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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/11/02 23:03:52 UTC
[GitHub] indhub closed pull request #12968: Updated / Deleted some examples
indhub closed pull request #12968: Updated / Deleted some examples
URL: https://github.com/apache/incubator-mxnet/pull/12968
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diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 43006f23974..dc96547ee7c 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -871,7 +871,6 @@ unittest_centos7_gpu() {
integrationtest_ubuntu_cpu_onnx() {
set -ex
export PYTHONPATH=./python/
- python example/onnx/super_resolution.py
pytest tests/python-pytest/onnx/import/mxnet_backend_test.py
pytest tests/python-pytest/onnx/import/onnx_import_test.py
pytest tests/python-pytest/onnx/import/gluon_backend_test.py
diff --git a/example/multivariate_time_series/README.md b/example/multivariate_time_series/README.md
index 704c86ae770..87baca36d35 100644
--- a/example/multivariate_time_series/README.md
+++ b/example/multivariate_time_series/README.md
@@ -3,6 +3,8 @@
- This repo contains an MXNet implementation of [this](https://arxiv.org/pdf/1703.07015.pdf) state of the art time series forecasting model.
- You can find my blog post on the model [here](https://opringle.github.io/2018/01/05/deep_learning_multivariate_ts.html)
+- A Gluon implementation is available [here](https://github.com/safrooze/LSTNet-Gluon)
+
## Running the code
@@ -22,7 +24,7 @@
## Hyperparameters
-The default arguements in `lstnet.py` achieve equivolent performance to the published results. For other datasets, the following hyperparameters provide a good starting point:
+The default arguements in `lstnet.py` achieve equivalent performance to the published results. For other datasets, the following hyperparameters provide a good starting point:
- q = {2^0, 2^1, ... , 2^9} (1 week is typical value)
- Convolutional num filters = {50, 100, 200}
diff --git a/example/named_entity_recognition/README.md b/example/named_entity_recognition/README.md
index 260c19d5ffb..2b28b3b5039 100644
--- a/example/named_entity_recognition/README.md
+++ b/example/named_entity_recognition/README.md
@@ -11,7 +11,6 @@ To reproduce the preprocessed training data:
1. Download and unzip the data: https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/downloads/ner_dataset.csv
2. Move ner_dataset.csv into `./data`
-3. create `./preprocessed_data` directory
3. `$ cd src && python preprocess.py`
To train the model:
diff --git a/example/named_entity_recognition/src/metrics.py b/example/named_entity_recognition/src/metrics.py
index 40c5015e81b..d3d73782c62 100644
--- a/example/named_entity_recognition/src/metrics.py
+++ b/example/named_entity_recognition/src/metrics.py
@@ -27,7 +27,7 @@ def load_obj(name):
with open(name + '.pkl', 'rb') as f:
return pickle.load(f)
-tag_dict = load_obj("../preprocessed_data/tag_to_index")
+tag_dict = load_obj("../data/tag_to_index")
not_entity_index = tag_dict["O"]
def classifer_metrics(label, pred):
diff --git a/example/named_entity_recognition/src/ner.py b/example/named_entity_recognition/src/ner.py
index 561db4c43d9..7f5dd84527c 100644
--- a/example/named_entity_recognition/src/ner.py
+++ b/example/named_entity_recognition/src/ner.py
@@ -34,7 +34,7 @@
parser = argparse.ArgumentParser(description="Deep neural network for multivariate time series forecasting",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-parser.add_argument('--data-dir', type=str, default='../preprocessed_data',
+parser.add_argument('--data-dir', type=str, default='../data',
help='relative path to input data')
parser.add_argument('--output-dir', type=str, default='../results',
help='directory to save model files to')
diff --git a/example/nce-loss/README.md b/example/nce-loss/README.md
index 70730b47729..56e43525a7c 100644
--- a/example/nce-loss/README.md
+++ b/example/nce-loss/README.md
@@ -29,7 +29,7 @@ The dataset used in the following examples is [text8](http://mattmahoney.net/dc/
* word2vec.py: a CBOW word2vec example using nce loss. You need to [download the text8 dataset](#dataset-download) before running this script. Command to start training on CPU (pass -g for training on GPU):
```
-python word2vec.py
+python wordvec.py
```
diff --git a/example/numpy-ops/numpy_softmax.py b/example/numpy-ops/numpy_softmax.py
deleted file mode 100644
index 88d24734929..00000000000
--- a/example/numpy-ops/numpy_softmax.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# 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.
