support for deterministic testing

python-code/Utils/evaluate.py

@@ -7,9 +7,6 @@
 # Created: Fri Mar  6 11:52:42 2015 (+0100)
 # Version:
 # Package-Requires: ()
-# Last-Updated: Thu Jul  7 10:54:17 2016 (+0200)
-#           By: Kwang Moo Yi
-#     Update #: 234
 # URL:
 # Doc URL:
 # Keywords:
@@ -42,7 +39,8 @@
 from Utils.networks.buildLayers import instantiateLayers
 
 
-def testModelNew(img_file_name, kp_file_name, pathconf, param, model_epoch=""):
+def testModelNew(img_file_name, kp_file_name, pathconf, param, model_epoch="",
+                 deterministic=False):
 
     if param.runType != "CVPR16":
         raise NotImplementedError(
@@ -104,7 +102,7 @@ def testModelNew(img_file_name, kp_file_name, pathconf, param, model_epoch=""):
     # Actual instantiation and setup
     myNet = SiameseOrientationLearner(myNetConfig)
 
-    instantiateLayers(myNet)
+    instantiateLayers(myNet, deterministic)
     # myNet.setupSGD()
     myNet.setupDataAndCompile4Test()
 

python-code/Utils/models.py

@@ -7,9 +7,6 @@
 # Created: Tue Jan 27 11:17:55 2015 (+0100)
 # Version:
 # Package-Requires: ()
-# Last-Updated: Mon Jul  4 05:51:24 2016 (-0700)
-#           By: Kwang Moo Yi
-#     Update #: 858
 # URL:
 # Doc URL:
 # Keywords:
@@ -117,12 +114,12 @@ def __init__(self, rng, input, filter_shape, image_shape,
             self.b = b_in
 
         # convolve input feature maps with filters
-        conv_out = conv.conv2d(input=input, filters=self.W,
-                               filter_shape=filter_shape,
-                               image_shape=image_shape)
+        self.conv_out = conv.conv2d(input=input, filters=self.W,
+                                    filter_shape=filter_shape,
+                                    image_shape=image_shape)
 
         # downsample each feature map individually, using maxpooling
-        pooled_out = downsample.max_pool_2d(input=conv_out,
+        pooled_out = downsample.max_pool_2d(input=self.conv_out,
                                             ds=poolsize, ignore_border=True)
 
         # add the bias term. Since the bias is a vector (1D array), we first
@@ -480,15 +477,15 @@ def __init__(self, rng, input, n_in, n_out, n_sum, n_max,
 
         W_vec = self.W.reshape([n_in, n_out * n_sum * n_max])
         raw_out = T.dot(input, W_vec) + self.b.flatten().dimshuffle('x', 0)
-        reshaped_raw_out = raw_out.reshape([batch_size, n_out, n_sum, n_max])
+        self.reshaped_raw_out = raw_out.reshape([batch_size, n_out, n_sum, n_max])
 
-        max_out = T.max(reshaped_raw_out, axis=3)
-        sum_out = T.sum(
-            max_out * self.delta.dimshuffle('x', 'x', 0),
+        self.max_out = T.max(self.reshaped_raw_out, axis=3)
+        self.sum_out = T.sum(
+            self.max_out * self.delta.dimshuffle('x', 'x', 0),
             axis=2, dtype=floatX
         )
 
-        lin_output = sum_out
+        lin_output = self.sum_out
         output = (lin_output if activation is None else activation(lin_output))
 
         # If nAtanPool is 2 then it means in this layer, we atan pool

python-code/Utils/networks/buildLayers.py

@@ -7,9 +7,6 @@
 # Created: Fri Aug 14 13:44:40 2015 (+0200)
 # Version:
 # Package-Requires: ()
-# Last-Updated: Tue Jul  5 16:19:41 2016 (+0200)
-#           By: Kwang Moo Yi
-#     Update #: 129
 # URL:
 # Doc URL:
 # Keywords:
@@ -52,7 +49,7 @@
 floatX = theano.config.floatX
 
