torchvision

问题 I am trying to understand how torchvision interacts with mathplotlib to produce a grid of images. It's easy to generate images and display them iteratively: import torch import torchvision import matplotlib.pyplot as plt w = torch.randn(10,3,640,640) for i in range (0,10): z = w[i] plt.imshow(z.permute(1,2,0)) plt.show() However, displaying these images in a grid does not seem to be as straightforward. w = torch.randn(10,3,640,640) grid = torchvision.utils.make_grid(w, nrow=5) plt.imshow(grid

问题 This line works fine self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) I introduced ResNet18 self.conv = ResNet18() **ResNet Class** '''ResNet in PyTorch. For Pre-activation ResNet, see 'preact_resnet.py'. Reference: [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Deep Residual Learning for Image Recognition. arXiv:1512.03385 ''' import torch import torch.nn as nn import torch.nn.functional as F class BasicBlock(nn.Module): expansion = 1 def __init__(self, in

问题 This line works fine self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) I introduced ResNet18 self.conv = ResNet18() **ResNet Class** '''ResNet in PyTorch. For Pre-activation ResNet, see 'preact_resnet.py'. Reference: [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Deep Residual Learning for Image Recognition. arXiv:1512.03385 ''' import torch import torch.nn as nn import torch.nn.functional as F class BasicBlock(nn.Module): expansion = 1 def __init__(self, in

问题 I wrote a image vgg classification model with pytorch's pretrained vgg16 model. import matplotlib.pyplot as plt import numpy as np import torch from PIL import Image import urllib from skimage.transform import resize from skimage import io import yaml # Downloading imagenet 1000 classes list file = urllib. request. urlopen("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt") classes = '' for f in file:

问题 I wrote a image vgg classification model with pytorch's pretrained vgg16 model. import matplotlib.pyplot as plt import numpy as np import torch from PIL import Image import urllib from skimage.transform import resize from skimage import io import yaml # Downloading imagenet 1000 classes list file = urllib. request. urlopen("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt") classes = '' for f in file:

问题 I am a little bit confused about the data augmentation performed in PyTorch. Because we are dealing with segmentation tasks, we need data and mask for the same data augmentation, but some of them are random, such as random rotation. Keras provides a random seed guarantee that data and mask do the same operation, as shown in the following code: data_gen_args = dict(featurewise_center=True, featurewise_std_normalization=True, rotation_range=25, horizontal_flip=True, vertical_flip=True) image

问题 I’m currently using Pipenv to maintain the Python packages used in a specific project. Most of the downloads I’ve tried so far have worked as intended; that is, I enter pipenv install [package] and it installs the package into the virtual environment, then records the package information into both the Pipfile and Pipfile.lock. However, I’m running into some problems installing PyTorch. I’ve tried running pipenv install torch , but every time the locking step fails. Instead, I’ve tried forcing

问题 I want to create an image classifier using transfer learning on a model already trained on ImageNet. How do I replace the final layer of a torchvision.models ImageNet classifier with my own custom classifier? 回答1: Get a pre-trained ImageNet model ( resnet152 has the best accuracy): from torchvision import models # https://pytorch.org/docs/stable/torchvision/models.html model = models.resnet152(pretrained=True) Print out its structure so we can compare to the final state: print(model) Remove

问题 I want to create an image classifier using transfer learning on a model already trained on ImageNet. How do I replace the final layer of a torchvision.models ImageNet classifier with my own custom classifier? 回答1: Get a pre-trained ImageNet model ( resnet152 has the best accuracy): from torchvision import models # https://pytorch.org/docs/stable/torchvision/models.html model = models.resnet152(pretrained=True) Print out its structure so we can compare to the final state: print(model) Remove

问题 I am in an university, and all the file system are in a remote system, wherever I log in with my account, I could aways access my home directory. even though I log into the GPU servers through SSH command. This is the condition where I employ the GPU servers to read data. Currently, I use the PyTorch to train ResNet from scratch on ImageNet, my codes only use all the GPUs in the same computer, I found that the "torchvision.datasets.ImageFolder" will take almost two hours. Would you please