init commit

LICENSE

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+MIT License
+
+Copyright (c) 2019 Alzayat Saleh
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

active_learning.py

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+''' Active learning wrapper model for pytoch Models.
+    Implement function get_active_learning_layers() and pass your model to constructor of
+    the ActiveLearning class.
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ActiveLearning(nn.Module):
+
+    def __init__(self, base_model, global_avg_pool_size=1, fc_width=128):
+        super(ActiveLearning, self).__init__()
+        self.base_model = base_model
+        self.channel_counts = base_model.get_active_learning_feature_channel_counts()
+        self.fc = []
+        for channels in self.channel_counts:
+            self.fc.append(nn.Linear(
+                channels * global_avg_pool_size * global_avg_pool_size, fc_width))
+
+        self.fc = nn.ModuleList(self.fc)
+        self.loss_pred = nn.Linear(len(self.channel_counts)*fc_width,1)
+        self.avgpool = nn.AdaptiveAvgPool2d((global_avg_pool_size, global_avg_pool_size))
+
+    def forward(self, x, detach_lp = False):
+        out_p = self.base_model.forward(x)
+        active_learning_features = self.base_model.get_active_learning_features()
+        fc_outputs = []
+        for idx, features in enumerate(active_learning_features):
+            if detach_lp:
+                features = features.detach()
+            out = self.avgpool(features)
+            out = torch.flatten(out, 1)
+            out = self.fc[idx](out)
+            fc_outputs.append(F.relu(out))
+        fc_cat = torch.cat(fc_outputs, dim=1)
+        loss_pred = self.loss_pred(fc_cat)
+        return out_p, loss_pred
+
+    # Used for segmentation with DRN only.
+    def optim_parameters(self):
+        for param in self.base_model.optim_parameters():
+            yield param
+        for param in self.fc.parameters():
+            yield param
+        for param in self.loss_pred.parameters():
+            yield param
+