, 2.38157120e+02,
        3.11596453e-01],
       [1.10099327e+02, 3.16593384e+02, 2.18731628e+02, 4.02757690e+02,
        3.06461185e-01],
       [3.08556671e+02, 3.22725494e+02, 3.99133270e+02, 4.17385376e+02,
        2.76122808e-01],
       [2.55284119e+02, 7.96735458e+01, 3.52827972e+02, 1.76994095e+02,
        2.71079659e-01],
       [4.13098221e+01, 3.35968933e+01, 1.38950943e+02, 1.35479111e+02,
        2.57438272e-01],
       [5.40790405e+02, 1.56372253e+02, 6.29017639e+02, 2.47990433e+02,
        1.95213586e-01],
       [1.00161911e+02, 3.04670849e+01, 2.07302948e+02, 1.25443214e+02,
        1.87867060e-01],
       [2.83764099e+02, 2.85599976e+02, 3.94084045e+02, 3.64408264e+02,
        1.63163185e-01],
       [2.64978821e+02, 4.21196533e+02, 3.46315430e+02, 4.78766205e+02,
        1.41224876e-01],
       [2.77171143e+02, 3.90356415e+02, 3.96530762e+02, 4.76050781e+02,
        1.08841062e-01],
       [3.63421173e+02, 2.76799530e+02, 4.78585846e+02, 3.69097046e+02,
        1.06620818e-01],
       [5.14309937e+02, 1.57601456e+02, 5.90797363e+02, 2.39995331e+02,
        8.63720253e-02],
       [5.17845581e+02, 2.97892273e+02, 5.94811646e+02, 3.79072815e+02,
        8.59191343e-02],
       [2.68425236e+01, 5.49468079e+01, 1.02478516e+02, 1.56944153e+02,
        6.32546842e-02],
       [4.56955231e+02, 1.16780136e+02, 5.46982178e+02, 1.81986649e+02,
        5.97867519e-02]], dtype=float32), array([[ 85.620834 , 155.01825  , 122.29311  , 190.68481  ,   0.9718275]],
      dtype=float32)], [array([[138.30522 ,   7.751706, 370.63516 , 235.91324 ,   0.997373]],
      dtype=float32), array([[3.4952014e+02, 1.4057625e+01, 4.4804242e+02, 1.1636261e+02,
        9.9265772e-01],
       [4.1895633e+02, 3.6016479e+02, 5.2936157e+02, 4.5338681e+02,
        9.9143094e-01],
       [5.4100995e+02, 4.1238670e+01, 6.4000000e+02, 1.4062958e+02,
        9.8074716e-01],
       [0.0000000e+00, 2.7675687e+02, 9.6883682e+01, 3.7851062e+02,
        9.6934140e-01],
       [6.6191711e+01, 5.8839077e+01, 1.6735468e+02, 1.6452756e+02,
        9.6020144e-01],
       [4.3816367e+02, 8.4912552e+01, 5.4050562e+02, 1.6429648e+02,
        9.4425410e-01],
       [2.6250839e+02, 4.0252045e+02, 3.7537808e+02, 4.7995941e+02,
        9.3964851e-01],
       [2.4212155e+02, 1.9977582e+02, 3.5084509e+02, 2.9997134e+02,
        9.1683686e-01],
       [5.1596684e+00, 3.9718445e+02, 1.1846395e+02, 4.8000000e+02,
        9.0481925e-01],
       [3.5978836e+02, 1.8956438e+02, 4.7576300e+02, 3.0561102e+02,
        8.6553842e-01],
       [4.3606198e+02, 5.5906677e-01, 5.3782202e+02, 6.6787231e+01,
        7.9426467e-01],
       [9.0288826e+01, 2.9590295e+02, 1.8536223e+02, 3.8595749e+02,
        7.9243284e-01],
       [3.5270181e+02, 1.3938739e+02, 4.3371881e+02, 2.2423456e+02,
        5.5954176e-01],
       [5.2889716e+02, 4.1184717e+02, 6.3579138e+02, 4.7938086e+02,
        5.0796676e-01],
       [2.4014659e+02, 2.8533609e+02, 3.4540714e+02, 3.6439755e+02,
        4.9197578e-01],
       [5.6594507e+02, 1.3914386e+02, 6.3902643e+02, 2.4632446e+02,
        4.5044756e-01],
       [1.3391468e+01, 1.3505254e+02, 1.1360587e+02, 2.4469232e+02,
        3.4531093e-01],
       [4.3207553e+02, 1.0785172e+02, 5.3346338e+02, 2.0137408e+02,
        1.8456598e-01],
       [1.8560927e+02, 2.8744589e+02, 2.8506760e+02, 3.7011456e+02,
        1.6264196e-01],
       [1.8290259e+02, 2.2899924e+02, 2.8131189e+02, 3.2405453e+02,
        1.4155442e-01],
       [7.8735931e+01, 1.7607793e+02, 1.7296671e+02, 2.7036801e+02,
        1.0886172e-01],
       [1.7314535e-01, 7.5049095e+01, 6.2725983e+01, 1.6023184e+02,
        9.6663155e-02]], dtype=float32), array([[2.9372400e+02, 3.7391013e+02, 3.3780313e+02, 4.1250116e+02,
        9.6625006e-01],
       [5.9145642e+02, 3.9034033e+02, 6.1865375e+02, 4.1537048e+02,
        4.2561728e-01],
       [1.4473222e+02, 0.0000000e+00, 1.8533698e+02, 2.6729359e+01,
        3.1437564e-01],
       [5.2624829e+02, 2.6351144e+02, 5.5820917e+02, 2.8668985e+02,
        2.0433955e-01]], dtype=float32)], [array([[156.94142   , 118.34243   , 321.09885   , 274.15103   ,
          0.99571997]], dtype=float32), array([[3.2087637e+02, 1.2678441e+02, 4.1934778e+02, 2.3214348e+02,
        9.9259186e-01],
       [1.2676303e+02, 3.3063480e+02, 2.4970993e+02, 4.4701877e+02,
        9.8464763e-01],
       [2.3675189e+02, 3.3305658e+02, 3.4736154e+02, 4.2988159e+02,
        9.6323073e-01],
       [4.4580756e+02, 8.3194771e+01, 5.4435352e+02, 1.6927538e+02,
        9.6050972e-01],
       [1.8133961e+01, 3.4602355e-02, 1.1211940e+02, 7.2091301e+01,
        8.9414757e-01],
       [4.3967313e+02, 3.3288736e+02, 5.3989764e+02, 4.3776205e+02,
        8.6803836e-01],
       [3.4746616e+02, 2.4508829e+02, 4.3399167e+02, 3.3232855e+02,
        8.3659530e-01],
       [1.4793726e+02, 2.4752522e+02, 2.4860004e+02, 3.3539313e+02,
        6.8812239e-01],
       [2.1892929e+02, 2.5640833e+02, 3.1040701e+02, 3.3514905e+02,
        6.8342537e-01],
       [5.1466028e+02, 2.4930895e+01, 6.3524847e+02, 1.6176363e+02,
        6.5377986e-01],
       [1.8474014e+01, 2.1958867e+02, 1.0413157e+02, 3.0981384e+02,
        6.3387471e-01],
       [4.2063489e+02, 3.6657590e+02, 5.2138098e+02, 4.7532202e+02,
        5.3669178e-01],
       [7.5348999e+01, 1.6124586e+02, 1.6733788e+02, 2.8994696e+02,
        5.1455981e-01],
       [5.3048297e+02, 2.6964996e+02, 6.3738452e+02, 3.6151569e+02,
        5.0893378e-01],
       [3.5603329e+02, 2.0988321e+02, 4.5528775e+02, 2.8744983e+02,
        2.8953338e-01],
       [9.1688766e+01, 2.5605255e+02, 1.7251498e+02, 3.3929169e+02,
        1.0101325e-01],
       [4.1252518e+02, 2.9526370e+02, 4.9054181e+02, 3.9265295e+02,
        9.9677391e-02],
       [4.4318918e+02, 2.5924277e+02, 5.5277710e+02, 3.5596371e+02,
        8.7636441e-02],
       [3.4831039e+02, 1.7957886e+02, 4.4638589e+02, 3.3324481e+02,
        8.1850946e-02],
       [4.5380182e+02, 2.3487360e+02, 5.4935370e+02, 2.9679782e+02,
        8.0386668e-02],
       [2.1824774e+02, 3.9021622e+02, 3.1223605e+02, 4.7770285e+02,
        5.7145637e-02],
       [3.8635014e+01, 1.7574222e+02, 1.4389905e+02, 2.9948904e+02,
        5.6977697e-02],
       [5.1883197e+02, 8.4794640e+01, 6.3256555e+02, 1.7653285e+02,
        5.1325936e-02]], dtype=float32), array([[ 95.29333   , 328.3315    , 131.0512    , 359.22632   ,
          0.92272353]], dtype=float32)], [array([[311.05496   ,   8.553493  , 477.3554    , 151.16432   ,
          0.99695253]], dtype=float32), array([[4.64230835e+02, 1.03838226e+02, 5.74651794e+02, 2.02901779e+02,
        9.53496218e-01],
       [1.28273697e+02, 2.88392448e+01, 2.39480072e+02, 1.31540375e+02,
        9.10221756e-01],
       [1.77914062e+02, 9.39278946e+01, 2.98174713e+02, 1.92846069e+02,
        9.01494205e-01],
       [3.00024231e+02, 1.61518112e+02, 4.08450317e+02, 2.56657043e+02,
        8.50637853e-01],
       [7.16448669e+01, 1.87242188e+02, 1.69068878e+02, 2.68430389e+02,
        8.38949323e-01],
       [4.10464844e+02, 4.00478485e+02, 5.11940582e+02, 4.79457520e+02,
        7.84120798e-01],
       [0.00000000e+00, 2.13441452e+02, 8.76940613e+01, 3.39733002e+02,
        7.67845392e-01],
       [4.06133057e+02, 2.99457397e+02, 5.04607666e+02, 4.03005005e+02,
        6.90204799e-01],
       [3.09704132e+02, 2.46236877e+02, 4.18938416e+02, 3.16135345e+02,
        6.82317376e-01],
       [1.68953079e+02, 1.89729477e+02, 2.86139008e+02, 3.05360901e+02,
        6.04284525e-01],
       [2.07800308e+02, 1.38097692e+00, 3.18511169e+02, 6.73501053e+01,
        5.32853901e-01],
       [5.66716003e+02, 7.41417236e+01, 6.39304443e+02, 1.79353348e+02,
        4.35142785e-01],
       [4.58741516e+02, 2.03166672e+02, 5.55233093e+02, 2.90850769e+02,
        4.16097373e-01],
       [1.23323669e+02, 3.81455109e+02, 2.31256363e+02, 4.75166504e+02,
        4.11829323e-01],
       [1.76752644e+01, 4.85858879e+01, 1.25767433e+02, 1.59749130e+02,
        3.23249280e-01],
       [1.87416412e+02, 3.49567688e+02, 2.76870972e+02, 4.34792816e+02,
        2.60212243e-01],
       [5.33875854e+02, 2.73547821e+02, 6.29996460e+02, 3.85675903e+02,
        2.35239133e-01],
       [3.59052490e+02, 2.89693512e+02, 4.83295166e+02, 3.75881104e+02,
        2.01021865e-01],
       [6.94640198e+01, 3.26938263e+02, 1.98504745e+02, 4.48128235e+02,
        1.93796545e-01],
       [1.43745728e+02, 2.61668182e+02, 2.22318939e+02, 3.48624512e+02,
        1.59831628e-01],
       [2.92799896e+02, 4.27953186e+02, 3.70041382e+02, 4.78857849e+02,
        1.37603953e-01],
       [5.78612854e+02, 3.54365936e+02, 6.39665039e+02, 4.58071899e+02,
