Тонкая настройка TorchVision Faster R-CNN, фактическое обнаружение пшеницы Kaggle

В этой статье будет использоваться предварительно обученная модель TorchVision Faster R-CNN дляKaggle: глобальное тестирование пшеницы? Практикуйте распространенную технику трансферного обучения: тонкую настройку.

Записные книжки Kaggle, связанные с этой статьей, доступны по адресу:

• TorchVision Faster R-CNN Finetuning
• TorchVision Faster R-CNN Inference

Если у вас нет графического процессора, вы также можете тренироваться онлайн на Kaggle. Введение в использование:

• Use Kaggle Notebooks

Итак, приступим ?

Подготовить данные

import os

import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from PIL import Image

Скачать данные

Kaggle: глобальное тестирование пшеницы DataДанные загрузки страницы, содержание следующее:

• train.csv - the training data
• sample_submission.csv - a sample submission file in the correct format
• train.zip - training images
• test.zip - test images

DIR_INPUT = 'global-wheat-detection'
DIR_TRAIN = f'{DIR_INPUT}/train'
DIR_TEST = f'{DIR_INPUT}/test'

читать данные

читатьtrain.csvсодержание:

train_df = pd.read_csv(f'{DIR_INPUT}/train.csv')
train_df.head()

• image_id - the unique image ID
• width, height - the width and height of the images
• bbox - a bounding box, formatted as a Python-style list of [xmin, ymin, width, height]
• etc.

Пучокbboxзаменитьx y w h:

train_df[['x','y','w','h']] = 0
train_df[['x','y','w','h']] = np.stack(train_df['bbox'].apply(lambda x: np.fromstring(x[1:-1], sep=','))).astype(np.float)
train_df.drop(columns=['bbox'], inplace=True)
train_df.head()

анализировать данные

Размер обучающих данных:

train_df.shape

(147793, 8)

Толькоimage_idколичество:

train_df['image_id'].nunique()

3373

trainКоличество картинок в каталоге:

len(os.listdir(DIR_TRAIN))

3423

Описание имеет3422-3373=49Картинка не отмечена.

Данные обучения, размер изображения:

train_df['width'].unique(), train_df['height'].unique()

(array([1024]), array([1024]))

оба1024x1024из.

Проверьте распределение количества аннотаций:

counts = train_df['image_id'].value_counts()
print(f'number of boxes, range [{min(counts)}, {max(counts)}]')
sns.displot(counts, kde=False)
plt.xlabel('boxes')
plt.ylabel('images')
plt.title('boxes vs. images')
plt.show()

number of boxes, range [1, 116]

На одной картинке больше всего116этикетка.

Проверьте распределение координат метки, ширины и высоты:

train_df['cx'] = train_df['x'] + train_df['w'] / 2
train_df['cy'] = train_df['y'] + train_df['h'] / 2

ax = plt.subplots(1, 4, figsize=(16, 4), tight_layout=True)[1].ravel()
ax[0].set_title('x vs. y')
ax[0].set_xlim(0, 1024)
ax[0].set_ylim(0, 1024)
ax[1].set_title('cx vs. cy')
ax[1].set_xlim(0, 1024)
ax[1].set_ylim(0, 1024)
ax[2].set_title('w vs. h')
ax[3].set_title('area size')
sns.histplot(data=train_df, x='x', y='y', ax=ax[0], bins=50, pmax=0.9)
sns.histplot(data=train_df, x='cx', y='cy', ax=ax[1], bins=50, pmax=0.9)
sns.histplot(data=train_df, x='w', y='h', ax=ax[2], bins=50, pmax=0.9)
sns.histplot(train_df['w'] * train_df['h'], ax=ax[3], bins=50, kde=False)
plt.show()

Разделите набор данных на обучающий набор и проверочный набор, пропорцию8:2:

image_ids = train_df['image_id'].unique()

split_len = round(len(image_ids)*0.8)

train_ids = image_ids[:split_len]
valid_ids = image_ids[split_len:]

train = train_df[train_df['image_id'].isin(train_ids)]
valid = train_df[train_df['image_id'].isin(valid_ids)]

train.shape, valid.shape

((122577, 10), (25216, 10))

