def inference(filename):
layers = 50
img = load_image(filename)
print(img.shape)
sess = tf.Session()
new_saver = tf.train.import_meta_graph(meta_fn(layers))
new_saver.restore(sess, checkpoint_fn(layers))
graph = tf.get_default_graph()
prob_tensor = graph.get_tensor_by_name("prob:0")
images = graph.get_tensor_by_name("images:0")
for op in graph.get_operations():
print op.name
#init = tf.initialize_all_variables()
#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)
return print_prob(prob[0])
def resnet(dataDir, layers):
from convert import print_prob, load_image, checkpoint_fn, meta_fn
import tensorflow as tf
import cv2
sess = tf.Session()
new_saver = tf.train.import_meta_graph('./models/' + meta_fn(layers))
new_saver.restore(sess, './models/' + checkpoint_fn(layers))
graph = tf.get_default_graph()
prob_tensor_1 = graph.get_tensor_by_name("prob:0")
prob_tensor_2 = graph.get_tensor_by_name("avg_pool:0")
images = graph.get_tensor_by_name("images:0")
for op in graph.get_operations():
print(op.name)
print("graph restored")
start_time = time.time()
EMB1, EMB2 = None, None
image_names = os.listdir(dataDir)
image_names = image_names[:]
images_list = np.zeros(shape=(len(image_names), cfg.EMB_IMAGE_HEIGHT,
cfg.EMB_IMAGE_WIDTH, 3))
for i, img_name in enumerate(image_names):
if i == 1:
start_time = time.time()
print(
str(i + 1).rjust(4) + '/' + str(len(image_names)) + ' - ' +
img_name)
img = load_image(os.path.join(dataDir, img_name))
image = cv2.imread(os.path.join(dataDir, img_name))
image = cv2.resize(image, (cfg.EMB_IMAGE_HEIGHT, cfg.EMB_IMAGE_WIDTH),
interpolation=cv2.INTER_CUBIC)
images_list[i] = image
batch = img.reshape((1, 224, 224, 3))
feed_dict = {images: batch}
prob = sess.run([prob_tensor_1, prob_tensor_2], feed_dict=feed_dict)
embedding = prob[0].flatten()
if EMB1 is None:
EMB1 = np.zeros((len(image_names), len(embedding)),
dtype='float32')
EMB1[i] = embedding
embedding = prob[1].flatten()
if EMB2 is None:
EMB2 = np.zeros((len(image_names), len(embedding)),
dtype='float32')
EMB2[i] = embedding
print("--- ResNet time: %s seconds ---" % (time.time() - start_time))
return sess, images_list, image_names, EMB1, EMB2
def test_image():
img = load_image("data/cat.jpg")
batch = img.reshape((1, 224, 224, 3))
feed_dict = {images: batch}
prob = sess.run(prob_tensor, feed_dict=feed_dict)
print_prob(prob[0])
vl = ['scale5/block3/Relu:0']
# vl = ['scale1/Relu:0', 'scale1/Conv2D:0', 'sub:0']
for v in vl:
vten = sess.run(graph.get_tensor_by_name(v), feed_dict=feed_dict)
print v
print vten
print vten.shape
# print sess.run(graph.get_tensor_by_name("scale1/Relu:0"),
# feed_dict=feed_dict)
# print sess.run(graph.get_tensor_by_name("scale1/Conv2D:0"),
# feed_dict=feed_dict)
# print sess.run(graph.get_tensor_by_name("sub:0"), feed_dict=feed_dict)
pass
def test_image():
img = load_image("data/cat.jpg")
batch = img.reshape((1, 224, 224, 3))
feed_dict = {images: batch}
prob = sess.run(prob_tensor, feed_dict=feed_dict)
print_prob(prob[0])
vl = ['scale5/block3/Relu:0']
# vl = ['scale1/Relu:0', 'scale1/Conv2D:0', 'sub:0']
for v in vl:
vten = sess.run(graph.get_tensor_by_name(v), feed_dict=feed_dict)
print v
print vten
print vten.shape
# print sess.run(graph.get_tensor_by_name("scale1/Relu:0"),
# feed_dict=feed_dict)
# print sess.run(graph.get_tensor_by_name("scale1/Conv2D:0"),
# feed_dict=feed_dict)
# print sess.run(graph.get_tensor_by_name("sub:0"), feed_dict=feed_dict)
pass
from convert import print_prob, load_image, checkpoint_fn, meta_fn, load_model
import tensorflow as tf
import os
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('resnet_model_base_dir', '/opt/storage/models/resnet',
"""Directory where to write trained models and checkpoints.""")
tf.app.flags.DEFINE_string('resnet_model_name', 'ResNet-',
"""Model name.""")
layers = 20
img = load_image("data/cat.jpg")
sess = tf.Session()
saver = tf.train.import_meta_graph(meta_fn(layers))
saver.restore(sess, checkpoint_fn(layers))
graph = tf.get_default_graph()
prob_tensor = graph.get_tensor_by_name("prob:0")
images = graph.get_tensor_by_name("images:0")
for op in graph.get_operations():
print op.name
#init = tf.initialize_all_variables()
#sess.run(init)
print "graph restored"
from convert import print_prob, load_image, checkpoint_fn, meta_fn
import tensorflow as tf
layers = 50
img = load_image("data/cat.jpg")
sess = tf.Session()
new_saver = tf.train.import_meta_graph(meta_fn(layers))
new_saver.restore(sess, checkpoint_fn(layers))
graph = tf.get_default_graph()
prob_tensor = graph.get_tensor_by_name("prob:0")
images = graph.get_tensor_by_name("images:0")
for op in graph.get_operations():
print op.name
#init = tf.initialize_all_variables()
#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])
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
import os
from convert import print_prob, load_image, checkpoint_fn, meta_fn
import tensorflow as tf
root = "/scratch/lyc/ai_challenger_scene"
layers = 50
img = load_image(os.path.join(root, "data/cat.jpg"))
sess = tf.Session()
model_dir = os.path.join(root, "model")
new_saver = tf.train.import_meta_graph(os.path.join(model_dir,
meta_fn(layers)))
new_saver.restore(sess, os.path.join(model_dir, checkpoint_fn(layers)))
graph = tf.get_default_graph()
prob_tensor = graph.get_tensor_by_name("prob:0")
images = graph.get_tensor_by_name("images:0")
sub_tensor = graph.get_tensor_by_name("sub:0")
file_path = os.path.join(root, "graph_op_in_forward.txt")
print("writing graph op to file {}".format(file_path))
f = open(file_path, "w+")
for op in graph.get_operations():