        1.27190799e-01],
       [3.05105255e+02, 2.67121307e+02, 4.35753998e+02, 3.54505157e+02,
        1.21316232e-01],
       [5.76610352e+02, 1.86289169e+02, 6.39749390e+02, 2.90705597e+02,
        1.20020226e-01],
       [1.01094675e+01, 1.32578754e+01, 1.14355598e+02, 1.02307053e+02,
        7.36863464e-02],
       [3.93220520e+02, 1.42544510e+02, 4.88029419e+02, 2.41080750e+02,
        5.91439232e-02]], dtype=float32), array([], shape=(0, 5), dtype=float32)], [array([[112.87249   ,  94.43067   , 297.386     , 280.60092   ,
          0.99693584]], dtype=float32), array([[3.86555176e+01, 1.73206863e+02, 1.31531982e+02, 2.75380341e+02,
        9.91971135e-01],
       [4.07521729e+02, 3.22568817e+02, 5.11544891e+02, 4.21538940e+02,
        9.89037693e-01],
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        5.16665399e-01],
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        5.07410109e-01],
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        4.04296726e-01],
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        3.78443927e-01],
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        6.67226538e-02],
       [5.31091431e+02, 3.14268494e+01, 6.35259216e+02, 2.92623169e+02,
        6.32044747e-02]], dtype=float32), array([[372.54633   , 421.93823   , 424.53754   , 468.90076   ,
          0.9669203 ],
       [ 54.364025  ,   0.        ,  95.534744  ,  33.470093  ,
          0.96675414]], dtype=float32)], [array([[214.80682  , 296.97073  , 440.22775  , 478.8366   ,   0.9983438]],
      dtype=float32), array([[4.16064789e+02, 1.87599686e+02, 5.29004761e+02, 2.94852844e+02,
        9.95000780e-01],
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        9.94070113e-01],
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        9.92408156e-01],
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        9.91823614e-01],
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        9.88777101e-01],
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        9.86014962e-01],
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        9.82830524e-01],
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        9.81555879e-01],
       [4.28561755e-02, 0.00000000e+00, 1.06079475e+02, 1.06129562e+02,
        9.81238484e-01],
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        9.79439437e-01],
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        9.78377759e-01],
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        9.71004725e-01],
       [1.32199752e+00, 3.73589844e+02, 1.04382797e+02, 4.77280273e+02,
        9.63817298e-01],
       [3.71976898e+02, 3.55989069e-01, 4.82252197e+02, 8.09778976e+01,
        9.04026091e-01],
       [5.64996948e+02, 2.99635742e+02, 6.39683350e+02, 4.06158142e+02,
        6.97173297e-01],
       [4.96070923e+02, 3.59889221e+02, 6.01647217e+02, 4.61917297e+02,
        5.00125408e-01],
       [1.28292252e+02, 4.19074585e+02, 2.23046417e+02, 4.79402344e+02,
        2.60286719e-01],
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        2.43279308e-01],
       [2.78467834e+02, 5.49532394e+01, 3.91378448e+02, 1.75895798e+02,
        1.12400264e-01]], dtype=float32), array([[187.11133   ,  86.57795   , 239.13657   , 136.17957   ,
          0.97666544],
       [  0.        , 336.78104   ,  35.337776  , 381.01132   ,
          0.9393976 ],
       [213.04648   ,   2.6357276 , 265.9985    ,  49.30934   ,
          0.84707564]], dtype=float32)], [array([[133.39375  , 298.44064  , 370.3856   , 480.       ,   0.9985794]],
      dtype=float32), array([[3.3777927e+02, 2.4516150e+02, 4.4130301e+02, 3.5269400e+02,
        9.9417996e-01],
       [3.7028446e+01, 2.0687666e+02, 1.5592552e+02, 3.0376645e+02,
        9.9245936e-01],
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        9.8909855e-01],
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        9.8192453e-01],
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        9.7559595e-01],
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        9.6081525e-01],
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        9.5060760e-01],
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        9.3547875e-01],
       [2.1763158e+00, 3.8171667e+02, 9.0547935e+01, 4.7718100e+02,
        7.2198427e-01],
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        6.8379259e-01],
       [5.4452948e+02, 3.6499567e+02, 6.3968860e+02, 4.7378757e+02,
        5.1410323e-01],
       [2.4594000e+02, 2.2349179e+02, 3.6206223e+02, 3.2143536e+02,
        5.8188755e-02]], dtype=float32), array([], shape=(0, 5), dtype=float32)], [array([[200.79124  , 161.00125  , 406.1593   , 390.8235   ,   0.9975141]],
      dtype=float32), array([[2.21376286e+01, 3.11166504e+02, 1.36989532e+02, 4.33726105e+02,
        9.94980991e-01],
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        9.93334293e-01],
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        9.92708385e-01],
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        9.91557717e-01],
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        9.88771379e-01],
       [3.50984406e+02, 7.60711655e-02, 4.73722382e+02, 1.02369896e+02,
        9.88093913e-01],
       [3.08126587e+02, 3.57169861e+02, 4.29944031e+02, 4.78077545e+02,
        9.85383451e-01],
       [4.76044189e+02, 1.55440018e+02, 5.95034790e+02, 2.54136826e+02,
        9.81745183e-01],
       [3.77150574e+02, 1.98562836e+02, 4.70244598e+02, 3.00547607e+02,
        9.78342175e-01],
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        9.70648348e-01],
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        9.64219093e-01],
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        9.48263347e-01],
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        9.36497629e-01],
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        8.13393235e-01],
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        7.25119889e-01],
       [7.19745560e+01, 4.23042908e+02, 1.80164307e+02, 4.79978363e+02,
        4.18101937e-01],
       [7.52479706e+01, 1.56946570e-01, 1.81825180e+02, 4.61374054e+01,
        8.63737538e-02]], dtype=float32), array([[454.7562    , 327.09427   , 504.40854   , 379.92502   ,
          0.85773027]], dtype=float32)], [array([[461.61487  , 282.74945  , 640.       , 479.31165  ,   0.9983298]],
      dtype=float32), array([[2.42222733e+02, 3.41052338e+02, 3.51366394e+02, 4.47291748e+02,
        9.88289595e-01],
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        9.87578213e-01],
       [3.01505585e+01, 1.65591080e+02, 1.38316910e+02, 2.61573853e+02,
        9.86746907e-01],
       [3.31905212e+02, 5.52774811e+01, 4.54921570e+02, 1.62402878e+02,
        9.86422718e-01],
       [1.71524765e+02, 1.42068791e+00, 2.95876495e+02, 1.05016861e+02,
        9.80155289e-01],
       [1.12973547e+01, 3.05600109e+01, 1.16099655e+02, 1.31793594e+02,
        9.79420900e-01],
       [3.50276703e+02, 2.92450043e+02, 4.58897247e+02, 3.85112183e+02,
        9.77155447e-01],
       [1.18635399e+02, 3.18869507e+02, 2.19957367e+02, 4.21075531e+02,
        9.75631237e-01],
       [5.24794861e+02, 2.16390015e+02, 6.34772888e+02, 3.09650909e+02,
        9.20614600e-01],
       [3.93202332e+02, 0.00000000e+00, 5.33183228e+02, 8.24931564e+01,
        8.83027792e-01],
       [5.12031860e+02, 9.35096741e+01, 6.08654297e+02, 1.95551285e+02,
        8.49205673e-01],
       [4.62535187e+02, 1.18474678e+02, 5.56261780e+02, 2.10281784e+02,
        8.06960225e-01],
       [3.95888367e+02, 1.30046082e+02, 5.03545013e+02, 2.32906433e+02,
        8.04393351e-01],
       [1.94942200e+02, 1.55126785e+02, 2.99493805e+02, 2.40399338e+02,
        7.11985588e-01],
       [1.57450211e+02, 2.34780334e+02, 2.74563599e+02, 3.58703217e+02,
        7.05446184e-01],
       [1.63177917e+02, 1.07295906e+02, 2.73954742e+02, 1.99046021e+02,
        6.82574332e-01],
       [2.67495209e+02, 2.68807648e+02, 3.81505524e+02, 3.79569916e+02,
        6.41149223e-01],
       [5.74545410e+02, 1.44370747e+01, 6.38489746e+02, 1.17637337e+02,
        1.59230113e-01],
       [1.46026077e+02, 2.40103668e+02, 2.31544617e+02, 3.32396851e+02,
        9.46123302e-02]], dtype=float32), array([[ 66.85258  , 109.761696 , 105.98781  , 147.87102  ,   0.9730454],
       [115.90851  ,  21.080624 , 162.34404  ,  62.225098 ,   0.9634706],
       [418.65884  , 227.71802  , 480.53766  , 293.78223  ,   0.9494921],
       [  0.       , 246.36829  ,  36.47911  , 284.09985  ,   0.8202262]],
      dtype=float32)]]

