Replace deprecated positional argument with --data-dir

bulk_runner.py

@@ -7,7 +7,7 @@
 python bulk_runner.py  --model-list 'vit*' --results-file vit_bench.csv benchmark.py --amp -b 512
 
 Validate all models:
-python bulk_runner.py  --model-list all --results-file val.csv --pretrained validate.py /imagenet/validation/ --amp -b 512 --retry
+python bulk_runner.py  --model-list all --results-file val.csv --pretrained validate.py --data-dir /imagenet/validation/ --amp -b 512 --retry
 
 Hacked together by Ross Wightman (https://github.com/rwightman)
 """

hfdocs/source/changes.mdx

@@ -228,7 +228,7 @@ Datasets & transform refactoring
   * Enabling either dynamic mode will break FX tracing unless PatchEmbed module added as leaf.
   * Existing method of resizing position embedding by passing different `img_size` (interpolate pretrained embed weights once) on creation still works.
   * Existing method of changing `patch_size` (resize pretrained patch_embed weights once) on creation still works.
-  * Example validation cmd `python validate.py /imagenet --model vit_base_patch16_224 --amp --amp-dtype bfloat16 --img-size 255 --crop-pct 1.0 --model-kwargs dynamic_img_size=True dyamic_img_pad=True`
+  * Example validation cmd `python validate.py --data-dir /imagenet --model vit_base_patch16_224 --amp --amp-dtype bfloat16 --img-size 255 --crop-pct 1.0 --model-kwargs dynamic_img_size=True dyamic_img_pad=True`
 
 ### Aug 25, 2023
 * Many new models since last release
@@ -245,8 +245,8 @@ Datasets & transform refactoring
 
 ### Aug 11, 2023
 * Swin, MaxViT, CoAtNet, and BEiT models support resizing of image/window size on creation with adaptation of pretrained weights
-* Example validation cmd to test w/ non-square resize `python validate.py /imagenet --model swin_base_patch4_window7_224.ms_in22k_ft_in1k --amp --amp-dtype bfloat16 --input-size 3 256 320 --model-kwargs window_size=8,10 img_size=256,320`
- 
+* Example validation cmd to test w/ non-square resize `python validate.py --data-dir /imagenet --model swin_base_patch4_window7_224.ms_in22k_ft_in1k --amp --amp-dtype bfloat16 --input-size 3 256 320 --model-kwargs window_size=8,10 img_size=256,320`
+
 ### Aug 3, 2023
 * Add GluonCV weights for HRNet w18_small and w18_small_v2. Converted by [SeeFun](https://github.com/seefun)
 * Fix `selecsls*` model naming regression
@@ -385,7 +385,7 @@ Datasets & transform refactoring
   * Refactor LeViT models to stages, add `features_only=True` support to new `conv` variants, weight remap required.
 * Move ImageNet meta-data (synsets, indices) from `/results` to [`timm/data/_info`](timm/data/_info/).
 * Add ImageNetInfo / DatasetInfo classes to provide labelling for various ImageNet classifier layouts in `timm`
-  * Update `inference.py` to use, try: `python inference.py /folder/to/images --model convnext_small.in12k --label-type detail --topk 5`
+  * Update `inference.py` to use, try: `python inference.py --data-dir /folder/to/images --model convnext_small.in12k --label-type detail --topk 5`
 * Ready for 0.8.10 pypi pre-release (final testing).
 
 ### Jan 20, 2023
@@ -449,8 +449,8 @@ Datasets & transform refactoring
 
 ### Jan 6, 2023
 * Finally got around to adding `--model-kwargs` and `--opt-kwargs` to scripts to pass through rare args directly to model classes from cmd line
-  * `train.py /imagenet --model resnet50 --amp --model-kwargs output_stride=16 act_layer=silu`
-  * `train.py /imagenet --model vit_base_patch16_clip_224 --img-size 240 --amp --model-kwargs img_size=240 patch_size=12`
+  * `train.py --data-dir /imagenet --model resnet50 --amp --model-kwargs output_stride=16 act_layer=silu`
+  * `train.py --data-dir /imagenet --model vit_base_patch16_clip_224 --img-size 240 --amp --model-kwargs img_size=240 patch_size=12`
 * Cleanup some popular models to better support arg passthrough / merge with model configs, more to go.
 
 ### Jan 5, 2023

hfdocs/source/training_script.mdx

@@ -12,7 +12,7 @@ The variety of training args is large and not all combinations of options (or ev
 To train an SE-ResNet34 on ImageNet, locally distributed, 4 GPUs, one process per GPU w/ cosine schedule, random-erasing prob of 50% and per-pixel random value:
 
 ```bash
-./distributed_train.sh 4 /data/imagenet --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 --amp -j 4
+./distributed_train.sh 4 --data-dir /data/imagenet --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 --amp -j 4
 ```
 
 <Tip>
@@ -27,13 +27,13 @@ Validation and inference scripts are similar in usage. One outputs metrics on a
 To validate with the model's pretrained weights (if they exist):
 
 ```bash
-python validate.py /imagenet/validation/ --model seresnext26_32x4d --pretrained
+python validate.py --data-dir /imagenet/validation/ --model seresnext26_32x4d --pretrained
 ```
 
 To run inference from a checkpoint:
 
 ```bash
-python inference.py /imagenet/validation/ --model mobilenetv3_large_100 --checkpoint ./output/train/model_best.pth.tar
+python inference.py --data-dir /imagenet/validation/ --model mobilenetv3_large_100 --checkpoint ./output/train/model_best.pth.tar
 ```
 
 ## Training Examples
@@ -43,23 +43,23 @@ python inference.py /imagenet/validation/ --model mobilenetv3_large_100 --checkp
 These params are for dual Titan RTX cards with NVIDIA Apex installed:
 
 ```bash
-./distributed_train.sh 2 /imagenet/ --model efficientnet_b2 -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .016
+./distributed_train.sh 2 --data-dir /imagenet/ --model efficientnet_b2 -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .016
 ```
 
 ### MixNet-XL with RandAugment - 80.5 top-1, 94.9 top-5
 
 This params are for dual Titan RTX cards with NVIDIA Apex installed:
 
 ```bash
-./distributed_train.sh 2 /imagenet/ --model mixnet_xl -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .969 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.3 --amp --lr .016 --dist-bn reduce
+./distributed_train.sh 2 --data-dir /imagenet/ --model mixnet_xl -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .969 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.3 --amp --lr .016 --dist-bn reduce
 ```
 
 ### SE-ResNeXt-26-D and SE-ResNeXt-26-T
 
 These hparams (or similar) work well for a wide range of ResNet architecture, generally a good idea to increase the epoch # as the model size increases... ie approx 180-200 for ResNe(X)t50, and 220+ for larger. Increase batch size and LR proportionally for better GPUs or with AMP enabled. These params were for 2 1080Ti cards:
 
 ```bash
-./distributed_train.sh 2 /imagenet/ --model seresnext26t_32x4d --lr 0.1 --warmup-epochs 5 --epochs 160 --weight-decay 1e-4 --sched cosine --reprob 0.4 --remode pixel -b 112
+./distributed_train.sh 2 --data-dir /imagenet/ --model seresnext26t_32x4d --lr 0.1 --warmup-epochs 5 --epochs 160 --weight-decay 1e-4 --sched cosine --reprob 0.4 --remode pixel -b 112
 ```
 ### EfficientNet-B3 with RandAugment - 81.5 top-1, 95.7 top-5
 
