[clean fluid api] replace fluid/contrib/slim api. (#1615)

ce_tests/dygraph/quant/src/qat.py

@@ -31,7 +31,7 @@ from paddle.metric.metrics import Accuracy
 import paddle.vision.models as models
 
 from paddleslim import QAT
-from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
+from paddle.quantization import ImperativeQuantAware
 
 from imagenet_dataset import ImageNetDataset
 

ce_tests/dygraph/quant/src/save_quant_model.py

@@ -23,7 +23,7 @@ import numpy as np
 import time
 import paddle
 from paddle.fluid.framework import IrGraph
-from paddle.fluid.contrib.slim.quantization import Quant2Int8MkldnnPass
+from paddle.quantization import Quant2Int8MkldnnPass
 from paddle.framework import core
 
 paddle.enable_static()

demo/mkldnn_quant/README.md

@@ -54,7 +54,7 @@ import numpy as np
 
 为了部署在CPU上，我们将保存的quant模型，通过一个转化脚本，移除fake_quantize/fake_dequantize op，进行算子融合和优化并且转化为INT8模型。
 
-脚本在官网的位置为[save_quant_model.py](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/contrib/slim/tests/save_quant_model.py)。
+脚本在官网的位置为[save_quant_model.py](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/static/quantization/tests/save_quant_model.py)。
 
 复制脚本到本样例所在目录(`/PATH_TO_PaddleSlim/demo/mkldnn_quant/`)，并执行如下命令：
 ```
@@ -181,4 +181,4 @@ INT8模型精度和性能结果参考[CPU部署预测INT8模型的精度和性
 ## FAQ
 
 - 自然语言处理模型在CPU上的部署和预测参考样例[ERNIE 模型 QUANT INT8 精度与性能复现](https://github.com/PaddlePaddle/benchmark/tree/master/Inference/c++/ernie/mkldnn)
-- 具体DNNL量化原理可以查看[SLIM Quant for INT8 MKLDNN](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/contrib/slim/tests/README.md)。
+- 具体DNNL量化原理可以查看[SLIM Quant for INT8 MKLDNN](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/static/quantization/tests/README.md)。

demo/mkldnn_quant/README_en.md

@@ -45,7 +45,7 @@ To generate fake quantized model with quant-aware strategy, see [Quant-aware tra
 To generate post-training fake quantized model, see [Offline post-training quantization tutorial](https://paddleslim.readthedocs.io/en/latest/quick_start/index_en.html)
 
 ## 3. Convert the fake quantized model to DNNL INT8 model
-In order to deploy an INT8 model on the CPU, we need to collect scales, remove all fake_quantize/fake_dequantize operators, optimize the graph and quantize it, turning it into the final DNNL INT8 model. This is done by the script [save_quant_model.py](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/contrib/slim/tests/save_quant_model.py). Copy the script to the directory where the demo is located: `/PATH_TO_PaddleSlim/demo/mkldnn_quant/` and run it as follows:
+In order to deploy an INT8 model on the CPU, we need to collect scales, remove all fake_quantize/fake_dequantize operators, optimize the graph and quantize it, turning it into the final DNNL INT8 model. This is done by the script [save_quant_model.py](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/static/quantization/tests/save_quant_model.py). Copy the script to the directory where the demo is located: `/PATH_TO_PaddleSlim/demo/mkldnn_quant/` and run it as follows:
 ```
 python save_quant_model.py --quant_model_path=/PATH/TO/SAVE/FLOAT32/quant/MODEL --int8_model_save_path=/PATH/TO/SAVE/INT8/MODEL
 ```
@@ -176,4 +176,4 @@ For INT8 models accuracy and performance results see [CPU deployment predicts th
 ## FAQ
 
 - For deploying INT8 NLP models on CPU, see [ERNIE model quant INT8 accuracy and performance reproduction](https://github.com/PaddlePaddle/benchmark/tree/master/Inference/c++/ernie/mkldnn)
-- The detailed DNNL quantification process can be viewed in [SLIM quant for INT8 DNNL](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/contrib/slim/tests/README.md)
+- The detailed DNNL quantification process can be viewed in [SLIM quant for INT8 DNNL](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/static/quantization/tests/README.md)

docs/zh_cn/FAQ/quantization_FAQ.md

@@ -35,9 +35,9 @@ config->EnableTensorRtEngine(1 << 20      /* workspace_size*/,
                         false             /* use_calib_mode*/);
 ```
 
--  如果量化模型在x86上线，需要使用[INT8 MKL-DNN](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/fluid/contrib/slim/tests)
+-  如果量化模型在x86上线，需要使用[INT8 MKL-DNN](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/static/quantization/tests)
 
