+scripts

configs/vit_imagenet_a100.py

@@ -0,0 +1,65 @@
+from libai.config import LazyCall
+from .common.models.vit.vit_base_patch16_224 import model #from .common.models.vit.vit_large_patch16_224 import model
+from .common.models.graph import graph
+from .common.train import train
+from .common.optim import optim
+from .common.data.imagenet import dataloader
+
+from flowvision.data import Mixup
+from flowvision.loss.cross_entropy import SoftTargetCrossEntropy
+
+# Refine data path to imagenet
+dataloader.train.dataset[0].root = "/data/ImageNet/extract"
+dataloader.test[0].dataset.root = "/data/ImageNet/extract"
+
+# Refine model cfg for vit training on imagenet
+model.cfg.num_classes = 1000
+model.cfg.loss_func = SoftTargetCrossEntropy()
+
+# Add Mixup Func
+dataloader.train.mixup_func = LazyCall(Mixup)(
+ mixup_alpha=0.8,
+ cutmix_alpha=1.0,
+ prob=1.0,
+ switch_prob=0.5,
+ mode="batch",
+ num_classes=model.cfg.num_classes,
+)
+
+# Refine optimizer cfg for vit model
+optim.lr = 1e-3 # 5e-4 * 1024 (batchsize) / 512
+optim.eps = 1e-8
+optim.weight_decay = 0.05
+optim.params.clip_grad_max_norm = None
+optim.params.clip_grad_norm_type = None
+optim.params.overrides = {"pos_embed": {"weight_decay": 0.0}, "cls_token": {"weight_decay": 0.0}}
+
+# Refine train cfg for vit model
+train.train_micro_batch_size = 128
+train.test_micro_batch_size = 128
+# train.train_epoch = 300
+train.train_epoch = 0
+import os
+train.train_iter = int(os.getenv("NUM_ITER_ENV"))
+train.warmup_ratio = 5 / 300
+train.evaluation.enabled = False
+# train.evaluation.eval_period = 100
+train.log_period = 1
+
+# Scheduler
+train.scheduler.warmup_factor = 0.001
+train.scheduler.alpha = 0.01
+train.scheduler.warmup_method = "linear"
+
+# Set fp16 ON
+train.amp.enabled = True
+
+# zero
+train.zero_optimization.enabled = False
+train.zero_optimization.stage = 1
+
+# Distributed Settings
+train.dist.pipeline_num_layers = model.cfg.depth
+train.dist.data_parallel_size = 2
+train.dist.tensor_parallel_size = 2
+train.dist.pipeline_parallel_size = 2