Merge branch 'PaddlePaddle:develop' into develop

Author: XYZ-916

paddlevideo/tasks/train.py

@@ -261,6 +261,10 @@ def evaluate(best):
                 rank = dist.get_rank()
             #single_gpu_test and multi_gpu_test
             for i, data in enumerate(valid_loader):
+                """Next two line of code only used in test_tipc,
+                ignore it most of the time"""
+                if max_iters is not None and i >= max_iters:
+                    break
                 outputs = model(data, mode='valid')
 
                 if cfg.MODEL.framework == "FastRCNN":

test_tipc/configs/AGCN/AGCN_train_infer_python.txt

@@ -5,7 +5,7 @@ gpu_list:0
 Global.use_gpu:null|null
 Global.auto_cast:null
 -o epochs:2
-null:null
+-o output_dir:null
 -o DATASET.batch_size:null
 null:null
 train_model_name:null
@@ -22,12 +22,12 @@ null:null
 ##
 ===========================eval_params===========================
 eval:main.py --test -c configs/recognition/agcn/agcn_fsd.yaml -o DATASET.train.file_path="data/fsd10/FSD_train_data.npy" -o DATASET.train.label_path="data/fsd10/FSD_train_label.npy" -o DATASET.test.file_path="data/fsd10/FSD_train_data.npy"
--w:output/AGCN/AGCN_epoch_00001.pdparams
+-w:./test_tipc/output/AGCN/AGCN_epoch_00001.pdparams
 ##
 ===========================infer_params===========================
 -o:inference/AGCN
 -p:null
-norm_export:tools/export_model.py -c configs/recognition/agcn/agcn_fsd.yaml
+norm_export:tools/export_model.py -c configs/recognition/agcn/agcn_fsd.yaml --save_name inference
 quant_export:null
 fpgm_export:null
 distill_export:null
@@ -44,8 +44,10 @@ inference:tools/predict.py --config configs/recognition/agcn/agcn_fsd.yaml
 --batch_size:1|2
 --use_tensorrt:False|True
 --precision:fp32|fp16
---model_file:inference/AGCN/AGCN.pdmodel
+--model_file:inference.pdmodel
 --input_file:./data/fsd10/example_skeleton.npy
 null:null
 --enable_benchmark:True
---params_file:inference/AGCN/AGCN.pdiparams
+--params_file:inference.pdiparams
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[2, 350, 25, 1]}]

test_tipc/configs/AttentionLSTM/AttentionLSTM_train_infer_python.txt

@@ -5,7 +5,7 @@ gpu_list:0|0,1
 Global.use_gpu:null|null
 Global.auto_cast:null
 -o epochs:2
-null:null
+-o output_dir:null
 -o DATASET.batch_size:64
 null:null
 train_model_name:null
@@ -21,8 +21,8 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval:main.py --test -c configs/recognition/attention_lstm/attention_lstm_youtube8m.yaml
--w:output/AttentionLSTM/AttentionLSTM_best.pdparams
+eval:main.py --test -c configs/recognition/attention_lstm/attention_lstm_youtube8m.yaml --save_name inference
+-w:./test_tipc/output/AttentionLSTM/AttentionLSTM_epoch_00001.pdparams
 ##
 ===========================infer_params===========================
 -o:inference/AttentionLSTM
@@ -44,8 +44,10 @@ inference:tools/predict.py --config configs/recognition/attention_lstm/attention
 --batch_size:1|2
 --use_tensorrt:False|True
 --precision:fp32|fp16
---model_file:inference/AttentionLSTM/AttentionLSTM.pdmodel
+--model_file:inference.pdmodel
 --input_file:./data/example.pkl
 null:null
 --enable_benchmark:True
---params_file:inference/AttentionLSTM/AttentionLSTM.pdiparams
+--params_file:inference.pdiparams
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[512, 1024]},{int32,[1]},{float32,[512, 1024]},{float32,[512, 128]},{int32,[1]},{float32,[512, 128]}]

