인프런 커뮤니티 질문&답변
작성한 질문수
efficientdet 학습시 'numpy.float64' object cannot be interpreted as an integer 오류
작성
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336
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이포크 5번 수행하고 validation 결과 출력시 오류가 나옵니다. 해결방법이 궁금합니다.
답변 2
0
권 철민
지식공유자
해당 오류 메시지를 보면, 학습은 제대로 되지만 coco evaluation으로 평가가 안됩니다.
git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI 에서 오류가 나온다고 하셨는데, 이 부분이 conda 환경에서 제대로 셋업이 되어야 할 것 같습니다.
그리고 code를 바꾼게 없으신건가요?
바꾸신 부분이 있으시면 적어주십시요. (전체 코드를 올려달라는게 소스 코드를 올려 주시라는 거지, 전체 output을 올려 달라는 애기는 아닙니다. 올려주신거 드래그 하느라 애먹었습니다 ^^)
권 철민
지식공유자
https://github.com/cocodataset/cocoapi/tree/master/PythonAPI 에 가셔서 setup.py를 이용해서 직접 install 해보시는게 어떨까요?
0
권 철민
지식공유자
colab이 아니라 개인 pc에서 수행하신것 같군요. 그리고 Esri 강의 실습코드에서 이렇게 나온다는 건가요?
기존 강의 실습 코드에서 변경한 부분이 있으시면 올려주십시요. 가능하면 전체 코드를요.
김태근
질문자
전체코드를 올립니다.
pip install -r requirements.txt에서 git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI 이 분이 설치오류가 나서 git+https://github.com/philferriere/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI로 대체해서 설치했습니다.
automl efficientdet 다운로드 및 설치
!git clone --depth 1 https://github.com/google/automl
Cloning into 'automl'...
remote: Enumerating objects: 155, done.
remote: Counting objects: 100% (155/155), done.
remote: Compressing objects: 100% (152/152), done.
remote: Total 155 (delta 16), reused 62 (delta 0), pack-reused 0
Receiving objects: 100% (155/155), 11.99 MiB | 29.36 MiB/s, done.
Resolving deltas: 100% (16/16), done.
!pip install -r requirements.txt
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting pycocotools
Cloning https://github.com/philferriere/cocoapi.git to c:\users\public\documents\estsoft\creatortemp\pip-install-d99izz2p\pycocotools_de0a17a6b99f4fb9bae9e002693b33f1
Resolved https://github.com/philferriere/cocoapi.git to commit 2929bd2ef6b451054755dfd7ceb09278f935f7ad
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Running command git clone -q https://github.com/philferriere/cocoapi.git 'C:\Users\Public\Documents\ESTsoft\CreatorTemp\pip-install-d99izz2p\pycocotools_de0a17a6b99f4fb9bae9e002693b33f1'
!nvidia-smi
Sun Dec 5 12:13:45 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 465.89 Driver Version: 465.89 CUDA Version: 11.3 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... WDDM | 00000000:01:00.0 On | N/A |
| 31% 46C P8 34W / 420W | 23958MiB / 24576MiB | 2% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1216 C+G Insufficient Permissions N/A |
| 0 N/A N/A 3712 C+G ...bbwe\Microsoft.Photos.exe N/A |
| 0 N/A N/A 3992 C ...da3\envs\tf270\python.exe N/A |
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| 0 N/A N/A 13352 C+G ...3.0.8.0\GoogleDriveFS.exe N/A |
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| 0 N/A N/A 14572 C+G ...n\Quick Menu\CNQMMAIN.EXE N/A |
+-----------------------------------------------------------------------------+
import os
import sys
import tensorflow.compat.v1 as tf
sys.path.append('E:/efd_work/automl/efficientdet')
import hparams_config
from tf2 import anchors
from model_inspect import ModelInspector
ESRI Object Detection Challenge Dataset 다운로드 및 파일 살펴 보기
Annotation은 PASCAL VOC 와 동일한 XML 포맷
os.makedirs('../../poolncar')
!wget https://github.com/chulminkw/DLCV/releases/download/1.0/swimming_pool_and_car.zip -o ../../poolncar
!unzip swimming_pool_and_car.zip -d ../../poolncar
wget: ../../poolncar: Permission denied
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inflating: ../../poolncar/training_data/training_data/labels/000003735.xml
inflating: ../../poolncar/training_data/training_data/labels/000003736.xml
inflating: ../../poolncar/training_data/training_data/labels/000003737.xml
inflating: ../../poolncar/training_data/training_data/labels/000003738.xml
inflating: ../../poolncar/training_data/training_data/labels/000003739.xml
inflating: ../../poolncar/training_data/training_data/labels/000003740.xml
inflating: ../../poolncar/training_data/training_data/labels/000003741.xml
inflating: ../../poolncar/training_data/training_data/labels/000003742.xml
inflating: ../../poolncar/training_data/training_data/labels/000003743.xml
inflating: ../../poolncar/training_data/training_data/labels/000003744.xml
inflating: ../../poolncar/training_data/training_data/labels/000003745.xml
inflating: ../../poolncar/training_data/training_data/labels/000003746.xml
inflating: ../../poolncar/training_data/training_data/labels/000003747.xml
import glob
import pandas as pd
import xml.etree.ElementTree as ET
import math
CLASSES = ['1','2']
# XML 파일을 Pandas DataFrame으로 변환 한뒤 DataFrame의 to_csv()를 이용하여 csv 파일로 생성하고 DataFrame반환
def get_bboxes_from_xml(xml_file):
# xml 파일을 parsing하여 XML Element형태의 Element Tree를 생성하여 object 정보를 추출.
tree = ET.parse(xml_file)
root = tree.getroot()
bboxes = []
bbox_names = []
if root.iter('object') is not None:
for obj in root.iter('object'):
bbox_name = obj.find('name').text
if bbox_name not in CLASSES:
continue
xmlbox = obj.find('bndbox')
# 위치 좌표가 소수점까지 표시됨. pixel 단위는 정수형이므로 변환하되 ceil로 조금 이동하여 변환
x1 = math.ceil(float(xmlbox.find('xmin').text))
y1 = math.ceil(float(xmlbox.find('ymin').text))
x2 = math.ceil(float(xmlbox.find('xmax').text))
y2 = math.ceil(float(xmlbox.find('ymax').text))
if x1 == x2 or y1 == y2:
continue
bbox = [x1, y1, x2, y2]
bboxes.append(bbox)
bbox_names.append(bbox_name)
return bbox_names, bboxes
bbox_names, bboxes = get_bboxes_from_xml('../../poolncar/training_data/training_data/labels/000000000.xml')
print('object별 class 명:', bbox_names)
print('object별 bbox 정보 list:', bboxes)
object별 class 명: ['1', '1', '1', '1']
object별 bbox 정보 list: [[59, 153, 70, 164], [11, 206, 22, 217], [41, 0, 51, 4], [47, 42, 58, 53]]
import matplotlib.pyplot as plt
import cv2
%matplotlib inline
img_rgb = cv2.cvtColor(cv2.imread('../../poolncar/training_data/training_data/images/000000002.jpg'), cv2.COLOR_BGR2RGB)
bbox_names, bboxes = get_bboxes_from_xml('../../poolncar/training_data/training_data/labels/000000002.xml')
for box_name, box in zip(bbox_names, bboxes):
cv2.rectangle(img_rgb, (box[0], box[1]), (box[2], box[3]), color=(0, 255, 0), thickness=1)
cv2.putText(img_rgb, box_name, (int(box[0]), int(box[1] - 7)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 1)
plt.figure(figsize=(12, 12))
plt.imshow(img_rgb)
<matplotlib.image.AxesImage at 0x221a4700f10>
import os
ANNO_DIR = '../../poolncar/training_data/training_data/labels'
file_list = os.listdir(ANNO_DIR)
xml_files = sorted([file for file in file_list if file.endswith('.xml')])
print('xml file list:', xml_files)
print('xml file 개수:', len(xml_files))
xml file list: ['000000000.xml', '000000001.xml', '000000002.xml', '000000003.xml', '000000004.xml', '000000005.xml', '000000006.xml', '000000007.xml', '000000008.xml', '000000009.xml', '000000010.xml', '000000011.xml', '000000012.xml', '000000013.xml', '000000014.xml', '000000015.xml', '000000016.xml', '000000017.xml', '000000018.xml', '000000019.xml', '000000020.xml', '000000021.xml', '000000022.xml', '000000023.xml', '000000024.xml', '000000025.xml', '000000026.xml', '000000027.xml', '000000028.xml', '000000029.xml', '000000030.xml', '000000031.xml', '000000032.xml', '000000033.xml', '000000034.xml', '000000035.xml', '000000036.xml', '000000037.xml', '000000038.xml', '000000039.xml', '000000040.xml', '000000041.xml', '000000042.xml', '000000043.xml', '000000044.xml', '000000045.xml', '000000046.xml', '000000047.xml', '000000048.xml', '000000049.xml', '000000050.xml', '000000051.xml', '000000052.xml', '000000053.xml', '000000054.xml', '000000055.xml', '000000056.xml', 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xml file 개수: 3748
개별 XML 정보와 이미지를 tf.train.Example로 변경하는 함수 생성.
https://github.com/google/automl/blob/master/efficientdet/dataset/create_pascal_tfrecord.py 참조
tfrecord.png
import xml.etree.ElementTree as ET
# xml 을 파싱해서 image와 object의 메타정보를 dict로 반환하는 함수
def get_anno_dict_from_xml(xml_filepath):
tree = ET.parse(xml_filepath)
root = tree.getroot()
bbox_names = []
objects = []
filename = root.find('filename').text
size = root.find('size')
width = int(size.find('width').text)
height = int(size.find('height').text)
# 파일내에 있는 모든 object Element를 찾음.
for obj in root.findall('object'):
name = obj.find('name').text
pose = 'Unspecified'
#truncated, poses, difficult_obj는 사용되지 않지만 create_pascal_voc.py 파일과의 호환성을 유지하기 위해 설정.
truncated = 0
difficult = 0
occluded = 0
#pose = obj.find('pose').text
#truncated = int(obj.find('truncated').text)
#difficult = int(obj.find('difficult').text)
#occluded = int(obj.find('occluded').text)
xmlbox = obj.find('bndbox')
# 위치 좌표가 소수점까지 표시됨. pixel 단위는 정수형이므로 변환하되 ceil로 조금 이동하여 변환
xmin = math.ceil(float(xmlbox.find('xmin').text))
ymin = math.ceil(float(xmlbox.find('ymin').text))
xmax = math.ceil(float(xmlbox.find('xmax').text))
ymax = math.ceil(float(xmlbox.find('ymax').text))
bbox = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax
}
single_obj = {'name':name, 'pose':pose, 'truncated':truncated, 'difficult':difficult, 'occluded':occluded,
'bndbox':bbox}
objects.append(single_obj)
anno_dict = {
'folder':'training_data', 'filename':filename, 'width':width, 'height':height,
'object':objects
}
return anno_dict
anno_dict = get_anno_dict_from_xml('../../poolncar/training_data/training_data/labels/000000000.xml')
anno_dict
{'folder': 'training_data',
'filename': '000000000.jpg',
'width': 224,
'height': 224,
'object': [{'name': '1',
'pose': 'Unspecified',
'truncated': 0,
'difficult': 0,
'occluded': 0,
'bndbox': {'xmin': 59, 'ymin': 153, 'xmax': 70, 'ymax': 164}},
{'name': '1',
'pose': 'Unspecified',
'truncated': 0,
'difficult': 0,
'occluded': 0,
'bndbox': {'xmin': 11, 'ymin': 206, 'xmax': 22, 'ymax': 217}},
{'name': '1',
'pose': 'Unspecified',
'truncated': 0,
'difficult': 0,
'occluded': 0,
'bndbox': {'xmin': 41, 'ymin': 0, 'xmax': 51, 'ymax': 4}},
{'name': '1',
'pose': 'Unspecified',
'truncated': 0,
'difficult': 0,
'occluded': 0,
'bndbox': {'xmin': 47, 'ymin': 42, 'xmax': 58, 'ymax': 53}}]}
# image와 object의 고유 id 부여.
class Unique_Id(object):
def __init__(self):
self.image_id = 0
self.ann_id = 0
def get_image_id(self):
self.image_id += 1
return self.image_id
def get_ann_id(self):
self.ann_id += 1
return self.ann_id
# https://github.com/google/automl/blob/master/efficientdet/dataset/create_pascal_tfrecord.py 참조
import hashlib
import io
import json
import os
import PIL.Image
import tensorflow as tf
from dataset import tfrecord_util
# 1개의 image 파일 PATH와 1개의 annotation XML 정보를 가지는 data dic를 이용하여 tf.train.Example를 생성.
def dict_to_tf_example(data, image_path, label_map_dict, unique_id, ignore_difficult_instances=False, ann_json_dict=None, debug=True):
'''
data는 1개의 xml 파일을 dictionary로 변환 anno_dict,
image는 1개의 xml에 매핑되는 image 파일의 절대 경로
unique_id는 고유한 image와 object id를 만들기 위한 Unique_Id객체
'''
#JPEG image를 binary 그대로 읽음.
with tf.io.gfile.GFile(image_path, 'rb') as fid:
encoded_jpg = fid.read()
# image가 JPEG 타입인지 확인.
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
# image의 고유 key값 생성.
key = hashlib.sha256(encoded_jpg).hexdigest()
#고유한 image id를 생성.
image_id = unique_id.get_image_id()
# image의 width와 height 가져옴.
width = data['width']
height = data['height']
xmins, ymins, xmaxes, ymaxes = [], [], [], []
areas, classes, classes_texts = [], [], []
truncated, poses, difficult_obj = [], [], []
# 만일 annotation에 1개 이상의 object가 있다면,
if 'object' in data:
# data(anno_dict)의 'object' key값으로 개별 object 별 annotation 정보를 dict로 가지는 list로 구성. 이 list를 iteration 수행.
for obj in data['object']:
difficult = bool(int(obj['difficult']))
if ignore_difficult_instances and difficult:
continue
# object 명이 '1', '2'가 아니면 제외.
if obj['name'] not in label_map_dict:
continue
difficult_obj.append(int(difficult))
# 개별 좌표 값을 이미지의 크기로 정규화하고 list로 저장.
xmins.append(float(obj['bndbox']['xmin']) / width)
ymins.append(float(obj['bndbox']['ymin']) / height)
xmaxes.append(float(obj['bndbox']['xmax']) / width)
ymaxes.append(float(obj['bndbox']['ymax']) / height)
areas.append((xmaxes[-1] - xmins[-1]) * (ymaxes[-1] - ymins[-1]))
# class명과 class_id를 list로 저장.
classes_texts.append(obj['name'].encode('utf8'))
classes.append(label_map_dict[obj['name']])
# truncated, poses, difficult_obj는 사용되지 않지만 create_pascal_voc.py 파일과의 호환성을 유지하기 위해 저장.
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
difficult_obj.append(obj['difficult'])
example_dict = {'height':height, 'width':width, 'filename':data['filename'].encode('utf8'),
'source_id': str(image_id).encode('utf8'), 'key_sha256': key.encode('utf8'),
'encoded': encoded_jpg, 'format':'jpeg'.encode('utf8'),
'xmin':xmins, 'xmax':xmaxes, 'ymin':ymins, 'ymax':ymaxes,
'area':areas, 'class_text':classes_texts, 'class_label':classes,
'difficult':difficult_obj, 'truncated':truncated, 'poses':poses}
if debug:
print('example_dict:', example_dict)
example = make_tfrecord_example(example_dict)
return example
import tensorflow as tf
from dataset import tfrecord_util
# 인자로 들어온 dict에 따라 tf.train.Example을 생성.
def make_tfrecord_example(example_dict):
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/height': tfrecord_util.int64_feature(example_dict['height']),
'image/width': tfrecord_util.int64_feature(example_dict['width']),
'image/filename': tfrecord_util.bytes_feature(example_dict['filename']),
'image/source_id': tfrecord_util.bytes_feature(example_dict['source_id']),
'image/key/sha256': tfrecord_util.bytes_feature(example_dict['key_sha256']),
'image/encoded': tfrecord_util.bytes_feature(example_dict['encoded']),
'image/format': tfrecord_util.bytes_feature('jpeg'.encode('utf8')),
'image/object/bbox/xmin': tfrecord_util.float_list_feature(example_dict['xmin']),
'image/object/bbox/xmax': tfrecord_util.float_list_feature(example_dict['xmax']),
'image/object/bbox/ymin': tfrecord_util.float_list_feature(example_dict['ymin']),
'image/object/bbox/ymax': tfrecord_util.float_list_feature(example_dict['ymax']),
'image/object/area': tfrecord_util.float_list_feature(example_dict['area']),
'image/object/class/text': tfrecord_util.bytes_list_feature(example_dict['class_text']),
'image/object/class/label': tfrecord_util.int64_list_feature(example_dict['class_label']),
'image/object/difficult': tfrecord_util.int64_list_feature(example_dict['difficult']),
'image/object/truncated': tfrecord_util.int64_list_feature(example_dict['truncated']),
'image/object/view': tfrecord_util.bytes_list_feature(example_dict['poses']),
}))
return example
label_map_dict = {
'1': 1,
'2': 2
}
# 테스트 용도로 한개의 Example 생성해 보기
unique_id = Unique_Id()
data = get_anno_dict_from_xml('../../poolncar/training_data/training_data/labels/000000000.xml')
print('## xml 파일을 data dic로 변경 결과:', data)
image_path = '../../poolncar/training_data/training_data/images/000000000.jpg'
example = dict_to_tf_example(data, image_path, label_map_dict, unique_id, ignore_difficult_instances=False, ann_json_dict=None)
## xml 파일을 data dic로 변경 결과: {'folder': 'training_data', 'filename': '000000000.jpg', 'width': 224, 'height': 224, 'object': [{'name': '1', 'pose': 'Unspecified', 'truncated': 0, 'difficult': 0, 'occluded': 0, 'bndbox': {'xmin': 59, 'ymin': 153, 'xmax': 70, 'ymax': 164}}, {'name': '1', 'pose': 'Unspecified', 'truncated': 0, 'difficult': 0, 'occluded': 0, 'bndbox': {'xmin': 11, 'ymin': 206, 'xmax': 22, 'ymax': 217}}, {'name': '1', 'pose': 'Unspecified', 'truncated': 0, 'difficult': 0, 'occluded': 0, 'bndbox': {'xmin': 41, 'ymin': 0, 'xmax': 51, 'ymax': 4}}, {'name': '1', 'pose': 'Unspecified', 'truncated': 0, 'difficult': 0, 'occluded': 0, 'bndbox': {'xmin': 47, 'ymin': 42, 'xmax': 58, 'ymax': 53}}]}
example_dict: {'height': 224, 'width': 224, 'filename': b'000000000.jpg', 'source_id': b'1', 'key_sha256': b'5ec019893b646aa03ec7cacb33b9038e4cc0c41848c6ea056074c1ae29c79bc4', 'encoded': 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example
features {
feature {
key: "image/encoded"
value {
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value {
bytes_list {
value: "Unspecified"
value: "Unspecified"
value: "Unspecified"
value: "Unspecified"
}
}
}
feature {
key: "image/source_id"
value {
bytes_list {
value: "1"
}
}
}
feature {
key: "image/width"
value {
int64_list {
value: 224
}
}
}
}
학습과 검증용 TFRecord 파일들을 생성.
