add ResNet graph construction and demo code

aiutils/examples/resnet_demo.py

@@ -0,0 +1,55 @@
+# Demonstrates use of pretrained ResNet model available here:
+# https://github.com/ry/tensorflow-resnet 
+
+from aiutils.tftools import placeholder_management
+from aiutils.vis import image, image_io
+import aiutils.vis.pretrained_models.resnet.model as model
+import os
+import numpy as np
+
+import tensorflow as tf
+
+
+if __name__=='__main__':
+    im_h, im_w = (224, 224)
+
+    # Path to the image to apply resnet on
+    image_path = './aiutils/examples/telephone.jpg'
+
+    # Checkpoint file to restore parameters from
+    model_dir = '/home/nfs/tgupta6/data/Resnet'
+    ckpt_filename = os.path.join(model_dir, 'ResNet-L50.ckpt')
+
+    # Graph construction
+    graph = tf.Graph()
+    with graph.as_default():
+        plh = placeholder_management.PlaceholderManager()
+        plh.add_placeholder(
+            'images',
+            tf.float32,
+            shape=[None,im_h,im_w,3])
+
+        resnet_model = model.ResnetInference(
+            plh['images'],
+            num_blocks = [3, 4, 6, 3],
+        )
+
+    # Create feed dict
+    im = image_io.imread(image_path)
+    im = image.imresize(im, output_size=(im_h, im_w)).astype(np.float32)
+    inputs = {
+        'images': im.reshape(1, im_h, im_w, 3)
+    }
+    feed_dict = plh.get_feed_dict(inputs)
+
+    # Restore model and get top-5 class predictions
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    config.gpu_options.per_process_gpu_memory_fraction = 0.8
+    sess = tf.Session(config=config, graph=graph)
+
+    resnet_model.restore_pretrained_model(sess, ckpt_filename)
+    logits = resnet_model.get_logits().eval(feed_dict,sess)
+    resnet_model.imagenet_class_prediction(logits[0,:])
+
+    sess.close()

aiutils/examples/resnet_fully_conv_demo.py

@@ -0,0 +1,58 @@
+# Demonstrates use of pretrained ResNet model available here:
+# https://github.com/ry/tensorflow-resnet 
+
+from aiutils.tftools import placeholder_management
+from aiutils.vis import image, image_io
+import aiutils.vis.pretrained_models.resnet.model_fully_conv as model
+import os
+import numpy as np
+
+from skimage.color import label2rgb
+import tensorflow as tf
+
+
+if __name__=='__main__':
+    im_h, im_w = (512, 512)
+
+    # Path to the image to apply resnet on
+    image_path = './aiutils/examples/telephone.jpg'
+
+    # Checkpoint file to restore parameters from
+    model_dir = '/home/nfs/tgupta6/data/Resnet'
+    ckpt_filename = os.path.join(model_dir, 'ResNet-L50.ckpt')
+
+    # Graph construction
+    graph = tf.Graph()
+    with graph.as_default():
+        plh = placeholder_management.PlaceholderManager()
+        plh.add_placeholder(
+            'images',
+            tf.float32,
+            shape=[None,im_h,im_w,3])
+
+        resnet_model = model.ResnetFullyConvInference(
+            plh['images'],
+            num_blocks = [3, 4, 6, 3]
+        )
+
+    # Create feed dict
+    im = image_io.imread(image_path)
+    im = image.imresize(im, output_size=(im_h, im_w)).astype(np.float32)
+    inputs = {
+        'images': im.reshape(1, im_h, im_w, 3)
+    }
+    feed_dict = plh.get_feed_dict(inputs)
+
+    # Restore model and get top-5 class predictions
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    config.gpu_options.per_process_gpu_memory_fraction = 1.0
+    sess = tf.Session(config=config, graph=graph)
+
+    resnet_model.restore_pretrained_model(sess, ckpt_filename)
+    logits = resnet_model.logits.eval(feed_dict,sess)
+    pred = np.argmax(logits, 3)
+    colored_pred = label2rgb(pred[0,:,:])
+    image_io.imwrite(np.uint8(colored_pred*255), './aiutils/examples/telephone_pred.jpg')
+
+    sess.close()

aiutils/tftools/var_collect.py

@@ -2,7 +2,7 @@
 
 
 def print_var_list(var_list, name='Variables'):
-    print name + ': \n' + '[' + ', '.join([var.name for var in var_list]) + ']'
+    print name + ': \n' + '[' + ',\n '.join([var.name for var in var_list]) + ']'
 
 
 def collect_name(var_name, graph=None, var_type=tf.GraphKeys.VARIABLES):

aiutils/vis/image.py

@@ -53,8 +53,8 @@ def imresize(np_im, method='bilinear', **kwargs):
     if 'output_size' in kwargs:
         h, w = kwargs['output_size']
     elif 'scale' in kwargs:
-        h = scale * im_h
-        w = scale * im_w
+        h = kwargs['scale'] * im_h
+        w = kwargs['scale'] * im_w
     else:
         assert_str = "Variable argument must be one of {'output_size','scale'}"
         assert (False), assert_str

aiutils/vis/pretrained_models/resnet/config.py

@@ -0,0 +1,82 @@
+# This is a variable scope aware configuation object for TensorFlow
+
+import tensorflow as tf
+
+FLAGS = tf.app.flags.FLAGS
+
+class Config:
+    def __init__(self):
+        root = self.Scope('')
+        for k, v in FLAGS.__dict__['__flags'].iteritems():
+            root[k] = v
+        self.stack = [ root ]
+
+    def _pop_stale(self):
+        var_scope_name = tf.get_variable_scope().name
+        top = self.stack[0]
+        while not top.contains(var_scope_name):
+            # We aren't in this scope anymore
+            self.stack.pop(0)
+            top = self.stack[0]
+
+    def __getitem__(self, name):
+        self._pop_stale()            
+        # Recursively extract value
+        for i in range(len(self.stack)):
+            cs = self.stack[i]
+            if name in cs:
+                return cs[name]
+
+        raise KeyError(name)
+
+    def __setitem__(self, name, value):
+        self._pop_stale()            
+        top = self.stack[0]
+        var_scope_name = tf.get_variable_scope().name
+        assert top.contains(var_scope_name)
+
+        if top.name != var_scope_name:
+            top = self.Scope(var_scope_name)
+            self.stack.insert(0, top)
+
+        top[name] = value
+
+    class Scope(dict):
+        def __init__(self, name):
+            self.name = name
+
+        def contains(self, var_scope_name):
+            return var_scope_name.startswith(self.name)
+
+
+
+# Test
+if __name__ == '__main__':
+
+    def assert_raises(exception, fn):
+        try:
+            fn()
+        except exception:
+            pass
+        else:
+            assert False, "Expected exception"
+
+    c = Config()
+
+    c['hello'] = 1
+    assert c['hello'] == 1
+
+    with tf.variable_scope('foo'):
+        c['bar'] = 2
+        assert c['bar'] == 2
+        assert c['hello'] == 1
+
+        with tf.variable_scope('meow'):
+            c['dog'] = 3
+            assert c['dog'] == 3
+            assert c['bar'] == 2
+            assert c['hello'] == 1
+
+        assert_raises(KeyError, lambda: c['dog'])
+        assert c['bar'] == 2
+        assert c['hello'] == 1