-
-# pylint: skip-file
-import mxnet as mx
-from mxnet.test_utils import get_mnist_iterator
-import numpy as np
-import logging
-
-
-class NumpySoftmax(mx.operator.NumpyOp):
- def __init__(self):
- super(NumpySoftmax, self).__init__(False)
-
- def list_arguments(self):
- return ['data', 'label']
-
- def list_outputs(self):
- return ['output']
-
- def infer_shape(self, in_shape):
- data_shape = in_shape[0]
- label_shape = (in_shape[0][0],)
- output_shape = in_shape[0]
- return [data_shape, label_shape], [output_shape]
-
- def forward(self, in_data, out_data):
- x = in_data[0]
- y = out_data[0]
- y[:] = np.exp(x - x.max(axis=1).reshape((x.shape[0], 1)))
- y /= y.sum(axis=1).reshape((x.shape[0], 1))
-
- def backward(self, out_grad, in_data, out_data, in_grad):
- l = in_data[1]
- l = l.reshape((l.size,)).astype(np.int)
- y = out_data[0]
- dx = in_grad[0]
- dx[:] = y
- dx[np.arange(l.shape[0]), l] -= 1.0
-
-# define mlp
-
-data = mx.symbol.Variable('data')
-fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
-act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu")
-fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)
-act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu")
-fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10)
-#mlp = mx.symbol.Softmax(data = fc3, name = 'mlp')
-mysoftmax = NumpySoftmax()
-mlp = mysoftmax(data=fc3, name = 'softmax')
-
-# data
-
-train, val = get_mnist_iterator(batch_size=100, input_shape = (784,))
-
-# train
-
-logging.basicConfig(level=logging.DEBUG)
-
-# MXNET_CPU_WORKER_NTHREADS must be greater than 1 for custom op to work on CPU
-context=mx.cpu()
-# Uncomment this line to train on GPU instead of CPU
-# context=mx.gpu(0)
-
-mod = mx.mod.Module(mlp, context=context)
-
-mod.fit(train_data=train, eval_data=val, optimizer='sgd',
- optimizer_params={'learning_rate':0.1, 'momentum': 0.9, 'wd': 0.00001},
- num_epoch=10, batch_end_callback=mx.callback.Speedometer(100, 100))
diff --git a/example/onnx/super_resolution.py b/example/onnx/super_resolution.py
deleted file mode 100644
index fcb8ccc88ed..00000000000
--- a/example/onnx/super_resolution.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# 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.
-
-"""Testing super_resolution model conversion"""
-from __future__ import absolute_import as _abs
-from __future__ import print_function
-from collections import namedtuple
-import logging
-import numpy as np
-from PIL import Image
-import mxnet as mx
-from mxnet.test_utils import download
-import mxnet.contrib.onnx as onnx_mxnet
-
-# set up logger
-logging.basicConfig()
-LOGGER = logging.getLogger()
-LOGGER.setLevel(logging.INFO)
-
-def import_onnx():
- """Import the onnx model into mxnet"""
- model_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_resolution.onnx'
- download(model_url, 'super_resolution.onnx')
-
- LOGGER.info("Converting onnx format to mxnet's symbol and params...")
- sym, arg_params, aux_params = onnx_mxnet.import_model('super_resolution.onnx')
- LOGGER.info("Successfully Converted onnx format to mxnet's symbol and params...")