-    - 首先对模型进行转化，可以参考[脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/contrib/slim/tests/save_quant_model.py)
+    - 首先对模型进行转化，可以参考[脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/static/quantization/tests/save_quant_model.py)
 
     - 转化之后可使用预测部署API进行加载。比如[c++ API](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/native_infer.html)
 

docs/zh_cn/tutorials/quant/AnalysisQAT.md

@@ -43,7 +43,7 @@ import paddle
 from PIL import Image
 from paddle.vision.datasets import DatasetFolder
 from paddle.vision.transforms import transforms
-from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization
+from paddle.quantization import PostTrainingQuantization
 from paddleslim.quant.analysis_qat import AnalysisQAT
 
 paddle.enable_static()

example/auto_compression/hyperparameter_tutorial.md

@@ -276,7 +276,7 @@ print(f"Operators in inference model:\n{op_types.keys()}")
 
 执行以下代码，查看当前PaddlePaddle版本的量化功能所支持的OP类型：
 ```
-from paddle.fluid.contrib.slim.quantization.utils import _weight_supported_quantizable_op_type, _act_supported_quantizable_op_type
+from paddle.static.quantization.utils import _weight_supported_quantizable_op_type, _act_supported_quantizable_op_type
 print(f"_supported_quantizable_op_type:\n{_weight_supported_quantizable_op_type}")
 print(f"_supported_quantizable_op_type:\n{_act_supported_quantizable_op_type}")
 ```

paddleslim/analysis/_utils.py

@@ -19,9 +19,11 @@ import paddle
 import paddleslim
 import subprocess
 import time
-import ssl
 import requests
 import shutil
+import logging
+from ..common import get_logger
+_logger = get_logger(__name__, level=logging.INFO)
 __all__ = [
     "save_cls_model", "save_det_model", "nearest_interpolate", "opt_model",
     "load_predictor"
@@ -36,7 +38,7 @@ def _get_download(url, fullname):
     try:
         req = requests.get(url, stream=True)
     except Exception as e:  # requests.exceptions.ConnectionError
-        logger.info("Downloading {} from {} failed with exception {}".format(
+        _logger.info("Downloading {} from {} failed with exception {}".format(
             fname, url, str(e)))
         return False
 

paddleslim/auto_compression/compressor.py

@@ -804,7 +804,7 @@ class AutoCompression:
                 else:
                     logging_iter = train_config.logging_iter
                 if batch_id % int(logging_iter) == 0:
-                    print_info = "Total iter: {}, epoch: {}, batch: {}, loss: {}".format(
+                    print_info = "Total iter: {}, epoch: {}, batch: {}, loss: {} ".format(
                         total_train_iter, epoch_id, batch_id, loss[0])
                     for idx, loss_value in enumerate(loss[1:]):
                         print_info += '{}: {} '.format(loss_names[idx],

paddleslim/auto_compression/utils/fake_ptq.py

@@ -2,10 +2,10 @@ import os
 import paddle
 from paddle.fluid.framework import IrGraph
 from paddle.framework import core
-from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass, QuantizationTransformPassV2, AddQuantDequantPass, AddQuantDequantPassV2, QuantizationFreezePass, QuantWeightPass
+from paddle.static.quantization import QuantizationTransformPass, QuantizationTransformPassV2, AddQuantDequantPass, AddQuantDequantPassV2, QuantizationFreezePass, QuantWeightPass
 
 try:
-    from paddle.fluid.contrib.slim.quantization import utils
+    from paddle.static.quantization import utils
     TRANSFORM_PASS_OP_TYPES = utils._weight_supported_quantizable_op_type
     QUANT_DEQUANT_PASS_OP_TYPES = utils._act_supported_quantizable_op_type
 except:

paddleslim/dygraph/quant/ptq.py

@@ -17,11 +17,16 @@ import logging
 
 import paddle
 import paddle.nn as nn
-import paddle.fluid.contrib.slim.quantization as Q
-from paddle.fluid.contrib.slim.quantization import AbsmaxQuantizer
-from paddle.fluid.contrib.slim.quantization import HistQuantizer
-from paddle.fluid.contrib.slim.quantization import KLQuantizer
-from paddle.fluid.contrib.slim.quantization import PerChannelAbsmaxQuantizer
+
+from paddle.quantization import (
+    PTQConfig,
+    ImperativePTQ,
+    AbsmaxQuantizer,
+    HistQuantizer,
+    KLQuantizer,
+    PerChannelAbsmaxQuantizer,
+    SUPPORT_ACT_QUANTIZERS,
+    SUPPORT_WT_QUANTIZERS, )
 from ...common import get_logger
 
