diff --git a/resnet/__init__.py b/resnet/__init__.py
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diff --git a/resnet/cat.jpg b/resnet/cat.jpg
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diff --git a/resnet/cat2.jpg b/resnet/cat2.jpg
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diff --git a/resnet/config.py b/resnet/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b31c32f18f2e509c754e80c95e5d7b7f929e3a4
--- /dev/null
+++ b/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
diff --git a/resnet/convert.py b/resnet/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..9785993a415b6f0c38a5726818d5bf89f4f1ab06
--- /dev/null
+++ b/resnet/convert.py
@@ -0,0 +1,333 @@
+import os
+os.environ["GLOG_minloglevel"] = "2"
+import sys
+import re
+#import caffe
+import numpy as np
+import tensorflow as tf
+#import skimage.io
+#from caffe.proto import caffe_pb2
+#from synset import *
+
+import inference as resnet
+
+
+class CaffeParamProvider():
+ def __init__(self, caffe_net):
+ self.caffe_net = caffe_net
+
+ def conv_kernel(self, name):
+ k = self.caffe_net.params[name][0].data
+ # caffe [out_channels, in_channels, filter_height, filter_width]
+ # 0 1 2 3
+ # tensorflow [filter_height, filter_width, in_channels, out_channels]
+ # 2 3 1 0
+ return k.transpose((2, 3, 1, 0))
+ return k
+
+ def bn_gamma(self, name):
+ return self.caffe_net.params[name][0].data
+
+ def bn_beta(self, name):
+ return self.caffe_net.params[name][1].data
+
+ def bn_mean(self, name):
+ return self.caffe_net.params[name][0].data
+
+ def bn_variance(self, name):
+ return self.caffe_net.params[name][1].data
+
+ def fc_weights(self, name):
+ w = self.caffe_net.params[name][0].data
+ w = w.transpose((1, 0))
+ return w
+
+ def fc_biases(self, name):
+ b = self.caffe_net.params[name][1].data
+ return b
+
+
+def preprocess(img):
+ """Changes RGB [0,1] valued image to BGR [0,255] with mean subtracted."""
+ mean_bgr = load_mean_bgr()
+ print 'mean blue', np.mean(mean_bgr[:, :, 0])
+ print 'mean green', np.mean(mean_bgr[:, :, 1])
+ print 'mean red', np.mean(mean_bgr[:, :, 2])
+ out = np.copy(img) * 255.0
+ out = out[:, :, [2, 1, 0]] # swap channel from RGB to BGR
+ out -= mean_bgr
+ return out
+
+
+def assert_almost_equal(caffe_tensor, tf_tensor):
+ t = tf_tensor[0]
+ c = caffe_tensor[0].transpose((1, 2, 0))
+
+ #for i in range(0, t.shape[-1]):
+ # print "tf", i, t[:,i]
+ # print "caffe", i, c[:,i]
+
+ if t.shape != c.shape:
+ print "t.shape", t.shape
+ print "c.shape", c.shape
+ sys.exit(1)
+
+ d = np.linalg.norm(t - c)
+ print "d", d
+ assert d < 500
+
+
+# returns image of shape [224, 224, 3]
+# [height, width, depth]
+def load_image(path, size=224):
+ img = skimage.io.imread(path)
+ short_edge = min(img.shape[:2])
+ yy = int((img.shape[0] - short_edge) / 2)
+ xx = int((img.shape[1] - short_edge) / 2)
+ crop_img = img[yy:yy + short_edge, xx:xx + short_edge]
+ resized_img = skimage.transform.resize(crop_img, (size, size))
+ return resized_img
+
+
+def load_mean_bgr():
+ """ bgr mean pixel value image, [0, 255]. [height, width, 3] """
+ with open("data/ResNet_mean.binaryproto", mode='rb') as f:
+ data = f.read()
+ blob = caffe_pb2.BlobProto()
+ blob.ParseFromString(data)
+
+ mean_bgr = caffe.io.blobproto_to_array(blob)[0]
+ assert mean_bgr.shape == (3, 224, 224)
+
+ return mean_bgr.transpose((1, 2, 0))
+
+
+def load_caffe(img_p, layers=50):
+ caffe.set_mode_cpu()
+
+ prototxt = "data/ResNet-%d-deploy.prototxt" % layers
+ caffemodel = "data/ResNet-%d-model.caffemodel" % layers
+ net = caffe.Net(prototxt, caffemodel, caffe.TEST)
+
+ net.blobs['data'].data[0] = img_p.transpose((2, 0, 1))
+ assert net.blobs['data'].data[0].shape == (3, 224, 224)
+ net.forward()
+
+ caffe_prob = net.blobs['prob'].data[0]
+ print_prob(caffe_prob)
+
+ return net
+
+
+# returns the top1 string
+def print_prob(prob):
+ #print prob
+ pred = np.argsort(prob)[::-1]
+
+ # Get top1 label
+ top1 = synset[pred[0]]
+ print "Top1: ", top1
+ # Get top5 label
+ top5 = [synset[pred[i]] for i in range(5)]
+ print "Top5: ", top5
+ return top1
+
+
+def parse_tf_varnames(p, tf_varname, num_layers):
+ if tf_varname == 'scale1/weights':
+ return p.conv_kernel('conv1')
+
+ elif tf_varname == 'scale1/gamma':
+ return p.bn_gamma('scale_conv1')
+
+ elif tf_varname == 'scale1/beta':
+ return p.bn_beta('scale_conv1')
+
+ elif tf_varname == 'scale1/moving_mean':
+ return p.bn_mean('bn_conv1')
+
+ elif tf_varname == 'scale1/moving_variance':
+ return p.bn_variance('bn_conv1')
+
+ elif tf_varname == 'fc/weights':
+ return p.fc_weights('fc1000')
+
+ elif tf_varname == 'fc/biases':
+ return p.fc_biases('fc1000')
+
+ # scale2/block1/shortcut/weights
+ # scale3/block2/c/moving_mean
+ # scale3/block6/c/moving_variance
+ # scale4/block3/c/moving_mean
+ # scale4/block8/a/beta
+ re1 = 'scale(\d+)/block(\d+)/(shortcut|a|b|c|A|B)'
+ m = re.search(re1, tf_varname)
+
+ def letter(i):
+ return chr(ord('a') + i - 1)
+
+ scale_num = int(m.group(1))
+
+ block_num = int(m.group(2))
+ if scale_num == 2:
+ # scale 2 always uses block letters
+ block_str = letter(block_num)
+ elif scale_num == 3 or scale_num == 4:
+ # scale 3 uses block letters for l=50 and numbered blocks for l=101, l=151
+ # scale 4 uses block letters for l=50 and numbered blocks for l=101, l=151
+ if num_layers == 50:
+ block_str = letter(block_num)
+ else:
+ if block_num == 1:
+ block_str = 'a'
+ else:
+ block_str = 'b%d' % (block_num - 1)
+ elif scale_num == 5:
+ # scale 5 always block letters
+ block_str = letter(block_num)
+ else:
+ raise ValueError("unexpected scale_num %d" % scale_num)
+
+ branch = m.group(3)
+ if branch == "shortcut":
+ branch_num = 1
+ conv_letter = ''
+ else:
+ branch_num = 2
+ conv_letter = branch.lower()
+
+ x = (scale_num, block_str, branch_num, conv_letter)
+ #print x
+
+ if 'weights' in tf_varname:
+ return p.conv_kernel('res%d%s_branch%d%s' % x)
+
+ if 'gamma' in tf_varname:
+ return p.bn_gamma('scale%d%s_branch%d%s' % x)
+
+ if 'beta' in tf_varname:
+ return p.bn_beta('scale%d%s_branch%d%s' % x)
+
+ if 'moving_mean' in tf_varname:
+ return p.bn_mean('bn%d%s_branch%d%s' % x)
+
+ if 'moving_variance' in tf_varname:
+ return p.bn_variance('bn%d%s_branch%d%s' % x)
+
+ raise ValueError('unhandled var ' + tf_varname)
+
+
+def checkpoint_fn(layers):
+ return 'ResNet-L%d.ckpt' % layers
+
+
+def meta_fn(layers):
+ return 'ResNet-L%d.meta' % layers
+
+
+def convert(graph, img, img_p, layers):
+ caffe_model = load_caffe(img_p, layers)
+
+ #for i, n in enumerate(caffe_model.params):
+ # print n
+
+ param_provider = CaffeParamProvider(caffe_model)
+
+ if layers == 50:
+ num_blocks = [3, 4, 6, 3]
+ elif layers == 101:
+ num_blocks = [3, 4, 23, 3]
+ elif layers == 152:
+ num_blocks = [3, 8, 36, 3]
+
+ with tf.device('/cpu:0'):
+ images = tf.placeholder("float32", [None, 224, 224, 3], name="images")
+ logits = resnet.inference(images,
+ is_training=False,
+ num_blocks=num_blocks,
+ preprocess=True,
+ bottleneck=True)
+ prob = tf.nn.softmax(logits, name='prob')
+
+ # We write the metagraph first to avoid adding a bunch of
+ # assign ops that are used to set variables from caffe.