test_tipc/configs/BMN/BMN_train_infer_python.txt

@@ -5,7 +5,7 @@ gpu_list:0|0,1
 Global.use_gpu:null|null
 Global.auto_cast:null
 -o epochs:2
-null:null
+-o output_dir:null
 -o DATASET.batch_size:16
 -o MODEL.backbone.pretrained:null
 train_model_name:null
@@ -22,12 +22,12 @@ null:null
 ##
 ===========================eval_params===========================
 eval:main.py --test -c configs/localization/bmn.yaml
--w:output/BMN/BMN_epoch_00001.pdparams
+-w:./test_tipc/output/BMN/BMN_epoch_00001.pdparams
 ##
 ===========================infer_params===========================
 -o:inference/BMN
 -p:null
-norm_export:tools/export_model.py -c configs/localization/bmn.yaml
+norm_export:tools/export_model.py -c configs/localization/bmn.yaml --save_name inference
 quant_export:null
 fpgm_export:null
 distill_export:null
@@ -44,14 +44,16 @@ inference:tools/predict.py --config configs/localization/bmn.yaml
 --batch_size:1
 --use_tensorrt:False|True
 --precision:fp32|fp16
---model_file:inference/BMN/BMN.pdmodel
+--model_file:inference.pdmodel
 --input_file:./data/example_feat.list
 null:null
 --enable_benchmark:True
---params_file:inference/BMN/BMN.pdiparams
+--params_file:inference.pdiparams
 ===========================train_benchmark_params==========================
 batch_size:8
 fp_items:fp32
 epoch:1
 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
 flags:FLAGS_conv_workspace_size_limit=800
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[400, 100]}]

test_tipc/configs/PP-TSM/PP-TSM_train_infer_python.txt

@@ -5,7 +5,7 @@ gpu_list:0|0,1
 Global.use_gpu:null|null
 Global.auto_cast:null
 -o epochs:2
-null:null
+-o output_dir:null
 -o DATASET.batch_size:2
 -o MODEL.backbone.pretrained:'data/ResNet50_vd_ssld_v2_pretrained.pdparams'
 train_model_name:null
@@ -22,12 +22,12 @@ null:null
 ##
 ===========================eval_params===========================
 eval:main.py --test -c configs/recognition/pptsm/pptsm_k400_frames_uniform.yaml
--w:output/ppTSM/ppTSM_best.pdparams
+-w:./test_tipc/output/ppTSM/ppTSM_epoch_00001.pdparams
 ##
 ===========================infer_params===========================
 -o:inference/ppTSM
 -p:null
-norm_export:tools/export_model.py -c configs/recognition/pptsm/pptsm_k400_frames_uniform.yaml
+norm_export:tools/export_model.py -c configs/recognition/pptsm/pptsm_k400_frames_uniform.yaml --save_name inference
 quant_export:null
 fpgm_export:null
 distill_export:null
@@ -44,8 +44,10 @@ inference:tools/predict.py --config configs/recognition/pptsm/pptsm_k400_frames_
 --batch_size:1|2
 --use_tensorrt:False|True
 --precision:fp32|fp16
---model_file:inference/ppTSM/ppTSM.pdmodel
+--model_file:inference.pdmodel
 --input_file:./data/example.avi
 null:null
 --enable_benchmark:True
---params_file:inference/ppTSM/ppTSM.pdiparams
+--params_file:inference.pdiparams
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[8, 3, 224, 224]}]