 _logger = get_logger(__name__, level=logging.INFO)
@@ -56,14 +61,14 @@ class PTQ(object):
         print("activation_quantizer", activation_quantizer)
         activation_quantizer = eval(activation_quantizer)(**kwargs)
         weight_quantizer = eval(weight_quantizer)()
-        assert isinstance(activation_quantizer, tuple(Q.SUPPORT_ACT_QUANTIZERS))
-        assert isinstance(weight_quantizer, tuple(Q.SUPPORT_WT_QUANTIZERS))
+        assert isinstance(activation_quantizer, tuple(SUPPORT_ACT_QUANTIZERS))
+        assert isinstance(weight_quantizer, tuple(SUPPORT_WT_QUANTIZERS))
 
-        quant_config = Q.PTQConfig(
+        quant_config = PTQConfig(
             activation_quantizer=activation_quantizer,
             weight_quantizer=weight_quantizer)
 
-        self.ptq = Q.ImperativePTQ(quant_config=quant_config)
+        self.ptq = ImperativePTQ(quant_config=quant_config)
 
     def quantize(self, model, inplace=False, fuse=False, fuse_list=None):
         """

paddleslim/dygraph/quant/qat.py

@@ -203,7 +203,7 @@ class QAT(object):
 
         # TODO: remove try-except when the version is stable
         try:
-            self.imperative_qat = paddle.fluid.contrib.slim.quantization.ImperativeQuantAware(
+            self.imperative_qat = paddle.quantization.ImperativeQuantAware(
                 weight_bits=self.config['weight_bits'],
                 activation_bits=self.config['activation_bits'],
                 weight_quantize_type=self.config['weight_quantize_type'],
@@ -220,7 +220,7 @@ class QAT(object):
                 onnx_format=self.config['onnx_format'],  # support Paddle >= 2.4
             )
         except:
-            self.imperative_qat = paddle.fluid.contrib.slim.quantization.ImperativeQuantAware(
+            self.imperative_qat = paddle.quantization.ImperativeQuantAware(
                 weight_bits=self.config['weight_bits'],
                 activation_bits=self.config['activation_bits'],
                 weight_quantize_type=self.config['weight_quantize_type'],
@@ -291,7 +291,7 @@ class QAT(object):
     def _remove_preprocess(self, model):
         state_dict = model.state_dict()
         try:
-            self.imperative_qat = paddle.fluid.contrib.slim.quantization.ImperativeQuantAware(
+            self.imperative_qat = paddle.quantization.ImperativeQuantAware(
                 weight_bits=self.config['weight_bits'],
                 activation_bits=self.config['activation_bits'],
                 weight_quantize_type=self.config['weight_quantize_type'],
@@ -302,7 +302,7 @@ class QAT(object):
                 onnx_format=self.config['onnx_format'],  # support Paddle >= 2.4
             )
         except:
-            self.imperative_qat = paddle.fluid.contrib.slim.quantization.ImperativeQuantAware(
+            self.imperative_qat = paddle.quantization.ImperativeQuantAware(
                 weight_bits=self.config['weight_bits'],
                 activation_bits=self.config['activation_bits'],
                 weight_quantize_type=self.config['weight_quantize_type'],

paddleslim/quant/analysis_ptq.py

@@ -165,7 +165,7 @@ class AnalysisPTQ(object):
         _logger.info('Activation Statistic is saved in {}'.format(save_path))
 
     def create_ptq(self, executor, skip_tensor_list):
-        return paddle.fluid.contrib.slim.quantization.PostTrainingQuantization(
+        return paddle.static.quantization.PostTrainingQuantization(
             executor=executor,
             data_loader=self.data_loader,
             model_dir=self.model_dir,
@@ -331,7 +331,7 @@ class AnalysisPTQ(object):
     def collect_vars(self, scope, var_names):
         all_vars = {}
         for var_name in var_names:
-            var_tensor = paddle.fluid.contrib.slim.quantization.utils.load_variable_data(
+            var_tensor = paddle.static.quantization.utils.load_variable_data(
                 scope, var_name)
             all_vars[var_name] = var_tensor
         return all_vars
@@ -446,7 +446,7 @@ class AnalysisPTQ(object):
         for op_name in weight_names:
             for block_id in range(len(program.blocks)):
                 for op in program.blocks[block_id].ops:
-                    var_name_list = paddle.fluid.contrib.slim.quantization.utils._get_op_input_var_names(
+                    var_name_list = paddle.static.quantization.utils._get_op_input_var_names(
                         op)
                     if op_name in var_name_list:
                         for var_name in var_name_list:

paddleslim/quant/post_quant_hpo.py

@@ -68,8 +68,8 @@ class QuantConfig(object):
                  eval_function=None,
                  model_filename=None,
                  params_filename=None,
-                 save_model_filename='__model__',
-                 save_params_filename='__params__',
+                 save_model_filename='model.pdmodel',
+                 save_params_filename='model.pdiparams',
                  scope=None,
                  quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
                  is_full_quantize=False,
@@ -190,14 +190,14 @@ def eval_quant_model():
     quant_scope = paddle.static.Scope()
     with paddle.static.scope_guard(float_scope):
         [infer_prog_float, feed_target_names_float, fetch_targets_float] = \
-            paddle.fluid.io.load_inference_model(dirname=g_quant_config.float_infer_model_path, \
+            paddle.static.load_inference_model(path_prefix=g_quant_config.float_infer_model_path, \
             model_filename=g_quant_config.model_filename, \
             params_filename=g_quant_config.params_filename, \
             executor=g_quant_config.executor)
 
     with paddle.static.scope_guard(quant_scope):
         [infer_prog_quant, feed_target_names_quant, fetch_targets_quant] = \
-            paddle.fluid.io.load_inference_model(dirname=g_quant_model_cache_path, \
+            paddle.static.load_inference_model(path_prefix=g_quant_model_cache_path, \
             model_filename=g_quant_config.save_model_filename, \
             params_filename=g_quant_config.save_params_filename, \
             executor=g_quant_config.executor)
@@ -304,7 +304,7 @@ def quantize(cfg):
         quant_scope = paddle.static.Scope()
         with paddle.static.scope_guard(float_scope):
             [float_inference_program, feed_target_names, fetch_targets]= paddle.static.load_inference_model( \
-                    dirname=g_quant_config.float_infer_model_path, \
+                    path_prefix=g_quant_config.float_infer_model_path, \
                     model_filename=g_quant_config.model_filename, params_filename=g_quant_config.params_filename,
                     executor=g_quant_config.executor)
             float_metric = g_quant_config.eval_function(
@@ -313,7 +313,7 @@ def quantize(cfg):
 
         with paddle.static.scope_guard(quant_scope):
             [quant_inference_program, feed_target_names, fetch_targets] = paddle.static.load_inference_model( \
-                    dirname=g_quant_model_cache_path, \
+                    path_prefix=g_quant_model_cache_path, \
                     model_filename=g_quant_config.model_filename, params_filename=g_quant_config.params_filename,
                     executor=g_quant_config.executor)
             quant_metric = g_quant_config.eval_function(
@@ -344,8 +344,8 @@ def quant_post_hpo(
         eval_function=None,
         model_filename=None,
         params_filename=None,
-        save_model_filename='__model__',
-        save_params_filename='__params__',
+        save_model_filename='model,pdmodel',
+        save_params_filename='model.pdiparams',
         scope=None,
         quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
         is_full_quantize=False,
@@ -388,9 +388,8 @@ def quant_post_hpo(
                 When all parameters are saved in a single file, set it
                 as filename. If parameters are saved in separate files,
                 set it as 'None'. Default : 'None'.
-        save_model_filename(str): The name of model file to save the quantized inference program.  Default: '__model__'.
-        save_params_filename(str): The name of file to save all related parameters.
-                If it is set None, parameters will be saved in separate files. Default: '__params__'.
+        save_model_filename(str): The name of model file to save the quantized inference program.  Default: 'model.pdmodel'.
+        save_params_filename(str): The name of file to save all related parameters. Default: 'model.pdiparams'.
         scope(paddle.static.Scope, optional): The scope to run program, use it to load
                         and save variables. If scope is None, will use paddle.static.global_scope().
         quantizable_op_type(list[str], optional): The list of op types

paddleslim/quant/quanter.py

@@ -21,15 +21,14 @@ import paddle
 from paddle.framework import core
 from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.framework import IrGraph
-from paddle.fluid.contrib.slim.quantization import WeightQuantization
-from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
-from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
-from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
-from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
-from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization
-from paddle.fluid.contrib.slim.quantization import AddQuantDequantPass
-from paddle.fluid.contrib.slim.quantization import OutScaleForTrainingPass
-from paddle.fluid.contrib.slim.quantization import OutScaleForInferencePass
+from paddle.static.quantization import WeightQuantization
+from paddle.static.quantization import QuantizationTransformPass
+from paddle.static.quantization import QuantizationFreezePass
+from paddle.static.quantization import ConvertToInt8Pass
+from paddle.static.quantization import PostTrainingQuantization
+from paddle.static.quantization import AddQuantDequantPass
+from paddle.static.quantization import OutScaleForTrainingPass
+from paddle.static.quantization import OutScaleForInferencePass
 from ..common import get_logger
 from ..common.patterns import get_patterns
 from ..common.patterns_common import has_trainable_var, get_weight
@@ -37,11 +36,11 @@ from ..core.graph_wrapper import GraphWrapper
 _logger = get_logger(__name__, level=logging.INFO)
 