+ # The checkpoint is written to at the end.
+ tf.train.export_meta_graph(filename=meta_fn(layers))
+
+ vars_to_restore = tf.all_variables()
+ saver = tf.train.Saver(vars_to_restore)
+
+ sess = tf.Session()
+ sess.run(tf.initialize_all_variables())
+
+ assigns = []
+ for var in vars_to_restore:
+ #print var.op.name
+ data = parse_tf_varnames(param_provider, var.op.name, layers)
+ #print "caffe data shape", data.shape
+ #print "tf shape", var.get_shape()
+ assigns.append(var.assign(data))
+ sess.run(assigns)
+
+ #for op in tf.get_default_graph().get_operations():
+ # print op.name
+
+ i = [
+ graph.get_tensor_by_name("scale1/Relu:0"),
+ graph.get_tensor_by_name("scale2/MaxPool:0"),
+ graph.get_tensor_by_name("scale2/block1/Relu:0"),
+ graph.get_tensor_by_name("scale2/block2/Relu:0"),
+ graph.get_tensor_by_name("scale2/block3/Relu:0"),
+ graph.get_tensor_by_name("scale3/block1/Relu:0"),
+ graph.get_tensor_by_name("scale5/block3/Relu:0"),
+ graph.get_tensor_by_name("avg_pool:0"),
+ graph.get_tensor_by_name("prob:0"),
+ ]
+
+ o = sess.run(i, {images: img[np.newaxis, :]})
+
+ assert_almost_equal(caffe_model.blobs['conv1'].data, o[0])
+ assert_almost_equal(caffe_model.blobs['pool1'].data, o[1])
+ assert_almost_equal(caffe_model.blobs['res2a'].data, o[2])
+ assert_almost_equal(caffe_model.blobs['res2b'].data, o[3])
+ assert_almost_equal(caffe_model.blobs['res2c'].data, o[4])
+ assert_almost_equal(caffe_model.blobs['res3a'].data, o[5])
+ assert_almost_equal(caffe_model.blobs['res5c'].data, o[6])
+ #assert_almost_equal(np.squeeze(caffe_model.blobs['pool5'].data), o[7])
+
+ print_prob(o[8][0])
+
+ prob_dist = np.linalg.norm(caffe_model.blobs['prob'].data - o[8])
+ print 'prob_dist ', prob_dist
+ assert prob_dist < 0.2 # XXX can this be tightened?
+
+ # We've already written the metagraph to avoid a bunch of assign ops.
+ saver.save(sess, checkpoint_fn(layers), write_meta_graph=False)
+
+
+def save_graph(save_path):
+ graph = tf.get_default_graph()
+ graph_def = graph.as_graph_def()
+ print "graph_def byte size", graph_def.ByteSize()
+ graph_def_s = graph_def.SerializeToString()
+
+ with open(save_path, "wb") as f:
+ f.write(graph_def_s)
+
+ print "saved model to %s" % save_path
+
+
+def main(_):
+ img = load_image("data/cat.jpg")
+ print img
+ img_p = preprocess(img)
+
+ for layers in [50, 101, 152]:
+ g = tf.Graph()
+ with g.as_default():
+ print "CONVERT", layers
+ convert(g, img, img_p, layers)
+
+
+if __name__ == '__main__':
+ tf.app.run()
diff --git a/resnet/dalmatian.jpg b/resnet/dalmatian.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3c53adfb70065e31c2bcc503fbff99bd8434ffd0
Binary files /dev/null and b/resnet/dalmatian.jpg differ
diff --git a/resnet/inference.py b/resnet/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d04bd4dd93ed52bc6ea8412ec7828de700c1291
--- /dev/null
+++ b/resnet/inference.py
@@ -0,0 +1,333 @@
+# import skimage.io # bug. need to import this before tensorflow
+# import skimage.transform # bug. need to import this before tensorflow
+import tensorflow as tf
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.training import moving_averages
+
+from config import Config
+
+# import datetime
+import numpy as np
+import os
+import time
+
+MOVING_AVERAGE_DECAY = 0.9997
+BN_DECAY = MOVING_AVERAGE_DECAY
+BN_EPSILON = 0.001
+CONV_WEIGHT_DECAY = 0.00004
+CONV_WEIGHT_STDDEV = 0.1
+FC_WEIGHT_DECAY = 0.00004
+FC_WEIGHT_STDDEV = 0.01
+RESNET_VARIABLES = 'resnet_variables'
+UPDATE_OPS_COLLECTION = 'resnet_update_ops' # must be grouped with training op
+IMAGENET_MEAN_BGR = [103.062623801, 115.902882574, 123.151630838, ]
+
+tf.app.flags.DEFINE_integer('input_size', 224, "input image size")
+
+
+activation = tf.nn.relu
+
+
+def inference(x, is_training,
+ num_classes=1000,
+ num_blocks=[3, 4, 6, 3], # defaults to 50-layer network
+ use_bias=False, # defaults to using batch norm
+ bottleneck=True):
+ c = Config()
+ c['bottleneck'] = bottleneck
+ c['is_training'] = tf.convert_to_tensor(is_training,
+ dtype='bool',
+ name='is_training')
+ c['ksize'] = 3
+ c['stride'] = 1
+ c['use_bias'] = use_bias
+ c['fc_units_out'] = num_classes
+ c['num_blocks'] = num_blocks
+ c['stack_stride'] = 2
+
+ with tf.variable_scope('scale1'):
+ c['conv_filters_out'] = 64
+ c['ksize'] = 7
+ c['stride'] = 2
+ x = conv(x, c)
+ x = bn(x, c)
+ x = activation(x)
+
+ with tf.variable_scope('scale2'):
+ x = _max_pool(x, ksize=3, stride=2)
+ c['num_blocks'] = num_blocks[0]
+ c['stack_stride'] = 1
+ c['block_filters_internal'] = 64
+ x = stack(x, c)
+
+ with tf.variable_scope('scale3'):
+ c['num_blocks'] = num_blocks[1]
+ c['block_filters_internal'] = 128
+ assert c['stack_stride'] == 2
+ x = stack(x, c)
+
+ with tf.variable_scope('scale4'):
+ c['num_blocks'] = num_blocks[2]
+ c['block_filters_internal'] = 256
+ x = stack(x, c)
+
+ with tf.variable_scope('scale5'):
+ c['num_blocks'] = num_blocks[3]
+ c['block_filters_internal'] = 512
+ x = stack(x, c)
+
+ # post-net
+ x = tf.reduce_mean(x, reduction_indices=[1, 2], name="avg_pool")
+
+ if num_classes != None:
+ with tf.variable_scope('fc'):
+ x = fc(x, c)
+
+ return x
+
+
+# This is what they use for CIFAR-10 and 100.
+# See Section 4.2 in http://arxiv.org/abs/1512.03385
+def inference_small(x,
+ is_training,
+ num_blocks=3, # 6n+2 total weight layers will be used.
+ use_bias=False, # defaults to using batch norm
+ num_classes=10):
+ c = Config()
+ c['is_training'] = tf.convert_to_tensor(is_training,
+ dtype='bool',
+ name='is_training')
+ c['use_bias'] = use_bias
+ c['fc_units_out'] = num_classes
+ c['num_blocks'] = num_blocks
+ c['num_classes'] = num_classes
+ inference_small_config(x, c)
+
+def inference_small_config(x, c):
+ c['bottleneck'] = False
+ c['ksize'] = 3
+ c['stride'] = 1
+ with tf.variable_scope('scale1'):
+ c['conv_filters_out'] = 16
+ c['block_filters_internal'] = 16
+ c['stack_stride'] = 1
+ x = conv(x, c)
+ x = bn(x, c)
+ x = activation(x)
+ x = stack(x, c)
+
+ with tf.variable_scope('scale2'):
+ c['block_filters_internal'] = 32
+ c['stack_stride'] = 2
+ x = stack(x, c)
+
+ with tf.variable_scope('scale3'):
+ c['block_filters_internal'] = 64
+ c['stack_stride'] = 2
+ x = stack(x, c)
+
+ # post-net
+ x = tf.reduce_mean(x, reduction_indices=[1, 2], name="avg_pool")
+
+ if c['num_classes'] != None:
+ with tf.variable_scope('fc'):
+ x = fc(x, c)
+
+ return x
+
+
+def _imagenet_preprocess(rgb):
+ """Changes RGB [0,1] valued image to BGR [0,255] with mean subtracted."""
+ red, green, blue = tf.split(3, 3, rgb * 255.0)
+ bgr = tf.concat(3, [blue, green, red])
+ bgr -= IMAGENET_MEAN_BGR
+ return bgr
+
+
+def loss(logits, labels):
+ cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels)
+ cross_entropy_mean = tf.reduce_mean(cross_entropy)
+
+ regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
+
+ loss_ = tf.add_n([cross_entropy_mean] + regularization_losses)
+ tf.scalar_summary('loss', loss_)
+
+ return loss_
+
+
+def stack(x, c):
+ for n in range(c['num_blocks']):
+ s = c['stack_stride'] if n == 0 else 1
+ c['block_stride'] = s
+ with tf.variable_scope('block%d' % (n + 1)):
+ x = block(x, c)
+ return x
+
+
+def block(x, c):
+ filters_in = x.get_shape()[-1]
+
+ # Note: filters_out isn't how many filters are outputed.
