modify infer_meta.py

models/multitask/metaheac/infer_meta.py

@@ -0,0 +1,243 @@
+# Copyright (c) 2020 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 paddle
+import os
+import time
+import logging
+import sys
+import numpy as np
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+print(os.path.abspath('/'.join(__dir__.split('/')[:-3])))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+sys.path.append(os.path.abspath('/'.join(__dir__.split('/')[:-3])))
+
+from tools.utils.utils_single import load_yaml, load_dy_model_class, get_abs_model, create_data_loader
+from tools.utils.save_load import save_model, load_model
+from paddle.io import DataLoader
+import argparse
+
+logging.basicConfig(
+    format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='paddle-rec run')
+    parser.add_argument("-m", "--config_yaml", type=str)
+    parser.add_argument("-o", "--opt", nargs='*', type=str)
+    args = parser.parse_args()
+    args.abs_dir = os.path.dirname(os.path.abspath(args.config_yaml))
+    args.config_yaml = get_abs_model(args.config_yaml)
+    return args
+
+
+def main(args):
+    paddle.seed(2021)
+    # load config
+    config = load_yaml(args.config_yaml)
+    dy_model_class = load_dy_model_class(args.abs_dir)
+    config["config_abs_dir"] = args.abs_dir
+    # modify config from command
+    if args.opt:
+        for parameter in args.opt:
+            parameter = parameter.strip()
+            key, value = parameter.split("=")
+            if type(config.get(key)) is int:
+                value = int(value)
+            if type(config.get(key)) is float:
+                value = float(value)
+            if type(config.get(key)) is bool:
+                value = (True if value.lower() == "true" else False)
+            config[key] = value
+
+    # tools.vars
+    use_gpu = config.get("runner.use_gpu", True)
+    use_xpu = config.get("runner.use_xpu", False)
+    use_npu = config.get("runner.use_npu", False)
+    use_visual = config.get("runner.use_visual", False)
+    test_data_dir = config.get("runner.test_data_dir", None)
+    print_interval = config.get("runner.print_interval", None)
+    infer_batch_size = config.get("runner.infer_batch_size", None)
+    model_load_path = config.get("runner.infer_load_path", "model_output")
+    start_epoch = config.get("runner.infer_start_epoch", 0)
+    end_epoch = config.get("runner.infer_end_epoch", 10)
+    infer_train_epoch = config.get("runner.infer_train_epoch", 2)
+    batchsize = config.get("hyper_parameters.batch_size", 32)
+
+    logger.info("**************common.configs**********")
+    logger.info(
+        "use_gpu: {}, use_xpu: {}, use_npu: {}, use_visual: {}, infer_batch_size: {}, test_data_dir: {}, start_epoch: {}, end_epoch: {}, print_interval: {}, model_load_path: {}".
+        format(use_gpu, use_xpu, use_npu, use_visual, infer_batch_size,
+               test_data_dir, start_epoch, end_epoch, print_interval,
+               model_load_path))
+    logger.info("**************common.configs**********")
+
+    if use_xpu:
+        xpu_device = 'xpu:{0}'.format(os.getenv('FLAGS_selected_xpus', 0))
+        place = paddle.set_device(xpu_device)
+    elif use_npu:
+        npu_device = 'npu:{0}'.format(os.getenv('FLAGS_selected_npus', 0))
+        place = paddle.set_device(npu_device)
+    else:
+        place = paddle.set_device('gpu' if use_gpu else 'cpu')
+
+    dy_model = dy_model_class.create_model(config)
+
+    # Create a log_visual object and store the data in the path
+    if use_visual:
+        from visualdl import LogWriter
+        log_visual = LogWriter(args.abs_dir + "/visualDL_log/infer")
+
+    # to do : add optimizer function
+    #optimizer = dy_model_class.create_optimizer(dy_model, config)
+
+    logger.info("read data")
+    infer_dataloader = create_data_loader(
+        config=config, place=place, mode="test")
+
+    epoch_begin = time.time()
+    interval_begin = time.time()
+
+    metric_list, metric_list_name = dy_model_class.create_metrics()
+    step_num = 0
+    print_interval = 1
+
+    for epoch_id in range(start_epoch, end_epoch):
+        logger.info("load model epoch {}".format(epoch_id))
+        model_path = os.path.join(model_load_path, str(epoch_id))
+
+        infer_reader_cost = 0.0
+        infer_run_cost = 0.0
+        reader_start = time.time()
+
+        assert any(infer_dataloader(
+        )), "test_dataloader is null, please ensure batch size < dataset size!"