- return sym, arg_params, aux_params
-
-def get_test_image():
- """Download and process the test image"""
- # Load test image
- input_image_dim = 224
- img_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_res_input.jpg'
- download(img_url, 'super_res_input.jpg')
- img = Image.open('super_res_input.jpg').resize((input_image_dim, input_image_dim))
- img_ycbcr = img.convert("YCbCr")
- img_y, img_cb, img_cr = img_ycbcr.split()
- input_image = np.array(img_y)[np.newaxis, np.newaxis, :, :]
- return input_image, img_cb, img_cr
-
-def perform_inference(sym, arg_params, aux_params, input_img, img_cb, img_cr):
- """Perform inference on image using mxnet"""
- metadata = onnx_mxnet.get_model_metadata('super_resolution.onnx')
- data_names = [input_name[0] for input_name in metadata.get('input_tensor_data')]
- # create module
- mod = mx.mod.Module(symbol=sym, data_names=data_names, label_names=None)
- mod.bind(for_training=False, data_shapes=[(data_names[0], input_img.shape)])
- mod.set_params(arg_params=arg_params, aux_params=aux_params)
-
- # run inference
- batch = namedtuple('Batch', ['data'])
- mod.forward(batch([mx.nd.array(input_img)]))
-
- # Save the result
- img_out_y = Image.fromarray(np.uint8(mod.get_outputs()[0][0][0].
- asnumpy().clip(0, 255)), mode='L')
-
- result_img = Image.merge(
- "YCbCr", [img_out_y,
- img_cb.resize(img_out_y.size, Image.BICUBIC),
- img_cr.resize(img_out_y.size, Image.BICUBIC)]).convert("RGB")
- output_img_dim = 672
- assert result_img.size == (output_img_dim, output_img_dim)
- LOGGER.info("Super Resolution example success.")
- result_img.save("super_res_output.jpg")
- return result_img
-
-if __name__ == '__main__':
- MX_SYM, MX_ARG_PARAM, MX_AUX_PARAM = import_onnx()
- INPUT_IMG, IMG_CB, IMG_CR = get_test_image()
- perform_inference(MX_SYM, MX_ARG_PARAM, MX_AUX_PARAM, INPUT_IMG, IMG_CB, IMG_CR)
diff --git a/example/python-howto/README.md b/example/python-howto/README.md
deleted file mode 100644
index 29652408e02..00000000000
--- a/example/python-howto/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-Python Howto Examples
-=====================
-
-* [Configuring Net to Get Multiple Ouputs](multiple_outputs.py)
-* [Configuring Image Record Iterator](data_iter.py)
-* [Monitor Intermediate Outputs in the Network](monitor_weights.py)
-* Set break point in C++ code of the symbol using gdb under Linux:
-
- * Build mxnet with following values:
-
- ```
- DEBUG=1
- USE_CUDA=0 # to make sure convolution-inl.h will be used
- USE_CUDNN=0 # to make sure convolution-inl.h will be used
- ```
-
- * run python under gdb: ```gdb --args python debug_conv.py```
- * in gdb set break point on particular line of the code and run execution:
-
-```
-(gdb) break src/operator/convolution-inl.h:120
-(gdb) run
-Breakpoint 1, mxnet::op::ConvolutionOp<mshadow::cpu, float>::Forward (this=0x12219d0, ctx=..., in_data=std::vector of length 3, capacity 4 = {...}, req=std::vector of length 1, capacity 1 = {...}, out_data=std::vector of length 1, capacity 1 = {...},
- aux_args=std::vector of length 0, capacity 0) at src/operator/./convolution-inl.h:121
-121 data.shape_[1] / param_.num_group * param_.kernel[0] * param_.kernel[1]);
-(gdb) list
-116 }
-117 Tensor<xpu, 4, DType> data = in_data[conv::kData].get<xpu, 4, DType>(s);
-118 Shape<3> wmat_shape =
-119 Shape3(param_.num_group,
-120 param_.num_filter / param_.num_group,
-121 data.shape_[1] / param_.num_group * param_.kernel[0] * param_.kernel[1]);
-122 Tensor<xpu, 3, DType> wmat =
-123 in_data[conv::kWeight].get_with_shape<xpu, 3, DType>(wmat_shape, s);
-124 Tensor<xpu, 4, DType> out = out_data[conv::kOut].get<xpu, 4, DType>(s);
-125 #if defined(__CUDACC__)
-```
diff --git a/example/python-howto/data_iter.py b/example/python-howto/data_iter.py
deleted file mode 100644
index 81c8988a8e5..00000000000
--- a/example/python-howto/data_iter.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# 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.