+ # That is the case when bottleneck=False but when bottleneck is
+ # True, filters_internal*4 filters are outputted. filters_internal is how many filters
+ # the 3x3 convs output internally.
+ m = 4 if c['bottleneck'] else 1
+ filters_out = m * c['block_filters_internal']
+
+ shortcut = x # branch 1
+
+ c['conv_filters_out'] = c['block_filters_internal']
+
+ if c['bottleneck']:
+ with tf.variable_scope('a'):
+ c['ksize'] = 1
+ c['stride'] = c['block_stride']
+ x = conv(x, c)
+ x = bn(x, c)
+ x = activation(x)
+
+ with tf.variable_scope('b'):
+ x = conv(x, c)
+ x = bn(x, c)
+ x = activation(x)
+
+ with tf.variable_scope('c'):
+ c['conv_filters_out'] = filters_out
+ c['ksize'] = 1
+ assert c['stride'] == 1
+ x = conv(x, c)
+ x = bn(x, c)
+ else:
+ with tf.variable_scope('A'):
+ c['stride'] = c['block_stride']
+ assert c['ksize'] == 3
+ x = conv(x, c)
+ x = bn(x, c)
+ x = activation(x)
+
+ with tf.variable_scope('B'):
+ c['conv_filters_out'] = filters_out
+ assert c['ksize'] == 3
+ assert c['stride'] == 1
+ x = conv(x, c)
+ x = bn(x, c)
+
+ with tf.variable_scope('shortcut'):
+ if filters_out != filters_in or c['block_stride'] != 1:
+ c['ksize'] = 1
+ c['stride'] = c['block_stride']
+ c['conv_filters_out'] = filters_out
+ shortcut = conv(shortcut, c)
+ shortcut = bn(shortcut, c)
+
+ return activation(x + shortcut)
+
+
+def bn(x, c):
+ x_shape = x.get_shape()
+ params_shape = x_shape[-1:]
+
+ if c['use_bias']:
+ bias = _get_variable('bias', params_shape,
+ initializer=tf.zeros_initializer)
+ return x + bias
+
+
+ axis = list(range(len(x_shape) - 1))
+
+ beta = _get_variable('beta',
+ params_shape,
+ initializer=tf.zeros_initializer)
+ gamma = _get_variable('gamma',
+ params_shape,
+ initializer=tf.ones_initializer)
+
+ moving_mean = _get_variable('moving_mean',
+ params_shape,
+ initializer=tf.zeros_initializer,
+ trainable=False)
+ moving_variance = _get_variable('moving_variance',
+ params_shape,
+ initializer=tf.ones_initializer,
+ trainable=False)
+
+ # These ops will only be preformed when training.
+ mean, variance = tf.nn.moments(x, axis)
+ update_moving_mean = moving_averages.assign_moving_average(moving_mean,
+ mean, BN_DECAY)
+ update_moving_variance = moving_averages.assign_moving_average(
+ moving_variance, variance, BN_DECAY)
+ tf.add_to_collection(UPDATE_OPS_COLLECTION, update_moving_mean)
+ tf.add_to_collection(UPDATE_OPS_COLLECTION, update_moving_variance)
+
+ mean, variance = control_flow_ops.cond(
+ c['is_training'], lambda: (mean, variance),
+ lambda: (moving_mean, moving_variance))
+
+ x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, BN_EPSILON)
+ #x.set_shape(inputs.get_shape()) ??
+
+ return x
+
+
+def fc(x, c):
+ num_units_in = x.get_shape()[1]
+ num_units_out = c['fc_units_out']
+ weights_initializer = tf.truncated_normal_initializer(
+ stddev=FC_WEIGHT_STDDEV)
+
+ weights = _get_variable('weights',
+ shape=[num_units_in, num_units_out],
+ initializer=weights_initializer,
+ weight_decay=FC_WEIGHT_STDDEV)
+ biases = _get_variable('biases',
+ shape=[num_units_out],
+ initializer=tf.zeros_initializer)
+ x = tf.nn.xw_plus_b(x, weights, biases)
+ return x
+
+
+def _get_variable(name,
+ shape,
+ initializer,
+ weight_decay=0.0,
+ dtype='float',
+ trainable=True):
+ "A little wrapper around tf.get_variable to do weight decay and add to"
+ "resnet collection"
+ if weight_decay > 0:
+ regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
+ else:
+ regularizer = None
+ collections = [tf.GraphKeys.VARIABLES, RESNET_VARIABLES]
+ return tf.get_variable(name,
+ shape=shape,
+ initializer=initializer,
+ dtype=dtype,
+ regularizer=regularizer,
+ collections=collections,
+ trainable=trainable)
+
+
+def conv(x, c):
+ ksize = c['ksize']
+ stride = c['stride']
+ filters_out = c['conv_filters_out']
+
+ filters_in = x.get_shape()[-1]
+ shape = [ksize, ksize, filters_in, filters_out]
+ initializer = tf.truncated_normal_initializer(stddev=CONV_WEIGHT_STDDEV)
+ weights = _get_variable('weights',
+ shape=shape,
+ dtype='float',
+ initializer=initializer,
+ weight_decay=CONV_WEIGHT_DECAY)
+ return tf.nn.conv2d(x, weights, [1, stride, stride, 1], padding='SAME')
+
+
+def _max_pool(x, ksize=3, stride=2):
+ return tf.nn.max_pool(x,
+ ksize=[1, ksize, ksize, 1],
+ strides=[1, stride, stride, 1],
+ padding='SAME')
diff --git a/resnet/schooner.jpg b/resnet/schooner.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1c4fc32b15b54c29de9fe5d06be4801fc4ff374d
Binary files /dev/null and b/resnet/schooner.jpg differ
diff --git a/resnet/synset.py b/resnet/synset.py
new file mode 100644
index 0000000000000000000000000000000000000000..74fb9ecea052f0aca253d2f6878d8413dc836f67
--- /dev/null
+++ b/resnet/synset.py
@@ -0,0 +1,1007 @@
+synset = [
+ "n01440764 tench, Tinca tinca",
+ "n01443537 goldfish, Carassius auratus",
+ "n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
+ "n01491361 tiger shark, Galeocerdo cuvieri",
+ "n01494475 hammerhead, hammerhead shark",
+ "n01496331 electric ray, crampfish, numbfish, torpedo",
+ "n01498041 stingray",
+ "n01514668 cock",
+ "n01514859 hen",
+ "n01518878 ostrich, Struthio camelus",
+ "n01530575 brambling, Fringilla montifringilla",
+ "n01531178 goldfinch, Carduelis carduelis",
+ "n01532829 house finch, linnet, Carpodacus mexicanus",
+ "n01534433 junco, snowbird",
+ "n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea",
+ "n01558993 robin, American robin, Turdus migratorius",
+ "n01560419 bulbul",
+ "n01580077 jay",
+ "n01582220 magpie",
+ "n01592084 chickadee",
+ "n01601694 water ouzel, dipper",
+ "n01608432 kite",
+ "n01614925 bald eagle, American eagle, Haliaeetus leucocephalus",
+ "n01616318 vulture",
+ "n01622779 great grey owl, great gray owl, Strix nebulosa",
+ "n01629819 European fire salamander, Salamandra salamandra",
+ "n01630670 common newt, Triturus vulgaris",
+ "n01631663 eft",
+ "n01632458 spotted salamander, Ambystoma maculatum",
+ "n01632777 axolotl, mud puppy, Ambystoma mexicanum",
+ "n01641577 bullfrog, Rana catesbeiana",
+ "n01644373 tree frog, tree-frog",
+ "n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
+ "n01664065 loggerhead, loggerhead turtle, Caretta caretta",
+ "n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
+ "n01667114 mud turtle",
+ "n01667778 terrapin",
+ "n01669191 box turtle, box tortoise",
+ "n01675722 banded gecko",
+ "n01677366 common iguana, iguana, Iguana iguana",
+ "n01682714 American chameleon, anole, Anolis carolinensis",
+ "n01685808 whiptail, whiptail lizard",
+ "n01687978 agama",
+ "n01688243 frilled lizard, Chlamydosaurus kingi",
+ "n01689811 alligator lizard",
+ "n01692333 Gila monster, Heloderma suspectum",
+ "n01693334 green lizard, Lacerta viridis",
+ "n01694178 African chameleon, Chamaeleo chamaeleon",
+ "n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
+ "n01697457 African crocodile, Nile crocodile, Crocodylus niloticus",
+ "n01698640 American alligator, Alligator mississipiensis",
+ "n01704323 triceratops",
+ "n01728572 thunder snake, worm snake, Carphophis amoenus",
+ "n01728920 ringneck snake, ring-necked snake, ring snake",
+ "n01729322 hognose snake, puff adder, sand viper",
+ "n01729977 green snake, grass snake",
+ "n01734418 king snake, kingsnake",
+ "n01735189 garter snake, grass snake",