 
-def instantiateLayers(myNet):
+def instantiateLayers(myNet, deterministic):
     '''
     Build layers here
 
@@ -100,7 +97,7 @@ def instantiateLayers(myNet):
         # ---------------------------------------------------------------------
         # If necessary, generate the dropout test layer and the corresponding
         # output
-        if bDropout:
+        if bDropout and deterministic:
             myNet.test_layers[idxSiam] = buildDropoutTestLayers(
                 myNet.layers[idxSiam], dropout_rates)
         else:
@@ -280,13 +277,24 @@ def buildDropoutTestLayers(orig_layer_list, dropout_rates):
     test_layer_list = []
 
     # For each layer in the network
+    prev_layer_output = None
     for cur_layer, dropout_rate in zip(orig_layer_list, dropout_rates):
 
         # Note that these will carry the normal name and args_in excluding the
         # dropout init
         layer_name = cur_layer.name
         layer_args = cur_layer.args_in
 
+        # Use input from the previous test layer
+        if prev_layer_output is not None:
+            # flatten the input layer into two dims if hidden layer
+            if cur_layer.name[-11:] == "HiddenLayer":
+                cur_layer_input = prev_layer_output.flatten(2)
+            else:
+                cur_layer_input = prev_layer_output
+            layer_args = (layer_args[0],) + \
+                (cur_layer_input,) + layer_args[2:]
+
         # Change the last arguments so that it simply uses the weights
         # of the  other layer!  note that the  original implementation
         # did not multiply the output for some reason
@@ -297,6 +305,9 @@ def buildDropoutTestLayers(orig_layer_list, dropout_rates):
         # Call the appropriate layer with it's name
         test_layer = globals()[layer_name](*new_layer_args)
 
+        # Store the output
+        prev_layer_output = test_layer.output
+
         test_layer_list += [test_layer]
 
     return test_layer_list

python-code/Utils/networks/cvpr16.py

@@ -209,8 +209,6 @@ def runTest(self, x_in, model_epoch=""):
             self.test_x[idxSiam].set_value(
                 numpy.asarray(test_x_in, dtype=floatX))
 
-        # self.test_x_id.set_value(test_x_id_in)
-        # self.test_y.set_value(test_y_in)
         test_result = self.get_output()
         test_raw_output = self.get_raw_output()
         # ---------------------------------------------------------------------

python-code/runSingleTestWithFiles.py

@@ -1,3 +1,4 @@
+#!/bin/env python2
 # runSingleTestWithFiles.py ---
 #
 # Filename: runSingleTestWithFiles.py
@@ -43,16 +44,22 @@
 if __name__ == '__main__':
     """ Main routine """
 
-    if len(sys.argv) != 5:
+    if len(sys.argv) != 5 and len(sys.argv) != 6:
         raise RuntimeError(
             "USAGE: python runSingleTestWithFiles.py "
-            "<image_file> <kp_file> <config_file> <output_file>")
+            "<image_file> <kp_file> <config_file> <output_file> "
+            "<isdeterministic(0/1)>")
 
     image_file_name = sys.argv[1]
     kp_file_name = sys.argv[2]
     config_file = sys.argv[3]
     output_file = sys.argv[4]
 
+    # Default test mode is stochastic
+    deterministic = False
+    if len(sys.argv) >= 6:
+        deterministic = bool(int(sys.argv[5]))
+
     # ------------------------------------------
     # Setup and load parameters
     param = paramStruct()
@@ -72,8 +79,8 @@
     # ------------------------------------------
     # Run Evaluate
     start_time = time.clock()
-    eval_res = testModelNew(
-        image_file_name, kp_file_name, pathconf, param, model_epoch)
+    eval_res = testModelNew(image_file_name, kp_file_name, pathconf, param,
+                            model_epoch, deterministic)
     end_time = time.clock()
     print("Time taken to compute for image {} (including compile time)"
           " is {} seconds".format(