학습과 검증용 DataFrame으로 학습, 검증 데이터세트 분류
개별 Example을 생성하는 dict_to_tf_example()을 이용하여 모든 데이터 세트를 TFRecord로 생성.
file_id_list = sorted([xml_file[:xml_file.rfind('.xml')] for xml_file in os.listdir('../../poolncar/training_data/training_data/labels') \
if '.xml' in xml_file])
print(file_id_list)
len(file_id_list)
['000000000', '000000001', '000000002', '000000003', '000000004', '000000005', '000000006', '000000007', '000000008', '000000009', '000000010', '000000011', '000000012', '000000013', '000000014', '000000015', '000000016', '000000017', '000000018', '000000019', '000000020', '000000021', '000000022', '000000023', '000000024', '000000025', '000000026', '000000027', '000000028', '000000029', '000000030', '000000031', '000000032', '000000033', '000000034', '000000035', '000000036', '000000037', '000000038', '000000039', '000000040', '000000041', '000000042', '000000043', '000000044', '000000045', '000000046', '000000047', '000000048', '000000049', '000000050', '000000051', '000000052', '000000053', '000000054', '000000055', '000000056', '000000057', '000000058', '000000059', '000000060', '000000061', '000000062', '000000063', '000000064', '000000065', '000000066', '000000067', '000000068', '000000069', '000000070', '000000071', '000000072', '000000073', '000000074', '000000075', '000000076', '000000077', '000000078', '000000079', '000000080', '000000081', '000000082', '000000083', '000000084', '000000085', '000000086', '000000087', '000000088', '000000089', '000000090', '000000091', '000000092', '000000093', '000000094', '000000095', '000000096', '000000097', '000000098', '000000099', '000000100', '000000101', '000000102', '000000103', '000000104', '000000105', '000000106', '000000107', '000000108', '000000109', '000000110', '000000111', '000000112', '000000113', '000000114', '000000115', '000000116', '000000117', '000000118', '000000119', '000000120', '000000121', '000000122', '000000123', '000000124', '000000125', '000000126', '000000127', '000000128', '000000129', '000000130', '000000131', '000000132', '000000133', '000000134', '000000135', '000000136', '000000137', '000000138', '000000139', '000000140', '000000141', '000000142', '000000143', '000000144', '000000145', '000000146', '000000147', '000000148', '000000149', '000000150', '000000151', '000000152', '000000153', '000000154', '000000155', '000000156', '000000157', '000000158', '000000159', '000000160', '000000161', '000000162', '000000163', '000000164', '000000165', '000000166', '000000167', '000000168', '000000169', '000000170', '000000171', '000000172', '000000173', '000000174', '000000175', '000000176', '000000177', '000000178', '000000179', '000000180', '000000181', '000000182', '000000183', '000000184', '000000185', '000000186', '000000187', '000000188', '000000189', '000000190', '000000191', '000000192', '000000193', '000000194', '000000195', '000000196', '000000197', '000000198', '000000199', '000000200', '000000201', '000000202', '000000203', '000000204', '000000205', '000000206', '000000207', '000000208', '000000209', '000000210', '000000211', '000000212', '000000213', '000000214', '000000215', '000000216', '000000217', '000000218', '000000219', '000000220', '000000221', '000000222', '000000223', '000000224', '000000225', '000000226', '000000227', '000000228', '000000229', '000000230', '000000231', '000000232', '000000233', '000000234', '000000235', '000000236', '000000237', '000000238', '000000239', '000000240', '000000241', '000000242', '000000243', '000000244', '000000245', '000000246', '000000247', '000000248', '000000249', '000000250', '000000251', '000000252', '000000253', '000000254', '000000255', '000000256', '000000257', '000000258', '000000259', '000000260', '000000261', '000000262', '000000263', '000000264', '000000265', '000000266', '000000267', '000000268', '000000269', '000000270', '000000271', '000000272', '000000273', '000000274', '000000275', '000000276', '000000277', '000000278', '000000279', '000000280', '000000281', '000000282', '000000283', '000000284', '000000285', '000000286', '000000287', '000000288', '000000289', '000000290', '000000291', '000000292', '000000293', '000000294', '000000295', '000000296', '000000297', '000000298', '000000299', '000000300', '000000301', '000000302', '000000303', '000000304', '000000305', '000000306', '000000307', '000000308', '000000309', '000000310', '000000311', '000000312', '000000313', '000000314', '000000315', '000000316', '000000317', '000000318', '000000319', '000000320', '000000321', '000000322', '000000323', '000000324', '000000325', '000000326', '000000327', '000000328', '000000329', '000000330', '000000331', '000000332', '000000333', '000000334', '000000335', '000000336', '000000337', '000000338', '000000339', '000000340', '000000341', '000000342', '000000343', '000000344', '000000345', '000000346', '000000347', '000000348', '000000349', '000000350', '000000351', '000000352', '000000353', '000000354', '000000355', '000000356', '000000357', '000000358', '000000359', '000000360', '000000361', '000000362', '000000363', '000000364', '000000365', '000000366', '000000367', '000000368', '000000369', '000000370', '000000371', '000000372', '000000373', '000000374', '000000375', '000000376', '000000377', '000000378', '000000379', '000000380', '000000381', '000000382', '000000383', '000000384', '000000385', '000000386', '000000387', '000000388', '000000389', '000000390', '000000391', '000000392', '000000393', '000000394', '000000395', '000000396', '000000397', '000000398', '000000399', '000000400', '000000401', '000000402', '000000403', '000000404', '000000405', '000000406', '000000407', '000000408', '000000409', '000000410', '000000411', '000000412', '000000413', '000000414', '000000415', '000000416', '000000417', '000000418', '000000419', '000000420', '000000421', '000000422', '000000423', '000000424', '000000425', '000000426', '000000427', '000000428', '000000429', '000000430', '000000431', '000000432', '000000433', '000000434', '000000435', '000000436', '000000437', '000000438', '000000439', '000000440', '000000441', '000000442', '000000443', '000000444', '000000445', '000000446', '000000447', '000000448', '000000449', '000000450', '000000451', '000000452', '000000453', '000000454', '000000455', '000000456', '000000457', '000000458', '000000459', '000000460', '000000461', '000000462', '000000463', '000000464', '000000465', '000000466', '000000467', '000000468', '000000469', '000000470', '000000471', '000000472', '000000473', '000000474', '000000475', '000000476', '000000477', '000000478', '000000479', '000000480', '000000481', '000000482', '000000483', '000000484', '000000485', '000000486', '000000487', '000000488', '000000489', '000000490', '000000491', '000000492', '000000493', '000000494', '000000495', '000000496', '000000497', '000000498', '000000499', '000000500', '000000501', '000000502', '000000503', '000000504', '000000505', '000000506', '000000507', '000000508', '000000509', '000000510', '000000511', '000000512', '000000513', '000000514', '000000515', '000000516', '000000517', '000000518', '000000519', '000000520', '000000521', '000000522', '000000523', '000000524', '000000525', '000000526', '000000527', '000000528', '000000529', '000000530', '000000531', '000000532', '000000533', '000000534', '000000535', '000000536', '000000537', 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'000000615', '000000616', '000000617', '000000618', '000000619', '000000620', '000000621', '000000622', '000000623', '000000624', '000000625', '000000626', '000000627', '000000628', '000000629', '000000630', '000000631', '000000632', '000000633', '000000634', '000000635', '000000636', '000000637', '000000638', '000000639', '000000640', '000000641', '000000642', '000000643', '000000644', '000000645', '000000646', '000000647', '000000648', '000000649', '000000650', '000000651', '000000652', '000000653', '000000654', '000000655', '000000656', '000000657', '000000658', '000000659', '000000660', '000000661', '000000662', '000000663', '000000664', '000000665', '000000666', '000000667', '000000668', '000000669', '000000670', '000000671', '000000672', '000000673', '000000674', '000000675', '000000676', '000000677', '000000678', '000000679', '000000680', '000000681', '000000682', '000000683', '000000684', '000000685', '000000686', '000000687', '000000688', '000000689', '000000690', '000000691', '000000692', '000000693', '000000694', '000000695', '000000696', '000000697', '000000698', '000000699', '000000700', '000000701', '000000702', '000000703', '000000704', '000000705', '000000706', '000000707', '000000708', '000000709', '000000710', '000000711', '000000712', '000000713', '000000714', '000000715', '000000716', '000000717', '000000718', '000000719', '000000720', '000000721', '000000722', '000000723', '000000724', '000000725', '000000726', '000000727', '000000728', '000000729', '000000730', '000000731', '000000732', '000000733', '000000734', '000000735', '000000736', '000000737', '000000738', '000000739', '000000740', '000000741', '000000742', '000000743', '000000744', '000000745', '000000746', '000000747', '000000748', '000000749', '000000750', '000000751', '000000752', '000000753', '000000754', '000000755', '000000756', '000000757', '000000758', '000000759', '000000760', '000000761', '000000762', '000000763', '000000764', '000000765', '000000766', '000000767', '000000768', 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'000000846', '000000847', '000000848', '000000849', '000000850', '000000851', '000000852', '000000853', '000000854', '000000855', '000000856', '000000857', '000000858', '000000859', '000000860', '000000861', '000000862', '000000863', '000000864', '000000865', '000000866', '000000867', '000000868', '000000869', '000000870', '000000871', '000000872', '000000873', '000000874', '000000875', '000000876', '000000877', '000000878', '000000879', '000000880', '000000881', '000000882', '000000883', '000000884', '000000885', '000000886', '000000887', '000000888', '000000889', '000000890', '000000891', '000000892', '000000893', '000000894', '000000895', '000000896', '000000897', '000000898', '000000899', '000000900', '000000901', '000000902', '000000903', '000000904', '000000905', '000000906', '000000907', '000000908', '000000909', '000000910', '000000911', '000000912', '000000913', '000000914', '000000915', '000000916', '000000917', '000000918', '000000919', '000000920', '000000921', '000000922', '000000923', '000000924', '000000925', '000000926', '000000927', '000000928', '000000929', '000000930', '000000931', '000000932', '000000933', '000000934', '000000935', '000000936', '000000937', '000000938', '000000939', '000000940', '000000941', '000000942', '000000943', '000000944', '000000945', '000000946', '000000947', '000000948', '000000949', '000000950', '000000951', '000000952', '000000953', '000000954', '000000955', '000000956', '000000957', '000000958', '000000959', '000000960', '000000961', '000000962', '000000963', '000000964', '000000965', '000000966', '000000967', '000000968', '000000969', '000000970', '000000971', '000000972', '000000973', '000000974', '000000975', '000000976', '000000977', '000000978', '000000979', '000000980', '000000981', '000000982', '000000983', '000000984', '000000985', '000000986', '000000987', '000000988', '000000989', '000000990', '000000991', '000000992', '000000993', '000000994', '000000995', '000000996', '000000997', '000000998', '000000999', 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'000001154', '000001155', '000001156', '000001157', '000001158', '000001159', '000001160', '000001161', '000001162', '000001163', '000001164', '000001165', '000001166', '000001167', '000001168', '000001169', '000001170', '000001171', '000001172', '000001173', '000001174', '000001175', '000001176', '000001177', '000001178', '000001179', '000001180', '000001181', '000001182', '000001183', '000001184', '000001185', '000001186', '000001187', '000001188', '000001189', '000001190', '000001191', '000001192', '000001193', '000001194', '000001195', '000001196', '000001197', '000001198', '000001199', '000001200', '000001201', '000001202', '000001203', '000001204', '000001205', '000001206', '000001207', '000001208', '000001209', '000001210', '000001211', '000001212', '000001213', '000001214', '000001215', '000001216', '000001217', '000001218', '000001219', '000001220', '000001221', '000001222', '000001223', '000001224', '000001225', '000001226', '000001227', '000001228', '000001229', '000001230', '000001231', '000001232', '000001233', '000001234', '000001235', '000001236', '000001237', '000001238', '000001239', '000001240', '000001241', '000001242', '000001243', '000001244', '000001245', '000001246', '000001247', '000001248', '000001249', '000001250', '000001251', '000001252', '000001253', '000001254', '000001255', '000001256', '000001257', '000001258', '000001259', '000001260', '000001261', '000001262', '000001263', '000001264', '000001265', '000001266', '000001267', '000001268', '000001269', '000001270', '000001271', '000001272', '000001273', '000001274', '000001275', '000001276', '000001277', '000001278', '000001279', '000001280', '000001281', '000001282', '000001283', '000001284', '000001285', '000001286', '000001287', '000001288', '000001289', '000001290', '000001291', '000001292', '000001293', '000001294', '000001295', '000001296', '000001297', '000001298', '000001299', '000001300', '000001301', '000001302', '000001303', '000001304', '000001305', '000001306', '000001307', 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'000003233', '000003234', '000003235', '000003236', '000003237', '000003238', '000003239', '000003240', '000003241', '000003242', '000003243', '000003244', '000003245', '000003246', '000003247', '000003248', '000003249', '000003250', '000003251', '000003252', '000003253', '000003254', '000003255', '000003256', '000003257', '000003258', '000003259', '000003260', '000003261', '000003262', '000003263', '000003264', '000003265', '000003266', '000003267', '000003268', '000003269', '000003270', '000003271', '000003272', '000003273', '000003274', '000003275', '000003276', '000003277', '000003278', '000003279', '000003280', '000003281', '000003282', '000003283', '000003284', '000003285', '000003286', '000003287', '000003288', '000003289', '000003290', '000003291', '000003292', '000003293', '000003294', '000003295', '000003296', '000003297', '000003298', '000003299', '000003300', '000003301', '000003302', '000003303', '000003304', '000003305', '000003306', '000003307', '000003308', '000003309', '000003310', '000003311', '000003312', '000003313', '000003314', '000003315', '000003316', '000003317', '000003318', '000003319', '000003320', '000003321', '000003322', '000003323', '000003324', '000003325', '000003326', '000003327', '000003328', '000003329', '000003330', '000003331', '000003332', '000003333', '000003334', '000003335', '000003336', '000003337', '000003338', '000003339', '000003340', '000003341', '000003342', '000003343', '000003344', '000003345', '000003346', '000003347', '000003348', '000003349', '000003350', '000003351', '000003352', '000003353', '000003354', '000003355', '000003356', '000003357', '000003358', '000003359', '000003360', '000003361', '000003362', '000003363', '000003364', '000003365', '000003366', '000003367', '000003368', '000003369', '000003370', '000003371', '000003372', '000003373', '000003374', '000003375', '000003376', '000003377', '000003378', '000003379', '000003380', '000003381', '000003382', '000003383', '000003384', '000003385', '000003386', '000003387', '000003388', '000003389', '000003390', '000003391', '000003392', '000003393', '000003394', '000003395', '000003396', '000003397', '000003398', '000003399', '000003400', '000003401', '000003402', '000003403', '000003404', '000003405', '000003406', '000003407', '000003408', '000003409', '000003410', '000003411', '000003412', '000003413', '000003414', '000003415', '000003416', '000003417', '000003418', '000003419', '000003420', '000003421', '000003422', '000003423', '000003424', '000003425', '000003426', '000003427', '000003428', '000003429', '000003430', '000003431', '000003432', '000003433', '000003434', '000003435', '000003436', '000003437', '000003438', '000003439', '000003440', '000003441', '000003442', '000003443', '000003444', '000003445', '000003446', '000003447', '000003448', '000003449', '000003450', '000003451', '000003452', '000003453', '000003454', '000003455', '000003456', '000003457', '000003458', '000003459', '000003460', '000003461', '000003462', '000003463', '000003464', '000003465', '000003466', '000003467', '000003468', '000003469', '000003470', '000003471', '000003472', '000003473', '000003474', '000003475', '000003476', '000003477', '000003478', '000003479', '000003480', '000003481', '000003482', '000003483', '000003484', '000003485', '000003486', '000003487', '000003488', '000003489', '000003490', '000003491', '000003492', '000003493', '000003494', '000003495', '000003496', '000003497', '000003498', '000003499', '000003500', '000003501', '000003502', '000003503', '000003504', '000003505', '000003506', '000003507', '000003508', '000003509', '000003510', '000003511', '000003512', '000003513', '000003514', '000003515', '000003516', '000003517', '000003518', '000003519', '000003520', '000003521', '000003522', '000003523', '000003524', '000003525', '000003526', '000003527', '000003528', '000003529', '000003530', '000003531', '000003532', '000003533', '000003534', '000003535', '000003536', '000003537', '000003538', '000003539', '000003540', '000003541', '000003542', '000003543', '000003544', '000003545', '000003546', '000003547', '000003548', '000003549', '000003550', '000003551', '000003552', '000003553', '000003554', '000003555', '000003556', '000003557', '000003558', '000003559', '000003560', '000003561', '000003562', '000003563', '000003564', '000003565', '000003566', '000003567', '000003568', '000003569', '000003570', '000003571', '000003572', '000003573', '000003574', '000003575', '000003576', '000003577', '000003578', '000003579', '000003580', '000003581', '000003582', '000003583', '000003584', '000003585', '000003586', '000003587', '000003588', '000003589', '000003590', '000003591', '000003592', '000003593', '000003594', '000003595', '000003596', '000003597', '000003598', '000003599', '000003600', '000003601', '000003602', '000003603', '000003604', '000003605', '000003606', '000003607', '000003608', '000003609', '000003610', '000003611', '000003612', '000003613', '000003614', '000003615', '000003616', '000003617', '000003618', '000003619', '000003620', '000003621', '000003622', '000003623', '000003624', '000003625', '000003626', '000003627', '000003628', '000003629', '000003630', '000003631', '000003632', '000003633', '000003634', '000003635', '000003636', '000003637', '000003638', '000003639', '000003640', '000003641', '000003642', '000003643', '000003644', '000003645', '000003646', '000003647', '000003648', '000003649', '000003650', '000003651', '000003652', '000003653', '000003654', '000003655', '000003656', '000003657', '000003658', '000003659', '000003660', '000003661', '000003662', '000003663', '000003664', '000003665', '000003666', '000003667', '000003668', '000003669', '000003670', '000003671', '000003672', '000003673', '000003674', '000003675', '000003676', '000003677', '000003678', '000003679', '000003680', '000003681', '000003682', '000003683', '000003684', '000003685', '000003686', '000003687', '000003688', '000003689', '000003690', '000003691', '000003692', '000003693', '000003694', '000003695', '000003696', '000003697', '000003698', '000003699', '000003700', '000003701', '000003702', '000003703', '000003704', '000003705', '000003706', '000003707', '000003708', '000003709', '000003710', '000003711', '000003712', '000003713', '000003714', '000003715', '000003716', '000003717', '000003718', '000003719', '000003720', '000003721', '000003722', '000003723', '000003724', '000003725', '000003726', '000003727', '000003728', '000003729', '000003730', '000003731', '000003732', '000003733', '000003734', '000003735', '000003736', '000003737', '000003738', '000003739', '000003740', '000003741', '000003742', '000003743', '000003744', '000003745', '000003746', '000003747']
3748
import pandas as pd
pd.set_option('display.max_colwidth', 300)
data_df = pd.DataFrame({'file_id': file_id_list})
data_df['image_path'] = '../../poolncar/training_data/training_data/images/' + data_df['file_id'] + '.jpg'
data_df['xml_path'] = '../../poolncar/training_data/training_data/labels/' + data_df['file_id'] + '.xml'
print(data_df.shape)
data_df.head()
(3748, 3)
file_id image_path xml_path
0 000000000 ../../poolncar/training_data/training_data/images/000000000.jpg ../../poolncar/training_data/training_data/labels/000000000.xml
1 000000001 ../../poolncar/training_data/training_data/images/000000001.jpg ../../poolncar/training_data/training_data/labels/000000001.xml
2 000000002 ../../poolncar/training_data/training_data/images/000000002.jpg ../../poolncar/training_data/training_data/labels/000000002.xml
3 000000003 ../../poolncar/training_data/training_data/images/000000003.jpg ../../poolncar/training_data/training_data/labels/000000003.xml
4 000000004 ../../poolncar/training_data/training_data/images/000000004.jpg ../../poolncar/training_data/training_data/labels/000000004.xml
from sklearn.model_selection import train_test_split
# 전체 데이터의 20%를 검증용으로 할당.
train_df, val_df = train_test_split(data_df, test_size=0.2, random_state=2021)
print(train_df.shape, val_df.shape)
(2998, 3) (750, 3)
# 모든 image/xml 파일들을 TFRecord로 생성.
def make_tfrecords(meta_df, output_dir, output_prefix, num_shards):
'''
meta_df: train/val DataFrame에서 입력 xml과 image의 경로를 가져옴.
