fix bs and unset update_freq to adapt to 8 gpus

ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_S.yaml

@@ -14,7 +14,6 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
-  update_freq: 3  # for 4 gpus
 
 # mixed precision training
 AMP:
@@ -85,7 +84,7 @@ DataLoader:
       scales: [256, 160, 192, 224, 288, 320]
       # first_bs: batch size for the first image resolution in the scales list
       # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-      first_bs: 32
+      first_bs: 48
       divided_factor: 32
       is_training: True
     loader:

ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_S_L2.yaml

@@ -14,7 +14,6 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
-  update_freq: 3  # for 4 gpus
 
 # mixed precision training
 AMP:
@@ -85,7 +84,7 @@ DataLoader:
       scales: [256, 160, 192, 224, 288, 320]
       # first_bs: batch size for the first image resolution in the scales list
       # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-      first_bs: 32
+      first_bs: 48
       divided_factor: 32
       is_training: True
     loader:

ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XS.yaml

@@ -14,7 +14,6 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
-  update_freq: 3  # for 4 gpus
 
 # mixed precision training
 AMP:
@@ -85,7 +84,7 @@ DataLoader:
       scales: [256, 160, 192, 224, 288, 320]
       # first_bs: batch size for the first image resolution in the scales list
       # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-      first_bs: 32
+      first_bs: 48
       divided_factor: 32
       is_training: True
     loader:

ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XS_L2.yaml

@@ -14,7 +14,6 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
-  update_freq: 3  # for 4 gpus
 
 # mixed precision training
 AMP:
@@ -85,7 +84,7 @@ DataLoader:
       scales: [256, 160, 192, 224, 288, 320]
       # first_bs: batch size for the first image resolution in the scales list
       # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-      first_bs: 32
+      first_bs: 48
       divided_factor: 32
       is_training: True
     loader:

ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XXS.yaml

@@ -14,7 +14,6 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
-  update_freq: 3  # for 4 gpus
 
 # mixed precision training
 AMP:
@@ -85,7 +84,7 @@ DataLoader:
       scales: [256, 160, 192, 224, 288, 320]
       # first_bs: batch size for the first image resolution in the scales list
       # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-      first_bs: 32
+      first_bs: 48
       divided_factor: 32
       is_training: True
     loader:

ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XXS_L2.yaml

@@ -14,7 +14,6 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
-  update_freq: 3  # for 4 gpus
 
 # mixed precision training
 AMP:
@@ -85,7 +84,7 @@ DataLoader:
       scales: [256, 160, 192, 224, 288, 320]
       # first_bs: batch size for the first image resolution in the scales list
       # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-      first_bs: 32
+      first_bs: 48
       divided_factor: 32
       is_training: True
     loader:

ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x0_5.yaml

@@ -14,7 +14,6 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
-  update_freq: 3  # for 4 gpus
 
 # mixed precision training
 AMP:
@@ -52,7 +51,7 @@ Optimizer:
   one_dim_param_no_weight_decay: True
   lr:
     name: Cosine
-    learning_rate: 0.002  # for total batch size 1020
+    learning_rate: 0.002  # for total batch size 1020 by referring to official
     eta_min: 0.0002
     warmup_epoch: 16  # 20000 iterations
     warmup_start_lr: 1e-6
@@ -103,7 +102,7 @@ DataLoader:
               prob: 0.25
     sampler:
       name: DistributedBatchSampler
-      batch_size: 85
+      batch_size: 128
       drop_last: False
       shuffle: True
     loader:

ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x0_75.yaml

@@ -14,7 +14,6 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
-  update_freq: 3  # for 4 gpus
 
 # mixed precision training
 AMP:
@@ -52,7 +51,7 @@ Optimizer:
   one_dim_param_no_weight_decay: True
   lr:
     name: Cosine
-    learning_rate: 0.002  # for total batch size 1020
+    learning_rate: 0.002  # for total batch size 1020 by referring to official
     eta_min: 0.0002
     warmup_epoch: 16  # 20000 iterations
     warmup_start_lr: 1e-6
@@ -103,7 +102,7 @@ DataLoader:
               prob: 0.25
     sampler:
       name: DistributedBatchSampler
-      batch_size: 85
+      batch_size: 128
       drop_last: False
       shuffle: True
     loader:

ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x1_0.yaml

@@ -14,7 +14,6 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
-  update_freq: 3  # for 4 gpus
 
 # mixed precision training
 AMP:
@@ -52,7 +51,7 @@ Optimizer:
   one_dim_param_no_weight_decay: True
   lr:
     name: Cosine
-    learning_rate: 0.002  # for total batch size 1020
+    learning_rate: 0.002  # for total batch size 1020 by referring to official
     eta_min: 0.0002
     warmup_epoch: 16  # 20000 iterations
     warmup_start_lr: 1e-6
@@ -103,7 +102,7 @@ DataLoader:
               prob: 0.25
     sampler:
       name: DistributedBatchSampler
-      batch_size: 85
+      batch_size: 128
       drop_last: False
       shuffle: True
     loader: