From d7f0c5064c1dfab8511b2e485361fdb3876494f6 Mon Sep 17 00:00:00 2001 From: yao_yf Date: Fri, 29 May 2020 09:17:15 +0800 Subject: [PATCH] adjust dir --- model_zoo/wide_and_deep/README.md | 93 +++++++++++ model_zoo/wide_and_deep/src/callbacks.py | 146 +++++++++--------- .../wide_and_deep/{tools => src}/config.py | 5 +- model_zoo/wide_and_deep/{ => src}/metrics.py | 0 model_zoo/wide_and_deep/src/wide_and_deep.py | 4 +- model_zoo/wide_and_deep/test.py | 12 +- model_zoo/wide_and_deep/train.py | 16 +- .../{tools => }/train_and_test.py | 14 +- .../wide_and_deep/train_and_test_multinpu.py | 8 +- 9 files changed, 196 insertions(+), 102 deletions(-) create mode 100644 model_zoo/wide_and_deep/README.md rename model_zoo/wide_and_deep/{tools => src}/config.py (97%) rename model_zoo/wide_and_deep/{ => src}/metrics.py (100%) rename model_zoo/wide_and_deep/{tools => }/train_and_test.py (87%) diff --git a/model_zoo/wide_and_deep/README.md b/model_zoo/wide_and_deep/README.md new file mode 100644 index 00000000000..f770297dd01 --- /dev/null +++ b/model_zoo/wide_and_deep/README.md @@ -0,0 +1,93 @@ +recommendation Model +## Overview +This is an implementation of WideDeep as described in the [Wide & Deep Learning for Recommender System](https://arxiv.org/pdf/1606.07792.pdf) paper. + +WideDeep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems. + +## Dataset +The [Criteo datasets](http://labs.criteo.com/2014/02/download-kaggle-display-advertising-challenge-dataset/) are used for model training and evaluation. + +## Running Code + +### Download and preprocess dataset +To download the dataset, please install Pandas package first. Then issue the following command: +``` +bash download.sh +``` + +### Code Structure +The entire code structure is as following: +``` +|--- wide_and_deep/ + train_and_test.py "Entrance of Wide&Deep model training and evaluation" + test.py "Entrance of Wide&Deep model evaluation" + train.py "Entrance of Wide&Deep model training" + train_and_test_multinpu.py "Entrance of Wide&Deep model data parallel training and evaluation" + |--- src/ "entrance of training and evaluation" + config.py "parameters configuration" + dataset.py "Dataset loader class" + WideDeep.py "Model structure" + callbacks.py "Callback class for training and evaluation" + metrics.py "Metric class" +``` + +### Train and evaluate model +To train and evaluate the model, issue the following command: +``` +python train_and_test.py +``` +Arguments: + * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument. + * `--epochs`: Total train epochs. + * `--batch_size`: Training batch size. + * `--eval_batch_size`: Eval batch size. + * `--field_size`: The number of features. + * `--vocab_size`： The total features of dataset. + * `--emb_dim`： The dense embedding dimension of sparse feature. + * `--deep_layers_dim`： The dimension of all deep layers. + * `--deep_layers_act`： The activation of all deep layers. + * `--keep_prob`： The rate to keep in dropout layer. + * `--ckpt_path`：The location of the checkpoint file. + * `--eval_file_name` : Eval output file. + * `--loss_file_name` : Loss output file. + +To train the model, issue the following command: +``` +python train.py +``` +Arguments: + * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument. + * `--epochs`: Total train epochs. + * `--batch_size`: Training batch size. + * `--eval_batch_size`: Eval batch size. + * `--field_size`: The number of features. + * `--vocab_size`： The total features of dataset. + * `--emb_dim`： The dense embedding dimension of sparse feature. + * `--deep_layers_dim`： The dimension of all deep layers. + * `--deep_layers_act`： The activation of all deep layers. + * `--keep_prob`： The rate to keep in dropout layer. + * `--ckpt_path`：The location of the checkpoint file. + * `--eval_file_name` : Eval output file. + * `--loss_file_name` : Loss output file. + +To evaluate the model, issue the following command: +``` +python test.py +``` +Arguments: + * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument. + * `--epochs`: Total train epochs. + * `--batch_size`: Training batch size. + * `--eval_batch_size`: Eval batch size. + * `--field_size`: The number of features. + * `--vocab_size`： The total features of dataset. + * `--emb_dim`： The dense embedding dimension of sparse feature. + * `--deep_layers_dim`： The dimension of all deep layers. + * `--deep_layers_act`： The activation of all deep layers. + * `--keep_prob`： The rate to keep in dropout layer. + * `--ckpt_path`：The