support voc dataset in act (#1185)

* support voc dataset in act

* fix readme

demo/auto_compression/detection/README.md

@@ -113,24 +113,29 @@ wget https://bj.bcebos.com/v1/paddle-slim-models/detection/ppyoloe_crn_l_300e_co
 tar -xf ppyoloe_crn_l_300e_coco.tar
 ```
 
-#### 3.4. 测试模型精度
+**注意**：TinyPose模型暂不支持精度测试。
 
-使用run.py脚本得到模型的mAP：
+#### 3.4 自动压缩并产出模型
+
+蒸馏量化自动压缩示例通过run.py脚本启动，会使用接口```paddleslim.auto_compression.AutoCompression```对模型进行自动压缩。配置config文件中模型路径、蒸馏、量化、和训练等部分的参数，配置完成后便可对模型进行量化和蒸馏。具体运行命令为：
 ```
+# 单卡
 export CUDA_VISIBLE_DEVICES=0
-python run.py --config_path=./configs/ppyoloe_l_qat_dis.yaml --eval=True
+# 多卡
+# export CUDA_VISIBLE_DEVICES=0,1,2,3
+python run.py --config_path=./configs/ppyoloe_l_qat_dis.yaml --save_dir='./output/'
 ```
 
-**注意**：TinyPose模型暂不支持精度测试。
+#### 3.5 测试模型精度
 
-#### 3.5 自动压缩并产出模型
-
-蒸馏量化自动压缩示例通过run.py脚本启动，会使用接口```paddleslim.auto_compression.AutoCompression```对模型进行自动压缩。配置config文件中模型路径、蒸馏、量化、和训练等部分的参数，配置完成后便可对模型进行量化和蒸馏。具体运行命令为：
+使用run.py脚本得到模型的mAP：
 ```
 export CUDA_VISIBLE_DEVICES=0
-python run.py --config_path=./configs/ppyoloe_l_qat_dis.yaml --save_dir='./output/'
+python eval.py --config_path=./configs/ppyoloe_l_qat_dis.yaml
 ```
 
+**注意**：要测试的模型路径可以在配置文件中`model_dir`字段下进行修改。
+
 
 ## 4.预测部署
 

demo/auto_compression/detection/configs/ssd_mbv1_voc_qat_dis.yaml

+Global:
+  reader_config: configs/ssd_reader.yml
+  input_list: ['image', 'scale_factor', 'im_shape']
+  Evaluation: True
+  model_dir: ./ssd_mobilenet_v1_300_120e_voc/
+  model_filename: model.pdmodel
+  params_filename: model.pdiparams
+
+Distillation:
+  alpha: 1.0
+  loss: l2
+  node:
+  - concat_0.tmp_0
+  - concat_2.tmp_0
+
+Quantization:
+  activation_quantize_type: 'range_abs_max'
+  quantize_op_types:
+  - conv2d
+  - depthwise_conv2d
+
+TrainConfig:
+  train_iter: 80000
+  eval_iter: 1000
+  learning_rate:  
+    type: CosineAnnealingDecay
+    learning_rate: 0.00001
+    T_max: 120000
+  optimizer_builder:
+    optimizer: 
+      type: SGD
+    weight_decay: 4.0e-05
+

demo/auto_compression/detection/configs/ssd_reader.yml

+metric: VOC
+map_type: 11point
+num_classes: 20
+
+# Datset configuration
+TrainDataset:
+  !VOCDataSet
+    dataset_dir: dataset/voc
+    anno_path: trainval.txt
+    label_list: label_list.txt
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
+
+EvalDataset:
+  !VOCDataSet
+    dataset_dir: dataset/voc
+    anno_path: test.txt
+    label_list: label_list.txt
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
+
+worker_num: 0
+
+# preprocess reader in test
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [300, 300], keep_ratio: False, interp: 1}
+    - NormalizeImage: {mean: [127.5, 127.5, 127.5], std: [127.502231, 127.502231, 127.502231], is_scale: false}
+    - Permute: {}
+  batch_size: 16
+  collate_batch: false

demo/auto_compression/detection/eval.py

+# Copyright (c) 2022 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.
