PyTorch之數據加載和處理

from __future__ import print_function, division import os import torch import pandas as pd #用于更容易地進行csv解析 from skimage import io, transform #用于圖像的IO和變換 import numpy as np import matplotlib.pyplot as plt from torch.utils.data import Dataset, DataLoader from torchvision import transforms, utilsimport warnings warnings.filterwarnings("ignore")plt.ion()""" 讀取數據集 將csv中的標注點數據讀入（N，2）數組中，其中N是特征點的數量。讀取數據代碼如下： """ landmarks_frame = pd.read_csv('data/faces/faces/face_landmarks.csv')n = 65 img_name = landmarks_frame.iloc[n, 0] #提取第n行，第0列中的數據 landmarks = landmarks_frame.iloc[n, 1:].values landmarks = landmarks.astype('float').reshape(-1, 2)print('Image name: {}'.format(img_name)) print('Landmarks shape: {}'.format(landmarks.shape)) print('First 4 Landmarks: {}'.format(landmarks[:4]))""" 寫一個簡單的函數來展示一張圖片和它對應的標注點作為例子。 """def show_landmarks(image, landmarks):"""顯示帶有地標的圖片"""plt.imshow(image)plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')plt.pause(5) # pause a bit so that plots are updatedplt.figure() show_landmarks(io.imread(os.path.join('data/faces/faces/', img_name)),landmarks) plt.show() """建立數據集類 """ class FaceLandmarksDataset(Dataset):"""面部標記數據集."""def __init__(self, csv_file, root_dir, transform=None):"""csv_file（string）：帶注釋的csv文件的路徑。root_dir（string）：包含所有圖像的目錄。transform（callable， optional）：一個樣本上的可用的可選變換"""self.landmarks_frame = pd.read_csv(csv_file)self.root_dir = root_dirself.transform = transformdef __len__(self):return len(self.landmarks_frame)def __getitem__(self, idx):img_name = os.path.join(self.root_dir,self.landmarks_frame.iloc[idx, 0])image = io.imread(img_name)landmarks = self.landmarks_frame.iloc[idx, 1:]landmarks = np.array([landmarks])landmarks = landmarks.astype('float').reshape(-1, 2)sample = {'image': image, 'landmarks': landmarks}if self.transform:sample = self.transform(sample)return sample""" 數據可視化 """face_dataset = FaceLandmarksDataset(csv_file='data/faces/faces/face_landmarks.csv',root_dir='data/faces/faces/') fig = plt.figure()for i in range(len(face_dataset)):sample = face_dataset[i]print(i, sample['image'].shape, sample['landmarks'].shape)ax = plt.subplot(1, 4, i + 1)plt.tight_layout()ax.set_title('Sample #{}'.format(i))ax.axis('off')show_landmarks(**sample)if i == 3:plt.show()break""" 數據變換,數據預處理 讓我們創建三個轉換: * Rescale：縮放圖片 * RandomCrop：對圖片進行隨機裁剪。這是一種數據增強操作 * ToTensor：把numpy格式圖片轉為torch格式圖片 (我們需要交換坐標軸). """class Rescale(object):"""將樣本中的圖像重新縮放到給定大小。.Args:output_size（tuple或int）：所需的輸出大小。 如果是元組，則輸出為與output_size匹配。 如果是int，則匹配較小的圖像邊緣到output_size保持縱橫比相同。"""