本篇内容主要讲解“PyTorch批量可视化怎么实现”,感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷,实用性强。下面就让小编来带大家学习“PyTorch批量可视化怎么实现”吧!
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1. 可视化任意网络模型训练的Loss,及Accuracy曲线图,Train与Valid必须在同一个图中
2. 采用make_grid,对任意图像训练输入数据进行批量可视化
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# -*- coding:utf-8 -*- """ @brief : 监控loss, accuracy, weights, gradients """ import os import numpy as np import torch import torch.nn as nn from torch.utils.data import DataLoader import torchvision.transforms as transforms from torch.utils.tensorboard import SummaryWriter import torch.optim as optim from matplotlib import pyplot as plt from model.lenet import LeNet from tools.my_dataset import RMBDataset from tools.common_tools2 import set_seed set_seed() # 设置随机种子 rmb_label = {"1": 0, "100": 1} # 参数设置 MAX_EPOCH = 10 BATCH_SIZE = 16 LR = 0.01 log_interval = 10 val_interval = 1 # ============================ step 1/5 数据 ============================ split_dir = os.path.join("..", "data", "rmb_split") train_dir = os.path.join(split_dir, "train") valid_dir = os.path.join(split_dir, "valid") norm_mean = [0.485, 0.456, 0.406] norm_std = [0.229, 0.224, 0.225] train_transform = transforms.Compose([ transforms.Resize((32, 32)), transforms.RandomCrop(32, padding=4), transforms.RandomGrayscale(p=0.8), transforms.ToTensor(), transforms.Normalize(norm_mean, norm_std), ]) valid_transform = transforms.Compose([ transforms.Resize((32, 32)), transforms.ToTensor(), transforms.Normalize(norm_mean, norm_std), ]) # 构建MyDataset实例 train_data = RMBDataset(data_dir=train_dir, transform=train_transform) valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform) # 构建DataLoder train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True) valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE) # ============================ step 2/5 模型 ============================ net = LeNet(classes=2) net.initialize_weights() # ============================ step 3/5 损失函数 ============================ criterion = nn.CrossEntropyLoss() # 选择损失函数 # ============================ step 4/5 优化器 ============================ optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9) # 选择优化器 scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1) # 设置学习率下降策略 # ============================ step 5/5 训练 ============================ train_curve = list() valid_curve = list() iter_count = 0 # 构建 SummaryWriter writer = SummaryWriter(comment='test_your_comment', filename_suffix="_test_your_filename_suffix") for epoch in range(MAX_EPOCH): loss_mean = 0. correct = 0. total = 0. net.train() for i, data in enumerate(train_loader): iter_count += 1 # forward inputs, labels = data outputs = net(inputs) # backward optimizer.zero_grad() loss = criterion(outputs, labels) loss.backward() # update weights optimizer.step() # 统计分类情况 _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).squeeze().sum().numpy() # 打印训练信息 loss_mean += loss.item() train_curve.append(loss.item()) if (i + 1) % log_interval == 0: loss_mean = loss_mean / log_interval print("Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format( epoch, MAX_EPOCH, i + 1, len(train_loader), loss_mean, correct / total)) loss_mean = 0. # 记录数据,保存于event file writer.add_scalars("Loss", {"Train": loss.item()}, iter_count) writer.add_scalars("Accuracy", {"Train": correct / total}, iter_count) # 每个epoch,记录梯度,权值 for name, param in net.named_parameters(): writer.add_histogram(name + '_grad', param.grad, epoch) writer.add_histogram(name + '_data', param, epoch) scheduler.step() # 更新学习率 # validate the model if (epoch + 1) % val_interval == 0: correct_val = 0. total_val = 0. loss_val = 0. net.eval() with torch.no_grad(): for j, data in enumerate(valid_loader): inputs, labels = data outputs = net(inputs) loss = criterion(outputs, labels) _, predicted = torch.max(outputs.data, 1) total_val += labels.size(0) correct_val += (predicted == labels).squeeze().sum().numpy() loss_val += loss.item() valid_curve.append(loss.item()) print("Valid:\t Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format( epoch, MAX_EPOCH, j + 1, len(valid_loader), loss_val, correct / total)) # 记录数据,保存于event file writer.add_scalars("Loss", {"Valid": np.mean(valid_curve)}, iter_count) writer.add_scalars("Accuracy", {"Valid": correct / total}, iter_count) train_x = range(len(train_curve)) train_y = train_curve train_iters = len(train_loader) valid_x = np.arange(1, len(valid_curve) + 1) * train_iters * val_interval # 由于valid中记录的是epochloss,需要对记录点进行转换到iterations valid_y = valid_curve plt.plot(train_x, train_y, label='Train') plt.plot(valid_x, valid_y, label='Valid') plt.legend(loc='upper right') plt.ylabel('loss value') plt.xlabel('Iteration') plt.show()
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# -*- coding:utf-8 -*- """ @brief : 卷积核和特征图的可视化 """ import torch.nn as nn from PIL import Image import torchvision.transforms as transforms from torch.utils.tensorboard import SummaryWriter import torchvision.utils as vutils from tools.common_tools import set_seed import torchvision.models as models set_seed(1) # 设置随机种子 # ----------------------------------- kernel visualization ----------------------------------- # flag = 0 flag = 1 if flag: writer = SummaryWriter(comment='test_your_comment', filename_suffix="_test_your_filename_suffix") alexnet = models.alexnet(pretrained=True) kernel_num = -1 vis_max = 1 for sub_module in alexnet.modules(): if isinstance(sub_module, nn.Conv2d): kernel_num += 1 if kernel_num > vis_max: break kernels = sub_module.weight c_out, c_int, k_w, k_h = tuple(kernels.shape) for o_idx in range(c_out): kernel_idx = kernels[o_idx, :, :, :].unsqueeze(1) # make_grid需要 BCHW,这里拓展C维度 kernel_grid = vutils.make_grid(kernel_idx, normalize=True, scale_each=True, nrow=c_int) writer.add_image('{}_Convlayer_split_in_channel'.format(kernel_num), kernel_grid, global_step=o_idx) kernel_all = kernels.view(-1, 3, k_h, k_w) # 3, h, w kernel_grid = vutils.make_grid(kernel_all, normalize=True, scale_each=True, nrow=8) # c, h, w writer.add_image('{}_all'.format(kernel_num), kernel_grid, global_step=322) print("{}_convlayer shape:{}".format(kernel_num, tuple(kernels.shape))) writer.close() # ----------------------------------- feature map visualization ----------------------------------- # flag = 0 flag = 1 if flag: writer = SummaryWriter(comment='test_your_comment', filename_suffix="_test_your_filename_suffix") # 数据 path_img = "./lena.png" # your path to image normMean = [0.49139968, 0.48215827, 0.44653124] normStd = [0.24703233, 0.24348505, 0.26158768] norm_transform = transforms.Normalize(normMean, normStd) img_transforms = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), norm_transform ]) img_pil = Image.open(path_img).convert('RGB') if img_transforms is not None: img_tensor = img_transforms(img_pil) img_tensor.unsqueeze_(0) # chw --> bchw # 模型 alexnet = models.alexnet(pretrained=True) # forward convlayer1 = alexnet.features[0] fmap_1 = convlayer1(img_tensor) # 预处理 fmap_1.transpose_(0, 1) # bchw=(1, 64, 55, 55) --> (64, 1, 55, 55) fmap_1_grid = vutils.make_grid(fmap_1, normalize=True, scale_each=True, nrow=8) writer.add_image('feature map in conv1', fmap_1_grid, global_step=322) writer.close()
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