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

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   ,
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       [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
# {"images": [{"file_name": "BloodImage_00001.jpg", "height": 480, "width": 640, "id": "BloodImage_00001"}, {"file_name": "BloodImage_00003.jpg", "height": 480, "width": 640, "id": "BloodImage_00003"}, {"file_name": "BloodImage_00004.jpg", "height": 480, "width": 640, "id": "BloodImage_00004"}, {"file_name": "BloodImage_00005.jpg", "height": 480, "width": 640, "id": "BloodImage_00005"}, {"file_name": "BloodImage_00006.jpg", "height": 480, "width": 640, "id": "BloodImage_00006"}, {"file_name": "BloodImage_00008.jpg", "height": 480, "width": 640, "id": "BloodImage_00008"}, {"file_name": "BloodImage_00009.jpg", "height": 480, "width": 640, "id": "BloodImage_00009"}, {"file_name": "BloodImage_00010.jpg", "height": 480, "width": 640, "id": "BloodImage_00010"}, {"file_name": "BloodImage_00012.jpg", "height": 480, "width": 640, "id": "BloodImage_00012"}, {"file_name": "BloodImage_00013.jpg", "height": 480, "width": 640, "id": "BloodImage_00013"}, {"file_name": "BloodImage_00020.jpg", "height": 480, "width": 640, "id": "BloodImage_00020"}, {"file_name": "BloodImage_00022.jpg", "height": 480, "width": 640, "id": "BloodImage_00022"}, {"file_name": "BloodImage_00023.jpg", "height": 480, "width": 640, "id": "BloodImage_00023"}, {"file_name": "BloodImage_00024.jpg", "height": 480, "width": 640, "id": "BloodImage_00024"}, {"file_name": "BloodImage_00026.jpg", "height": 480, "width": 640, "id": "BloodImage_00026"}, {"file_name": "BloodImage_00032.jpg", "height": 480, "width": 640, "id": "BloodImage_00032"}, {"file_name": "BloodImage_00034.jpg", "height": 480, "width": 640, "id": "BloodImage_00034"}, {"file_name": "BloodImage_00036.jpg", "height": 480, "width": 640, "id": "BloodImage_00036"}, {"file_name": "BloodImage_00038.jpg", "height": 480, "width": 640, "id": "BloodImage_00038"}, {"file_name": "BloodImage_00039.jpg", "height": 480, "width": 640, "id": "BloodImage_00039"}, {"file_name": "BloodImage_00040.jpg", "height": 480, "width": 640, "id": "BloodImage_00040"}, {"file_name": "BloodImage_00042.jpg", "height": 480, "width": 640, "id": "BloodImage_00042"}, {"file_name": "BloodImage_00043.jpg", "height": 480, "width": 640, "id": "BloodImage_00043"}, {"file_name": "BloodImage_00044.jpg", "height": 480, "width": 640, "id": "BloodImage_00044"}, {"file_name": "BloodImage_00045.jpg", "height": 480, "width": 640, "id": "BloodImage_00045"}, {"file_name": "BloodImage_00046.jpg", "height": 480, "width": 640, "id": "BloodImage_00046"}, {"file_name": "BloodImage_00047.jpg", "height": 480, "width": 640, "id": "BloodImage_00047"}, {"file_name": "BloodImage_00048.jpg", "height": 480, "width": 640, "id": "BloodImage_00048"}, {"file_name": "BloodImage_00049.jpg", "height": 480, "width": 640, "id": "BloodImage_00049"}, {"file_name": "BloodImage_00050.jpg", "height": 480, "width": 640, "id": "BloodImage_00050"}, {"file_name": "BloodImage_00052.jpg", "height": 480, "width": 640, "id": "BloodImage_00052"}, {"file_name": "BloodImage_00054.jpg", "height": 480, "width": 640, "id": "BloodImage_00054"}, {"file_name": "BloodImage_00056.jpg", "height": 480, "width": 640, "id": "BloodImage_00056"}, {"file_name": "BloodImage_00059.jpg", "height": 480, "width": 640, "id": "BloodImage_00059"}, {"file_name": "BloodImage_00069.jpg", "height": 480, "width": 640, "id": "BloodImage_00069"}, {"file_name": "BloodImage_00070.jpg", "height": 480, "width": 640, "id": "BloodImage_00070"}, {"file_name": "BloodImage_00071.jpg", "height": 480, "width": 640, "id": "BloodImage_00071"}, {"file_name": "BloodImage_00076.jpg", "height": 480, "width": 640, "id": "BloodImage_00076"}, {"file_name": "BloodImage_00078.jpg", "height": 480, "width": 640, "id": "BloodImage_00078"}, {"file_name": "BloodImage_00079.jpg", "height": 480, "width": 640, "id": "BloodImage_00079"}, {"file_name": "BloodImage_00081.jpg", "height": 480, "width": 640, "id": "BloodImage_00081"}, {"file_name": "BloodImage_00082.jpg", "height": 480, "width": 640, "id": "BloodImage_00082"}, {"file_name": "BloodImage_00083.jpg", "height": 480, "width": 640, "id": "BloodImage_00083"}, {"file_name": "BloodImage_00086.jpg", "height": 480, "width": 640, "id": "BloodImage_00086"}, {"file_name": "BloodImage_00087.jpg", "height": 