add benchmark_infer (#590)

* add benchmark_infer

ppgan/models/base_model.py

@@ -183,7 +183,7 @@ class BaseModel(ABC):
                 for param in net.parameters():
                     param.trainable = requires_grad
 
-    def export_model(self, export_model, output_dir=None, inputs_size=[], export_serving_model=False):
+    def export_model(self, export_model, output_dir=None, inputs_size=[], export_serving_model=False, model_name=None):
         inputs_num = 0
         for net in export_model:
             input_spec = [
@@ -196,11 +196,13 @@ class BaseModel(ABC):
                                                 input_spec=input_spec)
             if output_dir is None:
                 output_dir = 'inference_model'
+            if model_name is None:
+                model_name = '{}_{}'.format(self.__class__.__name__.lower(),
+                                               net["name"])
             paddle.jit.save(
                 static_model,
                 os.path.join(
-                    output_dir, '{}_{}'.format(self.__class__.__name__.lower(),
-                                               net["name"])))
+                    output_dir, model_name))
             if export_serving_model:
                 from paddle_serving_client.io import inference_model_to_serving
                 model_name = '{}_{}'.format(self.__class__.__name__.lower(),

ppgan/models/firstorder_model.py

@@ -214,7 +214,7 @@ class FirstOrderModel(BaseModel):
                                  kp_driving=kp_norm)
             return out['prediction']
 
-    def export_model(self, export_model=None, output_dir=None, inputs_size=[], export_serving_model=False):
+    def export_model(self, export_model=None, output_dir=None, inputs_size=[], export_serving_model=False, model_name=None):
 
         source = paddle.rand(shape=inputs_size[0], dtype='float32')
         driving = paddle.rand(shape=inputs_size[1], dtype='float32')

ppgan/models/styleganv2_model.py

@@ -310,13 +310,16 @@ class StyleGAN2Model(BaseModel):
                      export_model=None,
                      output_dir=None,
                      inputs_size=[[1, 1, 512], [1, 1]],
-                     export_serving_model=False):
+                     export_serving_model=False,
+                     model_name=None):
         infer_generator = self.InferGenerator()
         infer_generator.set_generator(self.nets['gen'])
         style = paddle.rand(shape=inputs_size[0], dtype='float32')
         truncation = paddle.rand(shape=inputs_size[1], dtype='float32')
         if output_dir is None:
             output_dir = 'inference_model'
+        if model_name is None:
+            model_name = "stylegan2model_gen"
         paddle.jit.save(infer_generator,
-                        os.path.join(output_dir, "stylegan2model_gen"),
+                        os.path.join(output_dir, model_name),
                         input_spec=[style, truncation])

test_tipc/configs/CycleGAN/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0|0,1
 ##
 auto_cast:null
-epochs:lite_train_lite_infer=1|whole_train_whole_infer=200
+epochs:lite_train_lite_infer=1|lite_train_whole_infer=1|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
@@ -27,13 +27,13 @@ null:null
 ===========================infer_params===========================
 --output_dir:./output/
 load:null
-norm_export:tools/export_model.py -c configs/cyclegan_horse2zebra.yaml  --inputs_size="-1,3,-1,-1;-1,3,-1,-1" --load 
+norm_export:tools/export_model.py -c configs/cyclegan_horse2zebra.yaml  --inputs_size="-1,3,-1,-1;-1,3,-1,-1"  --model_name inference --load 
 quant_export:null 
 fpgm_export:null
 distill_export:null
 export1:null
 export2:null
-inference_dir:cycleganmodel_netG_A
+inference_dir:inference
 train_model:./inference/cyclegan_horse2zebra/cycleganmodel_netG_A
 infer_export:null
 infer_quant:False
@@ -55,3 +55,5 @@ fp_items:fp32
 epoch:1
 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
 flags:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,256,256]}]
\ No newline at end of file

test_tipc/configs/FOMM/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0
 ##
 auto_cast:null
-epochs:lite_train_lite_infer=1|whole_train_whole_infer=100
+epochs:lite_train_lite_infer=1|lite_train_whole_infer=1|whole_train_whole_infer=100
 output_dir:./output/
 dataset.train.batch_size:lite_train_lite_infer=8|whole_train_whole_infer=8
 pretrained_model:null

test_tipc/configs/Pix2pix/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0
 ##
 auto_cast:null
-epochs:lite_train_lite_infer=10|whole_train_whole_infer=200
+epochs: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
@@ -27,13 +27,13 @@ null:null
 ===========================infer_params===========================
 --output_dir:./output/
 load:null
-norm_export:tools/export_model.py -c configs/pix2pix_facades.yaml --inputs_size="-1,3,-1,-1" --load 
+norm_export:tools/export_model.py -c configs/pix2pix_facades.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:pix2pixmodel_netG
+inference_dir:inference
 train_model:./inference/pix2pix_facade/pix2pixmodel_netG
 infer_export:null
 infer_quant:False
@@ -55,3 +55,5 @@ fp_items:fp32
 epoch:10
 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
 flags:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,256,256]}]

