using to modify cfg

mmpretrain/configs/_base_/datasets/imagenet_bs32.py

@@ -38,7 +38,7 @@ train_dataloader = dict(
     dataset=dict(
         type=dataset_type,
         data_root='data/imagenet',
-        ann_file='meta/train.txt',
+        # ann_file='meta/train.txt',
         data_prefix='train',
         pipeline=train_pipeline),
     sampler=dict(type=DefaultSampler, shuffle=True),
@@ -50,7 +50,7 @@ val_dataloader = dict(
     dataset=dict(
         type=dataset_type,
         data_root='data/imagenet',
-        ann_file='meta/val.txt',
+        # ann_file='meta/val.txt',
         data_prefix='val',
         pipeline=test_pipeline),
     sampler=dict(type=DefaultSampler, shuffle=False),

mmpretrain/configs/_base_/datasets/imagenet_bs64_pil_resize_autoaug.py

@@ -33,7 +33,7 @@ train_pipeline = [
         scale=224,
         backend='pillow',
         interpolation='bicubic'),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
     dict(
         type=AutoAugment,
         policies='imagenet',

mmpretrain/configs/vision_transformer/vit_base_p16_32xb128_mae_in1k.py

@@ -40,10 +40,10 @@ model = dict(
     ]))
 
 # dataset settings
-train_dataloader = dict(batch_size=128)
+train_dataloader.update(batch_size=128)
 
 # schedule settings
-optim_wrapper = dict(
+optim_wrapper.update(
     optimizer=dict(
         type=AdamW,
         lr=1e-4 * 4096 / 256,
@@ -64,4 +64,4 @@ custom_hooks = [dict(type=EMAHook, momentum=1e-4)]
 # NOTE: `auto_scale_lr` is for automatically scaling LR
 # based on the actual training batch size.
 # base_batch_size = (32 GPUs) x (128 samples per GPU)
-auto_scale_lr = dict(base_batch_size=4096)
+auto_scale_lr.update(base_batch_size=4096)

mmpretrain/configs/vision_transformer/vit_base_p16_64xb64_in1k.py

@@ -11,10 +11,10 @@ with read_base():
     from .._base_.default_runtime import *
 
 # model setting
-model = dict(
+model.update(
     head=dict(hidden_dim=3072),
     train_cfg=dict(augments=dict(type=Mixup, alpha=0.2)),
 )
 
 # schedule setting
-optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_base_p16_64xb64_in1k_384px.py

@@ -16,10 +16,10 @@ with read_base():
 
 
 # model setting
-model = dict(backbone=dict(img_size=384))
+model.update(backbone=dict(img_size=384))
 
 # dataset setting
-data_preprocessor = dict(
+data_preprocessor.update(
     mean=[127.5, 127.5, 127.5],
     std=[127.5, 127.5, 127.5],
     # convert image from BGR to RGB
@@ -40,9 +40,9 @@ test_pipeline = [
     dict(type=PackInputs),
 ]
 
-train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
-val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
-test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+train_dataloader.update(dataset=dict(pipeline=train_pipeline))
+val_dataloader.update(dataset=dict(pipeline=test_pipeline))
+test_dataloader.update(dataset=dict(pipeline=test_pipeline))
 
 # schedule setting
-optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_base_p32_64xb64_in1k.py

@@ -12,10 +12,10 @@ with read_base():
 
 
 # model setting
-model = dict(
+model.update(
     head=dict(hidden_dim=3072),
     train_cfg=dict(augments=dict(type=Mixup, alpha=0.2)),
 )
 
 # schedule setting
-optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_base_p32_64xb64_in1k_384px.py

@@ -15,10 +15,10 @@ with read_base():
     from .._base_.default_runtime import *
 
 # model setting
-model = dict(backbone=dict(img_size=384))
+model.update(backbone=dict(img_size=384))
 
 # dataset setting
-data_preprocessor = dict(
+data_preprocessor.update(
     mean=[127.5, 127.5, 127.5],
     std=[127.5, 127.5, 127.5],
     # convert image from BGR to RGB
@@ -39,9 +39,9 @@ test_pipeline = [
     dict(type=PackInputs),
 ]
 
-train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
-val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
-test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+train_dataloader.update(dataset=dict(pipeline=train_pipeline))
+val_dataloader.update(dataset=dict(pipeline=test_pipeline))
+test_dataloader.update(dataset=dict(pipeline=test_pipeline))
 
 # schedule setting
-optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p16_64xb64_in1k.py

@@ -11,10 +11,10 @@ with read_base():
 
 
 # model setting
-model = dict(
+model.update(
     head=dict(hidden_dim=3072),
     train_cfg=dict(augments=dict(type=Mixup, alpha=0.2)),
 )
 
 # schedule setting
-optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p16_64xb64_in1k_384px.py

@@ -16,10 +16,10 @@ with read_base():
 
 
 # model setting
-model = dict(backbone=dict(img_size=384))
+model.update(backbone=dict(img_size=384))
 
 # dataset setting
-data_preprocessor = dict(
+data_preprocessor.update(
     mean=[127.5, 127.5, 127.5],
     std=[127.5, 127.5, 127.5],
     # convert image from BGR to RGB
@@ -40,9 +40,9 @@ test_pipeline = [
     dict(type=PackInputs),
 ]
 
-train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
-val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
-test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+train_dataloader.update(dataset=dict(pipeline=train_pipeline))
+val_dataloader.update(dataset=dict(pipeline=test_pipeline))
+test_dataloader.update(dataset=dict(pipeline=test_pipeline))
 
 # schedule setting
-optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p32_64xb64_in1k.py

@@ -10,10 +10,10 @@ with read_base():
     from .._base_.default_runtime import *
 
 # model setting
-model = dict(
+model.update(
     head=dict(hidden_dim=3072),
     train_cfg=dict(augments=dict(type=Mixup, alpha=0.2)),
 )
 
 # schedule setting
-optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

mmpretrain/configs/vision_transformer/vit_large_p32_64xb64_in1k_384px.py

@@ -13,7 +13,7 @@ with read_base():
     from .._base_.default_runtime import *
 
 # model setting
-model = dict(backbone=dict(img_size=384))
+model.update(backbone=dict(img_size=384))
 
 # dataset setting
 data_preprocessor = dict(
@@ -37,9 +37,9 @@ test_pipeline = [
     dict(type=PackInputs),
 ]
 
-train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
-val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
-test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+train_dataloader.update(dataset=dict(pipeline=train_pipeline))
+val_dataloader.update(dataset=dict(pipeline=test_pipeline))
+test_dataloader.update(dataset=dict(pipeline=test_pipeline))
 
 # schedule setting
-optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+optim_wrapper.update(clip_grad=dict(max_norm=1.0))

tools/train.py

@@ -146,8 +146,17 @@ def main():
     # load config
     cfg = Config.fromfile(args.config)
 
-    cfg.train_dataloader.dataset.data_root = 'xyz'
-    cfg.val_dataloader.dataset.data_root = 'xyz'
+    # print('default train data root: ', cfg.train_dataloader.dataset.data_root)
+    # print('default val data root: ', cfg.val_dataloader.dataset.data_root)
+
+    cfg.train_dataloader.dataset.data_root = '/home/zeyuan.yin/imagenet'
+    cfg.val_dataloader.dataset.data_root = '/home/zeyuan.yin/imagenet'
+
+    print('dataset cfg', cfg.train_dataloader.dataset)
+    print('---')
+    # print('model cfg', cfg.model)
+    # print('optim_wrapper cfg', cfg.optim_wrapper)
+    exit()
 
     # merge cli arguments to config
     cfg = merge_args(cfg, args)