It is necessary to have both the model, and the data on the same device, either CPU or GPU, for the model to process data. Data on CPU and model on GPU, or vice-versa, will result in a Runtime error. You can set a variable device to cuda if it’s available, else it will be … Read more
Since Python 3.7 you can use a built-in breakpoint function. If this is not available, you can use import pdb pdb.set_trace() instead. If you want to execute the next line you can try n (next) instead of s (step).
Since the description of einsum is skimpy in torch documentation, I decided to write this post to document, compare and contrast how torch.einsum() behaves when compared to numpy.einsum(). Differences: NumPy allows both small case and capitalized letters [a-zA-Z] for the “subscript string” whereas PyTorch allows only the small case letters [a-z]. NumPy accepts nd-arrays, plain … Read more
While I tried your code, and it did not give me an error, I can say that usually the best practice to debug CUDA Runtime Errors: device-side assert like yours is to turn collab to CPU and recreate the error. It will give you a more useful traceback error. Most of the time CUDA Runtime … Read more
Input In [12]: aten = torch.tensor([[1, 2, 3], [4, 5, 6]]) In [13]: aten Out[13]: tensor([[ 1, 2, 3], [ 4, 5, 6]]) In [14]: aten.shape Out[14]: torch.Size([2, 3]) torch.view() reshapes the tensor to a different but compatible shape. For example, our input tensor aten has the shape (2, 3). This can be viewed as … Read more
The simplest code to do this relies on the broadcasting behavior of tf.multiply()*, which is based on numpy’s broadcasting behavior: x = tf.constant(5.0, shape=[5, 6]) w = tf.constant([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]) xw = tf.multiply(x, w) max_in_rows = tf.reduce_max(xw, 1) sess = tf.Session() print sess.run(xw) # ==> [[0.0, 5.0, 10.0, 15.0, 20.0, 25.0], # … Read more
Given two tensors A and B you can use either: A * B torch.mul(A, B) A.mul(B) Note: for matrix multiplication, you want to use A @ B which is equivalent to torch.matmul().
The tf.reduce_max() operator provides exactly this functionality. By default it computes the global maximum of the given tensor, but you can specify a list of reduction_indices, which has the same meaning as axis in NumPy. To complete your example: x = tf.constant([[1, 220, 55], [4, 3, -1]]) x_max = tf.reduce_max(x, reduction_indices=[1]) print sess.run(x_max) # ==> … Read more
The simplest and cleanest method I know: IntTensor.item() Returns the value of this tensor as a standard Python number. This only works for tensors with one element. For other cases, see tolist. PyTorch Docs
TensorFlow doesn’t have first-class Tensor objects, meaning that there are no notion of Tensor in the underlying graph that’s executed by the runtime. Instead the graph consists of op nodes connected to each other, representing operations. An operation allocates memory for its outputs, which are available on endpoints :0, :1, etc, and you can think … Read more