From b7debed719472922569af93d478dc66b65de19a5 Mon Sep 17 00:00:00 2001
From: Christopher Olah <colah@google.com>
Date: Wed, 8 Aug 2018 11:46:45 -0700
Subject: [PATCH 1/9] Update labels

---
 lucid/modelzoo/caffe_models/AlexNet.py     |  4 ++--
 lucid/modelzoo/caffe_models/InceptionV1.py |  6 +++---
 lucid/modelzoo/caffe_models/others.py      |  6 +++---
 lucid/modelzoo/other_models/InceptionV1.py |  2 +-
 lucid/modelzoo/slim_models/Inception.py    | 10 +++++-----
 lucid/modelzoo/slim_models/ResNetV1.py     |  6 +++---
 lucid/modelzoo/slim_models/ResNetV2.py     |  6 +++---
 lucid/modelzoo/slim_models/others.py       | 18 +++++++++---------
 8 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/lucid/modelzoo/caffe_models/AlexNet.py b/lucid/modelzoo/caffe_models/AlexNet.py
index 72d2d42a..a9fb4ad6 100644
--- a/lucid/modelzoo/caffe_models/AlexNet.py
+++ b/lucid/modelzoo/caffe_models/AlexNet.py
@@ -33,7 +33,7 @@ class AlexNet_caffe(Model):
   # http://jaina.cs.ucdavis.edu/datasets/adv/imagenet/alexnet_frozen.pb
   # but it seems more polite and reliable to host our own.
   model_path  = 'gs://modelzoo/AlexNet.pb'
-  labels_path = 'gs://modelzoo/ImageNet_labels_caffe.txt'
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
   image_shape = [227, 227, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
@@ -59,7 +59,7 @@ class AlexNet_caffe_Places365(Model):
   and then ported to TensorFlow using caffe-tensorflow.
   """
 
-  model_path  = 'gs://modelzoo/AlexNet_caffe_places365.pb'
+  model_path  = 'gs://modelzoo/labels/Places365.txt'
   labels_path = 'gs://modelzoo/InceptionV1_caffe_places365-labels.txt'
   image_shape = [227, 227, 3]
   is_BGR = True
diff --git a/lucid/modelzoo/caffe_models/InceptionV1.py b/lucid/modelzoo/caffe_models/InceptionV1.py
index 3d0b3715..73632754 100644
--- a/lucid/modelzoo/caffe_models/InceptionV1.py
+++ b/lucid/modelzoo/caffe_models/InceptionV1.py
@@ -27,7 +27,7 @@ class InceptionV1_caffe(Model):
   and then ported to TensorFlow using caffe-tensorflow.
   """
   model_path = 'gs://modelzoo/InceptionV1_caffe.pb'
-  labels_path = 'gs://modelzoo/InceptionV1_caffe-labels.txt'
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
   image_shape = [224, 224, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
@@ -60,7 +60,7 @@ class InceptionV1_caffe_Places205(Model):
   and then ported to TensorFlow using caffe-tensorflow.
   """
   model_path = 'gs://modelzoo/InceptionV1_caffe_places205.pb'
-  labels_path = 'gs://modelzoo/InceptionV1_caffe_places205-labels.txt'
+  labels_path = 'gs://modelzoo/labels/Places205.txt'
   image_shape = [224, 224, 3]
   # range based on emperical testing
   image_value_range = (-1,1)
@@ -94,7 +94,7 @@ class InceptionV1_caffe_Places365(Model):
   """
   model_path = 'gs://modelzoo/InceptionV1_caffe_places365.pb'
   # TODO - check labels match predictions
-  labels_path = 'gs://modelzoo/InceptionV1_caffe_places365-labels.txt'
+  labels_path = 'gs://modelzoo/labels/Places365.txt'
   image_shape = [224, 224, 3]
   # What is the correct input range???
   is_BGR = True
diff --git a/lucid/modelzoo/caffe_models/others.py b/lucid/modelzoo/caffe_models/others.py
index b043a30b..aceb6792 100644
--- a/lucid/modelzoo/caffe_models/others.py
+++ b/lucid/modelzoo/caffe_models/others.py
@@ -24,7 +24,7 @@ class CaffeNet_caffe(Model):
   """
 
