Following function basically returns numpy.ndarray
def getimage(id):
img = self.coco.loadImgs(id)
I = io.imread(img['coco_url'])
return I #returns 'numpy.ndarray'
The getimage function being called from main:
x = load.getimage(id).
x = torch.load(x)
Error thrown:
'numpy.ndarray' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.
Use torch.as_tensor instead of torch.load, and you won't have to create a buffer.
See this question and this answer.
If you want the pytorch tensor to be a copy of your numpy array, use torch.tensor(arr). If you want the torch.Tensor to share the same memory buffer, then use torch.as_tensor(arr). PyTorch will then reuse the buffer if it can.
If you really wanna make a buffer from your numpy array, use the BytesIO class from io and initialize it with arr.tobytes() like stream = io.BytesIO(arr.tobytes()). YMMV though; I just tried torch.load with a stream object from this and torch complained:
import io
import numpy as np
a = np.array([3, 4, 5])
stream = io.BytesIO(a.tobytes()) # implements seek()
torch.load(stream)
---------------------------------------------------------------------------
UnpicklingError Traceback (most recent call last)
...
UnpicklingError: invalid load key, '\x03'.
If you want to get that to work, you probably have to adjust the bytestream that numpy is generating. Good luck.
As the docs say, torch.load
Loads an object saved with torch.save() from a file.
To convert numpy.ndarray to torch.Tensor you want to use torch.from_numpy, documented clearly as
Creates a Tensor from a numpy.ndarray.