output_dir: 출력 TFRecord가 저장될 directory
output_prefix: 출력 TFRecord 파일의 prefix명.
num_shards: 만들어질 TFRecord 파일의 수
'''
xml_list = meta_df['xml_path'].to_list()
image_list = meta_df['image_path'].to_list()
unique_id = Unique_Id()
# num_shards 만큼의 TFRecordWriter객체 생성.
writers = [
tf.io.TFRecordWriter(output_dir + output_prefix +'-%05d-of-%05d.tfrecord' %
(i, num_shards))
for i in range(num_shards)
]
#xml 개수만큼 iteration을 돌려서 Example을 만들고 개별 Example을 특정 shard에 해당하는 TFRecord에 append
for idx, xml_path in enumerate(xml_list):
if idx % 100 == 0:
print('On image ', idx, 'of ', len(xml_list))
data = get_anno_dict_from_xml(xml_path)
image_path = image_list[idx]
# 개별 Example 생성.
tf_example = dict_to_tf_example(data, image_path, label_map_dict, unique_id,
ignore_difficult_instances=False, ann_json_dict=None, debug=False)
# num_shard만큼 만들어진 TFRecord에 Example을 iteration을 수행하면서 해당하는 TFRecord로 append
writers[idx % num_shards].write(tf_example.SerializeToString())
# num_shards 만큼 만들어진 TFRecordWriter 객체 close
for writer in writers:
writer.close()
os.makedirs("../../tfrecord/train", exist_ok=True)
os.makedirs("../../tfrecord/val", exist_ok=True)
print('#### train용 tfrecords 생성 시작 ####')
make_tfrecords(train_df, '../../tfrecord/train/', 'train', num_shards=100)
print('#### val용 tfrecords 생성 시작 ####')
make_tfrecords(val_df, '../../tfrecord/val/', 'val', num_shards=100)
#### train용 tfrecords 생성 시작 ####
On image 0 of 2998
On image 100 of 2998
On image 200 of 2998
On image 300 of 2998
On image 400 of 2998
On image 500 of 2998
On image 600 of 2998
On image 700 of 2998
On image 800 of 2998
On image 900 of 2998
On image 1000 of 2998
On image 1100 of 2998
On image 1200 of 2998
On image 1300 of 2998
On image 1400 of 2998
On image 1500 of 2998
On image 1600 of 2998
On image 1700 of 2998
On image 1800 of 2998
On image 1900 of 2998
On image 2000 of 2998
On image 2100 of 2998
On image 2200 of 2998
On image 2300 of 2998
On image 2400 of 2998
On image 2500 of 2998
On image 2600 of 2998
On image 2700 of 2998
On image 2800 of 2998
On image 2900 of 2998
#### val용 tfrecords 생성 시작 ####
On image 0 of 750
On image 100 of 750
On image 200 of 750
On image 300 of 750
On image 400 of 750
On image 500 of 750
On image 600 of 750
On image 700 of 750
os.listdir('../../tfrecord/train')
os.listdir('../../tfrecord/val')
['val-00000-of-00100.tfrecord',
'val-00001-of-00100.tfrecord',
'val-00002-of-00100.tfrecord',
'val-00003-of-00100.tfrecord',
'val-00004-of-00100.tfrecord',
'val-00005-of-00100.tfrecord',
'val-00006-of-00100.tfrecord',
'val-00007-of-00100.tfrecord',
'val-00008-of-00100.tfrecord',
'val-00009-of-00100.tfrecord',
'val-00010-of-00100.tfrecord',
'val-00011-of-00100.tfrecord',
'val-00012-of-00100.tfrecord',
'val-00013-of-00100.tfrecord',
'val-00014-of-00100.tfrecord',
'val-00015-of-00100.tfrecord',
'val-00016-of-00100.tfrecord',
'val-00017-of-00100.tfrecord',
'val-00018-of-00100.tfrecord',
'val-00019-of-00100.tfrecord',
'val-00020-of-00100.tfrecord',
'val-00021-of-00100.tfrecord',
'val-00022-of-00100.tfrecord',
'val-00023-of-00100.tfrecord',
'val-00024-of-00100.tfrecord',
'val-00025-of-00100.tfrecord',
'val-00026-of-00100.tfrecord',
'val-00027-of-00100.tfrecord',
'val-00028-of-00100.tfrecord',
'val-00029-of-00100.tfrecord',
'val-00030-of-00100.tfrecord',
'val-00031-of-00100.tfrecord',
'val-00032-of-00100.tfrecord',
'val-00033-of-00100.tfrecord',
'val-00034-of-00100.tfrecord',
'val-00035-of-00100.tfrecord',
'val-00036-of-00100.tfrecord',
'val-00037-of-00100.tfrecord',
'val-00038-of-00100.tfrecord',
'val-00039-of-00100.tfrecord',
'val-00040-of-00100.tfrecord',
'val-00041-of-00100.tfrecord',
'val-00042-of-00100.tfrecord',
'val-00043-of-00100.tfrecord',
'val-00044-of-00100.tfrecord',
'val-00045-of-00100.tfrecord',
'val-00046-of-00100.tfrecord',
'val-00047-of-00100.tfrecord',
'val-00048-of-00100.tfrecord',
'val-00049-of-00100.tfrecord',
'val-00050-of-00100.tfrecord',
'val-00051-of-00100.tfrecord',
'val-00052-of-00100.tfrecord',
'val-00053-of-00100.tfrecord',
'val-00054-of-00100.tfrecord',
'val-00055-of-00100.tfrecord',
'val-00056-of-00100.tfrecord',
'val-00057-of-00100.tfrecord',
'val-00058-of-00100.tfrecord',
'val-00059-of-00100.tfrecord',
'val-00060-of-00100.tfrecord',
'val-00061-of-00100.tfrecord',
'val-00062-of-00100.tfrecord',
'val-00063-of-00100.tfrecord',
'val-00064-of-00100.tfrecord',
'val-00065-of-00100.tfrecord',
'val-00066-of-00100.tfrecord',
'val-00067-of-00100.tfrecord',
'val-00068-of-00100.tfrecord',
'val-00069-of-00100.tfrecord',
'val-00070-of-00100.tfrecord',
'val-00071-of-00100.tfrecord',
'val-00072-of-00100.tfrecord',
'val-00073-of-00100.tfrecord',
'val-00074-of-00100.tfrecord',
'val-00075-of-00100.tfrecord',
'val-00076-of-00100.tfrecord',
'val-00077-of-00100.tfrecord',
'val-00078-of-00100.tfrecord',
'val-00079-of-00100.tfrecord',
'val-00080-of-00100.tfrecord',
'val-00081-of-00100.tfrecord',
'val-00082-of-00100.tfrecord',
'val-00083-of-00100.tfrecord',
'val-00084-of-00100.tfrecord',
'val-00085-of-00100.tfrecord',
'val-00086-of-00100.tfrecord',
'val-00087-of-00100.tfrecord',
'val-00088-of-00100.tfrecord',
'val-00089-of-00100.tfrecord',
'val-00090-of-00100.tfrecord',
'val-00091-of-00100.tfrecord',
'val-00092-of-00100.tfrecord',
'val-00093-of-00100.tfrecord',
'val-00094-of-00100.tfrecord',
'val-00095-of-00100.tfrecord',
'val-00096-of-00100.tfrecord',
'val-00097-of-00100.tfrecord',
'val-00098-of-00100.tfrecord',
'val-00099-of-00100.tfrecord']
Pretrained 모델을 다운로드 받고 Train을 위한 config 생성
pretrained 모델은 efficientdet-d2
num_classes는 2, max_instances_per_image는 기본 100개임. 1개의 이미지에 특정 object의 갯수가 100개를 초과하는 경우가 있음. max_instances_per_image = 200 으로 수정.
# epochs시마다 학습된 weight파일을 저장한 디렉토리 Google drive로 설정.
# 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
!mkdir -p /mydrive/model_trained_d2
Mounted at /content/gdrive
101_ObjectCategories Training400
오라클과_ADML_강의 train_santander.csv
application_test.csv ultra_workdir
application_train.csv Untitled
attention_01.ipynb Untitled0.ipynb
bkup Untitled10.ipynb
'Colab Notebooks' Untitled11.ipynb
credit_card_balance.csv Untitled12.ipynb
download Untitled13.ipynb
efficientdet01.ipynb Untitled14.ipynb
esri_work_dir Untitled15.ipynb
experiencor_yolov3_001.ipynb Untitled16.ipynb
face_mask.zip Untitled17.ipynb
HomeCredit_columns_description.csv Untitled18.ipynb
installments_payments.csv Untitled19.ipynb
kaggle.json Untitled1.ipynb
keras_yolov3_001.ipynb Untitled20.ipynb
mask_work_dir Untitled21.ipynb
melanoma_jpeg.zip Untitled22.ipynb
models Untitled23.ipynb
models_imsi Untitled24.ipynb
model_trained Untitled2.ipynb
model_trained_d2 Untitled3.ipynb
'My Drive' Untitled4.ipynb
perfect_guide Untitled5.ipynb
pet_work_dir Untitled6.ipynb
POS_CASH_balance.csv Untitled7.ipynb
President.ipynb Untitled8.ipynb
previous_application.csv Untitled9.ipynb
sample_submission.csv VOC
sea_depth yolov2_test01.ipynb
sea_depth.zip yolov3-tf2
test_colab01.ipynb yolo_weights
torch_efficientdet.ipynb
os.makedirs('../../model_trained_d2')
# MODEL = 'efficientdet-d0'
MODEL = 'efficientdet-d2'
# MODEL = 'efficientdet-d4'
def download(m):
if m not in os.listdir():
!wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/{m}.tar.gz
!tar zxf {m}.tar.gz
ckpt_path = os.path.join(os.getcwd(), m)
return ckpt_path
# Download checkpoint.
ckpt_path = download(MODEL)
print('Use model in {}'.format(ckpt_path))
--2021-12-05 11:12:06-- https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-d2.tar.gz
Resolving storage.googleapis.com (storage.googleapis.com)... 34.64.4.80, 34.64.4.16, 34.64.4.112, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|34.64.4.80|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 60307996 (58M) [application/octet-stream]
Saving to: 'efficientdet-d2.tar.gz'
0K .......... .......... .......... .......... .......... 0% 4.04M 14s
50K .......... .......... .......... .......... .......... 0% 8.19M 11s
100K .......... .......... .......... .......... .......... 0% 9.95M 9s
150K .......... .......... .......... .......... .......... 0% 9.80M 8s
200K .......... .......... .......... .......... .......... 0% 9.83M 8s
250K .......... .......... .......... .......... .......... 0% 4.32M 9s
300K .......... .......... .......... .......... .......... 0% 7.26M 9s
350K .......... .......... .......... .......... .......... 0% 9.16M 8s
400K .......... .......... .......... .......... .......... 0% 9.93M 8s
450K .......... .......... .......... .......... .......... 0% 6.18M 8s
500K .......... .......... .......... .......... .......... 0% 6.29M 8s
550K .......... .......... .......... .......... .......... 1% 7.82M 8s
600K .......... .......... .......... .......... .......... 1% 9.21M 8s
650K .......... .......... .......... .......... .......... 1% 9.27M 8s
700K .......... .......... .......... .......... .......... 1% 11.2M 8s
750K .......... .......... .......... .......... .......... 1% 10.4M 7s
800K .......... .......... .......... .......... .......... 1% 9.78M 7s
850K .......... .......... .......... .......... .......... 1% 10.8M 7s
900K .......... .......... .......... .......... .......... 1% 10.8M 7s
950K .......... .......... .......... .......... .......... 1% 10.2M 7s
1000K .......... .......... .......... .......... .......... 1% 12.0M 7s
1050K .......... .......... .......... .......... .......... 1% 66.5M 7s
1100K .......... .......... .......... .......... .......... 1% 75.0M 6s
1150K .......... .......... .......... .......... .......... 2% 49.1M 6s
1200K .......... .......... .......... .......... .......... 2% 62.8M 6s
1250K .......... .......... .......... .......... .......... 2% 70.8M 6s
1300K .......... .......... .......... .......... .......... 2% 74.5M 6s
1350K .......... .......... .......... .......... .......... 2% 46.9M 5s
1400K .......... .......... .......... .......... .......... 2% 57.3M 5s
1450K .......... .......... .......... .......... .......... 2% 41.2M 5s
1500K .......... .......... .......... .......... .......... 2% 41.9M 5s
1550K .......... .......... .......... .......... .......... 2% 39.6M 5s
1600K .......... .......... .......... .......... .......... 2% 68.1M 5s
1650K .......... .......... .......... .......... .......... 2% 53.1M 5s
1700K .......... .......... .......... .......... .......... 2% 62.0M 5s
1750K .......... .......... .......... .......... .......... 3% 54.4M 4s
1800K .......... .......... .......... .......... .......... 3% 58.0M 4s
1850K .......... .......... .......... .......... .......... 3% 67.7M 4s
1900K .......... .......... .......... .......... .......... 3% 29.0M 4s
1950K .......... .......... .......... .......... .......... 3% 37.7M 4s
2000K .......... .......... .......... .......... .......... 3% 67.4M 4s
2050K .......... .......... .......... .......... .......... 3% 34.2M 4s
2100K .......... .......... .......... .......... .......... 3% 63.4M 4s
2150K .......... .......... .......... .......... .......... 3% 55.0M 4s
2200K .......... .......... .......... .......... .......... 3% 52.3M 4s
2250K .......... .......... .......... .......... .......... 3% 45.1M 4s
2300K .......... .......... .......... .......... .......... 3% 24.5M 4s
2350K .......... .......... .......... .......... .......... 4% 32.8M 4s
2400K .......... .......... .......... .......... .......... 4% 28.9M 4s
2450K .......... .......... .......... .......... .......... 4% 52.4M 4s
2500K .......... .......... .......... .......... .......... 4% 23.5M 3s
2550K .......... .......... .......... .......... .......... 4% 36.0M 3s
2600K .......... .......... .......... .......... .......... 4% 9.87M 3s
2650K .......... .......... .......... .......... .......... 4% 29.3M 3s
2700K .......... .......... .......... .......... .......... 4% 45.7M 3s
2750K .......... .......... .......... .......... .......... 4% 38.0M 3s
2800K .......... .......... .......... .......... .......... 4% 26.2M 3s
2850K .......... .......... .......... .......... .......... 4% 21.5M 3s
2900K .......... .......... .......... .......... .......... 5% 49.4M 3s
2950K .......... .......... .......... .......... .......... 5% 33.5M 3s
3000K .......... .......... .......... .......... .......... 5% 46.9M 3s
3050K .......... .......... .......... .......... .......... 5% 37.9M 3s
3100K .......... .......... .......... .......... .......... 5% 21.0M 3s
3150K .......... .......... .......... .......... .......... 5% 31.6M 3s
3200K .......... .......... .......... .......... .......... 5% 21.1M 3s
3250K .......... .......... .......... .......... .......... 5% 22.3M 3s
3300K .......... .......... .......... .......... .......... 5% 43.6M 3s
3350K .......... .......... .......... .......... .......... 5% 7.56M 3s
3400K .......... .......... .......... .......... .......... 5% 53.9M 3s
3450K .......... .......... .......... .......... .......... 5% 32.8M 3s
3500K .......... .......... .......... .......... .......... 6% 52.6M 3s
3550K .......... .......... .......... .......... .......... 6% 25.3M 3s
3600K .......... .......... .......... .......... .......... 6% 52.6M 3s
3650K .......... .......... .......... .......... .......... 6% 49.5M 3s
3700K .......... .......... .......... .......... .......... 6% 45.4M 3s
3750K .......... .......... .......... .......... .......... 6% 21.6M 3s
3800K .......... .......... .......... .......... .......... 6% 62.1M 3s
3850K .......... .......... .......... .......... .......... 6% 39.6M 3s
3900K .......... .......... .......... .......... .......... 6% 57.1M 3s
3950K .......... .......... .......... .......... .......... 6% 20.9M 3s
4000K .......... .......... .......... .......... .......... 6% 48.0M 3s
4050K .......... .......... .......... .......... .......... 6% 49.0M 3s
4100K .......... .......... .......... .......... .......... 7% 32.2M 3s
4150K .......... .......... .......... .......... .......... 7% 49.8M 3s
4200K .......... .......... .......... .......... .......... 7% 69.9M 3s
4250K .......... .......... .......... .......... .......... 7% 13.5M 3s
4300K .......... .......... .......... .......... .......... 7% 23.7M 3s
4350K .......... .......... .......... .......... .......... 7% 32.5M 3s
4400K .......... .......... .......... .......... .......... 7% 45.8M 3s
4450K .......... .......... .......... .......... .......... 7% 58.4M 3s
4500K .......... .......... .......... .......... .......... 7% 46.7M 3s
4550K .......... .......... .......... .......... .......... 7% 52.3M 3s
4600K .......... .......... .......... .......... .......... 7% 44.4M 3s
4650K .......... .......... .......... .......... .......... 7% 50.4M 3s
4700K .......... .......... .......... .......... .......... 8% 46.7M 3s
4750K .......... .......... .......... .......... .......... 8% 41.9M 3s
4800K .......... .......... .......... .......... .......... 8% 16.9M 3s
4850K .......... .......... .......... .......... .......... 8% 21.2M 3s
4900K .......... .......... .......... .......... .......... 8% 41.3M 3s
4950K .......... .......... .......... .......... .......... 8% 43.8M 3s
5000K .......... .......... .......... .......... .......... 8% 59.1M 3s
5050K .......... .......... .......... .......... .......... 8% 46.5M 3s
5100K .......... .......... .......... .......... .......... 8% 28.1M 3s
5150K .......... .......... .......... .......... .......... 8% 36.9M 2s
5200K .......... .......... .......... .......... .......... 8% 54.1M 2s
5250K .......... .......... .......... .......... .......... 8% 73.7M 2s
5300K .......... .......... .......... .......... .......... 9% 43.9M 2s
5350K .......... .......... .......... .......... .......... 9% 58.0M 2s
5400K .......... .......... .......... .......... .......... 9% 28.5M 2s
5450K .......... .......... .......... .......... .......... 9% 67.1M 2s
5500K .......... .......... .......... .......... .......... 9% 25.9M 2s
5550K .......... .......... .......... .......... .......... 9% 36.0M 2s
5600K .......... .......... .......... .......... .......... 9% 40.3M 2s
5650K .......... .......... .......... .......... .......... 9% 33.7M 2s
5700K .......... .......... .......... .......... .......... 9% 47.3M 2s
5750K .......... .......... .......... .......... .......... 9% 49.7M 2s
5800K .......... .......... .......... .......... .......... 9% 64.7M 2s
5850K .......... .......... .......... .......... .......... 10% 19.4M 2s
5900K .......... .......... .......... .......... .......... 10% 62.7M 2s
5950K .......... .......... .......... .......... .......... 10% 49.8M 2s
6000K .......... .......... .......... .......... .......... 10% 41.0M 2s
6050K .......... .......... .......... .......... .......... 10% 51.4M 2s
6100K .......... .......... .......... .......... .......... 10% 28.7M 2s
6150K .......... .......... .......... .......... .......... 10% 37.6M 2s
6200K .......... .......... .......... .......... .......... 10% 33.0M 2s
6250K .......... .......... .......... .......... .......... 10% 57.7M 2s
6300K .......... .......... .......... .......... .......... 10% 44.8M 2s
6350K .......... .......... .......... .......... .......... 10% 39.7M 2s
6400K .......... .......... .......... .......... .......... 10% 31.6M 2s
6450K .......... .......... .......... .......... .......... 11% 57.7M 2s
6500K .......... .......... .......... .......... .......... 11% 57.6M 2s
6550K .......... .......... .......... .......... .......... 11% 30.5M 2s
6600K .......... .......... .......... .......... .......... 11% 58.8M 2s
6650K .......... .......... .......... .......... .......... 11% 23.1M 2s
6700K .......... .......... .......... .......... .......... 11% 59.8M 2s
6750K .......... .......... .......... .......... .......... 11% 30.5M 2s
6800K .......... .......... .......... .......... .......... 11% 13.5M 2s
6850K .......... .......... .......... .......... .......... 11% 40.4M 2s
6900K .......... .......... .......... .......... .......... 11% 35.4M 2s
6950K .......... .......... .......... .......... .......... 11% 47.4M 2s
7000K .......... .......... .......... .......... .......... 11% 71.8M 2s
7050K .......... .......... .......... .......... .......... 12% 27.7M 2s
7100K .......... .......... .......... .......... .......... 12% 41.8M 2s
7150K .......... .......... .......... .......... .......... 12% 52.9M 2s
7200K .......... .......... .......... .......... .......... 12% 13.8M 2s
7250K .......... .......... .......... .......... .......... 12% 23.5M 2s
7300K .......... .......... .......... .......... .......... 12% 42.2M 2s
7350K .......... .......... .......... .......... .......... 12% 30.7M 2s
7400K .......... .......... .......... .......... .......... 12% 43.0M 2s
7450K .......... .......... .......... .......... .......... 12% 55.3M 2s
7500K .......... .......... .......... .......... .......... 12% 29.4M 2s
7550K .......... .......... .......... .......... .......... 12% 44.6M 2s
7600K .......... .......... .......... .......... .......... 12% 54.6M 2s
7650K .......... .......... .......... .......... .......... 13% 28.3M 2s
7700K .......... .......... .......... .......... .......... 13% 25.6M 2s
7750K .......... .......... .......... .......... .......... 13% 23.6M 2s
7800K .......... .......... .......... .......... .......... 13% 34.4M 2s
7850K .......... .......... .......... .......... .......... 13% 39.0M 2s
7900K .......... .......... .......... .......... .......... 13% 35.5M 2s
7950K .......... .......... .......... .......... .......... 13% 32.1M 2s
