diff --git a/test_tipc/configs/GPEN/train_infer_python.txt b/test_tipc/configs/GPEN/train_infer_python.txt deleted file mode 100644 index 2deb575e23da1d704190b778ce467ca27268b15f..0000000000000000000000000000000000000000 --- a/test_tipc/configs/GPEN/train_infer_python.txt +++ /dev/null @@ -1,51 +0,0 @@ -===========================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 diff --git a/test_tipc/configs/PReNet/train_infer_python.txt b/test_tipc/configs/PReNet/train_infer_python.txt deleted file mode 100644 index cf30f759816db817f9a63d5ea9e9b96e2b975e44..0000000000000000000000000000000000000000 --- a/test_tipc/configs/PReNet/train_infer_python.txt +++ /dev/null @@ -1,59 +0,0 @@ -===========================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]}] diff --git a/test_tipc/docs/test_train_inference_python.md b/test_tipc/docs/test_train_inference_python.md index f7228e937b4ed1ba29a1ca3d948af00f4aaa5edd..0090fe606ace8e59e100cb46d69f4bd515c9c2d9 100644 --- a/test_tipc/docs/test_train_inference_python.md +++ b/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， diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index edd4934cf268310bba67bdc8e6f2cf2376edcd01..39a34343a7ca88bebd34d4e7a1ff679bc49c5aa8 100644 --- a/test_tipc/prepare.sh +++ b/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 diff --git a/tools/inference.py b/tools/inference.py index a8347421e4b6f405eaeab78f466c1e4cd2fcd5a3..6cb4698c4c6a677f4286d8dbeea81b1617e4dee1 100644 --- a/tools/inference.py +++ b/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')