+ "n01737021 water snake",
+ "n01739381 vine snake",
+ "n01740131 night snake, Hypsiglena torquata",
+ "n01742172 boa constrictor, Constrictor constrictor",
+ "n01744401 rock python, rock snake, Python sebae",
+ "n01748264 Indian cobra, Naja naja",
+ "n01749939 green mamba",
+ "n01751748 sea snake",
+ "n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
+ "n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus",
+ "n01756291 sidewinder, horned rattlesnake, Crotalus cerastes",
+ "n01768244 trilobite",
+ "n01770081 harvestman, daddy longlegs, Phalangium opilio",
+ "n01770393 scorpion",
+ "n01773157 black and gold garden spider, Argiope aurantia",
+ "n01773549 barn spider, Araneus cavaticus",
+ "n01773797 garden spider, Aranea diademata",
+ "n01774384 black widow, Latrodectus mactans",
+ "n01774750 tarantula",
+ "n01775062 wolf spider, hunting spider",
+ "n01776313 tick",
+ "n01784675 centipede",
+ "n01795545 black grouse",
+ "n01796340 ptarmigan",
+ "n01797886 ruffed grouse, partridge, Bonasa umbellus",
+ "n01798484 prairie chicken, prairie grouse, prairie fowl",
+ "n01806143 peacock",
+ "n01806567 quail",
+ "n01807496 partridge",
+ "n01817953 African grey, African gray, Psittacus erithacus",
+ "n01818515 macaw",
+ "n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
+ "n01820546 lorikeet",
+ "n01824575 coucal",
+ "n01828970 bee eater",
+ "n01829413 hornbill",
+ "n01833805 hummingbird",
+ "n01843065 jacamar",
+ "n01843383 toucan",
+ "n01847000 drake",
+ "n01855032 red-breasted merganser, Mergus serrator",
+ "n01855672 goose",
+ "n01860187 black swan, Cygnus atratus",
+ "n01871265 tusker",
+ "n01872401 echidna, spiny anteater, anteater",
+ "n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
+ "n01877812 wallaby, brush kangaroo",
+ "n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
+ "n01883070 wombat",
+ "n01910747 jellyfish",
+ "n01914609 sea anemone, anemone",
+ "n01917289 brain coral",
+ "n01924916 flatworm, platyhelminth",
+ "n01930112 nematode, nematode worm, roundworm",
+ "n01943899 conch",
+ "n01944390 snail",
+ "n01945685 slug",
+ "n01950731 sea slug, nudibranch",
+ "n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore",
+ "n01968897 chambered nautilus, pearly nautilus, nautilus",
+ "n01978287 Dungeness crab, Cancer magister",
+ "n01978455 rock crab, Cancer irroratus",
+ "n01980166 fiddler crab",
+ "n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
+ "n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus",
+ "n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
+ "n01985128 crayfish, crawfish, crawdad, crawdaddy",
+ "n01986214 hermit crab",
+ "n01990800 isopod",
+ "n02002556 white stork, Ciconia ciconia",
+ "n02002724 black stork, Ciconia nigra",
+ "n02006656 spoonbill",
+ "n02007558 flamingo",
+ "n02009229 little blue heron, Egretta caerulea",
+ "n02009912 American egret, great white heron, Egretta albus",
+ "n02011460 bittern",
+ "n02012849 crane",
+ "n02013706 limpkin, Aramus pictus",
+ "n02017213 European gallinule, Porphyrio porphyrio",
+ "n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana",
+ "n02018795 bustard",
+ "n02025239 ruddy turnstone, Arenaria interpres",
+ "n02027492 red-backed sandpiper, dunlin, Erolia alpina",
+ "n02028035 redshank, Tringa totanus",
+ "n02033041 dowitcher",
+ "n02037110 oystercatcher, oyster catcher",
+ "n02051845 pelican",
+ "n02056570 king penguin, Aptenodytes patagonica",
+ "n02058221 albatross, mollymawk",
+ "n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
+ "n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
+ "n02074367 dugong, Dugong dugon",
+ "n02077923 sea lion",
+ "n02085620 Chihuahua",
+ "n02085782 Japanese spaniel",
+ "n02085936 Maltese dog, Maltese terrier, Maltese",
+ "n02086079 Pekinese, Pekingese, Peke",
+ "n02086240 Shih-Tzu",
+ "n02086646 Blenheim spaniel",
+ "n02086910 papillon",
+ "n02087046 toy terrier",
+ "n02087394 Rhodesian ridgeback",
+ "n02088094 Afghan hound, Afghan",
+ "n02088238 basset, basset hound",
+ "n02088364 beagle",
+ "n02088466 bloodhound, sleuthhound",
+ "n02088632 bluetick",
+ "n02089078 black-and-tan coonhound",
+ "n02089867 Walker hound, Walker foxhound",
+ "n02089973 English foxhound",
+ "n02090379 redbone",
+ "n02090622 borzoi, Russian wolfhound",
+ "n02090721 Irish wolfhound",
+ "n02091032 Italian greyhound",
+ "n02091134 whippet",
+ "n02091244 Ibizan hound, Ibizan Podenco",
+ "n02091467 Norwegian elkhound, elkhound",
+ "n02091635 otterhound, otter hound",
+ "n02091831 Saluki, gazelle hound",
+ "n02092002 Scottish deerhound, deerhound",
+ "n02092339 Weimaraner",
+ "n02093256 Staffordshire bullterrier, Staffordshire bull terrier",
+ "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",
+ "n02093647 Bedlington terrier",
+ "n02093754 Border terrier",
+ "n02093859 Kerry blue terrier",
+ "n02093991 Irish terrier",
+ "n02094114 Norfolk terrier",
+ "n02094258 Norwich terrier",
+ "n02094433 Yorkshire terrier",
+ "n02095314 wire-haired fox terrier",
+ "n02095570 Lakeland terrier",
+ "n02095889 Sealyham terrier, Sealyham",
+ "n02096051 Airedale, Airedale terrier",
+ "n02096177 cairn, cairn terrier",
+ "n02096294 Australian terrier",
+ "n02096437 Dandie Dinmont, Dandie Dinmont terrier",
+ "n02096585 Boston bull, Boston terrier",
+ "n02097047 miniature schnauzer",
+ "n02097130 giant schnauzer",
+ "n02097209 standard schnauzer",
+ "n02097298 Scotch terrier, Scottish terrier, Scottie",
+ "n02097474 Tibetan terrier, chrysanthemum dog",
+ "n02097658 silky terrier, Sydney silky",
+ "n02098105 soft-coated wheaten terrier",
+ "n02098286 West Highland white terrier",
+ "n02098413 Lhasa, Lhasa apso",
+ "n02099267 flat-coated retriever",
+ "n02099429 curly-coated retriever",
+ "n02099601 golden retriever",
+ "n02099712 Labrador retriever",
+ "n02099849 Chesapeake Bay retriever",
+ "n02100236 German short-haired pointer",
+ "n02100583 vizsla, Hungarian pointer",
+ "n02100735 English setter",
+ "n02100877 Irish setter, red setter",
+ "n02101006 Gordon setter",
+ "n02101388 Brittany spaniel",
+ "n02101556 clumber, clumber spaniel",
+ "n02102040 English springer, English springer spaniel",
+ "n02102177 Welsh springer spaniel",
+ "n02102318 cocker spaniel, English cocker spaniel, cocker",
+ "n02102480 Sussex spaniel",
+ "n02102973 Irish water spaniel",
+ "n02104029 kuvasz",
+ "n02104365 schipperke",
+ "n02105056 groenendael",
+ "n02105162 malinois",
+ "n02105251 briard",
+ "n02105412 kelpie",
+ "n02105505 komondor",
+ "n02105641 Old English sheepdog, bobtail",
+ "n02105855 Shetland sheepdog, Shetland sheep dog, Shetland",
+ "n02106030 collie",
+ "n02106166 Border collie",
+ "n02106382 Bouvier des Flandres, Bouviers des Flandres",
+ "n02106550 Rottweiler",
+ "n02106662 German shepherd, German shepherd dog, German police dog, alsatian",
+ "n02107142 Doberman, Doberman pinscher",
+ "n02107312 miniature pinscher",
+ "n02107574 Greater Swiss Mountain dog",
+ "n02107683 Bernese mountain dog",
+ "n02107908 Appenzeller",
+ "n02108000 EntleBucher",
+ "n02108089 boxer",
+ "n02108422 bull mastiff",
+ "n02108551 Tibetan mastiff",
+ "n02108915 French bulldog",
+ "n02109047 Great Dane",
+ "n02109525 Saint Bernard, St Bernard",
+ "n02109961 Eskimo dog, husky",
+ "n02110063 malamute, malemute, Alaskan malamute",
+ "n02110185 Siberian husky",
