add slim support

tools/slim/config/ResNet50_vd.yaml

+# global configs
+Global:
+  checkpoints: null
+  # pretrained_model: ./output/ResNet50_vd/epoch_29
+  pretrained_model: null 
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 30
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+
+# for paddleslim
+Slim:
+  # for quantalization
+  quant:
+    name: pact
+  ## for prune
+  #prune:
+  #  name: fpgm
+  #  prune_ratio: 0.3
+
+# model architecture
+Arch:
+  name: MobileNetV3_large_x1_0
+  class_num: 1000
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - MixCELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.1
+  regularizer:
+    name: 'L2'
+    coeff: 0.00007
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+      batch_transform_ops:
+        - MixupOperator:
+            alpha: 0.2
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 64
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 64
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/whl/demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+  Train:
+  Eval:
+    - TopkAcc:
+        topk: [1, 5]

tools/slim/train.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import, division, print_function
+
+import os
+import sys
+
+import paddle
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
+from paddleslim.dygraph.quant import QAT
+from ppcls.engine.trainer import Trainer
+from ppcls.utils import config, logger
+from ppcls.utils.save_load import load_dygraph_pretrain
+
+quant_config = {
+    # weight preprocess type, default is None and no preprocessing is performed. 
+    'weight_preprocess_type': None,
+    # activation preprocess type, default is None and no preprocessing is performed.
+    'activation_preprocess_type': None,
+    # weight quantize type, default is 'channel_wise_abs_max'
+    'weight_quantize_type': 'channel_wise_abs_max',
+    # activation quantize type, default is 'moving_average_abs_max'
+    'activation_quantize_type': 'moving_average_abs_max',
+    # weight quantize bit num, default is 8
+    'weight_bits': 8,
+    # activation quantize bit num, default is 8
+    'activation_bits': 8,
+    # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
+    'dtype': 'int8',
+    # window size for 'range_abs_max' quantization. default is 10000
+    'window_size': 10000,
+    # The decay coefficient of moving average, default is 0.9
+    'moving_rate': 0.9,
+    # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
+    'quantizable_layer_type': ['Conv2D', 'Linear'],
+}
+
+
+class Trainer_slim(Trainer):
+    def __init__(self, config, mode="train"):
+        super().__init__(config, mode)
+        #  self.pact = self.config["Slim"].get("pact", False)
+        self.pact = True
+        if self.pact:
+            quant_config["activation_preprocess_type"] = "PACT"
+            self.quanter = QAT(config=quant_config)
+            self.quanter.quantize(self.model)
+            logger.info("QAT model summary:")
+            paddle.summary(self.model, (1, 3, 224, 224))
+        else:
+            self.quanter = None
+
+    def train(self):
+        super().train()
+        if self.config["Global"].get("save_inference_dir", None):
+            self.export_inference_model()
+
+    def export_inference_model(self):
+        if os.path.exists(
+                os.path.join(self.output_dir, self.config["Arch"]["name"],
+                             "best_model.pdparams")):
+            load_dygraph_pretrain(self.model,
+                                  os.path.join(self.output_dir,
+                                               self.config["Arch"]["name"],
+                                               "best_model"))
+        elif self.config["Global"].get(
+                "pretraine_model", False) and os.path.exists(self.config[
+                    "Global"]["pretraine_model"] + ".pdparams"):
+            load_dygraph_pretrain(self.model,
+                                  self.config["Global"]["pretraine_model"])
+        else:
+            raise RuntimeError(
+                "The best_model or pretraine_model should exist to generate inference model"
+            )
+
+        assert self.quanter
+        self.quanter.save_quantized_model(
+            self.model,
+            os.path.join(self.config["Global"]["save_inference_dir"],
+                         "inference"),
+            input_spec=[
+                paddle.static.InputSpec(
+                    shape=[None] + config["Global"]["image_shape"],
+                    dtype='float32')
+            ])
+
+
+if __name__ == "__main__":
+    args = config.parse_args()
+    config = config.get_config(
+        args.config, overrides=args.override, show=False)
+    trainer = Trainer_slim(config, mode="train")
+    trainer.train()