test_tipc/configs/PP-TSN/PP-TSN_train_infer_python.txt

@@ -5,7 +5,7 @@ gpu_list:0|0,1
 Global.use_gpu:null|null
 Global.auto_cast:null
 -o epochs:2
-null:null
+-o output_dir:null
 -o DATASET.batch_size:2
 -o MODEL.backbone.pretrained:'data/ResNet50_vd_ssld_v2_pretrained.pdparams'
 train_model_name:null
@@ -22,12 +22,12 @@ null:null
 ##
 ===========================eval_params===========================
 eval:main.py --test -c configs/recognition/pptsn/pptsn_k400_videos.yaml
--w:output/ppTSN/ppTSN_best.pdparams
+-w:./test_tipc/output/ppTSN_epoch_00001.pdparams
 ##
 ===========================infer_params===========================
 -o:inference/ppTSN
 -p:null
-norm_export:tools/export_model.py -c configs/recognition/pptsn/pptsn_k400_videos.yaml
+norm_export:tools/export_model.py -c configs/recognition/pptsn/pptsn_k400_videos.yaml --save_name inference
 quant_export:null
 fpgm_export:null
 distill_export:null
@@ -44,8 +44,10 @@ inference:tools/predict.py --config configs/recognition/pptsn/pptsn_k400_videos.
 --batch_size:1|2
 --use_tensorrt:False|True
 --precision:fp32|fp16
---model_file:inference/ppTSN/ppTSN.pdmodel
+--model_file:inference.pdmodel
 --input_file:./data/example.avi
 null:null
 --enable_benchmark:True
---params_file:inference/ppTSN/ppTSN.pdiparams
+--params_file:inference.pdiparams
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[250, 3, 224, 224]}]

test_tipc/configs/STGCN/STGCN_train_infer_python.txt

@@ -5,7 +5,7 @@ gpu_list:0
 Global.use_gpu:null|null
 Global.auto_cast:null
 -o epochs:2
-null:null
+-o output_dir:null
 -o DATASET.batch_size:null
 null:null
 train_model_name:null
@@ -22,12 +22,12 @@ null:null
 ##
 ===========================eval_params===========================
 eval:main.py --test -c configs/recognition/stgcn/stgcn_fsd.yaml -o DATASET.train.file_path="data/fsd10/FSD_train_data.npy" -o DATASET.train.label_path="data/fsd10/FSD_train_label.npy" -o DATASET.test.file_path="data/fsd10/FSD_train_data.npy"
--w:output/STGCN/STGCN_epoch_00001.pdparams
+-w:./test_tipc/output/STGCN/STGCN_epoch_00001.pdparams
 ##
 ===========================infer_params===========================
 -o:inference/STGCN
 -p:null
-norm_export:tools/export_model.py -c configs/recognition/stgcn/stgcn_fsd.yaml
+norm_export:tools/export_model.py -c configs/recognition/stgcn/stgcn_fsd.yaml --save_name inference
 quant_export:null
 fpgm_export:null
 distill_export:null
@@ -44,8 +44,10 @@ inference:tools/predict.py --config configs/recognition/stgcn/stgcn_fsd.yaml
 --batch_size:1|2
 --use_tensorrt:False|True
 --precision:fp32|fp16
---model_file:inference/STGCN/STGCN.pdmodel
+--model_file:inference.pdmodel
 --input_file:./data/fsd10/example_skeleton.npy
 null:null
 --enable_benchmark:True
---params_file:inference/STGCN/STGCN.pdiparams
+--params_file:inference.pdiparams
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[2, 350, 25, 1]}]

test_tipc/configs/SlowFast/SlowFast_train_infer_python.txt

@@ -5,7 +5,7 @@ gpu_list:0|0,1
 Global.use_gpu:null|null
 Global.auto_cast:null
 -o epochs:2
-null:null
+-o output_dir:null
 -o DATASET.batch_size:null
 -o MODEL.backbone.pretrained:null
 train_model_name:null
@@ -22,12 +22,12 @@ null:null
 ##
 ===========================eval_params===========================
 eval:main.py --test -c configs/recognition/slowfast/slowfast.yaml
--w:output/SlowFast/SlowFast_best.pdparams
+-w:./test_tipc/output/SlowFast/SlowFast_epoch_00001.pdparams
 ##
 ===========================infer_params===========================
 -o:inference/SlowFast
 -p:null
-norm_export:tools/export_model.py -c configs/recognition/slowfast/slowfast.yaml
+norm_export:tools/export_model.py -c configs/recognition/slowfast/slowfast.yaml --save_name inference
 quant_export:null
 fpgm_export:null
 distill_export:null
@@ -44,14 +44,16 @@ inference:tools/predict.py --config configs/recognition/slowfast/slowfast.yaml
 --batch_size:1|2
 --use_tensorrt:False|True
 --precision:fp32|fp16
---model_file:inference/SlowFast/SlowFast.pdmodel
+--model_file:inference.pdmodel
 --input_file:./data/example.avi
 null:null
 --enable_benchmark:True
---params_file:inference/SlowFast/SlowFast.pdiparams
+--params_file:inference.pdiparams
 ===========================train_benchmark_params==========================
 batch_size:8
 fp_items:fp32
 epoch:1
 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
 flags:FLAGS_conv_workspace_size_limit=800
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3, 4, 256, 256]},{float32,[3, 32, 256, 256]}]