   model_path  = 'gs://modelzoo/CaffeNet_caffe.pb'
-  labels_path = 'gs://modelzoo/ImageNet_labels_caffe.txt'
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
   image_shape = [227, 227, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
@@ -51,7 +51,7 @@ class VGG16_caffe(Model):
   and convert it with caffe-tensorflow.
   """
   model_path = 'gs://modelzoo/VGG16_caffe.pb'
-  labels_path = 'gs://modelzoo/InceptionV1_caffe-labels.txt'
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
   image_shape = [224, 224, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
@@ -89,7 +89,7 @@ class VGG19_caffe(Model):
   and convert it with caffe-tensorflow.
   """
   model_path = 'gs://modelzoo/VGG19_caffe.pb'
-  labels_path = 'gs://modelzoo/InceptionV1_caffe-labels.txt'
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
   image_shape = [224, 224, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
diff --git a/lucid/modelzoo/other_models/InceptionV1.py b/lucid/modelzoo/other_models/InceptionV1.py
index 01b3e672..a7df9aa7 100644
--- a/lucid/modelzoo/other_models/InceptionV1.py
+++ b/lucid/modelzoo/other_models/InceptionV1.py
@@ -48,7 +48,7 @@ class InceptionV1(Model):
   minor implementation differences (such as eliding the heads).
   """
   model_path = 'gs://modelzoo/InceptionV1.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt'
+  labels_path = 'gs://modelzoo/labels/ImageNet_alternate.txt'
   image_shape = [224, 224, 3]
   image_value_range = (-117, 255-117)
   input_name = 'input:0'
diff --git a/lucid/modelzoo/slim_models/Inception.py b/lucid/modelzoo/slim_models/Inception.py
index 25cd7029..e5308c39 100644
--- a/lucid/modelzoo/slim_models/Inception.py
+++ b/lucid/modelzoo/slim_models/Inception.py
@@ -28,7 +28,7 @@ class InceptionV1_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/InceptionV1_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -63,7 +63,7 @@ class InceptionV2_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/InceptionV2_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -99,7 +99,7 @@ class InceptionV3_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/InceptionV3_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt'
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt'
   image_shape = [299, 299, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -138,7 +138,7 @@ class InceptionV4_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/InceptionV4_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [299, 299, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -183,7 +183,7 @@ class InceptionResnetV2_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/InceptionResnetV2_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [299, 299, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
diff --git a/lucid/modelzoo/slim_models/ResNetV1.py b/lucid/modelzoo/slim_models/ResNetV1.py
index 8d659048..e1b8e789 100644
--- a/lucid/modelzoo/slim_models/ResNetV1.py
+++ b/lucid/modelzoo/slim_models/ResNetV1.py
@@ -25,7 +25,7 @@ class ResnetV1_50_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/ResnetV1_50_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -78,7 +78,7 @@ class ResnetV1_101_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/ResnetV1_101_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -166,7 +166,7 @@ class ResnetV1_152_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/ResnetV1_152_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
diff --git a/lucid/modelzoo/slim_models/ResNetV2.py b/lucid/modelzoo/slim_models/ResNetV2.py
index 661706ba..c3b92183 100644
--- a/lucid/modelzoo/slim_models/ResNetV2.py
+++ b/lucid/modelzoo/slim_models/ResNetV2.py
@@ -28,7 +28,7 @@ class ResnetV2_50_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/ResnetV2_50_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -72,7 +72,7 @@ class ResnetV2_101_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/ResnetV2_101_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -133,7 +133,7 @@ class ResnetV2_152_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/ResnetV2_152_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
diff --git a/lucid/modelzoo/slim_models/others.py b/lucid/modelzoo/slim_models/others.py
index 6738c5c4..6561294f 100644
--- a/lucid/modelzoo/slim_models/others.py
+++ b/lucid/modelzoo/slim_models/others.py
@@ -28,7 +28,7 @@ class VGG16_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/VGG16_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -65,7 +65,7 @@ class VGG19_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/VGG19_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -102,7 +102,7 @@ class MobilenetV1_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/MobilenetV1_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -119,7 +119,7 @@ class MobilenetV1_050_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/MobilenetV1050_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -136,7 +136,7 @@ class MobilenetV1_025_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/MobilenetV1025_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -153,7 +153,7 @@ class NasnetMobile_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/NasnetMobile_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -170,7 +170,7 @@ class NasnetLarge_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/NasnetLarge_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [331, 331, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -187,7 +187,7 @@ class PnasnetLarge_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/PnasnetLarge_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [331, 331, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -204,7 +204,7 @@ class PnasnetMobile_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/PnasnetMobile_slim.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280

From 5750508f76c0a62571b8131e26b463b49bcb1cbe Mon Sep 17 00:00:00 2001
From: Christopher Olah <colah@google.com>
Date: Wed, 8 Aug 2018 12:15:45 -0700
Subject: [PATCH 2/9] Fix ResNetV1_slim metadata

---
 lucid/modelzoo/slim_models/ResNetV1.py | 339 +++++++++----------------
 1 file changed, 121 insertions(+), 218 deletions(-)

diff --git a/lucid/modelzoo/slim_models/ResNetV1.py b/lucid/modelzoo/slim_models/ResNetV1.py
index e1b8e789..187c9950 100644
--- a/lucid/modelzoo/slim_models/ResNetV1.py
+++ b/lucid/modelzoo/slim_models/ResNetV1.py
@@ -25,48 +25,34 @@ class ResnetV1_50_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/ResnetV1_50_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
   image_shape = [224, 224, 3]
-  # inpute range taken from:
-  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
-  image_value_range = (-1, 1)
-  input_name = 'input'
   
+  
+  image_value_range = (-117, 255-117) # Inferred by testing, may not be exactly right
+  input_name = 'input'
+
+  # In ResNetV1, each add (joining the residual branch) is followed by a Relu 
+  # this seems to be the natural "layer" position
   layers = [
-     {'type': 'conv', 'name': 'resnet_v1_50/conv1/Relu', 'size': 64} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_1/bottleneck_v1/add', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_1/bottleneck_v1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_2/bottleneck_v1/add', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_2/bottleneck_v1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_3/bottleneck_v1/add', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_3/bottleneck_v1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_1/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_1/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_2/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_2/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_3/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_3/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_4/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_4/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_1/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_1/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_2/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_2/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_3/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_3/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_4/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_4/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_5/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_5/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_6/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_6/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_1/bottleneck_v1/add', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_1/bottleneck_v1/Relu', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_2/bottleneck_v1/add', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_2/bottleneck_v1/Relu', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_3/bottleneck_v1/add', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_3/bottleneck_v1/Relu', 'size': 2048} ,
-     {'type': 'dense', 'name': 'resnet_v1_50/predictions/Softmax', 'size': 1000} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/conv1/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_1/bottleneck_v1/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_2/bottleneck_v1/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_3/bottleneck_v1/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_1/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_2/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_3/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_4/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_1/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_2/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_3/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_4/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_5/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_6/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_1/bottleneck_v1/Relu', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_2/bottleneck_v1/Relu', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_3/bottleneck_v1/Relu', 'size': 2048},
+     {'type': 'dense', 'name': 'resnet_v1_50/predictions/Softmax', 'size': 1000},
    ]
 
