[TIPC] fix bug in train benchmark (#6509)

deploy/python/infer.py

@@ -152,17 +152,11 @@ class Detector(object):
     def postprocess(self, inputs, result):
         # postprocess output of predictor
         np_boxes_num = result['boxes_num']
-        out_result = {k: [] for k, v in result.items() if v is not None}
-        idx = 0
-        for num_box in np_boxes_num:
-            for k, v in out_result.items():
-                v.append(result[k][idx:idx + num_box])
-            idx += num_box
-            if num_box == 0:
+        if sum(np_boxes_num) <= 0:
             print('[WARNNING] No object detected.')
-        out_result = {k: np.concatenate(v) for k, v in out_result.items()}
-        out_result['boxes_num'] = result['boxes_num']
-        return out_result
+            result = {'boxes': np.zeros([0, 6]), 'boxes_num': [0]}
+        result = {k: v for k, v in result.items() if v is not None}
+        return result
 
     def filter_box(self, result, threshold):
         np_boxes_num = result['boxes_num']

ppdet/data/source/coco.py

@@ -39,6 +39,7 @@ class COCODataSet(DetDataset):
         empty_ratio (float): the ratio of empty record number to total 
             record's, if empty_ratio is out of [0. ,1.), do not sample the 
             records and use all the empty entries. 1. as default
+        repeat (int): repeat times for dataset, use in benchmark.
     """
 
     def __init__(self,
@@ -49,9 +50,15 @@ class COCODataSet(DetDataset):
                  sample_num=-1,
                  load_crowd=False,
                  allow_empty=False,
-                 empty_ratio=1.):
-        super(COCODataSet, self).__init__(dataset_dir, image_dir, anno_path,
-                                          data_fields, sample_num)
+                 empty_ratio=1.,
+                 repeat=1):
+        super(COCODataSet, self).__init__(
+            dataset_dir,
+            image_dir,
+            anno_path,
+            data_fields,
+            sample_num,
+            repeat=repeat)
         self.load_image_only = False
         self.load_semantic = False
         self.load_crowd = load_crowd

ppdet/data/source/dataset.py

@@ -38,6 +38,7 @@ class DetDataset(Dataset):
         data_fields (list): key name of data dictionary, at least have 'image'.
         sample_num (int): number of samples to load, -1 means all.
         use_default_label (bool): whether to load default label list.
+        repeat (int): repeat times for dataset, use in benchmark.
     """
 
     def __init__(self,
@@ -47,6 +48,7 @@ class DetDataset(Dataset):
                  data_fields=['image'],
                  sample_num=-1,
                  use_default_label=None,
+                 repeat=1,
                  **kwargs):
         super(DetDataset, self).__init__()
         self.dataset_dir = dataset_dir if dataset_dir is not None else ''
@@ -55,16 +57,19 @@ class DetDataset(Dataset):
         self.data_fields = data_fields
         self.sample_num = sample_num
         self.use_default_label = use_default_label
+        self.repeat = repeat
         self._epoch = 0
         self._curr_iter = 0
 
     def __len__(self, ):
-        return len(self.roidbs)
+        return len(self.roidbs) * self.repeat
 
     def __call__(self, *args, **kwargs):
         return self
 
     def __getitem__(self, idx):
+        if self.repeat > 1:
+            idx %= self.repeat
         # data batch
         roidb = copy.deepcopy(self.roidbs[idx])
         if self.mixup_epoch == 0 or self._epoch < self.mixup_epoch:

ppdet/data/source/mot.py

@@ -39,6 +39,7 @@ class MOTDataSet(DetDataset):
         image_lists (str|list): mot data image lists, muiti-source mot dataset.
         data_fields (list): key name of data dictionary, at least have 'image'.
         sample_num (int): number of samples to load, -1 means all.
+        repeat (int): repeat times for dataset, use in benchmark.
 
     Notes:
         MOT datasets root directory following this:
@@ -77,11 +78,13 @@ class MOTDataSet(DetDataset):
                  dataset_dir=None,
                  image_lists=[],
                  data_fields=['image'],
-                 sample_num=-1):
+                 sample_num=-1,
+                 repeat=1):
         super(MOTDataSet, self).__init__(
             dataset_dir=dataset_dir,
             data_fields=data_fields,
-            sample_num=sample_num)
+            sample_num=sample_num,
+            repeat=repeat)
         self.dataset_dir = dataset_dir
         self.image_lists = image_lists
         if isinstance(self.image_lists, str):
@@ -95,7 +98,8 @@ class MOTDataSet(DetDataset):
         # only used to get categories and metric
         # only check first data, but the label_list of all data should be same.
         first_mot_data = self.image_lists[0].split('.')[0]
-        anno_file = os.path.join(self.dataset_dir, first_mot_data, 'label_list.txt')
+        anno_file = os.path.join(self.dataset_dir, first_mot_data,
+                                 'label_list.txt')
         return anno_file
 
     def parse_dataset(self):
@@ -276,7 +280,8 @@ class MCMOTDataSet(DetDataset):
         # only used to get categories and metric
         # only check first data, but the label_list of all data should be same.
         first_mot_data = self.image_lists[0].split('.')[0]
-        anno_file = os.path.join(self.dataset_dir, first_mot_data, 'label_list.txt')
+        anno_file = os.path.join(self.dataset_dir, first_mot_data,
+                                 'label_list.txt')
         return anno_file
 
     def parse_dataset(self):
@@ -576,6 +581,7 @@ class MOTImageFolder(DetDataset):
     def get_anno(self):
         return self.anno_path
 
+
 def _is_valid_video(f, extensions=('.mp4', '.avi', '.mov', '.rmvb', 'flv')):
     return f.lower().endswith(extensions)
 

ppdet/data/source/voc.py

@@ -46,6 +46,7 @@ class VOCDataSet(DetDataset):
         empty_ratio (float): the ratio of empty record number to total 
             record's, if empty_ratio is out of [0. ,1.), do not sample the 
             records and use all the empty entries. 1. as default
+        repeat (int): repeat times for dataset, use in benchmark.
     """
 
     def __init__(self,
@@ -56,13 +57,15 @@ class VOCDataSet(DetDataset):
                  sample_num=-1,
                  label_list=None,
                  allow_empty=False,
-                 empty_ratio=1.):
+                 empty_ratio=1.,
+                 repeat=1):
         super(VOCDataSet, self).__init__(
             dataset_dir=dataset_dir,
             image_dir=image_dir,
             anno_path=anno_path,
             data_fields=data_fields,
-            sample_num=sample_num)
+            sample_num=sample_num,
+            repeat=repeat)
         self.label_list = label_list
         self.allow_empty = allow_empty
         self.empty_ratio = empty_ratio

test_tipc/benchmark_train.sh

@@ -83,6 +83,9 @@ line_num=`expr $line_num + 1`
 fp_items=$(func_parser_value "${lines[line_num]}")
 line_num=`expr $line_num + 1`
 epoch=$(func_parser_value "${lines[line_num]}")
+eval "sed -i '10i\    repeat: ${epoch}' configs/datasets/coco_detection.yml"
+eval "sed -i '10i\    repeat: ${epoch}' configs/datasets/coco_instance.yml"
+eval "sed -i '10i\    repeat: ${epoch}' configs/datasets/mot.yml"
 
 line_num=`expr $line_num + 1`
 profile_option_key=$(func_parser_key "${lines[line_num]}")
@@ -157,7 +160,7 @@ for batch_size in ${batch_size_list[*]}; do
             # sed batchsize and precision
             func_sed_params "$FILENAME" "${line_precision}" "$precision"
             func_sed_params "$FILENAME" "${line_batchsize}" "$MODE=$batch_size"
-            func_sed_params "$FILENAME" "${line_epoch}" "$MODE=$epoch"
+            func_sed_params "$FILENAME" "${line_epoch}" "$MODE=1"
             gpu_id=$(set_gpu_id $device_num)
 
             if [ ${#gpu_id} -le 1 ];then

test_tipc/configs/keypoint/tinypose_128x96_train_infer_python.txt

@@ -51,3 +51,9 @@ inference:./deploy/python/keypoint_infer.py
 null:null
 ===========================infer_benchmark_params===========================
 random_infer_input:[{float32,[3,128,96]}]
+===========================train_benchmark_params==========================
+batch_size:512
+fp_items:fp32|fp16
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:null
\ No newline at end of file

test_tipc/configs/picodet/picodet_lcnet_1_5x_416_coco_train_infer_python.txt

@@ -51,3 +51,9 @@ inference:./deploy/python/infer.py
 null:null
 ===========================infer_benchmark_params===========================
 numpy_infer_input:3x416x416_2.npy
+===========================train_benchmark_params==========================
+batch_size:80
+fp_items:fp32|fp16
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:null
\ No newline at end of file

test_tipc/configs/picodet/picodet_s_320_coco_lcnet_linux_gpu_normal_normal_paddle2onnx_python_linux_gpu_cpu.txt → test_tipc/configs/picodet/picodet_s_320_coco_lcnet_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

