Merge branch 'PaddlePaddle:develop' into develop

deploy/python/predict_cls.py

@@ -49,10 +49,15 @@ class ClsPredictor(Predictor):
             pid = os.getpid()
             size = config["PreProcess"]["transform_ops"][1]["CropImage"][
                 "size"]
+            if config["Global"].get("use_int8", False):
+                precision = "int8"
+            elif config["Global"].get("use_fp16", False):
+                precision = "fp16"
+            else:
+                precision = "fp32"
             self.auto_logger = auto_log.AutoLogger(
                 model_name=config["Global"].get("model_name", "cls"),
-                model_precision='fp16'
-                if config["Global"]["use_fp16"] else 'fp32',
+                model_precision=precision,
                 batch_size=config["Global"].get("batch_size", 1),
                 data_shape=[3, size, size],
                 save_path=config["Global"].get("save_log_path",

deploy/utils/predictor.py

@@ -42,8 +42,22 @@ class Predictor(object):
     def create_paddle_predictor(self, args, inference_model_dir=None):
         if inference_model_dir is None:
             inference_model_dir = args.inference_model_dir
-        params_file = os.path.join(inference_model_dir, "inference.pdiparams")
-        model_file = os.path.join(inference_model_dir, "inference.pdmodel")
+        if "inference_int8.pdiparams" in os.listdir(inference_model_dir):
+            params_file = os.path.join(inference_model_dir,
+                                       "inference_int8.pdiparams")
+            model_file = os.path.join(inference_model_dir,
+                                      "inference_int8.pdmodel")
+            assert args.get(
+                "use_fp16", False
+            ) is False, "fp16 mode is not supported for int8 model inference, please set use_fp16 as False during inference."
+        else:
+            params_file = os.path.join(inference_model_dir,
+                                       "inference.pdiparams")
+            model_file = os.path.join(inference_model_dir, "inference.pdmodel")
+            assert args.get(
+                "use_int8", False
+            ) is False, "int8 mode is not supported for fp32 model inference, please set use_int8 as False during inference."
+
         config = Config(model_file, params_file)
 
         if args.use_gpu:
@@ -63,12 +77,18 @@ class Predictor(object):
         config.disable_glog_info()
         config.switch_ir_optim(args.ir_optim)  # default true
         if args.use_tensorrt:
+            precision = Config.Precision.Float32
+            if args.get("use_int8", False):
+                precision = Config.Precision.Int8
+            elif args.get("use_fp16", False):
+                precision = Config.Precision.Half
+
             config.enable_tensorrt_engine(
-                precision_mode=Config.Precision.Half
-                if args.use_fp16 else Config.Precision.Float32,
+                precision_mode=precision,
                 max_batch_size=args.batch_size,
                 workspace_size=1 << 30,
-                min_subgraph_size=30)
+                min_subgraph_size=30,
+                use_calib_mode=False)
 
         config.enable_memory_optim()
         # use zero copy

docs/zh_CN/samples/.gitkeep

-

ppcls/arch/__init__.py

@@ -33,7 +33,7 @@ from ppcls.arch.distill.afd_attention import LinearTransformStudent, LinearTrans
 __all__ = ["build_model", "RecModel", "DistillationModel", "AttentionModel"]
 
 
-def build_model(config):
+def build_model(config, mode="train"):
     arch_config = copy.deepcopy(config["Arch"])
     model_type = arch_config.pop("name")
     use_sync_bn = arch_config.pop("use_sync_bn", False)
@@ -44,7 +44,7 @@ def build_model(config):
 
     if isinstance(arch, TheseusLayer):
         prune_model(config, arch)
-        quantize_model(config, arch)
+        quantize_model(config, arch, mode)
 
     logger.info("The FLOPs and Params of Arch:")
     try:

ppcls/arch/backbone/__init__.py

@@ -52,7 +52,7 @@ from ppcls.arch.backbone.model_zoo.darknet import DarkNet53
 from ppcls.arch.backbone.model_zoo.regnet import RegNetX_200MF, RegNetX_4GF, RegNetX_32GF, RegNetY_200MF, RegNetY_4GF, RegNetY_32GF
 from ppcls.arch.backbone.model_zoo.vision_transformer import ViT_small_patch16_224, ViT_base_patch16_224, ViT_base_patch16_384, ViT_base_patch32_384, ViT_large_patch16_224, ViT_large_patch16_384, ViT_large_patch32_384
 from ppcls.arch.backbone.model_zoo.distilled_vision_transformer import DeiT_tiny_patch16_224, DeiT_small_patch16_224, DeiT_base_patch16_224, DeiT_tiny_distilled_patch16_224, DeiT_small_distilled_patch16_224, DeiT_base_distilled_patch16_224, DeiT_base_patch16_384, DeiT_base_distilled_patch16_384
-from ppcls.arch.backbone.model_zoo.swin_transformer import SwinTransformer_tiny_patch4_window7_224, SwinTransformer_small_patch4_window7_224, SwinTransformer_base_patch4_window7_224, SwinTransformer_base_patch4_window12_384, SwinTransformer_large_patch4_window7_224, SwinTransformer_large_patch4_window12_384
+from ppcls.arch.backbone.legendary_models.swin_transformer import SwinTransformer_tiny_patch4_window7_224, SwinTransformer_small_patch4_window7_224, SwinTransformer_base_patch4_window7_224, SwinTransformer_base_patch4_window12_384, SwinTransformer_large_patch4_window7_224, SwinTransformer_large_patch4_window12_384
 from ppcls.arch.backbone.model_zoo.cswin_transformer import CSWinTransformer_tiny_224, CSWinTransformer_small_224, CSWinTransformer_base_224, CSWinTransformer_large_224, CSWinTransformer_base_384, CSWinTransformer_large_384
 from ppcls.arch.backbone.model_zoo.mixnet import MixNet_S, MixNet_M, MixNet_L
 from ppcls.arch.backbone.model_zoo.rexnet import ReXNet_1_0, ReXNet_1_3, ReXNet_1_5, ReXNet_2_0, ReXNet_3_0

ppcls/arch/backbone/model_zoo/swin_transformer.py → ppcls/arch/backbone/legendary_models/swin_transformer.py

@@ -21,8 +21,8 @@ import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle.nn.initializer import TruncatedNormal, Constant
 
-from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
-
+from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
+from ppcls.arch.backbone.model_zoo.vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
 from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
@@ -589,7 +589,7 @@ class PatchEmbed(nn.Layer):
         return flops
 
 
-class SwinTransformer(nn.Layer):
+class SwinTransformer(TheseusLayer):
     """ Swin Transformer
         A PaddlePaddle impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
           https://arxiv.org/pdf/2103.14030

ppcls/arch/slim/quant.py

@@ -40,12 +40,14 @@ QUANT_CONFIG = {
 }
 
 
-def quantize_model(config, model):
+def quantize_model(config, model, mode="train"):
     if config.get("Slim", False) and config["Slim"].get("quant", False):
         from paddleslim.dygraph.quant import QAT
         assert config["Slim"]["quant"]["name"].lower(
         ) == 'pact', 'Only PACT quantization method is supported now'
         QUANT_CONFIG["activation_preprocess_type"] = "PACT"
+        if mode in ["infer", "export"]:
+            QUANT_CONFIG['activation_preprocess_type'] = None
         model.quanter = QAT(config=QUANT_CONFIG)
         model.quanter.quantize(model)
         logger.info("QAT model summary:")

ppcls/engine/engine.py

@@ -189,7 +189,7 @@ class Engine(object):
             self.eval_metric_func = None
 
         # build model
-        self.model = build_model(self.config)
+        self.model = build_model(self.config, self.mode)
         # set @to_static for benchmark, skip this by default.
         apply_to_static(self.config, self.model)
 
@@ -472,23 +472,19 @@ class Engine(object):
 
         save_path = os.path.join(self.config["Global"]["save_inference_dir"],
                                  "inference")
-        if model.quanter:
-            model.quanter.save_quantized_model(
-                model.base_model,
-                save_path,
-                input_spec=[
-                    paddle.static.InputSpec(
-                        shape=[None] + self.config["Global"]["image_shape"],
-                        dtype='float32')
-                ])
+
+        model = paddle.jit.to_static(
+            model,
+            input_spec=[
+                paddle.static.InputSpec(
+                    shape=[None] + self.config["Global"]["image_shape"],
+                    dtype='float32')
+            ])
+        if hasattr(model.base_model,
+                   "quanter") and model.base_model.quanter is not None:
+            model.base_model.quanter.save_quantized_model(model,
+                                                          save_path + "_int8")
         else:
-            model = paddle.jit.to_static(
-                model,
-                input_spec=[
-                    paddle.static.InputSpec(
-                        shape=[None] + self.config["Global"]["image_shape"],
-                        dtype='float32')
-                ])
             paddle.jit.save(model, save_path)
         logger.info(
             f"Export succeeded! The inference model exported has been saved in \"{self.config['Global']['save_inference_dir']}\"."