480, "width": 640, "id": "BloodImage_00087"}, {"file_name": "BloodImage_00090.jpg", "height": 480, "width": 640, "id": "BloodImage_00090"}, {"file_name": "BloodImage_00091.jpg", "height": 480, "width": 640, "id": "BloodImage_00091"}, {"file_name": "BloodImage_00092.jpg", "height": 480, "width": 640, "id": "BloodImage_00092"}, {"file_name": "BloodImage_00094.jpg", "height": 480, "width": 640, "id": "BloodImage_00094"}, {"file_name": "BloodImage_00095.jpg", "height": 480, "width": 640, "id": "BloodImage_00095"}, {"file_name": "BloodImage_00097.jpg", "height": 480, "width": 640, "id": "BloodImage_00097"}, {"file_name": "BloodImage_00100.jpg", "height": 480, "width": 640, "id": "BloodImage_00100"}, {"file_name": "BloodImage_00101.jpg", "height": 480, "width": 640, "id": "BloodImage_00101"}, {"file_name": "BloodImage_00106.jpg", "height": 480, "width": 640, "id": "BloodImage_00106"}, {"file_name": "BloodImage_00107.jpg", "height": 480, "width": 640, "id": "BloodImage_00107"}, {"file_name": "BloodImage_00108.jpg", "height": 480, "width": 640, "id": "BloodImage_00108"}, {"file_name": "BloodImage_00110.jpg", "height": 480, "width": 640, "id": "BloodImage_00110"}, {"file_name": "BloodImage_00112.jpg", "height": 480, "width": 640, "id": "BloodImage_00112"}, {"file_name": "BloodImage_00113.jpg", "height": 480, "width": 640, "id": "BloodImage_00113"}, {"file_name": "BloodImage_00114.jpg", "height": 480, "width": 640, "id": "BloodImage_00114"}, {"file_name": "BloodImage_00117.jpg", "height": 480, "width": 640, "id": "BloodImage_00117"}, {"file_name": "BloodImage_00120.jpg", "height": 480, "width": 640, "id": "BloodImage_00120"}, {"file_name": "BloodImage_00123.jpg", "height": 480, "width": 640, "id": "BloodImage_00123"}, {"file_name": "BloodImage_00124.jpg", "height": 480, "width": 640, "id": "BloodImage_00124"}, {"file_name": "BloodImage_00126.jpg", "height": 480, "width": 640, "id": "BloodImage_00126"}, {"file_name": "BloodImage_00127.jpg", "height": 480, "width": 640, "id": "BloodImage_00127"}, {"file_name": "BloodImage_00133.jpg", "height": 480, "width": 640, "id": "BloodImage_00133"}, {"file_name": "BloodImage_00134.jpg", "height": 480, "width": 640, "id": "BloodImage_00134"}, {"file_name": "BloodImage_00139.jpg", "height": 480, "width": 640, "id": "BloodImage_00139"}, {"file_name": "BloodImage_00141.jpg", "height": 480, "width": 640, "id": "BloodImage_00141"}, {"file_name": "BloodImage_00142.jpg", "height": 480, "width": 640, "id": "BloodImage_00142"}, {"file_name": "BloodImage_00144.jpg", "height": 480, "width": 640, "id": "BloodImage_00144"}, {"file_name": "BloodImage_00150.jpg", "height": 480, "width": 640, "id": "BloodImage_00150"}, {"file_name": "BloodImage_00154.jpg", "height": 480, "width": 640, "id": "BloodImage_00154"}, {"file_name": "BloodImage_00156.jpg", "height": 480, "width": 640, "id": "BloodImage_00156"}, {"file_name": "BloodImage_00158.jpg", "height": 480, "width": 640, "id": "BloodImage_00158"}, {"file_name": "BloodImage_00159.jpg", "height": 480, "width": 640, "id": "BloodImage_00159"}, {"file_name": "BloodImage_00160.jpg", "height": 480, "width": 640, "id": "BloodImage_00160"}, {"file_name": "BloodImage_00163.jpg", "height": 480, "width": 640, "id": "BloodImage_00163"}, {"file_name": "BloodImage_00164.jpg", "height": 480, "width": 640, "id": "BloodImage_00164"}, {"file_name": "BloodImage_00165.jpg", "height": 480, "width": 640, "id": "BloodImage_00165"}, {"file_name": "BloodImage_00166.jpg", "height": 480, "width": 640, "id": "BloodImage_00166"}, {"file_name": "BloodImage_00168.jpg", "height": 480, "width": 640, "id": "BloodImage_00168"}, {"file_name": "BloodImage_00170.jpg", "height": 480, "width": 640, "id": "BloodImage_00170"}, {"file_name": "BloodImage_00174.jpg", "height": 480, "width": 640, "id": "BloodImage_00174"}, {"file_name": "BloodImage_00175.jpg", "height": 480, "width": 640, "id": "BloodImage_00175"}, {"file_name": "BloodImage_00176.jpg", "height": 480, "width": 640, "id": "BloodImage_00176"}, {"file_name": "BloodImage_00177.jpg", "height": 480, "width": 640, "id": "BloodImage_00177"}, {"file_name": "BloodImage_00179.jpg", "height": 480, "width": 640, "id": "BloodImage_00179"}, {"file_name": "BloodImage_00182.jpg", "height": 480, "width": 640, "id": "BloodImage_00182"}, {"file_name": "BloodImage_00187.jpg", "height": 480, "width": 640, "id": "BloodImage_00187"}, {"file_name": "BloodImage_00189.jpg", "height": 480, "width": 