def __init__(self, output_size):assert isinstance(output_size, (int, tuple))self.output_size = output_sizedef __call__(self, sample):image, landmarks = sample['image'], sample['landmarks']h, w = image.shape[:2]if isinstance(self.output_size, int):if h > w:new_h, new_w = self.output_size * h / w, self.output_sizeelse:new_h, new_w = self.output_size, self.output_size * w / helse:new_h, new_w = self.output_sizenew_h, new_w = int(new_h), int(new_w)img = transform.resize(image, (new_h, new_w))# h and w are swapped for landmarks because for images,# x and y axes are axis 1 and 0 respectivelylandmarks = landmarks * [new_w / w, new_h / h]return {'image': img, 'landmarks': landmarks}class RandomCrop(object):"""隨機裁剪樣本中的圖像.Args:output_size（tuple或int）：所需的輸出大小。 如果是int，方形裁剪是。"""def __init__(self, output_size):assert isinstance(output_size, (int, tuple))if isinstance(output_size, int):self.output_size = (output_size, output_size)else:assert len(output_size) == 2self.output_size = output_sizedef __call__(self, sample):image, landmarks = sample['image'], sample['landmarks']h, w = image.shape[:2]new_h, new_w = self.output_sizetop = np.random.randint(0, h - new_h)left = np.random.randint(0, w - new_w)image = image[top: top + new_h,left: left + new_w]landmarks = landmarks - [left, top]return {'image': image, 'landmarks': landmarks}class ToTensor(object):"""將樣本中的ndarrays轉換為Tensors."""def __call__(self, sample):image, landmarks = sample['image'], sample['landmarks']# 交換顏色軸因為# numpy包的圖片是: H * W * C# torch包的圖片是: C * H * Wimage = image.transpose((2, 0, 1))return {'image': torch.from_numpy(image),'landmarks': torch.from_numpy(landmarks)}scale = Rescale(256) crop = RandomCrop(128) composed = transforms.Compose([Rescale(256),RandomCrop(224)])""" 應用轉換 """# 在樣本上應用上述的每個變換。 fig = plt.figure() sample = face_dataset[65] for i, tsfrm in enumerate([scale, crop, composed]):transformed_sample = tsfrm(sample)ax = plt.subplot(1, 3, i + 1)plt.tight_layout()ax.set_title(type(tsfrm).__name__)show_landmarks(**transformed_sample)plt.show()""" torch.utils.data.DataLoader是一個提供 每次這個數據集被采樣時: 及時地從文件中讀取圖片 * 對讀取的圖片應用轉換 * 由于其中一步操作是隨機的 (randomcrop) , 數據被增強了 功能的迭代器 """transformed_dataset = FaceLandmarksDataset(csv_file='data/faces/faces/face_landmarks.csv',root_dir='data/faces/faces/',transform=transforms.Compose([Rescale(256),RandomCrop(224),ToTensor()]))dataloader = DataLoader(transformed_dataset, batch_size=4,shuffle=True, num_workers=4)# 輔助功能：顯示批次 def show_landmarks_batch(sample_batched):"""Show image with landmarks for a batch of samples."""images_batch, landmarks_batch = \sample_batched['image'], sample_batched['landmarks']batch_size = len(images_batch)im_size = images_batch.size(2)grid_border_size = 2grid = utils.make_grid(images_batch)plt.imshow(grid.numpy().transpose((1, 2, 0)))for i in range(batch_size):plt.scatter(landmarks_batch[i, :, 0].numpy() + i * im_size + (i + 1) * grid_border_size,landmarks_batch[i, :, 1].numpy() + grid_border_size,s=10, marker='.', c='r')plt.title('Batch from dataloader')for i_batch, sample_batched in enumerate(dataloader):print(i_batch, sample_batched['image'].size(),sample_batched['landmarks'].size())# 觀察第4批次并停止。if i_batch == 3:plt.figure()show_landmarks_batch(sample_batched)plt.axis('off')plt.ioff()plt.show()break