Предварительный просмотр данных

Определите вспомогательную функцию:

def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):
  figsize = (num_cols * scale, num_rows * scale)
  _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
  axes = axes.flatten()
  for i, (ax, img) in enumerate(zip(axes, imgs)):
    ax.imshow(img)
    ax.axes.get_xaxis().set_visible(False)
    ax.axes.get_yaxis().set_visible(False)
    if titles and len(titles) > i:
      ax.set_title(titles[i])
  return axes

def show_bboxes(axes, bboxes, labels=None, colors=None):
  def _make_list(obj, default_values=None):
    if obj is None:
      obj = default_values
    elif not isinstance(obj, (list, tuple)):
      obj = [obj]
    return obj

  labels = _make_list(labels)
  colors = _make_list(colors, ['b', 'g', 'r', 'm', 'c'])
  for i, bbox in enumerate(bboxes):
    color = colors[i % len(colors)]
    rect = plt.Rectangle(
      xy=(bbox[0], bbox[1]),
      width=bbox[2] - bbox[0],
      height=bbox[3] - bbox[1],
      fill=False,
      edgecolor=color,
      linewidth=2)
    axes.add_patch(rect)
    if labels and len(labels) > i:
      text_color = 'k' if color == 'w' else 'w'
      axes.text(rect.xy[0], rect.xy[1], labels[i], va='center',
                ha='center', fontsize=9, color=text_color,
                bbox=dict(facecolor=color, lw=0))

# https://github.com/d2l-ai/d2l-en/blob/master/d2l/torch.py

Предварительное изображение без аннотации:

num_rows, num_cols = 2, 4
imgs = [plt.imread(f'{DIR_TRAIN}/{n}.jpg') for n in train_df['image_id'].unique()[:num_rows*num_cols]]
show_images(imgs, num_rows, num_cols, scale=4)
plt.show()

Предварительный просмотр изображения, добавление аннотаций:

num_rows, num_cols = 1, 2
ids = train_df['image_id'].unique()[:num_rows*num_cols]
imgs = [plt.imread(f'{DIR_TRAIN}/{n}.jpg') for n in ids]
axes = show_images(imgs, num_rows, num_cols, scale=8)
for ax, id in zip(axes, ids):
  datas = train_df[train_df['image_id'] == id]
  bboxes = [(d['x'], d['y'], d['x']+d['w'], d['y']+d['h']) for _, d in datas.iterrows()]
  show_bboxes(ax, bboxes, labels=None, colors=['w'])
plt.show()

Создать набор данных

наследоватьtorch.utils.data.Datasetабстрактный класс, реализация__len__ __getitem__. и__getitem__Возвращаемые данные:

• image: a numpy.ndarray image
• target: a dict containing the following fields
  • boxes (FloatTensor[N, 4]): the coordinates of the N bounding boxes in [x0, y0, x1, y1] format, ranging from 0 to W and 0 to H
  • labels (Int64Tensor[N]): the label for each bounding box
  • image_id (Int64Tensor[1]): an image identifier. It should be unique between all the images in the dataset, and is used during evaluation
  • area (Tensor[N]): The area of the bounding box. This is used during evaluation with the COCO metric, to separate the metric scores between small, medium and large boxes.
  • iscrowd (UInt8Tensor[N]): instances with iscrowd=True will be ignored during evaluation.

import cv2 as cv
import numpy as np

import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2

import torch
from torch.utils.data import Dataset


class Wheat(Dataset):

  def __init__(self, dataframe, image_dir, transforms=None):
    super().__init__()
    self.image_ids = dataframe['image_id'].unique()
    self.df = dataframe
    self.image_dir = image_dir
    self.transforms = transforms

  def __getitem__(self, idx: int):
    image_id = self.image_ids[idx]
    records = self.df[self.df['image_id'] == image_id]

    image = cv.imread(f'{self.image_dir}/{image_id}.jpg', cv.IMREAD_COLOR)
    image = cv.cvtColor(image, cv.COLOR_BGR2RGB).astype(np.float32)
    image /= 255.0

    boxes = records[['x', 'y', 'w', 'h']].values

    area = boxes[:, 2] * boxes[:, 3]
    area = torch.as_tensor(area, dtype=torch.float32)

    boxes[:, 2] = boxes[:, 0] + boxes[:, 2]
    boxes[:, 3] = boxes[:, 1] + boxes[:, 3]

