东莞网站建设公司服务平台,平阳网站优化,软文网站名称,做网站是用什么软件做的✨博客主页#xff1a;王乐予#x1f388; ✨年轻人要#xff1a;Living for the moment#xff08;活在当下#xff09;#xff01;#x1f4aa; #x1f3c6;推荐专栏#xff1a;【图像处理】【千锤百炼Python】【深度学习】【排序算法】 目录 #x1f63a;〇、仓库… ✨博客主页王乐予 ✨年轻人要Living for the moment活在当下 推荐专栏【图像处理】【千锤百炼Python】【深度学习】【排序算法】 目录 〇、仓库源码一、数据集介绍1.1 GitHub原始数据集1.2 GitHub预处理后的数据集1.2.1 简化的绘图文件.ndjson1.2.2 二进制文件.bin1.2.3 Numpy位图.npy 1.3 Kaggle数据集 二、数据集准备三、获取png格式图片四、训练过程4.1 split_datasets.py4.2 option.py4.3 getdata.py4.4 model.py4.5 train-DDP.py4.6 model_transfer.py4.7 evaluate.py 〇、仓库源码
本文所有代码存放在GitHub仓库中QuickDraw-DDP欢迎fork和star
一、数据集介绍 Quick Draw 数据集是 345 个类别的 5000 万张图纸的集合由游戏 Quick Draw的玩家贡献。这些图画被捕获为带时间戳的矢量并标记有元数据包括要求玩家绘制的内容以及玩家所在的国家/地区。 GitHub数据集地址 The Quick, Draw! Dataset Kaggle数据集地址Quick, Draw! Doodle Recognition Challenge Github中提供了两种类型的数据集分别是 原始数据集 和 预处理后的数据集 。 Google Cloud提供了数据集下载链接quickdraw_dataset
1.1 GitHub原始数据集
原始数据以按类别分隔的 ndjson 文件的形式提供格式如下
键类型说明key_id64位无符号整型所有图形的唯一标识符word字符串类别recognized布尔值该类别是否被游戏识别timestamp日期时间绘制时间countrycode字符串玩家所在位置的双字母国家/地区代码 ISO 3166-1 alpha-2drawing字符串一个矢量绘制的 JSON 数组
每行包含一个绘图数据下面是单个绘图的示例 { key_id:5891796615823360,word:nose,countrycode:AE,timestamp:2017-03-01 20:41:36.70725 UTC,recognized:true,drawing:[[[129,128,129,129,130,130,131,132,132,133,133,133,133,...]]]}drawing字段格式如下
[ [ // First stroke [x0, x1, x2, x3, ...],[y0, y1, y2, y3, ...],[t0, t1, t2, t3, ...]],[ // Second stroke[x0, x1, x2, x3, ...],[y0, y1, y2, y3, ...],[t0, t1, t2, t3, ...]],... // Additional strokes
]其中x和y是像素坐标t是自第一个点以来的时间以毫秒为单位。由于用于显示和输入的设备不同原始绘图可能具有截然不同的边界框和点数。
1.2 GitHub预处理后的数据集
1.2.1 简化的绘图文件.ndjson
简化了向量删除了时序信息并将数据定位和缩放为256x256区域。数据以ndjson格式导出其元数据与raw格式相同。简化过程是
将绘图与左上角对齐最小值为 0。统一缩放绘图最大值为 255。以 1 像素的间距对所有描边重新取样。使用 epsilon 值为 2.0 的Ramer-Douglas-Peucker 算法简化所有笔画。
读取ndjson文件的代码如下
# read_ndjson.py
import jsonwith open(aircraft carrier.ndjson, r) as file:for line in file:data json.loads(line)key_id data[key_id]drawing data[drawing]# ……读取aircraft carrier.ndjsondebug之后的输出结果如下图所示。可以看到第一行数据包含8个笔触。
1.2.2 二进制文件.bin
简化的图纸和元数据也以自定义二进制格式提供以实现高效的压缩和加载。
读取bin文件的代码如下
# read_bin.py
import struct
from struct import unpackdef unpack_drawing(file_handle):key_id, unpack(Q, file_handle.read(8))country_code, unpack(2s, file_handle.read(2))recognized, unpack(b, file_handle.read(1))timestamp, unpack(I, file_handle.read(4))n_strokes, unpack(H, file_handle.read(2))image []for i in range(n_strokes):n_points, unpack(H, file_handle.read(2))fmt str(n_points) Bx unpack(fmt, file_handle.read(n_points))y unpack(fmt, file_handle.read(n_points))image.append((x, y))return {key_id: key_id,country_code: country_code,recognized: recognized,timestamp: timestamp,image: image}def unpack_drawings(filename):with open(filename, rb) as f:while True:try:yield unpack_drawing(f)except struct.error:breakfor drawing in unpack_drawings(nose.bin):# do something with the drawingprint(drawing[country_code])1.2.3 Numpy位图.npy
所有简化的绘图都已渲染为numpy格式的28x28灰度位图。这些图像是根据简化的数据生成的但与绘图边界框的中心对齐而不是与左上角对齐。
读取npy文件的代码如下
# read_npy.py
import numpy as npdata_path aircraft_carrier.npydata np.load(data_path)
print(data)1.3 Kaggle数据集
在Kaggle竞赛中使用的数据集为340个类别。数据格式统一为csv表格数据。数据集中有5个文件
sample_submission.csv - 正确格式的样本提交文件test_raw.csv - 矢量格式的测试数据rawtest_simplified.csv - 矢量格式的测试数据simplifiedtrain_raw.zip - 向量格式的训练数据;每个单词一个 CSV 文件rawtrain_simplified.zip - 向量格式的训练数据;每个单词一个 CSV 文件simplified 注csv文件的列title与ndjson文件的键名一致。 二、数据集准备
本文将使用kaggle提供的train_simplified数据集。案例流程包含
将所有类的csv格式文件保存为png图片格式对340个类别的png格式图片各抽取10000张用作后续实践对每个类别的10000张数据进行811的训练集、验证集、测试集的划分训练模型模型评估。
三、获取png格式图片
使用下面脚本可以将csv数据转为png图片格式保存。
# csv2png.py
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
from scipy import interpolate, misc
import matplotlib
matplotlib.use(Agg)input_dir kaggle/train_simplified
output_base_dir datasets256os.makedirs(output_base_dir, exist_okTrue)csv_files [f for f in os.listdir(input_dir) if f.endswith(.csv)] # Retrieve all CSV files from the folderskipped_files [] # Record skipped filesfor csv_file in csv_files:csv_file_path os.path.join(input_dir, csv_file) # Build a complete file pathoutput_dir os.path.join(output_base_dir, os.path.splitext(csv_file)[0]) # Build output directoryif os.path.exists(output_dir): # Check if the output directory existsskipped_files.append(csv_file)print(fThe directory already exists, skip file: {csv_file})continueos.makedirs(output_dir, exist_okTrue)data pd.read_csv(csv_file_path) # Read CSV filefor index, row in data.iterrows(): # Traverse each row of datadrawing eval(row[drawing])key_id row[key_id]word row[word]img np.zeros((256, 256)) # Initialize imagefig plt.figure(figsize(256/96, 256/96), dpi96)for stroke in drawing: # Draw each strokestroke_x stroke[0]stroke_y stroke[1]x np.array(stroke_x)y np.array(stroke_y)np.interp((x y) / 2, x, y)plt.plot(x, y, k)ax plt.gca()ax.xaxis.set_ticks_position(top)ax.invert_yaxis()plt.axis(off)plt.savefig(os.path.join(output_dir, f{word}-{key_id}.png))plt.close(fig)print(fConversion completed: {csv_file} the {index:06d}image)print(The skipped files are:)