location of the checkpoint file. + * `--eval_file_name` : Eval output file. + * `--loss_file_name` : Loss output file. + +There are other arguments about models and training process. Use the `--help` or `-h` flag to get a full list of possible arguments with detailed descriptions. + diff --git a/model_zoo/wide_and_deep/src/callbacks.py b/model_zoo/wide_and_deep/src/callbacks.py index f7f4d81ca3a..1ceefc115a6 100644 --- a/model_zoo/wide_and_deep/src/callbacks.py +++ b/model_zoo/wide_and_deep/src/callbacks.py @@ -26,79 +26,79 @@ def add_write(file_path, out_str): file_out.write(out_str + "\n") - class LossCallBack(Callback): +class LossCallBack(Callback): + """ + Monitor the loss in training. + + If the loss is NAN or INF, terminate the training. + + Note: + If per_print_times is 0, do NOT print loss. + + Args: + per_print_times (int): Print loss every times. Default: 1. + """ + def __init__(self, config=None, per_print_times=1): + super(LossCallBack, self).__init__() + if not isinstance(per_print_times, int) or per_print_times < 0: + raise ValueError("per_print_times must be in and >= 0.") + self._per_print_times = per_print_times + self.config = config + + def step_end(self, run_context): + cb_params = run_context.original_args() + wide_loss, deep_loss = cb_params.net_outputs[0].asnumpy(), cb_params.net_outputs[1].asnumpy() + cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1 + cur_num = cb_params.cur_step_num + print("===loss===", cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss) + + # raise ValueError + if self._per_print_times != 0 and cur_num % self._per_print_times == 0 and config is not None: + loss_file = open(self.config.loss_file_name, "a+") + loss_file.write("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" % + (cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss)) + loss_file.write("\n") + loss_file.close() + print("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" % + (cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss)) + + +class EvalCallBack(Callback): + """ + Monitor the loss in evaluating. + + If the loss is NAN or INF, terminate evaluating. + + Note: + If per_print_times is 0, do NOT print loss. + + Args: + print_per_step (int): Print loss every times. Default: 1. + """ + def __init__(self, model, eval_dataset, auc_metric, config, print_per_step=1): + super(EvalCallBack, self).__init__() + if not isinstance(print_per_step, int) or print_per_step < 0: + raise ValueError("print_per_step must be int and >= 0.") + self.print_per_step = print_per_step + self.model = model + self.eval_dataset = eval_dataset + self.aucMetric = auc_metric + self.aucMetric.clear() + self.eval_file_name = config.eval_file_name + + def epoch_name(self, run_context): """ - Monitor the loss in training. - - If the loss is NAN or INF, terminate the training. - - Note: - If per_print_times is 0, do NOT print loss. - - Args: - per_print_times (int): Print loss every times. Default: 1. + epoch name """ - def __init__(self, config, per_print_times=1): - super(LossCallBack, self).__init__() - if not isinstance(per_print_times, int) or per_print_times < 0: - raise ValueError("per_print_times must be in and >= 0.") - self._per_print_times = per_print_times - self.config = config + self.aucMetric.clear() + context.set_auto_parallel_context(strategy_ckpt_save_file="", + strategy_ckpt_load_file="./strategy_train.ckpt") + start_time = time.time() + out = self.model.eval(self.eval_dataset) + end_time = time.time() + eval_time = int(end_time - start_time) - def step_end(self, run_context): - cb_params = run_context.original_args() - wide_loss, deep_loss = cb_params.net_outputs[0].asnumpy(), cb_params.net_outputs[1].asnumpy() - cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1 - cur_num = cb_params.cur_step_num - print("===loss===", cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss) - - # raise ValueError - if self._per_print_times != 0 and cur_num % self._per_print_times == 0: - loss_file = open(self.config.loss_file_name, "a+") - loss_file.write("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" % - (cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss)) - loss_file.write("\n") - loss_file.close() - print("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" % - (cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss)) - - - class EvalCallBack(Callback): - """ - Monitor the loss in evaluating. - - If the loss is NAN or INF, terminate evaluating. - - Note: - If per_print_times is 0, do NOT print loss. - - Args: - print_per_step (int): Print loss every times. Default: 1. - """ - def __init__(self, model, eval_dataset, auc_metric, config, print_per_step=1): - super(EvalCallBack, self).__init__() - if not isinstance(print_per_step, int) or print_per_step < 0: - raise ValueError("print_per_step must be int and >= 0.") - self.print_per_step = print_per_step - self.model = model - self.eval_dataset = eval_dataset - self.aucMetric = auc_metric - self.aucMetric.clear() - self.eval_file_name = config.eval_file_name - - def epoch_name(self, run_context): - """ - epoch name - """ - self.aucMetric.clear() - context.set_auto_parallel_context(strategy_ckpt_save_file="", - strategy_ckpt_load_file="./strategy_train.ckpt") - start_time = time.time() - out = self.model.eval(self.eval_dataset) - end_time = time.time() - eval_time = int(end_time - start_time) - - time_str = time.strftime("%Y-%m-%d %H:%M%S", time.localtime()) - out_str = "{}==== EvalCallBack model.eval(): {}; eval_time: {}s".format(time_str, out.values(), eval_time) - print(out_str) - add_write(self.eval_file_name, out_str) + time_str = time.strftime("%Y-%m-%d %H:%M%S", time.localtime()) + out_str = "{}==== EvalCallBack model.eval(): {}; eval_time: {}s".format(time_str, out.values(), eval_time) + print(out_str) + add_write(self.eval_file_name, out_str) diff --git a/model_zoo/wide_and_deep/tools/config.py b/model_zoo/wide_and_deep/src/config.py similarity index 97% rename from model_zoo/wide_and_deep/tools/config.py rename to model_zoo/wide_and_deep/src/config.py index 8d87904be01..707750c97a4 100644 --- a/model_zoo/wide_and_deep/tools/config.py +++ b/model_zoo/wide_and_deep/src/config.py @@ -38,9 +38,9 @@ def argparse_init(): return parser -class Config_WideDeep(): +class WideDeepConfig(): """ - Config_WideDeep + WideDeepConfig """ def __init__(self): self.data_path = "./test_raw_data/" @@ -70,6 +70,7 @@ class Config_WideDeep(): """ parser = argparse_init() args, _ = parser.parse_known_args() + self.data_path = args.data_path self.epochs = args.epochs self.batch_size = args.batch_size self.eval_batch_size = args.eval_batch_size diff --git a/model_zoo/wide_and_deep/metrics.py b/model_zoo/wide_and_deep/src/metrics.py similarity index 100% rename from model_zoo/wide_and_deep/metrics.py rename to model_zoo/wide_and_deep/src/metrics.py diff --git a/model_zoo/wide_and_deep/src/wide_and_deep.py b/model_zoo/wide_and_deep/src/wide_and_deep.py index 4957ce2f490..7772431ab33 100644 --- a/model_zoo/wide_and_deep/src/wide_and_deep.py +++ b/model_zoo/wide_and_deep/src/wide_and_deep.py @@ -135,8 +135,8 @@ class WideDeepModel(nn.Cell): self.field_size = config.field_size self.vocab_size = config.vocab_size self.emb_dim = config.emb_dim - self.deep_layer_args = config.deep_layer_args - self.deep_layer_dims_list, self.deep_layer_act = self.deep_layer_args + self.deep_layer_dims_list = config.deep_layer_dim + self.deep_layer_act = config.deep_layer_act self.init_args = config.init_args self.weight_init, self.bias_init = config.weight_bias_init self.weight_bias_init = config.weight_bias_init diff --git a/model_zoo/wide_and_deep/test.py b/model_zoo/wide_and_deep/test.py index a8eb0cdbc47..54969e7c945 100644 --- a/model_zoo/wide_and_deep/test.py +++ b/model_zoo/wide_and_deep/test.py @@ -20,11 +20,11 @@ import os from mindspore import Model, context from mindspore.train.serialization import load_checkpoint, load_param_into_net -from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel -from wide_deep.utils.callbacks import LossCallBack, EvalCallBack -from wide_deep.data.datasets import create_dataset -from wide_deep.utils.metrics import AUCMetric -from tools.config import Config_WideDeep +from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel +from src.callbacks import LossCallBack, EvalCallBack +from src.datasets import create_dataset +from src.metrics import AUCMetric +from src.config import WideDeepConfig context.set_context(mode=context.GRAPH_MODE, device_target="Davinci", save_graphs=True) @@ -88,7 +88,7 @@ def test_eval(config): if __name__ == "__main__": - widedeep_config = Config_WideDeep() + widedeep_config = WideDeepConfig() widedeep_config.argparse_init() test_eval(widedeep_config.widedeep) diff --git a/model_zoo/wide_and_deep/train.py b/model_zoo/wide_and_deep/train.py index 4b73c1d4f7d..b3996e01cb5 100644 --- a/model_zoo/wide_and_deep/train.py +++ b/model_zoo/wide_and_deep/train.py @@ -16,19 +16,19 @@ import os from