 
@@ -78,82 +64,49 @@ class ResnetV1_101_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/ResnetV1_101_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
   image_shape = [224, 224, 3]
-  # inpute range taken from:
-  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
-  image_value_range = (-1, 1)
+  image_value_range = (-117, 255-117) # Inferred by testing, may not be exactly right
   input_name = 'input'
   
+  # In ResNetV1, each add (joining the residual branch) is followed by a Relu 
+  # this seems to be the natural "layer" position
   layers = [
-     {'type': 'conv', 'name': 'resnet_v1_101/conv1/Relu', 'size': 64} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_1/bottleneck_v1/add', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_1/bottleneck_v1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_2/bottleneck_v1/add', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_2/bottleneck_v1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_3/bottleneck_v1/add', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_3/bottleneck_v1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_1/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_1/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_2/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_2/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_3/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_3/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_4/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_4/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_1/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_1/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_2/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_2/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_3/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_3/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_4/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_4/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_5/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_5/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_6/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_6/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_7/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_7/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_8/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_8/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_9/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_9/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_10/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_10/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_11/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_11/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_12/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_12/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_13/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_13/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_14/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_14/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_15/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_15/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_16/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_16/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_17/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_17/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_18/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_18/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_19/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_19/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_20/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_20/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_21/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_21/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_22/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_22/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_23/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_23/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_1/bottleneck_v1/add', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_1/bottleneck_v1/Relu', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_2/bottleneck_v1/add', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_2/bottleneck_v1/Relu', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_3/bottleneck_v1/add', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_3/bottleneck_v1/Relu', 'size': 2048} ,
-     {'type': 'dense', 'name': 'resnet_v1_101/predictions/Softmax', 'size': 1000} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/conv1/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_1/bottleneck_v1/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_2/bottleneck_v1/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_3/bottleneck_v1/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_1/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_2/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_3/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_4/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_1/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_2/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_3/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_4/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_5/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_6/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_7/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_8/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_9/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_10/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_11/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_12/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_13/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_14/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_15/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_16/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_17/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_18/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_19/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_20/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_21/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_22/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_23/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_1/bottleneck_v1/Relu', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_2/bottleneck_v1/Relu', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_3/bottleneck_v1/Relu', 'size': 2048},
+     {'type': 'dense', 'name': 'resnet_v1_101/predictions/Softmax', 'size': 1000},
    ]
 
 
@@ -166,115 +119,65 @@ class ResnetV1_152_slim(Model):
   """
   
   model_path  = 'gs://modelzoo/ResnetV1_152_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
   image_shape = [224, 224, 3]
-  # inpute range taken from:
-  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
-  image_value_range = (-1, 1)
+  image_value_range = (-117, 255-117) # Inferred by testing, may not be exactly right
   input_name = 'input'
-  
+
+  # In ResNetV1, each add (joining the residual branch) is followed by a Relu 
+  # this seems to be the natural "layer" position
   layers = [
-     {'type': 'conv', 'name': 'resnet_v1_152/conv1/Relu', 'size': 64} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_1/bottleneck_v1/add', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_1/bottleneck_v1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_2/bottleneck_v1/add', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_2/bottleneck_v1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_3/bottleneck_v1/add', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_3/bottleneck_v1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_1/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_1/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_2/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_2/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_3/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_3/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_4/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_4/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_5/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_5/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_6/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_6/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_7/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_7/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_8/bottleneck_v1/add', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_8/bottleneck_v1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_1/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_1/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_2/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_2/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_3/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_3/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_4/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_4/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_5/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_5/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_6/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_6/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_7/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_7/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_8/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_8/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_9/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_9/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_10/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_10/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_11/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_11/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_12/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_12/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_13/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_13/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_14/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_14/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_15/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_15/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_16/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_16/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_17/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_17/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_18/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_18/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_19/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_19/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_20/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_20/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_21/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_21/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_22/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_22/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_23/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_23/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_24/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_24/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_25/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_25/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_26/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_26/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_27/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_27/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_28/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_28/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_29/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_29/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_30/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_30/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_31/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_31/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_32/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_32/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_33/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_33/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_34/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_34/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_35/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_35/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_36/bottleneck_v1/add', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_36/bottleneck_v1/Relu', 'size': 1024} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_1/bottleneck_v1/add', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_1/bottleneck_v1/Relu', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_2/bottleneck_v1/add', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_2/bottleneck_v1/Relu', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_3/bottleneck_v1/add', 'size': 2048} ,
-     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_3/bottleneck_v1/Relu', 'size': 2048} ,
-     {'type': 'dense', 'name': 'resnet_v1_152/predictions/Softmax', 'size': 1000} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/conv1/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_1/bottleneck_v1/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_2/bottleneck_v1/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_3/bottleneck_v1/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_1/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_2/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_3/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_4/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_5/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_6/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_7/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_8/bottleneck_v1/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_1/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_2/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_3/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_4/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_5/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_6/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_7/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_8/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_9/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_10/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_11/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_12/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_13/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_14/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_15/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_16/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_17/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_18/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_19/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_20/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_21/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_22/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_23/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_24/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_25/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_26/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_27/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_28/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_29/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_30/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_31/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_32/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_33/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_34/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_35/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_36/bottleneck_v1/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_1/bottleneck_v1/Relu', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_2/bottleneck_v1/Relu', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_3/bottleneck_v1/Relu', 'size': 2048},
+     {'type': 'dense', 'name': 'resnet_v1_152/predictions/Softmax', 'size': 1000},
    ]
   