640, "id": "BloodImage_00189"}, {"file_name": "BloodImage_00190.jpg", "height": 480, "width": 640, "id": "BloodImage_00190"}, {"file_name": "BloodImage_00191.jpg", "height": 480, "width": 640, "id": "BloodImage_00191"}, {"file_name": "BloodImage_00195.jpg", "height": 480, "width": 640, "id": "BloodImage_00195"}, {"file_name": "BloodImage_00196.jpg", "height": 480, "width": 640, "id": "BloodImage_00196"}, {"file_name": "BloodImage_00197.jpg", "height": 480, "width": 640, "id": "BloodImage_00197"}, {"file_name": "BloodImage_00198.jpg", "height": 480, "width": 640, "id": "BloodImage_00198"}, {"file_name": "BloodImage_00199.jpg", "height": 480, "width": 640, "id": "BloodImage_00199"}, {"file_name": "BloodImage_00200.jpg", "height": 480, "width": 640, "id": "BloodImage_00200"}, {"file_name": "BloodImage_00201.jpg", "height": 480, "width": 640, "id": "BloodImage_00201"}, {"file_name": "BloodImage_00202.jpg", "height": 480, "width": 640, "id": "BloodImage_00202"}, {"file_name": "BloodImage_00203.jpg", "height": 480, "width": 640, "id": "BloodImage_00203"}, {"file_name": "BloodImage_00204.jpg", "height": 480, "width": 640, "id": "BloodImage_00204"}, {"file_name": "BloodImage_00206.jpg", "height": 480, "width": 640, "id": "BloodImage_00206"}, {"file_name": "BloodImage_00210.jpg", "height": 480, "width": 640, "id": "BloodImage_00210"}, {"file_name": "BloodImage_00211.jpg", "height": 480, "width": 640, "id": "BloodImage_00211"}, {"file_name": "BloodImage_00212.jpg", "height": 480, "width": 640, "id": "BloodImage_00212"}, {"file_name": "BloodImage_00214.jpg", "height": 480, "width": 640, "id": "BloodImage_00214"}, {"file_name": "BloodImage_00215.jpg", "height": 480, "width": 640, "id": "BloodImage_00215"}, {"file_name": "BloodImage_00224.jpg", "height": 480, "width": 640, "id": "BloodImage_00224"}, {"file_name": "BloodImage_00231.jpg", "height": 480, "width": 640, "id": "BloodImage_00231"}, {"file_name": "BloodImage_00232.jpg", "height": 480, "width": 640, "id": "BloodImage_00232"}, {"file_name": "BloodImage_00235.jpg", "height": 480, "width": 640, "id": "BloodImage_00235"}, {"file_name": "BloodImage_00237.jpg", "height": 480, "width": 640, "id": "BloodImage_00237"}, {"file_name": "BloodImage_00239.jpg", "height": 480, "width": 640, "id": "BloodImage_00239"}, {"file_name": "BloodImage_00240.jpg", "height": 480, "width": 640, "id": "BloodImage_00240"}, {"file_name": "BloodImage_00242.jpg", "height": 480, "width": 640, "id": "BloodImage_00242"}, {"file_name": "BloodImage_00243.jpg", "height": 480, "width": 640, "id": "BloodImage_00243"}, {"file_name": "BloodImage_00247.jpg", "height": 480, "width": 640, "id": "BloodImage_00247"}, {"file_name": "BloodImage_00248.jpg", "height": 480, "width": 640, "id": "BloodImage_00248"}, {"file_name": "BloodImage_00250.jpg", "height": 480, "width": 640, "id": "BloodImage_00250"}, {"file_name": "BloodImage_00251.jpg", "height": 480, "width": 640, "id": "BloodImage_00251"}, {"file_name": "BloodImage_00252.jpg", "height": 480, "width": 640, "id": "BloodImage_00252"}, {"file_name": "BloodImage_00254.jpg", "height": 480, "width": 640, "id": "BloodImage_00254"}, {"file_name": "BloodImage_00256.jpg", "height": 480, "width": 640, "id": "BloodImage_00256"}, {"file_name": "BloodImage_00257.jpg", "height": 480, "width": 640, "id": "BloodImage_00257"}, {"file_name": "BloodImage_00258.jpg", "height": 480, "width": 640, "id": "BloodImage_00258"}, {"file_name": "BloodImage_00259.jpg", "height": 480, "width": 640, "id": "BloodImage_00259"}, {"file_name": "BloodImage_00263.jpg", "height": 480, "width": 640, "id": "BloodImage_00263"}, {"file_name": "BloodImage_00264.jpg", "height": 480, "width": 640, "id": "BloodImage_00264"}, {"file_name": "BloodImage_00265.jpg", "height": 480, "width": 640, "id": "BloodImage_00265"}, {"file_name": "BloodImage_00266.jpg", "height": 480, "width": 640, "id": "BloodImage_00266"}, {"file_name": "BloodImage_00267.jpg", "height": 480, "width": 640, "id": "BloodImage_00267"}, {"file_name": "BloodImage_00268.jpg", "height": 480, "width": 640, "id": "BloodImage_00268"}, {"file_name": "BloodImage_00270.jpg", "height": 480, "width": 640, "id": "BloodImage_00270"}, {"file_name": "BloodImage_00273.jpg", "height": 480, "width": 640, "id": "BloodImage_00273"}, {"file_name": "BloodImage_00275.jpg", "height": 480, 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"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)

