rename micronet_m(\d) to MicroNet_M(\d)

ppcls/arch/backbone/__init__.py

@@ -76,7 +76,7 @@ from .model_zoo.convnext import ConvNeXt_tiny, ConvNeXt_small, ConvNeXt_base_224
 from .model_zoo.nextvit import NextViT_small_224, NextViT_base_224, NextViT_large_224, NextViT_small_384, NextViT_base_384, NextViT_large_384
 from .model_zoo.cae import cae_base_patch16_224, cae_large_patch16_224
 from .model_zoo.cvt import CvT_13_224, CvT_13_384, CvT_21_224, CvT_21_384, CvT_W24_384
-from .model_zoo.micronet import micronet_m0, micronet_m1, micronet_m2, micronet_m3
+from .model_zoo.micronet import MicroNet_M0, MicroNet_M1, MicroNet_M2, MicroNet_M3
 
 from .variant_models.resnet_variant import ResNet50_last_stage_stride1
 from .variant_models.resnet_variant import ResNet50_adaptive_max_pool2d

ppcls/arch/backbone/model_zoo/micronet.py

@@ -23,10 +23,10 @@ import paddle.nn as nn
 from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
-    "micronet_m0": "",  # TODO
-    "micronet_m1": "",  # TODO
-    "micronet_m2": "",  # TODO
-    "micronet_m3": "",  # TODO
+    "MicroNet_M0": "",  # TODO
+    "MicroNet_M1": "",  # TODO
+    "MicroNet_M2": "",  # TODO
+    "MicroNet_M3": "",  # TODO
 }
 
 __all__ = MODEL_URLS.keys()
@@ -562,7 +562,7 @@ def _load_pretrained(pretrained, model, model_url, use_ssld):
         )
 
 
-def micronet_m0(pretrained=False, use_ssld=False, **kwargs):
+def MicroNet_M0(pretrained=False, use_ssld=False, **kwargs):
     model = MicroNet(
         NET_CONFIG["msnx_dy6_exp4_4M_221"],
         ACTIVATION_CONFIG["msnx_dy6_exp4_4M_221"],
@@ -571,11 +571,11 @@ def micronet_m0(pretrained=False, use_ssld=False, **kwargs):
         out_ch=640,
         dropout_rate=0.05,
         **kwargs)
-    _load_pretrained(pretrained, model, MODEL_URLS["micronet_m0"], use_ssld)
+    _load_pretrained(pretrained, model, MODEL_URLS["MicroNet_M0"], use_ssld)
     return model
 
 
-def micronet_m1(pretrained=False, use_ssld=False, **kwargs):
+def MicroNet_M1(pretrained=False, use_ssld=False, **kwargs):
     model = MicroNet(
         NET_CONFIG["msnx_dy6_exp6_6M_221"],
         ACTIVATION_CONFIG["msnx_dy6_exp6_6M_221"],
@@ -584,11 +584,11 @@ def micronet_m1(pretrained=False, use_ssld=False, **kwargs):
         out_ch=960,
         dropout_rate=0.05,
         **kwargs)
-    _load_pretrained(pretrained, model, MODEL_URLS["micronet_m1"], use_ssld)
+    _load_pretrained(pretrained, model, MODEL_URLS["MicroNet_M1"], use_ssld)
     return model
 
 
-def micronet_m2(pretrained=False, use_ssld=False, **kwargs):
+def MicroNet_M2(pretrained=False, use_ssld=False, **kwargs):
     model = MicroNet(
         NET_CONFIG["msnx_dy9_exp6_12M_221"],
         ACTIVATION_CONFIG["msnx_dy9_exp6_12M_221"],
@@ -597,11 +597,11 @@ def micronet_m2(pretrained=False, use_ssld=False, **kwargs):
         out_ch=1024,
         dropout_rate=0.1,
         **kwargs)
-    _load_pretrained(pretrained, model, MODEL_URLS["micronet_m2"], use_ssld)
+    _load_pretrained(pretrained, model, MODEL_URLS["MicroNet_M2"], use_ssld)
     return model
 
 
-def micronet_m3(pretrained=False, use_ssld=False, **kwargs):
+def MicroNet_M3(pretrained=False, use_ssld=False, **kwargs):
     model = MicroNet(
         NET_CONFIG["msnx_dy12_exp6_20M_020"],
         ACTIVATION_CONFIG["msnx_dy12_exp6_20M_020"],
@@ -610,5 +610,5 @@ def micronet_m3(pretrained=False, use_ssld=False, **kwargs):
         out_ch=1024,
         dropout_rate=0.1,
         **kwargs)
-    _load_pretrained(pretrained, model, MODEL_URLS["micronet_m3"], use_ssld)
+    _load_pretrained(pretrained, model, MODEL_URLS["MicroNet_M3"], use_ssld)
     return model

ppcls/configs/ImageNet/MicroNet/micronet_m0.yaml

@@ -18,7 +18,7 @@ Global:
 
 # model architecture
 Arch:
-  name: micronet_m0
+  name: MicroNet_M0
   class_num: 1000
  
 # loss function config for traing/eval process

ppcls/configs/ImageNet/MicroNet/micronet_m1.yaml

@@ -18,7 +18,7 @@ Global:
 
 # model architecture
 Arch:
-  name: micronet_m1
+  name: MicroNet_M1
   class_num: 1000
  
 # loss function config for traing/eval process

ppcls/configs/ImageNet/MicroNet/micronet_m2.yaml

@@ -18,7 +18,7 @@ Global:
 
 # model architecture
 Arch:
-  name: micronet_m2
+  name: MicroNet_M2
   class_num: 1000
  
 # loss function config for traing/eval process

ppcls/configs/ImageNet/MicroNet/micronet_m3.yaml

@@ -18,7 +18,7 @@ Global:
 
 # model architecture
 Arch:
-  name: micronet_m3
+  name: MicroNet_M3
   class_num: 1000
  
 # loss function config for traing/eval process

test_tipc/configs/MicroNet/micronet_m0_train_infer_python.txt

 ===========================train_params===========================
-model_name:micronet_m0
+model_name:MicroNet_M0
 python:python3
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MicroNet/micronet_m0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params=========================== 
-eval:tools/eval.py -c ppcls/configs/ImageNet/MicroNet/micronet_m0.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M0.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MicroNet/micronet_m0.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M0.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MicroNet/micronet_m1_train_infer_python.txt

 ===========================train_params===========================
-model_name:micronet_m1
+model_name:MicroNet_M1
 python:python3
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MicroNet/micronet_m1.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M1.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params=========================== 
-eval:tools/eval.py -c ppcls/configs/ImageNet/MicroNet/micronet_m1.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M1.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MicroNet/micronet_m1.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M1.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MicroNet/micronet_m2_train_infer_python.txt

 ===========================train_params===========================
-model_name:micronet_m2
+model_name:MicroNet_M2
 python:python3
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MicroNet/micronet_m2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params=========================== 
-eval:tools/eval.py -c ppcls/configs/ImageNet/MicroNet/micronet_m2.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M2.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MicroNet/micronet_m2.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M2.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MicroNet/micronet_m3_train_infer_python.txt

 ===========================train_params===========================
-model_name:micronet_m3
+model_name:MicroNet_M3
 python:python3
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MicroNet/micronet_m3.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M3.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params=========================== 
-eval:tools/eval.py -c ppcls/configs/ImageNet/MicroNet/micronet_m3.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M3.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MicroNet/micronet_m3.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MicroNet/MicroNet_M3.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null