[singa] branch dev updated: add alexnet model in cifar_distributed_cnn

[zh...@apache.org]

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zhaojing pushed a commit to branch dev
in repository https://gitbox.apache.org/repos/asf/singa.git


The following commit(s) were added to refs/heads/dev by this push:
     new 449ab35  add alexnet model in cifar_distributed_cnn
     new 92d617b  Merge pull request #854 from zlheui/updated-alexnet
449ab35 is described below

commit 449ab35438135d3de133aea99f9f81694d67322c
Author: zhulei <zl...@gmail.com>
AuthorDate: Fri May 21 21:38:37 2021 +0800

    add alexnet model in cifar_distributed_cnn
---
 examples/cifar_distributed_cnn/model/alexnet.py | 119 ++++++++++++++++++++++++
 1 file changed, 119 insertions(+)

diff --git a/examples/cifar_distributed_cnn/model/alexnet.py b/examples/cifar_distributed_cnn/model/alexnet.py
new file mode 100644
index 0000000..cad7b1e
--- /dev/null
+++ b/examples/cifar_distributed_cnn/model/alexnet.py
@@ -0,0 +1,119 @@
+#
+# 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.
+#
+
+from singa import layer
+from singa import model
+
+
+class AlexNet(model.Model):
+
+    def __init__(self, num_classes=10, num_channels=1):
+        super(AlexNet, self).__init__()
+        self.num_classes = num_classes
+        self.input_size = 224
+        self.dimension = 4
+        self.conv1 = layer.Conv2d(num_channels, 64, 11, stride=4, padding=2)
+        self.conv2 = layer.Conv2d(64, 192, 5, padding=2)
+        self.conv3 = layer.Conv2d(192, 384, 3, padding=1)
+        self.conv4 = layer.Conv2d(384, 256, 3, padding=1)
+        self.conv5 = layer.Conv2d(256, 256, 3, padding=1)
+        self.linear1 = layer.Linear(4096)
+        self.linear2 = layer.Linear(4096)
+        self.linear3 = layer.Linear(num_classes)
+        self.pooling1 = layer.MaxPool2d(2, 2, padding=0)
+        self.pooling2 = layer.MaxPool2d(2, 2, padding=0)
+        self.pooling3 = layer.MaxPool2d(2, 2, padding=0)
+        self.avg_pooling1 = layer.AvgPool2d(3, 2, padding=0)
+        self.relu1 = layer.ReLU()
+        self.relu2 = layer.ReLU()
+        self.relu3 = layer.ReLU()
+        self.relu4 = layer.ReLU()
+        self.relu5 = layer.ReLU()
+        self.relu6 = layer.ReLU()
+        self.relu7 = layer.ReLU()
+        self.flatten = layer.Flatten()
+        self.dropout1 = layer.Dropout()
+        self.dropout2 = layer.Dropout()
+        self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+
+    def forward(self, x):
+        y = self.conv1(x)
+        y = self.relu1(y)
+        y = self.pooling1(y)
+        y = self.conv2(y)
+        y = self.relu2(y)
+        y = self.pooling2(y)
+        y = self.conv3(y)
+        y = self.relu3(y)
+        y = self.conv4(y)
+        y = self.relu4(y)
+        y = self.conv5(y)
+        y = self.relu5(y)
+        y = self.pooling3(y)
+        y = self.avg_pooling1(y)
+        y = self.flatten(y)
+        y = self.dropout1(y)
+        y = self.linear1(y)
+        y = self.relu6(y)
+        y = self.dropout2(y)
+        y = self.linear2(y)
+        y = self.relu7(y)
+        y = self.linear3(y)
+        return y
+
+    def train_one_batch(self, x, y, dist_option, spars):
+        out = self.forward(x)
+        loss = self.softmax_cross_entropy(out, y)
+
+        if dist_option == 'plain':
+            self.optimizer(loss)
+        elif dist_option == 'half':
+            self.optimizer.backward_and_update_half(loss)
+        elif dist_option == 'partialUpdate':
+            self.optimizer.backward_and_partial_update(loss)
+        elif dist_option == 'sparseTopK':
+            self.optimizer.backward_and_sparse_update(loss,
+                                                      topK=True,
+                                                      spars=spars)
+        elif dist_option == 'sparseThreshold':
+            self.optimizer.backward_and_sparse_update(loss,
+                                                      topK=False,
+                                                      spars=spars)
+        return out, loss
+
+    def set_optimizer(self, optimizer):
+        self.optimizer = optimizer
+
+
+def create_model(pretrained=False, **kwargs):
+    """Constructs a AlexNet model.
+
+    Args:
+        pretrained (bool): If True, returns a pre-trained model.
+    
+    Returns:
+        The created AlexNet model.
+    
+    """
+    model = AlexNet(**kwargs)
+
+    return model
+
+
+__all__ = ['AlexNet', 'create_model']