    # there is only one class
    labels = torch.ones((records.shape[0],), dtype=torch.int64)
    # suppose all instances are not crowd
    iscrowd = torch.zeros((records.shape[0],), dtype=torch.uint8)

    target = {}
    target['boxes'] = boxes
    target['labels'] = labels
    target['image_id'] = torch.tensor([idx])
    target['area'] = area
    target['iscrowd'] = iscrowd

    if self.transforms:
      sample = {
        'image': image,
        'bboxes': target['boxes'],
        'labels': labels,
      }
      sample = self.transforms(**sample)
      image = sample['image']
      target['boxes'] = torch.stack(tuple(map(torch.tensor, zip(*sample['bboxes'])))).permute(1, 0)

    return image, target, image_id

  def __len__(self) -> int:
    return len(self.image_ids)

  # albumentations
  #  https://github.com/albumentations-team/albumentations

  @staticmethod
  def get_train_transform():
    return A.Compose([
      A.Flip(0.5),
      ToTensorV2(p=1.0)
    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})

  @staticmethod
  def get_valid_transform():
    return A.Compose([
      ToTensorV2(p=1.0)
    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})

albumentationsэто отличная библиотека улучшения изображений, которая определяетtrain validметод преобразования.

создать сейчасtrain validнабор данных:

train_dataset = Wheat(train, DIR_TRAIN, Wheat.get_train_transform())
valid_dataset = Wheat(valid, DIR_TRAIN, Wheat.get_valid_transform())

Предварительный просмотр изображений в наборе данных:

datas = [train_dataset[i] for i in range(2)]
imgs = [d[0].permute(1, 2, 0).numpy() for d in datas]
axes = show_images(imgs, 1, 2, scale=8)
for ax, (image, target, image_id) in zip(axes, datas):
  show_bboxes(ax, target['boxes'], labels=None, colors=['w'])
plt.show()

Подготовьте модель

import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator

Создать модель

Создайте предварительно обученную модель Faster R-CNN:

model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)

Точная настройка модели

Последний слой выходной модели:

print(model.roi_heads.box_predictor)

FastRCNNPredictor(
  (cls_score): Linear(in_features=1024, out_features=91, bias=True)
  (bbox_pred): Linear(in_features=1024, out_features=364, bias=True)
)

Замените слой, указав, что размер выходного объекта2:

num_classes = 2 # wheat or not(background)

# get number of input features for the classifier
in_features = model.roi_heads.box_predictor.cls_score.in_features

# replace the pre-trained model's head with a new one
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

Снова выведите последний слой модели:

print(model.roi_heads.box_predictor)

FastRCNNPredictor(
  (cls_score): Linear(in_features=1024, out_features=2, bias=True)
  (bbox_pred): Linear(in_features=1024, out_features=8, bias=True)
)

Обучите модель

Создать загрузчик данных

from torch.utils.data import DataLoader

def collate_fn(batch):
  return tuple(zip(*batch))

train_data_loader = DataLoader(
  train_dataset,
  batch_size=4,
  shuffle=False,
  num_workers=4,
  collate_fn=collate_fn
)

valid_data_loader = DataLoader(
  valid_dataset,
  batch_size=4,
  shuffle=False,
  num_workers=4,
  collate_fn=collate_fn
)

Создать параметры

# train on the GPU or on the CPU, if a GPU is not available
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')

# move model to the right device
model.to(device)

# create an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)

# create a learning rate scheduler
# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)
lr_scheduler = None

# train it for 10 epochs
num_epochs = 10

начать обучение

import time
from tqdm import tqdm
#from tqdm.notebook import tqdm as tqdm

itr = 1

total_train_loss = []
total_valid_loss = []

losses_value = 0

for epoch in range(num_epochs):

  start_time = time.time()

