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fix quant logic (#1941) 

* fix quant logic

* add support for trt+int8 inference

* add support for swin quant

* fix swin and quant

* fix assert info

* fix assert info

* fix log
pull/1922/head^2
littletomatodonkey 2022-05-23 19:04:27 +08:00 committed by GitHub
parent 821e550900
commit 6dac07f68d
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GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 54 additions and 31 deletions
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9
deploy/python/predict_cls.py
View File

@ -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",

30
deploy/utils/predictor.py
View File

@ -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

4
ppcls/arch/__init__.py
View File

@ -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:

2
ppcls/arch/backbone/__init__.py
View File

@ -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

6
ppcls/arch/backbone/model_zoo/swin_transformer.py → ppcls/arch/backbone/legendary_models/swin_transformer.py
View File

@ -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

4
ppcls/arch/slim/quant.py
View File

@ -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:")

30
ppcls/engine/engine.py
View File

@ -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']}\"."