8000K .......... .......... .......... .......... .......... 13% 48.3M 2s
8050K .......... .......... .......... .......... .......... 13% 30.5M 2s
8100K .......... .......... .......... .......... .......... 13% 51.3M 2s
8150K .......... .......... .......... .......... .......... 13% 83.3K 6s
8200K .......... .......... .......... .......... .......... 14% 24.8M 6s
8250K .......... .......... .......... .......... .......... 14% 37.7M 6s
8300K .......... .......... .......... .......... .......... 14% 26.3M 6s
8350K .......... .......... .......... .......... .......... 14% 38.4M 6s
8400K .......... .......... .......... .......... .......... 14% 14.5M 6s
8450K .......... .......... .......... .......... .......... 14% 50.8M 6s
8500K .......... .......... .......... .......... .......... 14% 48.4M 5s
8550K .......... .......... .......... .......... .......... 14% 35.8M 5s
8600K .......... .......... .......... .......... .......... 14% 54.4M 5s
8650K .......... .......... .......... .......... .......... 14% 43.4M 5s
8700K .......... .......... .......... .......... .......... 14% 57.5M 5s
8750K .......... .......... .......... .......... .......... 14% 38.2M 5s
8800K .......... .......... .......... .......... .......... 15% 14.2M 5s
8850K .......... .......... .......... .......... .......... 15% 50.3M 5s
8900K .......... .......... .......... .......... .......... 15% 34.3M 5s
8950K .......... .......... .......... .......... .......... 15% 41.0M 5s
9000K .......... .......... .......... .......... .......... 15% 48.6M 5s
9050K .......... .......... .......... .......... .......... 15% 27.6M 5s
9100K .......... .......... .......... .......... .......... 15% 62.3M 5s
9150K .......... .......... .......... .......... .......... 15% 61.5M 5s
9200K .......... .......... .......... .......... .......... 15% 29.8M 5s
9250K .......... .......... .......... .......... .......... 15% 59.5M 5s
9300K .......... .......... .......... .......... .......... 15% 35.1M 5s
9350K .......... .......... .......... .......... .......... 15% 66.3M 5s
9400K .......... .......... .......... .......... .......... 16% 28.0M 5s
9450K .......... .......... .......... .......... .......... 16% 72.4M 5s
9500K .......... .......... .......... .......... .......... 16% 27.3M 5s
9550K .......... .......... .......... .......... .......... 16% 32.6M 5s
9600K .......... .......... .......... .......... .......... 16% 32.9M 5s
9650K .......... .......... .......... .......... .......... 16% 63.5M 5s
9700K .......... .......... .......... .......... .......... 16% 39.1M 5s
9750K .......... .......... .......... .......... .......... 16% 29.0M 5s
9800K .......... .......... .......... .......... .......... 16% 47.0M 5s
9850K .......... .......... .......... .......... .......... 16% 61.1M 5s
9900K .......... .......... .......... .......... .......... 16% 58.3M 5s
9950K .......... .......... .......... .......... .......... 16% 43.9M 5s
10000K .......... .......... .......... .......... .......... 17% 40.7M 5s
10050K .......... .......... .......... .......... .......... 17% 50.3M 5s
10100K .......... .......... .......... .......... .......... 17% 17.7M 5s
10150K .......... .......... .......... .......... .......... 17% 36.5M 5s
10200K .......... .......... .......... .......... .......... 17% 50.1M 5s
10250K .......... .......... .......... .......... .......... 17% 61.3M 5s
10300K .......... .......... .......... .......... .......... 17% 52.5M 5s
10350K .......... .......... .......... .......... .......... 17% 63.7M 5s
10400K .......... .......... .......... .......... .......... 17% 53.8M 5s
10450K .......... .......... .......... .......... .......... 17% 20.9M 5s
10500K .......... .......... .......... .......... .......... 17% 60.3M 5s
10550K .......... .......... .......... .......... .......... 17% 51.0M 4s
10600K .......... .......... .......... .......... .......... 18% 47.7M 4s
10650K .......... .......... .......... .......... .......... 18% 57.3M 4s
10700K .......... .......... .......... .......... .......... 18% 56.3M 4s
10750K .......... .......... .......... .......... .......... 18% 26.5M 4s
10800K .......... .......... .......... .......... .......... 18% 18.0M 4s
10850K .......... .......... .......... .......... .......... 18% 50.2M 4s
10900K .......... .......... .......... .......... .......... 18% 47.2M 4s
10950K .......... .......... .......... .......... .......... 18% 52.5M 4s
11000K .......... .......... .......... .......... .......... 18% 26.9M 4s
11050K .......... .......... .......... .......... .......... 18% 57.9M 4s
11100K .......... .......... .......... .......... .......... 18% 71.8M 4s
11150K .......... .......... .......... .......... .......... 19% 36.7M 4s
11200K .......... .......... .......... .......... .......... 19% 25.4M 4s
11250K .......... .......... .......... .......... .......... 19% 64.5M 4s
11300K .......... .......... .......... .......... .......... 19% 63.2M 4s
11350K .......... .......... .......... .......... .......... 19% 66.4M 4s
11400K .......... .......... .......... .......... .......... 19% 32.5M 4s
11450K .......... .......... .......... .......... .......... 19% 17.6M 4s
11500K .......... .......... .......... .......... .......... 19% 47.8M 4s
11550K .......... .......... .......... .......... .......... 19% 24.3M 4s
11600K .......... .......... .......... .......... .......... 19% 36.4M 4s
11650K .......... .......... .......... .......... .......... 19% 43.3M 4s
11700K .......... .......... .......... .......... .......... 19% 37.4M 4s
11750K .......... .......... .......... .......... .......... 20% 57.3M 4s
11800K .......... .......... .......... .......... .......... 20% 17.4M 4s
11850K .......... .......... .......... .......... .......... 20% 46.0M 4s
11900K .......... .......... .......... .......... .......... 20% 36.3M 4s
11950K .......... .......... .......... .......... .......... 20% 41.5M 4s
12000K .......... .......... .......... .......... .......... 20% 26.7M 4s
12050K .......... .......... .......... .......... .......... 20% 58.7M 4s
12100K .......... .......... .......... .......... .......... 20% 54.7M 4s
12150K .......... .......... .......... .......... .......... 20% 55.1M 4s
12200K .......... .......... .......... .......... .......... 20% 33.0M 4s
12250K .......... .......... .......... .......... .......... 20% 58.6M 4s
12300K .......... .......... .......... .......... .......... 20% 33.3M 4s
12350K .......... .......... .......... .......... .......... 21% 59.9M 4s
12400K .......... .......... .......... .......... .......... 21% 40.6M 4s
12450K .......... .......... .......... .......... .......... 21% 36.7M 4s
12500K .......... .......... .......... .......... .......... 21% 26.1M 4s
12550K .......... .......... .......... .......... .......... 21% 49.5M 4s
12600K .......... .......... .......... .......... .......... 21% 52.1M 4s
12650K .......... .......... .......... .......... .......... 21% 62.9M 4s
12700K .......... .......... .......... .......... .......... 21% 26.7M 4s
12750K .......... .......... .......... .......... .......... 21% 21.3M 4s
12800K .......... .......... .......... .......... .......... 21% 39.6M 4s
12850K .......... .......... .......... .......... .......... 21% 42.3M 4s
12900K .......... .......... .......... .......... .......... 21% 57.5M 4s
12950K .......... .......... .......... .......... .......... 22% 36.7M 4s
13000K .......... .......... .......... .......... .......... 22% 33.8M 4s
13050K .......... .......... .......... .......... .......... 22% 54.4M 4s
13100K .......... .......... .......... .......... .......... 22% 31.5M 4s
13150K .......... .......... .......... .......... .......... 22% 54.2M 4s
13200K .......... .......... .......... .......... .......... 22% 46.6M 4s
13250K .......... .......... .......... .......... .......... 22% 24.6M 4s
13300K .......... .......... .......... .......... .......... 22% 54.0M 4s
13350K .......... .......... .......... .......... .......... 22% 62.5M 4s
13400K .......... .......... .......... .......... .......... 22% 34.0M 4s
13450K .......... .......... .......... .......... .......... 22% 58.7M 4s
13500K .......... .......... .......... .......... .......... 23% 64.9M 4s
13550K .......... .......... .......... .......... .......... 23% 49.1M 4s
13600K .......... .......... .......... .......... .......... 23% 11.6M 4s
13650K .......... .......... .......... .......... .......... 23% 32.9M 4s
13700K .......... .......... .......... .......... .......... 23% 22.2M 4s
13750K .......... .......... .......... .......... .......... 23% 36.2M 3s
13800K .......... .......... .......... .......... .......... 23% 54.5M 3s
13850K .......... .......... .......... .......... .......... 23% 16.4M 3s
13900K .......... .......... .......... .......... .......... 23% 69.3M 3s
13950K .......... .......... .......... .......... .......... 23% 38.8M 3s
14000K .......... .......... .......... .......... .......... 23% 44.1M 3s
14050K .......... .......... .......... .......... .......... 23% 47.6M 3s
14100K .......... .......... .......... .......... .......... 24% 34.0M 3s
14150K .......... .......... .......... .......... .......... 24% 51.6M 3s
14200K .......... .......... .......... .......... .......... 24% 28.0M 3s
14250K .......... .......... .......... .......... .......... 24% 43.7M 3s
14300K .......... .......... .......... .......... .......... 24% 16.6M 3s
14350K .......... .......... .......... .......... .......... 24% 61.1M 3s
14400K .......... .......... .......... .......... .......... 24% 49.1M 3s
14450K .......... .......... .......... .......... .......... 24% 50.0M 3s
14500K .......... .......... .......... .......... .......... 24% 14.3M 3s
14550K .......... .......... .......... .......... .......... 24% 50.4M 3s
14600K .......... .......... .......... .......... .......... 24% 42.6M 3s
14650K .......... .......... .......... .......... .......... 24% 36.4M 3s
14700K .......... .......... .......... .......... .......... 25% 46.5M 3s
14750K .......... .......... .......... .......... .......... 25% 45.1M 3s
14800K .......... .......... .......... .......... .......... 25% 30.9M 3s
14850K .......... .......... .......... .......... .......... 25% 35.5M 3s
14900K .......... .......... .......... .......... .......... 25% 22.9M 3s
14950K .......... .......... .......... .......... .......... 25% 61.8M 3s
15000K .......... .......... .......... .......... .......... 25% 55.1M 3s
15050K .......... .......... .......... .......... .......... 25% 4.94M 3s
15100K .......... .......... .......... .......... .......... 25% 21.9M 3s
15150K .......... .......... .......... .......... .......... 25% 31.8M 3s
15200K .......... .......... .......... .......... .......... 25% 49.3M 3s
15250K .......... .......... .......... .......... .......... 25% 71.3M 3s
15300K .......... .......... .......... .......... .......... 26% 53.0M 3s
15350K .......... .......... .......... .......... .......... 26% 62.0M 3s
15400K .......... .......... .......... .......... .......... 26% 58.0M 3s
15450K .......... .......... .......... .......... .......... 26% 69.1M 3s
15500K .......... .......... .......... .......... .......... 26% 68.3M 3s
15550K .......... .......... .......... .......... .......... 26% 66.5M 3s
15600K .......... .......... .......... .......... .......... 26% 57.8M 3s
15650K .......... .......... .......... .......... .......... 26% 65.9M 3s
15700K .......... .......... .......... .......... .......... 26% 71.3M 3s
15750K .......... .......... .......... .......... .......... 26% 74.8M 3s
15800K .......... .......... .......... .......... .......... 26% 56.2M 3s
15850K .......... .......... .......... .......... .......... 26% 71.0M 3s
15900K .......... .......... .......... .......... .......... 27% 66.2M 3s
15950K .......... .......... .......... .......... .......... 27% 74.2M 3s
16000K .......... .......... .......... .......... .......... 27% 11.5M 3s
16050K .......... .......... .......... .......... .......... 27% 34.1M 3s
16100K .......... .......... .......... .......... .......... 27% 44.8M 3s
16150K .......... .......... .......... .......... .......... 27% 72.0M 3s
16200K .......... .......... .......... .......... .......... 27% 53.4M 3s
16250K .......... .......... .......... .......... .......... 27% 63.5M 3s
16300K .......... .......... .......... .......... .......... 27% 61.3M 3s
16350K .......... .......... .......... .......... .......... 27% 41.4M 3s
16400K .......... .......... .......... .......... .......... 27% 48.2M 3s
16450K .......... .......... .......... .......... .......... 28% 16.1M 3s
16500K .......... .......... .......... .......... .......... 28% 10.2M 3s
16550K .......... .......... .......... .......... .......... 28% 29.7M 3s
16600K .......... .......... .......... .......... .......... 28% 36.4M 3s
16650K .......... .......... .......... .......... .......... 28% 38.2M 3s
16700K .......... .......... .......... .......... .......... 28% 42.7M 3s
16750K .......... .......... .......... .......... .......... 28% 41.8M 3s
16800K .......... .......... .......... .......... .......... 28% 15.6M 3s
16850K .......... .......... .......... .......... .......... 28% 53.8M 3s
16900K .......... .......... .......... .......... .......... 28% 34.0M 3s
16950K .......... .......... .......... .......... .......... 28% 61.5M 3s
17000K .......... .......... .......... .......... .......... 28% 26.5M 3s
17050K .......... .......... .......... .......... .......... 29% 11.2M 3s
17100K .......... .......... .......... .......... .......... 29% 33.7M 3s
17150K .......... .......... .......... .......... .......... 29% 48.1M 3s
17200K .......... .......... .......... .......... .......... 29% 51.9M 3s
17250K .......... .......... .......... .......... .......... 29% 62.9M 3s
17300K .......... .......... .......... .......... .......... 29% 73.4M 3s
17350K .......... .......... .......... .......... .......... 29% 23.2M 3s
17400K .......... .......... .......... .......... .......... 29% 49.5M 3s
17450K .......... .......... .......... .......... .......... 29% 53.7M 3s
17500K .......... .......... .......... .......... .......... 29% 55.1M 3s
17550K .......... .......... .......... .......... .......... 29% 53.8M 3s
17600K .......... .......... .......... .......... .......... 29% 43.8M 3s
17650K .......... .......... .......... .......... .......... 30% 54.0M 3s
17700K .......... .......... .......... .......... .......... 30% 38.4M 3s
17750K .......... .......... .......... .......... .......... 30% 48.7M 3s
17800K .......... .......... .......... .......... .......... 30% 46.1M 3s
17850K .......... .......... .......... .......... .......... 30% 18.8M 3s
17900K .......... .......... .......... .......... .......... 30% 63.5M 3s
17950K .......... .......... .......... .......... .......... 30% 47.2M 3s
18000K .......... .......... .......... .......... .......... 30% 16.8M 3s
18050K .......... .......... .......... .......... .......... 30% 58.3M 3s
18100K .......... .......... .......... .......... .......... 30% 49.6M 3s
18150K .......... .......... .......... .......... .......... 30% 39.5M 3s
18200K .......... .......... .......... .......... .......... 30% 54.8M 3s
18250K .......... .......... .......... .......... .......... 31% 51.2M 3s
18300K .......... .......... .......... .......... .......... 31% 24.9M 3s
18350K .......... .......... .......... .......... .......... 31% 47.6M 3s
18400K .......... .......... .......... .......... .......... 31% 49.9M 3s
18450K .......... .......... .......... .......... .......... 31% 23.3M 3s
18500K .......... .......... .......... .......... .......... 31% 59.6M 3s
18550K .......... .......... .......... .......... .......... 31% 57.6M 3s
18600K .......... .......... .......... .......... .......... 31% 44.2M 3s
18650K .......... .......... .......... .......... .......... 31% 67.7M 3s
18700K .......... .......... .......... .......... .......... 31% 49.7M 3s
18750K .......... .......... .......... .......... .......... 31% 51.7M 3s
18800K .......... .......... .......... .......... .......... 32% 41.5M 3s
18850K .......... .......... .......... .......... .......... 32% 50.0M 3s
18900K .......... .......... .......... .......... .......... 32% 70.8M 3s
18950K .......... .......... .......... .......... .......... 32% 39.1M 3s
19000K .......... .......... .......... .......... .......... 32% 41.6M 3s
19050K .......... .......... .......... .......... .......... 32% 13.1M 3s
19100K .......... .......... .......... .......... .......... 32% 44.4M 3s
19150K .......... .......... .......... .......... .......... 32% 69.9M 3s
19200K .......... .......... .......... .......... .......... 32% 58.4M 3s
19250K .......... .......... .......... .......... .......... 32% 68.3M 3s
19300K .......... .......... .......... .......... .......... 32% 61.7M 2s
19350K .......... .......... .......... .......... .......... 32% 61.7M 2s
19400K .......... .......... .......... .......... .......... 33% 11.9M 2s
19450K .......... .......... .......... .......... .......... 33% 58.2M 2s
19500K .......... .......... .......... .......... .......... 33% 48.3M 2s
19550K .......... .......... .......... .......... .......... 33% 34.4M 2s
19600K .......... .......... .......... .......... .......... 33% 24.3M 2s
19650K .......... .......... .......... .......... .......... 33% 43.7M 2s
19700K .......... .......... .......... .......... .......... 33% 64.9M 2s
19750K .......... .......... .......... .......... .......... 33% 68.1M 2s
19800K .......... .......... .......... .......... .......... 33% 33.3M 2s
19850K .......... .......... .......... .......... .......... 33% 57.5M 2s
19900K .......... .......... .......... .......... .......... 33% 53.1M 2s
19950K .......... .......... .......... .......... .......... 33% 50.3M 2s
20000K .......... .......... .......... .......... .......... 34% 12.0M 2s
20050K .......... .......... .......... .......... .......... 34% 49.3M 2s
20100K .......... .......... .......... .......... .......... 34% 59.1M 2s
20150K .......... .......... .......... .......... .......... 34% 57.4M 2s
20200K .......... .......... .......... .......... .......... 34% 47.1M 2s
20250K .......... .......... .......... .......... .......... 34% 59.5M 2s
20300K .......... .......... .......... .......... .......... 34% 41.0M 2s
20350K .......... .......... .......... .......... .......... 34% 42.9M 2s
20400K .......... .......... .......... .......... .......... 34% 40.9M 2s
20450K .......... .......... .......... .......... .......... 34% 44.8M 2s
20500K .......... .......... .......... .......... .......... 34% 53.3M 2s
20550K .......... .......... .......... .......... .......... 34% 58.9M 2s
20600K .......... .......... .......... .......... .......... 35% 50.8M 2s
20650K .......... .......... .......... .......... .......... 35% 61.1M 2s
20700K .......... .......... .......... .......... .......... 35% 62.2M 2s
20750K .......... .......... .......... .......... .......... 35% 15.3M 2s
20800K .......... .......... .......... .......... .......... 35% 45.1M 2s
20850K .......... .......... .......... .......... .......... 35% 53.0M 2s
20900K .......... .......... .......... .......... .......... 35% 50.6M 2s
20950K .......... .......... .......... .......... .......... 35% 50.3M 2s
21000K .......... .......... .......... .......... .......... 35% 23.7M 2s
21050K .......... .......... .......... .......... .......... 35% 51.7M 2s
21100K .......... .......... .......... .......... .......... 35% 35.7M 2s
21150K .......... .......... .......... .......... .......... 35% 40.5M 2s
21200K .......... .......... .......... .......... .......... 36% 53.6M 2s
21250K .......... .......... .......... .......... .......... 36% 45.0M 2s
21300K .......... .......... .......... .......... .......... 36% 35.7M 2s
21350K .......... .......... .......... .......... .......... 36% 57.6M 2s
21400K .......... .......... .......... .......... .......... 36% 49.3M 2s
21450K .......... .......... .......... .......... .......... 36% 35.6M 2s
21500K .......... .......... .......... .......... .......... 36% 48.3M 2s
21550K .......... .......... .......... .......... .......... 36% 25.3M 2s
21600K .......... .......... .......... .......... .......... 36% 9.21M 2s
21650K .......... .......... .......... .......... .......... 36% 49.7M 2s
21700K .......... .......... .......... .......... .......... 36% 63.3M 2s
21750K .......... .......... .......... .......... .......... 37% 44.3M 2s
21800K .......... .......... .......... .......... .......... 37% 45.1M 2s
21850K .......... .......... .......... .......... .......... 37% 22.0M 2s
21900K .......... .......... .......... .......... .......... 37% 49.6M 2s
21950K .......... .......... .......... .......... .......... 37% 35.0M 2s
22000K .......... .......... .......... .......... .......... 37% 44.3M 2s
22050K .......... .......... .......... .......... .......... 37% 46.7M 2s
22100K .......... .......... .......... .......... .......... 37% 51.4M 2s
22150K .......... .......... .......... .......... .......... 37% 32.6M 2s
22200K .......... .......... .......... .......... .......... 37% 52.6M 2s
22250K .......... .......... .......... .......... .......... 37% 60.5M 2s
22300K .......... .......... .......... .......... .......... 37% 42.4M 2s
22350K .......... .......... .......... .......... .......... 38% 31.1M 2s
22400K .......... .......... .......... .......... .......... 38% 56.7M 2s
22450K .......... .......... .......... .......... .......... 38% 21.7M 2s
22500K .......... .......... .......... .......... .......... 38% 60.2M 2s
22550K .......... .......... .......... .......... .......... 38% 61.9M 2s
22600K .......... .......... .......... .......... .......... 38% 47.9M 2s
22650K .......... .......... .......... .......... .......... 38% 64.9M 2s
22700K .......... .......... .......... .......... .......... 38% 55.4M 2s
22750K .......... .......... .......... .......... .......... 38% 20.2M 2s
22800K .......... .......... .......... .......... .......... 38% 48.3M 2s
22850K .......... .......... .......... .......... .......... 38% 60.1M 2s
22900K .......... .......... .......... .......... .......... 38% 13.5M 2s
22950K .......... .......... .......... .......... .......... 39% 40.9M 2s
23000K .......... .......... .......... .......... .......... 39% 50.7M 2s
23050K .......... .......... .......... .......... .......... 39% 27.1M 2s
23100K .......... .......... .......... .......... .......... 39% 44.7M 2s
23150K .......... .......... .......... .......... .......... 39% 50.7M 2s
23200K .......... .......... .......... .......... .......... 39% 41.8M 2s
23250K .......... .......... .......... .......... .......... 39% 57.3M 2s
23300K .......... .......... .......... .......... .......... 39% 58.8M 2s
23350K .......... .......... .......... .......... .......... 39% 46.6M 2s
23400K .......... .......... .......... .......... .......... 39% 57.1M 2s
23450K .......... .......... .......... .......... .......... 39% 9.02M 2s
23500K .......... .......... .......... .......... .......... 39% 41.5M 2s
23550K .......... .......... .......... .......... .......... 40% 54.4M 2s
23600K .......... .......... .......... .......... .......... 40% 37.3M 2s
23650K .......... .......... .......... .......... .......... 40% 61.9M 2s
23700K .......... .......... .......... .......... .......... 40% 43.7M 2s
23750K .......... .......... .......... .......... .......... 40% 60.4M 2s
23800K .......... .......... .......... .......... .......... 40% 24.9M 2s
23850K .......... .......... .......... .......... .......... 40% 52.5M 2s
23900K .......... .......... .......... .......... .......... 40% 73.8M 2s
23950K .......... .......... .......... .......... .......... 40% 46.9M 2s
24000K .......... .......... .......... .......... .......... 40% 21.3M 2s
24050K .......... .......... .......... .......... .......... 40% 66.0M 2s
24100K .......... .......... .......... .......... .......... 41% 53.5M 2s
24150K .......... .......... .......... .......... .......... 41% 64.1M 2s
24200K .......... .......... .......... .......... .......... 41% 29.5M 2s
24250K .......... .......... .......... .......... .......... 41% 45.0M 2s
24300K .......... .......... .......... .......... .......... 41% 52.9M 2s
24350K .......... .......... .......... .......... .......... 41% 62.9M 2s
24400K .......... .......... .......... .......... .......... 41% 14.7M 2s
24450K .......... .......... .......... .......... .......... 41% 54.8M 2s
24500K .......... .......... .......... .......... .......... 41% 30.2M 2s
24550K .......... .......... .......... .......... .......... 41% 46.4M 2s
24600K .......... .......... .......... .......... .......... 41% 49.6M 2s
24650K .......... .......... .......... .......... .......... 41% 59.1M 2s
24700K .......... .......... .......... .......... .......... 42% 22.9M 2s
24750K .......... .......... .......... .......... .......... 42% 62.8M 2s
24800K .......... .......... .......... .......... .......... 42% 36.7M 2s
24850K .......... .......... .......... .......... .......... 42% 34.2M 2s
24900K .......... .......... .......... .......... .......... 42% 54.0M 2s
24950K .......... .......... .......... .......... .......... 42% 67.5M 2s
25000K .......... .......... .......... .......... .......... 42% 19.6M 2s
25050K .......... .......... .......... .......... .......... 42% 66.0M 2s
25100K .......... .......... .......... .......... .......... 42% 68.4M 2s
25150K .......... .......... .......... .......... .......... 42% 59.7M 2s
25200K .......... .......... .......... .......... .......... 42% 58.3M 2s
25250K .......... .......... .......... .......... .......... 42% 52.6M 2s
25300K .......... .......... .......... .......... .......... 43% 52.1M 2s
25350K .......... .......... .......... .......... .......... 43% 14.2M 2s
25400K .......... .......... .......... .......... .......... 43% 41.6M 2s
25450K .......... .......... .......... .......... .......... 43% 69.8M 2s
25500K .......... .......... .......... .......... .......... 43% 41.5M 2s
25550K .......... .......... .......... .......... .......... 43% 57.0M 2s
25600K .......... .......... .......... .......... .......... 43% 49.0M 2s
25650K .......... .......... .......... .......... .......... 43% 65.3M 2s
25700K .......... .......... .......... .......... .......... 43% 70.4M 2s
25750K .......... .......... .......... .......... .......... 43% 73.4M 2s
25800K .......... .......... .......... .......... .......... 43% 15.0M 2s
25850K .......... .......... .......... .......... .......... 43% 68.1M 2s
25900K .......... .......... .......... .......... .......... 44% 58.3M 2s
25950K .......... .......... .......... .......... .......... 44% 64.6M 2s
26000K .......... .......... .......... .......... .......... 44% 12.1M 2s
26050K .......... .......... .......... .......... .......... 44% 67.8M 2s
26100K .......... .......... .......... .......... .......... 44% 61.3M 2s
26150K .......... .......... .......... .......... .......... 44% 10.8M 2s
26200K .......... .......... .......... .......... .......... 44% 38.8M 2s
26250K .......... .......... .......... .......... .......... 44% 51.7M 2s
26300K .......... .......... .......... .......... .......... 44% 64.1M 2s
26350K .......... .......... .......... .......... .......... 44% 37.2M 2s
26400K .......... .......... .......... .......... .......... 44% 48.7M 2s
26450K .......... .......... .......... .......... .......... 44% 30.0M 2s
26500K .......... .......... .......... .......... .......... 45% 59.8M 2s
26550K .......... .......... .......... .......... .......... 45% 61.9M 2s
26600K .......... .......... .......... .......... .......... 45% 44.8M 2s
26650K .......... .......... .......... .......... .......... 45% 30.0M 2s
26700K .......... .......... .......... .......... .......... 45% 22.5M 2s
26750K .......... .......... .......... .......... .......... 45% 41.3M 2s
26800K .......... .......... .......... .......... .......... 45% 45.1M 2s
26850K .......... .......... .......... .......... .......... 45% 59.7M 2s
26900K .......... .......... .......... .......... .......... 45% 63.2M 2s
26950K .......... .......... .......... .......... .......... 45% 27.4M 2s
27000K .......... .......... .......... .......... .......... 45% 45.8M 2s
27050K .......... .......... .......... .......... .......... 46% 59.6M 2s
27100K .......... .......... .......... .......... .......... 46% 52.1M 2s
27150K .......... .......... .......... .......... .......... 46% 19.8M 2s
27200K .......... .......... .......... .......... .......... 46% 54.9M 2s
27250K .......... .......... .......... .......... .......... 46% 65.4M 2s
27300K .......... .......... .......... .......... .......... 46% 54.9M 2s
27350K .......... .......... .......... .......... .......... 46% 47.0M 2s
27400K .......... .......... .......... .......... .......... 46% 56.8M 2s
27450K .......... .......... .......... .......... .......... 46% 30.7M 2s
27500K .......... .......... .......... .......... .......... 46% 52.3M 2s
27550K .......... .......... .......... .......... .......... 46% 19.3M 2s
27600K .......... .......... .......... .......... .......... 46% 43.7M 2s
27650K .......... .......... .......... .......... .......... 47% 53.9M 2s
27700K .......... .......... .......... .......... .......... 47% 55.2M 2s
27750K .......... .......... .......... .......... .......... 47% 50.0M 2s
27800K .......... .......... .......... .......... .......... 47% 54.2M 2s
27850K .......... .......... .......... .......... .......... 47% 54.5M 2s
27900K .......... .......... .......... .......... .......... 47% 28.8M 2s
27950K .......... .......... .......... .......... .......... 47% 61.8M 2s
28000K .......... .......... .......... .......... .......... 47% 61.2M 2s
28050K .......... .......... .......... .......... .......... 47% 15.2M 2s
28100K .......... .......... .......... .......... .......... 47% 52.1M 2s
28150K .......... .......... .......... .......... .......... 47% 62.0M 2s
28200K .......... .......... .......... .......... .......... 47% 53.5M 2s
28250K .......... .......... .......... .......... .......... 48% 56.3M 2s
28300K .......... .......... .......... .......... .......... 48% 8.02M 2s
28350K .......... .......... .......... .......... .......... 48% 25.8M 2s
28400K .......... .......... .......... .......... .......... 48% 41.1M 2s
28450K .......... .......... .......... .......... .......... 48% 28.8M 2s
28500K .......... .......... .......... .......... .......... 48% 60.5M 2s
28550K .......... .......... .......... .......... .......... 48% 66.5M 2s
28600K .......... .......... .......... .......... .......... 48% 52.8M 2s
28650K .......... .......... .......... .......... .......... 48% 50.8M 2s
28700K .......... .......... .......... .......... .......... 48% 57.5M 2s
28750K .......... .......... .......... .......... .......... 48% 26.0M 2s
28800K .......... .......... .......... .......... .......... 48% 46.6M 2s
28850K .......... .......... .......... .......... .......... 49% 48.3M 2s
28900K .......... .......... .......... .......... .......... 49% 33.1M 2s
28950K .......... .......... .......... .......... .......... 49% 66.4M 2s
29000K .......... .......... .......... .......... .......... 49% 35.9M 2s
29050K .......... .......... .......... .......... .......... 49% 68.3M 2s
29100K .......... .......... .......... .......... .......... 49% 42.5M 2s
29150K .......... .......... .......... .......... .......... 49% 26.0M 2s
29200K .......... .......... .......... .......... .......... 49% 51.8M 1s
29250K .......... .......... .......... .......... .......... 49% 61.5M 1s
29300K .......... .......... .......... .......... .......... 49% 60.5M 1s
29350K .......... .......... .......... .......... .......... 49% 65.6M 1s
29400K .......... .......... .......... .......... .......... 50% 23.2M 1s
29450K .......... .......... .......... .......... .......... 50% 51.8M 1s
29500K .......... .......... .......... .......... .......... 50% 65.0M 1s
29550K .......... .......... .......... .......... .......... 50% 73.5M 1s
29600K .......... .......... .......... .......... .......... 50% 16.7M 1s
29650K .......... .......... .......... .......... .......... 50% 67.0M 1s
29700K .......... .......... .......... .......... .......... 50% 56.7M 1s
29750K .......... .......... .......... .......... .......... 50% 60.3M 1s
29800K .......... .......... .......... .......... .......... 50% 42.5M 1s
29850K .......... .......... .......... .......... .......... 50% 59.8M 1s
29900K .......... .......... .......... .......... .......... 50% 60.4M 1s
29950K .......... .......... .......... .......... .......... 50% 65.9M 1s
30000K .......... .......... .......... .......... .......... 51% 23.3M 1s
30050K .......... .......... .......... .......... .......... 51% 52.2M 1s
30100K .......... .......... .......... .......... .......... 51% 18.1M 1s
30150K .......... .......... .......... .......... .......... 51% 65.2M 1s
30200K .......... .......... .......... .......... .......... 51% 49.0M 1s
30250K .......... .......... .......... .......... .......... 51% 61.2M 1s
30300K .......... .......... .......... .......... .......... 51% 20.6M 1s
30350K .......... .......... .......... .......... .......... 51% 59.1M 1s
30400K .......... .......... .......... .......... .......... 51% 25.5M 1s
30450K .......... .......... .......... .......... .......... 51% 30.9M 1s
30500K .......... .......... .......... .......... .......... 51% 36.5M 1s
30550K .......... .......... .......... .......... .......... 51% 31.0M 1s
30600K .......... .......... .......... .......... .......... 52% 40.0M 1s
30650K .......... .......... .......... .......... .......... 52% 42.7M 1s
30700K .......... .......... .......... .......... .......... 52% 44.3M 1s
30750K .......... .......... .......... .......... .......... 52% 49.8M 1s
30800K .......... .......... .......... .......... .......... 52% 43.2M 1s
30850K .......... .......... .......... .......... .......... 52% 57.6M 1s
30900K .......... .......... .......... .......... .......... 52% 68.4M 1s
30950K .......... .......... .......... .......... .......... 52% 73.2M 1s
31000K .......... .......... .......... .......... .......... 52% 45.0M 1s
31050K .......... .......... .......... .......... .......... 52% 17.3M 1s
31100K .......... .......... .......... .......... .......... 52% 64.6M 1s
31150K .......... .......... .......... .......... .......... 52% 59.8M 1s
31200K .......... .......... .......... .......... .......... 53% 38.7M 1s
31250K .......... .......... .......... .......... .......... 53% 57.0M 1s
31300K .......... .......... .......... .......... .......... 53% 60.2M 1s
31350K .......... .......... .......... .......... .......... 53% 25.8M 1s
31400K .......... .......... .......... .......... .......... 53% 19.9M 1s
31450K .......... .......... .......... .......... .......... 53% 23.3M 1s
31500K .......... .......... .......... .......... .......... 53% 54.7M 1s
31550K .......... .......... .......... .......... .......... 53% 39.3M 1s
31600K .......... .......... .......... .......... .......... 53% 35.6M 1s
31650K .......... .......... .......... .......... .......... 53% 32.5M 1s
31700K .......... .......... .......... .......... .......... 53% 57.0M 1s
31750K .......... .......... .......... .......... .......... 53% 36.1M 1s
31800K .......... .......... .......... .......... .......... 54% 26.6M 1s
31850K .......... .......... .......... .......... .......... 54% 67.8M 1s
31900K .......... .......... .......... .......... .......... 54% 35.6M 1s
31950K .......... .......... .......... .......... .......... 54% 64.4M 1s
32000K .......... .......... .......... .......... .......... 54% 56.1M 1s
32050K .......... .......... .......... .......... .......... 54% 40.9M 1s
32100K .......... .......... .......... .......... .......... 54% 51.1M 1s
32150K .......... .......... .......... .......... .......... 54% 24.5M 1s
32200K .......... .......... .......... .......... .......... 54% 55.7M 1s
32250K .......... .......... .......... .......... .......... 54% 57.8M 1s
32300K .......... .......... .......... .......... .......... 54% 36.4M 1s
32350K .......... .......... .......... .......... .......... 55% 30.2M 1s
32400K .......... .......... .......... .......... .......... 55% 55.3M 1s
32450K .......... .......... .......... .......... .......... 55% 49.8M 1s
32500K .......... .......... .......... .......... .......... 55% 31.5M 1s
32550K .......... .......... .......... .......... .......... 55% 59.4M 1s
32600K .......... .......... .......... .......... .......... 55% 21.9M 1s
32650K .......... .......... .......... .......... .......... 55% 53.8M 1s
32700K .......... .......... .......... .......... .......... 55% 56.1M 1s
32750K .......... .......... .......... .......... .......... 55% 43.2M 1s
32800K .......... .......... .......... .......... .......... 55% 37.7M 1s
32850K .......... .......... .......... .......... .......... 55% 71.5M 1s
32900K .......... .......... .......... .......... .......... 55% 55.3M 1s
32950K .......... .......... .......... .......... .......... 56% 18.5M 1s
33000K .......... .......... .......... .......... .......... 56% 51.3M 1s
33050K .......... .......... .......... .......... .......... 56% 59.1M 1s
33100K .......... .......... .......... .......... .......... 56% 14.0M 1s
33150K .......... .......... .......... .......... .......... 56% 29.2M 1s
33200K .......... .......... .......... .......... .......... 56% 28.0M 1s
33250K .......... .......... .......... .......... .......... 56% 25.0M 1s
33300K .......... .......... .......... .......... .......... 56% 55.8M 1s
33350K .......... .......... .......... .......... .......... 56% 51.7M 1s
33400K .......... .......... .......... .......... .......... 56% 54.4M 1s
33450K .......... .......... .......... .......... .......... 56% 55.1M 1s
33500K .......... .......... .......... .......... .......... 56% 59.2M 1s
33550K .......... .......... .......... .......... .......... 57% 47.1M 1s
33600K .......... .......... .......... .......... .......... 57% 19.1M 1s
33650K .......... .......... .......... .......... .......... 57% 54.8M 1s
33700K .......... .......... .......... .......... .......... 57% 54.0M 1s
33750K .......... .......... .......... .......... .......... 57% 67.5M 1s
33800K .......... .......... .......... .......... .......... 57% 32.7M 1s
33850K .......... .......... .......... .......... .......... 57% 52.7M 1s