test_tipc/configs/StyleGANv2/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0
 ##
 auto_cast:null
-total_iters:lite_train_lite_infer=10|whole_train_whole_infer=800
+total_iters:lite_train_lite_infer=10|lite_train_whole_infer=10|whole_train_whole_infer=800
 output_dir:./output/
 dataset.train.batch_size:lite_train_lite_infer=3|whole_train_whole_infer=3
 pretrained_model:null
@@ -27,13 +27,13 @@ null:null
 ===========================infer_params===========================
 --output_dir:./output/
 load:null
-norm_export:tools/export_model.py -c configs/stylegan_v2_256_ffhq.yaml  --inputs_size="1,1,512;1,1" --load 
+norm_export:tools/export_model.py -c configs/stylegan_v2_256_ffhq.yaml  --inputs_size="1,1,512;1,1"  --model_name inference --load 
 quant_export:null 
 fpgm_export:null
 distill_export:null
 export1:null
 export2:null
-inference_dir:stylegan2model_gen
+inference_dir:inference
 train_model:./inference/stylegan2/stylegan2model_gen
 infer_export:null
 infer_quant:False
@@ -55,3 +55,5 @@ fp_items:fp32
 epoch:100
 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
 flags:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[1，512]}, {float32,[1]}]
\ No newline at end of file

test_tipc/configs/basicvsr/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0
 ##
 auto_cast:null
-total_iters:lite_train_lite_infer=10|whole_train_whole_infer=200
+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
@@ -27,13 +27,13 @@ null:null
 ===========================infer_params===========================
 --output_dir:./output/
 load:null
-norm_export:tools/export_model.py -c configs/basicvsr_reds.yaml  --inputs_size="1,6,3,180,320" --load 
+norm_export:tools/export_model.py -c configs/basicvsr_reds.yaml  --inputs_size="1,6,3,180,320" --model_name inference --load 
 quant_export:null 
 fpgm_export:null
 distill_export:null
 export1:null
 export2:null
-inference_dir:basicvsrmodel_generator
+inference_dir:inference
 train_model:./inference/basicvsr/basicvsrmodel_generator
 infer_export:null
 infer_quant:False
@@ -55,3 +55,5 @@ 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]}]
\ No newline at end of file

test_tipc/configs/msvsr/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0
 ##
 auto_cast:null
-total_iters:lite_train_lite_infer=10|whole_train_whole_infer=200
+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
@@ -27,13 +27,13 @@ null:null
 ===========================infer_params===========================
 --output_dir:./output/
 load:null
-norm_export:tools/export_model.py -c configs/msvsr_reds.yaml  --inputs_size="1,2,3,180,320" --load 
+norm_export:tools/export_model.py -c configs/msvsr_reds.yaml  --inputs_size="1,2,3,180,320"  --model_name inference --load 
 quant_export:null 
 fpgm_export:null
 distill_export:null
 export1:null
 export2:null
-inference_dir:multistagevsrmodel_generator
+inference_dir:inference
 train_model:./inference/msvsr/multistagevsrmodel_generator
 infer_export:null
 infer_quant:False
@@ -49,3 +49,5 @@ null:null
 null:null
 --benchmark:True
 null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[2,3,180,320]}]

test_tipc/test_train_inference_python.sh

@@ -73,7 +73,7 @@ save_log_key=$(func_parser_key "${lines[48]}")
 infer_key1=$(func_parser_key "${lines[50]}")
 infer_value1=$(func_parser_value "${lines[50]}")
 
-LOG_PATH="./test_tipc/output"
+LOG_PATH="./test_tipc/output/${model_name}"
 mkdir -p ${LOG_PATH}
 status_log="${LOG_PATH}/results_python.log"
 

tools/export_model.py

@@ -57,6 +57,12 @@ def parse_args():
         type=bool,
         help="export serving model.",
     )
+    parser.add_argument(
+        "--model_name",
+        default=None,
+        type=str,
+        help="model_name.",
+    )
     args = parser.parse_args()
     return args
 
@@ -70,7 +76,8 @@ def main(args, cfg):
     for net_name, net in model.nets.items():
         if net_name in state_dicts:
             net.set_state_dict(state_dicts[net_name])
-    model.export_model(cfg.export_model, args.output_dir, inputs_size, args.export_serving_model)
+    model.export_model(cfg.export_model, args.output_dir, inputs_size, 
+                       args.export_serving_model, args.model_name)
 
 
 if __name__ == "__main__":