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用于图像分类的 RNN 是否仅适用于灰度图像？以下程序适用于灰度图像分类。

如果使用 RGB 图像，我有这个错误：

预期输入 batch_size (18) 与目标 batch_size (6) 匹配

在这条线上loss = criterion(outputs, labels)。

我的火车、有效和测试数据加载如下。

input_size  = 300
inputH = 300
inputW = 300

#Data transform (normalization & data augmentation)
stats = ((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
train_resize_tfms = tt.Compose([tt.Resize((inputH, inputW), interpolation=2),
                         tt.ToTensor(),
                         tt.Normalize(*stats)])

train_tfms = tt.Compose([tt.Resize((inputH, inputW), interpolation=2),
                         tt.RandomHorizontalFlip(),                                                  
                         tt.ToTensor(),
                         tt.Normalize(*stats)])
valid_tfms = tt.Compose([tt.Resize((inputH, inputW), interpolation=2),
                         tt.ToTensor(), 
                         tt.Normalize(*stats)])
test_tfms = tt.Compose([tt.Resize((inputH, inputW), interpolation=2),
                        tt.ToTensor(), 
                        tt.Normalize(*stats)])

#Create dataset
train_ds = ImageFolder('./data/train', train_tfms)
valid_ds = ImageFolder('./data/valid', valid_tfms)
test_ds = ImageFolder('./data/test', test_tfms)

from torch.utils.data.dataloader import DataLoader
batch_size = 6

#Training data loader
train_dl = DataLoader(train_ds, batch_size, shuffle = True, num_workers = 8, pin_memory=True)
#Validation data loader
valid_dl = DataLoader(valid_ds, batch_size, shuffle = True, num_workers = 8, pin_memory=True)
#Test data loader
test_dl = DataLoader(test_ds, 1, shuffle = False, num_workers = 1, pin_memory=True)

我的模型如下。

num_steps = 300
hidden_size = 256 #size of hidden layers
num_classes = 5
num_epochs = 20
learning_rate = 0.001
# Fully connected neural network with one hidden layer
num_layers = 2 # 2 RNN layers are stacked  
class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(RNN, self).__init__()
        self.num_layers = num_layers
        self.hidden_size = hidden_size
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True, dropout=0.2)#batch must have first dimension
        #our inpyt needs to have shape
        #x -> (batch_size, seq, input_size)
        self.fc = nn.Linear(hidden_size, num_classes)#this fc is after RNN. So needs the last hidden size of RNN

    def forward(self, x):
        #according to ducumentation of RNN in pytorch
        #rnn needs input, h_0 for inputs at RNN (h_0 is initial hidden state)

        #the following one is initial hidden layer
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)#first one is number of layers and second one is batch size
        #output has two outputs. The first tensor contains the output features of the hidden last layer for all time steps
        #the second one is hidden state f
        out, _ = self.rnn(x, h0)
        #output has batch_size, num_steps, hidden size
        #we need to decode hidden state only the last time step
        #out (N, 30, 128)
        #Since we need only the last time step
        #Out (N, 128)
        out = out[:, -1, :] #-1 for last time step, take all for N and 128
        out = self.fc(out)
        return out


stacked_rnn_model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()#cross entropy has softmax at output
#optimizer = torch.optim.Adam(stacked_rnn_model.parameters(), lr=learning_rate) #optimizer used gradient optimization using Adam 
optimizer = torch.optim.SGD(stacked_rnn_model.parameters(), lr=learning_rate)
# Train the model
n_total_steps = len(train_dl)
    for epoch in range(num_epochs):
        t_losses=[]
        for i, (images, labels) in enumerate(train_dl):  
            # origin shape: [6, 3, 300, 300]
            # resized: [6, 300, 300]
            images = images.reshape(-1, num_steps, input_size).to(device)
            print('images shape')
            print(images.shape)
            labels = labels.to(device)
            
            # Forward pass
            outputs = stacked_rnn_model(images)
            print('outputs shape')
            print(outputs.shape)
            loss = criterion(outputs, labels)
            t_losses.append(loss)
            # Backward and optimize
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

打印图像和输出形状是

images shape
torch.Size([18, 300, 300])
outputs shape
torch.Size([18, 5])

错误在哪里？

4

1 回答 1

1

Tl;dr：您正在展平前两个轴，即batch和channels。

我不确定您是否采用了正确的方法，但我会写关于该层的内容。

无论如何，让我们看看您面临的问题。您有一个生成的数据加载器(6, 3, 300, 300)，即 6 个三通道300x300图像的批次。从外观上看，您希望将每个批处理元素重塑(3, 300, 300)为(step_size=300, -1).

但是，您不应该影响第一个轴，而不是使用images.reshape(-1, num_steps, input_size). 这将在使用单通道图像时产生预期的效果，因为dim=1它不是“通道轴”。在您的情况下，您有3个通道，因此，生成的形状是：(6*3*300*300//300//300, 300, 300)这是(18, 300, 300)因为num_steps=300和input_size=300。结果，您剩下18 个批处理元素而不是6 个。

相反，您想要的是重塑(batch_size, num_steps, -1). 留下seq_length可变大小的最后一个轴（又名）。这将产生一个形状(6, 300, 900)。

这是一个更正和简化的片段：

batch_size = 6
channels = 3
inputH, inputW = 300, 300
train_ds = TensorDataset(torch.rand(100, 3, inputH, inputW), torch.rand(100, 5))
train_dl = DataLoader(train_ds, batch_size)

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(RNN, self).__init__()
        # (batch_size, seq, input_size)
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
        # (batch_size, hidden_size)
        self.fc = nn.Linear(hidden_size, num_classes)
        # (batch_size, num_classes)

    def forward(self, x):
        out, _ = self.rnn(x)
        out = out[:, -1, :]
        out = self.fc(out)
        return out

num_steps = 300
input_size = inputH*inputW*channels//num_steps
hidden_size = 256
num_classes = 5
num_layers = 2

rnn = RNN(input_size, hidden_size, num_layers, num_classes)
for x, y in train_dl:
    print(x.shape, y.shape)
    images = images.reshape(batch_size, num_steps, -1)
    print(images.shape)
    outputs = rnn(images)
    print(outputs.shape)
    break

正如我在一开始所说的那样，我对这种方法有点谨慎，因为你实际上是在以300 个300x300扁平向量序列的形式为你的 RNN 提供一个 RGB图像......我不能说这是否有意义和训练条款以及模型是否能够从中学习。我可能是错的！

于 2020-12-26T11:05:02.293 回答