33900K .......... .......... .......... .......... .......... 57% 68.0M 1s
33950K .......... .......... .......... .......... .......... 57% 22.8M 1s
34000K .......... .......... .......... .......... .......... 57% 45.6M 1s
34050K .......... .......... .......... .......... .......... 57% 17.7M 1s
34100K .......... .......... .......... .......... .......... 57% 39.9M 1s
34150K .......... .......... .......... .......... .......... 58% 54.5M 1s
34200K .......... .......... .......... .......... .......... 58% 57.2M 1s
34250K .......... .......... .......... .......... .......... 58% 70.8M 1s
34300K .......... .......... .......... .......... .......... 58% 60.1M 1s
34350K .......... .......... .......... .......... .......... 58% 62.0M 1s
34400K .......... .......... .......... .......... .......... 58% 54.2M 1s
34450K .......... .......... .......... .......... .......... 58% 23.2M 1s
34500K .......... .......... .......... .......... .......... 58% 35.9M 1s
34550K .......... .......... .......... .......... .......... 58% 63.7M 1s
34600K .......... .......... .......... .......... .......... 58% 59.6M 1s
34650K .......... .......... .......... .......... .......... 58% 64.9M 1s
34700K .......... .......... .......... .......... .......... 59% 65.7M 1s
34750K .......... .......... .......... .......... .......... 59% 52.4M 1s
34800K .......... .......... .......... .......... .......... 59% 34.0M 1s
34850K .......... .......... .......... .......... .......... 59% 67.3M 1s
34900K .......... .......... .......... .......... .......... 59% 27.2M 1s
34950K .......... .......... .......... .......... .......... 59% 47.5M 1s
35000K .......... .......... .......... .......... .......... 59% 55.8M 1s
35050K .......... .......... .......... .......... .......... 59% 14.0M 1s
35100K .......... .......... .......... .......... .......... 59% 39.8M 1s
35150K .......... .......... .......... .......... .......... 59% 57.6M 1s
35200K .......... .......... .......... .......... .......... 59% 55.1M 1s
35250K .......... .......... .......... .......... .......... 59% 65.3M 1s
35300K .......... .......... .......... .......... .......... 60% 56.7M 1s
35350K .......... .......... .......... .......... .......... 60% 59.4M 1s
35400K .......... .......... .......... .......... .......... 60% 50.9M 1s
35450K .......... .......... .......... .......... .......... 60% 56.6M 1s
35500K .......... .......... .......... .......... .......... 60% 18.0M 1s
35550K .......... .......... .......... .......... .......... 60% 53.5M 1s
35600K .......... .......... .......... .......... .......... 60% 57.3M 1s
35650K .......... .......... .......... .......... .......... 60% 69.7M 1s
35700K .......... .......... .......... .......... .......... 60% 14.2M 1s
35750K .......... .......... .......... .......... .......... 60% 53.4M 1s
35800K .......... .......... .......... .......... .......... 60% 42.8M 1s
35850K .......... .......... .......... .......... .......... 60% 70.4M 1s
35900K .......... .......... .......... .......... .......... 61% 46.0M 1s
35950K .......... .......... .......... .......... .......... 61% 67.6M 1s
36000K .......... .......... .......... .......... .......... 61% 44.9M 1s
36050K .......... .......... .......... .......... .......... 61% 60.6M 1s
36100K .......... .......... .......... .......... .......... 61% 51.9M 1s
36150K .......... .......... .......... .......... .......... 61% 48.0M 1s
36200K .......... .......... .......... .......... .......... 61% 48.8M 1s
36250K .......... .......... .......... .......... .......... 61% 67.8M 1s
36300K .......... .......... .......... .......... .......... 61% 12.6M 1s
36350K .......... .......... .......... .......... .......... 61% 49.7M 1s
36400K .......... .......... .......... .......... .......... 61% 36.5M 1s
36450K .......... .......... .......... .......... .......... 61% 51.3M 1s
36500K .......... .......... .......... .......... .......... 62% 53.6M 1s
36550K .......... .......... .......... .......... .......... 62% 53.4M 1s
36600K .......... .......... .......... .......... .......... 62% 64.4M 1s
36650K .......... .......... .......... .......... .......... 62% 10.7M 1s
36700K .......... .......... .......... .......... .......... 62% 30.5M 1s
36750K .......... .......... .......... .......... .......... 62% 66.1M 1s
36800K .......... .......... .......... .......... .......... 62% 53.0M 1s
36850K .......... .......... .......... .......... .......... 62% 73.0M 1s
36900K .......... .......... .......... .......... .......... 62% 64.5M 1s
36950K .......... .......... .......... .......... .......... 62% 69.1M 1s
37000K .......... .......... .......... .......... .......... 62% 58.7M 1s
37050K .......... .......... .......... .......... .......... 62% 55.8M 1s
37100K .......... .......... .......... .......... .......... 63% 60.1M 1s
37150K .......... .......... .......... .......... .......... 63% 63.9M 1s
37200K .......... .......... .......... .......... .......... 63% 56.9M 1s
37250K .......... .......... .......... .......... .......... 63% 68.0M 1s
37300K .......... .......... .......... .......... .......... 63% 68.2M 1s
37350K .......... .......... .......... .......... .......... 63% 66.7M 1s
37400K .......... .......... .......... .......... .......... 63% 52.4M 1s
37450K .......... .......... .......... .......... .......... 63% 64.9M 1s
37500K .......... .......... .......... .......... .......... 63% 13.7M 1s
37550K .......... .......... .......... .......... .......... 63% 35.9M 1s
37600K .......... .......... .......... .......... .......... 63% 46.8M 1s
37650K .......... .......... .......... .......... .......... 64% 60.4M 1s
37700K .......... .......... .......... .......... .......... 64% 44.5M 1s
37750K .......... .......... .......... .......... .......... 64% 58.6M 1s
37800K .......... .......... .......... .......... .......... 64% 51.9M 1s
37850K .......... .......... .......... .......... .......... 64% 68.1M 1s
37900K .......... .......... .......... .......... .......... 64% 63.9M 1s
37950K .......... .......... .......... .......... .......... 64% 62.2M 1s
38000K .......... .......... .......... .......... .......... 64% 56.0M 1s
38050K .......... .......... .......... .......... .......... 64% 70.3M 1s
38100K .......... .......... .......... .......... .......... 64% 70.8M 1s
38150K .......... .......... .......... .......... .......... 64% 8.74M 1s
38200K .......... .......... .......... .......... .......... 64% 32.4M 1s
38250K .......... .......... .......... .......... .......... 65% 55.7M 1s
38300K .......... .......... .......... .......... .......... 65% 40.1M 1s
38350K .......... .......... .......... .......... .......... 65% 38.5M 1s
38400K .......... .......... .......... .......... .......... 65% 31.7M 1s
38450K .......... .......... .......... .......... .......... 65% 42.4M 1s
38500K .......... .......... .......... .......... .......... 65% 51.6M 1s
38550K .......... .......... .......... .......... .......... 65% 54.6M 1s
38600K .......... .......... .......... .......... .......... 65% 59.4M 1s
38650K .......... .......... .......... .......... .......... 65% 67.8M 1s
38700K .......... .......... .......... .......... .......... 65% 70.3M 1s
38750K .......... .......... .......... .......... .......... 65% 59.9M 1s
38800K .......... .......... .......... .......... .......... 65% 32.6M 1s
38850K .......... .......... .......... .......... .......... 66% 50.4M 1s
38900K .......... .......... .......... .......... .......... 66% 52.9M 1s
38950K .......... .......... .......... .......... .......... 66% 56.4M 1s
39000K .......... .......... .......... .......... .......... 66% 48.2M 1s
39050K .......... .......... .......... .......... .......... 66% 71.0M 1s
39100K .......... .......... .......... .......... .......... 66% 64.6M 1s
39150K .......... .......... .......... .......... .......... 66% 72.2M 1s
39200K .......... .......... .......... .......... .......... 66% 18.7M 1s
39250K .......... .......... .......... .......... .......... 66% 56.5M 1s
39300K .......... .......... .......... .......... .......... 66% 47.3M 1s
39350K .......... .......... .......... .......... .......... 66% 60.9M 1s
39400K .......... .......... .......... .......... .......... 66% 55.2M 1s
39450K .......... .......... .......... .......... .......... 67% 51.0M 1s
39500K .......... .......... .......... .......... .......... 67% 61.0M 1s
39550K .......... .......... .......... .......... .......... 67% 49.3M 1s
39600K .......... .......... .......... .......... .......... 67% 52.2M 1s
39650K .......... .......... .......... .......... .......... 67% 65.9M 1s
39700K .......... .......... .......... .......... .......... 67% 74.1M 1s
39750K .......... .......... .......... .......... .......... 67% 9.57M 1s
39800K .......... .......... .......... .......... .......... 67% 30.2M 1s
39850K .......... .......... .......... .......... .......... 67% 53.5M 1s
39900K .......... .......... .......... .......... .......... 67% 69.0M 1s
39950K .......... .......... .......... .......... .......... 67% 72.1M 1s
40000K .......... .......... .......... .......... .......... 68% 54.7M 1s
40050K .......... .......... .......... .......... .......... 68% 64.8M 1s
40100K .......... .......... .......... .......... .......... 68% 39.5M 1s
40150K .......... .......... .......... .......... .......... 68% 51.8M 1s
40200K .......... .......... .......... .......... .......... 68% 11.1M 1s
40250K .......... .......... .......... .......... .......... 68% 46.0M 1s
40300K .......... .......... .......... .......... .......... 68% 50.1M 1s
40350K .......... .......... .......... .......... .......... 68% 42.3M 1s
40400K .......... .......... .......... .......... .......... 68% 50.2M 1s
40450K .......... .......... .......... .......... .......... 68% 60.6M 1s
40500K .......... .......... .......... .......... .......... 68% 62.6M 1s
40550K .......... .......... .......... .......... .......... 68% 58.7M 1s
40600K .......... .......... .......... .......... .......... 69% 45.0M 1s
40650K .......... .......... .......... .......... .......... 69% 63.2M 1s
40700K .......... .......... .......... .......... .......... 69% 50.5M 1s
40750K .......... .......... .......... .......... .......... 69% 71.5M 1s
40800K .......... .......... .......... .......... .......... 69% 45.0M 1s
40850K .......... .......... .......... .......... .......... 69% 47.6M 1s
40900K .......... .......... .......... .......... .......... 69% 65.0M 1s
40950K .......... .......... .......... .......... .......... 69% 291K 1s
41000K .......... .......... .......... .......... .......... 69% 15.3M 1s
41050K .......... .......... .......... .......... .......... 69% 48.4M 1s
41100K .......... .......... .......... .......... .......... 69% 50.3M 1s
41150K .......... .......... .......... .......... .......... 69% 65.7M 1s
41200K .......... .......... .......... .......... .......... 70% 39.4M 1s
41250K .......... .......... .......... .......... .......... 70% 44.8M 1s
41300K .......... .......... .......... .......... .......... 70% 62.3M 1s
41350K .......... .......... .......... .......... .......... 70% 68.1M 1s
41400K .......... .......... .......... .......... .......... 70% 54.3M 1s
41450K .......... .......... .......... .......... .......... 70% 40.6M 1s
41500K .......... .......... .......... .......... .......... 70% 41.8M 1s
41550K .......... .......... .......... .......... .......... 70% 61.0M 1s
41600K .......... .......... .......... .......... .......... 70% 56.4M 1s
41650K .......... .......... .......... .......... .......... 70% 71.5M 1s
41700K .......... .......... .......... .......... .......... 70% 33.3M 1s
41750K .......... .......... .......... .......... .......... 70% 49.3M 1s
41800K .......... .......... .......... .......... .......... 71% 47.8M 1s
41850K .......... .......... .......... .......... .......... 71% 12.4M 1s
41900K .......... .......... .......... .......... .......... 71% 35.3M 1s
41950K .......... .......... .......... .......... .......... 71% 54.9M 1s
42000K .......... .......... .......... .......... .......... 71% 55.0M 1s
42050K .......... .......... .......... .......... .......... 71% 44.3M 1s
42100K .......... .......... .......... .......... .......... 71% 67.8M 1s
42150K .......... .......... .......... .......... .......... 71% 46.9M 1s
42200K .......... .......... .......... .......... .......... 71% 59.9M 1s
42250K .......... .......... .......... .......... .......... 71% 54.9M 1s
42300K .......... .......... .......... .......... .......... 71% 63.8M 1s
42350K .......... .......... .......... .......... .......... 71% 67.9M 1s
42400K .......... .......... .......... .......... .......... 72% 52.6M 1s
42450K .......... .......... .......... .......... .......... 72% 58.9M 1s
42500K .......... .......... .......... .......... .......... 72% 71.2M 1s
42550K .......... .......... .......... .......... .......... 72% 30.0M 1s
42600K .......... .......... .......... .......... .......... 72% 40.3M 1s
42650K .......... .......... .......... .......... .......... 72% 58.1M 1s
42700K .......... .......... .......... .......... .......... 72% 73.3M 1s
42750K .......... .......... .......... .......... .......... 72% 9.00M 1s
42800K .......... .......... .......... .......... .......... 72% 29.1M 1s
42850K .......... .......... .......... .......... .......... 72% 54.5M 1s
42900K .......... .......... .......... .......... .......... 72% 41.4M 1s
42950K .......... .......... .......... .......... .......... 73% 59.0M 1s
43000K .......... .......... .......... .......... .......... 73% 59.8M 1s
43050K .......... .......... .......... .......... .......... 73% 24.5M 1s
43100K .......... .......... .......... .......... .......... 73% 50.5M 1s
43150K .......... .......... .......... .......... .......... 73% 69.5M 1s
43200K .......... .......... .......... .......... .......... 73% 49.6M 1s
43250K .......... .......... .......... .......... .......... 73% 45.6M 1s
43300K .......... .......... .......... .......... .......... 73% 17.6M 1s
43350K .......... .......... .......... .......... .......... 73% 37.8M 1s
43400K .......... .......... .......... .......... .......... 73% 45.7M 1s
43450K .......... .......... .......... .......... .......... 73% 58.9M 1s
43500K .......... .......... .......... .......... .......... 73% 55.5M 1s
43550K .......... .......... .......... .......... .......... 74% 56.7M 1s
43600K .......... .......... .......... .......... .......... 74% 58.6M 1s
43650K .......... .......... .......... .......... .......... 74% 46.9M 1s
43700K .......... .......... .......... .......... .......... 74% 70.7M 1s
43750K .......... .......... .......... .......... .......... 74% 33.0M 1s
43800K .......... .......... .......... .......... .......... 74% 35.3M 1s
43850K .......... .......... .......... .......... .......... 74% 53.4M 1s
43900K .......... .......... .......... .......... .......... 74% 59.9M 1s
43950K .......... .......... .......... .......... .......... 74% 50.3M 1s
44000K .......... .......... .......... .......... .......... 74% 47.8M 1s
44050K .......... .......... .......... .......... .......... 74% 69.7M 1s
44100K .......... .......... .......... .......... .......... 74% 71.3M 1s
44150K .......... .......... .......... .......... .......... 75% 66.6M 1s
44200K .......... .......... .......... .......... .......... 75% 62.8M 1s
44250K .......... .......... .......... .......... .......... 75% 8.23M 1s
44300K .......... .......... .......... .......... .......... 75% 42.3M 1s
44350K .......... .......... .......... .......... .......... 75% 55.6M 1s
44400K .......... .......... .......... .......... .......... 75% 33.5M 1s
44450K .......... .......... .......... .......... .......... 75% 46.0M 1s
44500K .......... .......... .......... .......... .......... 75% 55.1M 1s
44550K .......... .......... .......... .......... .......... 75% 64.3M 1s
44600K .......... .......... .......... .......... .......... 75% 50.2M 1s
44650K .......... .......... .......... .......... .......... 75% 35.1M 1s
44700K .......... .......... .......... .......... .......... 75% 50.8M 1s
44750K .......... .......... .......... .......... .......... 76% 55.2M 1s
44800K .......... .......... .......... .......... .......... 76% 36.7M 1s
44850K .......... .......... .......... .......... .......... 76% 38.5M 1s
44900K .......... .......... .......... .......... .......... 76% 48.2M 1s
44950K .......... .......... .......... .......... .......... 76% 46.7M 1s
45000K .......... .......... .......... .......... .......... 76% 54.1M 1s
45050K .......... .......... .......... .......... .......... 76% 73.4M 1s
45100K .......... .......... .......... .......... .......... 76% 70.5M 1s
45150K .......... .......... .......... .......... .......... 76% 75.2M 1s
45200K .......... .......... .......... .......... .......... 76% 63.3M 1s
45250K .......... .......... .......... .......... .......... 76% 13.8M 1s
45300K .......... .......... .......... .......... .......... 77% 66.6M 1s
45350K .......... .......... .......... .......... .......... 77% 56.5M 1s
45400K .......... .......... .......... .......... .......... 77% 41.1M 1s
45450K .......... .......... .......... .......... .......... 77% 71.8M 1s
45500K .......... .......... .......... .......... .......... 77% 67.9M 1s
45550K .......... .......... .......... .......... .......... 77% 64.7M 1s
45600K .......... .......... .......... .......... .......... 77% 43.4M 1s
45650K .......... .......... .......... .......... .......... 77% 68.4M 1s
45700K .......... .......... .......... .......... .......... 77% 64.6M 1s
45750K .......... .......... .......... .......... .......... 77% 59.6M 1s
45800K .......... .......... .......... .......... .......... 77% 58.8M 1s
45850K .......... .......... .......... .......... .......... 77% 69.8M 1s
45900K .......... .......... .......... .......... .......... 78% 66.4M 1s
45950K .......... .......... .......... .......... .......... 78% 58.8M 1s
46000K .......... .......... .......... .......... .......... 78% 58.1M 1s
46050K .......... .......... .......... .......... .......... 78% 8.07M 1s
46100K .......... .......... .......... .......... .......... 78% 48.8M 1s
46150K .......... .......... .......... .......... .......... 78% 54.7M 1s
46200K .......... .......... .......... .......... .......... 78% 55.5M 1s
46250K .......... .......... .......... .......... .......... 78% 63.2M 1s
46300K .......... .......... .......... .......... .......... 78% 69.1M 1s
46350K .......... .......... .......... .......... .......... 78% 69.6M 1s
46400K .......... .......... .......... .......... .......... 78% 44.3M 1s
46450K .......... .......... .......... .......... .......... 78% 65.0M 1s
46500K .......... .......... .......... .......... .......... 79% 49.8M 1s
46550K .......... .......... .......... .......... .......... 79% 72.0M 1s
46600K .......... .......... .......... .......... .......... 79% 9.15M 1s
46650K .......... .......... .......... .......... .......... 79% 46.3M 1s
46700K .......... .......... .......... .......... .......... 79% 51.3M 1s
46750K .......... .......... .......... .......... .......... 79% 62.1M 1s
46800K .......... .......... .......... .......... .......... 79% 38.6M 1s
