fix tipc (#635)

* fix tipc

* fix tipc

test_tipc/configs/GPEN/train_infer_python.txt

-===========================train_params===========================
-model_name:GPEN
-python:python3.7
-gpu_list:0
-##
-auto_cast:null
-total_iters:lite_train_lite_infer=10
-output_dir:./output/
-snapshot_config.interval:lite_train_lite_infer=10
-pretrained_model:null
-train_model_name:gpen*/*checkpoint.pdparams
-train_infer_img_dir:null
-null:null
-##
-trainer:norm_train
-norm_train:tools/main.py -c configs/gpen_256_ffhq.yaml --seed 100  -o log_config.interval=1
-pact_train:null
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params===========================
-eval:null
-null:null
-##
-===========================infer_params===========================
---output_dir:./output/
-load:null
-norm_export:tools/export_model.py -c configs/gpen_256_ffhq.yaml  --inputs_size=1,3,256,256 --model_name inference --load
-quant_export:null
-fpgm_export:null
-distill_export:null
-export1:null
-export2:null
-inference_dir:inference
-train_model:./inference/gpen/gpenmodel_g_ema
-infer_export:null
-infer_quant:False
-inference:tools/inference.py --model_type GPEN --seed 100 -c configs/gpen_256_ffhq.yaml --output_path test_tipc/output/ -o dataset.test.amount=5
---device:gpu
-null:null
-null:null
-null:null
-null:null
-null:null
---model_path:
-null:null
-null:null
---benchmark:True
-null:null

test_tipc/configs/PReNet/train_infer_python.txt

-===========================train_params===========================
-model_name:prenet
-python:python3.7
-gpu_list:0
-##
-auto_cast:null
-total_iters:lite_train_lite_infer=10|lite_train_whole_infer=10|whole_train_whole_infer=200
-output_dir:./output/
-dataset.train.batch_size:lite_train_lite_infer=1|whole_train_whole_infer=1
-pretrained_model:null
-train_model_name:prenet*/*checkpoint.pdparams
-train_infer_img_dir:./data/prenet/test
-null:null
-##
-trainer:norm_train
-norm_train:tools/main.py -c configs/prenet.yaml --seed 123 -o dataset.train.num_workers=0 log_config.interval=1 snapshot_config.interval=5
-pact_train:null
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params===========================
-eval:null
-null:null
-##
-===========================infer_params===========================
---output_dir:./output/
-load:null
-norm_export:tools/export_model.py -c configs/prenet.yaml  --inputs_size="-1,3,-1,-1" --model_name inference --load
-quant_export:null
-fpgm_export:null
-distill_export:null
-export1:null
-export2:null
-inference_dir:inference
-train_model:./inference/prenet/prenet_generator
-infer_export:null
-infer_quant:False
-inference:tools/inference.py --model_type prenet -c configs/prenet.yaml --seed 123  --output_path test_tipc/output/
---device:gpu
-null:null
-null:null
-null:null
-null:null
-null:null
---model_path:
-null:null
-null:null
---benchmark:True
-null:null
-===========================train_benchmark_params==========================
-batch_size:2|4
-fp_items:fp32
-total_iters:50
---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
-flags:null
-===========================infer_benchmark_params==========================
-random_infer_input:[{float32,[6,3,180,320]}]

test_tipc/docs/test_train_inference_python.md

@@ -15,7 +15,8 @@ Linux端基础训练预测功能测试的主程序为`test_train_inference_pytho
 | FOMM |FOMM | 生成  | 支持 | 多机多卡  | | |
 | BasicVSR |BasicVSR | 超分  | 支持 | 多机多卡  | | |
 |PP-MSVSR|PP-MSVSR | 超分|
-|SinGAN|SinGAN | 生成|支持|
+|edvr|edvr | 超分|支持|
+|esrgan|esrgan | 超分|支持|
 
 - 预测相关：预测功能汇总如下，
 

test_tipc/prepare.sh

@@ -54,9 +54,6 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         rm -rf ./data/ffhq*
         wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/ffhq.tar --no-check-certificate
         cd ./data/ && tar xf ffhq.tar && cd ../ ;;
-	GPEN)
-        rm -rf ./data/ffhq*
-        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/ffhq.tar --no-check-certificate
         cd ./data/ && tar xf ffhq.tar && cd ../ ;;
     FOMM)
         rm -rf ./data/fom_lite*
@@ -110,10 +107,6 @@ elif [ ${MODE} = "lite_train_whole_infer" ];then
         rm -rf ./data/ffhq*
         wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/ffhq.tar --no-check-certificate
         cd ./data/ && tar xf ffhq.tar && cd ../
-	elif [ ${model_name} == "GPEN" ]; then
-        rm -rf ./data/ffhq*
-        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/ffhq.tar --no-check-certificate
-        cd ./data/ && tar xf ffhq.tar && cd ../
     elif [ ${model_name} == "basicvsr" ]; then
         rm -rf ./data/reds*
         wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/reds_lite.tar --no-check-certificate

tools/inference.py

@@ -19,7 +19,7 @@ from ppgan.metrics import build_metric
 
 
 MODEL_CLASSES = ["pix2pix", "cyclegan", "wav2lip", "esrgan", \
-                 "edvr", "fom", "stylegan2", "basicvsr", "msvsr", "singan","prenet","GPEN"]
+                 "edvr", "fom", "stylegan2", "basicvsr", "msvsr", "singan"]
 
 
 def parse_args():
@@ -317,47 +317,6 @@ def main():
             metric_file = os.path.join(args.output_path, "singan/metric.txt")
             for metric in metrics.values():
                 metric.update(prediction, data['A'])
-        elif model_type == "prenet":
-            lq = data['lq'].numpy()
-            gt = data['gt'].numpy()
-            input_handles[0].copy_from_cpu(lq)
-            predictor.run()
-            prediction = output_handle.copy_to_cpu()
-            prediction = paddle.to_tensor(prediction)
-            gt = paddle.to_tensor(gt)
-            image_numpy = tensor2img(prediction, min_max)
-            gt_img = tensor2img(gt, min_max)
-            save_image(
-                image_numpy,
-                os.path.join(args.output_path, "prenet/{}.png".format(i)))
-            metric_file = os.path.join(args.output_path, "prenet/metric.txt")
-            for metric in metrics.values():
-                metric.update(image_numpy, gt_img)
-        elif model_type == "GPEN":
-            lq = data[0].numpy()
-            input_handles[0].copy_from_cpu(lq)
-            predictor.run()
-            prediction = output_handle.copy_to_cpu()
-            target = data[1].numpy()
-
-            metric_file = os.path.join(args.output_path, model_type,
-                                       "metric.txt")
-            for metric in metrics.values():
-                metric.update(prediction, target)
-
-            lq = paddle.to_tensor(lq)
-            target = paddle.to_tensor(target)
-            prediction = paddle.to_tensor(prediction)
-
-            lq = lq.transpose([0, 2, 3, 1])
-            target = target.transpose([0, 2, 3, 1])
-            prediction = prediction.transpose([0, 2, 3, 1])
-            sample_result = paddle.concat((lq[0], prediction[0], target[0]), 1)
-            sample = cv2.cvtColor((sample_result.numpy() + 1) / 2 * 255,
-                                  cv2.COLOR_RGB2BGR)
-            file_name = os.path.join(args.output_path, model_type,
-                                     "{}.png".format(i))
-            cv2.imwrite(file_name, sample)
 
     if metrics:
         log_file = open(metric_file, 'a')