+
+import os
+import sys
+import numpy as np
+import argparse
+import paddle
+from ppdet.core.workspace import load_config, merge_config
+from ppdet.core.workspace import create
+from ppdet.metrics import COCOMetric, VOCMetric
+from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
+
+from post_process import YOLOv5PostProcess
+
+
+def argsparser():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        '--config_path',
+        type=str,
+        default=None,
+        help="path of compression strategy config.",
+        required=True)
+    parser.add_argument(
+        '--devices',
+        type=str,
+        default='gpu',
+        help="which device used to compress.")
+
+    return parser
+
+
+def print_arguments(args):
+    print('-----------  Running Arguments -----------')
+    for arg, value in sorted(vars(args).items()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------')
+
+
+def reader_wrapper(reader, input_list):
+    def gen():
+        for data in reader:
+            in_dict = {}
+            if isinstance(input_list, list):
+                for input_name in input_list:
+                    in_dict[input_name] = data[input_name]
+            elif isinstance(input_list, dict):
+                for input_name in input_list.keys():
+                    in_dict[input_list[input_name]] = data[input_name]
+            yield in_dict
+
+    return gen
+
+
+def convert_numpy_data(data, metric):
+    data_all = {}
+    data_all = {k: np.array(v) for k, v in data.items()}
+    if isinstance(metric, VOCMetric):
+        for k, v in data_all.items():
+            if not isinstance(v[0], np.ndarray):
+                tmp_list = []
+                for t in v:
+                    tmp_list.append(np.array(t))
+                data_all[k] = np.array(tmp_list)
+    else:
+        data_all = {k: np.array(v) for k, v in data.items()}
+    return data_all
+
+
+def eval():
+
+    place = paddle.CUDAPlace(0) if FLAGS.devices == 'gpu' else paddle.CPUPlace()
+    exe = paddle.static.Executor(place)
+
+    val_program, feed_target_names, fetch_targets = paddle.fluid.io.load_inference_model(
+        global_config["model_dir"],
+        exe,
+        model_filename=global_config["model_filename"],
+        params_filename=global_config["params_filename"])
+    print('Loaded model from: {}'.format(global_config["model_dir"]))
+
+    metric = global_config['metric']
+    for batch_id, data in enumerate(val_loader):
+        data_all = convert_numpy_data(data, metric)
+        data_input = {}
+        for k, v in data.items():
+            if isinstance(global_config['input_list'], list):
+                if k in global_config['input_list']:
+                    data_input[k] = np.array(v)
+            elif isinstance(global_config['input_list'], dict):
+                if k in global_config['input_list'].keys():
+                    data_input[global_config['input_list'][k]] = np.array(v)
+
+        outs = exe.run(val_program,
+                       feed=data_input,
+                       fetch_list=fetch_targets,
+                       return_numpy=False)
+        res = {}
+        if 'arch' in global_config and global_config['arch'] == 'YOLOv5':
+            postprocess = YOLOv5PostProcess(
+                score_threshold=0.001, nms_threshold=0.6, multi_label=True)
+            res = postprocess(np.array(outs[0]), data_all['scale_factor'])
+        else:
+            for out in outs:
+                v = np.array(out)
+                if len(v.shape) > 1:
+                    res['bbox'] = v
+                else:
+                    res['bbox_num'] = v
+        metric.update(data_all, res)
+        if batch_id % 100 == 0:
+            print('Eval iter:', batch_id)
+    metric.accumulate()
+    metric.log()
+    metric.reset()
+
+
+def main():
+    global global_config
+    _, _, global_config = load_slim_config(FLAGS.config_path)
+    reader_cfg = load_config(global_config['reader_config'])
+
+    dataset = reader_cfg['EvalDataset']
+    global val_loader
+    val_loader = create('EvalReader')(reader_cfg['EvalDataset'],
+                                      reader_cfg['worker_num'],
+                                      return_list=True)
+    metric = None
+    if reader_cfg['metric'] == 'COCO':
+        clsid2catid = {v: k for k, v in dataset.catid2clsid.items()}
+        anno_file = dataset.get_anno()
+        metric = COCOMetric(
+            anno_file=anno_file, clsid2catid=clsid2catid, IouType='bbox')
+    elif reader_cfg['metric'] == 'VOC':
+        metric = VOCMetric(
+            label_list=dataset.get_label_list(),
+            class_num=reader_cfg['num_classes'],
+            map_type=reader_cfg['map_type'])
+    else:
+        raise ValueError("metric currently only supports COCO and VOC.")