From e466c701c2e90068d83ad68d4089449d7d239d75 Mon Sep 17 00:00:00 2001
From: Christopher Olah <colah@google.com>
Date: Wed, 8 Aug 2018 12:39:57 -0700
Subject: [PATCH 3/9] Add dataset metadata to modelzoo

---
 lucid/modelzoo/caffe_models/AlexNet.py     |   2 +
 lucid/modelzoo/caffe_models/InceptionV1.py |   4 +-
 lucid/modelzoo/caffe_models/others.py      |   3 +
 lucid/modelzoo/other_models/InceptionV1.py |   1 +
 lucid/modelzoo/slim_models/Inception.py    |  13 +-
 lucid/modelzoo/slim_models/ResNetV1.py     |   3 +
 lucid/modelzoo/slim_models/ResNetV2.py     |   9 +-
 lucid/modelzoo/slim_models/others.py       | 163 +++++++++++----------
 8 files changed, 115 insertions(+), 83 deletions(-)

diff --git a/lucid/modelzoo/caffe_models/AlexNet.py b/lucid/modelzoo/caffe_models/AlexNet.py
index a9fb4ad6..5f9b96f5 100644
--- a/lucid/modelzoo/caffe_models/AlexNet.py
+++ b/lucid/modelzoo/caffe_models/AlexNet.py
@@ -34,6 +34,7 @@ class AlexNet_caffe(Model):
   # but it seems more polite and reliable to host our own.
   model_path  = 'gs://modelzoo/AlexNet.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
+  dataset = 'ImageNet'
   image_shape = [227, 227, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
@@ -61,6 +62,7 @@ class AlexNet_caffe_Places365(Model):
 
   model_path  = 'gs://modelzoo/labels/Places365.txt'
   labels_path = 'gs://modelzoo/InceptionV1_caffe_places365-labels.txt'
+  dataset = 'Places365'
   image_shape = [227, 227, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
diff --git a/lucid/modelzoo/caffe_models/InceptionV1.py b/lucid/modelzoo/caffe_models/InceptionV1.py
index 73632754..b495677a 100644
--- a/lucid/modelzoo/caffe_models/InceptionV1.py
+++ b/lucid/modelzoo/caffe_models/InceptionV1.py
@@ -28,6 +28,7 @@ class InceptionV1_caffe(Model):
   """
   model_path = 'gs://modelzoo/InceptionV1_caffe.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
@@ -61,6 +62,7 @@ class InceptionV1_caffe_Places205(Model):
   """
   model_path = 'gs://modelzoo/InceptionV1_caffe_places205.pb'
   labels_path = 'gs://modelzoo/labels/Places205.txt'
+  dataset = 'Places205'
   image_shape = [224, 224, 3]
   # range based on emperical testing
   image_value_range = (-1,1)
@@ -93,8 +95,8 @@ class InceptionV1_caffe_Places365(Model):
   and then ported to TensorFlow using caffe-tensorflow.
   """
   model_path = 'gs://modelzoo/InceptionV1_caffe_places365.pb'
-  # TODO - check labels match predictions
   labels_path = 'gs://modelzoo/labels/Places365.txt'
+  dataset = 'Places365'
   image_shape = [224, 224, 3]
   # What is the correct input range???
   is_BGR = True
diff --git a/lucid/modelzoo/caffe_models/others.py b/lucid/modelzoo/caffe_models/others.py
index aceb6792..8ad3ad1a 100644
--- a/lucid/modelzoo/caffe_models/others.py
+++ b/lucid/modelzoo/caffe_models/others.py
@@ -25,6 +25,7 @@ class CaffeNet_caffe(Model):
 
   model_path  = 'gs://modelzoo/CaffeNet_caffe.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
+  dataset = 'ImageNet'
   image_shape = [227, 227, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
@@ -52,6 +53,7 @@ class VGG16_caffe(Model):
   """
   model_path = 'gs://modelzoo/VGG16_caffe.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
@@ -90,6 +92,7 @@ class VGG19_caffe(Model):
   """
   model_path = 'gs://modelzoo/VGG19_caffe.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   is_BGR = True
   image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
diff --git a/lucid/modelzoo/other_models/InceptionV1.py b/lucid/modelzoo/other_models/InceptionV1.py
index ed2e6d59..88bbabcf 100644
--- a/lucid/modelzoo/other_models/InceptionV1.py
+++ b/lucid/modelzoo/other_models/InceptionV1.py
@@ -50,6 +50,7 @@ class InceptionV1(Model):
   """
   model_path = 'gs://modelzoo/InceptionV1.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_alternate.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   image_value_range = (-117, 255-117)
   input_name = 'input:0'
diff --git a/lucid/modelzoo/slim_models/Inception.py b/lucid/modelzoo/slim_models/Inception.py
index e5308c39..8bacd3cc 100644
--- a/lucid/modelzoo/slim_models/Inception.py
+++ b/lucid/modelzoo/slim_models/Inception.py
@@ -28,7 +28,8 @@ class InceptionV1_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/InceptionV1_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -63,7 +64,8 @@ class InceptionV2_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/InceptionV2_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -100,6 +102,7 @@ class InceptionV3_slim(Model):
 
