统计学期末课程作业-python自定义实现CNN-KNN-NN-SVM网络模型源码+说明文件.zip

import os import cv2 import numpy as np import random import sys import torch from torch import nn from torch import optim import torch.utils.data # public leaderboard # 752 | 2123 : 0.88214 (v0) (6000 + 4802) * 4 = 10802 * 4 = 43208 # 3148 | 12842 : 0.88037 (v1) (6000 + 4633) * 4 = 10633 * 4 = 42532 epoch_num = 100000 batch_size = 64 lr = 0.001 train_path = '/home/dchen/dataset/ESL/quickdraw-data/train/' test_path = '/home/dchen/dataset/ESL/quickdraw-data/released_test/' # extra_train_path = '/home/dchen/dataset/ESL/extra_training_data/data/' extra_train_path = '/home/dchen/dataset/ESL/extra_v1/data/' model_path = '../models/extra_cnn_2.pkl' category_mapping = {'airplane': 0, 'ant': 1, 'bear': 2, 'bird': 3, 'bridge': 4, 'bus' : 5, 'calendar': 6, 'car': 7, 'chair': 8, 'dog': 9, 'dolphin' : 10, 'door': 11, 'flower': 12, 'fork': 13, 'truck': 14} data_pairs =[] def load_data(data_path, train=True, data_aug_mode=0): if (train): labels = os.listdir(data_path) for label in labels: filepath = data_path + label filename = os.listdir(filepath) for fname in filename: ffpath = filepath + "/" + fname data_pair = [ffpath, category_mapping[label]] data_pairs.append(data_pair) data_cnt = len(data_pairs) print(data_cnt) labels = os.listdir(extra_train_path) for label in labels: filepath = extra_train_path + label filename = os.listdir(filepath) for fname in filename: ffpath = filepath + "/" + fname data_pair = [ffpath, category_mapping[label]] data_pairs.append(data_pair) data_cnt = len(data_pairs) print(data_cnt) if (data_aug_mode == 1): data_cnt *= 4 elif (data_aug_mode == 2): data_cnt *= 16 elif (data_aug_mode == 3): data_cnt *= 64 data_x = np.empty((data_cnt, 1, 28, 28), dtype="float32") data_y = [] random.shuffle(data_pairs) i = 0 for data_pair in data_pairs: img = cv2.imread(data_pair[0], 0) img = cv2.resize(img, (28, 28)) if (data_aug_mode == 0): arr = np.asarray(img, dtype="float32") data_x[i, :, :, :] = arr i += 1 data_y.append(data_pair[1]) elif (data_aug_mode == 1): img_list = [img, cv2.flip(img, 1), cv2.flip(img, 0), cv2.flip(img, -1)] for tmp in img_list: arr = np.asarray(tmp, dtype="float32") data_x[i, :, :, :] = arr i += 1 data_y.append(data_pair[1]) elif (data_aug_mode == 2): img_list = [] for m in range(4): for n in range(4): arr = np.asarray(cv2.resize(img[m:m+25, n:n+25], (28, 28)), dtype="float32") data_x[i, :, :, :] = arr i += 1 data_y.append(data_pair[1]) elif (data_aug_mode == 3): img_list = [] for tmp in [img, cv2.flip(img, 1), cv2.flip(img, 0), cv2.flip(img, -1)]: for m in range(4): for n in range(4): arr = np.asarray(cv2.resize(img[m:m+25, n:n+25], (28, 28)), dtype="float32") data_x[i, :, :, :] = arr i += 1 data_y.append(data_pair[1]) data_x = data_x / 255 data_y = np.asarray(data_y) data_x = torch.from_numpy(data_x) data_y = torch.from_numpy(data_y) dataset = torch.utils.data.TensorDataset(data_x, data_y) loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True) return loader filename = os.listdir(data_path) for fname in filename: ffpath = data_path + fname data_pair = [ffpath, fname.split('.')[0]] data_pairs.append(data_pair) data_cnt = len(data_pairs) data_x = np.empty((data_cnt, 1, 28, 28), dtype="float32") data_y = [] i = 0 for data_pair in data_pairs: img = cv2.imread(data_pair[0], 0) img = cv2.resize(img, (28, 28)) arr = np.asarray(img, dtype="float32") data_x[i, :, :, :] = arr data_y.append(i) i += 1 data_x = data_x / 255 data_y = np.asarray(data_y) data_x = torch.from_numpy(data_x) data_y = torch.from_numpy(data_y) dataset = torch.utils.data.TensorDataset(data_x, data_y) loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False) return loader class ResBlock(nn.Module): def __init__(self, channels, kernel_size=3, stride=1, padding=1, mode=0): super(ResBlock, self).__init__() self.mode = mode self.bn1 = nn.BatchNorm2d(channels) self.bn2 = nn.BatchNorm2d(channels) self.conv1 = nn.Conv2d(channels, channels, kernel_size, stride, padding) self.ac1 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(channels, channels, kernel_size, stride, padding) self.ac2 = nn.ReLU(inplace=True) def forward(self, x): if (self.mode == 0): return self.ac2(x + self.conv2(self.ac1(self.conv1(x)))) if (self.mode == 1): return self.ac2(x + self.bn2(self.conv2(self.ac1(self.bn1(self.conv1(x)))))) if (self.mode == 2): return x + self.bn2(self.conv2(self.ac1(self.bn1(self.conv1(x))))) return x + self.conv2(self.ac1(self.conv1(x))) class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv = nn.Sequential( nn.Conv2d(1, 64, 3, 1, 1), nn.ReLU(inplace=True) ) self.res1 = nn.Sequential( ResBlock(channels=64, mode=1), nn.MaxPool2d(2, 2) ) self.res2 = nn.Sequential( ResBlock(channels=64, mode=1), nn.MaxPool2d(2, 2) ) self.fc1 = nn.Sequential( nn.Dropout(), nn.Linear(64 * 7 * 7, 512), nn.ReLU(inplace=True) ) self.fc2 = nn.Sequential( nn.Dropout(), nn.Linear(512, 512), nn.ReLU(inplace=True) ) self.fc3 = nn.Linear(512, 15) def forward(self, x): x = self.conv(x) x = self.res1(x) x = self.res2(x) x = x.view(x.size()[0], -1) x = self.fc1(x) x = self.fc2(x) x = self.fc3(x) return x model = CNN() # print(model) optimizer = optim.Adam(model.parameters(), lr=lr) criterion = nn.CrossEntropyLoss() if __name__ == "__main__": train = True if (len(sys.argv) == 2): if sys.argv[1] == 'test': train = False elif sys.argv[1] != 'train': print('Usage: python extra_cnn.py [train/test]') print(' Default: train') exit(0) elif (len(sys.argv) > 2): print('Usage: python extra_cnn.py [train/test]') print(' Default: train') exit(0) device = torch.device("cuda") torch.cuda.set_device(10) if (train): model.to(device) trainloader = load_data(train_path, data_aug_mode=1) minLoss = -1 lastSavedEpoch = 0 for epoch in range(epoch_num): for i, (inputs, labels) in enumerate(trainloader): inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = model.forward(inputs) loss = criterion(outputs, labels) loss.ba