dddd

Config를 설정하고 COCO로 Pretrained 된 모델을 Download

• config파일은 faster rcnn resnet 50 backbone 사용.
• Oxford Pet 데이터는 학습에 시간에 소모 되므로 학습으로 생성된 모델을 Google Drive에 저장

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

!cd mmdetection; mkdir checkpoints
!wget -O ./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

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)]

# Google Drive 접근을 위한 Mount 적용. 
import os, sys 
from google.colab import drive 

drive.mount('/content/gdrive')

# soft link로 Google Drive Directory 연결. 
!ln -s /content/gdrive/My\ Drive/ /mydrive
!ls /mydrive

# Google Drive 밑에 Directory 생성. 이미 생성 되어 있을 시 오류 발생. 
!mkdir "/mydrive/pet_work_dir"

!nvidia-smi
Sun Oct 17 09:04:09 2021       
# +-----------------------------------------------------------------------------+
# | NVIDIA-SMI 470.74       Driver Version: 460.32.03    CUDA Version: 11.2     |
# |-------------------------------+----------------------+----------------------+
# | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
# | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
# |                               |                      |               MIG M. |
# |===============================+======================+======================|
# |   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |
# | N/A   73C    P8    35W / 149W |      3MiB / 11441MiB |      0%      Default |
# |                               |                      |                  N/A |
# +-------------------------------+----------------------+----------------------+
#                                                                                
# +-----------------------------------------------------------------------------+
# | Processes:                                                                  |
# |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
# |        ID   ID                                                   Usage      |
# |=============================================================================|
# |  No running processes found                                                 |
# +-----------------------------------------------------------------------------+

from mmdet.apis import set_random_seed

# dataset에 대한 환경 파라미터 수정. 
cfg.dataset_type = 'PetDataset'
cfg.data_root = '/content/data/'

# train, val, test dataset에 대한 type, data_root, ann_file, img_prefix 환경 파라미터 수정. 
cfg.data.train.type = 'PetDataset'
cfg.data.train.data_root = '/content/data/'
cfg.data.train.ann_file = 'train.txt'
cfg.data.train.img_prefix = 'images'

cfg.data.val.type = 'PetDataset'
cfg.data.val.data_root = '/content/data/'
cfg.data.val.ann_file = 'val.txt'
cfg.data.val.img_prefix = 'images'

# class의 갯수 수정. 
cfg.model.roi_head.bbox_head.num_classes = 37
# pretrained 모델
cfg.load_from = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'

# 학습 weight 파일로 로그를 저장하기 위한 디렉토리로 구글 Drive 설정. 
cfg.work_dir = '/mydrive/pet_work_dir'

# 학습율 변경 환경 파라미터 설정. 
cfg.optimizer.lr = 0.02 / 8
cfg.lr_config.warmup = None
cfg.log_config.interval = 5

cfg.runner.max_epochs = 5

# 평가 metric 설정. 
cfg.evaluation.metric = 'mAP'
# 평가 metric 수행할 epoch interval 설정. 
cfg.evaluation.interval = 5
# 학습 iteration시마다 모델을 저장할 epoch interval 설정. 
cfg.checkpoint_config.interval = 5

# 학습 시 Batch size 설정(단일 GPU 별 Batch size로 설정됨)
cfg.data.samples_per_gpu = 4 
# 3000을 2장씩 1500번보다 4장씩 725번이라 좀더 빨리진
# 근데 너무 높이면, gpu 메모리를 많이 먹어서 다운됨

# Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
# 두번 config를 로드하면 lr_config의 policy가 사라지는 오류로 인하여 설정. 
cfg.lr_config.policy='step'
# We can initialize the logger for training and have a look
# at the final config used for training
print(f'Config:\n{cfg.pretty_text}')

Config:
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=37,
            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 = 'PetDataset'
data_root = '/content/data/'
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=4,
    workers_per_gpu=2,
    train=dict(
        type='PetDataset',
        ann_file='train.txt',
        img_prefix='images',
        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/data/'),
    val=dict(
        type='PetDataset',
        ann_file='val.txt',
        img_prefix='images',
        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/data/'),
    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=5, metric='mAP')
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=5)
checkpoint_config = dict(interval=5)
log_config = dict(interval=5, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
resume_from = None
workflow = [('train', 1)]
work_dir = '/mydrive/pet_work_dir'
seed = 0
gpu_ids = range(0, 1)

Train용 데이터를 생성하고 Oxford Dataset을 학습수행.

• build_dataset()로 train config 설정에 따른 Train용 dataset 생성.
• build_detector()로 train과 test config반영하여 model 생성.
• train_detector()로 model 학습.

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)]

datasets
# [
#  PetDataset Train dataset with number of images 3304, and instance counts: 
#  +-----------------------+-------+-------------------------+-------+-------------------------------+-------+---------------------+-------+---------------------------------+-------+
#  | category              | count | category                | count | category                      | count | category            | count | category                        | count |
#  +-----------------------+-------+-------------------------+-------+-------------------------------+-------+---------------------+-------+---------------------------------+-------+
#  | 0 [Abyssinian]        | 89    | 1 [american_bulldog]    | 90    | 2 [american_pit_bull_terrier] | 90    | 3 [basset_hound]    | 90    | 4 [beagle]                      | 90    |
#  | 5 [Bengal]            | 89    | 6 [Birman]              | 90    | 7 [Bombay]                    | 86    | 8 [boxer]           | 90    | 9 [British_Shorthair]           | 90    |
#  | 10 [chihuahua]        | 90    | 11 [Egyptian_Mau]       | 81    | 12 [english_cocker_spaniel]   | 86    | 13 [english_setter] | 90    | 14 [german_shorthaired]         | 90    |
#  | 15 [great_pyrenees]   | 90    | 16 [havanese]           | 90    | 17 [japanese_chin]            | 90    | 18 [keeshond]       | 90    | 19 [leonberger]                 | 90    |
#  | 20 [Maine_Coon]       | 90    | 21 [miniature_pinscher] | 90    | 22 [newfoundland]             | 87    | 23 [Persian]        | 90    | 24 [pomeranian]                 | 90    |
#  | 25 [pug]              | 90    | 26 [Ragdoll]            | 89    | 27 [Russian_Blue]             | 90    | 28 [saint_bernard]  | 89    | 29 [samoyed]                    | 90    |
#  | 30 [scottish_terrier] | 90    | 31 [shiba_inu]          | 90    | 32 [Siamese]                  | 89    | 33 [Sphynx]         | 90    | 34 [staffordshire_bull_terrier] | 90    |
#  |                       |       |                         |       |                               |       |                     |       |                                 |       |
#  | 35 [wheaten_terrier]  | 90    | 36 [yorkshire_terrier]  | 90    |                               |       |                     |       |                                 |       |
#  +-----------------------+-------+-------------------------+-------+-------------------------------+-------+---------------------+-------+---------------------------------+-------+]

%cd mmdetection

model = build_detector(cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg'))
model.CLASSES = datasets[0].CLASSES

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)

/content/mmdetection
/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 '
2021-10-17 09:04:41,047 - mmdet - INFO - load checkpoint from checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
2021-10-17 09:04:41,048 - mmdet - INFO - Use load_from_local loader
2021-10-17 09:04:41,215 - 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([38, 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([38]).
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([148, 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([148]).
2021-10-17 09:04:41,226 - mmdet - INFO - Start running, host: root@bcf0fa707f92, work_dir: /mydrive/pet_work_dir
2021-10-17 09:04:41,228 - 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                     
 -------------------- 
2021-10-17 09:04:41,231 - mmdet - INFO - workflow: [('train', 1)], max: 5 epochs
/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. '
2021-10-17 09:05:01,399 - mmdet - INFO - Epoch [1][5/827]	lr: 2.500e-03, eta: 4:29:38, time: 3.917, data_time: 0.507, memory: 9645, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0100, loss_cls: 2.2307, acc: 58.4863, loss_bbox: 0.1100, loss: 2.3629
# torch의 epoch방식 // batch size를 고려한  827, 1로 하면 3312개 // eta : 종료시간계산 // time  걸린 시간// memory : gpu메모리//      rpn  : rpn찾는 loss,// bbox찾는 loss        //                     main acc //                       main loss

# 런타임->런타임 다시 시작 후 아래 수행. 
from mmdet.apis import init_detector, inference_detector
import mmcv

Oxford Pet Dataset 다운로드

image와 annotations을 압축파일로 각각 download 수행.

!wget https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
!wget https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz

# /content/data 디렉토리를 만들고 해당 디렉토리에 다운로드 받은 압축 파일 풀기.
!mkdir /content/data
!tar -xvf images.tar.gz -C /content/data
!tar -xvf annotations.tar.gz -C /content/data

이미지 디렉토리와 annotation 파일 살펴 보기

!ls -lia ./data/images/Abyss*.jpg

!ls -lia ./data/images

!cat ./data/annotations/xmls/Abyssinian_1.xml

import glob
import xml.etree.ElementTree as ET

# annotation xml 파일 파싱해서 bbox정보 추출
def get_bboxes_from_xml_test(xml_file):
  tree = ET.parse(xml_file)
  root = tree.getroot()
  bbox_names = []
  bboxes = []
  # 파일내에 있는 모든 object Element를 찾음. 
  for obj in root.findall('object'):

    bbox_name = obj.find('name').text
    xmlbox = obj.find('bndbox')
    x1 = int(xmlbox.find('xmin').text)
    y1 = int(xmlbox.find('ymin').text)
    x2 = int(xmlbox.find('xmax').text)
    y2 = int(xmlbox.find('ymax').text)

    bbox_names.append(bbox_name)
    bboxes.append([x1, y1, x2, y2])

  return bbox_names, bboxes

get_bboxes_from_xml_test('./data/annotations/xmls/Abyssinian_1.xml')
# (['cat'], [[333, 72, 425, 158]])

!ls -lia ./data/annotations/xmls/Abys*.xml

train, val image/annotation 메타 파일 보기

• train과 valid 데이터로 나뉠 image와 annotation의 파일명을 가지는 메타 파일
• train과 valid용 meta 파일을 별도로 만듬.