 try:
-    from paddle.fluid.contrib.slim.quantization import QuantizationTransformPassV2
-    from paddle.fluid.contrib.slim.quantization import QuantWeightPass
-    from paddle.fluid.contrib.slim.quantization import AddQuantDequantPassV2
-    from paddle.fluid.contrib.slim.quantization import PostTrainingQuantizationProgram
-    from paddle.fluid.contrib.slim.quantization import AddQuantDequantForInferencePass
+    from paddle.static.quantization import QuantizationTransformPassV2
+    from paddle.static.quantization import QuantWeightPass
+    from paddle.static.quantization import AddQuantDequantPassV2
+    from paddle.static.quantization import PostTrainingQuantizationProgram
+    from paddle.static.quantization import AddQuantDequantForInferencePass
 except:
     _logger.warning(
         "Some functions fail to import, please update PaddlePaddle version to 2.4+"
@@ -62,7 +61,7 @@ ACTIVATION_QUANTIZATION_TYPES_TENSORRT = [
 
 VALID_DTYPES = ['int8']
 try:
-    from paddle.fluid.contrib.slim.quantization import utils
+    from paddle.static.quantization import utils
     TRANSFORM_PASS_OP_TYPES = utils._weight_supported_quantizable_op_type
     QUANT_DEQUANT_PASS_OP_TYPES = utils._act_supported_quantizable_op_type
 except:
@@ -383,6 +382,7 @@ def quant_aware(program,
             **calib_config)
         main_graph = post_training_quantization.quantize()
         scale_dict = post_training_quantization._scale_dict
+        sub_graphs = [sub_graph for sub_graph in main_graph.all_sub_graphs()]
     else:
         main_graph = IrGraph(core.Graph(program.desc), for_test=for_test)
         sub_graphs = [sub_graph for sub_graph in main_graph.all_sub_graphs()]
@@ -582,7 +582,7 @@ def quant_post_static(executor,
             sample_generator=sample_generator,
             batch_generator=batch_generator,
             data_loader=data_loader,
-            model_dir=model_dir,
+            model_dir=model_dir.rstrip('/'),
             model_filename=model_filename,
             params_filename=params_filename,
             batch_size=batch_size,
@@ -607,7 +607,7 @@ def quant_post_static(executor,
             sample_generator=sample_generator,
             batch_generator=batch_generator,
             data_loader=data_loader,
-            model_dir=model_dir,
+            model_dir=model_dir.rstrip('/'),
             model_filename=model_filename,
             params_filename=params_filename,
             batch_size=batch_size,
@@ -744,10 +744,10 @@ def convert(program,
 
 def quant_post_dynamic(model_dir,
                        save_model_dir,
-                       model_filename=None,
-                       params_filename=None,
-                       save_model_filename=None,
-                       save_params_filename=None,
+                       model_filename,
+                       params_filename,
+                       save_model_filename='model.pdmodel',
+                       save_params_filename='model.pdiparams',
                        quantizable_op_type=["conv2d", "mul"],
                        weight_bits=8,
                        generate_test_model=False):
@@ -764,22 +764,15 @@ def quant_post_dynamic(model_dir,
         model_dir(str): The path of the fp32 model that will be quantized,
                 and the model and params files are under the path.
         save_model_dir(str): The path to save the quantized model.
-        model_filename(str, optional): The name of file used to load the
-                inference program. If it is None, the default filename
-                '__model__' will be used. Default is 'None'.
-        params_filename(str, optional): The name of file used to load all
-                parameters. When all parameters were saved in a single
-                binary file, set it as the real filename. If parameters
-                were saved in separate files, set it as 'None'. Default is
-                'None'.
+        model_filename(str): The name of file used to load the
+                inference program. 
+        params_filename(str): The name of file used to load all
+                parameters.
         save_model_dir(str): The path used to save the quantized model.
         save_model_filename(str, optional): The name of file to 
-                save the inference program. If it is None, the default 
-                filename '__model__' will be used. Default is 'None'.
+                save the inference program. Default is 'model.pdmodel'.
         save_params_filename(str, optional): The name of file to 
-                save all parameters. If it is None, parameters were 
-                saved in separate files. If it is not None, all 
-                parameters were saved in a single binary file.
+                save all parameters. Default is 'model.pdiparams'.
         quantizable_op_type(list[str], optional): The list of ops 
                 that will be quantized, and the quantized ops should be
                 contained in ["conv2d", "depthwise_conv2d", "mul"]. 

