Add the implementations for the gan model in the peft

examples/singa_peft/examples/model/lsgan_mlp.py

@@ -0,0 +1,101 @@
+#
+# 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
+from singa import autograd
+
+
+class LSGAN_MLP(model.Model):
+
+    def __init__(self, noise_size=100, feature_size=784, hidden_size=128):
+        super(LSGAN_MLP, self).__init__()
+        self.noise_size = noise_size
+        self.feature_size = feature_size
+        self.hidden_size = hidden_size
+
+        # Generative Net
+        self.gen_net_fc_0 = layer.Linear(self.hidden_size)
+        self.gen_net_relu_0 = layer.ReLU()
+        self.gen_net_fc_1 = layer.Linear(self.feature_size)
+        self.gen_net_sigmoid_1 = layer.Sigmoid()
+
+        # Discriminative Net
+        self.dis_net_fc_0 = layer.Linear(self.hidden_size)
+        self.dis_net_relu_0 = layer.ReLU()
+        self.dis_net_fc_1 = layer.Linear(1)
+        self.mse_loss = layer.MeanSquareError()
+
+    def forward(self, x):
+        # Cascaded Net
+        y = self.forward_gen(x)
+        y = self.forward_dis(y)
+        return y
+
+    def forward_dis(self, x):
+        # Discriminative Net
+        y = self.dis_net_fc_0(x)
+        y = self.dis_net_relu_0(y)
+        y = self.dis_net_fc_1(y)
+        return y
+
+    def forward_gen(self, x):
+        # Generative Net
+        y = self.gen_net_fc_0(x)
+        y = self.gen_net_relu_0(y)
+        y = self.gen_net_fc_1(y)
+        y = self.gen_net_sigmoid_1(y)
+        return y
+
+    def train_one_batch(self, x, y):
+        # Training the Generative Net
+        out = self.forward(x)
+        loss = self.mse_loss(out, y)
+        # Only update the Generative Net
+        for p, g in autograd.backward(loss):
+            if "gen_net" in p.name:
+                self.optimizer.apply(p.name, p, g)
+        return out, loss
+
+    def train_one_batch_dis(self, x, y):
+        # Training the Discriminative Net
+        out = self.forward_dis(x)
+        loss = self.mse_loss(out, y)
+        # Only update the Discriminative Net
+        for p, g in autograd.backward(loss):
+            if "dis_net" in p.name:
+                self.optimizer.apply(p.name, p, g)
+        return out, loss
+
+    def set_optimizer(self, optimizer):
+        self.optimizer = optimizer
+
+
+def create_model(pretrained=False, **kwargs):
+    """Construct a CNN model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained
+    """
+    model = LSGAN_MLP(**kwargs)
+
+    return model
+
+
+__all__ = ['LSGAN_MLP', 'create_model']