   model_path  = 'gs://modelzoo/InceptionV3_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt'
+  dataset = 'ImageNet'
   image_shape = [299, 299, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -138,7 +141,8 @@ class InceptionV4_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/InceptionV4_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt'
+  dataset = 'ImageNet'
   image_shape = [299, 299, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -183,7 +187,8 @@ class InceptionResnetV2_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/InceptionResnetV2_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt'
+  dataset = 'ImageNet'
   image_shape = [299, 299, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
diff --git a/lucid/modelzoo/slim_models/ResNetV1.py b/lucid/modelzoo/slim_models/ResNetV1.py
index 187c9950..40e8c529 100644
--- a/lucid/modelzoo/slim_models/ResNetV1.py
+++ b/lucid/modelzoo/slim_models/ResNetV1.py
@@ -26,6 +26,7 @@ class ResnetV1_50_slim(Model):
   
   model_path  = 'gs://modelzoo/ResnetV1_50_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   
   
@@ -65,6 +66,7 @@ class ResnetV1_101_slim(Model):
   
   model_path  = 'gs://modelzoo/ResnetV1_101_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   image_value_range = (-117, 255-117) # Inferred by testing, may not be exactly right
   input_name = 'input'
@@ -120,6 +122,7 @@ class ResnetV1_152_slim(Model):
   
   model_path  = 'gs://modelzoo/ResnetV1_152_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   image_value_range = (-117, 255-117) # Inferred by testing, may not be exactly right
   input_name = 'input'
diff --git a/lucid/modelzoo/slim_models/ResNetV2.py b/lucid/modelzoo/slim_models/ResNetV2.py
index c3b92183..4223beb1 100644
--- a/lucid/modelzoo/slim_models/ResNetV2.py
+++ b/lucid/modelzoo/slim_models/ResNetV2.py
@@ -28,7 +28,8 @@ class ResnetV2_50_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/ResnetV2_50_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -72,7 +73,8 @@ class ResnetV2_101_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/ResnetV2_101_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -133,7 +135,8 @@ class ResnetV2_152_slim(Model):
   """
 
   model_path  = 'gs://modelzoo/ResnetV2_152_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt'
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
diff --git a/lucid/modelzoo/slim_models/others.py b/lucid/modelzoo/slim_models/others.py
index 6561294f..a43a2f1a 100644
--- a/lucid/modelzoo/slim_models/others.py
+++ b/lucid/modelzoo/slim_models/others.py
@@ -17,81 +17,87 @@
 from lucid.modelzoo.vision_base import Model, IMAGENET_MEAN
 