46850K .......... .......... .......... .......... .......... 79% 57.7M 1s
46900K .......... .......... .......... .......... .......... 79% 61.2M 1s
46950K .......... .......... .......... .......... .......... 79% 68.5M 1s
47000K .......... .......... .......... .......... .......... 79% 62.9M 1s
47050K .......... .......... .......... .......... .......... 79% 24.0M 1s
47100K .......... .......... .......... .......... .......... 80% 51.5M 1s
47150K .......... .......... .......... .......... .......... 80% 37.4M 1s
47200K .......... .......... .......... .......... .......... 80% 38.6M 1s
47250K .......... .......... .......... .......... .......... 80% 42.8M 1s
47300K .......... .......... .......... .......... .......... 80% 38.8M 1s
47350K .......... .......... .......... .......... .......... 80% 31.9M 1s
47400K .......... .......... .......... .......... .......... 80% 55.4M 1s
47450K .......... .......... .......... .......... .......... 80% 57.4M 1s
47500K .......... .......... .......... .......... .......... 80% 19.6M 0s
47550K .......... .......... .......... .......... .......... 80% 37.7M 0s
47600K .......... .......... .......... .......... .......... 80% 52.0M 0s
47650K .......... .......... .......... .......... .......... 80% 50.0M 0s
47700K .......... .......... .......... .......... .......... 81% 48.4M 0s
47750K .......... .......... .......... .......... .......... 81% 60.0M 0s
47800K .......... .......... .......... .......... .......... 81% 52.0M 0s
47850K .......... .......... .......... .......... .......... 81% 49.2M 0s
47900K .......... .......... .......... .......... .......... 81% 47.0M 0s
47950K .......... .......... .......... .......... .......... 81% 46.0M 0s
48000K .......... .......... .......... .......... .......... 81% 49.4M 0s
48050K .......... .......... .......... .......... .......... 81% 49.9M 0s
48100K .......... .......... .......... .......... .......... 81% 51.9M 0s
48150K .......... .......... .......... .......... .......... 81% 62.2M 0s
48200K .......... .......... .......... .......... .......... 81% 51.5M 0s
48250K .......... .......... .......... .......... .......... 82% 23.9M 0s
48300K .......... .......... .......... .......... .......... 82% 40.7M 0s
48350K .......... .......... .......... .......... .......... 82% 54.0M 0s
48400K .......... .......... .......... .......... .......... 82% 56.8M 0s
48450K .......... .......... .......... .......... .......... 82% 69.0M 0s
48500K .......... .......... .......... .......... .......... 82% 66.1M 0s
48550K .......... .......... .......... .......... .......... 82% 39.9M 0s
48600K .......... .......... .......... .......... .......... 82% 13.2M 0s
48650K .......... .......... .......... .......... .......... 82% 61.3M 0s
48700K .......... .......... .......... .......... .......... 82% 64.7M 0s
48750K .......... .......... .......... .......... .......... 82% 32.2M 0s
48800K .......... .......... .......... .......... .......... 82% 36.1M 0s
48850K .......... .......... .......... .......... .......... 83% 37.0M 0s
48900K .......... .......... .......... .......... .......... 83% 72.2M 0s
48950K .......... .......... .......... .......... .......... 83% 13.6M 0s
49000K .......... .......... .......... .......... .......... 83% 51.3M 0s
49050K .......... .......... .......... .......... .......... 83% 53.2M 0s
49100K .......... .......... .......... .......... .......... 83% 68.3M 0s
49150K .......... .......... .......... .......... .......... 83% 56.4M 0s
49200K .......... .......... .......... .......... .......... 83% 37.1M 0s
49250K .......... .......... .......... .......... .......... 83% 63.6M 0s
49300K .......... .......... .......... .......... .......... 83% 12.6M 0s
49350K .......... .......... .......... .......... .......... 83% 42.1M 0s
49400K .......... .......... .......... .......... .......... 83% 29.7M 0s
49450K .......... .......... .......... .......... .......... 84% 41.3M 0s
49500K .......... .......... .......... .......... .......... 84% 67.6M 0s
49550K .......... .......... .......... .......... .......... 84% 45.8M 0s
49600K .......... .......... .......... .......... .......... 84% 43.2M 0s
49650K .......... .......... .......... .......... .......... 84% 68.9M 0s
49700K .......... .......... .......... .......... .......... 84% 70.8M 0s
49750K .......... .......... .......... .......... .......... 84% 67.8M 0s
49800K .......... .......... .......... .......... .......... 84% 54.6M 0s
49850K .......... .......... .......... .......... .......... 84% 64.0M 0s
49900K .......... .......... .......... .......... .......... 84% 68.6M 0s
49950K .......... .......... .......... .......... .......... 84% 65.8M 0s
50000K .......... .......... .......... .......... .......... 84% 56.7M 0s
50050K .......... .......... .......... .......... .......... 85% 63.9M 0s
50100K .......... .......... .......... .......... .......... 85% 70.3M 0s
50150K .......... .......... .......... .......... .......... 85% 73.7M 0s
50200K .......... .......... .......... .......... .......... 85% 11.3M 0s
50250K .......... .......... .......... .......... .......... 85% 58.4M 0s
50300K .......... .......... .......... .......... .......... 85% 58.5M 0s
50350K .......... .......... .......... .......... .......... 85% 47.4M 0s
50400K .......... .......... .......... .......... .......... 85% 47.8M 0s
50450K .......... .......... .......... .......... .......... 85% 56.0M 0s
50500K .......... .......... .......... .......... .......... 85% 55.7M 0s
50550K .......... .......... .......... .......... .......... 85% 39.6M 0s
50600K .......... .......... .......... .......... .......... 86% 42.8M 0s
50650K .......... .......... .......... .......... .......... 86% 50.5M 0s
50700K .......... .......... .......... .......... .......... 86% 46.8M 0s
50750K .......... .......... .......... .......... .......... 86% 53.6M 0s
50800K .......... .......... .......... .......... .......... 86% 31.5M 0s
50850K .......... .......... .......... .......... .......... 86% 66.3M 0s
50900K .......... .......... .......... .......... .......... 86% 63.3M 0s
50950K .......... .......... .......... .......... .......... 86% 12.1M 0s
51000K .......... .......... .......... .......... .......... 86% 19.7M 0s
51050K .......... .......... .......... .......... .......... 86% 45.3M 0s
51100K .......... .......... .......... .......... .......... 86% 52.6M 0s
51150K .......... .......... .......... .......... .......... 86% 64.9M 0s
51200K .......... .......... .......... .......... .......... 87% 53.6M 0s
51250K .......... .......... .......... .......... .......... 87% 64.6M 0s
51300K .......... .......... .......... .......... .......... 87% 66.1M 0s
51350K .......... .......... .......... .......... .......... 87% 70.3M 0s
51400K .......... .......... .......... .......... .......... 87% 22.1M 0s
51450K .......... .......... .......... .......... .......... 87% 66.6M 0s
51500K .......... .......... .......... .......... .......... 87% 16.3M 0s
51550K .......... .......... .......... .......... .......... 87% 64.7M 0s
51600K .......... .......... .......... .......... .......... 87% 34.4M 0s
51650K .......... .......... .......... .......... .......... 87% 55.2M 0s
51700K .......... .......... .......... .......... .......... 87% 64.6M 0s
51750K .......... .......... .......... .......... .......... 87% 68.9M 0s
51800K .......... .......... .......... .......... .......... 88% 16.2M 0s
51850K .......... .......... .......... .......... .......... 88% 62.0M 0s
51900K .......... .......... .......... .......... .......... 88% 60.0M 0s
51950K .......... .......... .......... .......... .......... 88% 68.8M 0s
52000K .......... .......... .......... .......... .......... 88% 21.7M 0s
52050K .......... .......... .......... .......... .......... 88% 53.7M 0s
52100K .......... .......... .......... .......... .......... 88% 62.4M 0s
52150K .......... .......... .......... .......... .......... 88% 18.8M 0s
52200K .......... .......... .......... .......... .......... 88% 54.6M 0s
52250K .......... .......... .......... .......... .......... 88% 65.3M 0s
52300K .......... .......... .......... .......... .......... 88% 68.6M 0s
52350K .......... .......... .......... .......... .......... 88% 69.2M 0s
52400K .......... .......... .......... .......... .......... 89% 44.3M 0s
52450K .......... .......... .......... .......... .......... 89% 61.1M 0s
52500K .......... .......... .......... .......... .......... 89% 27.5M 0s
52550K .......... .......... .......... .......... .......... 89% 57.5M 0s
52600K .......... .......... .......... .......... .......... 89% 50.3M 0s
52650K .......... .......... .......... .......... .......... 89% 17.2M 0s
52700K .......... .......... .......... .......... .......... 89% 63.2M 0s
52750K .......... .......... .......... .......... .......... 89% 68.1M 0s
52800K .......... .......... .......... .......... .......... 89% 23.2M 0s
52850K .......... .......... .......... .......... .......... 89% 50.4M 0s
52900K .......... .......... .......... .......... .......... 89% 28.3M 0s
52950K .......... .......... .......... .......... .......... 89% 65.3M 0s
53000K .......... .......... .......... .......... .......... 90% 46.7M 0s
53050K .......... .......... .......... .......... .......... 90% 46.8M 0s
53100K .......... .......... .......... .......... .......... 90% 54.3M 0s
53150K .......... .......... .......... .......... .......... 90% 62.9M 0s
53200K .......... .......... .......... .......... .......... 90% 39.5M 0s
53250K .......... .......... .......... .......... .......... 90% 58.1M 0s
53300K .......... .......... .......... .......... .......... 90% 17.0M 0s
53350K .......... .......... .......... .......... .......... 90% 57.4M 0s
53400K .......... .......... .......... .......... .......... 90% 39.4M 0s
53450K .......... .......... .......... .......... .......... 90% 46.2M 0s
53500K .......... .......... .......... .......... .......... 90% 63.7M 0s
53550K .......... .......... .......... .......... .......... 91% 45.1M 0s
53600K .......... .......... .......... .......... .......... 91% 42.1M 0s
53650K .......... .......... .......... .......... .......... 91% 64.0M 0s
53700K .......... .......... .......... .......... .......... 91% 18.3M 0s
53750K .......... .......... .......... .......... .......... 91% 55.6M 0s
53800K .......... .......... .......... .......... .......... 91% 55.4M 0s
53850K .......... .......... .......... .......... .......... 91% 33.2M 0s
53900K .......... .......... .......... .......... .......... 91% 49.6M 0s
53950K .......... .......... .......... .......... .......... 91% 49.5M 0s
54000K .......... .......... .......... .......... .......... 91% 52.6M 0s
54050K .......... .......... .......... .......... .......... 91% 16.9M 0s
54100K .......... .......... .......... .......... .......... 91% 60.5M 0s
54150K .......... .......... .......... .......... .......... 92% 62.9M 0s
54200K .......... .......... .......... .......... .......... 92% 51.5M 0s
54250K .......... .......... .......... .......... .......... 92% 61.4M 0s
54300K .......... .......... .......... .......... .......... 92% 52.9M 0s
54350K .......... .......... .......... .......... .......... 92% 22.9M 0s
54400K .......... .......... .......... .......... .......... 92% 53.7M 0s
54450K .......... .......... .......... .......... .......... 92% 66.4M 0s
54500K .......... .......... .......... .......... .......... 92% 63.9M 0s
54550K .......... .......... .......... .......... .......... 92% 59.1M 0s
54600K .......... .......... .......... .......... .......... 92% 55.5M 0s
54650K .......... .......... .......... .......... .......... 92% 60.3M 0s
54700K .......... .......... .......... .......... .......... 92% 16.2M 0s
54750K .......... .......... .......... .......... .......... 93% 51.8M 0s
54800K .......... .......... .......... .......... .......... 93% 40.4M 0s
54850K .......... .......... .......... .......... .......... 93% 63.4M 0s
54900K .......... .......... .......... .......... .......... 93% 21.9M 0s
54950K .......... .......... .......... .......... .......... 93% 28.6M 0s
55000K .......... .......... .......... .......... .......... 93% 49.2M 0s
55050K .......... .......... .......... .......... .......... 93% 58.6M 0s
55100K .......... .......... .......... .......... .......... 93% 57.0M 0s
55150K .......... .......... .......... .......... .......... 93% 56.9M 0s
55200K .......... .......... .......... .......... .......... 93% 49.8M 0s
55250K .......... .......... .......... .......... .......... 93% 30.9M 0s
55300K .......... .......... .......... .......... .......... 93% 57.8M 0s
55350K .......... .......... .......... .......... .......... 94% 52.5M 0s
55400K .......... .......... .......... .......... .......... 94% 15.6M 0s
55450K .......... .......... .......... .......... .......... 94% 54.3M 0s
55500K .......... .......... .......... .......... .......... 94% 52.7M 0s
55550K .......... .......... .......... .......... .......... 94% 65.1M 0s
55600K .......... .......... .......... .......... .......... 94% 55.6M 0s
55650K .......... .......... .......... .......... .......... 94% 52.9M 0s
55700K .......... .......... .......... .......... .......... 94% 46.7M 0s
55750K .......... .......... .......... .......... .......... 94% 28.8M 0s
55800K .......... .......... .......... .......... .......... 94% 48.0M 0s
55850K .......... .......... .......... .......... .......... 94% 55.6M 0s
55900K .......... .......... .......... .......... .......... 95% 70.3M 0s
55950K .......... .......... .......... .......... .......... 95% 19.1M 0s
56000K .......... .......... .......... .......... .......... 95% 44.9M 0s
56050K .......... .......... .......... .......... .......... 95% 47.2M 0s
56100K .......... .......... .......... .......... .......... 95% 45.1M 0s
56150K .......... .......... .......... .......... .......... 95% 65.9M 0s
56200K .......... .......... .......... .......... .......... 95% 58.2M 0s
56250K .......... .......... .......... .......... .......... 95% 60.8M 0s
56300K .......... .......... .......... .......... .......... 95% 59.5M 0s
56350K .......... .......... .......... .......... .......... 95% 68.0M 0s
56400K .......... .......... .......... .......... .......... 95% 25.7M 0s
56450K .......... .......... .......... .......... .......... 95% 53.0M 0s
56500K .......... .......... .......... .......... .......... 96% 53.7M 0s
56550K .......... .......... .......... .......... .......... 96% 14.1M 0s
56600K .......... .......... .......... .......... .......... 96% 41.2M 0s
56650K .......... .......... .......... .......... .......... 96% 69.2M 0s
56700K .......... .......... .......... .......... .......... 96% 64.2M 0s
56750K .......... .......... .......... .......... .......... 96% 47.9M 0s
56800K .......... .......... .......... .......... .......... 96% 25.9M 0s
56850K .......... .......... .......... .......... .......... 96% 47.9M 0s
56900K .......... .......... .......... .......... .......... 96% 44.4M 0s
56950K .......... .......... .......... .......... .......... 96% 43.6M 0s
57000K .......... .......... .......... .......... .......... 96% 57.8M 0s
57050K .......... .......... .......... .......... .......... 96% 56.2M 0s
57100K .......... .......... .......... .......... .......... 97% 9.06M 0s
57150K .......... .......... .......... .......... .......... 97% 54.1M 0s
57200K .......... .......... .......... .......... .......... 97% 56.7M 0s
57250K .......... .......... .......... .......... .......... 97% 63.4M 0s
57300K .......... .......... .......... .......... .......... 97% 60.4M 0s
57350K .......... .......... .......... .......... .......... 97% 44.2M 0s
57400K .......... .......... .......... .......... .......... 97% 52.0M 0s
57450K .......... .......... .......... .......... .......... 97% 69.7M 0s
57500K .......... .......... .......... .......... .......... 97% 68.6M 0s
57550K .......... .......... .......... .......... .......... 97% 68.6M 0s
57600K .......... .......... .......... .......... .......... 97% 58.3M 0s
57650K .......... .......... .......... .......... .......... 97% 67.7M 0s
57700K .......... .......... .......... .......... .......... 98% 65.1M 0s
57750K .......... .......... .......... .......... .......... 98% 71.9M 0s
57800K .......... .......... .......... .......... .......... 98% 10.8M 0s
57850K .......... .......... .......... .......... .......... 98% 56.1M 0s
57900K .......... .......... .......... .......... .......... 98% 53.2M 0s
57950K .......... .......... .......... .......... .......... 98% 58.3M 0s
58000K .......... .......... .......... .......... .......... 98% 43.0M 0s
58050K .......... .......... .......... .......... .......... 98% 54.7M 0s
58100K .......... .......... .......... .......... .......... 98% 21.5M 0s
58150K .......... .......... .......... .......... .......... 98% 51.2M 0s
58200K .......... .......... .......... .......... .......... 98% 51.5M 0s
58250K .......... .......... .......... .......... .......... 98% 58.7M 0s
58300K .......... .......... .......... .......... .......... 99% 41.7M 0s
58350K .......... .......... .......... .......... .......... 99% 46.1M 0s
58400K .......... .......... .......... .......... .......... 99% 41.9M 0s
58450K .......... .......... .......... .......... .......... 99% 42.1M 0s
58500K .......... .......... .......... .......... .......... 99% 42.9M 0s
58550K .......... .......... .......... .......... .......... 99% 26.6M 0s
58600K .......... .......... .......... .......... .......... 99% 49.3M 0s
58650K .......... .......... .......... .......... .......... 99% 57.2M 0s
58700K .......... .......... .......... .......... .......... 99% 56.8M 0s
58750K .......... .......... .......... .......... .......... 99% 46.6M 0s
58800K .......... .......... .......... .......... .......... 99% 37.0M 0s
58850K .......... .......... .......... .......... .... 100% 58.0M=2.4s
2021-12-05 11:12:09 (24.4 MB/s) - 'efficientdet-d2.tar.gz' saved [60307996/60307996]
Use model in E:\efd_work\automl\efficientdet\efficientdet-d2
class TRAIN_CFG:
model_name = 'efficientdet-d2' # efficientdet 모델명
strategy = '' # tpu, 여러개의 GPU들, 단일 GPU 일때 학습 strategy 설정.
model_dir = '../../model_trained_d2' # 학습된 모델이 저장될 위치
pretrained_ckpt = './efficientdet-d2' # coco로 pretrained된 checkpoint 파일이 있는 디렉토리 위치
# num_classes는 2개임.
hparams = 'num_classes=2,moving_average_decay=0,mixed_precision=true'
use_xla = False
use_fake_data = False
# max_instances_per_image를 200으로 설정하고, D2 모델이 상대적으로 더 크기 때문에 batch_size를 8로 설정시 GPU Out of Memory 발생
batch_size = 4
eval_samples = 5000 # evaluation image 데이터 갯수
steps_per_execution = 1 # train 시 steps 횟수
num_examples_per_epoch = 3000 # 1 epochs 시 적용하는 examples 개수
num_epochs = 20 # epochs 횟수
train_file_pattern = '../../tfrecord/train/train-*.tfrecord' # 학습용 tfrecords를 glob 형태로 가져오는 표현식.
val_file_pattern = '../../tfrecord/val/val-*.tfrecord' # 검증용 tfrecords를 glob 형태로 가져오는 표현식.
val_json_file = None # optional coco validation json
mode = 'traineval' # train만 적용 또는 train과 eval함께 적용(traineval)
num_shards = 100 #
# max_instances_per_image는 기본 100개임. 1개의 이미지에 특정 object의 갯수가 100개를 초과하는 경우가 있음. 200으로 수정.
max_instances_per_image = 200
num_cores = 2 # tpu 일때 8 적용.