Pytorch小試牛刀

""" 使用NumPy，手動實現網絡的 前向和反向傳播，來擬合隨機數據 """import numpy as np# N是批量大小; D_in是輸入維度; # 49/5000 H是隱藏的維度; D_out是輸出維度。 N, D_in, H, D_out = 64, 1000, 100, 10# 創建隨機輸入和輸出數據 x = np.random.randn(N, D_in) y = np.random.randn(N, D_out)# 隨機初始化權重 w1 = np.random.randn(D_in, H) w2 = np.random.randn(H, D_out)learning_rate = 1e-6 for t in range(500):# 前向傳遞：計算預測值yh = x.dot(w1)h_relu = np.maximum(h, 0)y_pred = h_relu.dot(w2)# 計算和打印損失lossloss = np.square(y_pred - y).sum()print(t, loss)# 反向傳播，計算w1和w2對loss的梯度grad_y_pred = 2.0 * (y_pred - y)grad_w2 = h_relu.T.dot(grad_y_pred)grad_h_relu = grad_y_pred.dot(w2.T)grad_h = grad_h_relu.copy()grad_h[h < 0] = 0grad_w1 = x.T.dot(grad_h)# 更新權重w1 -= learning_rate * grad_w1w2 -= learning_rate * grad_w2""" 使用PyTorch的tensor，手動在網絡中實現前向傳播和反向傳播 """import torchdtype = torch.float device = torch.device("cpu") # device = torch.device（“cuda：0”）＃取消注釋以在GPU上運行# N是批量大小; D_in是輸入維度; # H是隱藏的維度; D_out是輸出維度。 N, D_in, H, D_out = 64, 1000, 100, 10#創建隨機輸入和輸出數據 x = torch.randn(N, D_in, device=device, dtype=dtype) y = torch.randn(N, D_out, device=device, dtype=dtype)# 隨機初始化權重 w1 = torch.randn(D_in, H, device=device, dtype=dtype) w2 = torch.randn(H, D_out, device=device, dtype=dtype)learning_rate = 1e-6 for t in range(500):# 前向傳遞：計算預測yh = x.mm(w1)h_relu = h.clamp(min=0)y_pred = h_relu.mm(w2) #(tensor).mm == (numpy).dot# 計算和打印損失loss = (y_pred - y).pow(2).sum().item()print(t, loss)# Backprop計算w1和w2相對于損耗的梯度grad_y_pred = 2.0 * (y_pred - y)grad_w2 = h_relu.t().mm(grad_y_pred)grad_h_relu = grad_y_pred.mm(w2.t())grad_h = grad_h_relu.clone()grad_h[h < 0] = 0grad_w1 = x.t().mm(grad_h)# 使用梯度下降更新權重w1 -= learning_rate * grad_w1w2 -= learning_rate * grad_w2""" 使用PyTorch的Tensors和autograd來實現我們的兩層的神經網絡 """import torchdtype = torch.float device = torch.device("cpu") # device = torch.device（“cuda：0”）＃取消注釋以在GPU上運行# N是批量大小; D_in是輸入維度; # H是隱藏的維度; D_out是輸出維度。 N, D_in, H, D_out = 64, 1000, 100, 10# 創建隨機Tensors以保持輸入和輸出。 # 設置requires_grad = False表示我們不需要計算漸變 # 在向后傳球期間對于這些Tensors。 x = torch.randn(N, D_in, device=device, dtype=dtype) y = torch.randn(N, D_out, device=device, dtype=dtype)# 為權重創建隨機Tensors。 # 設置requires_grad = True表示我們想要計算漸變 # 在向后傳球期間尊重這些張貼。 w1 = torch.randn(D_in, H, device=device, dtype=dtype, requires_grad=True) w2 = torch.randn(H, D_out, device=device, dtype=dtype, requires_grad=True)learning_rate = 1e-6 for t in range(500):# 前向傳播：使用tensors上的操作計算預測值y;# 由于w1和w2有requires_grad=True，涉及這些張量的操作將讓PyTorch構建計算圖，# 從而允許自動計算梯度。由于我們不再手工實現反向傳播，所以不需要保留中間值的引用。y_pred = x.mm(w1).clamp(min=0).mm(w2)# 使用Tensors上的操作計算和打印丟失。# loss是一個形狀為()的張量# loss.item() 得到這個張量對應的python數值loss = (y_pred - y).pow(2).sum()print(t, loss.item())# 使用autograd計算反向傳播。這個調用將計算loss對所有requires_grad=True的tensor的梯度。# 這次調用后，w1.grad和w2.grad將分別是loss對w1和w2的梯度張量。loss.backward()# 使用梯度下降更新權重。