+ "n02110341 dalmatian, coach dog, carriage dog",
+ "n02110627 affenpinscher, monkey pinscher, monkey dog",
+ "n02110806 basenji",
+ "n02110958 pug, pug-dog",
+ "n02111129 Leonberg",
+ "n02111277 Newfoundland, Newfoundland dog",
+ "n02111500 Great Pyrenees",
+ "n02111889 Samoyed, Samoyede",
+ "n02112018 Pomeranian",
+ "n02112137 chow, chow chow",
+ "n02112350 keeshond",
+ "n02112706 Brabancon griffon",
+ "n02113023 Pembroke, Pembroke Welsh corgi",
+ "n02113186 Cardigan, Cardigan Welsh corgi",
+ "n02113624 toy poodle",
+ "n02113712 miniature poodle",
+ "n02113799 standard poodle",
+ "n02113978 Mexican hairless",
+ "n02114367 timber wolf, grey wolf, gray wolf, Canis lupus",
+ "n02114548 white wolf, Arctic wolf, Canis lupus tundrarum",
+ "n02114712 red wolf, maned wolf, Canis rufus, Canis niger",
+ "n02114855 coyote, prairie wolf, brush wolf, Canis latrans",
+ "n02115641 dingo, warrigal, warragal, Canis dingo",
+ "n02115913 dhole, Cuon alpinus",
+ "n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
+ "n02117135 hyena, hyaena",
+ "n02119022 red fox, Vulpes vulpes",
+ "n02119789 kit fox, Vulpes macrotis",
+ "n02120079 Arctic fox, white fox, Alopex lagopus",
+ "n02120505 grey fox, gray fox, Urocyon cinereoargenteus",
+ "n02123045 tabby, tabby cat",
+ "n02123159 tiger cat",
+ "n02123394 Persian cat",
+ "n02123597 Siamese cat, Siamese",
+ "n02124075 Egyptian cat",
+ "n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
+ "n02127052 lynx, catamount",
+ "n02128385 leopard, Panthera pardus",
+ "n02128757 snow leopard, ounce, Panthera uncia",
+ "n02128925 jaguar, panther, Panthera onca, Felis onca",
+ "n02129165 lion, king of beasts, Panthera leo",
+ "n02129604 tiger, Panthera tigris",
+ "n02130308 cheetah, chetah, Acinonyx jubatus",
+ "n02132136 brown bear, bruin, Ursus arctos",
+ "n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus",
+ "n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
+ "n02134418 sloth bear, Melursus ursinus, Ursus ursinus",
+ "n02137549 mongoose",
+ "n02138441 meerkat, mierkat",
+ "n02165105 tiger beetle",
+ "n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
+ "n02167151 ground beetle, carabid beetle",
+ "n02168699 long-horned beetle, longicorn, longicorn beetle",
+ "n02169497 leaf beetle, chrysomelid",
+ "n02172182 dung beetle",
+ "n02174001 rhinoceros beetle",
+ "n02177972 weevil",
+ "n02190166 fly",
+ "n02206856 bee",
+ "n02219486 ant, emmet, pismire",
+ "n02226429 grasshopper, hopper",
+ "n02229544 cricket",
+ "n02231487 walking stick, walkingstick, stick insect",
+ "n02233338 cockroach, roach",
+ "n02236044 mantis, mantid",
+ "n02256656 cicada, cicala",
+ "n02259212 leafhopper",
+ "n02264363 lacewing, lacewing fly",
+ "n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
+ "n02268853 damselfly",
+ "n02276258 admiral",
+ "n02277742 ringlet, ringlet butterfly",
+ "n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
+ "n02280649 cabbage butterfly",
+ "n02281406 sulphur butterfly, sulfur butterfly",
+ "n02281787 lycaenid, lycaenid butterfly",
+ "n02317335 starfish, sea star",
+ "n02319095 sea urchin",
+ "n02321529 sea cucumber, holothurian",
+ "n02325366 wood rabbit, cottontail, cottontail rabbit",
+ "n02326432 hare",
+ "n02328150 Angora, Angora rabbit",
+ "n02342885 hamster",
+ "n02346627 porcupine, hedgehog",
+ "n02356798 fox squirrel, eastern fox squirrel, Sciurus niger",
+ "n02361337 marmot",
+ "n02363005 beaver",
+ "n02364673 guinea pig, Cavia cobaya",
+ "n02389026 sorrel",
+ "n02391049 zebra",
+ "n02395406 hog, pig, grunter, squealer, Sus scrofa",
+ "n02396427 wild boar, boar, Sus scrofa",
+ "n02397096 warthog",
+ "n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius",
+ "n02403003 ox",
+ "n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
+ "n02410509 bison",
+ "n02412080 ram, tup",
+ "n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
+ "n02417914 ibex, Capra ibex",
+ "n02422106 hartebeest",
+ "n02422699 impala, Aepyceros melampus",
+ "n02423022 gazelle",
+ "n02437312 Arabian camel, dromedary, Camelus dromedarius",
+ "n02437616 llama",
+ "n02441942 weasel",
+ "n02442845 mink",
+ "n02443114 polecat, fitch, foulmart, foumart, Mustela putorius",
+ "n02443484 black-footed ferret, ferret, Mustela nigripes",
+ "n02444819 otter",
+ "n02445715 skunk, polecat, wood pussy",
+ "n02447366 badger",
+ "n02454379 armadillo",
+ "n02457408 three-toed sloth, ai, Bradypus tridactylus",
+ "n02480495 orangutan, orang, orangutang, Pongo pygmaeus",
+ "n02480855 gorilla, Gorilla gorilla",
+ "n02481823 chimpanzee, chimp, Pan troglodytes",
+ "n02483362 gibbon, Hylobates lar",
+ "n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus",
+ "n02484975 guenon, guenon monkey",
+ "n02486261 patas, hussar monkey, Erythrocebus patas",
+ "n02486410 baboon",
+ "n02487347 macaque",
+ "n02488291 langur",
+ "n02488702 colobus, colobus monkey",
+ "n02489166 proboscis monkey, Nasalis larvatus",
+ "n02490219 marmoset",
+ "n02492035 capuchin, ringtail, Cebus capucinus",
+ "n02492660 howler monkey, howler",
+ "n02493509 titi, titi monkey",
+ "n02493793 spider monkey, Ateles geoffroyi",
+ "n02494079 squirrel monkey, Saimiri sciureus",
+ "n02497673 Madagascar cat, ring-tailed lemur, Lemur catta",
+ "n02500267 indri, indris, Indri indri, Indri brevicaudatus",
+ "n02504013 Indian elephant, Elephas maximus",
+ "n02504458 African elephant, Loxodonta africana",
+ "n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
+ "n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
+ "n02514041 barracouta, snoek",
+ "n02526121 eel",
+ "n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
+ "n02606052 rock beauty, Holocanthus tricolor",
+ "n02607072 anemone fish",
+ "n02640242 sturgeon",
+ "n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus",
+ "n02643566 lionfish",
+ "n02655020 puffer, pufferfish, blowfish, globefish",
+ "n02666196 abacus",
+ "n02667093 abaya",
+ "n02669723 academic gown, academic robe, judge's robe",
+ "n02672831 accordion, piano accordion, squeeze box",
+ "n02676566 acoustic guitar",
+ "n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier",
+ "n02690373 airliner",
+ "n02692877 airship, dirigible",
+ "n02699494 altar",
+ "n02701002 ambulance",
+ "n02704792 amphibian, amphibious vehicle",
+ "n02708093 analog clock",
+ "n02727426 apiary, bee house",
+ "n02730930 apron",
+ "n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
+ "n02749479 assault rifle, assault gun",
+ "n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack",
+ "n02776631 bakery, bakeshop, bakehouse",
+ "n02777292 balance beam, beam",
+ "n02782093 balloon",
+ "n02783161 ballpoint, ballpoint pen, ballpen, Biro",
+ "n02786058 Band Aid",
+ "n02787622 banjo",
+ "n02788148 bannister, banister, balustrade, balusters, handrail",
+ "n02790996 barbell",
+ "n02791124 barber chair",
+ "n02791270 barbershop",
+ "n02793495 barn",
+ "n02794156 barometer",
+ "n02795169 barrel, cask",
+ "n02797295 barrow, garden cart, lawn cart, wheelbarrow",
+ "n02799071 baseball",
+ "n02802426 basketball",
+ "n02804414 bassinet",
+ "n02804610 bassoon",
+ "n02807133 bathing cap, swimming cap",
+ "n02808304 bath towel",
+ "n02808440 bathtub, bathing tub, bath, tub",
+ "n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
+ "n02814860 beacon, lighthouse, beacon light, pharos",
+ "n02815834 beaker",
+ "n02817516 bearskin, busby, shako",
+ "n02823428 beer bottle",
+ "n02823750 beer glass",
+ "n02825657 bell cote, bell cot",
+ "n02834397 bib",
+ "n02835271 bicycle-built-for-two, tandem bicycle, tandem",
+ "n02837789 bikini, two-piece",
+ "n02840245 binder, ring-binder",
+ "n02841315 binoculars, field glasses, opera glasses",
+ "n02843684 birdhouse",
+ "n02859443 boathouse",
+ "n02860847 bobsled, bobsleigh, bob",
+ "n02865351 bolo tie, bolo, bola tie, bola",
+ "n02869837 bonnet, poke bonnet",
+ "n02870880 bookcase",
+ "n02871525 bookshop, bookstore, bookstall",
+ "n02877765 bottlecap",
+ "n02879718 bow",
+ "n02883205 bow tie, bow-tie, bowtie",
+ "n02892201 brass, memorial tablet, plaque",
+ "n02892767 brassiere, bra, bandeau",
+ "n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty",
+ "n02895154 breastplate, aegis, egis",
+ "n02906734 broom",
+ "n02909870 bucket, pail",
+ "n02910353 buckle",
+ "n02916936 bulletproof vest",
+ "n02917067 bullet train, bullet",
+ "n02927161 butcher shop, meat market",
+ "n02930766 cab, hack, taxi, taxicab",
+ "n02939185 caldron, cauldron",
+ "n02948072 candle, taper, wax light",
+ "n02950826 cannon",
+ "n02951358 canoe",
+ "n02951585 can opener, tin opener",
+ "n02963159 cardigan",
+ "n02965783 car mirror",
+ "n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig",
+ "n02966687 carpenter's kit, tool kit",
+ "n02971356 carton",
+ "n02974003 car wheel",
+ "n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
+ "n02978881 cassette",
+ "n02979186 cassette player",
+ "n02980441 castle",
+ "n02981792 catamaran",
+ "n02988304 CD player",
+ "n02992211 cello, violoncello",
+ "n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone",
+ "n02999410 chain",
+ "n03000134 chainlink fence",
+ "n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
+ "n03000684 chain saw, chainsaw",
+ "n03014705 chest",
+ "n03016953 chiffonier, commode",
+ "n03017168 chime, bell, gong",
+ "n03018349 china cabinet, china closet",
+ "n03026506 Christmas stocking",
+ "n03028079 church, church building",
+ "n03032252 cinema, movie theater, movie theatre, movie house, picture palace",
+ "n03041632 cleaver, meat cleaver, chopper",
+ "n03042490 cliff dwelling",
+ "n03045698 cloak",
+ "n03047690 clog, geta, patten, sabot",
+ "n03062245 cocktail shaker",
+ "n03063599 coffee mug",
+ "n03063689 coffeepot",
+ "n03065424 coil, spiral, volute, whorl, helix",
+ "n03075370 combination lock",
+ "n03085013 computer keyboard, keypad",
+ "n03089624 confectionery, confectionary, candy store",
+ "n03095699 container ship, containership, container vessel",
+ "n03100240 convertible",
+ "n03109150 corkscrew, bottle screw",
+ "n03110669 cornet, horn, trumpet, trump",
+ "n03124043 cowboy boot",
+ "n03124170 cowboy hat, ten-gallon hat",
+ "n03125729 cradle",
+ "n03126707 crane",
+ "n03127747 crash helmet",
+ "n03127925 crate",
+ "n03131574 crib, cot",
+ "n03133878 Crock Pot",
+ "n03134739 croquet ball",
+ "n03141823 crutch",
+ "n03146219 cuirass",
+ "n03160309 dam, dike, dyke",
+ "n03179701 desk",
+ "n03180011 desktop computer",
+ "n03187595 dial telephone, dial phone",
+ "n03188531 diaper, nappy, napkin",
+ "n03196217 digital clock",
+ "n03197337 digital watch",
+ "n03201208 dining table, board",
+ "n03207743 dishrag, dishcloth",
+ "n03207941 dishwasher, dish washer, dishwashing machine",
+ "n03208938 disk brake, disc brake",
+ "n03216828 dock, dockage, docking facility",
+ "n03218198 dogsled, dog sled, dog sleigh",
+ "n03220513 dome",
+ "n03223299 doormat, welcome mat",
+ "n03240683 drilling platform, offshore rig",
+ "n03249569 drum, membranophone, tympan",
+ "n03250847 drumstick",
+ "n03255030 dumbbell",
+ "n03259280 Dutch oven",
+ "n03271574 electric fan, blower",
+ "n03272010 electric guitar",
+ "n03272562 electric locomotive",
+ "n03290653 entertainment center",
+ "n03291819 envelope",
+ "n03297495 espresso maker",
+ "n03314780 face powder",
+ "n03325584 feather boa, boa",
+ "n03337140 file, file cabinet, filing cabinet",
+ "n03344393 fireboat",
+ "n03345487 fire engine, fire truck",
+ "n03347037 fire screen, fireguard",
+ "n03355925 flagpole, flagstaff",
+ "n03372029 flute, transverse flute",
+ "n03376595 folding chair",
+ "n03379051 football helmet",
+ "n03384352 forklift",
+ "n03388043 fountain",
+ "n03388183 fountain pen",
+ "n03388549 four-poster",
+ "n03393912 freight car",
+ "n03394916 French horn, horn",
+ "n03400231 frying pan, frypan, skillet",
+ "n03404251 fur coat",
+ "n03417042 garbage truck, dustcart",
+ "n03424325 gasmask, respirator, gas helmet",
+ "n03425413 gas pump, gasoline pump, petrol pump, island dispenser",
+ "n03443371 goblet",
+ "n03444034 go-kart",
+ "n03445777 golf ball",
+ "n03445924 golfcart, golf cart",
+ "n03447447 gondola",
+ "n03447721 gong, tam-tam",
+ "n03450230 gown",
+ "n03452741 grand piano, grand",
+ "n03457902 greenhouse, nursery, glasshouse",
+ "n03459775 grille, radiator grille",
+ "n03461385 grocery store, grocery, food market, market",
+ "n03467068 guillotine",
+ "n03476684 hair slide",
+ "n03476991 hair spray",
+ "n03478589 half track",
+ "n03481172 hammer",
+ "n03482405 hamper",
+ "n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier",
+ "n03485407 hand-held computer, hand-held microcomputer",
+ "n03485794 handkerchief, hankie, hanky, hankey",
+ "n03492542 hard disc, hard disk, fixed disk",
+ "n03494278 harmonica, mouth organ, harp, mouth harp",
+ "n03495258 harp",
+ "n03496892 harvester, reaper",
+ "n03498962 hatchet",
+ "n03527444 holster",
+ "n03529860 home theater, home theatre",
+ "n03530642 honeycomb",
+ "n03532672 hook, claw",
+ "n03534580 hoopskirt, crinoline",
+ "n03535780 horizontal bar, high bar",
+ "n03538406 horse cart, horse-cart",
+ "n03544143 hourglass",
+ "n03584254 iPod",
+ "n03584829 iron, smoothing iron",
+ "n03590841 jack-o'-lantern",
+ "n03594734 jean, blue jean, denim",
+ "n03594945 jeep, landrover",
+ "n03595614 jersey, T-shirt, tee shirt",
+ "n03598930 jigsaw puzzle",
+ "n03599486 jinrikisha, ricksha, rickshaw",
+ "n03602883 joystick",
+ "n03617480 kimono",
+ "n03623198 knee pad",
+ "n03627232 knot",
+ "n03630383 lab coat, laboratory coat",
+ "n03633091 ladle",
+ "n03637318 lampshade, lamp shade",
+ "n03642806 laptop, laptop computer",
+ "n03649909 lawn mower, mower",
+ "n03657121 lens cap, lens cover",
+ "n03658185 letter opener, paper knife, paperknife",
+ "n03661043 library",
+ "n03662601 lifeboat",
+ "n03666591 lighter, light, igniter, ignitor",
+ "n03670208 limousine, limo",
+ "n03673027 liner, ocean liner",
+ "n03676483 lipstick, lip rouge",
+ "n03680355 Loafer",
+ "n03690938 lotion",
+ "n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
+ "n03692522 loupe, jeweler's loupe",
+ "n03697007 lumbermill, sawmill",
+ "n03706229 magnetic compass",
+ "n03709823 mailbag, postbag",
+ "n03710193 mailbox, letter box",
+ "n03710637 maillot",
+ "n03710721 maillot, tank suit",
+ "n03717622 manhole cover",
+ "n03720891 maraca",
+ "n03721384 marimba, xylophone",
+ "n03724870 mask",
+ "n03729826 matchstick",
+ "n03733131 maypole",
+ "n03733281 maze, labyrinth",
+ "n03733805 measuring cup",
+ "n03742115 medicine chest, medicine cabinet",
+ "n03743016 megalith, megalithic structure",
