bug occur at this function line 7
def visualize_embedding(h, color, epoch=None, loss=None):
plt.figure(figsize=(7,7))
plt.xticks([])
plt.yticks([])
h = h.detach().clone().cpu().numpy()
print(type(h))
plt.scatter(h[:, 0], h[:, 1], s=140, c=color, cmap="Set2")
if epoch is not None and loss is not None:
plt.xlabel(f'Epoch: {epoch}, Loss: {loss.item():.4f}', fontsize=16)
plt.show()
error:
<class 'numpy.ndarray'>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[17], line 21
19 loss, h = train(data)
20 if epoch % 10 == 0:
---> 21 visualize_embedding(h, color=data.y, epoch=epoch, loss=loss)
22 time.sleep(0.3)
Cell In[16], line 16
14 h = h.detach().clone().cpu().numpy()
15 print(type(h))
---> 16 plt.scatter(h[:, 0], h[:, 1], s=140, c=color, cmap="Set2")
17 if epoch is not None and loss is not None:
18 plt.xlabel(f'Epoch: {epoch}, Loss: {loss.item():.4f}', fontsize=16)
File c:\Users\polyu\Documents\RA\hkjc_dm\hkjc_dm\model\src\venvModel4\lib\site-packages\matplotlib\pyplot.py:3684, in scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)
3665 @_copy_docstring_and_deprecators(Axes.scatter)
3666 def scatter(
3667 x: float | ArrayLike,
(...)
3682 **kwargs,
3683 ) -> PathCollection:
-> 3684 __ret = gca().scatter(
3685 x,
3686 y,
...
1030 return self.numpy()
1031 else:
-> 1032 return self.numpy().astype(dtype, copy=False)
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
h is already ndarray, why it still gives me the convert cuda tensor error? By the way h is the representation of shape [batch_size, 2]
I would guess the error is raised by something other than h, perhaps the color! Check whether data.y is a GPU tensor, in that case you can give it the same treatment as h by calling detach/cpu/numpy on it.