!cd ./data/annotations; cat trainval.txt

import pandas as pd

pet_df = pd.read_csv('./data/annotations/trainval.txt', sep=' ', header=None, names=['img_name', 'class_id', 'etc1', 'etc2'])
pet_df.head()

img_name	class_id	etc1	etc2
0	Abyssinian_100	1	1	1
1	Abyssinian_101	1	1	1
2	Abyssinian_102	1	1	1
3	Abyssinian_103	1	1	1
4	Abyssinian_104	1	1	1

pet_df['class_id'].value_counts()
37    100
22    100
34    100
32    100
30    100
28    100
26    100
24    100
20    100
35    100
18    100
16    100
14    100
10    100
6     100
4     100
36    100
1     100
3     100
19    100
31    100
29    100
27    100
25    100
5     100
21    100
17    100
15    100
11    100
9     100
7     100
2     100
33     99
23     96
13     96
8      96
12     93
Name: class_id, dtype: int64

pet_df['class_name'] = pet_df['img_name'].apply(lambda x:x[:x.rfind('_')])
pet_df.head()
	img_name	class_id	etc1	etc2	class_name
0	Abyssinian_100	1	1	1	Abyssinian
1	Abyssinian_101	1	1	1	Abyssinian
2	Abyssinian_102	1	1	1	Abyssinian
3	Abyssinian_103	1	1	1	Abyssinian
4	Abyssinian_104	1	1	1	Abyssinian

from sklearn.model_selection import train_test_split

train_df, val_df = train_test_split(pet_df, test_size=0.1, stratify=pet_df['class_id'], random_state=2021)

print(train_df['class_id'].value_counts(), val_df['class_id'].value_counts())
37    90
22    90
34    90
32    90
30    90
28    90
26    90
24    90
20    90
35    90
18    90
16    90
14    90
10    90
6     90
4     90
36    90
1     90
3     90
19    90
31    90
29    90
27    90
25    90
5     90
21    90
17    90
15    90
11    90
9     90
7     90
2     90
33    89
23    87
13    86
8     86
12    84
Name: class_id, dtype: int64 37    10
36    10
17    10
16    10
15    10
14    10
13    10
11    10
10    10
9     10
8     10
7     10
6     10
5     10
4     10
3     10
2     10
18    10
19    10
20    10
21    10
35    10
34    10
33    10
32    10
31    10
30    10
29    10
28    10
27    10
26    10
25    10
24    10
22    10
1     10
12     9
23     9
Name: class_id, dtype: int64

train_df = train_df.sort_values(by='img_name')
val_df = val_df.sort_values(by='img_name')

# ann_file로 주어지는 메타파일은 가급적이면 소스데이터의 가장 상단 디렉토리에 저장하는 것이 바람직. 
train_df['img_name'].to_csv('./data/train.txt', sep=' ', header=False, index=False)
val_df['img_name'].to_csv('./data/val.txt', sep=' ', header=False, index=False)

pet_classes_list = pet_df['class_name'].unique().tolist()
print(pet_classes_list)
# ['Abyssinian', 'american_bulldog', 'american_pit_bull_terrier', 'basset_hound', 'beagle', 'Bengal', 'Birman', 'Bombay', 'boxer', 'British_Shorthair', 'chihuahua', 'Egyptian_Mau', 'english_cocker_spaniel', 'english_setter', 'german_shorthaired', 'great_pyrenees', 'havanese', 'japanese_chin', 'keeshond', 'leonberger', 'Maine_Coon', 'miniature_pinscher', 'newfoundland', 'Persian', 'pomeranian', 'pug', 'Ragdoll', 'Russian_Blue', 'saint_bernard', 'samoyed', 'scottish_terrier', 'shiba_inu', 'Siamese', 'Sphynx', 'staffordshire_bull_terrier', 'wheaten_terrier', 'yorkshire_terrier']

!echo 'train list #####'; cat ./data/train.txt
train list #####
Abyssinian_1
Abyssinian_10
...
yorkshire_terrier_188
yorkshire_terrier_189

!echo ' valid list ###'; cat ./data/val.txt
 valid list ###
Abyssinian_100
Abyssinian_11
Abyssinian_122
...
yorkshire_terrier_185
yorkshire_terrier_190

mmdetection의 중립 annotation 포맷 변환

• CLASSES 는 pet_df의 'class_name' 컬럼에 unique 데이터로 지정. class id는 tuple(list)형의 CLASSES의 index값에 따라 설정.
• ann_file로 입력되는 메타 파일을 읽어서 개별 image정보와 ann 정보를 dict로 생성하여 data_infos list에 입력
• 개별 XML 읽어서 ann 정보를 만드는 것은 get_bboxes_from_xml() 함수 이용.
• 디버깅용으로 CustomDataset을 만들어서 미리 테스트 하는 방법도 고려.

