update slim for new trainer

deploy/slim/README.md

@@ -66,12 +66,11 @@ cd PaddleClas
 以CPU为例，若使用GPU，则将命令中改成`cpu`改成`gpu`
 
 ```bash
-python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantization.yaml -o Global.device=cpu
+python3.7 tools/train.py -c ppcls/configs/slim/ResNet50_vd_quantization.yaml -o Global.device=cpu
 ```
 
 其中`yaml`文件解析详见[参考文档](../../docs/zh_CN/tutorials/config_description.md)。为了保证精度，`yaml`文件中已经使用`pretrained model`.
 
-`-m`：表示`slim.py`支持的模式，有`train、eval、infer、export`，4种模式，分别为：训练、测试、动态图预测、导出`inference model`
 
 * 单机多卡/多机多卡启动
 
@@ -79,8 +78,7 @@ python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantiz
 export CUDA_VISIBLE_DEVICES=0,1,2,3
 python3.7 -m paddle.distributed.launch \
     --gpus="0,1,2,3" \
-      deploy/slim/slim.py \
-      -m train \
+      tools/train.py \
       -c ppcls/configs/slim/ResNet50_vd_quantization.yaml
 ```
 
@@ -109,7 +107,7 @@ python3.7 deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/ResNet/ResN
 以CPU为例，若使用GPU，则将命令中改成`cpu`改成`gpu`
 
 ```bash
-python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_prune.yaml -o Global.device=cpu
+python3.7 tools/train.py -c ppcls/configs/slim/ResNet50_vd_prune.yaml -o Global.device=cpu
 ```
 
 - 单机单卡/单机多卡/多机多卡启动
@@ -118,8 +116,7 @@ python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_prune.y
 export CUDA_VISIBLE_DEVICES=0,1,2,3
 python3.7 -m paddle.distributed.launch \
     --gpus="0,1,2,3" \
-      deploy/slim/slim.py \
-      -m train \
+      tools/train.py \
       -c ppcls/configs/slim/ResNet50_vd_prune.yaml
 ```
 
@@ -128,9 +125,9 @@ python3.7 -m paddle.distributed.launch \
 在得到在线量化训练、模型剪枝保存的模型后，可以将其导出为inference model，用于预测部署，以模型剪枝为例：
 
 ```bash
-python3.7 deploy/slim/slim.py \
-    -m export \
+python3.7 tools/export.py \
     -c ppcls/configs/slim/ResNet50_vd_prune.yaml \
+    -o Global.pretrained_model=./output/ResNet50_vd/best_model \
     -o Global.save_inference_dir=./inference
 ```
 

deploy/slim/README_en.md

@@ -67,12 +67,11 @@ The training command is as follow:
 If using GPU, change the `cpu` to `gpu` in the following command.
 
 ```bash
-python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantization.yaml -o Global.device=cpu
+python3.7 tools/train.py -c ppcls/configs/slim/ResNet50_vd_quantization.yaml -o Global.device=cpu
 ```
 
 The description of `yaml` file can be found  in this [doc](../../docs/en/tutorials/config_en.md). To get better accuracy, the `pretrained model`is used in `yaml`.
 
-`-m`: the mode of `slim.py` supported, include ` train, eval, infer, export`, means training models, evaluating model, inferring images using dygraph model and exporting inference model for deploy respectively.
 
 * Distributed training
 
@@ -80,7 +79,7 @@ The description of `yaml` file can be found  in this [doc](../../docs/en/tutoria
 export CUDA_VISIBLE_DEVICES=0,1,2,3
 python3.7 -m paddle.distributed.launch \
     --gpus="0,1,2,3" \
-      deploy/slim/slim.py \
+      tools/train.py \
       -m train \
       -c ppcls/configs/slim/ResNet50_vd_quantization.yaml
 ```
@@ -108,7 +107,7 @@ If run successfully, the directory `quant_post_static_model` is generated in `Gl
 If using GPU, change the `cpu` to `gpu` in the following command.
 
 ```bash
-python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_prune.yaml -o Global.device=cpu
+python3.7 tools/train.py -c ppcls/configs/slim/ResNet50_vd_prune.yaml -o Global.device=cpu
 ```
 
 - Distributed training
@@ -117,8 +116,7 @@ python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_prune.y
 export CUDA_VISIBLE_DEVICES=0,1,2,3
 python3.7 -m paddle.distributed.launch \
     --gpus="0,1,2,3" \
-      deploy/slim/slim.py \
-      -m train \
+      tools/train.py \
       -c ppcls/configs/slim/ResNet50_vd_prune.yaml
 ```
 
@@ -129,9 +127,9 @@ python3.7 -m paddle.distributed.launch \
 After getting the compressed model, we can export it as inference model for predictive deployment. Using pruned model as example:
 
 ```bash
-python3.7 deploy/slim/slim.py \
-    -m export \
+python3.7 tools/export.py \
     -c ppcls/configs/slim/ResNet50_vd_prune.yaml \
+    -o Global.pretrained_model=./output/ResNet50_vd/best_model
     -o Global.save_inference_dir=./inference
 ```
 

ppcls/arch/backbone/legendary_models/mobilenet_v3.py

@@ -162,7 +162,7 @@ class MobileNetV3(TheseusLayer):
             if_act=True,
             act="hardswish")
 
-        self.blocks = nn.Sequential(*[
+        self.blocks = nn.Sequential(* [
             ResidualUnit(
                 in_c=_make_divisible(self.inplanes * self.scale if i == 0 else
                                      self.cfg[i - 1][2] * self.scale),
@@ -333,6 +333,8 @@ class SEModule(TheseusLayer):
             stride=1,
             padding=0)
         self.hardsigmoid = Hardsigmoid(slope=0.2, offset=0.5)
+        self.conv1.skip_quant = True
+        self.conv2.skip_quant = True
 
     def forward(self, x):
         identity = x

ppcls/engine/engine.py

@@ -42,6 +42,7 @@ from ppcls.data import create_operators
 from ppcls.engine.train import train_epoch
 from ppcls.engine import evaluation
 from ppcls.arch.gears.identity_head import IdentityHead
+from ppcls.engine.slim import get_pruner, get_quaner
 
 
 class Engine(object):
@@ -170,6 +171,8 @@ class Engine(object):
                     self.model, self.config["Global"]["pretrained_model"])
 
         # for slim
+        self.pruner = get_pruner(self.config, self.model)
+        self.quanter = get_quaner(self.config, self.model)
 
         # build optimizer
         if self.mode == 'train':
@@ -334,18 +337,26 @@ class Engine(object):
                                   self.config["Global"]["pretrained_model"])
 
         model.eval()
-
-        model = paddle.jit.to_static(
-            model,
-            input_spec=[
-                paddle.static.InputSpec(
-                    shape=[None] + self.config["Global"]["image_shape"],
-                    dtype='float32')
-            ])
-        paddle.jit.save(
-            model,
-            os.path.join(self.config["Global"]["save_inference_dir"],
-                         "inference"))
+        save_path = os.path.join(self.config["Global"]["save_inference_dir"],
+                                 "inference")
+        if self.quanter:
+            self.quanter.save_quantized_model(
+                model,
+                save_path,
+                input_spec=[
+                    paddle.static.InputSpec(
+                        shape=[None] + self.config["Global"]["image_shape"],
+                        dtype='float32')
+                ])
+        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)
 
 
 class ExportModel(nn.Layer):

ppcls/engine/slim/__init__.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 ppcls.engine.slim.prune import get_pruner
+from ppcls.engine.slim.quant import get_quaner

ppcls/engine/slim/prune.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 paddle
+from ppcls.utils import logger
+
+
+def get_pruner(config, model):
+    if config.get("Slim", False) and config["Slim"].get("prune", False):
+        import paddleslim
+        prune_method_name = config["Slim"]["prune"]["name"].lower()
+        assert prune_method_name in [
+            "fpgm", "l1_norm"
+        ], "The prune methods only support 'fpgm' and 'l1_norm'"
+        if prune_method_name == "fpgm":
+            pruner = paddleslim.dygraph.FPGMFilterPruner(
+                model, [1] + config["Global"]["image_shape"])
+        else:
+            pruner = paddleslim.dygraph.L1NormFilterPruner(
+                model, [1] + config["Global"]["image_shape"])
+
+        # prune model
+        _prune_model(pruner, config, model)
+    else:
+        pruner = None
+
+    return pruner
+
+
+def _prune_model(pruner, config, model):
+    from paddleslim.analysis import dygraph_flops as flops
+    logger.info("FLOPs before pruning: {}GFLOPs".format(
+        flops(model, [1] + config["Global"]["image_shape"]) / 1e9))
+    model.eval()
+
+    params = []
+    for sublayer in model.sublayers():
+        for param in sublayer.parameters(include_sublayers=False):
+            if isinstance(sublayer, paddle.nn.Conv2D):
+                params.append(param.name)
+    ratios = {}
+    for param in params:
+        ratios[param] = config["Slim"]["prune"]["pruned_ratio"]
+    plan = pruner.prune_vars(ratios, [0])
+
+    logger.info("FLOPs after pruning: {}GFLOPs; pruned ratio: {}".format(
+        flops(model, [1] + config["Global"]["image_shape"]) / 1e9,
+        plan.pruned_flops))
+
+    for param in model.parameters():
+        if "conv2d" in param.name:
+            logger.info("{}\t{}".format(param.name, param.shape))
+
+    model.train()

ppcls/engine/slim/quant.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 paddle
+from ppcls.utils import logger
+
+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'],
+}
+
+
+def get_quaner(config, model):
+    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"
+        quanter = QAT(config=QUANT_CONFIG)
+        quanter.quantize(model)
+        logger.info("QAT model summary:")
+        paddle.summary(model, (1, 3, 224, 224))
+    else:
+        quanter = None
+    return quanter