From 421c293f3d74bdd54828375ef38452dc6d7ec984 Mon Sep 17 00:00:00 2001 From: xjqbest <173596896@qq.com> Date: Thu, 28 May 2020 01:08:17 +0800 Subject: [PATCH] fix --- core/trainers/single_trainer.py | 64 +++++++++++---------------------- models/rank/dnn/config.yaml | 4 +-- 2 files changed, 22 insertions(+), 46 deletions(-) diff --git a/core/trainers/single_trainer.py b/core/trainers/single_trainer.py index 50e512ed..9b58ca45 100755 --- a/core/trainers/single_trainer.py +++ b/core/trainers/single_trainer.py @@ -137,35 +137,9 @@ class SingleTrainer(TranspileTrainer): return self._get_dataset(dataset_name) - reader = envs.path_adapter("paddlerec.core.utils") + "/dataset_instance.py" - pipe_cmd = "python {} {} {} {} {} {} {} {}".format( - reader, "slot", "slot", self._config_yaml, "fake", \ - sparse_slots.replace(" ", "#"), dense_slots.replace(" ", "#"), str(padding)) - - if type_name == "QueueDataset": - dataset = fluid.DatasetFactory().create_dataset() - dataset.set_batch_size(envs.get_global_env(name + "batch_size")) - dataset.set_pipe_command(pipe_cmd) - train_data_path = envs.get_global_env(name + "data_path") - file_list = [ - os.path.join(train_data_path, x) - for x in os.listdir(train_data_path) - ] - dataset.set_filelist(file_list) - for model_dict in self._env["executor"]: - if model_dict["dataset_name"] == dataset_name: - model = self._model[model_dict["name"]][3] - inputs = model.get_inputs() - dataset.set_use_var(inputs) - break - else: - pass - - return dataset - def init(self, context): for model_dict in self._env["executor"]: - self._model[model_dict["name"]] = [None] * 4 + self._model[model_dict["name"]] = [None] * 5 train_program = fluid.Program() startup_program = fluid.Program() scope = fluid.Scope() @@ -175,19 +149,21 @@ class SingleTrainer(TranspileTrainer): opt_strategy = envs.get_global_env("hyper_parameters.optimizer.strategy") with fluid.program_guard(train_program, startup_program): with fluid.unique_name.guard(): - model_path = model_dict["model"].replace("{workspace}", envs.path_adapter(self._env["workspace"])) - model = envs.lazy_instance_by_fliename(model_path, "Model")(self._env) - model._data_var = model.input_data(dataset_name=model_dict["dataset_name"]) - if envs.get_global_env("dataset." + dataset_name + ".type") == "DataLoader": - model._init_dataloader() - self._get_dataloader(dataset_name, model._data_loader) - model.net(model._data_var, is_infer=model_dict["is_infer"]) - optimizer = model._build_optimizer(opt_name, opt_lr, opt_strategy) - optimizer.minimize(model._cost) + with fluid.scope_guard(scope): + model_path = model_dict["model"].replace("{workspace}", envs.path_adapter(self._env["workspace"])) + model = envs.lazy_instance_by_fliename(model_path, "Model")(self._env) + model._data_var = model.input_data(dataset_name=model_dict["dataset_name"]) + if envs.get_global_env("dataset." + dataset_name + ".type") == "DataLoader": + model._init_dataloader() + self._get_dataloader(dataset_name, model._data_loader) + model.net(model._data_var, is_infer=model_dict["is_infer"]) + optimizer = model._build_optimizer(opt_name, opt_lr, opt_strategy) + optimizer.minimize(model._cost) self._model[model_dict["name"]][0] = train_program self._model[model_dict["name"]][1] = startup_program self._model[model_dict["name"]][2] = scope self._model[model_dict["name"]][3] = model + self._model[model_dict["name"]][4] = train_program.clone() for dataset in self._env["dataset"]: if dataset["type"] != "DataLoader": @@ -219,7 +195,7 @@ class SingleTrainer(TranspileTrainer): else: self._executor_dataset_train(model_dict) with fluid.scope_guard(self._model[model_dict["name"]][2]): - train_prog = self._model[model_dict["name"]][0] + train_prog = self._model[model_dict["name"]][4] startup_prog = self._model[model_dict["name"]][1] with fluid.program_guard(train_prog, startup_prog): self.save(j) @@ -250,13 +226,13 @@ class SingleTrainer(TranspileTrainer): fetch_info=fetch_alias, print_period=fetch_period) - def _executor_dataloader_train(self, model_dict): reader_name = model_dict["dataset_name"] model_name = model_dict["name"] model_class = self._model[model_name][3] - self._model[model_name][0] = fluid.compiler.CompiledProgram( - self._model[model_name][0]).with_data_parallel(loss_name=model_class.get_avg_cost().name) + program = self._model[model_name][0].clone() + program = fluid.compiler.CompiledProgram( + program).with_data_parallel(loss_name=model_class.get_avg_cost().name) fetch_vars = [] fetch_alias = [] fetch_period = 20 @@ -266,7 +242,8 @@ class SingleTrainer(TranspileTrainer): fetch_alias = metrics.keys() metrics_varnames = [] metrics_format = [] - metrics_format.append("{}: {{}}".format("epoch")) + fetch_period = 20 + #metrics_format.append("{}: {{}}".format("epoch")) metrics_format.append("{}: {{}}".format("batch")) for name, var in model_class.get_metrics().items(): metrics_varnames.append(var.name) @@ -277,16 +254,15 @@ class SingleTrainer(TranspileTrainer): reader.start() batch_id = 0 scope = self._model[model_name][2] - program = self._model[model_name][0] with fluid.scope_guard(scope): try: while True: metrics_rets = self._exe.run(program=program, fetch_list=metrics_varnames) - metrics = [epoch, batch_id] + metrics = [batch_id]#[epoch, batch_id] metrics.extend(metrics_rets) - if batch_id % self.fetch_period == 0 and batch_id != 0: + if batch_id % fetch_period == 0 and batch_id != 0: print(metrics_format.format(*metrics)) batch_id += 1 except fluid.core.EOFException: diff --git a/models/rank/dnn/config.yaml b/models/rank/dnn/config.yaml index f15e67e9..a51647d0 100755 --- a/models/rank/dnn/config.yaml +++ b/models/rank/dnn/config.yaml @@ -21,8 +21,8 @@ workspace: "paddlerec.models.rank.dnn" dataset: - name: dataset_2 batch_size: 2 - type: QueueDataset - #type: DataLoader + #type: QueueDataset + type: DataLoader data_path: "{workspace}/data/sample_data/train" sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26" dense_slots: "dense_var:13" -- GitLab