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The following commit(s) were added to refs/heads/fit-api by this push:
new b1ef99a [MXNet-1343][Fit API]Add CNN integration test for fit() API
(#14405)
b1ef99a is described below
commit b1ef99ae4f84da2fbd9c25115040e612e7250323
Author: Abhinav Sharma <[email protected]>
AuthorDate: Wed Apr 3 15:12:56 2019 -0700
[MXNet-1343][Fit API]Add CNN integration test for fit() API (#14405)
* added cnn intg tests for fit api
* updated cnn intg tests
* added functions for nightly test
* updated runtime_function
* updated intg tests
* updated init, datapath, refs
* added validation data
* update cpu test
* refactor code
* updated context
---
ci/docker/runtime_functions.sh | 14 +++
tests/nightly/JenkinsfileForBinaries | 16 +++
tests/nightly/estimator/test_estimator_cnn.py | 153 ++++++++++++++++++++++++++
3 files changed, 183 insertions(+)
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index 128ae2b..88cd972 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -1296,6 +1296,20 @@ nightly_scala_demo_test_cpu() {
bash bin/run_im.sh
}
+nightly_estimator_cnn_gpu() {
+ set -ex
+ cd /work/mxnet/tests/nightly/estimator
+ export PYTHONPATH=/work/mxnet/python/
+ python test_estimator_cnn.py --type gpu
+}
+
+nightly_estimator_cnn_cpu() {
+ set -ex
+ cd /work/mxnet/tests/nightly/estimator
+ export PYTHONPATH=/work/mxnet/python/
+ python test_estimator_cnn.py --type cpu
+}
+
nightly_estimator_rnn_gpu() {
set -ex
cd /work/mxnet/tests/nightly/estimator
diff --git a/tests/nightly/JenkinsfileForBinaries
b/tests/nightly/JenkinsfileForBinaries
index 53e1c30..53572c8 100755
--- a/tests/nightly/JenkinsfileForBinaries
+++ b/tests/nightly/JenkinsfileForBinaries
@@ -106,6 +106,22 @@ core_logic: {
utils.docker_run('ubuntu_nightly_gpu',
'nightly_tutorial_test_ubuntu_python3_gpu', true, '1500m')
}
}
+ },
+ 'estimator: CNN GPU': {
+ node(NODE_LINUX_GPU) {
+ ws('workspace/estimator-test-cnn-gpu') {
+ utils.unpack_and_init('gpu', mx_lib)
+ utils.docker_run('ubuntu_nightly_gpu',
'nightly_estimator_test_cnn_gpu', true)
+ }
+ }
+ },
+ 'estimator: CNN CPU': {
+ node(NODE_LINUX_CPU) {
+ ws('workspace/estimator-test-cnn-cpu') {
+ utils.unpack_and_init('cpu', mx_lib)
+ utils.docker_run('ubuntu_nightly_cpu',
'nightly_estimator_test_cnn_cpu', true)
+ }
+ }
}
}
}
diff --git a/tests/nightly/estimator/test_estimator_cnn.py
b/tests/nightly/estimator/test_estimator_cnn.py
new file mode 100644
index 0000000..b99e99a
--- /dev/null
+++ b/tests/nightly/estimator/test_estimator_cnn.py
@@ -0,0 +1,153 @@
+# 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.