對于這一步，我們只想對w1和w2的值進行原地改變；不想為更新階段構建計算圖，# 所以我們使用torch.no_grad()上下文管理器防止PyTorch為更新構建計算圖with torch.no_grad():w1 -= learning_rate * w1.gradw2 -= learning_rate * w2.grad# 反向傳播后手動將梯度設置為零w1.grad.zero_()w2.grad.zero_()""" 自定義nn模塊: 自定義Module的子類構建兩層網絡： """import torchclass TwoLayerNet(torch.nn.Module):def __init__(self, D_in, H, D_out):"""在構造函數中，我們實例化了兩個nn.Linear模塊，并將它們作為成員變量。"""super(TwoLayerNet, self).__init__()self.linear1 = torch.nn.Linear(D_in, H)self.linear2 = torch.nn.Linear(H, D_out)def forward(self, x):"""在前向傳播的函數中，我們接收一個輸入的張量，也必須返回一個輸出張量。我們可以使用構造函數中定義的模塊以及張量上的任意的（可微分的）操作。"""h_relu = self.linear1(x).clamp(min=0)y_pred = self.linear2(h_relu)return y_pred# N是批大小； D_in 是輸入維度； # H 是隱藏層維度； D_out 是輸出維度 N, D_in, H, D_out = 64, 1000, 100, 10# 產生輸入和輸出的隨機張量 x = torch.randn(N, D_in) y = torch.randn(N, D_out)# 通過實例化上面定義的類來構建我們的模型。 model = TwoLayerNet(D_in, H, D_out)# 構造損失函數和優化器。 # SGD構造函數中對model.parameters()的調用， # 將包含模型的一部分，即兩個nn.Linear模塊的可學習參數。 loss_fn = torch.nn.MSELoss(reduction='sum') optimizer = torch.optim.SGD(model.parameters(), lr=1e-4) for t in range(500):# 前向傳播：通過向模型傳遞x計算預測值yy_pred = model(x)#計算并輸出lossloss = loss_fn(y_pred, y)print(t, loss.item())# 清零梯度，反向傳播，更新權重optimizer.zero_grad()loss.backward()optimizer.step()""" 控制流和權重共享 使用普通的Python流控制來實現循環，并且我們可以通過在定義轉發時多次重用同一個模塊來實現最內層之間的權重共享。 """import random import torchclass DynamicNet(torch.nn.Module):def __init__(self, D_in, H, D_out):"""在構造函數中，我們構造了三個nn.Linear實例，它們將在前向傳播時被使用。"""super(DynamicNet, self).__init__()self.input_linear = torch.nn.Linear(D_in, H)self.middle_linear = torch.nn.Linear(H, H)self.output_linear = torch.nn.Linear(H, D_out)def forward(self, x):"""對于模型的前向傳播，我們隨機選擇0、1、2、3，并重用了多次計算隱藏層的middle_linear模塊。由于每個前向傳播構建一個動態計算圖，我們可以在定義模型的前向傳播時使用常規Python控制流運算符，如循環或條件語句。在這里，我們還看到，在定義計算圖形時多次重用同一個模塊是完全安全的。這是Lua Torch的一大改進，因為Lua Torch中每個模塊只能使用一次。"""h_relu = self.input_linear(x).clamp(min=0)for _ in range(random.randint(0, 3)): #權重共享的實現h_relu = self.middle_linear(h_relu).clamp(min=0)y_pred = self.output_linear(h_relu)return y_pred# N是批大小；D是輸入維度 # H是隱藏層維度；D_out是輸出維度 N, D_in, H, D_out = 64, 1000, 100, 10# 產生輸入和輸出隨機張量 x = torch.randn(N, D_in) y = torch.randn(N, D_out)# 實例化上面定義的類來構造我們的模型 model = DynamicNet(D_in, H, D_out)# 構造我們的損失函數（loss function）和優化器（Optimizer）。 # 用平凡的隨機梯度下降訓練這個奇怪的模型是困難的，所以我們使用了momentum方法。 criterion = torch.nn.MSELoss(reduction='sum') optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) for t in range(500):# 前向傳播：通過向模型傳入x計算預測的y。y_pred = model(x)# 計算并打印損失loss = criterion(y_pred, y)print(t, loss.item())# 清零梯度，反向傳播，更新權重optimizer.zero_grad()loss.backward()optimizer.step()

PyTorch之遷移學習

