Comments (2)
For the model size and flops, you can calculate them precisely based on the sum of all basic operations.
For the memory, you can record the memory occupation of GPUs (use nvidia-smi to check).
Also, some packages can make this process easier, such as thop.
from predrnn-pytorch.
Thank you!
from predrnn-pytorch.
Related Issues (20)
- The traffic4cast model
- Inconsistent dataset of Moving_MNIST in the paper and code? HOT 1
- Whether the Moving_MNIST dataset volume in PredRNN++ is the same as the code repo? HOT 3
- What's the validation dataset for KTH_action, TaxiBJ, and Human3.6M? HOT 2
- Prediction images don't change. HOT 4
- Random output images HOT 2
- misconfigured parameter `num_action_ch` for `action_cond_predrnn_v2` HOT 2
- when i use CIKM_predrnn.py ,it run wrong HOT 1
- Guidance for custom dataset HOT 1
- Pretrained model-Unexpected key HOT 1
- Generate a new Moving-MNIST dataset HOT 1
- Generate new Mnist dataset HOT 3
- Radar echo dataset HOT 3
- FileNotFoundError: [Errno 2] No such file or directory HOT 2
- radar echo dataset HOT 1
- Kth dataset: inf error HOT 2
- link of pretrained models from Tsinghua Cloud need to update
- A list of dependencies
- Problem with hidden layers HOT 1
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