+ "n03759954 microphone, mike",
+ "n03761084 microwave, microwave oven",
+ "n03763968 military uniform",
+ "n03764736 milk can",
+ "n03769881 minibus",
+ "n03770439 miniskirt, mini",
+ "n03770679 minivan",
+ "n03773504 missile",
+ "n03775071 mitten",
+ "n03775546 mixing bowl",
+ "n03776460 mobile home, manufactured home",
+ "n03777568 Model T",
+ "n03777754 modem",
+ "n03781244 monastery",
+ "n03782006 monitor",
+ "n03785016 moped",
+ "n03786901 mortar",
+ "n03787032 mortarboard",
+ "n03788195 mosque",
+ "n03788365 mosquito net",
+ "n03791053 motor scooter, scooter",
+ "n03792782 mountain bike, all-terrain bike, off-roader",
+ "n03792972 mountain tent",
+ "n03793489 mouse, computer mouse",
+ "n03794056 mousetrap",
+ "n03796401 moving van",
+ "n03803284 muzzle",
+ "n03804744 nail",
+ "n03814639 neck brace",
+ "n03814906 necklace",
+ "n03825788 nipple",
+ "n03832673 notebook, notebook computer",
+ "n03837869 obelisk",
+ "n03838899 oboe, hautboy, hautbois",
+ "n03840681 ocarina, sweet potato",
+ "n03841143 odometer, hodometer, mileometer, milometer",
+ "n03843555 oil filter",
+ "n03854065 organ, pipe organ",
+ "n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO",
+ "n03866082 overskirt",
+ "n03868242 oxcart",
+ "n03868863 oxygen mask",
+ "n03871628 packet",
+ "n03873416 paddle, boat paddle",
+ "n03874293 paddlewheel, paddle wheel",
+ "n03874599 padlock",
+ "n03876231 paintbrush",
+ "n03877472 pajama, pyjama, pj's, jammies",
+ "n03877845 palace",
+ "n03884397 panpipe, pandean pipe, syrinx",
+ "n03887697 paper towel",
+ "n03888257 parachute, chute",
+ "n03888605 parallel bars, bars",
+ "n03891251 park bench",
+ "n03891332 parking meter",
+ "n03895866 passenger car, coach, carriage",
+ "n03899768 patio, terrace",
+ "n03902125 pay-phone, pay-station",
+ "n03903868 pedestal, plinth, footstall",
+ "n03908618 pencil box, pencil case",
+ "n03908714 pencil sharpener",
+ "n03916031 perfume, essence",
+ "n03920288 Petri dish",
+ "n03924679 photocopier",
+ "n03929660 pick, plectrum, plectron",
+ "n03929855 pickelhaube",
+ "n03930313 picket fence, paling",
+ "n03930630 pickup, pickup truck",
+ "n03933933 pier",
+ "n03935335 piggy bank, penny bank",
+ "n03937543 pill bottle",
+ "n03938244 pillow",
+ "n03942813 ping-pong ball",
+ "n03944341 pinwheel",
+ "n03947888 pirate, pirate ship",
+ "n03950228 pitcher, ewer",
+ "n03954731 plane, carpenter's plane, woodworking plane",
+ "n03956157 planetarium",
+ "n03958227 plastic bag",
+ "n03961711 plate rack",
+ "n03967562 plow, plough",
+ "n03970156 plunger, plumber's helper",
+ "n03976467 Polaroid camera, Polaroid Land camera",
+ "n03976657 pole",
+ "n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
+ "n03980874 poncho",
+ "n03982430 pool table, billiard table, snooker table",
+ "n03983396 pop bottle, soda bottle",
+ "n03991062 pot, flowerpot",
+ "n03992509 potter's wheel",
+ "n03995372 power drill",
+ "n03998194 prayer rug, prayer mat",
+ "n04004767 printer",
+ "n04005630 prison, prison house",
+ "n04008634 projectile, missile",
+ "n04009552 projector",
+ "n04019541 puck, hockey puck",
+ "n04023962 punching bag, punch bag, punching ball, punchball",
+ "n04026417 purse",
+ "n04033901 quill, quill pen",
+ "n04033995 quilt, comforter, comfort, puff",
+ "n04037443 racer, race car, racing car",
+ "n04039381 racket, racquet",
+ "n04040759 radiator",
+ "n04041544 radio, wireless",
+ "n04044716 radio telescope, radio reflector",
+ "n04049303 rain barrel",
+ "n04065272 recreational vehicle, RV, R.V.",
+ "n04067472 reel",
+ "n04069434 reflex camera",
+ "n04070727 refrigerator, icebox",
+ "n04074963 remote control, remote",
+ "n04081281 restaurant, eating house, eating place, eatery",
+ "n04086273 revolver, six-gun, six-shooter",
+ "n04090263 rifle",
+ "n04099969 rocking chair, rocker",
+ "n04111531 rotisserie",
+ "n04116512 rubber eraser, rubber, pencil eraser",
+ "n04118538 rugby ball",
+ "n04118776 rule, ruler",
+ "n04120489 running shoe",
+ "n04125021 safe",
+ "n04127249 safety pin",
+ "n04131690 saltshaker, salt shaker",
+ "n04133789 sandal",
+ "n04136333 sarong",
+ "n04141076 sax, saxophone",
+ "n04141327 scabbard",
+ "n04141975 scale, weighing machine",
+ "n04146614 school bus",
+ "n04147183 schooner",
+ "n04149813 scoreboard",
+ "n04152593 screen, CRT screen",
+ "n04153751 screw",
+ "n04154565 screwdriver",
+ "n04162706 seat belt, seatbelt",
+ "n04179913 sewing machine",
+ "n04192698 shield, buckler",
+ "n04200800 shoe shop, shoe-shop, shoe store",
+ "n04201297 shoji",
+ "n04204238 shopping basket",
+ "n04204347 shopping cart",
+ "n04208210 shovel",
+ "n04209133 shower cap",
+ "n04209239 shower curtain",
+ "n04228054 ski",
+ "n04229816 ski mask",
+ "n04235860 sleeping bag",
+ "n04238763 slide rule, slipstick",
+ "n04239074 sliding door",
+ "n04243546 slot, one-armed bandit",
+ "n04251144 snorkel",
+ "n04252077 snowmobile",
+ "n04252225 snowplow, snowplough",
+ "n04254120 soap dispenser",
+ "n04254680 soccer ball",
+ "n04254777 sock",
+ "n04258138 solar dish, solar collector, solar furnace",
+ "n04259630 sombrero",
+ "n04263257 soup bowl",
+ "n04264628 space bar",
+ "n04265275 space heater",
+ "n04266014 space shuttle",
+ "n04270147 spatula",
+ "n04273569 speedboat",
+ "n04275548 spider web, spider's web",
+ "n04277352 spindle",
+ "n04285008 sports car, sport car",
+ "n04286575 spotlight, spot",
+ "n04296562 stage",
+ "n04310018 steam locomotive",
+ "n04311004 steel arch bridge",
+ "n04311174 steel drum",
+ "n04317175 stethoscope",
+ "n04325704 stole",
+ "n04326547 stone wall",
+ "n04328186 stopwatch, stop watch",
+ "n04330267 stove",
+ "n04332243 strainer",
+ "n04335435 streetcar, tram, tramcar, trolley, trolley car",
+ "n04336792 stretcher",
+ "n04344873 studio couch, day bed",
+ "n04346328 stupa, tope",
+ "n04347754 submarine, pigboat, sub, U-boat",
+ "n04350905 suit, suit of clothes",
+ "n04355338 sundial",
+ "n04355933 sunglass",
+ "n04356056 sunglasses, dark glasses, shades",
+ "n04357314 sunscreen, sunblock, sun blocker",
+ "n04366367 suspension bridge",
+ "n04367480 swab, swob, mop",
+ "n04370456 sweatshirt",
+ "n04371430 swimming trunks, bathing trunks",
+ "n04371774 swing",
+ "n04372370 switch, electric switch, electrical switch",
+ "n04376876 syringe",
+ "n04380533 table lamp",
+ "n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle",
+ "n04392985 tape player",
+ "n04398044 teapot",
+ "n04399382 teddy, teddy bear",
+ "n04404412 television, television system",
+ "n04409515 tennis ball",
+ "n04417672 thatch, thatched roof",
+ "n04418357 theater curtain, theatre curtain",
+ "n04423845 thimble",
+ "n04428191 thresher, thrasher, threshing machine",
+ "n04429376 throne",
+ "n04435653 tile roof",
+ "n04442312 toaster",
+ "n04443257 tobacco shop, tobacconist shop, tobacconist",
+ "n04447861 toilet seat",
+ "n04456115 torch",
+ "n04458633 totem pole",
+ "n04461696 tow truck, tow car, wrecker",
+ "n04462240 toyshop",
+ "n04465501 tractor",
+ "n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
+ "n04476259 tray",
+ "n04479046 trench coat",
+ "n04482393 tricycle, trike, velocipede",
+ "n04483307 trimaran",
+ "n04485082 tripod",
+ "n04486054 triumphal arch",
+ "n04487081 trolleybus, trolley coach, trackless trolley",
+ "n04487394 trombone",
+ "n04493381 tub, vat",