+    global_config['metric'] = metric
+
+    eval()
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    parser = argsparser()
+    FLAGS = parser.parse_args()
+    print_arguments(FLAGS)
+
+    assert FLAGS.devices in ['cpu', 'gpu', 'xpu', 'npu']
+    paddle.set_device(FLAGS.devices)
+
+    main()

demo/auto_compression/detection/run.py

@@ -19,7 +19,7 @@ import argparse
 import paddle
 from ppdet.core.workspace import load_config, merge_config
 from ppdet.core.workspace import create
-from ppdet.metrics import COCOMetric
+from ppdet.metrics import COCOMetric, VOCMetric
 from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
 from paddleslim.auto_compression import AutoCompression
 
@@ -72,65 +72,25 @@ def reader_wrapper(reader, input_list):
     return gen
 
 
-def eval(config):
-
-    place = paddle.CUDAPlace(0) if FLAGS.devices == 'gpu' else paddle.CPUPlace()
-    exe = paddle.static.Executor(place)
-
-    val_program, feed_target_names, fetch_targets = paddle.fluid.io.load_inference_model(
-        config["model_dir"],
-        exe,
-        model_filename=config["model_filename"],
-        params_filename=config["params_filename"], )
-    clsid2catid = {v: k for k, v in dataset.catid2clsid.items()}
-
-    anno_file = dataset.get_anno()
-    metric = COCOMetric(
-        anno_file=anno_file, clsid2catid=clsid2catid, IouType='bbox')
-    for batch_id, data in enumerate(val_loader):
+def convert_numpy_data(data, metric):
+    data_all = {}
     data_all = {k: np.array(v) for k, v in data.items()}
-        data_input = {}
-        for k, v in data.items():
-            if isinstance(config['input_list'], list):
-                if k in config['input_list']:
-                    data_input[k] = np.array(v)
-            elif isinstance(config['input_list'], dict):
-                if k in config['input_list'].keys():
-                    data_input[config['input_list'][k]] = np.array(v)
-
-        outs = exe.run(val_program,
-                       feed=data_input,
-                       fetch_list=fetch_targets,
-                       return_numpy=False)
-        res = {}
-        if 'arch' in config and config['arch'] == 'YOLOv5':
-            postprocess = YOLOv5PostProcess(
-                score_threshold=0.001, nms_threshold=0.6, multi_label=True)
-            res = postprocess(np.array(outs[0]), data_all['scale_factor'])
-        else:
-            for out in outs:
-                v = np.array(out)
-                if len(v.shape) > 1:
-                    res['bbox'] = v
+    if isinstance(metric, VOCMetric):
+        for k, v in data_all.items():
+            if not isinstance(v[0], np.ndarray):
+                tmp_list = []
+                for t in v:
+                    tmp_list.append(np.array(t))
+                data_all[k] = np.array(tmp_list)
     else:
-                    res['bbox_num'] = v
-
-        metric.update(data_all, res)
-        if batch_id % 100 == 0:
-            print('Eval iter:', batch_id)
-    metric.accumulate()
-    metric.log()
-    metric.reset()
+        data_all = {k: np.array(v) for k, v in data.items()}
+    return data_all
 
 
 def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
-    clsid2catid = {v: k for k, v in dataset.catid2clsid.items()}
-
-    anno_file = dataset.get_anno()
-    metric = COCOMetric(
-        anno_file=anno_file, clsid2catid=clsid2catid, IouType='bbox')
+    metric = global_config['metric']
     for batch_id, data in enumerate(val_loader):
-        data_all = {k: np.array(v) for k, v in data.items()}
+        data_all = convert_numpy_data(data, metric)
         data_input = {}
         for k, v in data.items():
             if isinstance(global_config['input_list'], list):
@@ -177,16 +137,25 @@ def main():
                                         return_list=True)
     train_loader = reader_wrapper(train_loader, global_config['input_list'])
 
-    global dataset
