[TIPC]Support yolov3 dy2st train (#7325)

* Support yolov3 dy2st train

* Fix device_num

* Polish apply_to_static

* Modify config txt to support dy2st tipc

ppdet/engine/export_utils.py

@@ -57,6 +57,39 @@ TRT_MIN_SUBGRAPH = {
 KEYPOINT_ARCH = ['HigherHRNet', 'TopDownHRNet']
 MOT_ARCH = ['DeepSORT', 'JDE', 'FairMOT', 'ByteTrack']
 
+TO_STATIC_SPEC = {
+    'yolov3_darknet53_270e_coco': [{
+        'im_id': paddle.static.InputSpec(
+            name='im_id', shape=[-1, 1], dtype='float32'),
+        'is_crowd': paddle.static.InputSpec(
+            name='is_crowd', shape=[-1, 50], dtype='float32'),
+        'gt_bbox': paddle.static.InputSpec(
+            name='gt_bbox', shape=[-1, 50, 4], dtype='float32'),
+        'curr_iter': paddle.static.InputSpec(
+            name='curr_iter', shape=[-1], dtype='float32'),
+        'image': paddle.static.InputSpec(
+            name='image', shape=[-1, 3, -1, -1], dtype='float32'),
+        'im_shape': paddle.static.InputSpec(
+            name='im_shape', shape=[-1, 2], dtype='float32'),
+        'scale_factor': paddle.static.InputSpec(
+            name='scale_factor', shape=[-1, 2], dtype='float32'),
+        'target0': paddle.static.InputSpec(
+            name='target0', shape=[-1, 3, 86, -1, -1], dtype='float32'),
+        'target1': paddle.static.InputSpec(
+            name='target1', shape=[-1, 3, 86, -1, -1], dtype='float32'),
+        'target2': paddle.static.InputSpec(
+            name='target2', shape=[-1, 3, 86, -1, -1], dtype='float32'),
+    }],
+}
+
+
+def apply_to_static(config, model):
+    filename = config.get('filename', None)
+    spec = TO_STATIC_SPEC.get(filename, None)
+    model = paddle.jit.to_static(model, input_spec=spec)
+    logger.info("Successfully to apply @to_static with specs: {}".format(spec))
+    return model
+
 
 def _prune_input_spec(input_spec, program, targets):
     # try to prune static program to figure out pruned input spec

ppdet/engine/trainer.py

@@ -48,7 +48,7 @@ from ppdet.utils import profiler
 from ppdet.modeling.post_process import multiclass_nms
 
 from .callbacks import Callback, ComposeCallback, LogPrinter, Checkpointer, WiferFaceEval, VisualDLWriter, SniperProposalsGenerator, WandbCallback
-from .export_utils import _dump_infer_config, _prune_input_spec
+from .export_utils import _dump_infer_config, _prune_input_spec, apply_to_static
 
 from paddle.distributed.fleet.utils.hybrid_parallel_util import fused_allreduce_gradients
 
@@ -419,6 +419,8 @@ class Trainer(object):
                 "EvalDataset")()
 
         model = self.model
+        if self.cfg.get('to_static', False):
+            model = apply_to_static(self.cfg, model)
         sync_bn = (getattr(self.cfg, 'norm_type', None) == 'sync_bn' and
                    (self.cfg.use_gpu or self.cfg.use_mlu) and self._nranks > 1)
         if sync_bn:

test_tipc/benchmark_train.sh

@@ -165,6 +165,16 @@ else
     device_num_list=($device_num)
 fi
 
+# for log name
+to_static=""
+# parse "to_static" options and modify trainer into "to_static_trainer"
+if [[ ${model_type} = "dynamicTostatic" ]];then
+    to_static="d2sT_"
+    sed -i 's/trainer:norm_train/trainer:to_static_train/g' $FILENAME
+fi
+
+
+
 if [[ ${model_name} =~ "higherhrnet" ]] || [[ ${model_name} =~ "hrnet" ]] || [[ ${model_name} =~ "tinypose" ]];then
     echo "${model_name} run on full coco dataset"
     epoch=$(set_dynamic_epoch $device_num $epoch)
@@ -189,7 +199,7 @@ for batch_size in ${batch_size_list[*]}; do
             if [ ${#gpu_id} -le 1 ];then
                 log_path="$SAVE_LOG/profiling_log"
                 mkdir -p $log_path
-                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_profiling"
+                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_${to_static}profiling"
                 func_sed_params "$FILENAME" "${line_gpuid}" "0"  # sed used gpu_id
                 # set profile_option params
                 tmp=`sed -i "${line_profile}s/.*/${profile_option}/" "${FILENAME}"`
@@ -205,8 +215,8 @@ for batch_size in ${batch_size_list[*]}; do
                 speed_log_path="$SAVE_LOG/index"
                 mkdir -p $log_path
                 mkdir -p $speed_log_path
-                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_log"
-                speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_speed"
+                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_${to_static}log"
+                speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_${to_static}speed"
                 func_sed_params "$FILENAME" "${line_profile}" "null"  # sed profile_id as null
                 cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
                 echo $cmd
@@ -240,8 +250,8 @@ for batch_size in ${batch_size_list[*]}; do
                 speed_log_path="$SAVE_LOG/index"
                 mkdir -p $log_path
                 mkdir -p $speed_log_path
-                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_log"
-                speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_speed"
+                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_${to_static}log"
+                speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_${to_static}speed"
                 func_sed_params "$FILENAME" "${line_gpuid}" "$gpu_id"  # sed used gpu_id
                 func_sed_params "$FILENAME" "${line_profile}" "null"  # sed --profile_option as null
                 cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "

test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_infer_python.txt

@@ -17,7 +17,7 @@ norm_train:tools/train.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml -o wo
 pact_train:tools/train.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --slim_config configs/slim/quant/yolov3_darknet_qat.yml -o
 fpgm_train:tools/train.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --slim_config configs/slim/prune/yolov3_darknet_prune_fpgm.yml -o
 distill_train:null
-null:null
+to_static_train:--to_static
 null:null
 ##
 ===========================eval_params===========================

test_tipc/test_train_inference_python.sh

@@ -41,8 +41,8 @@ fpgm_key=$(func_parser_key "${lines[17]}")
 fpgm_trainer=$(func_parser_value "${lines[17]}")
 distill_key=$(func_parser_key "${lines[18]}")
 distill_trainer=$(func_parser_value "${lines[18]}")
-trainer_key1=$(func_parser_key "${lines[19]}")
-trainer_value1=$(func_parser_value "${lines[19]}")
+to_static_key=$(func_parser_key "${lines[19]}")
+to_static_trainer=$(func_parser_value "${lines[19]}")
 trainer_key2=$(func_parser_key "${lines[20]}")
 trainer_value2=$(func_parser_value "${lines[20]}")
 
@@ -237,6 +237,7 @@ else
         for autocast in ${autocast_list[*]}; do
             for trainer in ${trainer_list[*]}; do
                 flag_quant=False
+                set_to_static=""
                 if [ ${trainer} = "${norm_key}" ]; then
                     run_train=${norm_trainer}
                     run_export=${norm_export}
@@ -250,9 +251,10 @@ else
                 elif [ ${trainer} = "${distill_key}" ]; then
                     run_train=${distill_trainer}
                     run_export=${distill_export}
-                elif [ ${trainer} = "${trainer_key1}" ]; then
-                    run_train=${trainer_value1}
-                    run_export=${export_value1}
+                elif [ ${trainer} = "${to_static_key}" ]; then
+                    run_train=${norm_trainer}
+                    run_export=${norm_export}
+                    set_to_static=${to_static_trainer}
                 elif [ ${trainer} = "${trainer_key2}" ]; then
                     run_train=${trainer_value2}
                     run_export=${export_value2}
@@ -289,9 +291,9 @@ else
                 set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
                 nodes="1"
                 if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
-                    cmd="${python} ${run_train} LearningRate.base_lr=0.0001 log_iter=1 ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_filename} ${set_shuffle} ${set_amp_level} ${set_enable_ce} ${set_autocast} ${set_train_params1}"
+                    cmd="${python} ${run_train} LearningRate.base_lr=0.0001 log_iter=1 ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_filename} ${set_shuffle} ${set_amp_level} ${set_enable_ce} ${set_autocast} ${set_to_static} ${set_train_params1}"
                 elif [ ${#ips} -le 15 ];then  # train with multi-gpu
-                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} log_iter=1 ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_filename} ${set_shuffle} ${set_amp_level} ${set_enable_ce} ${set_autocast} ${set_train_params1}"
+                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} log_iter=1 ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_filename} ${set_shuffle} ${set_amp_level} ${set_enable_ce} ${set_autocast} ${set_to_static} ${set_train_params1}"
                 else     # train with multi-machine
                     IFS=","
                     ips_array=(${ips})
@@ -299,7 +301,7 @@ else
                     save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
                     IFS="|"
                     set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
-                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} log_iter=1 ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_filename} ${set_shuffle} ${set_amp_level} ${set_enable_ce} ${set_autocast} ${set_train_params1}"
+                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} log_iter=1 ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_filename} ${set_shuffle} ${set_amp_level} ${set_enable_ce} ${set_autocast} ${set_to_static} ${set_train_params1}"
                 fi
                 # run train
                 train_log_path="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}.log"

tools/train.py

@@ -103,6 +103,11 @@ def parse_args():
         type=str,
         default="sniper/proposals.json",
         help='Train proposals directory')
+    parser.add_argument(
+        "--to_static",
+        action='store_true',
+        default=False,
+        help="Enable dy2st to train.")
 
     args = parser.parse_args()
     return args