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predict_ilsvrc12_inception-2015-12-05.py
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predict_ilsvrc12_inception-2015-12-05.py
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import tensorflow.python.platform
from six.moves import urllib
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import gfile
import sys, os, re, time
class NodeLookup(object):
"""Converts integer node ID's to human readable labels."""
def __init__(self,
label_lookup_path=None,
uid_lookup_path=None):
if not label_lookup_path:
label_lookup_path = os.path.join(
'imagenet', 'imagenet_2012_challenge_label_map_proto.pbtxt')
if not uid_lookup_path:
uid_lookup_path = os.path.join(
'imagenet', 'imagenet_synset_to_human_label_map.txt')
self.node_lookup, self.node_id_lookup = self.load(label_lookup_path, uid_lookup_path)
def load(self, label_lookup_path, uid_lookup_path):
"""Loads a human readable English name for each softmax node.
Args:
label_lookup_path: string UID to integer node ID.
uid_lookup_path: string UID to human-readable string.
Returns:
dict from integer node ID to human-readable string.
"""
if not gfile.Exists(uid_lookup_path):
tf.logging.fatal('File does not exist %s', uid_lookup_path)
if not gfile.Exists(label_lookup_path):
tf.logging.fatal('File does not exist %s', label_lookup_path)
# Loads mapping from string UID to human-readable string
proto_as_ascii_lines = gfile.GFile(uid_lookup_path).readlines()
uid_to_human = {}
p = re.compile(r'[n\d]*[ \S,]*')
for line in proto_as_ascii_lines:
parsed_items = p.findall(line)
uid = parsed_items[0]
human_string = parsed_items[2]
uid_to_human[uid] = human_string
# Loads mapping from string UID to integer node ID.
node_id_to_uid = {}
proto_as_ascii = gfile.GFile(label_lookup_path).readlines()
for line in proto_as_ascii:
if line.startswith(' target_class:'):
target_class = int(line.split(': ')[1])
if line.startswith(' target_class_string:'):
target_class_string = line.split(': ')[1]
node_id_to_uid[target_class] = target_class_string[1:-2]
# Loads the final mapping of integer node ID to human-readable string
node_id_to_name = {}
for key, val in node_id_to_uid.items():
if val not in uid_to_human:
tf.logging.fatal('Failed to locate: %s', val)
name = uid_to_human[val]
node_id_to_name[key] = name
return node_id_to_name, node_id_to_uid
def id_to_string(self, node_id):
if node_id not in self.node_lookup:
return ''
return self.node_lookup[node_id]
def id_to_uid(self, node_id):
if node_id not in self.node_id_lookup:
return ''
return self.node_id_lookup[node_id]
def create_graph(graph_def_pb='classify_image_graph_def.pb'):
with gfile.FastGFile(graph_def_pb, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
filename_list = [entry.strip().split(' ') for entry in open(
'/storage/ImageNet/ILSVRC2012/val_synset.txt',
'r'
)]
synset_label_map = [entry.strip().split(' ')[0] for entry in open(
'/storage/ImageNet/ILSVRC2012/synset_words.txt',
'r'
)]
synset_label_dic = {}
for id, synset in enumerate(synset_label_map):
synset_label_dic[synset] = id
path_prefix = '/storage/ImageNet/ILSVRC2012/val/%s'
num_top_predictions = 5
graph_def_pb = 'imagenet/classify_image_graph_def.pb'
create_graph(graph_def_pb)
node_lookup = NodeLookup()
sess = tf.Session(
config=tf.ConfigProto(
log_device_placement=False
)
)
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
filepath = 'imagenet/cropped_panda.jpg'
image_data = gfile.FastGFile(filepath, 'rb').read()
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
top_k = predictions.argsort()[-num_top_predictions:][::-1]
#import pdb; pdb.set_trace()
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
uid = node_lookup.id_to_uid(node_id)
score = predictions[node_id]
print('(predicted: %d, score = %.5f), %s' % (node_id, score, human_string))
#import pdb; pdb.set_trace()
top_1 = 0
top_5 = 0
for n, item in enumerate(filename_list):
filepath = path_prefix % item[0]
label = int(item[1])
if not gfile.Exists(filepath):
tf.logging.fatal('File does not exist %s', filepath)
image_data = gfile.FastGFile(filepath, 'rb').read()
#import pdb; pdb.set_trace()
start_predict = time.time()
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
elapsed_predict = time.time() - start_predict
predictions = np.squeeze(predictions)
top_k = predictions.argsort()[-num_top_predictions:][::-1]
for k, node_id in enumerate(top_k):
uid = node_lookup.id_to_uid(node_id)
if k == 0 and synset_label_dic[uid] == label:
top_1 += 1.0
top_5 += 1.0
break
if k > 0 and synset_label_dic[uid] == label:
top_5 += 1.0
break
print('%s, top@1: %d/%d = %.4f, top@5: %d/%d = %.4f in %.3f sec.' % \
(n+1,
top_1, n+1, top_1/(n+1)*100,
top_5, n+1, top_5/(n+1)*100,
elapsed_predict))
sys.stdout.flush()