+ "n04501370 turnstile",
+ "n04505470 typewriter keyboard",
+ "n04507155 umbrella",
+ "n04509417 unicycle, monocycle",
+ "n04515003 upright, upright piano",
+ "n04517823 vacuum, vacuum cleaner",
+ "n04522168 vase",
+ "n04523525 vault",
+ "n04525038 velvet",
+ "n04525305 vending machine",
+ "n04532106 vestment",
+ "n04532670 viaduct",
+ "n04536866 violin, fiddle",
+ "n04540053 volleyball",
+ "n04542943 waffle iron",
+ "n04548280 wall clock",
+ "n04548362 wallet, billfold, notecase, pocketbook",
+ "n04550184 wardrobe, closet, press",
+ "n04552348 warplane, military plane",
+ "n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin",
+ "n04554684 washer, automatic washer, washing machine",
+ "n04557648 water bottle",
+ "n04560804 water jug",
+ "n04562935 water tower",
+ "n04579145 whiskey jug",
+ "n04579432 whistle",
+ "n04584207 wig",
+ "n04589890 window screen",
+ "n04590129 window shade",
+ "n04591157 Windsor tie",
+ "n04591713 wine bottle",
+ "n04592741 wing",
+ "n04596742 wok",
+ "n04597913 wooden spoon",
+ "n04599235 wool, woolen, woollen",
+ "n04604644 worm fence, snake fence, snake-rail fence, Virginia fence",
+ "n04606251 wreck",
+ "n04612504 yawl",
+ "n04613696 yurt",
+ "n06359193 web site, website, internet site, site",
+ "n06596364 comic book",
+ "n06785654 crossword puzzle, crossword",
+ "n06794110 street sign",
+ "n06874185 traffic light, traffic signal, stoplight",
+ "n07248320 book jacket, dust cover, dust jacket, dust wrapper",
+ "n07565083 menu",
+ "n07579787 plate",
+ "n07583066 guacamole",
+ "n07584110 consomme",
+ "n07590611 hot pot, hotpot",
+ "n07613480 trifle",
+ "n07614500 ice cream, icecream",
+ "n07615774 ice lolly, lolly, lollipop, popsicle",
+ "n07684084 French loaf",
+ "n07693725 bagel, beigel",
+ "n07695742 pretzel",
+ "n07697313 cheeseburger",
+ "n07697537 hotdog, hot dog, red hot",
+ "n07711569 mashed potato",
+ "n07714571 head cabbage",
+ "n07714990 broccoli",
+ "n07715103 cauliflower",
+ "n07716358 zucchini, courgette",
+ "n07716906 spaghetti squash",
+ "n07717410 acorn squash",
+ "n07717556 butternut squash",
+ "n07718472 cucumber, cuke",
+ "n07718747 artichoke, globe artichoke",
+ "n07720875 bell pepper",
+ "n07730033 cardoon",
+ "n07734744 mushroom",
+ "n07742313 Granny Smith",
+ "n07745940 strawberry",
+ "n07747607 orange",
+ "n07749582 lemon",
+ "n07753113 fig",
+ "n07753275 pineapple, ananas",
+ "n07753592 banana",
+ "n07754684 jackfruit, jak, jack",
+ "n07760859 custard apple",
+ "n07768694 pomegranate",
+ "n07802026 hay",
+ "n07831146 carbonara",
+ "n07836838 chocolate sauce, chocolate syrup",
+ "n07860988 dough",
+ "n07871810 meat loaf, meatloaf",
+ "n07873807 pizza, pizza pie",
+ "n07875152 potpie",
+ "n07880968 burrito",
+ "n07892512 red wine",
+ "n07920052 espresso",
+ "n07930864 cup",
+ "n07932039 eggnog",
+ "n09193705 alp",
+ "n09229709 bubble",
+ "n09246464 cliff, drop, drop-off",
+ "n09256479 coral reef",
+ "n09288635 geyser",
+ "n09332890 lakeside, lakeshore",
+ "n09399592 promontory, headland, head, foreland",
+ "n09421951 sandbar, sand bar",
+ "n09428293 seashore, coast, seacoast, sea-coast",
+ "n09468604 valley, vale",
+ "n09472597 volcano",
+ "n09835506 ballplayer, baseball player",
+ "n10148035 groom, bridegroom",
+ "n10565667 scuba diver",
+ "n11879895 rapeseed",
+ "n11939491 daisy",
+ "n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
+ "n12144580 corn",
+ "n12267677 acorn",
+ "n12620546 hip, rose hip, rosehip",
+ "n12768682 buckeye, horse chestnut, conker",
+ "n12985857 coral fungus",
+ "n12998815 agaric",
+ "n13037406 gyromitra",
+ "n13040303 stinkhorn, carrion fungus",
+ "n13044778 earthstar",
+ "n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
+ "n13054560 bolete",
+ "n13133613 ear, spike, capitulum",
+ "n15075141 toilet tissue, toilet paper, bathroom tissue",
+]
+
+synset_map = {}
+for i, l in enumerate(synset):
+ label, desc = l.split(' ', 1)
+ synset_map[label] = {"index": i, "desc": desc, }
diff --git a/test_resnet_inference.py b/test_resnet_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..763ecbe2b1161ac25ed8a902917e2e2979522ce7
--- /dev/null
+++ b/test_resnet_inference.py
@@ -0,0 +1,59 @@
+import os
+import json
+import image_io
+import numpy as np
+from resnet.synset import *
+import resnet.inference as resnet_inference
+import tftools.var_collect as var_collect
+
+import tensorflow as tf
+
+
+def print_prob(prob):
+ #print prob
+ pred = np.argsort(prob)[::-1]
+
+ # Get top1 label
+ top1 = synset[pred[0]]
+ print "Top1: ", top1
+ # Get top5 label
+ top5 = [synset[pred[i]] for i in range(5)]
+ print "Top5: ", top5
+ return top1
+
+
+if __name__=='__main__':
+ model_dir = '/home/tanmay/Downloads/pretrained_networks/' + \
+ 'Resnet/tensorflow-resnet-pretrained-20160509'
+ ckpt_filename = os.path.join(model_dir, 'ResNet-L50.ckpt')
+
+ img = image_io.imread("/home/tanmay/Code/GenVQA/GenVQA/resnet/dalmatian.jpg")
+ img = image_io.imresize(img, output_size=(224,224))
+ img = img.astype(np.float32)
+
+ sess = tf.Session()
+
+ images = tf.placeholder(tf.float32, shape=[1, 224, 224, 3], name='images')
+ logits = resnet_inference.inference(images, False)
+ prob_tensor = tf.nn.softmax(logits, name='prob')
+
+ vars_to_restore = []
+ for s in xrange(5):
+ vars_to_restore += var_collect.collect_scope('scale'+str(s+1))
+ vars_to_restore += var_collect.collect_scope('fc')
+
+ saver = tf.train.Saver(vars_to_restore)
+ saver.restore(sess, ckpt_filename)
+
+ all_vars = var_collect.collect_all()
+ vars_to_init = [var for var in all_vars if var not in vars_to_restore]
+
+ init = tf.initialize_variables(vars_to_init)
+ sess.run(init)
+
+ print "graph restored"
+
+ batch = img.reshape((1, 224, 224, 3))
+ feed_dict = {images: batch}
+ prob = sess.run(prob_tensor, feed_dict=feed_dict)
+ print_prob(prob[0])
diff --git a/tftools/var_collect.py b/tftools/var_collect.py
new file mode 100644
index 0000000000000000000000000000000000000000..3900633460f05e8cb1d9896fce78715b4c7f90a6
--- /dev/null
+++ b/tftools/var_collect.py
@@ -0,0 +1,50 @@
+import tensorflow as tf
+
+
+def print_var_list(var_list, name='Variables'):
+ print name + ': \n' + '[' + ', '.join([var.name for var in var_list]) + ']'
+
+
+def collect_name(var_name, graph=None):
+ if graph == None:
+ graph = tf.get_default_graph()
+
+ var_list = graph.get_collection(tf.GraphKeys.VARIABLES, scope=var_name)
+
+ assert_str = "No variable exists with name '{}'".format(var_name)
+ assert len(var_list) != 0, assert_str
+
+ assert_str = \
+ "Multiple variables exist with name_scope '{}'".format(var_name)
+ assert len(var_list) == 1, assert_str
+
+ return var_list[0]
+
+
+def collect_scope(name_scope, graph=None):
+ if graph == None:
+ graph = tf.get_default_graph()
+
+ var_list = graph.get_collection(tf.GraphKeys.VARIABLES, scope=name_scope)
+
+ assert_str = "No variable exists with name_scope '{}'".format(name_scope)
+ assert len(var_list) != 0, assert_str
+
+ return var_list
+
+
+def collect_all(graph=None):
+ if graph == None:
+ graph = tf.get_default_graph()
+
+ var_list = graph.get_collection(tf.GraphKeys.VARIABLES)
+
+ return var_list
+
+
+def collect_list(var_name_list, graph=None):
+ var_dict = dict()
+ for var_name in var_name_list:
+ var_dict[var_name] = collect_name(var_name, graph=graph)
+
+ return var_dict