[ ]

import xml.etree.ElementTree as ET

# 1개의 annotation 파일에서 bbox 정보 추출. 여러개의 object가 있을 경우 이들 object의 name과 bbox 좌표들을 list로 반환.
def get_bboxes_from_xml(anno_dir, xml_file):
  anno_xml_file = osp.join(anno_dir, xml_file)
  tree = ET.parse(anno_xml_file)
  root = tree.getroot()
  bbox_names = []
  bboxes = []

  # 파일내에 있는 모든 object Element를 찾음. 
  for obj in root.findall('object'):
    #obj.find('name').text는 cat 이나 dog을 반환     
    #bbox_name = obj.find('name').text
    # object의 클래스명은 파일명에서 추출. 
    bbox_name = xml_file[:xml_file.rfind('_')]

    xmlbox = obj.find('bndbox')
    x1 = int(xmlbox.find('xmin').text)
    y1 = int(xmlbox.find('ymin').text)
    x2 = int(xmlbox.find('xmax').text)
    y2 = int(xmlbox.find('ymax').text)

    bboxes.append([x1, y1, x2, y2])
    bbox_names.append(bbox_name)

  return bbox_names, bboxes

PET_CLASSES = pet_df['class_name'].unique().tolist()
PET_CLASSES

['Abyssinian',
 'american_bulldog',
 'american_pit_bull_terrier',
 'basset_hound',
 'beagle',
 'Bengal',
 'Birman',
 'Bombay',
 'boxer',
 'British_Shorthair',
 'chihuahua',
 'Egyptian_Mau',
 'english_cocker_spaniel',
 'english_setter',
 'german_shorthaired',
 'great_pyrenees',
 'havanese',
 'japanese_chin',
 'keeshond',
 'leonberger',
 'Maine_Coon',
 'miniature_pinscher',
 'newfoundland',
 'Persian',
 'pomeranian',
 'pug',
 'Ragdoll',
 'Russian_Blue',
 'saint_bernard',
 'samoyed',
 'scottish_terrier',
 'shiba_inu',
 'Siamese',
 'Sphynx',
 'staffordshire_bull_terrier',
 'wheaten_terrier',
 'yorkshire_terrier']

import copy
import os.path as osp

import mmcv
import numpy as np
import cv2

from mmdet.datasets.builder import DATASETS
from mmdet.datasets.custom import CustomDataset

import xml.etree.ElementTree as ET

PET_CLASSES = pet_df['class_name'].unique().tolist()

@DATASETS.register_module(force=True)
class PetDataset(CustomDataset):
  CLASSES = PET_CLASSES

  # annotation에 대한 모든 파일명을 가지고 있는 텍스트 파일을 __init__(self, ann_file)로 입력 받고, 
  # 이 self.ann_file이 load_annotations()의 인자로 입력
  def load_annotations(self, ann_file):
    cat2label = {k:i for i, k in enumerate(self.CLASSES)}
    image_list = mmcv.list_from_file(self.ann_file)
    # 포맷 중립 데이터를 담을 list 객체
    data_infos = []

    for image_id in image_list:
      # self.img_prefix는 images 가 입력될 것임. 
      filename = '{0:}/{1:}.jpg'.format(self.img_prefix, image_id)
      # 원본 이미지의 너비, 높이를 image를 직접 로드하여 구함. 
      image = cv2.imread(filename)
      height, width = image.shape[:2]
      # 개별 image의 annotation 정보 저장용 Dict 생성. key값 filename에는 image의 파일명만 들어감(디렉토리는 제외)
      data_info = {'filename': filename,
                  'width': width, 'height': height}
      # 개별 annotation XML 파일이 있는 서브 디렉토리의 prefix 변환. 
      label_prefix = self.img_prefix.replace('images', 'annotations')
      
      # 개별 annotation XML 파일을 1개 line 씩 읽어서 list 로드. annotation XML파일이 xmls 밑에 있음에 유의
      anno_xml_file = osp.join(label_prefix, 'xmls/'+str(image_id)+'.xml')
      # 메타 파일에는 이름이 있으나 실제로는 존재하지 않는 XML이 있으므로 이는 제외. 
      if not osp.exists(anno_xml_file):
          continue
      
      # get_bboxes_from_xml() 를 이용하여 개별 XML 파일에 있는 이미지의 모든 bbox 정보를 list 객체로 생성. 
      anno_dir = osp.join(label_prefix, 'xmls')
      bbox_names, bboxes = get_bboxes_from_xml(anno_dir, str(image_id)+'.xml')
      #print('#########:', bbox_names)
                  
      gt_bboxes = []
      gt_labels = []
      gt_bboxes_ignore = []
      gt_labels_ignore = []
        
