%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as Image
import numpy as np
im1 = Image.imread("work/data/10img11.jpg")
im1 = im1/255.
im2 = Image.imread("work/data/14img01.jpg")
im2 = im2/255.
for i in range(1,10):
    lam= i*0.1
 im_mixup = (im1*lam+im2*(1-lam))
 plt.subplot(3,3,i)
 plt.imshow(im_mixup)
plt.show()

  # bbx1 = np.clip(cx - cut_w // 2, 0, W)
    # bby1 = np.clip(cy - cut_h // 2, 0, H)
    # bbx2 = np.clip(cx + cut_w // 2, 0, W)
    # bby2 = np.clip(cy + cut_h // 2, 0, H)
    bbx1 = 10
    bby1 = 600
    bbx2 = 10
    bby2 = 600
%matplotlib inline
import glob
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [10,10]
import cv2
# Path to data
data_folder = f"/home/aistudio/work/data/"
# Read filenames in the data folder
filenames = glob.glob(f"{data_folder}*.jpg")
# Read first 10 filenames
image_paths = filenames[:4]
image_batch = []
image_batch_labels = []
n_images = 4
print(image_paths)
for i in range(4):
    image = cv2.cvtColor(cv2.imread(image_paths[i]), cv2.COLOR_BGR2RGB)
 image_batch.append(image)
image_batch_labels=np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]])
def rand_bbox(size, lamb):
    W = size[0]
    H = size[1]
 cut_rat = np.sqrt(1. - lamb)
 cut_w = np.int(W * cut_rat)
 cut_h = np.int(H * cut_rat)
 # uniform
    cx = np.random.randint(W)
    cy = np.random.randint(H)
 # bbx1 = np.clip(cx - cut_w // 2, 0, W)
 # bby1 = np.clip(cy - cut_h // 2, 0, H)
 # bbx2 = np.clip(cx + cut_w // 2, 0, W)
 # bby2 = np.clip(cy + cut_h // 2, 0, H)
    bbx1 = 10
    bby1 = 600
    bbx2 = 10
    bby2 = 600
 return bbx1, bby1, bbx2, bby2
image = cv2.cvtColor(cv2.imread(image_paths[0]), cv2.COLOR_BGR2RGB)
# Crop a random bounding box
lamb = 0.3
size = image.shape
print('size',size)
def generate_cutmix_image(image_batch, image_batch_labels, beta):
    c=[1,0,3,2]
 # generate mixed sample
    lam = np.random.beta(beta, beta)
 rand_index = np.random.permutation(len(image_batch))
 print(f'iamhere{rand_index}')
 target_a = image_batch_labels
 target_b = np.array(image_batch_labels)[c]
 print('img.shape',image_batch[0].shape)
    bbx1, bby1, bbx2, bby2 = rand_bbox(image_batch[0].shape, lam)
 print('bbx1',bbx1)
 print('bby1',bby1)
 print('bbx2',bbx2)
 print('bby2',bby2)
 image_batch_updated = image_batch.copy()
 image_batch_updated=np.array(image_batch_updated)
 image_batch=np.array(image_batch)
 image_batch_updated[:, bbx1:bby1, bbx2:bby2, :] = image_batch[[c], bbx1:bby1, bbx2:bby2, :]
 # adjust lambda to exactly match pixel ratio
    lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (image_batch.shape[1] * image_batch.shape[2]))
 print(f'lam is {lam}')
    label = target_a * lam + target_b * (1. - lam)
 return image_batch_updated, label
# Generate CutMix image
input_image = image_batch[0]
image_batch_updated, image_batch_labels_updated = generate_cutmix_image(image_batch, image_batch_labels, 1.0)
# Show original images
print("Original Images")
for i in range(2):
 for j in range(2):
 plt.subplot(2,2,2*i+j+1)
 plt.imshow(image_batch[2*i+j])
plt.show()
# Show CutMix images
print("CutMix Images")
for i in range(2):
 for j in range(2):
 plt.subplot(2,2,2*i+j+1)
 plt.imshow(image_batch_updated[2*i+j])
plt.show()
# Print labels
print('Original labels:')
print(image_batch_labels)
print('Updated labels')
print(image_batch_labels_updated)
['/home/aistudio/work/data/11img01.jpg', '/home/aistudio/work/data/10img11.jpg', '/home/aistudio/work/data/14img01.jpg', '/home/aistudio/work/data/12img11.jpg']
size (2016, 1512, 3)
iamhere[2 1 0 3]
img.shape (2016, 1512, 3)
bbx1 10
bby1 600
bbx2 10
bby2 600
lam is 1.0
Original Images

CutMix Images

Original labels:
[[1 0 0 0]
 [0 1 0 0]
 [0 0 1 0]
 [0 0 0 1]]
Updated labels
[[1. 0. 0. 0.]
 [0. 1. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]]

!cd 'data/data97595' && unzip -q nongzuowu.zip
from paddle.io import Dataset
import cv2
import paddle
import random
# 导入所需要的库
from sklearn.utils import shuffle
import os
import pandas as pd
import numpy as np
from PIL import Image
import paddle
import paddle.nn as nn
from paddle.io import Dataset
import paddle.vision.transforms as T
import paddle.nn.functional as F
from paddle.metric import Accuracy
import warnings
warnings.filterwarnings("ignore")
# 读取数据
train_images = pd.read_csv('data/data97595/nongzuowu/train.csv')
# 划分训练集和校验集
all_size = len(train_images)
# print(all_size)
train_size = int(all_size * 0.8)
train_df = train_images[:train_size]
val_df = train_images[train_size:]
#  CutMix 的切块功能
def rand_bbox(size, lam):
 if len(size) == 4:
        W = size[2]
        H = size[3]
 elif len(size) == 3:
        W = size[0]
        H = size[1]
 else:
 raise Exception
 cut_rat = np.sqrt(1. - lam)
 cut_w = np.int(W * cut_rat)
 cut_h = np.int(H * cut_rat)
 # uniform
    cx = np.random.randint(W)
    cy = np.random.randint(H)
    bbx1 = np.clip(cx - cut_w // 2, 0, W)
    bby1 = np.clip(cy - cut_h // 2, 0, H)
    bbx2 = np.clip(cx + cut_w // 2, 0, W)
    bby2 = np.clip(cy + cut_h // 2, 0, H)
 return bbx1, bby1, bbx2, bby2
# 定义数据预处理
data_transforms = T.Compose([
 T.Resize(size=(256, 256)),
 T.Transpose(), # HWC -> CHW
 T.Normalize(
        mean=[0, 0, 0], # 归一化
        std=[255, 255, 255],
 to_rgb=True) 
])
class JSHDataset(Dataset):
 def __init__(self, df, transforms, train=False):
 self.df = df
 self.transfoms = transforms
 self.train = train
 def __getitem__(self, idx):
        row = self.df.iloc[idx]
 fn = row.image
 # 读取图片数据
        image = cv2.imread(os.path.join('data/data97595/nongzuowu/train', fn))
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = cv2.resize(image, (256, 256), interpolation=cv2.INTER_LINEAR)
 # 读取 mask 数据
 # masks = cv2.imread(os.path.join(row['mask_path'], fn), cv2.IMREAD_GRAYSCALE)/255
 # masks = cv2.resize(masks, (1024, 1024), interpolation=cv2.INTER_LINEAR)
 # 读取 label
        label = paddle.zeros([4])
        label[row.label] = 1
 # ------------------------------  CutMix  ------------------------------------------
        prob = 20 # 将 prob 设置为 0 即可关闭 CutMix
 if random.randint(0, 99) < prob and self.train:
 rand_index = random.randint(0, len(self.df) - 1)
 rand_row = self.df.iloc[rand_index]
 rand_fn = rand_row.image
 rand_image = cv2.imread(os.path.join('data/data97595/nongzuowu/train', rand_fn))
 rand_image = cv2.cvtColor(rand_image, cv2.COLOR_BGR2RGB)
 rand_image = cv2.resize(rand_image, (256, 256), interpolation=cv2.INTER_LINEAR)
 # rand_masks = cv2.imread(os.path.join(rand_row['mask_path'], rand_fn), cv2.IMREAD_GRAYSCALE)/255
 # rand_masks = cv2.resize(rand_masks, (1024, 1024), interpolation=cv2.INTER_LINEAR)
            lam = np.random.beta(1,1)
            bbx1, bby1, bbx2, bby2 = rand_bbox(image.shape, lam)
 image[bbx1:bbx2, bby1:bby2, :] = rand_image[bbx1:bbx2, bby1:bby2, :]
 # masks[bbx1:bbx2, bby1:bby2] = rand_masks[bbx1:bbx2, bby1:bby2]
            lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (image.shape[1] * image.shape[0]))
 rand_label = paddle.zeros([4])
 rand_label[rand_row.label] = 1
            label = label * lam + rand_label * (1. - lam)
 # ---------------------------------  CutMix  ---------------------------------------
 # 应用之前我们定义的各种数据增广
 # augmented = self.transforms(image=image, mask=masks)
 # img, mask = augmented['image'], augmented['mask']
 img = image
 return self.transfoms(img), label
 def __len__(self):
 return len(self.df)
train_dataset = JSHDataset(train_df, data_transforms, train=True)
val_dataset = JSHDataset(val_df, data_transforms)
#train_loader
train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=8, shuffle=True, num_workers=0)
#val_loader
val_loader = paddle.io.DataLoader(val_dataset, places=paddle.CPUPlace(), batch_size=8, shuffle=True, num_workers=0)
for batch_id, data in enumerate(train_loader()):
 x_data = data[0]
 y_data = data[1]
 print(x_data.dtype)
 print(y_data)
 break
paddle.float32
Tensor(shape=[8, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
 [[0. , 0. , 1. , 0. ],
 [0.54284668, 0.45715332, 0. , 0. ],
 [0. , 1. , 0. , 0. ],
 [0. , 0. , 1. , 0. ],
 [0.32958984, 0. , 0.67041016, 0. ],
 [0. , 0. , 0. , 1. ],
 [0. , 0. , 0. , 1. ],
 [0. , 0. , 0. , 1. ]])
from paddle.vision.models import resnet18
model = resnet18(num_classes=4)
# 模型封装
model = paddle.Model(model)
# 定义优化器
optim = paddle.optimizer.Adam(learning_rate=3e-4, parameters=model.parameters())
# 配置模型
model.prepare(
 optim,
 paddle.nn.CrossEntropyLoss(soft_label=True),
 Accuracy()
 )
# 模型训练与评估
model.fit(train_loader,
 val_loader,
 log_freq=1,
        epochs=2,
        verbose=1,
 )
The loss value printed in the log is the current step, and the metric is the average value of previous steps.
Epoch 1/2
step 56/56 [==============================] - loss: 1.2033 - acc: 0.5843 - 96ms/step        
Eval begin...
step 14/14 [==============================] - loss: 1.6905 - acc: 0.5625 - 73ms/step         
Eval samples: 112
Epoch 2/2
step 56/56 [==============================] - loss: 0.5297 - acc: 0.7708 - 82ms/step        
Eval begin...
step 14/14 [==============================] - loss: 0.5764 - acc: 0.7857 - 67ms/step        
Eval samples: 112