AttributeError：模块 'utils' 没有属性 'read'

Question

我正在尝试使用 PyTorch 训练模型，但出现此错误 AttributeError: module 'utils' has no attribute 'read'

在主项目中，我有 utils.pyx 文件，并且在此特定行中出现错误 for i, data in enumerate(trainloader):

这是有问题的代码（如果您对此问题有任何想法，请告诉我谢谢）

import os
import copy
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import numpy as np
import matplotlib.pyplot as plt
#from dataset import NTUSkeletonDataset
from torch.utils.data import Dataset, DataLoader
#import GAN
from torch.autograd import Variable
import matplotlib.pyplot as plt
import time

# Root directory for dataset
dataroot = "Data/nturgb+d_skeletons"

# Batch size during training
batch_size = 5

# Size of z latent vector (i.e. size of generator input)
latent_dim = 20

# Number of training epochs
num_epochs = 200

# Learning rate for optimizers
lrG = 0.00005
lrD = 0.00005

clip_value = 0.01
n_critic = 20

trainset = NTUSkeletonDataset(root_dir=dataroot, pinpoint=10)
trainloader = DataLoader(trainset, batch_size=batch_size,
                         shuffle=True, num_workers=4)

cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if cuda else "cpu")
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor

generator = Gen0(latent_dim).to(device)
discriminator = Dis0().to(device)

optimizer_G = torch.optim.RMSprop(generator.parameters(), lr=lrG)
optimizer_D = torch.optim.RMSprop(discriminator.parameters(), lr=lrD)

epoch_loss = np.zeros((num_epochs, 3, len(trainloader)//n_critic+1))

for epoch in range(num_epochs):
    j = 0
    print("Boucle 1")
    epoch_start = time.time()
    for i, data in enumerate(trainloader):
        print("something")
        size = (-1, data.size(-1))
        data = data.reshape(size)
        print
        optimizer_D.zero_grad()

        real_skeleton = Variable(data.type(Tensor)).to(device)

        critic_real = -torch.mean(discriminator(real_skeleton))
        # critic_real.backward()

        # sample noise as generator input
        z = torch.randn(real_skeleton.size(0), latent_dim).to(device)

        # Generate a batch of fake skeleton
        fake_skeleton = generator(z).detach()

        critic_fake = torch.mean(discriminator(fake_skeleton))
        # critic_fake.backward()

        loss_D = critic_real + critic_fake
        loss_D.backward()

        optimizer_D.step()

        # clip weights of discriminator
        for p in discriminator.parameters():
            p.data.clamp_(-clip_value, clip_value)

        # Train the generator every n_critic iterations:
        if i % n_critic == n_critic - 1:
            optimizer_G.zero_grad()

            # Generate a batch of
            gen_skeleton = generator(z)
            # adversarial loss
            loss_G = -torch.mean(discriminator(gen_skeleton))

            loss_G.backward()
            optimizer_G.step()

            for k, l in enumerate((loss_G, critic_real, critic_fake)):
                epoch_loss[epoch, k, j] = l.item()
            j += 1

    epoch_end = time.time()
    print('[%d] time eplased: %.3f' % (epoch, epoch_end-epoch_start))
    for k, l in enumerate(('G', 'critic real', 'critic fake')):
        print('\t', l, epoch_loss[epoch, k].mean(axis=-1))

    if epoch % 20 == 19:
        m = copy.deepcopy(generator.state_dict())
        torch.save(m, 'gen0_%d.pt' % epoch)

np.save('gen0_epoch_loss.npy', epoch_loss)

完整的错误是：

utils.pyx

#cython: language_level=3
import numpy as np
cimport numpy as np
import os
cimport cython

def read(fname, max_bodies=2):
    with open(fname, 'r') as f:
        num_frames = int(f.readline())
        keypoints = np.zeros((2, num_frames, 25, 2), dtype=np.float64)

        for t in range(num_frames):
            num_bodies = int(f.readline())

            for m in range(num_bodies):
                f.readline() # Body info, skip
                num_keypoints = int(f.readline())
                for k in range(num_keypoints): # Read joints
                    x, y = f.readline().split()[:2]
                    if m >= max_bodies:
                        continue

                    keypoints[m, t, k, 0] = x
                    keypoints[m, t, k, 1] = y
    return keypoints


@cython.boundscheck(False)
@cython.wraparound(False)
@cython.initializedcheck(False)
@cython.cdivision(True)
cpdef void ins_frames(double[:,:,:,::1] buf, double[:,:,:,::1] data, int diff):
    cdef int n0 = data.shape[1]
    cdef int i = 0
    cdef int j = 0
    cdef int k = 0
    cdef int l = 0
    cdef double v = 0
    cdef int count = 0

    indices = np.linspace(1, n0, num=diff, endpoint=False, dtype=np.int32) \
              + np.arange(diff, dtype=np.int32)
    cdef np.ndarray[np.int32_t, ndim=1] to_ins = indices

    for i in range(to_ins.shape[0]):
        buf[0, to_ins[i], 0, 0] = -10000001 # Marker

    recur = 0

    for i in range(buf.shape[1]):
        if buf[0, i, 0, 0] == -10000001:
            recur += 1
            continue

        for j in range(2):
            for k in range(25):
                for l in range(2):
                    v = data[j, count, k, l]
                    buf[j, i, k, l] = v # Copy
                    if recur != 0: # Calculate the mean
                        buf[j, i-1, k, l] = (v + data[j, count-1, k, l]) * 0.5

        if recur > 0: recur -= 1 # Reset

        count += 1

为了更好地了解整个代码，您可以在这里找到我的notebook。以及dataset。

Answer 1

当我做了pip install utils 并尝试：

>> import utils
>> utils.read()

我收到了AttributeError: module 'utils' has no attribute 'read'。我认为你的代码也发生了同样的事情。

尝试重命名您的utils.pyx（例如myutils.pyx）并更改：

f = utils.read(...)

到：

f = myutils.read(...)

别忘了先导入。