Comments (6)
I also have the same needs. Is it solved?
from tensorwatch.
Is there anybody who can hlep use with this issue?
from tensorwatch.
Same question,
from tensorwatch.
Hey guys, I may solve this problem by input a composed tensor and split it into several inputs before the forward process begin.
Like call tensorwatch by tw.draw_model(model, [2,1,3,224,224])
, and add torch.split in forward
function of your model, which split tensor([2,1,3,224,224]) into tensor([1,3,224,224]) and tensor([1,3,224,224])
from tensorwatch.
Hey guys, I may solve this problem by input a composed tensor and split it into several inputs before the forward process begin.
Like call tensorwatch bytw.draw_model(model, [2,1,3,224,224])
, and add torch.split inforward
function of your model, which split tensor([2,1,3,224,224]) into tensor([1,3,224,224]) and tensor([1,3,224,224])
What if we need two inputs with different shape? For example, if our inputs are [1, 3, 224, 224] and [1, 3, 112, 112]?
from tensorwatch.
Hey guys, I may solve this problem by input a composed tensor and split it into several inputs before the forward process begin.
Like call tensorwatch bytw.draw_model(model, [2,1,3,224,224])
, and add torch.split inforward
function of your model, which split tensor([2,1,3,224,224]) into tensor([1,3,224,224]) and tensor([1,3,224,224])What if we need two inputs with different shape? For example, if our inputs are [1, 3, 224, 224] and [1, 3, 112, 112]?
Just input [1, 15, 112, 112]
, then divide it into [1, 12, 112, 112]
and [1, 3, 112, 112]
along the second dimension. The first tensor can be reshaped/viewed to [1, 3, 224, 224]
as you need.
from tensorwatch.
Related Issues (20)
- Jupyter Lab
- Problem in model_draw HOT 5
- Logging PyTorch scalars
- stats.log file is empty, yet weighs in at 500+ meg
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- AttributeError: module 'torch.onnx' has no attribute 'set_training' HOT 13
- AttributeError: 'torch._C.Node' object has no attribute 'ival' HOT 7
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- Question & Discussion
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- TypeError: 'mode' should be a torch.onnx.TrainingMode enum, but got '<class 'bool'>'.
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from tensorwatch.