for file in skipped_files:print(file)需要注意的是绘图数据有5000万左右处理时间非常久建议多开几个脚本运行PS代码中添加了文件夹是否存在的判断语句不用担心会重复写入。也可以使用joblib库多线程加速玩不好容易宕机不建议。 相关文件存储空间大小如下 GitHub 预处理后的ndjson文件有23GKaggle 的train_raw.zip文件有206GKaggle 的train_simplified.zip文件有23GKaggle 的train_simplified转为256*256大小的图片有470G 如果磁盘空间不足进行png转化时可以选择128128大小或者6464大小。也可以保存单通道图像。
建议处理完毕之后使用下面的脚本检查一下有没有没处理的类别
# check_class_num.py
import osfolder datasets256subfolders [f.path for f in os.scandir(folder) if f.is_dir()]for subfolder in subfolders: # Traverse each subfoldersfolder_name os.path.basename(subfolder) # Get the name of the subfoldersfiles [f for f in os.scandir(subfolder) if f.is_file()] # Retrieve all files in the subfoldersimage_count sum(1 for f in files if f.name.lower().endswith((.png, .jpg, .jpeg, .bmp, .gif))) # Calculate the number of imagesif image_count 0: # If the number of images is 0, print out the names of the subfolders and delete themprint(fThere are no images in the subfolders {folder_name}, deleting them...)os.rmdir(subfolder)print(fsubfolders {folder_name} deleted)else:print(fNumber of images in subfolders: {folder_name} : {image_count})如果检查到有空文件夹需要再运行csv2png.py的代码。
四、训练过程
4.1 split_datasets.py
首先要划分数据集原始数据为png图片格式数据集。
import os
import shutil
import randomoriginal_dataset_path datasets256 # Original dataset path
new_dataset_path datasets # Divide the dataset pathtrain_path os.path.join(new_dataset_path, train)
val_path os.path.join(new_dataset_path, val)
test_path os.path.join(new_dataset_path, test)if not os.path.exists(train_path):os.makedirs(train_path)if not os.path.exists(val_path):os.makedirs(val_path)if not os.path.exists(test_path):os.makedirs(test_path)classes os.listdir(original_dataset_path) # Get all categoriesrandom.seed(42)for class_name in classes: # Traverse each categorysrc_folder os.path.join(original_dataset_path, class_name) # Source folder path# Check if the folder for this category already exists under train, val, and testtrain_folder os.path.join(train_path, class_name)val_folder os.path.join(val_path, class_name)test_folder os.path.join(test_path, class_name)# If the train, val, and test folders already exist, skip the folder creation sectionif os.path.exists(train_folder) and os.path.exists(val_folder) and os.path.exists(test_folder):# Check if the folder is emptyif os.listdir(train_folder) and os.listdir(val_folder) and os.listdir(test_folder):print(fCategory {class_name} already exists and is not empty, skip processing.)continue# create folderif not os.path.exists(train_folder):os.makedirs(train_folder)if not os.path.exists(val_folder):os.makedirs(val_folder)if not os.path.exists(test_folder):os.makedirs(test_folder)files os.listdir(src_folder) # Retrieve all file names under this categoryfiles files[:10000] # Only retrieve the first 10000 filesrandom.shuffle(files) # Shuffle file listtotal_files len(files)train_split_index int(total_files * 0.8)val_split_index int(total_files * 0.9)train_files files[:train_split_index]val_files files[train_split_index:val_split_index]test_files files[val_split_index:]for file in train_files:src_file os.path.join(src_folder, file)dst_file os.path.join(train_folder, file)shutil.copy(src_file, dst_file)for file in val_files:src_file os.path.join(src_folder, file)dst_file os.path.join(val_folder, file)shutil.copy(src_file, dst_file)for file in test_files:src_file os.path.join(src_folder, file)dst_file os.path.join(test_folder, file)shutil.copy(src_file, dst_file)print(Dataset partitioning completed!)代码运行完毕之后datasets目录下面会出现三个文件夹分别是train、val和test。
4.2 option.py
定义后续我们需要的一些参数。
import argparsedef get_args():parser argparse.ArgumentParser(descriptionall argument)parser.add_argument(--num_classes, typeint, default340, helpimage num classes)parser.add_argument(--loadsize, typeint, default64, helpimage size)parser.add_argument(--epochs, typeint, default100, helpall epochs)parser.add_argument(--batch_size, typeint, default1024, helpbatch size)parser.add_argument(--lr, typefloat, default0.001, helpinit lr)parser.add_argument(--use_lr_scheduler, typebool, defaultTrue, helpuse lr scheduler)parser.add_argument(--dataset_train, typestr, default./datasets/train, helptrain path)parser.add_argument(--dataset_val, typestr, default./datasets/val, helpval path)parser.add_argument(--dataset_test, typestr, default./datasets/test, helptest path)parser.add_argument(--checkpoints, typestr, default./checkpoints/, helpckpt path)parser.add_argument(--tensorboard_dir, typestr, default./tensorboard_dir, helplog path)parser.add_argument(--resume, typebool, defaultFalse, helpcontinue training)parser.add_argument(--resume_ckpt, typestr, default./checkpoints/model_best.pth, helpchoose breakpoint ckpt)parser.add_argument(--local-rank, typeint, default-1, helplocal rank)parser.add_argument(--use_mix_precision, typebool, defaultFalse, helpuse mix pretrain)parser.add_argument(--test_img_path, typestr, defaultdatasets/test/zigzag/zigzag-4508464694951936.png, helpchoose test image)parser.add_argument(--test_dir_path, typestr, default./datasets/test, helpchoose test path)return parser.parse_args()由于后续将使用DDP单机多卡以及AMP策略进行训练因此额外加入了local-rank和use_mix_precision参数。