+
+# Test gluon estimator on CNN models
+
+import argparse
+import numpy as np
+import mxnet as mx
+from mxnet import gluon, init, nd
+from mxnet.gluon import data
+from mxnet.gluon.estimator import estimator
+from mxnet.gluon.model_zoo import vision
+
+def load_data_mnist(batch_size, resize=None, num_workers=4):
+ '''
+ Load MNIST dataset
+ '''
+ transformer = []
+ if resize:
+ transformer += [data.vision.transforms.Resize(resize)]
+ transformer += [data.vision.transforms.ToTensor()]
+ transformer = data.vision.transforms.Compose(transformer)
+ mnist_train = data.vision.MNIST(train=True)
+ mnist_test = data.vision.MNIST(train=False)
+ train_iter = data.DataLoader(
+ mnist_train.transform_first(transformer), batch_size, shuffle=True,
+ num_workers=num_workers)
+ test_iter = data.DataLoader(
+ mnist_test.transform_first(transformer), batch_size, shuffle=False,
+ num_workers=num_workers)
+ return train_iter, test_iter
+
+def bilinear_kernel(in_channels, out_channels, kernel_size):
+ '''
+ Bilinear interpolation using transposed convolution
+ https://github.com/d2l-ai/d2l-en/blob/master/chapter_computer-vision/fcn.md
+ '''
+ factor = (kernel_size + 1) // 2
+ if kernel_size % 2 == 1:
+ center = factor - 1
+ else:
+ center = factor - 0.5
+ og = np.ogrid[:kernel_size, :kernel_size]
+ filt = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) /
factor)
+ weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size),
dtype='float32')
+ weight[range(in_channels), range(out_channels), :, :] = filt
+ return nd.array(weight)
+
+def get_net(model_name, context):
+ if model_name == 'FCN':
+ num_classes = 21
+ pretrained_net = vision.resnet18_v2(pretrained=True, ctx=context)
+ net = gluon.nn.HybridSequential()
+ for layer in pretrained_net.features[:-2]:
+ net.add(layer)
+ net.add(gluon.nn.Conv2D(num_classes, kernel_size=1),
+ gluon.nn.Conv2DTranspose(num_classes, kernel_size=64,
padding=16, strides=32))
+ net[-1].initialize(init.Constant(bilinear_kernel(num_classes,
num_classes, 64)), ctx=context)
+ net[-2].initialize(init=init.Xavier(), ctx=context)
+ input_shape = (1, 3, 320, 480)
+ label_shape = (1, 320, 480)
+ loss_axis = 1
+ else:
+ net = vision.get_model(model_name, classes=10)
+ net.initialize(mx.init.Xavier(), ctx=context)
+ input_shape = (1, 1, 224, 224)
+ label_shape = 1
+ loss_axis = -1
+ return net, input_shape, label_shape, loss_axis
+
+def test_estimator_cpu():
+ '''
+ Test estimator by doing one pass over each model with synthetic data
+ '''
+ models = ['resnet18_v1',
+ 'FCN'
+ ]
+ context = mx.cpu()
+ for model_name in models:
+ net, input_shape, label_shape, loss_axis = get_net(model_name, context)
+ train_dataset =
gluon.data.dataset.ArrayDataset(mx.nd.random.uniform(shape=input_shape),
+
mx.nd.zeros(shape=label_shape))
+ val_dataset =
gluon.data.dataset.ArrayDataset(mx.nd.random.uniform(shape=input_shape),
+
mx.nd.zeros(shape=label_shape))
+ loss = gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis)
+ train_data = gluon.data.DataLoader(train_dataset, batch_size=1)
+ val_data = gluon.data.DataLoader(val_dataset, batch_size=1)
+ net.hybridize()
+ trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate':
0.001})
+ # Define estimator
+ est = estimator.Estimator(net=net,
+ loss=loss,
+ metrics=mx.metric.Accuracy(),
+ trainers=trainer,
+ context=context)
+ # Call fit()
+ est.fit(train_data=train_data,
+ val_data=val_data,
+ epochs=1,
+ batch_size=1)
+
+def test_estimator_gpu():
+ '''
+ Test estimator by training resnet18_v1 for 5 epochs on MNIST and verify
accuracy
+ '''
+ model_name = 'resnet18_v1'
+ batch_size = 128
+ num_epochs = 5
+ context = mx.gpu(0)
+ net, _, _, _ = get_net(model_name, context)
+ train_data, test_data = load_data_mnist(batch_size, resize=224)
+ loss = gluon.loss.SoftmaxCrossEntropyLoss()
+ net.hybridize()
+ acc = mx.metric.Accuracy()
+ trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate':
0.001})
+ # Define estimator
+ est = estimator.Estimator(net=net,
+ loss=loss,
+ metrics=acc,
+ trainers=trainer,
+ context=context)
+ # Call fit()
+ est.fit(train_data=train_data,
+ val_data=test_data,
+ epochs=num_epochs,
+ batch_size=batch_size)
+
+ assert est.train_stats['train_'+acc.name][num_epochs-1] > 0.80
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(description='test gluon estimator')
+ parser.add_argument('--type', type=str, default='cpu')
+ opt = parser.parse_args()
+ if opt.type == 'cpu':
+ test_estimator_cpu()
+ elif opt.type == 'gpu':
+ test_estimator_gpu()
+ else:
+ raise RuntimeError("Unknown test type")
