Detectron2 使用 deepfashion2 數據集，訓練服飾偵測

2021 年 6 月 28 日 1,167 則留言

deepfashion2 數據集位置與目錄

├── DeepFashion2
│   └── train
│       ├── annos
│       └── image

下載 Detectron2 環境

docker run --gpus all -d \
-it \
-e DISPLAY \
-e QT_X11_NO_MITSHM=1 \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-v $HOME/.Xauthority:/root/.Xauthority \
-v /home/ubuntu/dataset/:/app/detectron2_repo/datasets/data \
--name Detectron2-deepfashion2 \
--restart=always \
--shm-size="1g" \
raidavid/detectron2:base

注意掛載目錄對應上面 deepfashion2 實際路徑

轉換為 COCO 格式 

使用 deepfashion2 提供的轉換工具 將格式轉換為 coco

from PIL import Image
import numpy as np
import json

dataset = {
    "info": {},
    "licenses": [],
    "images": [],
    "annotations": [],
    "categories": []
}

lst_name = ['short_sleeved_shirt', 'long_sleeved_shirt', 'short_sleeved_outwear', 'long_sleeved_outwear',
            'vest', 'sling', 'shorts', 'trousers', 'skirt', 'short_sleeved_dress',
            'long_sleeved_dress', 'vest_dress', 'sling_dress']

for idx, e  in enumerate(lst_name):
    dataset['categories'].append({
        'id': idx + 1,
        'name': e,
        'supercategory': "clothes",
        'keypoints': ['%i' % (i) for i in range(1, 295)],
        'skeleton': []
    })

num_images = 32153 #191961 
sub_index = 0  # the index of ground truth instance
for num in range(1, num_images + 1):
    json_name = '/app/detectron2_repo/datasets/data/DeepFashion2/train/annos/' + str(num).zfill(6) + '.json'
    image_name = '/app/detectron2_repo/datasets/data/DeepFashion2/train/image/' + str(num).zfill(6) + '.jpg'

    if (num >= 0):
        imag = Image.open(image_name)
        width, height = imag.size
        with open(json_name, 'r') as f:
            temp = json.loads(f.read())
            pair_id = temp['pair_id']

            dataset['images'].append({
                'coco_url': '',
                'date_captured': '',
                'file_name': str(num).zfill(6) + '.jpg',
                'flickr_url': '',
                'id': num,
                'license': 0,
                'width': width,
                'height': height
            })
            for i in temp:
                if i == 'source' or i == 'pair_id':
                    continue
                else:
                    points = np.zeros(294 * 3)
                    sub_index = sub_index + 1
                    box = temp[i]['bounding_box']
                    w = box[2] - box[0]
                    h = box[3] - box[1]
                    x_1 = box[0]
                    y_1 = box[1]
                    bbox = [x_1, y_1, w, h]
                    cat = temp[i]['category_id']
                    style = temp[i]['style']
                    seg = temp[i]['segmentation']
                    landmarks = temp[i]['landmarks']

                    points_x = landmarks[0::3]
                    points_y = landmarks[1::3]
                    points_v = landmarks[2::3]
                    points_x = np.array(points_x)
                    points_y = np.array(points_y)
                    points_v = np.array(points_v)
                    case = [0, 25, 58, 89, 128, 143, 158, 168, 182, 190, 219, 256, 275, 294]
                    idx_i, idx_j = case[cat - 1], case[cat]

                    for n in range(idx_i, idx_j):
                        points[3 * n] = points_x[n - idx_i]
                        points[3 * n + 1] = points_y[n - idx_i]
                        points[3 * n + 2] = points_v[n - idx_i]

                    num_points = len(np.where(points_v > 0)[0])

                    dataset['annotations'].append({
                        'area': w * h,
                        'bbox': bbox,
                        'category_id': cat,
                        'id': sub_index,
                        'pair_id': pair_id,
                        'image_id': num,
                        'iscrowd': 0,
                        'style': style,
                        'num_keypoints': num_points,
                        'keypoints': points.tolist(),
                        'segmentation': seg,
                    })


json_name = '/app/detectron2_repo/datasets/data/deepfashion2_validation.json'
with open(json_name, 'w') as f:
    json.dump(dataset, f)

訓練模型

建立 train.py

# Setup detectron2 logger
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()

# import some common libraries
import os
import numpy as np
import cv2
import random

# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.engine import DefaultTrainer
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
from detectron2.data.datasets import register_coco_instances
#register_coco_instances("deepfashion_train", {}, "/content/DeepFashion2/deepfashion2_train.json", "/content/DeepFashion2/train/image")
register_coco_instances("deepfashion_val", {}, "/app/detectron2_repo/datasets/data/deepfashion2_validation.json", "/app/detectron2_repo/datasets/data/DeepFashion2/train/image/")

cfg = get_cfg()

cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml"))
cfg.DATASETS.TRAIN = ("deepfashion_val",)
cfg.DATASETS.TEST = ()
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml")  # Let training initialize from model zoo

cfg.SOLVER.IMS_PER_BATCH = 4
cfg.SOLVER.BASE_LR = 0.001
cfg.SOLVER.WARMUP_ITERS = 1000
cfg.SOLVER.MAX_ITER = 1500
# cfg.SOLVER.STEPS = (1000, 1500)
cfg.SOLVER.STEPS = []
cfg.SOLVER.GAMMA = 0.05
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 64
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 13

cfg.TEST.EVAL_PERIOD = 500
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
trainer = DefaultTrainer(cfg) 
trainer.resume_or_load(resume=False)
trainer.train()

觀看訓練狀況

tensorboard --logdir output

在〈“Detectron2 使用 deepfashion2 數據集，訓練服飾偵測”〉中有 1 則留言

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