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deepcore/nets/mlp.py

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+import torch.nn as nn
+import torch.nn.functional as F
+from torch import set_grad_enabled
+from .nets_utils import EmbeddingRecorder
+
+# Acknowledgement to
+# https://github.com/kuangliu/pytorch-cifar,
+# https://github.com/BIGBALLON/CIFAR-ZOO,
+
+
+''' MLP '''
+
+
+class MLP(nn.Module):
+    def __init__(self, channel, num_classes, im_size, record_embedding: bool = False, no_grad: bool = False,
+                 pretrained: bool = False):
+        if pretrained:
+            raise NotImplementedError("torchvison pretrained models not available.")
+        super(MLP, self).__init__()
+        self.fc_1 = nn.Linear(im_size[0] * im_size[1] * channel, 128)
+        self.fc_2 = nn.Linear(128, 128)
+        self.fc_3 = nn.Linear(128, num_classes)
+
+        self.embedding_recorder = EmbeddingRecorder(record_embedding)
+        self.no_grad = no_grad
+
+    def get_last_layer(self):
+        return self.fc_3
+
+    def forward(self, x):
+        with set_grad_enabled(not self.no_grad):
+            out = x.view(x.size(0), -1)
+            out = F.relu(self.fc_1(out))
+            out = F.relu(self.fc_2(out))
+            out = self.embedding_recorder(out)
+            out = self.fc_3(out)
+        return out