[extractor] added the possibility to have an untrained model (for test purposes)

bob/ip/pytorch_extractor/CNN8.py

 import numpy
+import sys
 
 import torch
 import torch.nn as nn
@@ -7,6 +8,10 @@ from torch.autograd import Variable
 
 import torchvision.transforms as transforms
 
+import bob.core
+logger = bob.core.log.setup("bob.learn.pytorch")
+
+
 CNN8_CONFIG = [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M']
 
 def make_conv_layers(cfg, input_c = 3):
@@ -43,14 +48,17 @@ from bob.bio.base.extractor import Extractor
 
 class CNN8Extractor(Extractor):
   
-  def __init__(self, model_file, num_classes=10575):
+  def __init__(self, model_file=None, num_classes=10575):
 
     Extractor.__init__(self, skip_extractor_training=True)
     
     # model
     self.network = CNN8(num_classes)
-    cp = torch.load(model_file)
-    if 'state_dict' in cp:
+    if model_file is None:
+      logger.warning("No model file provided, building network from scratch !")
+    else:
+      cp = torch.load(model_file)
+      if 'state_dict' in cp:
         self.network.load_state_dict(cp['state_dict'])
     self.network.eval()
 

bob/ip/pytorch_extractor/CasiaNet.py

@@ -7,6 +7,9 @@ from torch.autograd import Variable
 
 import torchvision.transforms as transforms
 
+import bob.core
+logger = bob.core.log.setup("bob.learn.pytorch")
+
 CASIA_CONFIG = [32, 64, 'M', 64, 128, 'M', 96, 192, 'M', 128, 256, 'M', 160, 320] 
 
 def make_conv_layers(cfg, input_c = 3):
@@ -44,14 +47,17 @@ from bob.bio.base.extractor import Extractor
 
 class CasiaNetExtractor(Extractor):
   
-  def __init__(self, model_file, num_classes=10575):
+  def __init__(self, model_file=None, num_classes=10575):
 
     Extractor.__init__(self, skip_extractor_training=True)
     
     # model
     self.network = CASIA_NET(num_classes)
-    cp = torch.load(model_file)
-    if 'state_dict' in cp:
+    if model_file is None:
+      logger.warning("No model file provided, building network from scratch !")
+    else:
+      cp = torch.load(model_file)
+      if 'state_dict' in cp:
         self.network.load_state_dict(cp['state_dict'])
     self.network.eval()