Hi. Thanks for great code.
I modified you code to utilize my own image dataset, and I failing with out of memory.
Traceback (most recent calls WITHOUT Sacred internals):
File "experiments/patch_experiment.py", line 128, in main
learning_loop.train_step(observer, c.inner_loop_steps, c.mnist_classes)
File "/lhi/GTN/GTN_clean/gtn/models/learning_loop.py", line 146, in train_step
self.optimizer_teacher.step()
File "/opt/conda/lib/python3.7/site-packages/torch/optim/adam.py", line 103, in step
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
RuntimeError: CUDA out of memory. Tried to allocate 2.00 GiB (GPU 0; 11.93 GiB total capacity; 9.53 GiB already allocated; 1.85 GiB free; 9.62 GiB reserved in total by PyTorch)
I'm using 64x64 images so that I changed input and output of learner/teacher to 64.
I reduced batch size to 4 / test_batch_size 4
Hi. Thanks for great code.
I modified you code to utilize my own image dataset, and I failing with out of memory.
Traceback (most recent calls WITHOUT Sacred internals):
File "experiments/patch_experiment.py", line 128, in main
learning_loop.train_step(observer, c.inner_loop_steps, c.mnist_classes)
File "/lhi/GTN/GTN_clean/gtn/models/learning_loop.py", line 146, in train_step
self.optimizer_teacher.step()
File "/opt/conda/lib/python3.7/site-packages/torch/optim/adam.py", line 103, in step
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
RuntimeError: CUDA out of memory. Tried to allocate 2.00 GiB (GPU 0; 11.93 GiB total capacity; 9.53 GiB already allocated; 1.85 GiB free; 9.62 GiB reserved in total by PyTorch)
I'm using 64x64 images so that I changed input and output of learner/teacher to 64.
I reduced batch size to 4 / test_batch_size 4