-
-"""Create a Cifar data iterator.
-
-This example shows how to create a iterator reading from recordio,
-introducing image augmentations and using a backend thread to hide IO cost.
-All you need to do is to set some parameters.
-"""
-import mxnet as mx
-
-dataiter = mx.io.ImageRecordIter(
- # Dataset Parameter
- # Impulsary
- # indicating the data file, please check the data is already there
- path_imgrec="data/cifar/train.rec",
- # Dataset/Augment Parameter
- # Impulsary
- # indicating the image size after preprocessing
- data_shape=(3,28,28),
- # Batch Parameter
- # Impulsary
- # tells how many images in a batch
- batch_size=100,
- # Augmentation Parameter
- # Optional
- # when offers mean_img, each image will subtract the mean value at each pixel
- mean_img="data/cifar/cifar10_mean.bin",
- # Augmentation Parameter
- # Optional
- # randomly crop a patch of the data_shape from the original image
- rand_crop=True,
- # Augmentation Parameter
- # Optional
- # randomly mirror the image horizontally
- rand_mirror=True,
- # Augmentation Parameter
- # Optional
- # randomly shuffle the data
- shuffle=False,
- # Backend Parameter
- # Optional
- # Preprocessing thread number
- preprocess_threads=4,
- # Backend Parameter
- # Optional
- # Prefetch buffer size
- prefetch_buffer=4,
- # Backend Parameter,
- # Optional
- # Whether round batch,
- round_batch=True)
-
-batchidx = 0
-for dbatch in dataiter:
- data = dbatch.data[0]
- label = dbatch.label[0]
- pad = dbatch.pad
- index = dbatch.index
- print("Batch", batchidx)
- print(label.asnumpy().flatten())
- batchidx += 1
diff --git a/example/python-howto/debug_conv.py b/example/python-howto/debug_conv.py
deleted file mode 100644
index 9de421d8e88..00000000000
--- a/example/python-howto/debug_conv.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# 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
-
-data_shape = (1,3,5,5)
-class SimpleData(object):
-
- def __init__(self, data):
- self.data = data
-
-data = mx.sym.Variable('data')
-conv = mx.sym.Convolution(data=data, kernel=(3,3), pad=(1,1), stride=(1,1), num_filter=1)
-mon = mx.mon.Monitor(1)
-
-
-mod = mx.mod.Module(conv)
-mod.bind(data_shapes=[('data', data_shape)])
-mod._exec_group.install_monitor(mon)
-mod.init_params()
-
-input_data = mx.nd.ones(data_shape)
-mod.forward(data_batch=SimpleData([input_data]))
-res = mod.get_outputs()[0].asnumpy()
-print(res)
diff --git a/example/python-howto/monitor_weights.py b/example/python-howto/monitor_weights.py
deleted file mode 100644
index 929b0e7bf79..00000000000
--- a/example/python-howto/monitor_weights.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# 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.
-
-# pylint: skip-file
-import mxnet as mx
-from mxnet.test_utils import get_mnist_iterator
-import numpy as np
-import logging
-
-# network
-data = mx.symbol.Variable('data')
-fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
-act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu")
-fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)
-act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu")
-fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10)
-mlp = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax')
-
-# data
-train, val = get_mnist_iterator(batch_size=100, input_shape = (784,))
-
-# monitor
-def norm_stat(d):
- return mx.nd.norm(d)/np.sqrt(d.size)
-mon = mx.mon.Monitor(100, norm_stat)
-
-# train with monitor
-logging.basicConfig(level=logging.DEBUG)
-module = mx.module.Module(context=mx.cpu(), symbol=mlp)
-module.fit(train_data=train, eval_data=val, monitor=mon, num_epoch=2,
- batch_end_callback = mx.callback.Speedometer(100, 100),
- optimizer_params=(('learning_rate', 0.1), ('momentum', 0.9), ('wd', 0.00001)))
diff --git a/example/python-howto/multiple_outputs.py b/example/python-howto/multiple_outputs.py
deleted file mode 100644
index 7c1ddd22055..00000000000
--- a/example/python-howto/multiple_outputs.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# 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.
-
-"""Create a Multiple output configuration.
-
-This example shows how to create a multiple output configuration.
-"""
-from __future__ import print_function
-import mxnet as mx
-
-net = mx.symbol.Variable('data')
-fc1 = mx.symbol.FullyConnected(data=net, name='fc1', num_hidden=128)
-net = mx.symbol.Activation(data=fc1, name='relu1', act_type="relu")
-net = mx.symbol.FullyConnected(data=net, name='fc2', num_hidden=64)
-out = mx.symbol.SoftmaxOutput(data=net, name='softmax')
-# group fc1 and out together
-group = mx.symbol.Group([fc1, out])
-print(group.list_outputs())
-
-# You can go ahead and bind on the group
-# executor = group.simple_bind(data=data_shape)
-# executor.forward()
-# executor.output[0] will be value of fc1
-# executor.output[1] will be value of softmax
diff --git a/example/mxnet_adversarial_vae/README.md b/example/vae-gan/README.md
similarity index 100%
rename from example/mxnet_adversarial_vae/README.md
rename to example/vae-gan/README.md
diff --git a/example/mxnet_adversarial_vae/convert_data.py b/example/vae-gan/convert_data.py
similarity index 100%
rename from example/mxnet_adversarial_vae/convert_data.py
rename to example/vae-gan/convert_data.py
diff --git a/example/mxnet_adversarial_vae/vaegan_mxnet.py b/example/vae-gan/vaegan_mxnet.py
similarity index 100%
rename from example/mxnet_adversarial_vae/vaegan_mxnet.py
rename to example/vae-gan/vaegan_mxnet.py
diff --git a/tests/python-pytest/onnx/import/onnx_import_test.py b/tests/python-pytest/onnx/import/onnx_import_test.py
index 573dd74a471..c2d1e9cb2d3 100644
--- a/tests/python-pytest/onnx/import/onnx_import_test.py
+++ b/tests/python-pytest/onnx/import/onnx_import_test.py
@@ -149,21 +149,6 @@ def test_equal():
output = bkd_rep.run([input1, input2])
npt.assert_almost_equal(output[0], numpy_op)
-def test_super_resolution_example():
- """Test the super resolution example in the example/onnx folder"""
- sys.path.insert(0, os.path.join(CURR_PATH, '../../../../example/onnx/'))
- import super_resolution
-
- sym, arg_params, aux_params = super_resolution.import_onnx()
-
- logging.info("Asserted the result of the onnx model conversion")
- output_img_dim = 672
- input_image, img_cb, img_cr = super_resolution.get_test_image()
- result_img = super_resolution.perform_inference(sym, arg_params, aux_params,
- input_image, img_cb, img_cr)
-
- assert hashlib.md5(result_img.tobytes()).hexdigest() == '0d98393a49b1d9942106a2ed89d1e854'
- assert result_img.size == (output_img_dim, output_img_dim)
def get_test_files(name):
"""Extract tar file and returns model path and input, output data"""
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