@@ -6,23 +6,25 @@ filename:null
 --output_dir:./output_inference
 weights:https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams
 norm_export:tools/export_model.py -c configs/picodet/picodet_s_320_coco_lcnet.yml -o
-quant_export:tools/export_model.py -c configs/picodet/picodet_s_320_coco_lcnet.yml --slim_config configs/picodet/picodet_s_320_coco_lcnet.yml -o
-fpgm_export:tools/export_model.py -c configs/picodet/picodet_s_320_coco_lcnet.yml --slim_config configs/picodet/picodet_s_320_coco_lcnet.yml -o
+quant_export:tools/export_model.py -c configs/picodet/picodet_s_320_coco_lcnet.yml --slim_config _template_pact -o
+fpgm_export:tools/export_model.py -c configs/picodet/picodet_s_320_coco_lcnet.yml --slim_config _template_fpgm -o
 distill_export:null
 export1:null
-export2:null
-kl_quant_export:tools/post_quant.py -c configs/picodet/picodet_s_320_coco_lcnet.yml --slim_config configs/picodet/picodet_s_320_coco_lcnet.yml -o
+export_param:null
+kl_quant_export:tools/post_quant.py -c configs/picodet/yolov3_darknet53_270e_coco.yml --slim_config _template_kl_quant -o
 ##
-2onnx: paddle2onnx
---model_dir:./output_inference/picodet_s_320_coco_lcnet/
+infer_mode:norm
+infer_quant:False
+cmd:paddle2onnx
+--model_dir:null
 --model_filename:model.pdmodel
 --params_filename:model.pdiparams
---save_file:./deploy/third_engine/demo_onnxruntime/onnx_file/picodet_s_320_coco.onnx
+--save_file:model.onnx
 --opset_version:11
-##
-inference:infer_demo.py
---modelpath:./onnx_file/picodet_s_320_coco.onnx
---img_fold:./imgs
---result_fold:results
-infer_mode:norm
-null:null
+--enable_onnx_checker:True
+paddle2onnx_param1:null
+infer_py:./deploy/third_engine/onnx/infer.py
+--infer_cfg:null
+--onnx_file:null
+--image_file:./demo/000000014439.jpg
+infer_param1:null
\ No newline at end of file

test_tipc/configs/picodet/picodet_s_320_coco_lcnet_train_infer_python.txt

@@ -51,3 +51,9 @@ inference:./deploy/python/infer.py
 null:null
 ===========================infer_benchmark_params===========================
 numpy_infer_input:3x320x320_2.npy
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32|fp16
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:null
\ No newline at end of file

test_tipc/configs/picodet/picodet_s_320_coco_train_infer_python.txt

@@ -51,3 +51,9 @@ inference:./deploy/python/infer.py
 null:null
 ===========================infer_benchmark_params===========================
 numpy_infer_input:3x320x320_2.npy
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32|fp16
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:null
\ No newline at end of file

test_tipc/configs/ppyolo/ppyolo_mbv3_large_coco_train_infer_python.txt

@@ -51,3 +51,9 @@ inference:./deploy/python/infer.py
 null:null
 ===========================infer_benchmark_params===========================
 numpy_infer_input:3x320x320.npy
+===========================train_benchmark_params==========================
+batch_size:24
+fp_items:fp32|fp16
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:null
\ No newline at end of file

test_tipc/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco_train_infer_python.txt

@@ -51,3 +51,9 @@ inference:./deploy/python/infer.py
 null:null
 ===========================infer_benchmark_params===========================
 numpy_infer_input:3x608x608.npy
+===========================train_benchmark_params==========================
+batch_size:24
+fp_items:fp32|fp16
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:null
\ No newline at end of file

test_tipc/configs/ppyolo/ppyolo_tiny_650e_coco_train_infer_python.txt

@@ -51,3 +51,9 @@ inference:./deploy/python/infer.py
 null:null
 ===========================infer_benchmark_params===========================
 numpy_infer_input:3x320x320.npy
+===========================train_benchmark_params==========================
+batch_size:32
+fp_items:fp32|fp16
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:null
\ No newline at end of file

test_tipc/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco_train_infer_python.txt

@@ -51,3 +51,9 @@ inference:./deploy/python/infer.py
 null:null
 ===========================infer_benchmark_params===========================
 numpy_infer_input:3x640x640.npy
+===========================train_benchmark_params==========================
+batch_size:12
+fp_items:fp32|fp16
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:null
\ No newline at end of file

test_tipc/configs/ppyoloe/ppyoloe_crn_s_300e_coco_train_infer_python.txt

@@ -51,3 +51,9 @@ inference:./deploy/python/infer.py
 --trt_max_shape:1600
 ===========================infer_benchmark_params===========================
 numpy_infer_input:3x640x640.npy
+===========================train_benchmark_params==========================
+batch_size:32
+fp_items:fp32|fp16
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:null
\ No newline at end of file