You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by ta...@apache.org on 2019/02/16 11:06:51 UTC
[incubator-mxnet] branch master updated: Add an inference script
providing both accuracy and benchmark result for original wide_n_deep
example (#13895)
This is an automated email from the ASF dual-hosted git repository.
taolv pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 8bfbb7d Add an inference script providing both accuracy and benchmark result for original wide_n_deep example (#13895)
8bfbb7d is described below
commit 8bfbb7de46ce309e1935967ea2dfeb99f8d8a1f0
Author: Shufan <33...@users.noreply.github.com>
AuthorDate: Sat Feb 16 19:06:25 2019 +0800
Add an inference script providing both accuracy and benchmark result for original wide_n_deep example (#13895)
* Add a inference script can provide both accuracy and benchmark result
* minor changes
* minor fix to use keep similar coding style as other examples
* fix typo
* remove code redundance and other minor changes
* Addressing review comments and minor pylint fix
* remove parameter 'accuracy' to make logic simple
---
example/sparse/wide_deep/README.md | 5 +-
example/sparse/wide_deep/config.py | 28 +++++++++
example/sparse/wide_deep/inference.py | 106 ++++++++++++++++++++++++++++++++++
example/sparse/wide_deep/train.py | 20 ++-----
4 files changed, 142 insertions(+), 17 deletions(-)
diff --git a/example/sparse/wide_deep/README.md b/example/sparse/wide_deep/README.md
index 769d723..d0ae8ad 100644
--- a/example/sparse/wide_deep/README.md
+++ b/example/sparse/wide_deep/README.md
@@ -20,5 +20,8 @@
The example demonstrates how to train [wide and deep model](https://arxiv.org/abs/1606.07792). The [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) that this example uses for training is hosted by the [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/). Tricks of feature engineering are adapted from tensorflow's [wide and deep tutorial](https://github.com/tensorflow/models/tree/master/official/wide_deep).
The final accuracy should be around 85%.
-
+For training:
- `python train.py`
+
+For inference:
+- `python inference.py`
diff --git a/example/sparse/wide_deep/config.py b/example/sparse/wide_deep/config.py
new file mode 100644
index 0000000..c0d20c4
--- /dev/null
+++ b/example/sparse/wide_deep/config.py
@@ -0,0 +1,28 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+
+# Related to feature engineering, please see preprocess in data.py
+ADULT = {
+ 'train': 'adult.data',
+ 'test': 'adult.test',
+ 'url': 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/',
+ 'num_linear_features': 3000,
+ 'num_embed_features': 2,
+ 'num_cont_features': 38,
+ 'embed_input_dims': [1000, 1000],
+ 'hidden_units': [8, 50, 100],
+}
diff --git a/example/sparse/wide_deep/inference.py b/example/sparse/wide_deep/inference.py
new file mode 100644
index 0000000..e14396e
--- /dev/null
+++ b/example/sparse/wide_deep/inference.py
@@ -0,0 +1,106 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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 mxnet as mx
+from mxnet.test_utils import *
+from config import *
+from data import get_uci_adult
+from model import wide_deep_model
+import argparse
+import os
+import time
+
+parser = argparse.ArgumentParser(description="Run sparse wide and deep inference",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('--num-infer-batch', type=int, default=100,
+ help='number of batches to inference')
+parser.add_argument('--load-epoch', type=int, default=0,
+ help='loading the params of the corresponding training epoch.')
+parser.add_argument('--batch-size', type=int, default=100,
+ help='number of examples per batch')
+parser.add_argument('--benchmark', action='store_true', default=False,
+ help='run the script for benchmark mode, not set for accuracy test.')