metric = dataset.evaluate(outputs, metric='bbox')
print(metric)

# Evaluating bbox...
# Loading and preparing results...
# DONE (t=0.00s)
# creating index...
# index created!
# Running per image evaluation...
# Evaluate annotation type *bbox*
# DONE (t=0.81s).
# Accumulating evaluation results...
# DONE (t=0.07s).
#  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.627
#  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.920
#  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.736
#  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.615
#  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.499
#  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.475
#  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.711
#  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.711
#  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.711
#  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.640
#  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.625
#  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.523
# OrderedDict([('bbox_mAP', 0.627), ('bbox_mAP_50', 0.92), ('bbox_mAP_75', 0.736), ('bbox_mAP_s', 0.615), ('bbox_mAP_m', 0.499), ('bbox_mAP_l', 0.475), ('bbox_mAP_copypaste', '0.627 0.920 0.736 0.615 0.499 0.475')])

VOC는 ANNOTATION이 XML형태

MMDetection 설치

!pip install mmcv-full
!git clone https://github.com/open-mmlab/mmdetection.git
!cd mmdetection; python setup.py install

# 아래를 수행하기 전에 kernel을 restart 해야 함. 
from mmdet.apis import init_detector, inference_detector
import mmcv

PASCAL VOC형태의 BCCD Dataset를 Download 후 MS-COCO 형태로 변경

• BCCD Dataset은 백혈구(WBC), 적혈구(RBC), 혈소판(Platelets) 세가지 유형의 Object Class를 가짐.
• 다운로드 받은 Dataset은 Pascal VOC 형태이므로 이를 별도의 유틸리티를 이용하여 MS-COCO 형태로 변환

!git clone https://github.com/Shenggan/BCCD_Dataset.git

VOC를 COCO로 변환하는 package적용하기

!git clone https://github.com/yukkyo/voc2coco.git

import os

# colab 버전은 아래 명령어로 ballnfish_classes.txt 를 수정합니다. 
with open('/content/BCCD_Dataset/BCCD/labels.txt', "w") as f:
    f.write("WBC\n")
    f.write("RBC\n")
    f.write("Platelets\n")

!cat /content/BCCD_Dataset/BCCD/labels.txt
# WBC
# RBC
# Platelets

# VOC를 COCO로 변환 수행. 학습/검증/테스트 용 json annotation을 생성. 
# voc2coco.py를 수행하여, annotation, jpgimage의 metafile를 매칭시켜서 json파일로 만들겠다.
# train, test, val 별로
%cd voc2coco
!python voc2coco.py --ann_dir /content/BCCD_Dataset/BCCD/Annotations \
--ann_ids /content/BCCD_Dataset/BCCD/ImageSets/Main/train.txt \
--labels /content/BCCD_Dataset/BCCD/labels.txt \
--output /content/BCCD_Dataset/BCCD/train.json \
--ext xml

!python voc2coco.py --ann_dir /content/BCCD_Dataset/BCCD/Annotations \
--ann_ids /content/BCCD_Dataset/BCCD/ImageSets/Main/val.txt \
--labels /content/BCCD_Dataset/BCCD/labels.txt \
--output /content/BCCD_Dataset/BCCD/val.json \
--ext xml

!python voc2coco.py --ann_dir /content/BCCD_Dataset/BCCD/Annotations \
--ann_ids /content/BCCD_Dataset/BCCD/ImageSets/Main/test.txt \
--labels /content/BCCD_Dataset/BCCD/labels.txt \
--output /content/BCCD_Dataset/BCCD/test.json \
--ext xml

# /content/voc2coco
# Start converting !
# 100% 205/205 [00:00<00:00, 3822.02it/s]
# Start converting !
# 100% 87/87 [00:00<00:00, 3999.13it/s]
# Start converting !
# 100% 72/72 [00:00<00:00, 3397.08it/s]

!cat /content/BCCD_Dataset/BCCD/train.json
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11856, "iscrowd": 0, "bbox": [255, 0, 114, 104], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00393", "id": 2707}, {"area": 11988, "iscrowd": 0, "bbox": [320, 270, 111, 108], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00393", "id": 2708}, {"area": 47174, "iscrowd": 0, "bbox": [209, 91, 229, 206], "category_id": 1, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00393", "id": 2709}, {"area": 12360, "iscrowd": 0, "bbox": [24, 89, 103, 120], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2710}, {"area": 10094, "iscrowd": 0, "bbox": [5, 0, 103, 98], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2711}, {"area": 9548, "iscrowd": 0, "bbox": [108, 0, 124, 77], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2712}, {"area": 13108, "iscrowd": 0, "bbox": [273, 5, 116, 113], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2713}, {"area": 13572, "iscrowd": 0, "bbox": [363, 172, 116, 117], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2714}, {"area": 10094, "iscrowd": 0, "bbox": [85, 265, 98, 103], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2715}, {"area": 10656, "iscrowd": 0, "bbox": [0, 313, 96, 111], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2716}, {"area": 10767, "iscrowd": 0, "bbox": [176, 330, 97, 111], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2717}, {"area": 9898, "iscrowd": 0, "bbox": [282, 305, 98, 101], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2718}, {"area": 11526, "iscrowd": 0, "bbox": [391, 295, 113, 102], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2719}, {"area": 11526, "iscrowd": 0, "bbox": [500, 369, 113, 102], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2720}, {"area": 10098, "iscrowd": 0, "bbox": [387, 61, 99, 102], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2721}, {"area": 6256, "iscrowd": 0, "bbox": [232, 411, 92, 68], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2722}, {"area": 7571, "iscrowd": 0, "bbox": [89, 411, 113, 67], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2723}, {"area": 54502, "iscrowd": 0, "bbox": [113, 65, 238, 229], "category_id": 1, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2724}, {"area": 4692, "iscrowd": 0, "bbox": [471, 200, 69, 68], "category_id": 3, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2725}, {"area": 1974, "iscrowd": 0, "bbox": [469, 42, 47, 42], "category_id": 3, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00395", "id": 2726}, {"area": 12920, "iscrowd": 0, "bbox": [461, 367, 136, 95], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2727}, {"area": 14175, "iscrowd": 0, "bbox": [375, 103, 135, 105], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2728}, {"area": 11868, "iscrowd": 0, "bbox": [218, 223, 129, 92], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2729}, {"area": 10395, "iscrowd": 0, "bbox": [123, 285, 99, 105], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2730}, {"area": 9222, "iscrowd": 0, "bbox": [68, 392, 106, 87], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2731}, {"area": 11009, "iscrowd": 0, "bbox": [218, 324, 109, 101], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2732}, {"area": 14756, "iscrowd": 0, "bbox": [292, 290, 124, 119], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2733}, {"area": 11009, "iscrowd": 0, "bbox": [361, 202, 109, 101], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2734}, {"area": 11155, "iscrowd": 0, "bbox": [34, 0, 97, 115], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2735}, {"area": 10302, "iscrowd": 0, "bbox": [460, 194, 101, 102], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2736}, {"area": 10900, "iscrowd": 0, "bbox": [373, 0, 109, 100], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2737}, {"area": 10108, "iscrowd": 0, "bbox": [0, 109, 76, 133], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2738}, {"area": 52102, "iscrowd": 0, "bbox": [116, 17, 239, 218], "category_id": 1, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00400", "id": 2739}, {"area": 13312, "iscrowd": 0, "bbox": [453, 196, 128, 104], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2740}, {"area": 7632, "iscrowd": 0, "bbox": [302, 283, 72, 106], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2741}, {"area": 13312, "iscrowd": 0, "bbox": [338, 158, 128, 104], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2742}, {"area": 13312, "iscrowd": 0, "bbox": [336, 156, 128, 104], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2743}, {"area": 6970, "iscrowd": 0, "bbox": [322, 119, 85, 82], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2744}, {"area": 13312, "iscrowd": 0, "bbox": [233, 83, 128, 104], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2745}, {"area": 11556, "iscrowd": 0, "bbox": [210, 0, 107, 108], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2746}, {"area": 9810, "iscrowd": 0, "bbox": [0, 13, 90, 109], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2747}, {"area": 6426, "iscrowd": 0, "bbox": [0, 137, 63, 102], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2748}, {"area": 11663, "iscrowd": 0, "bbox": [209, 335, 107, 109], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2749}, {"area": 10296, "iscrowd": 0, "bbox": [8, 370, 104, 99], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2750}, {"area": 10296, "iscrowd": 0, "bbox": [10, 271, 104, 99], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2751}, {"area": 8692, "iscrowd": 0, "bbox": [77, 367, 106, 82], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2752}, {"area": 10807, "iscrowd": 0, "bbox": [489, 9, 101, 107], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2753}, {"area": 6120, "iscrowd": 0, "bbox": [555, 125, 85, 72], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2754}, {"area": 44472, "iscrowd": 0, "bbox": [69, 164, 218, 204], "category_id": 1, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2755}, {"area": 4158, "iscrowd": 0, "bbox": [368, 259, 63, 66], "category_id": 3, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2756}, {"area": 2288, "iscrowd": 0, "bbox": [563, 0, 52, 44], "category_id": 3, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00403", "id": 2757}, {"area": 11484, "iscrowd": 0, "bbox": [397, 195, 132, 87], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2758}, {"area": 14152, "iscrowd": 0, "bbox": [415, 52, 122, 116], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2759}, {"area": 11368, "iscrowd": 0, "bbox": [542, 61, 98, 116], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2760}, {"area": 11025, "iscrowd": 0, "bbox": [278, 42, 105, 105], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2761}, {"area": 9765, "iscrowd": 0, "bbox": [187, 354, 105, 93], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2762}, {"area": 6699, "iscrowd": 0, "bbox": [283, 347, 77, 87], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2763}, {"area": 10404, "iscrowd": 0, "bbox": [338, 305, 102, 102], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2764}, {"area": 11340, "iscrowd": 0, "bbox": [396, 343, 126, 90], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2765}, {"area": 11340, "iscrowd": 0, "bbox": [433, 295, 126, 90], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2766}, {"area": 9765, "iscrowd": 0, "bbox": [0, 363, 93, 105], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2767}, {"area": 10527, "iscrowd": 0, "bbox": [69, 70, 121, 87], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2768}, {"area": 8540, "iscrowd": 0, "bbox": [184, 0, 122, 70], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2769}, {"area": 7370, "iscrowd": 0, "bbox": [573, 178, 67, 110], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2770}, {"area": 58764, "iscrowd": 0, "bbox": [114, 133, 249, 236], "category_id": 1, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2771}, {"area": 1776, "iscrowd": 0, "bbox": [23, 290, 48, 37], "category_id": 3, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00404", "id": 2772}, {"area": 11770, "iscrowd": 0, "bbox": [52, 305, 110, 107], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2773}, {"area": 8160, "iscrowd": 0, "bbox": [0, 202, 68, 120], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2774}, {"area": 11770, "iscrowd": 0, "bbox": [161, 113, 110, 107], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2775}, {"area": 12992, "iscrowd": 0, "bbox": [109, 81, 112, 116], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2776}, {"area": 8455, "iscrowd": 0, "bbox": [150, 0, 95, 89], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2777}, {"area": 8455, "iscrowd": 0, "bbox": [242, 0, 95, 89], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2778}, {"area": 10290, "iscrowd": 0, "bbox": [508, 205, 105, 98], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2779}, {"area": 10290, "iscrowd": 0, "bbox": [478, 104, 105, 98], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2780}, {"area": 10290, "iscrowd": 0, "bbox": [375, 141, 105, 98], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2781}, {"area": 12792, "iscrowd": 0, "bbox": [237, 234, 123, 104], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2782}, {"area": 12064, "iscrowd": 0, "bbox": [175, 211, 116, 104], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2783}, {"area": 12870, "iscrowd": 0, "bbox": [157, 319, 99, 130], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2784}, {"area": 8640, "iscrowd": 0, "bbox": [306, 335, 72, 120], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2785}, {"area": 20808, "iscrowd": 0, "bbox": [315, 0, 136, 153], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2786}, {"area": 45675, "iscrowd": 0, "bbox": [337, 247, 225, 203], "category_id": 1, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00405", "id": 2787}, {"area": 10412, "iscrowd": 0, "bbox": [63, 278, 76, 137], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2788}, {"area": 11500, "iscrowd": 0, "bbox": [117, 119, 115, 100], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2789}, {"area": 9200, "iscrowd": 0, "bbox": [0, 177, 92, 100], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2790}, {"area": 10504, "iscrowd": 0, "bbox": [212, 303, 104, 101], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2791}, {"area": 8910, "iscrowd": 0, "bbox": [214, 390, 99, 90], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2792}, {"area": 9880, "iscrowd": 0, "bbox": [453, 240, 104, 95], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2793}, {"area": 10944, "iscrowd": 0, "bbox": [307, 177, 114, 96], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2794}, {"area": 12317, "iscrowd": 0, "bbox": [283, 46, 109, 113], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2795}, {"area": 7140, "iscrowd": 0, "bbox": [469, 0, 105, 68], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2796}, {"area": 9504, "iscrowd": 0, "bbox": [390, 43, 99, 96], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2797}, {"area": 11092, "iscrowd": 0, "bbox": [386, 135, 94, 118], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2798}, {"area": 7905, "iscrowd": 0, "bbox": [541, 270, 85, 93], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2799}, {"area": 10476, "iscrowd": 0, "bbox": [8, 27, 97, 108], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2800}, {"area": 9405, "iscrowd": 0, "bbox": [102, 0, 99, 95], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2801}, {"area": 7128, "iscrowd": 0, "bbox": [197, 0, 88, 81], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2802}, {"area": 756, "iscrowd": 0, "bbox": [0, 280, 21, 36], "category_id": 3, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2803}, {"area": 1302, "iscrowd": 0, "bbox": [382, 253, 42, 31], "category_id": 3, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2804}, {"area": 42120, "iscrowd": 0, "bbox": [309, 318, 260, 162], "category_id": 1, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2805}], "categories": [{"supercategory": "none", "id": 1, "name": "WBC"}, {"supercategory": "none", "id": 2, "name": "RBC"}, {"supercategory": "none", "id": 3, "name": "Platelets"}]}