tpu = None
gcp_project = None
tpu_zone = None
eval_master = ''
eval_name = None
tf_random_seed = 2021
profile = False
debug = False
# 강의영상에는 from keras import anchors 이지만 efficientdet 패키지의 keras 모듈이 tf2 로 변경됨.
from tf2.train import setup_model
import hparams_config
import utils
from tf2 import tfmot
from tf2 import train_lib
from tf2 import util_keras
config = hparams_config.get_detection_config(TRAIN_CFG.model_name)
config.override(TRAIN_CFG.hparams)
steps_per_epoch = TRAIN_CFG.num_examples_per_epoch // TRAIN_CFG.batch_size
if tf.config.list_physical_devices('GPU'):
ds_strategy = tf.distribute.OneDeviceStrategy('device:GPU:0')
else:
ds_strategy = tf.distribute.OneDeviceStrategy('device:CPU:0')
print(ds_strategy)
params = dict(
profile=TRAIN_CFG.profile,
mode = TRAIN_CFG.mode,
model_name=TRAIN_CFG.model_name,
steps_per_execution=TRAIN_CFG.steps_per_execution,
num_epochs = TRAIN_CFG.num_epochs,
model_dir=TRAIN_CFG.model_dir,
steps_per_epoch=steps_per_epoch,
strategy=TRAIN_CFG.strategy,
batch_size=TRAIN_CFG.batch_size,
tf_random_seed=TRAIN_CFG.tf_random_seed,
debug=TRAIN_CFG.debug,
val_json_file=TRAIN_CFG.val_json_file,
eval_samples=TRAIN_CFG.eval_samples,
num_shards=ds_strategy.num_replicas_in_sync,
# max_instances_per_image는 기본 100개임. 1개의 이미지에 특정 object의 갯수가 100개를 초과하는 경우가 있음. 200으로 수정.
max_instances_per_image = TRAIN_CFG.max_instances_per_image
)
config.override(params, True)
# image size를 tuple 형태로 변환. 512는 (512, 512)로 '1920x880' 은 (1920, 880) 으로 변환.
config.image_size = utils.parse_image_size(config.image_size)
print(config)
<tensorflow.python.distribute.one_device_strategy.OneDeviceStrategy object at 0x0000027FD0789580>
act_type: swish
alpha: 0.25
anchor_scale: 4.0
apply_bn_for_resampling: true
aspect_ratios:
- 1.0
- 2.0
- 0.5
autoaugment_policy: null
backbone_config: null
backbone_name: efficientnet-b2
batch_size: 4
box_class_repeats: 3
box_loss_weight: 50.0
ckpt_var_scope: null
clip_gradients_norm: 10.0
conv_after_downsample: false
conv_bn_act_pattern: false
data_format: channels_last
dataset_type: null
debug: false
delta: 0.1
drop_remainder: true
eval_samples: 5000
first_lr_drop_epoch: 200.0
fpn_cell_repeats: 5
fpn_config: null
fpn_name: null
fpn_num_filters: 112
fpn_weight_method: null
gamma: 1.5
grad_checkpoint: false
grid_mask: false
heads:
- object_detection
image_size: !!python/tuple
- 768
- 768
img_summary_steps: null
input_rand_hflip: true
iou_loss_type: null
iou_loss_weight: 1.0
is_training_bn: true
jitter_max: 2.0
jitter_min: 0.1
label_map: null
label_smoothing: 0.0
learning_rate: 0.08
loss_scale: null
lr_decay_method: cosine
lr_warmup_epoch: 1.0
lr_warmup_init: 0.008
map_freq: 5
max_instances_per_image: 200
max_level: 7
mean_rgb:
- 123.675
- 116.28
- 103.53
min_level: 3
mixed_precision: true
mode: traineval
model_dir: ../../model_trained_d2
model_name: efficientdet-d2
model_optimizations: {}
momentum: 0.9
moving_average_decay: 0
name: efficientdet-d2
nms_configs:
iou_thresh: null
max_nms_inputs: 0
max_output_size: 100
method: gaussian
pyfunc: false
score_thresh: 0.0
sigma: null
num_classes: 2
num_epochs: 20
num_scales: 3
num_shards: 1
optimizer: sgd
poly_lr_power: 0.9
positives_momentum: null
profile: false
regenerate_source_id: false
sample_image: null
save_freq: epoch
scale_range: false
second_lr_drop_epoch: 250.0
seg_num_classes: 3
separable_conv: true
skip_crowd_during_training: true
skip_mismatch: true
stddev_rgb:
- 58.395
- 57.120000000000005
- 57.375
steps_per_epoch: 750
steps_per_execution: 1
strategy: ''
survival_prob: null
target_size: null
tf_random_seed: 2021
tflite_max_detections: 100
use_keras_model: true
val_json_file: null
var_freeze_expr: null
verbose: 1
weight_decay: 4.0e-05
import utils
from tf2 import tfmot
from tf2 import train_lib
from tf2 import util_keras
# P100에서는 적용할 필요 없음.
'''
precision = utils.get_precision(config.strategy, config.mixed_precision)
policy = tf.keras.mixed_precision.Policy(precision)
tf.keras.mixed_precision.set_global_policy(policy)
'''
'\nprecision = utils.get_precision(config.strategy, config.mixed_precision)\npolicy = tf.keras.mixed_precision.Policy(precision)\ntf.keras.mixed_precision.set_global_policy(policy)\n'
Dataset과 Model 생성하고 학습 수행.
앞에서 만든 train과 val용 TFRecord를 이용하여 dataloader.InputReader를 생성하는 함수 생성.
efficientdet-d2 모델 생성 후 pretrained 모델의 weight 전이.
batch_size 4로 설정. 8로 설정 시 GPU Memory OOM 발생 가능성 높음(Out Of Memory), epochs 20으로 학습 수행.
import dataloader
def get_dataset(is_training, config):
# is_training이 True이면 TRAIN_CFG의 train_file_pattern, 그렇지 아니면 val_file_pattern
file_pattern = (
TRAIN_CFG.train_file_pattern
if is_training else TRAIN_CFG.val_file_pattern)
if not file_pattern:
raise ValueError('No matching files.')
return dataloader.InputReader(
file_pattern,
is_training=is_training,
use_fake_data=TRAIN_CFG.use_fake_data,
max_instances_per_image=config.max_instances_per_image,
debug=TRAIN_CFG.debug)(
config.as_dict())
from tf2 import train_lib
from tf2 import train
def get_efficientdet_model(config):
# 2개의 class를 가진 efficientdet d2 모델을 생성.
model = train_lib.EfficientDetNetTrain(config=config)
model = train.setup_model(model, config)
# 만약 pretrained 모델이 있으면, 해당 checkpoint weight를 모델로 로딩. 이때 classification layer는 제외.
#class TRAIN_CFG: pretrained_ckpt = '/content/efficientdet-d2'
if TRAIN_CFG.pretrained_ckpt:
ckpt_path = tf.train.latest_checkpoint(TRAIN_CFG.pretrained_ckpt)
# classfication layer를 제외하고 pretrained weight를 생성된 모델로 로딩.
util_keras.restore_ckpt(
model,
ckpt_path,
config.moving_average_decay,
exclude_layers=['class_net'])
train.init_experimental(config)
return model
model = get_efficientdet_model(config)
model.summary()
C:\Users\Public\Documents\ESTsoft\CreatorTemp\__autograph_generated_file44g4u0f6.py:23: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
ag__.for_stmt(ag__.ld(self).updates, None, loop_body, get_state, set_state, (), {'iterate_names': 'u'})
E:\efd_work\automl\efficientdet\utils.py:255: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
for u in self.updates:
WARNING:absl:Shape mismatch: class_net/class-predict/pointwise_kernel
WARNING:absl:Shape mismatch: class_net/class-predict/bias
Model: ""
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
efficientnet-b2 (Model) multiple 7267314
resample_p6 (ResampleFeatur multiple 39984
eMap)
resample_p7 (ResampleFeatur multiple 0
eMap)
fpn_cells (FPNCells) multiple 678479
class_net (ClassNet) multiple 50754
box_net (BoxNet) multiple 52788
=================================================================
Total params: 8,089,319
Trainable params: 8,007,543
Non-trainable params: 81,776
_________________________________________________________________
print(config.mode)
traineval
from tf2 import train
import numpy as np
config.batch_size = 4
tr_steps_per_epoch = train_df.shape[0]//config.batch_size
val_steps_per_epoch = val_df.shape[0]//config.batch_size
print('tr_steps_per_epoch:', tr_steps_per_epoch, 'val_steps_per_epoch:', val_steps_per_epoch)
val_dataset = get_dataset(False, config) if 'eval' in config.mode else None
model.fit(
get_dataset(True, config),
epochs=20,
steps_per_epoch=tr_steps_per_epoch,
callbacks=train_lib.get_callbacks(config.as_dict(), val_dataset),
validation_data=val_dataset,
validation_steps=val_steps_per_epoch)
tf.keras.backend.clear_session()
tr_steps_per_epoch: 749 val_steps_per_epoch: 187
Epoch 1/20
749/749 [==============================] - ETA: 0s - det_loss: 7.3323 - cls_loss: 7.0140 - box_loss: 0.0064 - reg_l2_loss: 0.0953 - loss: 7.4277 - learning_rate: 0.0065 - gradient_norm: 4.4740
Epoch 00001: saving model to ../../model_trained_d2\ckpt-1
749/749 [==============================] - 675s 834ms/step - det_loss: 7.3237 - cls_loss: 7.0053 - box_loss: 0.0064 - reg_l2_loss: 0.0953 - loss: 7.4191 - learning_rate: 0.0065 - gradient_norm: 4.4728 - val_det_loss: 0.7131 - val_cls_loss: 0.5166 - val_box_loss: 0.0039 - val_reg_l2_loss: 0.0957 - val_loss: 0.8087
Epoch 2/20
749/749 [==============================] - ETA: 0s - det_loss: 0.6172 - cls_loss: 0.3818 - box_loss: 0.0047 - reg_l2_loss: 0.0958 - loss: 0.7130 - learning_rate: 0.0049 - gradient_norm: 3.5588
Epoch 00002: saving model to ../../model_trained_d2\ckpt-2
749/749 [==============================] - 621s 829ms/step - det_loss: 0.6170 - cls_loss: 0.3817 - box_loss: 0.0047 - reg_l2_loss: 0.0958 - loss: 0.7128 - learning_rate: 0.0049 - gradient_norm: 3.5565 - val_det_loss: 0.5032 - val_cls_loss: 0.3372 - val_box_loss: 0.0033 - val_reg_l2_loss: 0.0959 - val_loss: 0.5991
Epoch 3/20
749/749 [==============================] - ETA: 0s - det_loss: 0.5405 - cls_loss: 0.3423 - box_loss: 0.0040 - reg_l2_loss: 0.0960 - loss: 0.6365 - learning_rate: 0.0048 - gradient_norm: 3.3900
Epoch 00003: saving model to ../../model_trained_d2\ckpt-3
749/749 [==============================] - 620s 828ms/step - det_loss: 0.5412 - cls_loss: 0.3425 - box_loss: 0.0040 - reg_l2_loss: 0.0960 - loss: 0.6372 - learning_rate: 0.0048 - gradient_norm: 3.3988 - val_det_loss: 0.4668 - val_cls_loss: 0.3517 - val_box_loss: 0.0023 - val_reg_l2_loss: 0.0960 - val_loss: 0.5628
Epoch 4/20
749/749 [==============================] - ETA: 0s - det_loss: 0.5292 - cls_loss: 0.3381 - box_loss: 0.0038 - reg_l2_loss: 0.0961 - loss: 0.6253 - learning_rate: 0.0046 - gradient_norm: 3.3804
Epoch 00004: saving model to ../../model_trained_d2\ckpt-4
749/749 [==============================] - 626s 836ms/step - det_loss: 0.5298 - cls_loss: 0.3384 - box_loss: 0.0038 - reg_l2_loss: 0.0961 - loss: 0.6259 - learning_rate: 0.0046 - gradient_norm: 3.3849 - val_det_loss: 0.3676 - val_cls_loss: 0.2398 - val_box_loss: 0.0026 - val_reg_l2_loss: 0.0962 - val_loss: 0.4638
Epoch 5/20
749/749 [==============================] - ETA: 0s - det_loss: 0.4957 - cls_loss: 0.3154 - box_loss: 0.0036 - reg_l2_loss: 0.0963 - loss: 0.5920 - learning_rate: 0.0043 - gradient_norm: 3.3270
Epoch 00005: saving model to ../../model_trained_d2\ckpt-5
creating index...
index created!
Loading and preparing results...
Converting ndarray to lists...
(74800, 7)
0/74800
DONE (t=0.56s)
creating index...
index created!
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
C:\Users\Public\Documents\ESTsoft\CreatorTemp/ipykernel_3992/2928440334.py in <module>
8
9 val_dataset = get_dataset(False, config) if 'eval' in config.mode else None
---> 10 model.fit(
11 get_dataset(True, config),
12 epochs=20,
e:\anaconda3\envs\tf270\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
E:\efd_work\automl\efficientdet\tf2\train_lib.py in on_epoch_end(self, epoch, logs)
356 for (images, labels) in dataset:
357 strategy.run(self._get_detections, (images, labels))
--> 358 metrics = self.evaluator.result()
359 eval_results = {}
360 with self.file_writer.as_default(), tf.summary.record_if(True):
E:\efd_work\automl\efficientdet\coco_metric.py in result(self, log_level)
172 """Return the metric values (and compute it if needed)."""
173 if self.metric_values is None:
--> 174 self.metric_values = self.evaluate(log_level)
175 return self.metric_values
176
E:\efd_work\automl\efficientdet\coco_metric.py in evaluate(self, log_level)
142 image_ids = list(set(detections[:, 0]))
143 coco_dt = coco_gt.loadRes(detections)
--> 144 coco_eval = COCOeval(coco_gt, coco_dt, iouType='bbox')
145 coco_eval.params.imgIds = image_ids
146 coco_eval.evaluate()
e:\anaconda3\envs\tf270\lib\site-packages\pycocotools\cocoeval.py in __init__(self, cocoGt, cocoDt, iouType)
74 self._gts = defaultdict(list) # gt for evaluation
75 self._dts = defaultdict(list) # dt for evaluation
---> 76 self.params = Params(iouType=iouType) # parameters
77 self._paramsEval = {} # parameters for evaluation
78 self.stats = [] # result summarization
e:\anaconda3\envs\tf270\lib\site-packages\pycocotools\cocoeval.py in __init__(self, iouType)
525 def __init__(self, iouType='segm'):
526 if iouType == 'segm' or iouType == 'bbox':
--> 527 self.setDetParams()
528 elif iouType == 'keypoints':
529 self.setKpParams()
e:\anaconda3\envs\tf270\lib\site-packages\pycocotools\cocoeval.py in setDetParams(self)
505 self.catIds = []
506 # np.arange causes trouble. the data point on arange is slightly larger than the true value
--> 507 self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True)
508 self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True)
509 self.maxDets = [1, 10, 100]
<__array_function__ internals> in linspace(*args, **kwargs)
e:\anaconda3\envs\tf270\lib\site-packages\numpy\core\function_base.py in linspace(start, stop, num, endpoint, retstep, dtype, axis)
118
119 """
--> 120 num = operator.index(num)
121 if num < 0:
122 raise ValueError("Number of samples, %s, must be non-negative." % num)
TypeError: 'numpy.float64' object cannot be interpreted as an integer
학습된 모델 파일을 이용하여 Inference 수행.
import sys
sys.path.append('E:/efd_work/automl/efficientdet')
import hparams_config
infer_config = hparams_config.get_efficientdet_config('efficientdet-d2')
# config의 특정 항목을 update
infer_config.model_name = 'efficientdet-d2'
infer_config.model_dir = './model_trained_d2'
# infer_config의 num_classes는 2로 바뀌어야 함.
infer_config.num_classes = 2
infer_config.is_training_bn = False
infer_config.nms_configs.score_thresh = 0.4
print(infer_config)
import inference
from tf2 import efficientdet_keras
import tensorflow as tf
model = efficientdet_keras.EfficientDetModel(config=infer_config)
model.build((None, None, None, 3))
print('#### checkpoint name:', tf.train.latest_checkpoint(infer_config.model_dir))
model.load_weights(tf.train.latest_checkpoint(infer_config.model_dir))
model.summary()
import time
class ExportModel(tf.Module):
def __init__(self, model):
super().__init__()
self.model = model
@tf.function
def f(self, imgs):
#model(imgs, training=False, post_mode='global')
return self.model(imgs, training=False, post_mode='global')
export_model = ExportModel(model)
labels_to_names = {1:'Car', 2:'Pool'}
def get_detected_img(export_model, img_array, is_print=True):
# automl efficent은 반환 bbox 좌표값이 원본 이미지 좌표값으로 되어 있으므로 별도의 scaling작업 필요 없음.
'''
height = img_array.shape[0]
width = img_array.shape[1]
'''
# cv2의 rectangle()은 인자로 들어온 이미지 배열에 직접 사각형을 업데이트 하므로 그림 표현을 위한 별도의 이미지 배열 생성.
draw_img = img_array.copy()
# bounding box의 테두리와 caption 글자색 지정
green_color=(0, 255, 0)
red_color=(255, 0, 0)
# cv2로 만들어진 numpy image array를 tensor로 변환
img_tensor = tf.convert_to_tensor(img_array, dtype=tf.uint8)[tf.newaxis, ...]
#img_tensor = tf.convert_to_tensor(img_array, dtype=tf.float32)[tf.newaxis, ...]
# efficientdet 모델을 다운로드 한 뒤 inference 수행.
start_time = time.time()
# automl efficientdet 모델은 boxes, score, classes, num_detections를 각각 Tensor로 반환.
boxes, scores, classes, valid_len = export_model.f(img_tensor)
# Tensor값을 시각화를 위해 numpy 로 변환.
boxes = boxes.numpy()
scores = scores.numpy()
classes = classes.numpy()
valid_len = valid_len.numpy()
# detected 된 object들을 iteration 하면서 정보 추출. detect된 object의 갯수는 100개
for i in range(valid_len[0]):
# detection score를 iteration시 마다 높은 순으로 추출하고 SCORE_THRESHOLD보다 낮으면 loop 중단.
score = scores[0, i]
# detected된 object들은 scale된 기준으로 예측되었으므로 다시 원본 이미지 비율로 계산
box = boxes[0, i]
''' **** 주의 ******
box는 ymin, xmin, ymax, xmax 순서로 되어 있음. 또한 원본 좌표값으로 되어 있음. '''
left = box[1]
top = box[0]
right = box[3]
bottom = box[2]
# class id 추출하고 class 명으로 매핑
class_id = classes[0, i]
caption = "{}: {:.4f}".format(labels_to_names[class_id], score)
print(caption)
#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=1)
cv2.putText(draw_img, caption, (int(left), int(top - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.2, red_color, 1)
if is_print:
print('Detection 수행시간:',round(time.time() - start_time, 2),"초")
return draw_img
import cv2
import matplotlib.pyplot as plt
img_array = cv2.cvtColor(cv2.imread('../../poolncar/test_data_images/test_data_images/images/000000000.jpg'), cv2.COLOR_BGR2RGB)
draw_img = get_detected_img(export_model, img_array, is_print=True)
plt.figure(figsize=(16, 16))
plt.imshow(draw_img)
코드 부분에서 수정한 것은 데이터나 모델 경로를 제 컴에 맞게 바꾼 정도입니다.
제가 사용하는 콘다환경이 python 3.8, tensorflow 2.7입니다.
혹시 git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI 설치에 맞는 가상환경 버전이 있나요?
오류해결에 맞는 numpy 버전이 따로 있나요?