4.3 getdata.py
接下来定义数据管道。
import torch
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision import transforms
from option import get_args
opt get_args()mean [0.9367, 0.9404, 0.9405]
std [0.1971, 0.1970, 0.1972]
def data_augmentation():data_transform {train: transforms.Compose([transforms.Resize((opt.loadsize, opt.loadsize)),transforms.ToTensor(), # HWC - CHWtransforms.Normalize(mean, std)]),val: transforms.Compose([transforms.Resize((opt.loadsize, opt.loadsize)),transforms.ToTensor(),transforms.Normalize(mean, std)]),}return data_transformdef MyData():data_transform data_augmentation()image_datasets {train: ImageFolder(opt.dataset_train, data_transform[train]),val: ImageFolder(opt.dataset_val, data_transform[val]),}data_sampler {train: torch.utils.data.distributed.DistributedSampler(image_datasets[train]),val: torch.utils.data.distributed.DistributedSampler(image_datasets[val]),}dataloaders {train: DataLoader(image_datasets[train], batch_sizeopt.batch_size, shuffleFalse, num_workers0, pin_memoryTrue, samplerdata_sampler[train]),val: DataLoader(image_datasets[val], batch_sizeopt.batch_size, shuffleFalse, num_workers0, pin_memoryTrue, samplerdata_sampler[val])}return dataloadersclass_names [The Eiffel Tower, The Great Wall of China, The Mona Lisa, airplane, alarm clock, ambulance, angel, animal migration, ant, anvil, apple, arm, asparagus, axe, backpack, banana, bandage, barn, baseball, baseball bat, basket, basketball, bat, bathtub, beach, bear, beard, bed, bee, belt, bench, bicycle, binoculars, bird, birthday cake, blackberry, blueberry, book, boomerang, bottlecap, bowtie, bracelet, brain, bread, bridge, broccoli, broom, bucket, bulldozer, bus, bush, butterfly, cactus, cake, calculator, calendar, camel, camera, camouflage, campfire, candle, cannon, canoe, car, carrot, castle, cat, ceiling fan, cell phone, cello, chair, chandelier, church, circle, clarinet, clock, cloud, coffee cup, compass, computer, cookie, cooler, couch, cow, crab, crayon, crocodile, crown, cruise ship, cup, diamond, dishwasher, diving board, dog, dolphin, donut, door, dragon, dresser, drill, drums, duck, dumbbell, ear, elbow, elephant, envelope, eraser, eye, eyeglasses, face, fan, feather, fence, finger, fire hydrant, fireplace, firetruck, fish, flamingo, flashlight, flip flops, floor lamp, flower, flying saucer, foot, fork, frog, frying pan, garden, garden hose, giraffe, goatee, golf club, grapes, grass, guitar, hamburger, hammer, hand, harp, hat, headphones, hedgehog, helicopter, helmet, hexagon, hockey puck, hockey stick, horse, hospital, hot air balloon, hot dog, hot tub, hourglass, house, house plant, hurricane, ice cream, jacket, jail, kangaroo, key, keyboard, knee, ladder, lantern, laptop, leaf, leg, light bulb, lighthouse, lightning, line, lion, lipstick, lobster, lollipop, mailbox, map, marker, matches, megaphone, mermaid, microphone, microwave, monkey, moon, mosquito, motorbike, mountain, mouse, moustache, mouth, mug, mushroom, nail, necklace, nose, ocean, octagon, octopus, onion, oven, owl, paint can, paintbrush, palm tree, panda, pants, paper clip, parachute, parrot, passport, peanut, pear, peas, pencil, penguin, piano, pickup truck, picture frame, pig, pillow, pineapple, pizza, pliers, police car, pond, pool, popsicle, postcard, potato, power outlet, purse, rabbit, raccoon, radio, rain, rainbow, rake, remote control, rhinoceros, river, roller coaster, rollerskates, sailboat, sandwich, saw, saxophone, school bus, scissors, scorpion, screwdriver, sea turtle, see saw, shark, sheep, shoe, shorts, shovel, sink, skateboard, skull, skyscraper, sleeping bag, smiley face, snail, snake, snorkel, snowflake, snowman, soccer ball, sock, speedboat, spider, spoon, spreadsheet, square, squiggle, squirrel, stairs, star, steak, stereo, stethoscope, stitches, stop sign, stove, strawberry, streetlight, string bean, submarine, suitcase, sun, swan, sweater, swing set, sword, t-shirt, table, teapot, teddy-bear, telephone, television, tennis racquet, tent, tiger, toaster, toe, toilet, tooth, toothbrush, toothpaste, tornado, tractor, traffic light, train, tree, triangle, trombone, truck, trumpet, umbrella, underwear, van, vase, violin, washing machine, watermelon, waterslide, whale, wheel, windmill, wine bottle, wine glass, wristwatch, yoga, zebra, zigzag
