size of input tensor and input format are different. tensor shape: (64, 3, 32, 32), input_format: CHW 报错应该怎么改
时间: 2024-10-06 11:04:19 浏览: 20
当你遇到输入张量大小(tensor shape)(64, 3, 32, 32) 和输入格式(input format) 为 CHW(Channel First, Height, Width)的情况下,如果某个深度学习模型或者库期望的输入格式是不一样的,比如可能是NHWC(Channel Last, Height, Width),你需要调整你的数据预处理步骤。
首先,确认你的模型或者库期望的确切输入格式。通常,对于卷积神经网络(CNN),CHW 是常见的顺序,但在某些框架如 TensorFlow 或 PyTorch 中,默认设置可能会是 NHWC。
如果你正在使用 PyTorch,你可以这样做:
```python
import torch
# 假设data是一个Tensor in CHW格式
data = data.permute(0, 2, 3, 1) # 将通道从第一位移动到最后一位
```
如果你使用 TensorFlow,则需要先转换到 NHWC:
```python
import tensorflow as tf
data = tf.transpose(data, perm=[0, 3, 1, 2]) # Transpose the channels to the last position
```
一旦数据转换成正确的格式,就可以将它传入模型了。如果还是报错,检查一下是否所有维度都按照新格式排列,并且输入的张量大小和模型预期一致。
相关问题
AssertionError: size of input tensor and input format are different. tensor shape: (1, 8, 32, 56, 56), input_format: CHW
This error occurs when the size of the input tensor and the expected input format do not match. In this case, the input tensor has a shape of (1, 8, 32, 56, 56), which suggests that it has 8 channels and a spatial resolution of 56x56 pixels. However, the expected input format is CHW, which stands for channel-first format. It means that the channel dimension should come first, followed by the height and width dimensions.
To fix this error, you need to rearrange the dimensions of the input tensor to match the expected format. You can use the transpose function to swap the dimensions as follows:
```
input_tensor = input_tensor.transpose(0, 1, 2, 4, 3)
```
This code will swap the last two dimensions of the input tensor, which correspond to the height and width, respectively. After this, the input tensor should have a shape of (1, 8, 56, 56, 32), which matches the expected input format.
修改import torch import torchvision.models as models vgg16_model = models.vgg16(pretrained=True) import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as transforms from PIL import Image # 加载图片 img_path = "pic.jpg" img = Image.open(img_path) # 定义预处理函数 preprocess = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # 预处理图片,并添加一个维度(batch_size) img_tensor = preprocess(img).unsqueeze(0) # 提取特征 features = vgg16_model.features(img_tensor) import numpy as np import matplotlib.pyplot as plt def deconv_visualization(model, features, layer_idx, iterations=30, lr=1, figsize=(10, 10)): # 获取指定层的输出特征 output = features[layer_idx] # 定义随机输入张量,并启用梯度计算 #input_tensor = torch.randn(output.shape, requires_grad=True) input_tensor = torch.randn(1, 3, output.shape[2], output.shape[3], requires_grad=True) # 定义优化器 optimizer = torch.optim.Adam([input_tensor], lr=lr) for i in range(iterations): # 将随机张量输入到网络中,得到对应的输出 model.zero_grad() #x = model.features(input_tensor) x = model.features:layer_idx # 计算输出与目标特征之间的距离,并进行反向传播 loss = F.mse_loss(x[layer_idx], output) loss.backward() # 更新输入张量 optimizer.step() # 反归一化 input_tensor = (input_tensor - input_tensor.min()) / (input_tensor.max() - input_tensor.min()) # 将张量转化为numpy数组 img = input_tensor.squeeze(0).detach().numpy().transpose((1, 2, 0)) # 绘制图像 plt.figure(figsize=figsize) plt.imshow(img) plt.axis("off") plt.show() # 可视化第一层特征 deconv_visualization(vgg16_model, features, 0)使其不产生报错IndexError: tuple index out of range
修改后的代码如下:
```
import torch
import torchvision.models as models
vgg16_model = models.vgg16(pretrained=True)
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from PIL import Image
# 加载图片
img_path = "pic.jpg"
img = Image.open(img_path)
# 定义预处理函数
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# 预处理图片,并添加一个维度(batch_size)
img_tensor = preprocess(img).unsqueeze(0)
# 提取特征
features = vgg16_model.features(img_tensor)
import numpy as np
import matplotlib.pyplot as plt
def deconv_visualization(model, features, layer_idx, iterations=30, lr=1, figsize=(10, 10)):
# 获取指定层的输出特征
output = features[layer_idx]
# 定义随机输入张量,并启用梯度计算
input_tensor = torch.randn(1, 3, output.shape[2], output.shape[3], requires_grad=True)
# 定义优化器
optimizer = torch.optim.Adam([input_tensor], lr=lr)
for i in range(iterations):
# 将随机张量输入到网络中,得到对应的输出
model.zero_grad()
x = model.features[:layer_idx+1](input_tensor)
# 计算输出与目标特征之间的距离,并进行反向传播
loss = F.mse_loss(x, output)
loss.backward()
# 更新输入张量
optimizer.step()
# 反归一化
input_tensor = (input_tensor - input_tensor.min()) / (input_tensor.max() - input_tensor.min())
# 将张量转化为numpy数组
img = input_tensor.squeeze(0).detach().numpy().transpose((1, 2, 0))
# 绘制图像
plt.figure(figsize=figsize)
plt.imshow(img)
plt.axis("off")
plt.show()
# 可视化第一层特征
deconv_visualization(vgg16_model, features, 0)
```
改动的主要是在定义随机输入张量后,将其输入到网络中获取对应的输出,同时在获取输出时,需要指定截取到哪一层。然后计算输出与目标特征之间的距离,并进行反向传播,更新输入张量。最后将张量转化为numpy数组,绘制图像。
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