# annotation json 파일을 잘 볼수 있는 jq 유틸리티 셋업. 
!sudo apt-get install jq
# Reading package lists... Done
# Building dependency tree       
# Reading state information... Done
# The following additional packages will be installed:
#   libjq1 libonig4
# The following NEW packages will be installed:
#   jq libjq1 libonig4
# 0 upgraded, 3 newly installed, 0 to remove and 37 not upgraded.
# Need to get 276 kB of archives.
# After this operation, 930 kB of additional disk space will be used.
# Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libonig4 amd64 6.7.0-1 [119 kB]
# Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libjq1 amd64 1.5+dfsg-2 [111 kB]
# Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 jq amd64 1.5+dfsg-2 [45.6 kB]
# Fetched 276 kB in 1s (332 kB/s)
# debconf: unable to initialize frontend: Dialog
# debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 3.)
# debconf: falling back to frontend: Readline
# debconf: unable to initialize frontend: Readline
# debconf: (This frontend requires a controlling tty.)
# debconf: falling back to frontend: Teletype
# dpkg-preconfigure: unable to re-open stdin: 
# Selecting previously unselected package libonig4:amd64.
# (Reading database ... 155047 files and directories currently installed.)
# Preparing to unpack .../libonig4_6.7.0-1_amd64.deb ...
# Unpacking libonig4:amd64 (6.7.0-1) ...
# Selecting previously unselected package libjq1:amd64.
# Preparing to unpack .../libjq1_1.5+dfsg-2_amd64.deb ...
# Unpacking libjq1:amd64 (1.5+dfsg-2) ...
# Selecting previously unselected package jq.
# Preparing to unpack .../jq_1.5+dfsg-2_amd64.deb ...
# Unpacking jq (1.5+dfsg-2) ...
# Setting up libonig4:amd64 (6.7.0-1) ...
# Setting up libjq1:amd64 (1.5+dfsg-2) ...
# Setting up jq (1.5+dfsg-2) ...
# Processing triggers for man-db (2.8.3-2ubuntu0.1) ...
# Processing triggers for libc-bin (2.27-3ubuntu1.3) ...
# /sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link

!jq . /content/BCCD_Dataset/BCCD/train.json > output.json
!tail -100 output.json
      "category_id": 2,
      "ignore": 0,
      "segmentation": [],
      "image_id": "BloodImage_00408",
      "id": 2800
    },
    {
      "area": 9405,
      "iscrowd": 0,
      "bbox": [
        102,
        0,
        99,
        95
      ],
      "category_id": 2,
      "ignore": 0,
      "segmentation": [],
      "image_id": "BloodImage_00408",
      "id": 2801
    },
    {
      "area": 7128,
      "iscrowd": 0,
      "bbox": [
        197,
        0,
        88,
        81
      ],
      "category_id": 2,
      "ignore": 0,
      "segmentation": [],
      "image_id": "BloodImage_00408",
      "id": 2802
    },
    {
      "area": 756,
      "iscrowd": 0,
      "bbox": [
        0,
        280,
        21,
        36
      ],
      "category_id": 3,
      "ignore": 0,
      "segmentation": [],
      "image_id": "BloodImage_00408",
      "id": 2803
    },
    {
      "area": 1302,
      "iscrowd": 0,
      "bbox": [
        382,
        253,
        42,
        31
      ],
      "category_id": 3,
      "ignore": 0,
      "segmentation": [],
      "image_id": "BloodImage_00408",
      "id": 2804
    },
    {
      "area": 42120,
      "iscrowd": 0,
      "bbox": [
        309,
        318,
        260,
        162
      ],
      "category_id": 1,
      "ignore": 0,
      "segmentation": [],
      "image_id": "BloodImage_00408",
      "id": 2805
    }
  ],
  "categories": [
    {
      "supercategory": "none",
      "id": 1,
      "name": "WBC"
    },
    {
      "supercategory": "none",
      "id": 2,
      "name": "RBC"
    },
    {
      "supercategory": "none",
      "id": 3,
      "name": "Platelets"
    }
  ]
}

!jq . /content/BCCD_Dataset/BCCD/test.json > output.json
!cat output.json

CocoDataset 클래스를 활용하여 BCCD Dataset을 로딩하기

• MS-COCO Dataset의 경우 별다른 Custom Code없이 Object들의 Class만 지정해 주면 됨

%cd /content
# /content

from mmdet.datasets.builder import DATASETS
from mmdet.datasets.coco import CocoDataset

@DATASETS.register_module(force=True)
class BCCDDataset(CocoDataset):
  CLASSES = ('WBC', 'RBC', 'Platelets')
  # 혹시 단일 class이더라도 ( 'wbc', ) 이렇게 , 을 남겨야함

Config 설정하고 Checkpoint 파일 다운로드 받기 ( pretrained file

config_file = '/content/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
checkpoint_file = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'

!pwd
# /content

!cd /content/mmdetection; mkdir checkpoints
!wget -O /content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth

!ls -lia /content/mmdetection/checkpoints
# total 163376
# 4983200 drwxr-xr-x  2 root root      4096 Oct 18 14:47 .
# 4456450 drwxr-xr-x 19 root root      4096 Oct 18 14:47 ..
# 4983201 -rw-r--r--  1 root root 167287506 Aug 28  2020 faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth

from mmcv import Config

cfg = Config.fromfile(config_file)
print(cfg.pretty_text)
model = dict(
    type='FasterRCNN',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=80,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='CocoDataset',
        ann_file='data/coco/annotations/instances_train2017.json',
        img_prefix='data/coco/train2017/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ]),
    val=dict(
        type='CocoDataset',
        ann_file='data/coco/annotations/instances_val2017.json',
        img_prefix='data/coco/val2017/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='CocoDataset',
        ann_file='data/coco/annotations/instances_val2017.json',
        img_prefix='data/coco/val2017/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]

from mmdet.apis import set_random_seed

# dataset에 대한 환경 파라미터 수정. 
cfg.dataset_type = 'BCCDDataset'
cfg.data_root = '/content/BCCD_Dataset/BCCD/'

# train, val, test dataset에 대한 type, data_root, ann_file, img_prefix 환경 파라미터 수정. 
cfg.data.train.type = 'BCCDDataset'
cfg.data.train.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.train.ann_file = 'train.json'
cfg.data.train.img_prefix = 'JPEGImages'

cfg.data.val.type = 'BCCDDataset'
cfg.data.val.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.val.ann_file = 'val.json'
cfg.data.val.img_prefix = 'JPEGImages'

cfg.data.test.type = 'BCCDDataset'
cfg.data.test.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.test.ann_file = 'test.json'
cfg.data.test.img_prefix = 'JPEGImages'

# class의 갯수 수정. 
cfg.model.roi_head.bbox_head.num_classes = 3
# pretrained 모델
cfg.load_from = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'

# 학습 weight 파일로 로그를 저장하기 위한 디렉토리 설정. 
cfg.work_dir = './tutorial_exps'

# 학습율 변경 환경 파라미터 설정. 
cfg.optimizer.lr = 0.02 / 8
cfg.lr_config.warmup = None
cfg.log_config.interval = 10

# CocoDataset의 경우 metric을 bbox로 설정해야 함.(mAP아님. bbox로 설정하면 mAP를 iou threshold를 0.5 ~ 0.95까지 변경하면서 측정)
cfg.evaluation.metric = 'bbox'
cfg.evaluation.interval = 12
cfg.checkpoint_config.interval = 12

# 두번 config를 로드하면 lr_config의 policy가 사라지는 오류로 인하여 설정. 
cfg.lr_config.policy='step'
# Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)

print(cfg.pretty_text)
model = dict(
    type='FasterRCNN',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=3,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100)))
dataset_type = 'BCCDDataset'
data_root = '/content/BCCD_Dataset/BCCD/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='BCCDDataset',
        ann_file='train.json',
        img_prefix='JPEGImages',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        data_root='/content/BCCD_Dataset/BCCD/'),
    val=dict(
        type='BCCDDataset',
        ann_file='val.json',
        img_prefix='JPEGImages',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        data_root='/content/BCCD_Dataset/BCCD/'),
    test=dict(
        type='BCCDDataset',
        ann_file='test.json',
        img_prefix='JPEGImages',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        data_root='/content/BCCD_Dataset/BCCD/'))
evaluation = dict(interval=12, metric='bbox')
optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup=None,
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=12)
log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
resume_from = None
workflow = [('train', 1)]
work_dir = './tutorial_exps'
seed = 0
gpu_ids = range(0, 1)

