The Expanded Size Of The Tensor 8 Must Match The Existing Size 9 At

Runtimeerror The Expanded Size Of The Tensor 3 Must Match The When working with pytorch, a common framework for deep learning, you might encounter an error message like: runtimeerror: the expanded size of the tensor (x) must match the existing size (y) at non singleton dimension. Returns a new view of the self tensor with singleton dimensions expanded to a larger size. […] any dimension of size 1 can be expanded to an arbitrary value without allocating new memory. you could use x.repeat (2, 1) for your use case. note that this operation would allocate new memory for the tensor. 2 likes.

Runtimeerror The Expanded Size Of The Tensor 3 Must Match The Runtimeerror: the expanded size of the tensor (585) must match the existing size (514) at non singleton dimension 1. target sizes: [1, 585]. tensor sizes: [1, 514] this post suggests a way to fix the issue but doesn't say how to fix it in pipeline. the size of tensor a (707) must match the size of tensor b (512) at non singleton dimension 1. Runtimeerror: the expanded size of the tensor (32768) must match the existing size (32767) at non singleton dimension 1. target sizes: [16, 32768, 32, 128]. tensor sizes: [1, 32767, 1, 1]. Print(b.shape) # torch.size([7484, 100, 200]) # fails b = a.expand(19, 100, 200) # runtimeerror: the expanded size of the tensor (19) must match the existing size (7484) at non singleton dimension 0. target sizes: [19, 100, 200]. tensor sizes: [7484, 1, 1] note that i’ve added two additional dimensions to show how expand can be used. Runtimeerror: the expanded size of the tensor (26) must match the existing size (27) at non singleton dimension 3. target sizes: [2, 128, 35, 26]. tensor sizes: [2, 128, 36, 27] i modified two files to fix the issue. create sinusoidal timestep embeddings. args: timesteps (torch.tensor): a 1 d tensor of n indices, one per batch element.

The Expanded Size Of The Tensor 8 Must Match The Existing Size 9 At Print(b.shape) # torch.size([7484, 100, 200]) # fails b = a.expand(19, 100, 200) # runtimeerror: the expanded size of the tensor (19) must match the existing size (7484) at non singleton dimension 0. target sizes: [19, 100, 200]. tensor sizes: [7484, 1, 1] note that i’ve added two additional dimensions to show how expand can be used. Runtimeerror: the expanded size of the tensor (26) must match the existing size (27) at non singleton dimension 3. target sizes: [2, 128, 35, 26]. tensor sizes: [2, 128, 36, 27] i modified two files to fix the issue. create sinusoidal timestep embeddings. args: timesteps (torch.tensor): a 1 d tensor of n indices, one per batch element. X out[a:b] = self.inner model(x in[a:b], sigma in[a:b], cond=make condition dict(c crossattn, image cond in[a:b])) runtimeerror: the expanded size of the tensor (1) must match the existing size (2) at non singleton dimension 0. target sizes: [1, 4, 64, 64]. tensor sizes: [2, 4, 64, 64]. The expanded size of the tensor (8) must match the existing size (9) at non singleton dimension 3. target sizes: [2, 8, 8, 8]. tensor sizes: [2, 1, 9, 9]. I have got this error while running my code, the tensor shape is (batchxchannelsxhightxwidth) [8,204,15,15], however, it worked perfectly for the same image with different height and width [8,204,11,11]. could you post the shapes of target and pred before trying to calculate correct?. >>> runtimeerror: the expanded size of the tensor (2188) must match the existing size (514) at non singleton dimension 1. target sizes: [4, 2188]. tensor sizes: [1, 514] i believe the reason for this issue is that camembert's tokenizer config specifies "model max len" and not "model max length" as flair expects it.

The Expanded Size Of The Tensor 8 Must Match The Existing Size 9 At X out[a:b] = self.inner model(x in[a:b], sigma in[a:b], cond=make condition dict(c crossattn, image cond in[a:b])) runtimeerror: the expanded size of the tensor (1) must match the existing size (2) at non singleton dimension 0. target sizes: [1, 4, 64, 64]. tensor sizes: [2, 4, 64, 64]. The expanded size of the tensor (8) must match the existing size (9) at non singleton dimension 3. target sizes: [2, 8, 8, 8]. tensor sizes: [2, 1, 9, 9]. I have got this error while running my code, the tensor shape is (batchxchannelsxhightxwidth) [8,204,15,15], however, it worked perfectly for the same image with different height and width [8,204,11,11]. could you post the shapes of target and pred before trying to calculate correct?. >>> runtimeerror: the expanded size of the tensor (2188) must match the existing size (514) at non singleton dimension 1. target sizes: [4, 2188]. tensor sizes: [1, 514] i believe the reason for this issue is that camembert's tokenizer config specifies "model max len" and not "model max length" as flair expects it.