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cloudpanl avatar cloudpanl commented on May 25, 2024

I also have the same needs. Is it solved?

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StrugglingForBetter avatar StrugglingForBetter commented on May 25, 2024

Is there anybody who can hlep use with this issue?

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violetcodet avatar violetcodet commented on May 25, 2024

Same question,

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AlphaGoMK avatar AlphaGoMK commented on May 25, 2024

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])

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StrugglingForBetter avatar StrugglingForBetter commented on May 25, 2024

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])

What if we need two inputs with different shape? For example, if our inputs are [1, 3, 224, 224] and [1, 3, 112, 112]?

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AlphaGoMK avatar AlphaGoMK commented on May 25, 2024

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])

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.

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