 
-class VGG16_slim(Model):
-  """VGG16 as implemented by the TensorFlow slim framework.
-  
-  VGG16 was introduced by Simonyan & Zisserman (2014):
-  https://arxiv.org/pdf/1409.1556.pdf
-  This function provides the pre-trained reimplementation from TF slim:
-  https://github.com/tensorflow/models/tree/master/research/slim
-  We believe the weights were actually trained in caffe and ported.
-  """
-  
-  model_path  = 'gs://modelzoo/VGG16_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
-  image_shape = [224, 224, 3]
-  # inpute range taken from:
-  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
-  image_value_range = (-1, 1)
-  input_name = 'input'
-  
-  layers = [
-     {'type': 'conv', 'name': 'vgg_16/conv1/conv1_1/Relu', 'size': 64} ,
-     {'type': 'conv', 'name': 'vgg_16/conv1/conv1_2/Relu', 'size': 64} ,
-     {'type': 'conv', 'name': 'vgg_16/conv2/conv2_1/Relu', 'size': 128} ,
-     {'type': 'conv', 'name': 'vgg_16/conv2/conv2_2/Relu', 'size': 128} ,
-     {'type': 'conv', 'name': 'vgg_16/conv3/conv3_1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'vgg_16/conv3/conv3_2/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'vgg_16/conv3/conv3_3/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'vgg_16/conv4/conv4_1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_16/conv4/conv4_2/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_16/conv4/conv4_3/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_16/conv5/conv5_1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_16/conv5/conv5_2/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_16/conv5/conv5_3/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_16/fc6/Relu', 'size': 4096} ,
-     {'type': 'conv', 'name': 'vgg_16/fc7/Relu', 'size': 4096} ,
-   ]
-
-
-class VGG19_slim(Model):
-  """VGG19 as implemented by the TensorFlow slim framework.
-  
-  VGG19 was introduced by Simonyan & Zisserman (2014):
-  https://arxiv.org/pdf/1409.1556.pdf
-  This function provides the pre-trained reimplementation from TF slim:
-  https://github.com/tensorflow/models/tree/master/research/slim
-  We believe the weights were actually trained in caffe and ported.
-  """
-  
-  model_path  = 'gs://modelzoo/VGG19_slim.pb'
-  labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
-  image_shape = [224, 224, 3]
-  # inpute range taken from:
-  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
-  image_value_range = (-1, 1)
-  input_name = 'input'
-  
-  layers = [
-     {'type': 'conv', 'name': 'vgg_19/conv1/conv1_1/Relu', 'size': 64} ,
-     {'type': 'conv', 'name': 'vgg_19/conv1/conv1_2/Relu', 'size': 64} ,
-     {'type': 'conv', 'name': 'vgg_19/conv2/conv2_1/Relu', 'size': 128} ,
-     {'type': 'conv', 'name': 'vgg_19/conv2/conv2_2/Relu', 'size': 128} ,
-     {'type': 'conv', 'name': 'vgg_19/conv3/conv3_1/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'vgg_19/conv3/conv3_2/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'vgg_19/conv3/conv3_3/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'vgg_19/conv3/conv3_4/Relu', 'size': 256} ,
-     {'type': 'conv', 'name': 'vgg_19/conv4/conv4_1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_19/conv4/conv4_2/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_19/conv4/conv4_3/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_19/conv4/conv4_4/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_19/conv5/conv5_1/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_19/conv5/conv5_2/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_19/conv5/conv5_3/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_19/conv5/conv5_4/Relu', 'size': 512} ,
-     {'type': 'conv', 'name': 'vgg_19/fc6/Relu', 'size': 4096} ,
-     {'type': 'conv', 'name': 'vgg_19/fc7/Relu', 'size': 4096} ,
-   ]
+## Commenting out slim implementations of VGG16 and VGG19 because:
+##
+##  * They are supposed to be ports of the caffe implementation, 
+##    which we also provide.
+##  * We can't determine the correct input range or whether they are BGR.
+##  * They do no provide a softmax layer, leaving us no clear way to test them.
+##
+
+# class VGG16_slim(Model):
+#   """VGG16 as implemented by the TensorFlow slim framework.
+# 
+#   VGG16 was introduced by Simonyan & Zisserman (2014):
+#   https://arxiv.org/pdf/1409.1556.pdf
+#   This function provides the pre-trained reimplementation from TF slim:
+#   https://github.com/tensorflow/models/tree/master/research/slim
+#   We believe the weights were actually trained in caffe and ported.
+#   """
+# 
+#   model_path  = 'gs://modelzoo/VGG16_slim.pb'
+#   labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+#   dataset = 'ImageNet'
+#   image_shape = [224, 224, 3]
+#   image_value_range = (-1, 1)
+#   input_name = 'input'
+# 
+#   layers = [
+#      {'type': 'conv', 'name': 'vgg_16/conv1/conv1_1/Relu', 'size': 64} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv1/conv1_2/Relu', 'size': 64} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv2/conv2_1/Relu', 'size': 128} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv2/conv2_2/Relu', 'size': 128} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv3/conv3_1/Relu', 'size': 256} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv3/conv3_2/Relu', 'size': 256} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv3/conv3_3/Relu', 'size': 256} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv4/conv4_1/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv4/conv4_2/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv4/conv4_3/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv5/conv5_1/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv5/conv5_2/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_16/conv5/conv5_3/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_16/fc6/Relu', 'size': 4096} ,
+#      {'type': 'conv', 'name': 'vgg_16/fc7/Relu', 'size': 4096} ,
+#    ]
+
+
+# class VGG19_slim(Model):
+#   """VGG19 as implemented by the TensorFlow slim framework.
+# 
+#   VGG19 was introduced by Simonyan & Zisserman (2014):
+#   https://arxiv.org/pdf/1409.1556.pdf
+#   This function provides the pre-trained reimplementation from TF slim:
+#   https://github.com/tensorflow/models/tree/master/research/slim
+#   We believe the weights were actually trained in caffe and ported.
+#   """
+# 
+#   model_path  = 'gs://modelzoo/VGG19_slim.pb'
+#   labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+#   dataset = 'ImageNet'
+#   image_shape = [224, 224, 3]
+#   image_value_range = (-1, 1)
+#   input_name = 'input'
+# 
+#   layers = [
+#      {'type': 'conv', 'name': 'vgg_19/conv1/conv1_1/Relu', 'size': 64} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv1/conv1_2/Relu', 'size': 64} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv2/conv2_1/Relu', 'size': 128} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv2/conv2_2/Relu', 'size': 128} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv3/conv3_1/Relu', 'size': 256} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv3/conv3_2/Relu', 'size': 256} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv3/conv3_3/Relu', 'size': 256} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv3/conv3_4/Relu', 'size': 256} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv4/conv4_1/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv4/conv4_2/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv4/conv4_3/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv4/conv4_4/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv5/conv5_1/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv5/conv5_2/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv5/conv5_3/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_19/conv5/conv5_4/Relu', 'size': 512} ,
+#      {'type': 'conv', 'name': 'vgg_19/fc6/Relu', 'size': 4096} ,
+#      {'type': 'conv', 'name': 'vgg_19/fc7/Relu', 'size': 4096} ,
+#    ]
 
 
 class MobilenetV1_slim(Model):
@@ -103,6 +109,7 @@ class MobilenetV1_slim(Model):
   
   model_path  = 'gs://modelzoo/MobilenetV1_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -120,6 +127,7 @@ class MobilenetV1_050_slim(Model):
   
   model_path  = 'gs://modelzoo/MobilenetV1050_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -137,6 +145,7 @@ class MobilenetV1_025_slim(Model):
   
   model_path  = 'gs://modelzoo/MobilenetV1025_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -154,6 +163,7 @@ class NasnetMobile_slim(Model):
   
   model_path  = 'gs://modelzoo/NasnetMobile_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -171,6 +181,7 @@ class NasnetLarge_slim(Model):
   
   model_path  = 'gs://modelzoo/NasnetLarge_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  dataset = 'ImageNet'
   image_shape = [331, 331, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -188,6 +199,7 @@ class PnasnetLarge_slim(Model):
   
   model_path  = 'gs://modelzoo/PnasnetLarge_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  dataset = 'ImageNet'
   image_shape = [331, 331, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
@@ -205,6 +217,7 @@ class PnasnetMobile_slim(Model):
   
   model_path  = 'gs://modelzoo/PnasnetMobile_slim.pb'
   labels_path = 'gs://modelzoo/labels/ImageNet_standard_with_dummy.txt' #TODO
+  dataset = 'ImageNet'
   image_shape = [224, 224, 3]
   # inpute range taken from:
   # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280