+parser.add_argument('--verbose', action='store_true', default=False,
+ help='accurcy for each batch will be logged if set')
+parser.add_argument('--gpu', action='store_true', default=False,
+ help='Inference on GPU with CUDA')
+parser.add_argument('--model-prefix', type=str, default='checkpoint',
+ help='the model prefix')
+
+if __name__ == '__main__':
+ import logging
+ head = '%(asctime)-15s %(message)s'
+ logging.basicConfig(level=logging.INFO, format=head)
+
+ # arg parser
+ args = parser.parse_args()
+ logging.info(args)
+ num_iters = args.num_infer_batch
+ batch_size = args.batch_size
+ benchmark = args.benchmark
+ verbose = args.verbose
+ model_prefix = args.model_prefix
+ load_epoch = args.load_epoch
+ ctx = mx.gpu(0) if args.gpu else mx.cpu()
+ # dataset
+ data_dir = os.path.join(os.getcwd(), 'data')
+ val_data = os.path.join(data_dir, ADULT['test'])
+ val_csr, val_dns, val_label = get_uci_adult(data_dir, ADULT['test'], ADULT['url'])
+ # load parameters and symbol
+ sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, load_epoch)
+ # data iterator
+ eval_data = mx.io.NDArrayIter({'csr_data': val_csr, 'dns_data': val_dns},
+ {'softmax_label': val_label}, batch_size,
+ shuffle=True, last_batch_handle='discard')
+ # module
+ mod = mx.mod.Module(symbol=sym, context=ctx, data_names=['csr_data', 'dns_data'],
+ label_names=['softmax_label'])
+ mod.bind(data_shapes=eval_data.provide_data, label_shapes=eval_data.provide_label)
+ # get the sparse weight parameter
+ mod.set_params(arg_params=arg_params, aux_params=aux_params)
+
+ data_iter = iter(eval_data)
+ nbatch = 0
+ if benchmark:
+ logging.info('Inference benchmark started ...')
+ tic = time.time()
+ for i in range(num_iters):
+ try:
+ batch = data_iter.next()
+ except StopIteration:
+ data_iter.reset()
+ else:
+ mod.forward(batch, is_train=False)
+ for output in mod.get_outputs():
+ output.wait_to_read()
+ nbatch += 1
+ score = (nbatch*batch_size)/(time.time() - tic)
+ logging.info('batch size %d, process %s samples/s' % (batch_size, score))
+ else:
+ logging.info('Inference started ...')
+ # use accuracy as the metric
+ metric = mx.metric.create(['acc'])
+ accuracy_avg = 0.0
+ for batch in data_iter:
+ nbatch += 1
+ metric.reset()
+ mod.forward(batch, is_train=False)
+ mod.update_metric(metric, batch.label)
+ accuracy_avg += metric.get()[1][0]
+ if args.verbose:
+ logging.info('batch %d, accuracy = %s' % (nbatch, metric.get()))
+ logging.info('averged accuracy on eval set is %.5f' % (accuracy_avg/nbatch))
diff --git a/example/sparse/wide_deep/train.py b/example/sparse/wide_deep/train.py
index 6fd81b7..eea7030 100644
--- a/example/sparse/wide_deep/train.py
+++ b/example/sparse/wide_deep/train.py
@@ -17,6 +17,7 @@
import mxnet as mx
from mxnet.test_utils import *
+from config import *
from data import get_uci_adult
from model import wide_deep_model
import argparse
@@ -31,7 +32,7 @@ parser.add_argument('--batch-size', type=int, default=100,
help='number of examples per batch')
parser.add_argument('--lr', type=float, default=0.001,
help='learning rate')
-parser.add_argument('--cuda', action='store_true', default=False,
+parser.add_argument('--gpu', action='store_true', default=False,
help='Train on GPU with CUDA')
parser.add_argument('--optimizer', type=str, default='adam',
help='what optimizer to use',
@@ -40,19 +41,6 @@ parser.add_argument('--log-interval', type=int, default=100,
help='number of batches to wait before logging training status')
-# Related to feature engineering, please see preprocess in data.py
-ADULT = {
- 'train': 'adult.data',
- 'test': 'adult.test',
- 'url': 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/',
- 'num_linear_features': 3000,
- 'num_embed_features': 2,
- 'num_cont_features': 38,
- 'embed_input_dims': [1000, 1000],
- 'hidden_units': [8, 50, 100],
-}
-
-
if __name__ == '__main__':
import logging
head = '%(asctime)-15s %(message)s'
@@ -66,7 +54,7 @@ if __name__ == '__main__':
optimizer = args.optimizer
log_interval = args.log_interval
lr = args.lr
- ctx = mx.gpu(0) if args.cuda else mx.cpu()
+ ctx = mx.gpu(0) if args.gpu else mx.cpu()
# dataset
data_dir = os.path.join(os.getcwd(), 'data')
@@ -88,7 +76,7 @@ if __name__ == '__main__':
shuffle=True, last_batch_handle='discard')
# module
- mod = mx.mod.Module(symbol=model, context=ctx ,data_names=['csr_data', 'dns_data'],
+ mod = mx.mod.Module(symbol=model, context=ctx, data_names=['csr_data', 'dns_data'],
label_names=['softmax_label'])
mod.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label)
mod.init_params()