update code for the latest timm

.gitignore

@@ -134,6 +134,7 @@ figures/*
 plots/*
 .idea/*
 checkpoints/
+output/*
 *.out
 *.err
 .vscode/

README.md

@@ -8,6 +8,8 @@ By [Zizheng Pan](https://scholar.google.com.au/citations?user=w_VMopoAAAAJ&hl=en
 
 ## News
 
+- **13/10/2022.** Fix compatibility with higher version of timm.
+
 - **30/09/2022.** Add [benchmarking results](https://github.com/ziplab/LITv2#single-attention-layer-benchmark) for single attention layer. HiLo is super fast on both CPU and GPU!
 
 - **15/09/2022.** LITv2 is accepted by NeurIPS 2022! 🔥🔥🔥
@@ -55,7 +57,8 @@ torch.Size([64, 196, 384])
 ### Requirements
 
 - Linux with Python ≥ 3.6
-- PyTorch 1.8.1
+- PyTorch >= 1.8.1
+- timm >= 0.3.2
 - CUDA 11.1
 - An NVIDIA GPU
 
@@ -70,7 +73,7 @@ conda activate lit
 # Install Pytorch and TorchVision
 pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
 
-pip install timm==0.3.2
+pip install timm
 pip install ninja
 pip install tensorboard
 

classification/data/build.py

@@ -6,7 +6,10 @@
 from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.data import Mixup
 from timm.data import create_transform
-from timm.data.transforms import _pil_interp
+try:
+    from timm.data.transforms import _pil_interp
+except:
+    from timm.data.transforms import str_to_pil_interp as _pil_interp
 
 from .cached_image_folder import CachedImageFolder
 from .samplers import SubsetRandomSampler

classification/lr_scheduler.py

@@ -14,7 +14,7 @@ def build_scheduler(config, optimizer, n_iter_per_epoch):
         lr_scheduler = CosineLRScheduler(
             optimizer,
             t_initial=num_steps,
-            t_mul=1.,
+            # t_mul=1.,
             lr_min=config.TRAIN.MIN_LR,
             warmup_lr_init=config.TRAIN.WARMUP_LR,
             warmup_t=warmup_steps,