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207 lines
5.9 KiB
207 lines
5.9 KiB
import torch
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import numpy as np
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import cv2, os, sys
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from torch.utils.data import Dataset
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from matplotlib import pyplot as plt
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from torch.utils.data import ConcatDataset, DataLoader, Subset
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import torch.nn as nn
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import torchvision.transforms as transforms
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from torchvision.datasets import DatasetFolder
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from PIL import Image
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from BinaryNetpytorch.models.binarized_modules import BinarizeLinear,BinarizeConv2d
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from BinaryNetpytorch.models.binarized_modules import Binarize,HingeLoss
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import seaborn as sns
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import random
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batch_size = 8
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num_epoch = 10
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seed = 777
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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np.random.seed(seed)
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random.seed(seed)
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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train_tfm = transforms.Compose([
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#transforms.Grayscale(),
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#transforms.RandomHorizontalFlip(),
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#transforms.RandomResizedCrop((40,30)),
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#transforms.RandomCrop((40,30)),
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#transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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#transforms.RandomResizedCrop((40,30)),
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#transforms.TenCrop((40,30)),
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#transforms.Normalize(0.5,0.5),
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])
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test_tfm = transforms.Compose([
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#transforms.Grayscale(),
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transforms.ToTensor()
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])
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class Classifier(nn.Module):
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def __init__(self):
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super(Classifier, self).__init__()
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self.cnn_layers = nn.Sequential(
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# BinarizeConv2d(in_channels=1, out_channels=128, kernel_size=9, padding=9//2, bias=False),
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# nn.BatchNorm2d(128),
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# nn.ReLU(),
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# BinarizeConv2d(in_channels=128, out_channels=64, kernel_size=1, padding=1//2, bias=False),
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# nn.BatchNorm2d(64),
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#input_size(1,30,40)
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BinarizeConv2d(1, 128, 3, 1), #output_size(16,28,38)
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nn.BatchNorm2d(128),
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nn.ReLU(),
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#nn.Dropout(0.2),
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nn.MaxPool2d(kernel_size = 2), #output_size(16,14,19)
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BinarizeConv2d(128, 64, 3, 1), #output_size(24,12,17)
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nn.BatchNorm2d(64),
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nn.ReLU(),
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#nn.Dropout(0.2),
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nn.MaxPool2d(kernel_size = 2), #output_size(24,6,8)
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BinarizeConv2d(64, 32, 3, 1), #output_size(32,4,6)
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nn.BatchNorm2d(32),
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nn.ReLU(),
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#nn.Dropout(0.2),
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nn.MaxPool2d(kernel_size = 2), #ouput_size(32,2,3)
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#nn.LogSoftmax(),
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BinarizeConv2d(32, 3, (3,2), 1) #ouput_size(4,2,3) without max :(32,24,34)
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)
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def forward(self, x):
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x = self.cnn_layers(x)
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#x = x.flatten(1)
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#x = self.fc_layers(x)
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#print(x.shape)
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x = x.view(x.size(0), -1)
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#print(x.shape)
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#x = nn.LogSoftmax(x)
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#print(x)
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return x
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def main():
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train_set = DatasetFolder("./dataset/data_0711/grideye/train", loader=lambda x: Image.open(x), extensions="bmp", transform=train_tfm)
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test_set = DatasetFolder("./dataset/data_0711/grideye/test", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm)
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val_set = DatasetFolder("./dataset/data_0711/grideye/train", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm)
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train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
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test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True)
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val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=True)
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save_path = 'models.ckpt'
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = Classifier().to(device)
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optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
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criterion = nn.CrossEntropyLoss()
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best_accuracy = 0.0
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for epoch in range(num_epoch):
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running_loss = 0.0
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total = 0
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correct = 0
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for i, data in enumerate(train_loader):
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inputs, labels = data
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inputs = inputs.to(device)
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labels = labels.to(device)
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#print(labels)
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optimizer.zero_grad()
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outputs = model(inputs)
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#print(outputs.shape)
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loss = criterion(outputs, labels)
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loss.backward()
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for p in list(model.parameters()):
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if hasattr(p,'org'):
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p.data.copy_(p.org)
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optimizer.step()
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for p in list(model.parameters()):
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if hasattr(p,'org'):
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p.org.copy_(p.data.clamp_(-1,1))
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running_loss += loss.item()
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total += labels.size(0)
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_,predicted = torch.max(outputs.data,1)
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#print(predicted)
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#print("label",labels)
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correct += (predicted == labels).sum().item()
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train_acc = correct / total
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print(f"[ Train | {epoch + 1:03d}/{num_epoch:03d} ] loss = {running_loss:.5f}, acc = {train_acc:.5f}")
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model.eval()
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with torch.no_grad():
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correct = 0
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total = 0
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for i, data in enumerate(val_loader):
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inputs, labels = data
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inputs = inputs.to(device)
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labels = labels.to(device)
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outputs = model(inputs)
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_,predicted = torch.max(outputs.data,1)
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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val_acc = correct / total
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if val_acc > best_accuracy:
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best_accuracy = val_acc
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torch.save(model.state_dict(), save_path)
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print("Save Model")
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print(f"[ Val | {epoch + 1:03d}/{num_epoch:03d} ] acc = {val_acc:.5f}")
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model = Classifier().to(device)
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model.load_state_dict(torch.load(save_path))
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model.eval()
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stat = np.zeros((3,3))
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with torch.no_grad():
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correct = 0
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total = 0
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print(model)
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for i, data in enumerate(test_loader):
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inputs, labels = data
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inputs = inputs.to(device)
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labels = labels.to(device)
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outputs = model(inputs)
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#print(outputs.data)
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_,predicted = torch.max(outputs.data,1)
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#print(predicted)
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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for k in range(len(predicted)):
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if predicted[k] != labels[k]:
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img = inputs[k].mul(255).byte()
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img = img.cpu().numpy().squeeze(0)
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img = np.moveaxis(img, 0, -1)
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predict = predicted[k].cpu().numpy()
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label = labels[k].cpu().numpy()
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path = "test_result/predict:"+str(predict)+"_labels:"+str(label)+".jpg"
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stat[int(label)][int(predict)] += 1
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ax = sns.heatmap(stat, linewidth=0.5)
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plt.xlabel('Prediction')
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plt.ylabel('Label')
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plt.savefig('heatmap.jpg')
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#print(predicted)
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#print("labels:",labels)
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print('Test Accuracy:{} %'.format((correct / total) * 100))
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if __name__ == '__main__':
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main()
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