     dataset = reader_cfg['EvalDataset']
     global val_loader
     val_loader = create('EvalReader')(reader_cfg['EvalDataset'],
                                       reader_cfg['worker_num'],
                                       return_list=True)
-
-    if FLAGS.eval:
-        eval(global_config)
-        sys.exit(0)
+    metric = None
+    if reader_cfg['metric'] == 'COCO':
+        clsid2catid = {v: k for k, v in dataset.catid2clsid.items()}
+        anno_file = dataset.get_anno()
+        metric = COCOMetric(
+            anno_file=anno_file, clsid2catid=clsid2catid, IouType='bbox')
+    elif reader_cfg['metric'] == 'VOC':
+        metric = VOCMetric(
+            label_list=dataset.get_label_list(),
+            class_num=reader_cfg['num_classes'],
+            map_type=reader_cfg['map_type'])
+    else:
+        raise ValueError("metric currently only supports COCO and VOC.")
+    global_config['metric'] = metric
 
     if 'Evaluation' in global_config.keys() and global_config['Evaluation']:
         eval_func = eval_function

paddleslim/auto_compression/compressor.py

@@ -611,7 +611,8 @@ class AutoCompression:
 
     def _start_train(self, train_program_info, test_program_info, strategy):
         best_metric = -1.0
-        total_epochs = self.train_config.epochs if self.train_config.epochs else 1
+        total_epochs = self.train_config.epochs if self.train_config.epochs else 100
+        total_train_iter = 0
         for epoch_id in range(total_epochs):
             for batch_id, data in enumerate(self.train_dataloader()):
                 np_probs_float, = self._exe.run(train_program_info.program, \
@@ -627,11 +628,13 @@ class AutoCompression:
                 else:
                     logging_iter = self.train_config.logging_iter
                 if batch_id % int(logging_iter) == 0:
-                    _logger.info("epoch: {}, batch: {}, loss: {}".format(
-                        epoch_id, batch_id, np_probs_float))
-
-                if batch_id % int(
-                        self.train_config.eval_iter) == 0 and batch_id != 0:
+                    _logger.info(
+                        "Total iter: {}, epoch: {}, batch: {}, loss: {}".format(
+                            total_train_iter, epoch_id, batch_id,
+                            np_probs_float))
+                total_train_iter += 1
+                if total_train_iter % int(self.train_config.eval_iter
+                                          ) == 0 and total_train_iter != 0:
                     if self.eval_function is not None:
 
                         # GMP pruner step 3: update params before summrizing sparsity, saving model or evaluation. 
@@ -644,8 +647,9 @@ class AutoCompression:
                             test_program_info.fetch_targets)
 
                         _logger.info(
-                            "epoch: {}, batch: {} metric of compressed model is: {}, best metric of compressed model is {}".
-                            format(epoch_id, batch_id, metric, best_metric))
+                            "epoch: {} metric of compressed model is: {:.6f}, best metric of compressed model is {:.6f}".
+                            format(epoch_id, metric, best_metric))
+
                         if metric > best_metric:
                             paddle.static.save(
                                 program=test_program_info.program._program,
@@ -665,7 +669,7 @@ class AutoCompression:
                         _logger.warning(
                             "Not set eval function, so unable to test accuracy performance."
                         )
-                if self.train_config.train_iter and batch_id >= self.train_config.train_iter:
+                if self.train_config.train_iter and total_train_iter >= self.train_config.train_iter:
                     break
 
         if 'unstructure' in self._strategy or self.train_config.sparse_model: