预期输入 batch_size (18) 以匹配目标 batch_size (6)
Expected input batch_size (18) to match target batch_size (6)
图像分类的RNN只能用于灰度图像吗？
以下程序适用于灰度图像分类。
如果使用RGB图像，我会出现这个错误：
Expected input batch_size (18) to match target batch_size (6)
在这一行 loss = criterion(outputs, labels).
我的训练、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为
images shape
torch.Size([18, 300, 300])
outputs shape
torch.Size([18, 5])
哪里错了？
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，但我会写下该层。
无论如何，让我们看看您面临的问题。您有一个生成 (6, 3, 300, 300) 的数据加载器，即 6 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 (3, 300, 300) 重塑为 (step_size=300, -1).
然而，您没有影响第一个轴 - 您不应该使用 images.reshape(-1, num_steps, input_size)。这将在处理单通道图像时产生预期的效果，因为 dim=1 不是“通道轴”。在您的情况下，您有 3 个通道，因此，结果形状为：(6*3*300*300//300//300, 300, 300)，即 (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.
相反，您想要的是使用 (batch_size, num_steps, -1) 重塑。留下可变大小的最后一个轴 (a.k.a. seq_length)。这将导致形状 (6, 300, 900).
这是经过更正和简化的片段：
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
正如我在开始时所说的那样，我对这种方法有点担心，因为您实际上是以 300[=51 的序列形式为 RNN 提供 RGB 300x300 图像=] 扁平向量......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！
图像分类的RNN只能用于灰度图像吗？ 以下程序适用于灰度图像分类。
如果使用RGB图像，我会出现这个错误：
Expected input batch_size (18) to match target batch_size (6)
在这一行 loss = criterion(outputs, labels).
我的训练、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为
images shape
torch.Size([18, 300, 300])
outputs shape
torch.Size([18, 5])
哪里错了？
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，但我会写下该层。
无论如何，让我们看看您面临的问题。您有一个生成 (6, 3, 300, 300) 的数据加载器，即 6 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 (3, 300, 300) 重塑为 (step_size=300, -1).
然而，您没有影响第一个轴 - 您不应该使用 images.reshape(-1, num_steps, input_size)。这将在处理单通道图像时产生预期的效果，因为 dim=1 不是“通道轴”。在您的情况下，您有 3 个通道，因此，结果形状为：(6*3*300*300//300//300, 300, 300)，即 (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.
相反，您想要的是使用 (batch_size, num_steps, -1) 重塑。留下可变大小的最后一个轴 (a.k.a. seq_length)。这将导致形状 (6, 300, 900).
这是经过更正和简化的片段：
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
正如我在开始时所说的那样，我对这种方法有点担心，因为您实际上是以 300[=51 的序列形式为 RNN 提供 RGB 300x300 图像=] 扁平向量......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！