make sure all networks have a broadcastable dimension, fix for certai…

…n versions of theano
EliasVansteenkiste · Apr 10, 2017 · c71ee09 · c71ee09
1 parent d4913b4
commit c71ee09
Show file tree

Hide file tree

Showing 17 changed files with 19 additions and 17 deletions.
diff --git a/configs_class_dsb/dsb_a_eliasq10_mal2_s5_p8a1_all.py b/configs_class_dsb/dsb_a_eliasq10_mal2_s5_p8a1_all.py
@@ -75,6 +75,8 @@ def candidates_prep_function(all_candidates, n_selection=None):
 
     return selected_candidates
 
+batch_size = 1
+
 train_valid_ids = utils.load_pkl(pathfinder.VALIDATION_SPLIT_PATH)
 train_pids, valid_pids, test_pids, stage2_pids = train_valid_ids['training'], train_valid_ids['validation'], train_valid_ids['test'], train_valid_ids['test_stage2']
 print 'n train', len(train_pids)

diff --git a/configs_class_dsb/dsb_a_eliasq10_mal2_s5_p8a1_spl.py b/configs_class_dsb/dsb_a_eliasq10_mal2_s5_p8a1_spl.py
@@ -247,7 +247,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq11_mal2_s5_p8a1_all.py b/configs_class_dsb/dsb_a_eliasq11_mal2_s5_p8a1_all.py
@@ -265,7 +265,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq11_mal2_s5_p8a1_spl.py b/configs_class_dsb/dsb_a_eliasq11_mal2_s5_p8a1_spl.py
@@ -238,7 +238,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq14_mal2_s5_p8a1_all.py b/configs_class_dsb/dsb_a_eliasq14_mal2_s5_p8a1_all.py
@@ -273,7 +273,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq14_mal2_s5_p8a1_spl.py b/configs_class_dsb/dsb_a_eliasq14_mal2_s5_p8a1_spl.py
@@ -247,7 +247,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq15_mal7_s5_p8a1_all.py b/configs_class_dsb/dsb_a_eliasq15_mal7_s5_p8a1_all.py
@@ -276,7 +276,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq15_mal7_s5_p8a1_spl.py b/configs_class_dsb/dsb_a_eliasq15_mal7_s5_p8a1_spl.py
@@ -252,7 +252,7 @@ def load_pretrained_model(l_in):
                 b=nn.init.Constant(0))
 
 
-    metadata = utils.load_pkl(os.path.join("/home/eavsteen/dsb3/storage/metadata/dsb3/models/eavsteen/","r_fred_malignancy_7-20170404-163552.pkl"))
+    metadata = utils.load_pkl(os.path.join("/home/eavsteen/dsb3/storage/metadata/dsb3/models/","r_fred_malignancy_7-20170404-163552.pkl"))
     nn.layers.set_all_param_values(l, metadata['param_values'])
 
     return l
@@ -261,7 +261,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq1_mal2_s5_p8a1_spl.py b/configs_class_dsb/dsb_a_eliasq1_mal2_s5_p8a1_spl.py
@@ -241,7 +241,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq3_mal2_s5_p8a1_all.py b/configs_class_dsb/dsb_a_eliasq3_mal2_s5_p8a1_all.py
@@ -262,7 +262,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq3_mal2_s5_p8a1_spl.py b/configs_class_dsb/dsb_a_eliasq3_mal2_s5_p8a1_spl.py
@@ -236,7 +236,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq4_mal2_s5_p8a1_all.py b/configs_class_dsb/dsb_a_eliasq4_mal2_s5_p8a1_all.py
@@ -272,7 +272,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq4_mal2_s5_p8a1_spl.py b/configs_class_dsb/dsb_a_eliasq4_mal2_s5_p8a1_spl.py
@@ -247,7 +247,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq5_mal2_s5_p8a1_all.py b/configs_class_dsb/dsb_a_eliasq5_mal2_s5_p8a1_all.py
@@ -274,7 +274,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq5_mal2_s5_p8a1_spl.py b/configs_class_dsb/dsb_a_eliasq5_mal2_s5_p8a1_spl.py
@@ -247,7 +247,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq6_mal2_s5_p8a1_all.py b/configs_class_dsb/dsb_a_eliasq6_mal2_s5_p8a1_all.py
@@ -264,7 +264,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)
 

diff --git a/configs_class_dsb/dsb_a_eliasq6_mal2_s5_p8a1_spl.py b/configs_class_dsb/dsb_a_eliasq6_mal2_s5_p8a1_spl.py
@@ -237,7 +237,7 @@ def load_pretrained_model(l_in):
 def build_model():
     l_in = nn.layers.InputLayer((None, n_candidates_per_patient,) + p_transform['patch_size'])
     l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
-    l_target = nn.layers.InputLayer((batch_size,))
+    l_target = nn.layers.InputLayer((None,))
 
     l = load_pretrained_model(l_in_rshp)