I cannot reproduce the issue and I guess you might be calling reshape or view on the input image in order to move the channel dimension, which would interleave the image. If that’s the case, use permute instead.
For an input of

and this code snippet:
class vgg16(torch.nn.Module):
    def __init__(self):
        super(vgg16, self).__init__()
        vgg_pretrained_features = models.vgg16().features
        self.layerss = torch.nn.Sequential()
        for x in range(30):
            self.layerss.add_module(str(x), vgg_pretrained_features[x])
        self.layerss.eval()
    def forward(self, x):
      output=[]
      for i,layer in enumerate( self.layerss):
        # print (i)
        x=layer(x)
        output.append(x)
      return output

img = PIL.Image.open('./drums.png')
x = TF.resize(img, (224, 224)) 
x = TF.to_tensor(x)[None, :3, :, :]

model = vgg16()
output = model(x)

import matplotlib.pyplot as plt
plt.imshow(output[0][0][0].detach())

I get: