From c99ce7c4e781712e0252c6127ad1a4e8021cc489 Mon Sep 17 00:00:00 2001
From: junyanz <junyanz@berkeley.edu>
Date: Tue, 18 Apr 2017 03:38:47 -0700
Subject: first commit

---
 models/base_model.py | 56 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 models/base_model.py

(limited to 'models/base_model.py')

diff --git a/models/base_model.py b/models/base_model.py
new file mode 100644
index 0000000..0ea83d8
--- /dev/null
+++ b/models/base_model.py
@@ -0,0 +1,56 @@
+import os
+import torch
+from pdb import set_trace as st
+
+class BaseModel():
+    def name(self):
+        return 'BaseModel'
+
+    def initialize(self, opt):
+        self.opt = opt
+        self.gpu_ids = opt.gpu_ids
+        self.isTrain = opt.isTrain
+        self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
+        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
+
+    def set_input(self, input):
+        self.input = input
+
+    def forward(self):
+        pass
+
+    # used in test time, no backprop
+    def test(self):
+        pass
+
+    def get_image_paths(self):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        return self.input
+
+    def get_current_errors(self):
+        return {}
+
+    def save(self, label):
+        pass
+
+    # helper saving function that can be used by subclasses
+    def save_network(self, network, network_label, epoch_label, use_gpu):
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        torch.save(network.cpu().state_dict(), save_path)
+        if use_gpu and torch.cuda.is_available():
+            network.cuda()
+
+    # helper loading function that can be used by subclasses
+    def load_network(self, network, network_label, epoch_label):
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        network.load_state_dict(torch.load(save_path))
+
+    def update_learning_rate():
+        pass
-- 
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