test_tipc/configs/TSM/TSM_train_infer_python.txt

@@ -5,7 +5,7 @@ gpu_list:0|0,1
 Global.use_gpu:null|null
 Global.auto_cast:null
 -o epochs:2
-null:null
+-o output_dir:null
 -o DATASET.batch_size:2
 -o MODEL.backbone.pretrained:'data/ResNet50_pretrain.pdparams'
 train_model_name:null
@@ -22,12 +22,12 @@ null:null
 ##
 ===========================eval_params===========================
 eval:main.py --test -c configs/recognition/tsm/tsm_k400_frames.yaml
--w:output/TSM/TSM_best.pdparams
+-w:./test_tipc/output/TSM/TSM_epoch_00001.pdparams
 ##
 ===========================infer_params===========================
 -o:inference/TSM
 -p:null
-norm_export:tools/export_model.py -c configs/recognition/tsm/tsm_k400_frames.yaml
+norm_export:tools/export_model.py -c configs/recognition/tsm/tsm_k400_frames.yaml --save_name inference
 quant_export:null
 fpgm_export:null
 distill_export:null
@@ -44,14 +44,16 @@ inference:tools/predict.py --config configs/recognition/tsm/tsm_k400_frames.yaml
 --batch_size:1|2
 --use_tensorrt:False|True
 --precision:fp32|fp16
---model_file:inference/TSM/TSM.pdmodel
+--model_file:inference.pdmodel
 --input_file:./data/example.avi
 null:null
 --enable_benchmark:True
---params_file:inference/TSM/TSM.pdiparams
+--params_file:inference.pdiparams
 ===========================train_benchmark_params==========================
 batch_size:30
 fp_items:fp32|fp16
 epoch:1
 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
 flags:FLAGS_conv_workspace_size_limit=800
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[8, 3, 224, 224]}]

test_tipc/configs/TSN/TSN_train_infer_python.txt

@@ -5,7 +5,7 @@ gpu_list:0|0,1
 Global.use_gpu:null|null
 Global.auto_cast:null
 -o epochs:2
-null:null
+-o output_dir:null
 -o DATASET.batch_size:2
 -o MODEL.backbone.pretrained:'data/ResNet50_pretrain.pdparams'
 train_model_name:null
@@ -22,12 +22,12 @@ null:null
 ##
 ===========================eval_params===========================
 eval:main.py --test -c configs/recognition/tsn/tsn_k400_frames.yaml
--w:output/TSN/TSN_best.pdparams
+-w:./test_tipc/output/TSN/TSN_epoch_00001.pdparams
 ##
 ===========================infer_params===========================
 -o:inference/TSN
 -p:null
-norm_export:tools/export_model.py -c configs/recognition/tsn/tsn_k400_frames.yaml
+norm_export:tools/export_model.py -c configs/recognition/tsn/tsn_k400_frames.yaml --save_name inference
 quant_export:null
 fpgm_export:null
 distill_export:null
@@ -44,14 +44,16 @@ inference:tools/predict.py --config configs/recognition/tsn/tsn_k400_frames.yaml
 --batch_size:1|2
 --use_tensorrt:False|True
 --precision:fp32|fp16
---model_file:inference/TSN/TSN.pdmodel
+--model_file:inference.pdmodel
 --input_file:./data/example.avi
 null:null
 --enable_benchmark:True
---params_file:inference/TSN/TSN.pdiparams
+--params_file:inference.pdiparams
 ===========================train_benchmark_params==========================
 batch_size:32
 fp_items:fp32
 epoch:1
 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
 flags:FLAGS_conv_workspace_size_limit=800
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[250, 3, 224, 224]}]