_base_ = [
    'configs/_base_/models/swin_transformer/tiny_large_224.py',
    'configs/_base_/datasets/tiny_imagenet_bs64_swin_224.py',
    'configs/_base_/schedules/imagenet_bs1024_adamw_swin.py',
    'configs/_base_/default_runtime.py'
]
import torch

#torch.backends.cuda.matmul.allow_tf32=False
#torch.backends.cudnn.allow_tf32=False

# schedule settings
optim_wrapper = dict(
        #type='AmpOptimWrapper',
        #dtype='float16',
        clip_grad=dict(max_norm=5.0))

#custom_hooks = [
#        dict(type='ProfilerHook', by_epoch=False,
#        profile_times=5,
#        with_stack=True,
#        with_flops=True,
#        json_trace_path="trace_swin-l-pure-fp32.json",
#        on_trace_ready=dict(type="log_trace", sort_by="self_cuda_time_total"),
#        activity_with_cuda=True,
#        schedule=dict(wait=3, warmup=1, active=1, repeat=1)),
#        ]