]if __name__ __main__:mena_std_transform transforms.Compose([transforms.ToTensor()])dataset ImageFolder(opt.dataset_val, transformmena_std_transform)print(dataset.class_to_idx) # Index for each category
4.4 model.py
定义模型这里使用mobilenet的small版本。需要将模型的classifier层的输出改为类别数量。 可以使用更多优质的模型对数据集进行训练例如shufflenet、squeezenet等。
import torch.nn as nn
from torchvision.models import mobilenet_v3_small
from torchsummary import summary
from option import get_args
opt get_args()def CustomMobileNetV3():model mobilenet_v3_small(weightsMobileNet_V3_Small_Weights.IMAGENET1K_V1)model.classifier[-1] nn.Linear(model.classifier[-1].in_features, opt.num_classes)return modelif __name__ __main__:model CustomMobileNetV3()print(model)print(summary(model.to(opt.device), (3, opt.loadsize, opt.loadsize), opt.batch_size))模型结构如下
----------------------------------------------------------------Layer (type) Output Shape Param #
Conv2d-1 [1024, 16, 32, 32] 432BatchNorm2d-2 [1024, 16, 32, 32] 32Hardswish-3 [1024, 16, 32, 32] 0Conv2d-4 [1024, 16, 16, 16] 144BatchNorm2d-5 [1024, 16, 16, 16] 32ReLU-6 [1024, 16, 16, 16] 0AdaptiveAvgPool2d-7 [1024, 16, 1, 1] 0Conv2d-8 [1024, 8, 1, 1] 136ReLU-9 [1024, 8, 1, 1] 0Conv2d-10 [1024, 16, 1, 1] 144Hardsigmoid-11 [1024, 16, 1, 1] 0
SqueezeExcitation-12 [1024, 16, 16, 16] 0Conv2d-13 [1024, 16, 16, 16] 256BatchNorm2d-14 [1024, 16, 16, 16] 32InvertedResidual-15 [1024, 16, 16, 16] 0Conv2d-16 [1024, 72, 16, 16] 1,152BatchNorm2d-17 [1024, 72, 16, 16] 144ReLU-18 [1024, 72, 16, 16] 0Conv2d-19 [1024, 72, 8, 8] 648BatchNorm2d-20 [1024, 72, 8, 8] 144ReLU-21 [1024, 72, 8, 8] 0Conv2d-22 [1024, 24, 8, 8] 1,728BatchNorm2d-23 [1024, 24, 8, 8] 48InvertedResidual-24 [1024, 24, 8, 8] 0Conv2d-25 [1024, 88, 8, 8] 2,112BatchNorm2d-26 [1024, 88, 8, 8] 176ReLU-27 [1024, 88, 8, 8] 0Conv2d-28 [1024, 88, 8, 8] 792BatchNorm2d-29 [1024, 88, 8, 8] 176ReLU-30 [1024, 88, 8, 8] 0Conv2d-31 [1024, 24, 8, 8] 2,112BatchNorm2d-32 [1024, 24, 8, 8] 48InvertedResidual-33 [1024, 24, 8, 8] 0Conv2d-34 [1024, 96, 8, 8] 2,304BatchNorm2d-35 [1024, 96, 8, 8] 192Hardswish-36 [1024, 96, 8, 8] 0Conv2d-37 [1024, 96, 4, 4] 2,400BatchNorm2d-38 [1024, 96, 4, 4] 192Hardswish-39 [1024, 96, 4, 4] 0
AdaptiveAvgPool2d-40 [1024, 96, 1, 1] 0Conv2d-41 [1024, 24, 1, 1] 2,328ReLU-42 [1024, 24, 1, 1] 0Conv2d-43 [1024, 96, 1, 1] 2,400Hardsigmoid-44 [1024, 96, 1, 1] 0
SqueezeExcitation-45 [1024, 96, 4, 4] 0Conv2d-46 [1024, 40, 4, 4] 3,840BatchNorm2d-47 [1024, 40, 4, 4] 80InvertedResidual-48 [1024, 40, 4, 4] 0Conv2d-49 [1024, 240, 4, 4] 9,600BatchNorm2d-50 [1024, 240, 4, 4] 480Hardswish-51 [1024, 240, 4, 4] 0Conv2d-52 [1024, 240, 4, 4] 6,000BatchNorm2d-53 [1024, 240, 4, 4] 480Hardswish-54 [1024, 240, 4, 4] 0
AdaptiveAvgPool2d-55 [1024, 240, 1, 1] 0Conv2d-56 [1024, 64, 1, 1] 15,424ReLU-57 [1024, 64, 1, 1] 0Conv2d-58 [1024, 240, 1, 1] 15,600Hardsigmoid-59 [1024, 240, 1, 1] 0
SqueezeExcitation-60 [1024, 240, 4, 4] 0Conv2d-61 [1024, 40, 4, 4] 9,600BatchNorm2d-62 [1024, 40, 4, 4] 80InvertedResidual-63 [1024, 40, 4, 4] 0Conv2d-64 [1024, 240, 4, 4] 9,600BatchNorm2d-65 [1024, 240, 4, 4] 480Hardswish-66 [1024, 240, 4, 4] 0Conv2d-67 [1024, 240, 4, 4] 6,000BatchNorm2d-68 [1024, 240, 4, 4] 480Hardswish-69 [1024, 240, 4, 4] 0
AdaptiveAvgPool2d-70 [1024, 240, 1, 1] 0Conv2d-71 [1024, 64, 1, 1] 15,424ReLU-72 [1024, 64, 1, 1] 0Conv2d-73 [1024, 240, 1, 1] 15,600Hardsigmoid-74 [1024, 240, 1, 1] 0
SqueezeExcitation-75 [1024, 240, 4, 4] 0Conv2d-76 [1024, 40, 4, 4] 9,600BatchNorm2d-77 [1024, 40, 4, 4] 80InvertedResidual-78 [1024, 40, 4, 4] 0Conv2d-79 [1024, 120, 4, 4] 4,800BatchNorm2d-80 [1024, 120, 4, 4] 240Hardswish-81 [1024, 120, 4, 4] 0Conv2d-82 [1024, 120, 4, 4] 3,000BatchNorm2d-83 [1024, 120, 4, 4] 240Hardswish-84 [1024, 120, 4, 4] 0
AdaptiveAvgPool2d-85 [1024, 120, 1, 1] 0Conv2d-86 [1024, 32, 1, 1] 3,872ReLU-87 [1024, 32, 1, 1] 0Conv2d-88 [1024, 120, 1, 1] 3,960Hardsigmoid-89 [1024, 120, 1, 1] 0
SqueezeExcitation-90 [1024, 120, 4, 4] 0Conv2d-91 [1024, 48, 4, 4] 5,760BatchNorm2d-92 [1024, 48, 4, 4] 96InvertedResidual-93 [1024, 48, 4, 4] 0Conv2d-94 [1024, 144, 4, 4] 6,912BatchNorm2d-95 [1024, 144, 4, 4] 288Hardswish-96 [1024, 144, 4, 4] 0Conv2d-97 [1024, 144, 4, 4] 3,600BatchNorm2d-98 [1024, 144, 4, 4] 288Hardswish-99 [1024, 144, 4, 4] 0
AdaptiveAvgPool2d-100 [1024, 144, 1, 1] 0Conv2d-101 [1024, 40, 1, 1] 5,800ReLU-102 [1024, 40, 1, 1] 0Conv2d-103 [1024, 144, 1, 1] 5,904Hardsigmoid-104 [1024, 144, 1, 1] 0
SqueezeExcitation-105 [1024, 144, 4, 4] 0Conv2d-106 [1024, 48, 4, 4] 6,912BatchNorm2d-107 [1024, 48, 4, 4] 96
InvertedResidual-108 [1024, 48, 4, 4] 0Conv2d-109 [1024, 288, 4, 4] 13,824BatchNorm2d-110 [1024, 288, 4, 4] 576Hardswish-111 [1024, 288, 4, 4] 0Conv2d-112 [1024, 288, 2, 2] 7,200BatchNorm2d-113 [1024, 288, 2, 2] 576Hardswish-114 [1024, 288, 2, 2] 0
AdaptiveAvgPool2d-115 [1024, 288, 1, 1] 0Conv2d-116 [1024, 72, 1, 1] 20,808ReLU-117 [1024, 72, 1, 1] 0Conv2d-118 [1024, 288, 1, 1] 21,024Hardsigmoid-119 [1024, 288, 1, 1] 0
SqueezeExcitation-120 [1024, 288, 2, 2] 0Conv2d-121 [1024, 96, 2, 2] 27,648BatchNorm2d-122 [1024, 96, 2, 2] 192
InvertedResidual-123 [1024, 96, 2, 2] 0Conv2d-124 [1024, 576, 2, 2] 55,296BatchNorm2d-125 [1024, 576, 2, 2] 1,152Hardswish-126 [1024, 576, 2, 2] 0Conv2d-127 [1024, 576, 2, 2] 14,400BatchNorm2d-128 [1024, 576, 2, 2] 1,152Hardswish-129 [1024, 576, 2, 2] 0
AdaptiveAvgPool2d-130 [1024, 576, 1, 1] 0Conv2d-131 [1024, 144, 1, 1] 83,088ReLU-132 [1024, 144, 1, 1] 0Conv2d-133 [1024, 576, 1, 1] 83,520Hardsigmoid-134 [1024, 576, 1, 1] 0
SqueezeExcitation-135 [1024, 576, 2, 2] 0Conv2d-136 [1024, 96, 2, 2] 55,296BatchNorm2d-137 [1024, 96, 2, 2] 192
InvertedResidual-138 [1024, 96, 2, 2] 0Conv2d-139 [1024, 576, 2, 2] 55,296BatchNorm2d-140 [1024, 576, 2, 2] 1,152Hardswish-141 [1024, 576, 2, 2] 0Conv2d-142 [1024, 576, 2, 2] 14,400BatchNorm2d-143 [1024, 576, 2, 2] 1,152Hardswish-144 [1024, 576, 2, 2] 0
AdaptiveAvgPool2d-145 [1024, 576, 1, 1] 0Conv2d-146 [1024, 144, 1, 1] 83,088ReLU-147 [1024, 144, 1, 1] 0Conv2d-148 [1024, 576, 1, 1] 83,520Hardsigmoid-149 [1024, 576, 1, 1] 0
SqueezeExcitation-150 [1024, 576, 2, 2] 0Conv2d-151 [1024, 96, 2, 2] 55,296BatchNorm2d-152 [1024, 96, 2, 2] 192
InvertedResidual-153 [1024, 96, 2, 2] 0Conv2d-154 [1024, 576, 2, 2] 55,296BatchNorm2d-155 [1024, 576, 2, 2] 1,152Hardswish-156 [1024, 576, 2, 2] 0
AdaptiveAvgPool2d-157 [1024, 576, 1, 1] 0Linear-158 [1024, 1024] 590,848Hardswish-159 [1024, 1024] 0Dropout-160 [1024, 1024] 0Linear-161 [1024, 340] 348,500Total params: 1,866,356
Trainable params: 1,866,356
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 48.00
Forward/backward pass size (MB): 2979.22
Params size (MB): 7.12
Estimated Total Size (MB): 3034.34
----------------------------------------------------------------4.5 train-DDP.py
需要注意的是train-DDP.py中包含许多训练策略
DDP分布式训练单机双卡AMP混合精度训练学习率衰减早停断点继续训练。
# python -m torch.distributed.launch --nproc_per_node2 --nnodes1 --node_rank0 --master_addr192.168.8.89 --master_port12345 train-DDP.py --use_mix_precision True
# Watch Training Logtensorboard --logdirtensorboard_dir
from tqdm import tqdm
import torch
import torch.nn.parallel
import torch.distributed as dist
from torch.utils.tensorboard import SummaryWriter
import time
import os
import torch.optim
import torch.utils.data
import torch.nn as nn
from collections import OrderedDict
from model import CustomMobileNetV3
from getdata import MyData
from torch.cuda.amp import GradScaler
from option import get_args
opt get_args()
dist.init_process_group(backendnccl, init_methodenv://)os.makedirs(opt.checkpoints, exist_okTrue)def train(gpu):rank dist.get_rank()model CustomMobileNetV3()model.cuda(gpu)criterion nn.CrossEntropyLoss().to(gpu)optimizer torch.optim.Adam(model.parameters(), lropt.lr)model nn.SyncBatchNorm.convert_sync_batchnorm(model)model nn.parallel.DistributedDataParallel(model, device_ids[gpu])scaler GradScaler(enabledopt.use_mix_precision) dataloaders MyData()train_loader dataloaders[train]test_loader dataloaders[val]if opt.use_lr_scheduler:scheduler torch.optim.lr_scheduler.StepLR(optimizer, step_size10, gamma0.8)start_time time.time()best_val_acc 0.0no_improve_epochs 0early_stopping_patience 6 # Early Stopping Patiencebreakckpt resumeif opt.resume:checkpoint torch.load(opt.resume_ckpt)print(Loading checkpoint from:, opt.resume_ckpt)new_state_dict OrderedDict() # Create a new ordered dictionary and remove prefixesfor k, v in checkpoint[model].items():name k[7:] # Remove module. To match the original model definitionnew_state_dict[name] vmodel.load_state_dict(new_state_dict, strictFalse) # Load a new state dictionaryoptimizer.load_state_dict(checkpoint[optimizer])start_epoch checkpoint[epoch] # Set the starting epochif opt.use_lr_scheduler:scheduler.load_state_dict(checkpoint[scheduler])else:start_epoch 0for epoch in range(start_epoch 1, opt.epochs):tqdm_trainloader tqdm(train_loader, descfEpoch {epoch})running_loss, running_correct_top1, running_correct_top3, running_correct_top5 0.0, 0.0, 0.0, 0.0total_samples 0for i, (images, target) in enumerate(tqdm_trainloader if rank 0 else train_loader, 0):images images.to(gpu)target target.to(gpu)with torch.cuda.amp.autocast(enabledopt.use_mix_precision):output model(images)loss criterion(output, target)optimizer.zero_grad()scaler.scale(loss).backward()scaler.step(optimizer)scaler.update() running_loss loss.item() * images.size(0)_, predicted torch.max(output.data, 1)running_correct_top1 (predicted target).sum().item()_, predicted_top3 torch.topk(output.data, 3, dim1)_, predicted_top5 torch.topk(output.data, 5, dim1)running_correct_top3 (predicted_top3[:, :3] target.unsqueeze(1).expand_as(predicted_top3)).sum().item()running_correct_top5 (predicted_top5[:, :5] target.unsqueeze(1).expand_as(predicted_top5)).sum().item()total_samples target.size(0)state {epoch: epoch,model: model.module.state_dict(),optimizer: optimizer.state_dict(),scheduler: scheduler.state_dict()}if rank 0:current_lr scheduler.get_last_lr()[0] if opt.use_lr_scheduler else opt.lrprint(f[Epoch {epoch}] f[Train Loss: {running_loss / len(train_loader.dataset):.6f}] f[Train Top-1 Acc: {running_correct_top1 / len(train_loader.dataset):.6f}] f[Train Top-3 Acc: {running_correct_top3 / len(train_loader.dataset):.6f}] f[Train Top-5 Acc: {running_correct_top5 / len(train_loader.dataset):.6f}] f[Learning Rate: {current_lr:.6f}] f[Time: {time.time() - start_time:.6f} seconds])writer.add_scalar(Train/Loss, running_loss / len(train_loader.dataset), epoch)writer.add_scalar(Train/Top-1 Accuracy, running_correct_top1 / len(train_loader.dataset), epoch)writer.add_scalar(Train/Top-3 Accuracy, running_correct_top3 / len(train_loader.dataset), epoch)writer.add_scalar(Train/Top-5 Accuracy, running_correct_top5 / len(train_loader.dataset), epoch)writer.add_scalar(Train/Learning Rate, current_lr, epoch)torch.save(state, f{opt.checkpoints}model_epoch_{epoch}.pth)# dist.barrier()tqdm_trainloader.close()if opt.use_lr_scheduler: # Learning-rate Schedulerscheduler.step()acc_top1 valid(test_loader, model, epoch, gpu, rank)if acc_top1 is not None:if acc_top1 best_val_acc:best_val_acc acc_top1no_improve_epochs 0torch.save(state, f{opt.checkpoints}/model_best.pth)else:no_improve_epochs 1if no_improve_epochs early_stopping_patience:print(fEarly stopping triggered after {early_stopping_patience} epochs without improvement.)breakelse:print(Warning: acc_top1 is None, skipping this epoch.)dist.destroy_process_group()def valid(val_loader, model, epoch, gpu, rank):model.eval()correct_top1, correct_top3, correct_top5, total torch.tensor(0.).to(gpu), torch.tensor(0.).to(gpu), torch.tensor(0.).to(gpu), torch.tensor(0.).to(gpu)with torch.no_grad():tqdm_valloader tqdm(val_loader, descfEpoch {epoch})for i, (images, target) in enumerate(tqdm_valloader, 0) :images images.to(gpu)target target.to(gpu)output model(images)total target.size(0)correct_top1 (output.argmax(1) target).type(torch.float).sum()_, predicted_top3 torch.topk(output, 3, dim1)_, predicted_top5 torch.topk(output, 5, dim1)correct_top3 (predicted_top3[:, :3] target.unsqueeze(1).expand_as(predicted_top3)).sum().item()correct_top5 (predicted_top5[:, :5] target.unsqueeze(1).expand_as(predicted_top5)).sum().item()dist.reduce(total, 0, opdist.ReduceOp.SUM) # Group communication reduce operation (change to allreduce if Gloo)dist.reduce(correct_top1, 0, opdist.ReduceOp.SUM)dist.reduce(correct_top3, 0, opdist.ReduceOp.SUM)dist.reduce(correct_top5, 0, opdist.ReduceOp.SUM)if rank 0:print(f[Epoch {epoch}] f[Val Top-1 Acc: {correct_top1 / total:.6f}] f[Val Top-3 Acc: {correct_top3 / total:.6f}] f[Val Top-5 Acc: {correct_top5 / total:.6f}])writer.add_scalar(Validation/Top-1 Accuracy, correct_top1 / total, epoch)writer.add_scalar(Validation/Top-3 Accuracy, correct_top3 / total, epoch)writer.add_scalar(Validation/Top-5 Accuracy, correct_top5 / total, epoch)return float(correct_top1 / total) # Return top 1 precisiontqdm_valloader.close()def main():train(opt.local_rank)if __name__ __main__:writer SummaryWriter(log_diropt.tensorboard_dir)main()writer.close()在终端使用下面命令可以启动多卡分布式训练
python -m torch.distributed.launch --nproc_per_node2 --nnodes1 --node_rank0 --master_addr192.168.8.89 --master_port12345 train-DDP.py --use_mix_precision True相关参数含义如下
nproc_per_node显卡数量nnodes机器数量node_rank机器编号master_addr机器ip地址master_port机器端口
如果使用nohup启动训练会存在一个bug
W0914 18:33:15.081479 140031432897728 torch/distributed/elastic/agent/server/api.py:741] Received Signals.SIGHUP death signal, shutting down workers
W0914 18:33:15.085310 140031432897728 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1685186 closing signal SIGHUP
W0914 18:33:15.085644 140031432897728 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1685192 closing signal SIGHUP具体原因可以参考pytorch官方的discussDDP Error: torch.distributed.elastic.agent.server.api:Received 1 death signal, shutting down workers
我们可以使用tmux解决这个问题。
安装tmuxsudo apt-get install tmux新建会话tmux new -s train-DDP会话名称自定义激活虚拟环境conda activate pytorch虚拟环境以实际需要为准启动训练任务python -m torch.distributed.launch --nproc_per_node2 --nnodes1 --node_rank0 --master_addr192.168.8.89 --master_port12345 train-DDP.py --use_mix_precision True
tmux常用命令如下
查看当前全部的tmux会话tmux ls新建会话tmux new -s 会话名字重新进入会话tmux attach -t 会话名字kill会话tmux kill-session -t 会话名字
本文训练过程中的日志如下图所示 模型在第11轮发生早停。
4.6 model_transfer.py
代码作用是将pth模型转为移动端的ptl格式和onnx格式方便模型端侧部署。
from torch.utils.mobile_optimizer import optimize_for_mobile
import torch
from model import CustomMobileNetV3
import onnx
from onnxsim import simplify
from torch.autograd import Variable
from option import get_args
opt get_args()model CustomMobileNetV3()
model.load_state_dict(torch.load(f{opt.checkpoints}model_best.pth, map_locationcpu)[model])
model.eval()
print(Model loaded successfully.)Save .pth format model
torch.save(model, f{opt.checkpoints}/model.pth)Save .ptl format model
example torch.rand(1, 3, 64, 64)
traced_script_module torch.jit.trace(model, example)
traced_script_module_optimized optimize_for_mobile(traced_script_module)
traced_script_module_optimized._save_for_lite_interpreter(f{opt.checkpoints}model.ptl)Save .onnx format model
input_name [input]
output_name [output]
input Variable(torch.randn(1, 3, opt.loadsize, opt.loadsize))
torch.onnx.export(model, input, f{opt.checkpoints}model.onnx, input_namesinput_name, output_namesoutput_name, verboseTrue)
onnx.save(onnx.shape_inference.infer_shapes(onnx.load(f{opt.checkpoints}model.onnx)), f{opt.checkpoints}model.onnx) # Perform shape judgment
# simplified model
model_onnx onnx.load(f{opt.checkpoints}model.onnx)
model_simplified, check simplify(model_onnx)
assert check, Simplified ONNX model could not be validated
onnx.save(model_simplified, f{opt.checkpoints}model_simplified.onnx)4.7 evaluate.py
代码定义了三个函数
evaluate_image_single对单张图像进行预测evaluate_image_dir对文件夹图像进行预测evaluate_onnx_modelonnx模型对图像进行预测
代码提供了多个可视化图像与评估指标。包括 混淆矩阵、F1score 等。
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision import transforms
import torch.nn.functional as F
import torch.utils.data
import onnxruntime
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix, roc_curve, auc
from tqdm import tqdm
from getdata import mean, std, class_names
from option import get_args
opt get_args()
device cuda:1Predicting a single image
def evaluate_image_single(img_path, transform_test, model, class_names, top_k):image Image.open(img_path).convert(RGB)img transform_test(image).to(device)img img.unsqueeze_(0)out model(img)pred_softmax F.softmax(out, dim1)top_n, top_n_indices torch.topk(pred_softmax, top_k)confs top_n[0].cpu().detach().numpy().tolist()class_names_top [class_names[i] for i in top_n_indices[0]]for i in range(top_k):print(fPre: {class_names_top[i]} Conf: {confs[i]:.3f})confs_max confs[0]plt.figure(figsize(10, 5))plt.subplot(1, 2, 1)plt.axis(off)plt.title(fPre: {class_names_top[0]} Conf: {confs_max:.3f})plt.imshow(image)sorted_pairs sorted(zip(class_names_top, confs), keylambda x: x[1], reverseTrue)sorted_class_names_top, sorted_confs zip(*sorted_pairs)plt.subplot(1, 2, 2)bars plt.bar(sorted_class_names_top, sorted_confs, colorlightcoral)plt.xlabel(Class Names)plt.ylabel(Confidence)plt.title(Top 5 Predictions (Descending Order))plt.xticks(rotation45)plt.ylim(0, 1)plt.tight_layout()for bar, conf in zip(bars, sorted_confs):yval bar.get_height()plt.text(bar.get_x() bar.get_width()/2, yval 0.01, f{conf:.3f}, hacenter, vabottom)plt.savefig(predict_image_with_bars.jpg)Predicting folder images
def evaluate_image_dir(model, dataloader, class_names):model.eval()all_preds []all_labels []correct_top1, correct_top3, correct_top5, total torch.tensor(0.).to(device), torch.tensor(0.).to(device), torch.tensor(0.).to(device), torch.tensor(0.).to(device)with torch.no_grad():for images, labels in tqdm(dataloader, descEvaluating):images images.to(device)labels labels.to(device)outputs model(images)total labels.size(0)correct_top1 (outputs.argmax(1) labels).type(torch.float).sum()_, predicted_top3 torch.topk(outputs, 3, dim1)_, predicted_top5 torch.topk(outputs, 5, dim1)correct_top3 (predicted_top3[:, :3] labels.unsqueeze(1).expand_as(predicted_top3)).sum().item()correct_top5 (predicted_top5[:, :5] labels.unsqueeze(1).expand_as(predicted_top5)).sum().item()_, preds torch.max(outputs, 1)all_preds.extend(preds)all_labels.extend(labels)all_preds torch.tensor(all_preds)all_labels torch.tensor(all_labels)top1 correct_top1 / totaltop3 correct_top3 / totaltop5 correct_top5 / totalprint(fTop-1 Accuracy: {top1:.4f})print(fTop-3 Accuracy: {top3:.4f})print(fTop-5 Accuracy: {top5:.4f})accuracy accuracy_score(all_labels.cpu().numpy(), all_preds.cpu().numpy())precision precision_score(all_labels.cpu().numpy(), all_preds.cpu().numpy(), averagemacro)recall recall_score(all_labels.cpu().numpy(), all_preds.cpu().numpy(), averagemacro)f1 f1_score(all_labels.cpu().numpy(), all_preds.cpu().numpy(), averagemacro)cm confusion_matrix(all_labels.cpu().numpy(), all_preds.cpu().numpy())report classification_report(all_labels.cpu().numpy(), all_preds.cpu().numpy(), target_namesclass_names)print(fAccuracy: {accuracy:.4f})print(fPrecision: {precision:.4f})print(fRecall: {recall:.4f})print(fF1 Score: {f1:.4f})print(report)plt.figure(figsize(100, 100))sns.heatmap(cm, annotTrue, fmtd, cmapBlues, xticklabelsclass_names, yticklabelsclass_names, annot_kws{size: 8})plt.xticks(rotation90) plt.yticks(rotation0) plt.xlabel(Predicted Label)plt.ylabel(True Label)plt.title(Confusion Matrix)plt.savefig(confusion_matrix.jpg)Using .onnx model to predict images
def evaluate_onnx_model(img_path, data_transform, onnx_model_path, class_names, top_k5):ort_session onnxruntime.InferenceSession(onnx_model_path)img_pil Image.open(img_path).convert(RGB)input_img data_transform(img_pil)input_tensor input_img.unsqueeze(0).numpy()ort_inputs {input: input_tensor}out ort_session.run([output], ort_inputs)[0]def softmax(x):return np.exp(x) / np.sum(np.exp(x), axis1, keepdimsTrue)prob_dist softmax(out)result_dict {label: float(prob_dist[0][i]) for i, label in enumerate(class_names)}result_dict dict(sorted(result_dict.items(), keylambda item: item[1], reverseTrue))for key, value in list(result_dict.items())[:top_k]:print(fPre: {key} Conf: {value:.3f})confs_max list(result_dict.values())[0]class_names_top list(result_dict.keys())plt.figure(figsize(10, 5))plt.subplot(1, 2, 1)plt.axis(off)plt.title(fPre: {class_names_top[0]} Conf: {confs_max:.3f})plt.imshow(img_pil)plt.subplot(1, 2, 2)bars plt.bar(class_names_top[:top_k], list(result_dict.values())[:top_k], colorlightcoral)plt.xlabel(Class Names)plt.ylabel(Confidence)plt.title(Top 5 Predictions (Descending Order))plt.xticks(rotation45)plt.ylim(0, 1)plt.tight_layout()for bar, conf in zip(bars, list(result_dict.values())[:top_k]):yval bar.get_height()plt.text(bar.get_x() bar.get_width()/2, yval 0.01, f{conf:.3f}, hacenter, vabottom)plt.savefig(predict_image_with_bars.jpg)if __name__ __main__:data_transform transforms.Compose([transforms.Resize((opt.loadsize, opt.loadsize)), transforms.ToTensor(),transforms.Normalize(mean, std)])image_datasets ImageFolder(opt.dataset_test, data_transform)dataloaders DataLoader(image_datasets, batch_size512, shuffleTrue)ptl_model_path opt.checkpoints model.ptlpth_model_path opt.checkpoints model.pthonnx_model_path opt.checkpoints model.onnxptl_model torch.jit.load(ptl_model_path).to(device)pth_model torch.load(pth_model_path).to(device)evaluate_image_single(opt.test_img_path, data_transform, pth_model, class_names, top_k5) # Predicting a single image# evaluate_image_dir(pth_model, dataloaders, class_names) # Predicting folder images# evaluate_onnx_model(opt.test_img_path, data_transform, onnx_model_path, class_names, top_k5) # Predicting a single image使用evaluate_image_single函数对datasets/test/zigzag/zigzag-4508464694951936.png图片进行预测结果如下 使用evaluate_image_dir函数对datasets/test路径内的图像进行预测结果如下
Top-1 Accuracy: 0.6833
Top-3 Accuracy: 0.8521
Top-5 Accuracy: 0.8933
Accuracy: 0.6833
Precision: 0.6875
Recall: 0.6833
F1 Score: 0.6817precision recall f1-score supportThe Eiffel Tower 0.83 0.88 0.85 1000
The Great Wall of China 0.47 0.36 0.41 1000The Mona Lisa 0.68 0.86 0.76 1000airplane 0.83 0.74 0.78 1000alarm clock 0.76 0.76 0.76 1000ambulance 0.70 0.65 0.67 1000angel 0.87 0.78 0.82 1000animal migration 0.47 0.66 0.55 1000ant 0.77 0.74 0.75 1000anvil 0.80 0.66 0.72 1000apple 0.82 0.85 0.83 1000arm 0.74 0.69 0.71 1000asparagus 0.54 0.44 0.48 1000axe 0.69 0.67 0.68 1000backpack 0.61 0.75 0.67 1000banana 0.68 0.72 0.70 1000bandage 0.83 0.71 0.77 1000barn 0.66 0.68 0.67 1000baseball 0.77 0.71 0.74 1000baseball bat 0.75 0.73 0.74 1000basket 0.71 0.62 0.66 1000basketball 0.62 0.72 0.66 1000bat 0.79 0.62 0.69 1000bathtub 0.60 0.64 0.62 1000beach 0.58 0.65 0.61 1000bear 0.46 0.31 0.37 1000beard 0.56 0.73 0.63 1000bed 0.80 0.67 0.73 1000bee 0.82 0.74 0.78 1000belt 0.78 0.55 0.64 1000bench 0.59 0.53 0.56 1000bicycle 0.73 0.72 0.72 1000binoculars 0.74 0.77 0.76 1000bird 0.47 0.43 0.45 1000birthday cake 0.52 0.64 0.57 1000blackberry 0.46 0.42 0.44 1000blueberry 0.58 0.47 0.52 1000book 0.72 0.78 0.75 1000boomerang 0.73 0.70 0.71 1000bottlecap 0.58 0.54 0.56 1000bowtie 0.87 0.86 0.86 1000bracelet 0.68 0.60 0.64 1000brain 0.59 0.60 0.59 1000bread 0.54 0.63 0.58 1000bridge 0.61 0.64 0.63 1000broccoli 0.58 0.70 0.64 1000broom 0.56 0.68 0.61 1000bucket 0.62 0.66 0.64 1000bulldozer 0.69 0.70 0.70 1000bus 0.56 0.42 0.48 1000bush 0.47 0.65 0.55 1000butterfly 0.86 0.88 0.87 1000cactus 0.69 0.87 0.77 1000cake 0.53 0.42 0.47 1000calculator 0.76 0.82 0.79 1000calendar 0.54 0.50 0.52 1000camel 0.82 0.84 0.83 1000camera 0.87 0.74 0.80 1000camouflage 0.23 0.43 0.30 1000campfire 0.72 0.77 0.75 1000candle 0.75 0.73 0.74 1000cannon 0.77 0.69 0.72 1000canoe 0.67 0.63 0.65 1000car 0.65 0.63 0.64 1000carrot 0.75 0.82 0.78 1000castle 0.79 0.72 0.75 1000cat 0.69 0.66 0.68 1000ceiling fan 0.83 0.64 0.72 1000cell phone 0.62 0.60 0.61 1000cello 0.51 0.67 0.58 1000chair 0.83 0.80 0.81 1000chandelier 0.74 0.71 0.73 1000church 0.72 0.67 0.69 1000circle 0.53 0.86 0.66 1000clarinet 0.53 0.63 0.58 1000clock 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