mindspore import Model, context from mindspore.train.callback import ModelCheckpoint, CheckpointConfig -from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWarp, NetWithLossClass, WideDeepModel -from wide_deep.utils.callbacks import LossCallBack -from wide_deep.data.datasets import create_dataset -from tools.config import Config_WideDeep +from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel +from src.callbacks import LossCallBack +from src.datasets import create_dataset +from src.config import WideDeepConfig -context.set_context(model=context.GRAPH_MODE, device_target="Ascend", save_graphs=True) +context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True) def get_WideDeep_net(configure): WideDeep_net = WideDeepModel(configure) loss_net = NetWithLossClass(WideDeep_net, configure) - train_net = TrainStepWarp(loss_net) + train_net = TrainStepWrap(loss_net) eval_net = PredictWithSigmoid(WideDeep_net) return train_net, eval_net @@ -71,7 +71,7 @@ def test_train(configure): train_net.set_train() model = Model(train_net) - callback = LossCallBack(configure) + callback = LossCallBack(config=configure) ckptconfig = CheckpointConfig(save_checkpoint_steps=1, keep_checkpoint_max=5) ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=configure.ckpt_path, config=ckptconfig) @@ -79,7 +79,7 @@ def test_train(configure): if __name__ == "__main__": - config = Config_WideDeep() + config = WideDeepConfig() config.argparse_init() test_train(config) diff --git a/model_zoo/wide_and_deep/tools/train_and_test.py b/model_zoo/wide_and_deep/train_and_test.py similarity index 87% rename from model_zoo/wide_and_deep/tools/train_and_test.py rename to model_zoo/wide_and_deep/train_and_test.py index 9f08377c75e..5d9832b84bc 100644 --- a/model_zoo/wide_and_deep/tools/train_and_test.py +++ b/model_zoo/wide_and_deep/train_and_test.py @@ -17,11 +17,11 @@ import os from mindspore import Model, context from mindspore.train.callback import ModelCheckpoint, CheckpointConfig -from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel -from wide_deep.utils.callbacks import LossCallBack, EvalCallBack -from wide_deep.data.datasets import create_dataset -from wide_deep.utils.metrics import AUCMetric -from tools.config import Config_WideDeep +from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel +from src.callbacks import LossCallBack, EvalCallBack +from src.datasets import create_dataset +from src.metrics import AUCMetric +from src.config import WideDeepConfig context.set_context(mode=context.GRAPH_MODE, device_target="Davinci") @@ -81,7 +81,7 @@ def test_train_eval(config): eval_callback = EvalCallBack(model, ds_eval, auc_metric, config) - callback = LossCallBack() + callback = LossCallBack(config=config) ckptconfig = CheckpointConfig(save_checkpoint_steps=1, keep_checkpoint_max=5) ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=config.ckpt_path, config=ckptconfig) @@ -91,7 +91,7 @@ def test_train_eval(config): if __name__ == "__main__": - wide_deep_config = Config_WideDeep() + wide_deep_config = WideDeepConfig() wide_deep_config.argparse_init() test_train_eval(wide_deep_config) diff --git a/model_zoo/wide_and_deep/train_and_test_multinpu.py b/model_zoo/wide_and_deep/train_and_test_multinpu.py index 8c7686988cf..9e4b99f5462 100644 --- a/model_zoo/wide_and_deep/train_and_test_multinpu.py +++ b/model_zoo/wide_and_deep/train_and_test_multinpu.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ -"""train_imagenet.""" +"""train_multinpu.""" import os @@ -27,7 +27,7 @@ from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClas from src.callbacks import LossCallBack, EvalCallBack from src.datasets import create_dataset from src.metrics import AUCMetric -from src.config import Config_WideDeep +from src.config import WideDeepConfig sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) context.set_context(mode=GRAPH_MODE, device_target="Davinci", save_graph=True) @@ -71,7 +71,7 @@ def test_train_eval(): test_train_eval """ np.random.seed(1000) - config = Config_WideDeep + config = WideDeepConfig data_path = Config.data_path batch_size = config.batch_size epochs = config.epochs @@ -93,7 +93,7 @@ def test_train_eval(): eval_callback = EvalCallBack(model, ds_eval, auc_metric, config) - callback = LossCallBack(config) + callback = LossCallBack(config=config) ckptconfig = CheckpointConfig(save_checkpoint_steps=1, keep_checkpoint_max=5) ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=config.ckpt_path, config=ckptconfig)