+
+        aid_flag = -1
+
+        for batch_id, batch in enumerate(infer_dataloader()):
+            infer_reader_cost += time.time() - reader_start
+            infer_start = time.time()
+
+            aid_flag = batch[0][0].item()
+            x_spt, y_spt, x_qry, y_qry = batch[1], batch[2], batch[3], batch[4]
+
+            load_model(model_path, dy_model)
+            # 对每个子任务进行训练
+            optimizer = dy_model_class.create_optimizer(dy_model, config,
+                                                        "infer")
+            dy_model.train()
+
+            for i in range(infer_train_epoch):
+                n_samples = y_spt.shape[0]
+                n_batch = int(np.ceil(n_samples / batchsize))
+                optimizer.clear_grad()
+
+                for i_batch in range(n_batch):
+                    batch_input = list()
+                    batch_x = []
+                    batch_x.append(x_spt[0][i_batch * batchsize:(i_batch + 1) *
+                                            batchsize])
+                    batch_x.append(x_spt[1][i_batch * batchsize:(i_batch + 1) *
+                                            batchsize])
+                    batch_x.append(x_spt[2][i_batch * batchsize:(i_batch + 1) *
+                                            batchsize])
+                    batch_x.append(x_spt[3][i_batch * batchsize:(i_batch + 1) *
+                                            batchsize])
+
+                    batch_y = y_spt[i_batch * batchsize:(i_batch + 1) *
+                                    batchsize]
+
+                    batch_input.append(batch_x)
+                    batch_input.append(batch_y)
+
+                    loss = dy_model_class.infer_train_forward(
+                        dy_model, batch_input, config)
+
+                    dy_model.clear_gradients()
+                    loss.backward()
+                    optimizer.step()
+            # 对每个子任务进行测试
+            dy_model.eval()
+            metric_list_local, metric_list_local_name = dy_model_class.create_metrics(
+            )
+            with paddle.no_grad():
+                n_samples = y_qry.shape[0]
+                n_batch = int(np.ceil(n_samples / batchsize))
+
+                for i_batch in range(n_batch):
+                    batch_input = list()
+                    batch_x = []
+                    batch_x.append(x_qry[0][i_batch * batchsize:(i_batch + 1) *
+                                            batchsize])
+                    batch_x.append(x_qry[1][i_batch * batchsize:(i_batch + 1) *
+                                            batchsize])
+                    batch_x.append(x_qry[2][i_batch * batchsize:(i_batch + 1) *
+                                            batchsize])
+                    batch_x.append(x_qry[3][i_batch * batchsize:(i_batch + 1) *
+                                            batchsize])
+
+                    batch_y = y_qry[i_batch * batchsize:(i_batch + 1) *
+                                    batchsize]
+
+                    batch_input.append(batch_x)
+                    batch_input.append(batch_y)
+
+                    metric_list, metric_list_local = dy_model_class.infer_forward(
+                        dy_model, metric_list, metric_list_local, batch_input,
+                        config)
+
+            infer_run_cost += time.time() - infer_start
+
+            metric_str_local = ""
+            for metric_id in range(len(metric_list_local_name)):
+                metric_str_local += (
+                    metric_list_local_name[metric_id] + ": {:.6f},".format(
+                        metric_list_local[metric_id].accumulate()))
+                if use_visual:
+                    log_visual.add_scalar(
+                        tag="infer/" + metric_list_local_name[metric_id],
+                        step=step_num,
+                        value=metric_list_local[metric_id].accumulate())
+            logger.info(
+                "epoch: {}, batch_id: {}, aid: {} ".format(
+                    epoch_id, batch_id, aid_flag) + metric_str_local +
+                " avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.2f} ins/s".
+                format(infer_reader_cost / print_interval, (
+                    infer_reader_cost + infer_run_cost) / print_interval,
+                       batchsize, print_interval * batchsize / (time.time(
+                       ) - interval_begin)))
+
+            interval_begin = time.time()
+            infer_reader_cost = 0.0
+            infer_run_cost = 0.0
+            step_num = step_num + 1
+            reader_start = time.time()
+
+        metric_str = ""
+        for metric_id in range(len(metric_list_name)):
+            metric_str += (
+                metric_list_name[metric_id] +
+                ": {:.6f},".format(metric_list[metric_id].accumulate()))
+
+        logger.info("epoch: {} done, ".format(epoch_id) + metric_str +
+                    " epoch time: {:.2f} s".format(time.time() - epoch_begin))
+        epoch_begin = time.time()
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)