From 1e060e9a1730cfa7c7a8ea7a743f8123f3784136 Mon Sep 17 00:00:00 2001
From: Christopher Olah <colah@google.com>
Date: Wed, 8 Aug 2018 14:04:10 -0700
Subject: [PATCH 4/9] Add caricature recipe

---
 lucid/recipes/caricature.py | 61 +++++++++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 lucid/recipes/caricature.py

diff --git a/lucid/recipes/caricature.py b/lucid/recipes/caricature.py
new file mode 100644
index 00000000..42363713
--- /dev/null
+++ b/lucid/recipes/caricature.py
@@ -0,0 +1,61 @@
+import numpy as np
+import tensorflow as tf
+import scipy.ndimage as nd
+
+import lucid.modelzoo.vision_models as models
+import lucid.optvis.objectives as objectives
+import lucid.optvis.param as param
+import lucid.optvis.render as render
+import lucid.optvis.transform as transform
+from lucid.misc.io import show, load
+from lucid.misc.io.reading import read
+
+def imgToModelSize(arr, model):
+  W = model.image_shape[0]
+  w, h, _ = arr.shape
+  s = float(W) / min(w,h)
+  arr = nd.zoom(arr, [s, s, 1], mode="nearest")
+  w, h, _ = arr.shape
+  dw, dh = (w-W)//2, (h-W)//3
+  return arr[dw:dw+W, dh:dh+W]
+  
+  
+@objectives.wrap_objective
+def dot_compare(layer, batch=1, cossim_pow=0):
+  def inner(T):
+    dot = tf.reduce_sum(T(layer)[batch] * T(layer)[0])
+    mag = tf.sqrt(tf.reduce_sum(T(layer)[0]**2))
+    cossim = dot / (1e-6 + mag)
+    cossim = tf.maximum(0.1, cossim)
+    return dot * cossim ** cossim_pow
+  return inner
+
+def feature_inversion(img, model, layer=None, n_steps=512, cossim_pow=0.0):
+  with tf.Graph().as_default(), tf.Session() as sess:
+    img = imgToModelSize(img, model)
+    
+    objective = objectives.Objective.sum([
+        1.0 * dot_compare(layer, cossim_pow=cossim_pow),
+        objectives.blur_input_each_step(),
+    ])
+
+    t_input = tf.placeholder(tf.float32, img.shape)
+    param_f = param.image(img.shape[0], decorrelate=True, fft=True, alpha=False)
+    param_f = tf.stack([param_f[0], t_input])
+
+    transforms = [
+      transform.pad(8, mode='constant', constant_value=.5),
+      transform.jitter(8),
+      transform.random_scale([0.9, 0.95, 1.05, 1.1] + [1]*4),
+      transform.random_rotate(range(-5, 5) + [0]*5),
+      transform.jitter(2),
+    ]
+
+    T = render.make_vis_T(model, objective, param_f, transforms=transforms)
+    loss, vis_op, t_image = T("loss"), T("vis_op"), T("input")
+
+    tf.global_variables_initializer().run()
+    for i in range(n_steps): _ = sess.run([vis_op], {t_input: img})
+
+    result = t_image.eval(feed_dict={t_input: img})
+    show(result[0])

From 5d76328f44ea9fdd1e60c863c724864b49b22c3e Mon Sep 17 00:00:00 2001
From: Christopher Olah <colah@google.com>
Date: Wed, 8 Aug 2018 14:10:50 -0700
Subject: [PATCH 5/9] Fix AlexNet

---
 lucid/modelzoo/caffe_models/AlexNet.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/lucid/modelzoo/caffe_models/AlexNet.py b/lucid/modelzoo/caffe_models/AlexNet.py
index 5f9b96f5..b635d411 100644
--- a/lucid/modelzoo/caffe_models/AlexNet.py
+++ b/lucid/modelzoo/caffe_models/AlexNet.py
@@ -60,8 +60,8 @@ class AlexNet_caffe_Places365(Model):
   and then ported to TensorFlow using caffe-tensorflow.
   """
 
-  model_path  = 'gs://modelzoo/labels/Places365.txt'
-  labels_path = 'gs://modelzoo/InceptionV1_caffe_places365-labels.txt'
+  model_path  = 'gs://modelzoo/AlexNet_caffe_places365.pb'
+  labels_path = 'gs://modelzoo/labels/Places365.txt'
   dataset = 'Places365'
   image_shape = [227, 227, 3]
   is_BGR = True

From 1722a5341236c9378498d274e338164b87060438 Mon Sep 17 00:00:00 2001
From: Christopher Olah <colah@google.com>
Date: Wed, 8 Aug 2018 14:19:00 -0700
Subject: [PATCH 6/9] Fix InceptionV1_caffe_Places205 input format

---
 lucid/modelzoo/caffe_models/InceptionV1.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/lucid/modelzoo/caffe_models/InceptionV1.py b/lucid/modelzoo/caffe_models/InceptionV1.py
index b495677a..b976d196 100644
--- a/lucid/modelzoo/caffe_models/InceptionV1.py
+++ b/lucid/modelzoo/caffe_models/InceptionV1.py
@@ -64,8 +64,8 @@ class InceptionV1_caffe_Places205(Model):
   labels_path = 'gs://modelzoo/labels/Places205.txt'
   dataset = 'Places205'
   image_shape = [224, 224, 3]
-  # range based on emperical testing
-  image_value_range = (-1,1)
+  is_BGR = True
+  image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
   input_name = 'data'
 
   layers = [

From d50d3b649a0ae804ac4f15b76585cfb91a05055a Mon Sep 17 00:00:00 2001
From: Ludwig Schubert <github-email@ludwigschubert.de>
Date: Wed, 8 Aug 2018 14:55:18 -0700
Subject: [PATCH 7/9] Clean up vision_models module namespace in Python 2
 specifically.

---
 lucid/modelzoo/caffe_models/__init__.py | 7 +++++++
 lucid/modelzoo/other_models/__init__.py | 7 +++++++
 lucid/modelzoo/slim_models/__init__.py  | 7 +++++++
 lucid/modelzoo/vision_models.py         | 7 +++++++
 4 files changed, 28 insertions(+)

diff --git a/lucid/modelzoo/caffe_models/__init__.py b/lucid/modelzoo/caffe_models/__init__.py
index 1fa91d01..27597d11 100644
--- a/lucid/modelzoo/caffe_models/__init__.py
+++ b/lucid/modelzoo/caffe_models/__init__.py
@@ -22,3 +22,10 @@
 del print_function
 
 del IMAGENET_MEAN_BGR
+
+# in Python 2 only, list comprehensions leak bound vars to a broader scope
+try:
+  del _obj
+  del _name
+except:
+  pass
diff --git a/lucid/modelzoo/other_models/__init__.py b/lucid/modelzoo/other_models/__init__.py
index 480c8e6d..8131a895 100644
--- a/lucid/modelzoo/other_models/__init__.py
+++ b/lucid/modelzoo/other_models/__init__.py
@@ -15,3 +15,10 @@
 __all__ = [_name for _name, _obj in list(globals().items())
            if isinstance(_obj, type) and issubclass(_obj, _Model)
            and _obj is not _Model]
+
+# in Python 2 only, list comprehensions leak bound vars to a broader scope
+try:
+  del _obj
+  del _name
+except:
+  pass
diff --git a/lucid/modelzoo/slim_models/__init__.py b/lucid/modelzoo/slim_models/__init__.py
index 35851b3d..bf8fef0b 100644
--- a/lucid/modelzoo/slim_models/__init__.py
+++ b/lucid/modelzoo/slim_models/__init__.py
@@ -24,3 +24,10 @@
 del print_function
 
 del IMAGENET_MEAN
+
+# in Python 2 only, list comprehensions leak bound vars to a broader scope
+try:
+  del _obj
+  del _name
+except:
+  pass
diff --git a/lucid/modelzoo/vision_models.py b/lucid/modelzoo/vision_models.py
index 2c0f8f26..bcbdf26c 100644
--- a/lucid/modelzoo/vision_models.py
+++ b/lucid/modelzoo/vision_models.py
@@ -29,3 +29,10 @@
 
 __all__ = [_name for _name, _obj in list(globals().items())
            if isinstance(_obj, type) and issubclass(_obj, Model)]
+
+# in Python 2 only, list comprehensions leak bound vars to a broader scope
+try:
+  del _obj
+  del _name
+except:
+  pass

From c4a10d843ced779fce542fa1816241536197a6fe Mon Sep 17 00:00:00 2001
From: Ludwig Schubert <github-email@ludwigschubert.de>
Date: Wed, 8 Aug 2018 14:55:35 -0700
Subject: [PATCH 8/9] Re-raise exception when load_graphdef fails.

---
 lucid/modelzoo/util.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/lucid/modelzoo/util.py b/lucid/modelzoo/util.py
index 48f30e2a..958c1f4b 100644
--- a/lucid/modelzoo/util.py
+++ b/lucid/modelzoo/util.py
@@ -39,6 +39,8 @@ def load_graphdef(model_url, reset_device=True):
   except DecodeError:
     cache_path = local_cache_path(model_url)
     log.error("Could not decode graphdef protobuf. Maybe check if you have a corrupted file cached at {}?".format(cache_path))
+    raise RuntimeError('Could not load_graphdef!')
+
   if reset_device:
     for n in graph_def.node:
       n.device = ""

From 6645aaf33a95f0ca613f629d372388e7401a0409 Mon Sep 17 00:00:00 2001
From: Christopher Olah <colah@google.com>
Date: Wed, 8 Aug 2018 15:03:30 -0700
Subject: [PATCH 9/9] Add anotehr model test

---
 tests/modelzoo/test_vision_models.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/tests/modelzoo/test_vision_models.py b/tests/modelzoo/test_vision_models.py
index 921f59c2..c1a7604a 100644
--- a/tests/modelzoo/test_vision_models.py
+++ b/tests/modelzoo/test_vision_models.py
@@ -43,6 +43,19 @@ def test_consistent_namespaces():
         assert difference == 'Model' or difference.startswith("__")
 
 
+@pytest.mark.parametrize("name,model_class", models_map.items())
+def test_model_properties(name, model_class):
+    assert hasattr(model_class, "model_path")
+    assert model_class.model_path.endswith(".pb")
+    assert hasattr(model_class, "labels_path")
+    assert model_class.labels_path.endswith(".txt")
+    assert hasattr(model_class, "dataset")
+    assert hasattr(model_class, "image_shape")
+    assert len(model_class.image_shape) == 3
+    assert hasattr(model_class, "image_value_range")
+    assert hasattr(model_class, "input_name")
+    assert hasattr(model_class, "layers")
+
 @pytest.mark.slow
 @pytest.mark.parametrize("name,model_class", models_map.items())
 def test_model_layers_shapes(name, model_class):