  # train ------------------------------

  model.train()
  train_loss = []

  pbar = tqdm(train_data_loader, desc='let\'s train')
  for images, targets, image_ids in pbar:

    images = list(image.to(device) for image in images)
    targets = [{k: v.to(device) for k, v in t.items()} for t in targets]

    loss_dict = model(images, targets)

    losses = sum(loss for loss in loss_dict.values())
    losses_value = losses.item()
    train_loss.append(losses_value)

    optimizer.zero_grad()
    losses.backward()
    optimizer.step()

    pbar.set_description(f"Epoch: {epoch+1}, Batch: {itr}, Loss: {losses_value}")
    itr += 1

  epoch_train_loss = np.mean(train_loss)
  total_train_loss.append(epoch_train_loss)

  # update the learning rate
  if lr_scheduler is not None:
    lr_scheduler.step()

  # valid ------------------------------

  with torch.no_grad():
    valid_loss = []

    for images, targets, image_ids in valid_data_loader:
      images = list(image.to(device) for image in images)
      targets = [{k: v.to(device) for k, v in t.items()} for t in targets]

      loss_dict = model(images, targets)

      losses = sum(loss for loss in loss_dict.values())
      loss_value = losses.item()
      valid_loss.append(loss_value)

  epoch_valid_loss = np.mean(valid_loss)
  total_valid_loss.append(epoch_valid_loss)

  # print ------------------------------

  print(f"Epoch Completed: {epoch+1}/{num_epochs}, Time: {time.time()-start_time}, "
        f"Train Loss: {epoch_train_loss}, Valid Loss: {epoch_valid_loss}")

Epoch: 1, Batch: 675, Loss: 0.8600306952323752: 100%|██████████| 675/675 [08:26<00:00,  1.33it/s]
Epoch Completed: 1/2, Time: 566.7130048274994, Train Loss: 0.909390335455138, Valid Loss: 0.8790632066434829
Epoch: 2, Batch: 1350, Loss: 0.8195391336080114: 100%|██████████| 675/675 [08:27<00:00,  1.33it/s]
Epoch Completed: 2/2, Time: 569.5055477619171, Train Loss: 0.8158109236268262, Valid Loss: 0.8743081629490502
...

Проигрыш в розыгрыше

plt.figure(figsize=(8, 5))
sns.set_style(style="whitegrid")
sns.lineplot(x=range(1, len(total_train_loss)+1), y=total_train_loss, label="Train Loss")
sns.lineplot(x=range(1, len(total_train_loss)+1), y=total_valid_loss, label="Valid Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.show()

сохранить модель

torch.save(model.state_dict(), 'fasterrcnn_resnet50_fpn.pth')

Предсказание модели

Здесь мы готовим данные с нуля, загружаем модель и делаем прогнозы.

Подготовить данные

import os

import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from PIL import Image

DIR_INPUT = 'global-wheat-detection'
DIR_TRAIN = f'{DIR_INPUT}/train'
DIR_TEST = f'{DIR_INPUT}/test'

WEIGHTS_FILE = 'fasterrcnn_resnet50_fpn.pth'

Документ подачи

Файл, используемый для отправки результатов. Строка содержимого, представляющая результат предсказания изображения. следующее:

ce4833752,0.5 0 0 100 100

image_id ce4833752картина, предсказаниеx y w h 0 0 100 100где пшеница, уверенность0.5. Если есть несколько полей подсказок, их можно разделить пробелами.

test_df = pd.read_csv(f'{DIR_INPUT}/sample_submission.csv')
test_df.tail()

Создать набор данных

import cv2 as cv
import numpy as np

import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2

import torch
from torch.utils.data import Dataset


class WheatTest(Dataset):

  def __init__(self, image_ids, image_dir, transforms=None):
    super().__init__()
    self.image_ids = image_ids
    self.image_dir = image_dir
    self.transforms = transforms

  def __getitem__(self, idx: int):
    image_id = self.image_ids[idx]

    image = cv.imread(f'{self.image_dir}/{image_id}.jpg', cv.IMREAD_COLOR)
    image = cv.cvtColor(image, cv.COLOR_BGR2RGB).astype(np.float32)
    image /= 255.0

    if self.transforms:
      sample = {
        'image': image,
      }
      sample = self.transforms(**sample)
      image = sample['image']

    return image, image_id

  def __len__(self) -> int:
    return len(self.image_ids)

  @staticmethod
  def get_test_transform():
    return A.Compose([
      ToTensorV2(p=1.0)
    ])

Создайте тестовый набор данных:

def get_image_ids(p):
  import glob
  image_ids = []
  for p in glob.glob(f'{p}/*.jpg'):
    n, _ = os.path.splitext(os.path.basename(p))
    image_ids.append(n)
  return image_ids

# try more images for submission
#test_dataset = WheatTest(get_image_ids(DIR_TRAIN), DIR_TRAIN, WheatTest.get_test_transform())

test_dataset = WheatTest(test_df["image_id"].unique(), DIR_TEST, WheatTest.get_test_transform())

Создать загрузчик данных

from torch.utils.data import DataLoader

def collate_fn(batch):
  return tuple(zip(*batch))

test_data_loader = DataLoader(
  test_dataset,
  batch_size=2,
  shuffle=False,
  num_workers=4,
  drop_last=False,
  collate_fn=collate_fn
)

модель нагрузки

import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator

device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')

# create a Faster R-CNN model without pre-trained
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)

num_classes = 2 # wheat or not(background)

# get number of input features for the classifier
in_features = model.roi_heads.box_predictor.cls_score.in_features

# replace the pre-trained model's head with a new one
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

# load the trained weights
model.load_state_dict(torch.load(WEIGHTS_FILE, map_location=device))
model.eval()

# move model to the right device
_ = model.to(device)

начать прогнозирование

score_threshold = 0.7
image_outputs = []

for images, image_ids in test_data_loader:
  images = list(image.to(device) for image in images)
  outputs = model(images)

  for image_id, output in zip(image_ids, outputs):
    boxes = output['boxes'].data.cpu().numpy()
    scores = output['scores'].data.cpu().numpy()

    mask = scores >= score_threshold
    boxes = boxes[mask].astype(np.int32)
    scores = scores[mask]

    image_outputs.append((image_id, boxes, scores))

Предварительный просмотр результатов

num_rows, num_cols = 1, 2
scale = 16
figsize = (num_rows * scale, num_cols * scale)
_, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
axes = axes.flatten()

fig_n = num_rows * num_cols
fig_i = 0

for i in range(1, 1+fig_n):
  image, image_id = test_dataset[i]
  _, boxes, scores = image_outputs[i]

  sample = image.permute(1, 2, 0).cpu().numpy()

  for box in boxes:
    cv.rectangle(sample, (box[0], box[1]), (box[2], box[3]), (220, 0, 0), 2)

  axes[fig_i].imshow(sample)
  fig_i += 1

сохранить результаты

def format_prediction_string(boxes, scores):
  pred_strings = []
  for score, box in zip(scores, boxes):
    pred_strings.append(round(score, 4))
    pred_strings.extend(box)
  return ' '.join(map(str, pred_strings))

results = []

for image_id, boxes, scores in image_outputs:
  #boxes = boxes_.copy()
  boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
  boxes[:, 3] = boxes[:, 3] - boxes[:, 1]

  result = {
    'image_id': image_id,
    'PredictionString': format_prediction_string(boxes, scores)
  }
  results.append(result)

results[0]

{'image_id': 'aac893a91',

'PredictionString': '0.9928 72 2 96 166 0.9925 553 528 123 203 0.9912 613 921 85 102 0.9862 691 392 125 193 0.9855 819 708 105 204 0.9842 356 531 100 88 0.982 586 781 100 119 0.9795 739 768 82 122 0.9779 324 662 126 160 0.9764 27 454 102 156 0.9763 545 76 145 182 0.9736 450 858 90 95 0.9626 241 91 137 146 0.9406 306 0 75 68 0.9404 89 618 128 80 0.9366 177 576 114 182 0.9363 234 845 144 91 0.9265 64 857 115 69 0.824 822 630 90 124 0.7516 815 921 134 100'}

test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df

test_df.to_csv('submission.csv', index=False)

Отправить результаты

Это базовый уровень, можно попробовать продолжить настройку ?

Ссылаться на

• TorchVision Instance Segmentation Finetuning Tutorial
• Kaggle: Global Wheat Detection
  • Pytorch Starter - FasterRCNN Train
  • Global Wheat Detection: Starter EDA

Обмен опытом личной практики GoCoding, вы можете обратить внимание на общедоступный номер!