[TIPC] Support ENet

test_tipc/README.md

@@ -32,6 +32,7 @@
 | STDC | STDC_STDC1 | 支持 | - | - | - |
 | MODNet | PP-Matting | 支持 | - | - | - |
 | PFPNNet | PFPNNet | 支持 | - | - | - |
+| ENet | ENet | 支持 | - | - | - |
 
 
 ## 3. 测试工具简介

test_tipc/configs/enet/enet_cityscapes_1024x512_adam_0.002_80k.yml

@@ -0,0 +1,34 @@
+_base_: '../_base_/cityscapes.yml'
+batch_size: 8
+
+train_dataset:
+  transforms:
+    - type: RandomPaddingCrop
+      crop_size: [1024, 512]
+    - type: RandomDistort
+      brightness_range: 0.4
+      contrast_range: 0.4
+      saturation_range: 0.4
+    - type: Normalize
+  mode: train
+
+model:
+  type: ENet
+  num_classes: 19
+  pretrained: Null
+
+export:
+  transforms:
+    - type: Normalize
+
+optimizer:
+  _inherited_: False
+  type: adam
+  weight_decay: 0.0002
+
+lr_scheduler:
+  type: PolynomialDecay
+  end_lr: 0
+  learning_rate: 0.001
+  power: 0.9
+

test_tipc/configs/enet/train_infer_python.txt

@@ -0,0 +1,52 @@
+===========================train_params===========================
+model_name:enet
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:null|null
+Global.auto_cast:null
+--iters:lite_train_lite_infer=50|lite_train_whole_infer=50|whole_train_whole_infer=1000
+--save_dir:
+--batch_size:lite_train_lite_infer=2|lite_train_whole_infer=2|whole_train_whole_infer=3
+Global.pretrained_model:null
+train_model_name:best_model/model.pdparams
+train_infer_img_dir:test_tipc/data/cityscapes/val.list
+null:null
+##
+trainer:norm
+norm_train:train.py --config test_tipc/configs/enet/enet_cityscapes_1024x512_adam_0.002_80k.yml --do_eval --save_interval 500 --seed 100
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:null
+null:null
+##
+===========================export_params===========================
+--save_dir:
+--model_path:
+norm_export:export.py --config test_tipc/configs/enet/enet_cityscapes_1024x512_adam_0.002_80k.yml --input_shape 1 3 1024 2048
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+===========================infer_params===========================
+infer_model:./test_tipc/output/enet/model.pdparams
+infer_export:export.py --config test_tipc/configs/enet/enet_cityscapes_1024x512_adam_0.002_80k.yml --input_shape 1 3 1024 2048
+infer_quant:False
+inference:deploy/python/infer.py --save_dir ./test_tipc/output/enet/
+--device:cpu|gpu
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--batch_size:1
+--use_trt:False
+--precision:fp32|int8|fp16
+--config:
+--image_path:./test_tipc/data/cityscapes/val.list
+--save_log_path:null
+--benchmark:True
+--save_dir:
+--model_name:enet

test_tipc/docs/test_train_inference_python.md

@@ -18,6 +18,7 @@ Linux端基础训练预测功能测试的主程序为`test_train_inference_pytho
 | STDC | STDC_STDC1 | 正常训练  | 正常训练  |  |  |
 | MODNet | PP-Matting | 正常训练  | 正常训练  |  |  |
 | PFPNNet | PFPNNet | 正常训练 | 正常训练 |  |  |
+| ENet | ENet | 正常训练 | 正常训练 |  |  |
 
 
 - 预测相关：基于训练是否使用量化，可以将训练产出的模型可以分为`正常模型`和`量化模型`，这两类模型对应的预测功能汇总如下，

test_tipc/prepare.sh

@@ -50,7 +50,7 @@ if [ ${model_name} == "fcn_hrnetw18_small" ] || [ ${model_name} == "pphumanseg_l
     rm -rf ./test_tipc/data/mini_supervisely
     wget -nc -P ./test_tipc/data/ https://paddleseg.bj.bcebos.com/humanseg/data/mini_supervisely.zip
     cd ./test_tipc/data/ && unzip mini_supervisely.zip && cd -
-elif [ ${model_name} == "ocrnet_hrnetw18" ] || [ ${model_name} == "bisenetv2" ] || [ ${model_name} == "segformer_b0" ] || [ ${model_name} == "stdc_stdc1" ] || [ ${model_name} == "pfpnnet" ];then
+elif [ ${model_name} == "ocrnet_hrnetw18" ] || [ ${model_name} == "bisenetv2" ] || [ ${model_name} == "segformer_b0" ] || [ ${model_name} == "stdc_stdc1" ] || [ ${model_name} == "pfpnnet" ] || [ ${model_name} == "enet" ];then
     rm -rf ./test_tipc/data/cityscapes
     wget -nc -P ./test_tipc/data/ https://paddleseg.bj.bcebos.com/dataset/cityscapes.tar
     cd ./test_tipc/data/ && tar -xvf cityscapes.tar && cd -