Dataset을 만들고, 모델 학습 및 Inference 적용

from mmdet.datasets import build_dataset
from mmdet.models import build_detector
from mmdet.apis import train_detector

# train용 Dataset 생성. 
datasets = [build_dataset(cfg.data.train)]
# loading annotations into memory...
# Done (t=0.02s)
# creating index...
# index created!

print(datasets[0])
# datasets[0].__dict__ 로 모든 self variables의 key와 value값을 볼 수 있음. 
datasets[0].__dict__.keys()

# BCCDDataset Train dataset with number of images 205, and instance counts: 
# +----------+-------+----------+-------+---------------+-------+----------+-------+----------+-------+
# | category | count | category | count | category      | count | category | count | category | count |
# +----------+-------+----------+-------+---------------+-------+----------+-------+----------+-------+
# |          |       |          |       |               |       |          |       |          |       |
# | 0 [WBC]  | 214   | 1 [RBC]  | 2382  | 2 [Platelets] | 209   |          |       |          |       |
# +----------+-------+----------+-------+---------------+-------+----------+-------+----------+-------+
# dict_keys(['ann_file', 'data_root', 'img_prefix', 'seg_prefix', 'proposal_file', 'test_mode', 'filter_empty_gt', 'CLASSES', 'coco', 'cat_ids', 'cat2label', 'img_ids', 'data_infos', 'proposals', 'flag', 'pipeline'])

datasets[0].data_infos
# [{'file_name': 'BloodImage_00001.jpg',
#   'filename': 'BloodImage_00001.jpg',
#   'height': 480,
#   'id': 'BloodImage_00001',
#   'width': 640},
#    ...
#  {'file_name': 'BloodImage_00408.jpg',
#   'filename': 'BloodImage_00408.jpg',
#   'height': 480,
#   'id': 'BloodImage_00408',
#   'width': 640}]

datasets[0].pipeline
# Compose(
#     LoadImageFromFile(to_float32=False, color_type='color', file_client_args={'backend': 'disk'})
#     LoadAnnotations(with_bbox=True, with_label=True, with_mask=False, with_seg=False, poly2mask=True, poly2mask={'backend': 'disk'})
#     Resize(img_scale=[(1333, 800)], multiscale_mode=range, ratio_range=None, keep_ratio=True, bbox_clip_border=True)
#     RandomFlip(flip_ratio=0.5)
#     Normalize(mean=[123.675 116.28  103.53 ], std=[58.395 57.12  57.375], to_rgb=True)
#     Pad(size=None, size_divisor=32, pad_to_square=False, pad_val={'img': 0, 'masks': 0, 'seg': 255})
#     DefaultFormatBundle
#     Collect(keys=['img', 'gt_bboxes', 'gt_labels'], meta_keys=('filename', 'ori_filename', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg'))
# )

model = build_detector(cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg'))
model.CLASSES = datasets[0].CLASSES
print(model.CLASSES)
# ('WBC', 'RBC', 'Platelets')
# /usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/builder.py:17: UserWarning: ``build_anchor_generator`` would be deprecated soon, please use ``build_prior_generator`` 
#   '``build_anchor_generator`` would be deprecated soon, please use '

import os.path as osp
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# epochs는 config의 runner 파라미터로 지정됨. 기본 12회 
train_detector(model, datasets, cfg, distributed=False, validate=True) # val 불러와서 모델평가
2021-10-18 14:47:52,688 - mmdet - INFO - load checkpoint from /content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
2021-10-18 14:47:52,690 - mmdet - INFO - Use load_from_local loader
2021-10-18 14:47:52,824 - mmdet - WARNING - The model and loaded state dict do not match exactly

size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).
size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([4]).
size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([12, 1024]).
size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([12]).
2021-10-18 14:47:52,828 - mmdet - INFO - Start running, host: root@f8acc20ddcac, work_dir: /content/tutorial_exps
2021-10-18 14:47:52,834 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) CheckpointHook                     
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) NumClassCheckHook                  
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_iter:
(VERY_HIGH   ) StepLrUpdaterHook                  
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
 -------------------- 
after_train_iter:
(ABOVE_NORMAL) OptimizerHook                      
(NORMAL      ) CheckpointHook                     
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) CheckpointHook                     
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) NumClassCheckHook                  
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_epoch:
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
2021-10-18 14:47:52,835 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
/usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/anchor_generator.py:324: UserWarning: ``grid_anchors`` would be deprecated soon. Please use ``grid_priors`` 
  warnings.warn('``grid_anchors`` would be deprecated soon. '
/usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/anchor_generator.py:361: UserWarning: ``single_level_grid_anchors`` would be deprecated soon. Please use ``single_level_grid_priors`` 
  '``single_level_grid_anchors`` would be deprecated soon. '
2021-10-18 14:48:11,147 - mmdet - INFO - Epoch [1][10/103]	lr: 2.500e-03, eta: 0:36:37, time: 1.792, data_time: 0.238, memory: 3163, loss_rpn_cls: 0.1477, loss_rpn_bbox: 0.0886, loss_cls: 0.8380, acc: 66.0742, loss_bbox: 0.7945, loss: 1.8688
2021-10-18 14:48:26,850 - mmdet - INFO - Epoch [1][20/103]	lr: 2.500e-03, eta: 0:34:05, time: 1.572, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0410, loss_rpn_bbox: 0.0682, loss_cls: 0.4894, acc: 84.2676, loss_bbox: 0.6111, loss: 1.2097
2021-10-18 14:48:42,486 - mmdet - INFO - Epoch [1][30/103]	lr: 2.500e-03, eta: 0:33:00, time: 1.564, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0711, loss_cls: 0.3802, acc: 86.5137, loss_bbox: 0.4905, loss: 0.9619
2021-10-18 14:48:58,107 - mmdet - INFO - Epoch [1][40/103]	lr: 2.500e-03, eta: 0:32:20, time: 1.561, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0792, loss_cls: 0.3563, acc: 85.7715, loss_bbox: 0.4395, loss: 0.8997
2021-10-18 14:49:13,799 - mmdet - INFO - Epoch [1][50/103]	lr: 2.500e-03, eta: 0:31:51, time: 1.569, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0775, loss_cls: 0.3678, acc: 84.5312, loss_bbox: 0.4920, loss: 0.9588
2021-10-18 14:49:29,424 - mmdet - INFO - Epoch [1][60/103]	lr: 2.500e-03, eta: 0:31:25, time: 1.563, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0652, loss_cls: 0.3114, acc: 87.1484, loss_bbox: 0.4320, loss: 0.8256
2021-10-18 14:49:44,975 - mmdet - INFO - Epoch [1][70/103]	lr: 2.500e-03, eta: 0:31:01, time: 1.556, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0583, loss_cls: 0.3246, acc: 85.8203, loss_bbox: 0.4269, loss: 0.8280
2021-10-18 14:50:00,647 - mmdet - INFO - Epoch [1][80/103]	lr: 2.500e-03, eta: 0:30:41, time: 1.567, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0650, loss_cls: 0.2901, acc: 86.6016, loss_bbox: 0.3662, loss: 0.7446
2021-10-18 14:50:16,314 - mmdet - INFO - Epoch [1][90/103]	lr: 2.500e-03, eta: 0:30:22, time: 1.567, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0606, loss_cls: 0.2966, acc: 86.9824, loss_bbox: 0.3820, loss: 0.7508
2021-10-18 14:50:31,911 - mmdet - INFO - Epoch [1][100/103]	lr: 2.500e-03, eta: 0:30:02, time: 1.560, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0609, loss_cls: 0.2727, acc: 88.0469, loss_bbox: 0.3575, loss: 0.7150
2021-10-18 14:50:54,057 - mmdet - INFO - Epoch [2][10/103]	lr: 2.500e-03, eta: 0:29:10, time: 1.742, data_time: 0.233, memory: 3163, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0590, loss_cls: 0.2809, acc: 87.5391, loss_bbox: 0.3818, loss: 0.7377
2021-10-18 14:51:09,722 - mmdet - INFO - Epoch [2][20/103]	lr: 2.500e-03, eta: 0:28:55, time: 1.566, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0605, loss_cls: 0.2494, acc: 89.4141, loss_bbox: 0.3764, loss: 0.6997
2021-10-18 14:51:25,435 - mmdet - INFO - Epoch [2][30/103]	lr: 2.500e-03, eta: 0:28:40, time: 1.570, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0557, loss_cls: 0.2251, acc: 90.4688, loss_bbox: 0.3036, loss: 0.6002
2021-10-18 14:51:41,089 - mmdet - INFO - Epoch [2][40/103]	lr: 2.500e-03, eta: 0:28:25, time: 1.566, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0608, loss_cls: 0.2599, acc: 88.5352, loss_bbox: 0.3750, loss: 0.7120
2021-10-18 14:51:56,764 - mmdet - INFO - Epoch [2][50/103]	lr: 2.500e-03, eta: 0:28:10, time: 1.568, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0562, loss_cls: 0.2804, acc: 87.6074, loss_bbox: 0.3292, loss: 0.6856
2021-10-18 14:52:12,388 - mmdet - INFO - Epoch [2][60/103]	lr: 2.500e-03, eta: 0:27:55, time: 1.563, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0455, loss_cls: 0.2401, acc: 89.0723, loss_bbox: 0.2994, loss: 0.5979
2021-10-18 14:52:28,030 - mmdet - INFO - Epoch [2][70/103]	lr: 2.500e-03, eta: 0:27:39, time: 1.564, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0524, loss_cls: 0.2657, acc: 88.3008, loss_bbox: 0.3692, loss: 0.7033
2021-10-18 14:52:43,643 - mmdet - INFO - Epoch [2][80/103]	lr: 2.500e-03, eta: 0:27:23, time: 1.560, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0513, loss_cls: 0.2644, acc: 88.4668, loss_bbox: 0.3327, loss: 0.6608
2021-10-18 14:52:59,301 - mmdet - INFO - Epoch [2][90/103]	lr: 2.500e-03, eta: 0:27:08, time: 1.567, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0097, loss_rpn_bbox: 0.0493, loss_cls: 0.2427, acc: 89.4336, loss_bbox: 0.3167, loss: 0.6184
2021-10-18 14:53:14,918 - mmdet - INFO - Epoch [2][100/103]	lr: 2.500e-03, eta: 0:26:52, time: 1.561, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0551, loss_cls: 0.2767, acc: 87.0215, loss_bbox: 0.3340, loss: 0.6810
2021-10-18 14:53:37,139 - mmdet - INFO - Epoch [3][10/103]	lr: 2.500e-03, eta: 0:26:19, time: 1.750, data_time: 0.234, memory: 3163, loss_rpn_cls: 0.0064, loss_rpn_bbox: 0.0491, loss_cls: 0.2134, acc: 90.7812, loss_bbox: 0.3132, loss: 0.5822
2021-10-18 14:53:52,789 - mmdet - INFO - Epoch [3][20/103]	lr: 2.500e-03, eta: 0:26:04, time: 1.565, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0580, loss_cls: 0.2401, acc: 89.4336, loss_bbox: 0.3260, loss: 0.6377
2021-10-18 14:54:08,426 - mmdet - INFO - Epoch [3][30/103]	lr: 2.500e-03, eta: 0:25:49, time: 1.563, data_time: 0.046, memory: 3163, loss_rpn_cls: 0.0095, loss_rpn_bbox: 0.0522, loss_cls: 0.2173, acc: 90.1660, loss_bbox: 0.3065, loss: 0.5856
2021-10-18 14:54:24,065 - mmdet - INFO - Epoch [3][40/103]	lr: 2.500e-03, eta: 0:25:34, time: 1.564, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0107, loss_rpn_bbox: 0.0587, loss_cls: 0.2301, acc: 89.9121, loss_bbox: 0.3299, loss: 0.6293
2021-10-18 14:54:39,614 - mmdet - INFO - Epoch [3][50/103]	lr: 2.500e-03, eta: 0:25:19, time: 1.555, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0530, loss_cls: 0.2393, acc: 89.2285, loss_bbox: 0.3204, loss: 0.6281
2021-10-18 14:54:55,205 - mmdet - INFO - Epoch [3][60/103]	lr: 2.500e-03, eta: 0:25:04, time: 1.559, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0108, loss_rpn_bbox: 0.0466, loss_cls: 0.2780, acc: 86.8262, loss_bbox: 0.2879, loss: 0.6234
2021-10-18 14:55:10,793 - mmdet - INFO - Epoch [3][70/103]	lr: 2.500e-03, eta: 0:24:49, time: 1.559, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0498, loss_cls: 0.2277, acc: 90.0195, loss_bbox: 0.3022, loss: 0.5930
2021-10-18 14:55:26,348 - mmdet - INFO - Epoch [3][80/103]	lr: 2.500e-03, eta: 0:24:33, time: 1.556, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0486, loss_cls: 0.2372, acc: 89.6484, loss_bbox: 0.3007, loss: 0.5996
2021-10-18 14:55:41,922 - mmdet - INFO - Epoch [3][90/103]	lr: 2.500e-03, eta: 0:24:18, time: 1.557, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0096, loss_rpn_bbox: 0.0524, loss_cls: 0.2315, acc: 89.4434, loss_bbox: 0.3501, loss: 0.6436
2021-10-18 14:55:57,458 - mmdet - INFO - Epoch [3][100/103]	lr: 2.500e-03, eta: 0:24:02, time: 1.553, data_time: 0.046, memory: 3163, loss_rpn_cls: 0.0110, loss_rpn_bbox: 0.0490, loss_cls: 0.2349, acc: 89.6582, loss_bbox: 0.2973, loss: 0.5922
2021-10-18 14:56:19,591 - mmdet - INFO - Epoch [4][10/103]	lr: 2.500e-03, eta: 0:23:34, time: 1.739, data_time: 0.233, memory: 3163, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0588, loss_cls: 0.2577, acc: 88.3301, loss_bbox: 0.2968, loss: 0.6307
2021-10-18 14:56:35,179 - mmdet - INFO - Epoch [4][20/103]	lr: 2.500e-03, eta: 0:23:19, time: 1.559, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0088, loss_rpn_bbox: 0.0496, loss_cls: 0.2249, acc: 89.9219, loss_bbox: 0.2827, loss: 0.5660
2021-10-18 14:56:50,736 - mmdet - INFO - Epoch [4][30/103]	lr: 2.500e-03, eta: 0:23:04, time: 1.556, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0524, loss_cls: 0.2309, acc: 89.2188, loss_bbox: 0.3273, loss: 0.6242
2021-10-18 14:57:06,282 - mmdet - INFO - Epoch [4][40/103]	lr: 2.500e-03, eta: 0:22:49, time: 1.555, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0089, loss_rpn_bbox: 0.0451, loss_cls: 0.2151, acc: 90.6738, loss_bbox: 0.3046, loss: 0.5737
2021-10-18 14:57:21,844 - mmdet - INFO - Epoch [4][50/103]	lr: 2.500e-03, eta: 0:22:34, time: 1.556, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0106, loss_rpn_bbox: 0.0526, loss_cls: 0.2454, acc: 88.8770, loss_bbox: 0.3133, loss: 0.6219
2021-10-18 14:57:37,402 - mmdet - INFO - Epoch [4][60/103]	lr: 2.500e-03, eta: 0:22:19, time: 1.556, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0112, loss_rpn_bbox: 0.0513, loss_cls: 0.2401, acc: 89.4336, loss_bbox: 0.2884, loss: 0.5910
2021-10-18 14:57:52,980 - mmdet - INFO - Epoch [4][70/103]	lr: 2.500e-03, eta: 0:22:04, time: 1.557, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0554, loss_cls: 0.2413, acc: 89.2578, loss_bbox: 0.2870, loss: 0.5970
2021-10-18 14:58:08,563 - mmdet - INFO - Epoch [4][80/103]	lr: 2.500e-03, eta: 0:21:48, time: 1.559, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0454, loss_cls: 0.2204, acc: 90.3125, loss_bbox: 0.2928, loss: 0.5706
2021-10-18 14:58:24,113 - mmdet - INFO - Epoch [4][90/103]	lr: 2.500e-03, eta: 0:21:33, time: 1.555, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0106, loss_rpn_bbox: 0.0541, loss_cls: 0.2269, acc: 89.9609, loss_bbox: 0.3076, loss: 0.5992
2021-10-18 14:58:39,697 - mmdet - INFO - Epoch [4][100/103]	lr: 2.500e-03, eta: 0:21:18, time: 1.559, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0090, loss_rpn_bbox: 0.0505, loss_cls: 0.2398, acc: 89.2383, loss_bbox: 0.3206, loss: 0.6198
2021-10-18 14:59:01,793 - mmdet - INFO - Epoch [5][10/103]	lr: 2.500e-03, eta: 0:20:53, time: 1.741, data_time: 0.233, memory: 3163, loss_rpn_cls: 0.0099, loss_rpn_bbox: 0.0457, loss_cls: 0.2310, acc: 89.3457, loss_bbox: 0.2852, loss: 0.5719
2021-10-18 14:59:17,360 - mmdet - INFO - Epoch [5][20/103]	lr: 2.500e-03, eta: 0:20:38, time: 1.556, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0093, loss_rpn_bbox: 0.0483, loss_cls: 0.2239, acc: 89.6484, loss_bbox: 0.2795, loss: 0.5610
2021-10-18 14:59:33,025 - mmdet - INFO - Epoch [5][30/103]	lr: 2.500e-03, eta: 0:20:23, time: 1.566, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0110, loss_rpn_bbox: 0.0564, loss_cls: 0.2414, acc: 89.4238, loss_bbox: 0.3214, loss: 0.6302
2021-10-18 14:59:48,632 - mmdet - INFO - Epoch [5][40/103]	lr: 2.500e-03, eta: 0:20:08, time: 1.560, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0476, loss_cls: 0.2286, acc: 89.6875, loss_bbox: 0.2757, loss: 0.5645
2021-10-18 15:00:04,209 - mmdet - INFO - Epoch [5][50/103]	lr: 2.500e-03, eta: 0:19:53, time: 1.559, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0106, loss_rpn_bbox: 0.0517, loss_cls: 0.2213, acc: 90.2930, loss_bbox: 0.2719, loss: 0.5556
2021-10-18 15:00:19,786 - mmdet - INFO - Epoch [5][60/103]	lr: 2.500e-03, eta: 0:19:37, time: 1.558, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0102, loss_rpn_bbox: 0.0393, loss_cls: 0.1849, acc: 92.0898, loss_bbox: 0.2542, loss: 0.4886
2021-10-18 15:00:35,359 - mmdet - INFO - Epoch [5][70/103]	lr: 2.500e-03, eta: 0:19:22, time: 1.557, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0430, loss_cls: 0.2312, acc: 89.7266, loss_bbox: 0.2958, loss: 0.5815
2021-10-18 15:00:50,928 - mmdet - INFO - Epoch [5][80/103]	lr: 2.500e-03, eta: 0:19:07, time: 1.557, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0454, loss_cls: 0.2032, acc: 91.3281, loss_bbox: 0.2920, loss: 0.5536
2021-10-18 15:01:06,512 - mmdet - INFO - Epoch [5][90/103]	lr: 2.500e-03, eta: 0:18:52, time: 1.558, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0101, loss_rpn_bbox: 0.0529, loss_cls: 0.2109, acc: 90.4590, loss_bbox: 0.3155, loss: 0.5895
2021-10-18 15:01:22,111 - mmdet - INFO - Epoch [5][100/103]	lr: 2.500e-03, eta: 0:18:37, time: 1.560, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0537, loss_cls: 0.2242, acc: 89.6289, loss_bbox: 0.3117, loss: 0.6031
2021-10-18 15:01:44,312 - mmdet - INFO - Epoch [6][10/103]	lr: 2.500e-03, eta: 0:18:13, time: 1.746, data_time: 0.233, memory: 3163, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0506, loss_cls: 0.2074, acc: 90.7520, loss_bbox: 0.2752, loss: 0.5469
2021-10-18 15:01:59,964 - mmdet - INFO - Epoch [6][20/103]	lr: 2.500e-03, eta: 0:17:58, time: 1.565, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0074, loss_rpn_bbox: 0.0402, loss_cls: 0.2013, acc: 91.2305, loss_bbox: 0.2628, loss: 0.5118
2021-10-18 15:02:15,565 - mmdet - INFO - Epoch [6][30/103]	lr: 2.500e-03, eta: 0:17:43, time: 1.559, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0107, loss_rpn_bbox: 0.0541, loss_cls: 0.2096, acc: 91.0547, loss_bbox: 0.3057, loss: 0.5801
2021-10-18 15:02:31,228 - mmdet - INFO - Epoch [6][40/103]	lr: 2.500e-03, eta: 0:17:28, time: 1.567, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0102, loss_rpn_bbox: 0.0431, loss_cls: 0.2095, acc: 90.8008, loss_bbox: 0.3012, loss: 0.5640
2021-10-18 15:02:46,946 - mmdet - INFO - Epoch [6][50/103]	lr: 2.500e-03, eta: 0:17:13, time: 1.572, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0093, loss_rpn_bbox: 0.0565, loss_cls: 0.2169, acc: 90.1953, loss_bbox: 0.3056, loss: 0.5884
2021-10-18 15:03:02,644 - mmdet - INFO - Epoch [6][60/103]	lr: 2.500e-03, eta: 0:16:58, time: 1.569, data_time: 0.046, memory: 3163, loss_rpn_cls: 0.0087, loss_rpn_bbox: 0.0444, loss_cls: 0.1868, acc: 91.9824, loss_bbox: 0.2689, loss: 0.5088
2021-10-18 15:03:18,398 - mmdet - INFO - Epoch [6][70/103]	lr: 2.500e-03, eta: 0:16:43, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0076, loss_rpn_bbox: 0.0476, loss_cls: 0.2094, acc: 90.6543, loss_bbox: 0.3088, loss: 0.5733
2021-10-18 15:03:34,123 - mmdet - INFO - Epoch [6][80/103]	lr: 2.500e-03, eta: 0:16:28, time: 1.573, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0468, loss_cls: 0.2358, acc: 89.4141, loss_bbox: 0.2845, loss: 0.5796
2021-10-18 15:03:49,854 - mmdet - INFO - Epoch [6][90/103]	lr: 2.500e-03, eta: 0:16:13, time: 1.573, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0107, loss_rpn_bbox: 0.0418, loss_cls: 0.2364, acc: 89.4629, loss_bbox: 0.2580, loss: 0.5468
2021-10-18 15:04:05,618 - mmdet - INFO - Epoch [6][100/103]	lr: 2.500e-03, eta: 0:15:58, time: 1.576, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0515, loss_cls: 0.2274, acc: 90.1855, loss_bbox: 0.2945, loss: 0.5862
2021-10-18 15:04:27,956 - mmdet - INFO - Epoch [7][10/103]	lr: 2.500e-03, eta: 0:15:35, time: 1.757, data_time: 0.234, memory: 3163, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0552, loss_cls: 0.1878, acc: 91.8750, loss_bbox: 0.2967, loss: 0.5517
2021-10-18 15:04:43,678 - mmdet - INFO - Epoch [7][20/103]	lr: 2.500e-03, eta: 0:15:20, time: 1.572, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0113, loss_rpn_bbox: 0.0543, loss_cls: 0.2078, acc: 91.2988, loss_bbox: 0.2867, loss: 0.5601
2021-10-18 15:04:59,445 - mmdet - INFO - Epoch [7][30/103]	lr: 2.500e-03, eta: 0:15:05, time: 1.577, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0064, loss_rpn_bbox: 0.0436, loss_cls: 0.2141, acc: 90.7910, loss_bbox: 0.2589, loss: 0.5230
2021-10-18 15:05:15,222 - mmdet - INFO - Epoch [7][40/103]	lr: 2.500e-03, eta: 0:14:50, time: 1.578, data_time: 0.046, memory: 3163, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0425, loss_cls: 0.1989, acc: 91.2402, loss_bbox: 0.2928, loss: 0.5463
2021-10-18 15:05:30,968 - mmdet - INFO - Epoch [7][50/103]	lr: 2.500e-03, eta: 0:14:35, time: 1.574, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0069, loss_rpn_bbox: 0.0476, loss_cls: 0.1792, acc: 92.3438, loss_bbox: 0.2747, loss: 0.5085
2021-10-18 15:05:46,673 - mmdet - INFO - Epoch [7][60/103]	lr: 2.500e-03, eta: 0:14:20, time: 1.571, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0118, loss_rpn_bbox: 0.0427, loss_cls: 0.1889, acc: 91.3867, loss_bbox: 0.2528, loss: 0.4962
2021-10-18 15:06:02,363 - mmdet - INFO - Epoch [7][70/103]	lr: 2.500e-03, eta: 0:14:04, time: 1.569, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0059, loss_rpn_bbox: 0.0406, loss_cls: 0.2097, acc: 90.7910, loss_bbox: 0.2512, loss: 0.5075
2021-10-18 15:06:18,129 - mmdet - INFO - Epoch [7][80/103]	lr: 2.500e-03, eta: 0:13:49, time: 1.577, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0104, loss_rpn_bbox: 0.0499, loss_cls: 0.2154, acc: 90.3320, loss_bbox: 0.2776, loss: 0.5534
2021-10-18 15:06:33,890 - mmdet - INFO - Epoch [7][90/103]	lr: 2.500e-03, eta: 0:13:34, time: 1.576, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0420, loss_cls: 0.2420, acc: 89.1309, loss_bbox: 0.2749, loss: 0.5739
2021-10-18 15:06:49,605 - mmdet - INFO - Epoch [7][100/103]	lr: 2.500e-03, eta: 0:13:19, time: 1.571, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0089, loss_rpn_bbox: 0.0460, loss_cls: 0.2090, acc: 91.2109, loss_bbox: 0.2762, loss: 0.5402
2021-10-18 15:07:12,005 - mmdet - INFO - Epoch [8][10/103]	lr: 2.500e-03, eta: 0:12:57, time: 1.764, data_time: 0.234, memory: 3163, loss_rpn_cls: 0.0058, loss_rpn_bbox: 0.0533, loss_cls: 0.2009, acc: 91.5137, loss_bbox: 0.2877, loss: 0.5477
2021-10-18 15:07:27,751 - mmdet - INFO - Epoch [8][20/103]	lr: 2.500e-03, eta: 0:12:42, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0523, loss_cls: 0.2126, acc: 90.6543, loss_bbox: 0.2808, loss: 0.5536
2021-10-18 15:07:43,526 - mmdet - INFO - Epoch [8][30/103]	lr: 2.500e-03, eta: 0:12:27, time: 1.578, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0421, loss_cls: 0.1987, acc: 91.4160, loss_bbox: 0.2803, loss: 0.5331
2021-10-18 15:07:59,324 - mmdet - INFO - Epoch [8][40/103]	lr: 2.500e-03, eta: 0:12:12, time: 1.579, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0097, loss_rpn_bbox: 0.0436, loss_cls: 0.1987, acc: 91.4746, loss_bbox: 0.2670, loss: 0.5191
2021-10-18 15:08:15,074 - mmdet - INFO - Epoch [8][50/103]	lr: 2.500e-03, eta: 0:11:56, time: 1.575, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0104, loss_rpn_bbox: 0.0430, loss_cls: 0.2119, acc: 90.8203, loss_bbox: 0.2779, loss: 0.5433
2021-10-18 15:08:30,775 - mmdet - INFO - Epoch [8][60/103]	lr: 2.500e-03, eta: 0:11:41, time: 1.569, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0331, loss_cls: 0.1876, acc: 91.7578, loss_bbox: 0.2415, loss: 0.4749
2021-10-18 15:08:46,585 - mmdet - INFO - Epoch [8][70/103]	lr: 2.500e-03, eta: 0:11:26, time: 1.582, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0112, loss_rpn_bbox: 0.0361, loss_cls: 0.2252, acc: 89.8438, loss_bbox: 0.2437, loss: 0.5161
2021-10-18 15:09:02,432 - mmdet - INFO - Epoch [8][80/103]	lr: 2.500e-03, eta: 0:11:11, time: 1.584, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0099, loss_rpn_bbox: 0.0537, loss_cls: 0.1993, acc: 91.5234, loss_bbox: 0.2850, loss: 0.5480
2021-10-18 15:09:18,211 - mmdet - INFO - Epoch [8][90/103]	lr: 2.500e-03, eta: 0:10:56, time: 1.578, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0065, loss_rpn_bbox: 0.0534, loss_cls: 0.1822, acc: 92.0117, loss_bbox: 0.2840, loss: 0.5262
2021-10-18 15:09:33,972 - mmdet - INFO - Epoch [8][100/103]	lr: 2.500e-03, eta: 0:10:40, time: 1.576, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0079, loss_rpn_bbox: 0.0499, loss_cls: 0.1804, acc: 92.2559, loss_bbox: 0.2750, loss: 0.5133
2021-10-18 15:09:56,375 - mmdet - INFO - Epoch [9][10/103]	lr: 2.500e-04, eta: 0:10:19, time: 1.764, data_time: 0.235, memory: 3163, loss_rpn_cls: 0.0109, loss_rpn_bbox: 0.0461, loss_cls: 0.2038, acc: 91.1133, loss_bbox: 0.2733, loss: 0.5341
2021-10-18 15:10:12,120 - mmdet - INFO - Epoch [9][20/103]	lr: 2.500e-04, eta: 0:10:04, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0114, loss_rpn_bbox: 0.0459, loss_cls: 0.1855, acc: 91.9824, loss_bbox: 0.2692, loss: 0.5121
2021-10-18 15:10:27,837 - mmdet - INFO - Epoch [9][30/103]	lr: 2.500e-04, eta: 0:09:48, time: 1.571, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0085, loss_rpn_bbox: 0.0349, loss_cls: 0.1752, acc: 92.5488, loss_bbox: 0.2350, loss: 0.4536
2021-10-18 15:10:43,597 - mmdet - INFO - Epoch [9][40/103]	lr: 2.500e-04, eta: 0:09:33, time: 1.576, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0415, loss_cls: 0.1967, acc: 91.6895, loss_bbox: 0.2577, loss: 0.5040
2021-10-18 15:10:59,391 - mmdet - INFO - Epoch [9][50/103]	lr: 2.500e-04, eta: 0:09:18, time: 1.580, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0343, loss_cls: 0.1593, acc: 93.1445, loss_bbox: 0.2243, loss: 0.4261
2021-10-18 15:11:15,188 - mmdet - INFO - Epoch [9][60/103]	lr: 2.500e-04, eta: 0:09:03, time: 1.580, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0093, loss_rpn_bbox: 0.0392, loss_cls: 0.1843, acc: 92.0215, loss_bbox: 0.2262, loss: 0.4590
2021-10-18 15:11:30,975 - mmdet - INFO - Epoch [9][70/103]	lr: 2.500e-04, eta: 0:08:47, time: 1.578, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0394, loss_cls: 0.1610, acc: 93.2422, loss_bbox: 0.2546, loss: 0.4630
2021-10-18 15:11:46,744 - mmdet - INFO - Epoch [9][80/103]	lr: 2.500e-04, eta: 0:08:32, time: 1.578, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0070, loss_rpn_bbox: 0.0434, loss_cls: 0.1895, acc: 91.8262, loss_bbox: 0.2544, loss: 0.4944
2021-10-18 15:12:02,500 - mmdet - INFO - Epoch [9][90/103]	lr: 2.500e-04, eta: 0:08:17, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0069, loss_rpn_bbox: 0.0364, loss_cls: 0.1707, acc: 92.8613, loss_bbox: 0.2301, loss: 0.4440
2021-10-18 15:12:18,240 - mmdet - INFO - Epoch [9][100/103]	lr: 2.500e-04, eta: 0:08:01, time: 1.574, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0102, loss_rpn_bbox: 0.0396, loss_cls: 0.1664, acc: 92.9297, loss_bbox: 0.2421, loss: 0.4583
2021-10-18 15:12:40,644 - mmdet - INFO - Epoch [10][10/103]	lr: 2.500e-04, eta: 0:07:41, time: 1.762, data_time: 0.235, memory: 3163, loss_rpn_cls: 0.0087, loss_rpn_bbox: 0.0417, loss_cls: 0.1784, acc: 92.4121, loss_bbox: 0.2568, loss: 0.4856
2021-10-18 15:12:56,354 - mmdet - INFO - Epoch [10][20/103]	lr: 2.500e-04, eta: 0:07:25, time: 1.571, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0081, loss_rpn_bbox: 0.0380, loss_cls: 0.1600, acc: 93.1641, loss_bbox: 0.2438, loss: 0.4498
2021-10-18 15:13:12,156 - mmdet - INFO - Epoch [10][30/103]	lr: 2.500e-04, eta: 0:07:10, time: 1.581, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0433, loss_cls: 0.1610, acc: 93.0371, loss_bbox: 0.2484, loss: 0.4598
2021-10-18 15:13:27,901 - mmdet - INFO - Epoch [10][40/103]	lr: 2.500e-04, eta: 0:06:55, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0066, loss_rpn_bbox: 0.0392, loss_cls: 0.1850, acc: 91.7676, loss_bbox: 0.2658, loss: 0.4967
2021-10-18 15:13:43,628 - mmdet - INFO - Epoch [10][50/103]	lr: 2.500e-04, eta: 0:06:39, time: 1.572, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0416, loss_cls: 0.1646, acc: 93.3887, loss_bbox: 0.2515, loss: 0.4659
2021-10-18 15:13:59,433 - mmdet - INFO - Epoch [10][60/103]	lr: 2.500e-04, eta: 0:06:24, time: 1.581, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0089, loss_rpn_bbox: 0.0459, loss_cls: 0.1665, acc: 92.9102, loss_bbox: 0.2580, loss: 0.4793
2021-10-18 15:14:15,196 - mmdet - INFO - Epoch [10][70/103]	lr: 2.500e-04, eta: 0:06:08, time: 1.577, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0066, loss_rpn_bbox: 0.0378, loss_cls: 0.1633, acc: 93.1055, loss_bbox: 0.2305, loss: 0.4381
2021-10-18 15:14:30,971 - mmdet - INFO - Epoch [10][80/103]	lr: 2.500e-04, eta: 0:05:53, time: 1.577, data_time: 0.051, memory: 3163, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0348, loss_cls: 0.1681, acc: 92.8906, loss_bbox: 0.2326, loss: 0.4426
2021-10-18 15:14:46,770 - mmdet - INFO - Epoch [10][90/103]	lr: 2.500e-04, eta: 0:05:38, time: 1.581, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0106, loss_rpn_bbox: 0.0360, loss_cls: 0.1589, acc: 93.0371, loss_bbox: 0.2178, loss: 0.4232
2021-10-18 15:15:02,550 - mmdet - INFO - Epoch [10][100/103]	lr: 2.500e-04, eta: 0:05:22, time: 1.578, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0357, loss_cls: 0.1748, acc: 92.5098, loss_bbox: 0.2288, loss: 0.4508
2021-10-18 15:15:24,986 - mmdet - INFO - Epoch [11][10/103]	lr: 2.500e-04, eta: 0:05:02, time: 1.768, data_time: 0.235, memory: 3163, loss_rpn_cls: 0.0066, loss_rpn_bbox: 0.0436, loss_cls: 0.1668, acc: 92.7246, loss_bbox: 0.2477, loss: 0.4647
2021-10-18 15:15:40,764 - mmdet - INFO - Epoch [11][20/103]	lr: 2.500e-04, eta: 0:04:46, time: 1.577, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0059, loss_rpn_bbox: 0.0340, loss_cls: 0.1574, acc: 93.5645, loss_bbox: 0.2274, loss: 0.4246
2021-10-18 15:15:56,568 - mmdet - INFO - Epoch [11][30/103]	lr: 2.500e-04, eta: 0:04:31, time: 1.581, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0415, loss_cls: 0.1643, acc: 93.2520, loss_bbox: 0.2272, loss: 0.4409
2021-10-18 15:16:12,338 - mmdet - INFO - Epoch [11][40/103]	lr: 2.500e-04, eta: 0:04:16, time: 1.577, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0105, loss_rpn_bbox: 0.0426, loss_cls: 0.1746, acc: 92.7051, loss_bbox: 0.2327, loss: 0.4604
2021-10-18 15:16:28,127 - mmdet - INFO - Epoch [11][50/103]	lr: 2.500e-04, eta: 0:04:00, time: 1.579, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0105, loss_rpn_bbox: 0.0357, loss_cls: 0.1502, acc: 93.6719, loss_bbox: 0.2220, loss: 0.4184
2021-10-18 15:16:43,887 - mmdet - INFO - Epoch [11][60/103]	lr: 2.500e-04, eta: 0:03:45, time: 1.575, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0074, loss_rpn_bbox: 0.0433, loss_cls: 0.1712, acc: 92.9785, loss_bbox: 0.2520, loss: 0.4739
2021-10-18 15:16:59,694 - mmdet - INFO - Epoch [11][70/103]	lr: 2.500e-04, eta: 0:03:30, time: 1.581, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0077, loss_rpn_bbox: 0.0385, loss_cls: 0.1562, acc: 93.4082, loss_bbox: 0.2385, loss: 0.4409
2021-10-18 15:17:15,410 - mmdet - INFO - Epoch [11][80/103]	lr: 2.500e-04, eta: 0:03:14, time: 1.572, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0046, loss_rpn_bbox: 0.0372, loss_cls: 0.1741, acc: 92.6953, loss_bbox: 0.2438, loss: 0.4596
2021-10-18 15:17:31,165 - mmdet - INFO - Epoch [11][90/103]	lr: 2.500e-04, eta: 0:02:59, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0058, loss_rpn_bbox: 0.0340, loss_cls: 0.1713, acc: 92.7344, loss_bbox: 0.2241, loss: 0.4351
2021-10-18 15:17:46,930 - mmdet - INFO - Epoch [11][100/103]	lr: 2.500e-04, eta: 0:02:43, time: 1.577, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0061, loss_rpn_bbox: 0.0409, loss_cls: 0.1846, acc: 92.2852, loss_bbox: 0.2562, loss: 0.4879
2021-10-18 15:18:09,435 - mmdet - INFO - Epoch [12][10/103]	lr: 2.500e-05, eta: 0:02:23, time: 1.772, data_time: 0.239, memory: 3163, loss_rpn_cls: 0.0067, loss_rpn_bbox: 0.0461, loss_cls: 0.1533, acc: 93.8379, loss_bbox: 0.2564, loss: 0.4625
2021-10-18 15:18:25,199 - mmdet - INFO - Epoch [12][20/103]	lr: 2.500e-05, eta: 0:02:08, time: 1.576, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0078, loss_rpn_bbox: 0.0337, loss_cls: 0.1636, acc: 92.9395, loss_bbox: 0.2393, loss: 0.4444
2021-10-18 15:18:40,980 - mmdet - INFO - Epoch [12][30/103]	lr: 2.500e-05, eta: 0:01:52, time: 1.578, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0074, loss_rpn_bbox: 0.0378, loss_cls: 0.1535, acc: 93.3203, loss_bbox: 0.2322, loss: 0.4308
2021-10-18 15:18:56,750 - mmdet - INFO - Epoch [12][40/103]	lr: 2.500e-05, eta: 0:01:37, time: 1.577, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0078, loss_rpn_bbox: 0.0415, loss_cls: 0.1613, acc: 93.3594, loss_bbox: 0.2299, loss: 0.4405
2021-10-18 15:19:12,512 - mmdet - INFO - Epoch [12][50/103]	lr: 2.500e-05, eta: 0:01:21, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0061, loss_rpn_bbox: 0.0396, loss_cls: 0.1502, acc: 93.3105, loss_bbox: 0.2375, loss: 0.4334
2021-10-18 15:19:28,253 - mmdet - INFO - Epoch [12][60/103]	lr: 2.500e-05, eta: 0:01:06, time: 1.575, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0077, loss_rpn_bbox: 0.0355, loss_cls: 0.1670, acc: 92.8809, loss_bbox: 0.2007, loss: 0.4109
2021-10-18 15:19:43,993 - mmdet - INFO - Epoch [12][70/103]	lr: 2.500e-05, eta: 0:00:50, time: 1.574, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0359, loss_cls: 0.1480, acc: 93.9160, loss_bbox: 0.2361, loss: 0.4271
2021-10-18 15:19:59,777 - mmdet - INFO - Epoch [12][80/103]	lr: 2.500e-05, eta: 0:00:35, time: 1.578, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0109, loss_rpn_bbox: 0.0388, loss_cls: 0.1823, acc: 92.3828, loss_bbox: 0.2493, loss: 0.4813
2021-10-18 15:20:15,526 - mmdet - INFO - Epoch [12][90/103]	lr: 2.500e-05, eta: 0:00:20, time: 1.575, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0038, loss_rpn_bbox: 0.0387, loss_cls: 0.1636, acc: 93.0078, loss_bbox: 0.2283, loss: 0.4344
2021-10-18 15:20:31,262 - mmdet - INFO - Epoch [12][100/103]	lr: 2.500e-05, eta: 0:00:04, time: 1.573, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0065, loss_rpn_bbox: 0.0373, loss_cls: 0.1759, acc: 92.7246, loss_bbox: 0.2317, loss: 0.4514
2021-10-18 15:20:35,682 - mmdet - INFO - Saving checkpoint at 12 epochs
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 87/87, 2.7 task/s, elapsed: 32s, ETA:     0s2021-10-18 15:21:09,102 - mmdet - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
2021-10-18 15:21:10,170 - mmdet - INFO - Epoch(val) [12][87]	bbox_mAP: 0.6290, bbox_mAP_50: 0.8990, bbox_mAP_75: 0.7130, bbox_mAP_s: 0.1520, bbox_mAP_m: 0.4960, bbox_mAP_l: 0.7330, bbox_mAP_copypaste: 0.629 0.899 0.713 0.152 0.496 0.733
DONE (t=0.94s).
Accumulating evaluation results...
DONE (t=0.09s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.629
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.899
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.713
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.152
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.496
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.733
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.714
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.714
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.714
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.179
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.633
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.797

import cv2
from mmdet.apis import inference_detector, init_detector, show_result_pyplot

img = cv2.imread('/content/BCCD_Dataset/BCCD/JPEGImages/BloodImage_00007.jpg')

model.cfg = cfg

result = inference_detector(model, img)
show_result_pyplot(model, img, result)