paddleslim/quant/reconstruction_quantization.py

@@ -13,19 +13,17 @@
 # limitations under the License.
 import copy
 import logging
-import math
-import os
-import re
-import shutil
 import sys
 import time
 
 import numpy as np
 import paddle
+from paddle.static.quantization import utils
+from paddle.static.quantization import PostTrainingQuantization
 
 from ..dist import merge
 from ..core.graph_wrapper import GraphWrapper
-from ..common import get_logger, recover_program
+from ..common import get_logger
 
 __all__ = ['ReconstructionQuantization', ]
 
@@ -48,8 +46,7 @@ class Collections(object):
         return self._config
 
 
-class ReconstructionQuantization(
-        paddle.fluid.contrib.slim.quantization.PostTrainingQuantization):
+class ReconstructionQuantization(PostTrainingQuantization):
     """
     Utilizing reconstruction quantization method to quantize the FP32 model,
     and it uses calibrate data to get the quantization information for all
@@ -92,7 +89,7 @@ class ReconstructionQuantization(
 
     def _preparation(self):
         batch_id = 0
-        with paddle.fluid.contrib.slim.quantization.utils.tqdm(
+        with utils.tqdm(
                 total=self._batch_nums,
                 bar_format='Preparation stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
                 ncols=80, ) as t:
@@ -112,7 +109,7 @@ class ReconstructionQuantization(
 
     def _sampling_threshold(self):
         batch_id = 0
-        with paddle.fluid.contrib.slim.quantization.utils.tqdm(
+        with utils.tqdm(
                 total=self._batch_nums,
                 bar_format='Sampling stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
                 ncols=80, ) as t:
@@ -175,7 +172,7 @@ class ReconstructionQuantization(
             self._quantized_threshold = self._scale_dict
 
     def _postprocessing(self):
-        if self._algo is 'min_max':
+        if self._algo == 'min_max':
             self._save_input_threhold()
         else:
             self._update_program()
@@ -323,8 +320,7 @@ class ReconstructionQuanter(object):
         self._input_weight_pairs = {}
         for block_id in range(len(self._program.blocks)):
             for op in self._program.blocks[block_id].ops:
-                in_var_names = paddle.fluid.contrib.slim.quantization.utils._get_op_input_var_names(
-                    op)
+                in_var_names = utils._get_op_input_var_names(op)
                 for in_var_name in in_var_names:
                     if in_var_name in persistable_var_names:
                         in_var_names.remove(in_var_name)
@@ -435,14 +431,14 @@ class ReconstructionQuanter(object):
         return self._program, self._scale_dict
 
     def _init_alpha(self, name, scale):
-        _tensor = paddle.fluid.contrib.slim.quantization.utils.load_variable_data(
+        _tensor = paddle.static.quantization.utils.load_variable_data(
             self._scope, "teacher_" + name)
-        tensor_scaled = paddle.fluid.contrib.slim.quantization.utils.quant_tensor(
+        tensor_scaled = paddle.static.quantization.utils.quant_tensor(
             x=_tensor,
             scale=scale,
             weight_bits=self._weight_bits,
-            quant_axis=0 if self._weight_op_pairs[name] not in paddle.fluid.
-            contrib.slim.quantization.utils._channelwise_quant_axis1_ops else 1)
+            quant_axis=0 if self._weight_op_pairs[name] not in
+            utils._channelwise_quant_axis1_ops else 1)
         tensor_floor = np.floor(tensor_scaled)
         tensor = tensor_scaled - tensor_floor
         alpha = -np.log((ZETA - GAMMA) / (tensor - GAMMA) - 1)
@@ -744,11 +740,10 @@ class ReconstructionQuanter(object):
             if self._skip_tensor_list is not None and _name in self._skip_tensor_list:
                 continue
             scale_name = _name + '.scale'
-            scale_tensor = paddle.fluid.contrib.slim.quantization.utils.load_variable_data(
-                self._scope, scale_name)
+            scale_tensor = utils.load_variable_data(self._scope, scale_name)
             scale_list = []
             if self._weight_op_pairs[
-                    _name] in paddle.fluid.contrib.slim.quantization.utils._channelwise_quant_axis1_ops:
+                    _name] in utils._channelwise_quant_axis1_ops:
                 scale_list = list(scale_tensor[0])
             else:
                 for i in range(scale_tensor.shape[0]):
@@ -759,23 +754,21 @@ class ReconstructionQuanter(object):
         for weight_var_name in self._weight_var_names:
             if self._skip_tensor_list is not None and weight_var_name in self._skip_tensor_list:
                 continue
-            alpha_tensor = paddle.fluid.contrib.slim.quantization.utils.load_variable_data(
+            alpha_tensor = utils.load_variable_data(
                 self._scope,
                 weight_var_name + '.alpha', )
             h_alpha_tensor = self._compute_soft_rounding_np(alpha_tensor)
-            weight_tensor = paddle.fluid.contrib.slim.quantization.utils.load_variable_data(
+            weight_tensor = utils.load_variable_data(
                 self._scope,
                 weight_var_name, )
-            weight_quant_tensor = paddle.fluid.contrib.slim.quantization.utils.quant_tensor(
+            weight_quant_tensor = utils.quant_tensor(
                 x=weight_tensor,
                 scale=self._scale_dict[weight_var_name],
                 weight_bits=self._weight_bits,
-                quant_axis=0
-                if self._weight_op_pairs[weight_var_name] not in paddle.fluid.
-                contrib.slim.quantization.utils._channelwise_quant_axis1_ops
-                else 1)
+                quant_axis=0 if self._weight_op_pairs[weight_var_name] not in
+                utils._channelwise_quant_axis1_ops else 1)
 
-            paddle.fluid.contrib.slim.quantization.utils.set_variable_data(
+            utils.set_variable_data(
                 self._scope,
                 self._place,
                 weight_var_name,
@@ -783,23 +776,21 @@ class ReconstructionQuanter(object):
 
     def _bias_correction_w(self):
         for weight_var_name in self._weight_var_names:
-            weight_var_tensor = paddle.fluid.contrib.slim.quantization.utils.load_variable_data(
+            weight_var_tensor = utils.load_variable_data(
                 self._scope,
                 "teacher_" + weight_var_name, )
-            weight_quant_tensor = paddle.fluid.contrib.slim.quantization.utils.load_variable_data(
+            weight_quant_tensor = utils.load_variable_data(
                 self._scope,
                 weight_var_name, )
             scale = self._scale_dict[weight_var_name]
-            final_weight_tensor = paddle.fluid.contrib.slim.quantization.utils.bias_correction_w(
+            final_weight_tensor = utils.bias_correction_w(
                 weight_var_tensor,
                 weight_quant_tensor,
                 scale,
-                quant_axis=0
-                if self._weight_op_pairs[weight_var_name] not in paddle.fluid.
-                contrib.slim.quantization.utils._channelwise_quant_axis1_ops
-                else 1,
+                quant_axis=0 if self._weight_op_pairs[weight_var_name] not in
+                utils._channelwise_quant_axis1_ops else 1,
                 weight_bits=self._weight_bits, )
-            paddle.fluid.contrib.slim.quantization.utils.set_variable_data(
+            utils.set_variable_data(
                 self._scope,
                 self._place,
                 weight_var_name,
@@ -807,8 +798,7 @@ class ReconstructionQuanter(object):
 
     def _compute_soft_rounding_np(self, alpha_v):
         return np.clip(
-            paddle.fluid.contrib.slim.quantization.utils.stable_sigmoid(alpha_v)
-            * (ZETA - GAMMA) + GAMMA,
+            utils.stable_sigmoid(alpha_v) * (ZETA - GAMMA) + GAMMA,
             a_min=0,
             a_max=1, )
 
@@ -1206,7 +1196,7 @@ def quant_recon_static(executor,
         sample_generator=sample_generator,
         batch_generator=batch_generator,
         data_loader=data_loader,
-        model_dir=model_dir,
+        model_dir=model_dir.rstrip('/'),
         model_filename=model_filename,
         params_filename=params_filename,
         batch_size=batch_size,

tests/quant_analysis/test_analysis_qat.py

@@ -7,7 +7,7 @@ import paddle
 from PIL import Image
 from paddle.vision.datasets import DatasetFolder
 from paddle.vision.transforms import transforms
-from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization
+from paddle.static.quantization import PostTrainingQuantization
 from paddleslim.quant.analysis_qat import AnalysisQAT
 
 paddle.enable_static()

tests/quant_analysis/test_analysis_qat_eval_func.py

@@ -9,7 +9,7 @@ from PIL import Image
 from paddle.vision.datasets import DatasetFolder
 from paddle.vision.transforms import transforms
 from paddleslim.quant.analysis_qat import AnalysisQAT
-from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization
+from paddle.static.quantization import PostTrainingQuantization
 
 paddle.enable_static()
 

tests/test_latency_predictor.py

@@ -116,7 +116,7 @@ class ModelCase5(paddle.nn.Layer):
             anchors=anchors,
             conf_thresh=0.01,
             downsample_ratio=32)
-        out = paddle.fluid.layers.matrix_nms(
+        out = paddle.vision.ops.matrix_nms(
             bboxes=boxes,
             scores=scores,
             background_label=0,
@@ -125,7 +125,7 @@ class ModelCase5(paddle.nn.Layer):
             nms_top_k=400,
             keep_top_k=200,
             normalized=False)
-        box, var = paddle.fluid.layers.prior_box(
+        box, var = paddle.vision.ops.prior_box(
             input=image, image=image, min_sizes=[2.], clip=True, flip=True)
         return boxes, scores, box, var, out
 
@@ -185,7 +185,7 @@ class ModelCase7(paddle.nn.Layer):
             anchors=anchors,
             conf_thresh=0.01,
             downsample_ratio=32)
-        box, var = paddle.fluid.layers.prior_box(
+        box, var = paddle.vision.ops.prior_box(
             input=image, image=image, min_sizes=[2.], clip=True, flip=True)
         return boxes, scores, box, var
 

tests/test_quant_post_hpo.py

@@ -116,14 +116,12 @@ class TestQuantPostHpoCase1(StaticCase):
 
         train(main_prog)
         top1_1, top5_1 = test(val_prog)
-        paddle.fluid.io.save_inference_model(
-            dirname='./test_quant_post_hpo',
-            feeded_var_names=[image.name, label.name],
-            target_vars=[avg_cost, acc_top1, acc_top5],
-            main_program=val_prog,
+        paddle.static.save_inference_model(
+            path_prefix='./test_quant_post_hpo/model',
+            feed_vars=[image, label],
+            fetch_vars=[avg_cost, acc_top1, acc_top5],
             executor=exe,
-            model_filename='model',
-            params_filename='params')
+            program=val_prog)
 
         quant_post_hpo(
             exe,
@@ -132,16 +130,13 @@ class TestQuantPostHpoCase1(StaticCase):
             "./test_quant_post_hpo_inference",
             train_sample_generator=sample_generator_creator(),
             eval_sample_generator=sample_generator_creator(),
-            model_filename="model",
-            params_filename="params",
-            save_model_filename='__model__',
-            save_params_filename='__params__',
+            model_filename="model.pdmodel",
+            params_filename="model.pdiparams",
+            save_model_filename='model.pdmodel',
+            save_params_filename='model.pdiparams',
             runcount_limit=2)
-        quant_post_prog, feed_target_names, fetch_targets = paddle.fluid.io.load_inference_model(
-            dirname='./test_quant_post_hpo_inference',
-            executor=exe,
-            model_filename='__model__',
-            params_filename='__params__')
+        quant_post_prog, feed_target_names, fetch_targets = paddle.static.load_inference_model(
+            path_prefix='./test_quant_post_hpo_inference/model', executor=exe)
         top1_2, top5_2 = test(quant_post_prog, fetch_targets)
         print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1))
         print("after quantization: top1: {}, top5: {}".format(top1_2, top5_2))

tests/test_quant_post_only_weight.py

@@ -101,23 +101,22 @@ class TestQuantPostOnlyWeightCase1(StaticCase):
 
         train(main_prog)
         top1_1, top5_1 = test(val_prog)
-        paddle.fluid.io.save_inference_model(
-            dirname='./test_quant_post_dynamic',
-            feeded_var_names=[image.name, label.name],
-            target_vars=[avg_cost, acc_top1, acc_top5],
-            main_program=val_prog,
+        paddle.static.save_inference_model(
+            path_prefix='./test_quant_post_dynamic/model',
+            feed_vars=[image, label],
+            fetch_vars=[avg_cost, acc_top1, acc_top5],
             executor=exe,
-            model_filename='model',
-            params_filename='params')
+            program=val_prog)
 
         quant_post_dynamic(
             model_dir='./test_quant_post_dynamic',
             save_model_dir='./test_quant_post_inference',
-            model_filename='model',
-            params_filename='params',
+            model_filename='model.pdmodel',
+            params_filename='model.pdiparams',
             generate_test_model=True)
-        quant_post_prog, feed_target_names, fetch_targets = paddle.fluid.io.load_inference_model(
-            dirname='./test_quant_post_inference/test_model', executor=exe)
+        quant_post_prog, feed_target_names, fetch_targets = paddle.static.load_inference_model(
+            path_prefix='./test_quant_post_inference/test_model/model',
+            executor=exe)
         top1_2, top5_2 = test(quant_post_prog, fetch_targets)
         print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1))
         print("after quantization: top1: {}, top5: {}".format(top1_2, top5_2))