      # bbox별 Object들의 class name을 class id로 매핑. class id는 tuple(list)형의 CLASSES의 index값에 따라 설정
      for bbox_name, bbox in zip(bbox_names, bboxes):
        # 만약 bbox_name이 클래스명에 해당 되면, gt_bboxes와 gt_labels에 추가, 그렇지 않으면 gt_bboxes_ignore, gt_labels_ignore에 추가
        # bbox_name이 CLASSES중에 반드시 하나 있어야 함. 안 그러면 FILTERING 되므로 주의 할것. 
        if bbox_name in cat2label:
            gt_bboxes.append(bbox)
            # gt_labels에는 class id를 입력
            gt_labels.append(cat2label[bbox_name])
        else:
            gt_bboxes_ignore.append(bbox)
            gt_labels_ignore.append(-1)
      
      # 개별 image별 annotation 정보를 가지는 Dict 생성. 해당 Dict의 value값을 np.array형태로 bbox의 좌표와 label값으로 생성. 
      data_anno = {
        'bboxes': np.array(gt_bboxes, dtype=np.float32).reshape(-1, 4),
        'labels': np.array(gt_labels, dtype=np.long),
        'bboxes_ignore': np.array(gt_bboxes_ignore, dtype=np.float32).reshape(-1, 4),
        'labels_ignore': np.array(gt_labels_ignore, dtype=np.long)
      }
      
      # image에 대한 메타 정보를 가지는 data_info Dict에 'ann' key값으로 data_anno를 value로 저장. 
      data_info.update(ann=data_anno)
      # 전체 annotation 파일들에 대한 정보를 가지는 data_infos에 data_info Dict를 추가
      data_infos.append(data_info)
      #print(data_info)

    return data_infos

# 디버깅 용도로 생성한 클래스를 생성하고 data_infos를 10개만 추출하여 생성된 데이터 확인. 
train_ds = PetDataset_imsi(data_root='/content/data', ann_file='train.txt', img_prefix='images')
print(train_ds.data_infos[:10])
[{'filename': '/content/data/images/Abyssinian_1.jpg', 'width': 600, 'height': 400, 'ann': {'bboxes': array([[333.,  72., 425., 158.]], dtype=float32), 'labels': array([0]), 'bboxes_ignore': array([], shape=(0, 4), dtype=float32), 'labels_ignore': array([], dtype=int64)}}, {'filename': '/content/data/images/Abyssinian_10.jpg', 'width': 375, 'height': 500, 'ann': {'bboxes': array([[ 72., 105., 288., 291.]], dtype=float32), 'labels': array([0]), 'bboxes_ignore': array([], shape=(0, 4), dtype=float32), 'labels_ignore': array([], dtype=int64)}}, {'filename': '/content/data/images/Abyssinian_101.jpg', 'width': 450, 'height': 313, 'ann': {'bboxes': array([[ 54.,  36., 319., 235.]], dtype=float32), 'labels': array([0]), 'bboxes_ignore': array([], shape=(0, 4), dtype=float32), 'labels_ignore': array([], dtype=int64)}}, {'filename': '/content/data/images/Abyssinian_102.jpg', 'width': 500, 'height': 465, 'ann': {'bboxes': array([[ 23.,  27., 325., 320.]], dtype=float32), 'labels': array([0]), 'bboxes_ignore': array([], shape=(0, 4), dtype=float32), 'labels_ignore': array([], dtype=int64)}}, {'filename': '/content/data/images/Abyssinian_103.jpg', 'width': 500, 'height': 351, 'ann': {'bboxes': array([[241.,  68., 362., 196.]], dtype=float32), 'labels': array([0]), 'bboxes_ignore': array([], shape=(0, 4), dtype=float32), 'labels_ignore': array([], dtype=int64)}}, {'filename': '/content/data/images/Abyssinian_105.jpg', 'width': 500, 'height': 375, 'ann': {'bboxes': array([[237., 101., 373., 227.]], dtype=float32), 'labels': array([0]), 'bboxes_ignore': array([], shape=(0, 4), dtype=float32), 'labels_ignore': array([], dtype=int64)}}, {'filename': '/content/data/images/Abyssinian_106.jpg', 'width': 1536, 'height': 1024, 'ann': {'bboxes': array([[ 861.,  156., 1302.,  563.]], dtype=float32), 'labels': array([0]), 'bboxes_ignore': array([], shape=(0, 4), dtype=float32), 'labels_ignore': array([], dtype=int64)}}, {'filename': '/content/data/images/Abyssinian_107.jpg', 'width': 500, 'height': 448, 'ann': {'bboxes': array([[ 94.,  76., 275., 271.]], dtype=float32), 'labels': array([0]), 'bboxes_ignore': array([], shape=(0, 4), dtype=float32), 'labels_ignore': array([], dtype=int64)}}, {'filename': '/content/data/images/Abyssinian_108.jpg', 'width': 500, 'height': 404, 'ann': {'bboxes': array([[ 50.,  14., 336., 304.]], dtype=float32), 'labels': array([0]), 'bboxes_ignore': array([], shape=(0, 4), dtype=float32), 'labels_ignore': array([], dtype=int64)}}, {'filename': '/content/data/images/Abyssinian_109.jpg', 'width': 282, 'height': 450, 'ann': {'bboxes': array([[ 81.,   7., 246., 146.]], dtype=float32), 'labels': array([0]), 'bboxes_ignore': array([], shape=(0, 4), dtype=float32), 'labels_ignore': array([], dtype=int64)}}]