from __future__ import print_function, division import torch import torch.nn as nn import torch.optim as optim from torch.optim import lr_scheduler import numpy as np import torchvision from torchvision import datasets, models, transforms import matplotlib.pyplot as plt import time import os import copyplt.ion() # interactive mode""" 加載數據 """#訓練集數據擴充和歸一化 #在驗證集上僅需要歸一化 data_transforms = {'train': transforms.Compose([transforms.RandomResizedCrop(224), #隨機裁剪一個area然后再resizetransforms.RandomHorizontalFlip(), #隨機水平翻轉transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),'val': transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224), #圖像裁剪transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]), }data_dir = 'data/hymenoptera_data' image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),data_transforms[x])for x in ['train', 'val']} dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,shuffle=True, num_workers=4)for x in ['train', 'val']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} class_names = image_datasets['train'].classesdevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")""" 可視化部分圖像數據 """def imshow(inp, title=None):"""Imshow for Tensor."""inp = inp.numpy().transpose((1, 2, 0))mean = np.array([0.485, 0.456, 0.406])std = np.array([0.229, 0.224, 0.225])inp = std * inp + meaninp = np.clip(inp, 0, 1)plt.imshow(inp)if title is not None:plt.title(title)plt.pause(1) # pause a bit so that plots are updated# 獲取一批訓練數據 inputs, classes = next(iter(dataloaders['train']))# 批量制作網格 out = torchvision.utils.make_grid(inputs)imshow(out, title=[class_names[x] for x in classes])""" 訓練模型 """def train_model(model, criterion, optimizer, scheduler, num_epochs=25):since = time.time()best_model_wts = copy.deepcopy(model.state_dict())best_acc = 0.0for epoch in range(num_epochs):print('Epoch {}/{}'.format(epoch, num_epochs - 1))print('-' * 10)# 每個epoch都有一個訓練和驗證階段for phase in ['train', 'val']:if phase == 'train':scheduler.step()model.train() # Set model to training modeelse:model.eval() # Set model to evaluate moderunning_loss = 0.0running_corrects = 0# 迭代數據.for inputs, labels in dataloaders[phase]:inputs = inputs.to(device)labels = labels.to(device)# 零參數梯度optimizer.zero_grad()# 前向# track history if only in trainwith torch.set_grad_enabled(phase == 'train'):outputs = model(inputs)_, preds = torch.max(outputs, 1)loss = criterion(outputs, labels)# 后向+僅在訓練階段進行優化if phase == 'train':loss.backward()optimizer.step()# 統計running_loss += loss.item() * inputs.size(0)running_corrects += torch.sum(preds == labels.data)epoch_loss = running_loss / dataset_sizes[phase]epoch_acc = running_corrects.double() / dataset_sizes[phase]print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))# 深度復制moif phase == 'val' and epoch_acc > best_acc:best_acc = epoch_accbest_model_wts = copy.deepcopy(model.state_dict())print()time_elapsed = time.time() - sinceprint('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))print('Best val Acc: {:4f}'.format(best_acc))# 加載最佳模型權重model.load_state_dict(best_model_wts)return model""" 可視化模型的預測結果 """ #一個通用的展示少量預測圖片的函數 def visualize_model(model, num_images=6):was_training = model.trainingmodel.eval()images_so_far = 0fig = plt.figure()with torch.no_grad():for i, (inputs, labels) in enumerate(dataloaders['val']):inputs = inputs.to(device)labels = labels.to(device)outputs = model(inputs)_, preds = torch.max(outputs, 1)for j in range(inputs.size()[0]):images_so_far += 1ax = plt.subplot(num_images//2, 2, images_so_far)ax.axis('off')ax.set_title('predicted: {}'.format(class_names[preds[j]]))imshow(inputs.cpu().data[j])if images_so_far == num_images:model.train(mode=was_training)returnmodel.train(mode=was_training)""" 場景1：微調ConvNet 加載預訓練模型并重置最終完全連接的圖層。 """model_ft = models.resnet18(pretrained=True) #直接加載pre-train模型中預先訓練好的參數 num_ftrs = model_ft.fc.in_features #提取fc層中固定的參數 model_ft.fc = nn.Linear(num_ftrs, 2) #resnet網絡最后一層分類層fc是對1000種類型進行劃分，對于自己的數據集，如果只有2類model_ft = model_ft.to(device) #模型加載到指定設備上。(device = 'cpu')criterion = nn.CrossEntropyLoss()# 觀察所有參數都正在優化 optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)# 每7個epochs衰減LR通過設置gamma=0.1 exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)#訓練和評估模型 model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,num_epochs=25)#模型評估效果可視化 visualize_model(model_ft)""" 場景2：ConvNet作為固定特征提取器 在這里需要凍結除最后一層之外的所有網絡。通過設置requires_grad == False backward()來凍結參數，這樣在反向傳播backward()的時候他們的梯度就不會被計算。 """model_conv = torchvision.models.resnet18(pretrained=True) for param in model_conv.parameters():param.requires_grad = False#默認情況下，新構造模塊的參數需要_grad=True num_ftrs = model_conv.fc.in_features model_conv.fc = nn.Linear(num_ftrs, 2)model_conv = model_conv.to(device)criterion = nn.CrossEntropyLoss()# 觀察到只有最后一層的參數被優化為 # 與以前相反 optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)# LR每7個周期衰減0.1倍 exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)#訓練和評估 model_conv = train_model(model_conv, criterion, optimizer_conv,exp_lr_scheduler, num_epochs=25) #模型評估效果可視化 visualize_model(model_conv)plt.ioff() plt.show()

保存和加載模型

import torch import torch.nn as nn import torch.functional as F import torch.utils.data as Data import torchvision import torch.optim as optim""" 什么是狀態字典：state_dict? """class TheModelClass(nn.Module):def __init__(self):super(TheModelClass, self).__init__()self.conv1 = nn.Conv2d(3, 6, 5)self.pool = nn.MaxPool2d(2, 2)self.conv2 = nn.Conv2d(6, 16, 5)self.fc1 = nn.Linear(16 * 5 * 5, 120)self.fc2 = nn.Linear(120, 84)self.fc3 = nn.Linear(84, 10)def forward(self, x):x = self.pool(F.relu(self.conv1(x)))x = self.pool(F.relu(self.conv2(x)))x = x.view(-1, 16 * 5 * 5)x = F.relu(self.fc1(x))x = F.relu(self.fc2(x))x = self.fc3(x)return x# 初始化模型 model = TheModelClass()# 初始化優化器 optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)# 打印模型的狀態字典 print("Model's state_dict:") for param_tensor in model.state_dict():print(param_tensor, "\t", model.state_dict()[param_tensor].size())# 打印優化器的狀態字典 print("Optimizer's state_dict:") for var_name in optimizer.state_dict():print(var_name, "\t", optimizer.state_dict()[var_name])# """ # 保存和加載推理模型 # """ # # #2.1 保存/加載state_dict（推薦使用） # # #保存 # torch.save(model.state_dict(), PATH) #(path是存儲路徑) # # #加載 # model = TheModelClass(*args, **kwargs) # model.load_state_dict(torch.load(PATH)) # model.eval() # #在 PyTorch 中最常見的模型保存使‘.pt’或者是‘.pth’作為模型文件擴展名。 # #請記住，在運行推理之前，務必調用model.eval()去設置 dropout 和 batch normalization 層為評估模式。如果不這么做，可能導致模型推斷結果不一致。 # # #2.2 保存/加載完整模型 # # #保存 # torch.save(model, PATH) # # #加載 # # # 模型類必須在此之前被定義 # model = torch.load(PATH) # model.eval() # # """ # 保存和加載 Checkpoint 用于推理/繼續訓練 # """ # # #保存 # torch.save({ # 'epoch': epoch, # 'model_state_dict': model.state_dict(), # 'optimizer_state_dict': optimizer.state_dict(), # 'loss': loss, # ... # }, PATH) # # #加載 # # model = TheModelClass(*args, **kwargs) # optimizer = TheOptimizerClass(*args, **kwargs) # # checkpoint = torch.load(PATH) # model.load_state_dict(checkpoint['model_state_dict']) # optimizer.load_state_dict(checkpoint['optimizer_state_dict']) # epoch = checkpoint['epoch'] # loss = checkpoint['loss'] # # model.eval() # # - or - # model.train() # #PyTorch 中常見的保存checkpoint 是使用 .tar 文件擴展名。 # # """ # 在一個文件中保存多個模型 # """ # # #保存 # torch.save({ # 'modelA_state_dict': modelA.state_dict(), # 'modelB_state_dict': modelB.state_dict(), # 'optimizerA_state_dict': optimizerA.state_dict(), # 'optimizerB_state_dict': optimizerB.state_dict(), # ... # }, PATH) # # #加載 # modelA = TheModelAClass(*args, **kwargs) # modelB = TheModelBClass(*args, **kwargs) # optimizerA = TheOptimizerAClass(*args, **kwargs) # optimizerB = TheOptimizerBClass(*args, **kwargs) # # checkpoint = torch.load(PATH) # modelA.load_state_dict(checkpoint['modelA_state_dict']) # modelB.load_state_dict(checkpoint['modelB_state_dict']) # optimizerA.load_state_dict(checkpoint['optimizerA_state_dict']) # optimizerB.load_state_dict(checkpoint['optimizerB_state_dict']) # # modelA.eval() # modelB.eval() # # - or - # modelA.train() # modelB.train() # #PyTorch 中常見的保存 checkpoint 是使用 .tar 文件擴展名。 # #請記住在運行推理之前，務必調用model.eval()去設置 dropout 和 batch normalization 為評估。如果不這樣做，有可能得到不一致的推斷結果。 # # 如果你想要恢復訓練，請調用model.train()以確保這些層處于訓練模式。 # # """ # 使用在不同模型參數下的熱啟動模式 # """ # # #保存 # torch.save(modelA.state_dict(), PATH) # # #加載 # modelB = TheModelBClass(*args, **kwargs) # modelB.load_state_dict(torch.load(PATH), strict=False) # # """ # 保存到 CPU、加載到 CPU # """ # # #保存 # torch.save(model.state_dict(), PATH) # # #加載 # device = torch.device('cpu') # model = TheModelClass(*args, **kwargs) # model.load_state_dict(torch.load(PATH, map_location=device)) # # """ # 保存到 GPU、加載到 GPU # """ # # #保存 # torch.save(model.state_dict(), PATH) # # #加載 # device = torch.device("cuda") # model = TheModelClass(*args, **kwargs) # model.load_state_dict(torch.load(PATH, map_location="cuda:0")) # Choose whatever GPU device number you want # model.to(device) # # 確保在你提供給模型的任何輸入張量上調用input = input.to(device) # # """ # 保存 torch.nn.DataParallel 模型 # """ # # #保存： # torch.save(model.module.state_dict(), PATH) 創作挑戰賽新人創作獎勵來咯，堅持創作打卡瓜分現金大獎

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