@@ -70,32 +70,32 @@ The training of this model started with the same command line as EfficientNet-B2
 [Michael Klachko](https://github.com/michaelklachko) achieved these results with the command line for B2 adapted for larger batch size, with the recommended B0 dropout rate of 0.2.
 
 ```bash
-./distributed_train.sh 2 /imagenet/ --model efficientnet_b0 -b 384 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .048
+./distributed_train.sh 2 --data-dir /imagenet/ --model efficientnet_b0 -b 384 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .048
 ```
 ### ResNet50 with JSD loss and RandAugment (clean + 2x RA augs) - 79.04 top-1, 94.39 top-5
 
 Trained on two older 1080Ti cards, this took a while. Only slightly, non statistically better ImageNet validation result than my first good AugMix training of 78.99. However, these weights are more robust on tests with ImageNetV2, ImageNet-Sketch, etc. Unlike my first AugMix runs, I've enabled SplitBatchNorm, disabled random erasing on the clean split, and cranked up random erasing prob on the 2 augmented paths.
 
 ```bash
-./distributed_train.sh 2 /imagenet -b 64 --model resnet50 --sched cosine --epochs 200 --lr 0.05 --amp --remode pixel --reprob 0.6 --aug-splits 3 --aa rand-m9-mstd0.5-inc1 --resplit --split-bn --jsd --dist-bn reduce
+./distributed_train.sh 2 --data-dir /imagenet -b 64 --model resnet50 --sched cosine --epochs 200 --lr 0.05 --amp --remode pixel --reprob 0.6 --aug-splits 3 --aa rand-m9-mstd0.5-inc1 --resplit --split-bn --jsd --dist-bn reduce
 ```
 ### EfficientNet-ES (EdgeTPU-Small) with RandAugment - 78.066 top-1, 93.926 top-5
 
 Trained by [Andrew Lavin](https://github.com/andravin) with 8 V100 cards. Model EMA was not used, final checkpoint is the average of 8 best checkpoints during training.
 
 ```bash
-./distributed_train.sh 8 /imagenet --model efficientnet_es -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2  --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064
+./distributed_train.sh 8 --data-dir /imagenet --model efficientnet_es -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2  --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064
 ```
 ### MobileNetV3-Large-100 - 75.766 top-1, 92,542 top-5
 
 ```bash
-./distributed_train.sh 2 /imagenet/ --model mobilenetv3_large_100 -b 512 --sched step --epochs 600 --decay-epochs 2.4 --decay-rate .973 --opt rmsproptf --opt-eps .001 -j 7 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064 --lr-noise 0.42 0.9
+./distributed_train.sh 2 /--data-dir imagenet/ --model mobilenetv3_large_100 -b 512 --sched step --epochs 600 --decay-epochs 2.4 --decay-rate .973 --opt rmsproptf --opt-eps .001 -j 7 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064 --lr-noise 0.42 0.9
 ```
 
 ### ResNeXt-50 32x4d w/ RandAugment - 79.762 top-1, 94.60 top-5
 These params will also work well for SE-ResNeXt-50 and SK-ResNeXt-50 and likely 101. I used them for the SK-ResNeXt-50 32x4d that I trained with 2 GPU using a slightly higher LR per effective batch size (lr=0.18, b=192 per GPU). The cmd line below are tuned for 8 GPU training.
 
 
 ```bash
-./distributed_train.sh 8 /imagenet --model resnext50_32x4d --lr 0.6 --warmup-epochs 5 --epochs 240 --weight-decay 1e-4 --sched cosine --reprob 0.4 --recount 3 --remode pixel --aa rand-m7-mstd0.5-inc1 -b 192 -j 6 --amp --dist-bn reduce
+./distributed_train.sh 8 --data-dir /imagenet --model resnext50_32x4d --lr 0.6 --warmup-epochs 5 --epochs 240 --weight-decay 1e-4 --sched cosine --reprob 0.4 --recount 3 --remode pixel --aa rand-m7-mstd0.5-inc1 -b 192 -j 6 --amp --dist-bn reduce
 ```