def evaluate_graph(graph_file_name):
with load_graph(graph_file_name).as_default() as graph:
ground_truth_input = tf.placeholder(tf.float32, [None, 5],
name='GroundTruthInput')
image_buffer_input = graph.get_tensor_by_name('input:0')
final_tensor = graph.get_tensor_by_name('final_result:0')
accuracy, _ = retrain.add_evaluation_step(final_tensor,
ground_truth_input)
logits = graph.get_tensor_by_name("final_training_ops/Wx_plus_b/add:0")
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=ground_truth_input,
logits=logits))
image_dir = 'tf_files/flower_photos'
testing_percentage = 10
validation_percentage = 10
validation_batch_size = 100
category = 'testing'
image_lists = retrain.create_image_lists(image_dir, testing_percentage,
validation_percentage)
class_count = len(image_lists.keys())
ground_truths = []
filenames = []
for label_index, label_name in enumerate(image_lists.keys()):
for image_index, image_name in enumerate(
image_lists[label_name][category]):
image_name = retrain.get_image_path(image_lists, label_name,
image_index, image_dir,
category)
ground_truth = np.zeros([1, class_count], dtype=np.float32)
ground_truth[0, label_index] = 1.0
ground_truths.append(ground_truth)
filenames.append(image_name)
accuracies = []
xents = []
with tf.Session(graph=graph) as sess:
for filename, ground_truth in zip(filenames, ground_truths):
image = Image.open(filename).resize((224, 224), Image.ANTIALIAS)
image = np.array(image, dtype=np.float32)[None, ...]
image = (image - 128) / 128.0
feed_dict = {
image_buffer_input: image,
ground_truth_input: ground_truth
}
eval_accuracy, eval_xent = sess.run([accuracy, xent], feed_dict)
accuracies.append(eval_accuracy)
xents.append(eval_xent)
return np.mean(accuracies), np.mean(xents)
def get_graph(path):
clss = os.listdir(path)
clss.sort()
print(clss)
graphs = []
for cls in clss:
graph_classifier = load_graph(os.path.join(path, cls))
graphs.append(graph_classifier)
return graphs
if args.labels:
label_file = args.labels
if args.input_height:
input_height = args.input_height
if args.input_width:
input_width = args.input_width
if args.input_mean:
input_mean = args.input_mean
if args.input_std:
input_std = args.input_std
if args.input_layer:
input_layer = args.input_layer
if args.output_layer:
output_layer = args.output_layer
graph = load_graph(model_file)
t = read_tensor_from_image_file(file_name,
input_height=input_height,
input_width=input_width,
input_mean=input_mean,
input_std=input_std)
input_name = "import/" + input_layer
output_name = "import/" + output_layer
input_operation = graph.get_operation_by_name(input_name)
output_operation = graph.get_operation_by_name(output_name)
with tf.Session(graph=graph) as sess:
start = time.time()
results = sess.run(output_operation.outputs[0],
{input_operation.outputs[0]: t})
def extract():
input_layer = "input"
output_layer = "MobilenetV2/Predictions/Reshape_1"
graph = load_graph('./frozen_pb/frozen_0-0-0.pb')
#with graph.as_default() as g:
# image_buffer_input = g.get_tensor_by_name('input:0')
# final_tensor = g.get_tensor_by_name('MobilenetV2/Logits/AvgPool:0')
input_operation = graph.get_operation_by_name(input_layer)
output_operation = graph.get_operation_by_name(output_layer)
#image_dir = '/home/xxxx/PHICOMM/ai-share/dataset/imagenet/raw-data/imagenet-data/validation'
image_dir = "/home/deepl/PHICOMM/dataset/cifar10_tf/cifar-10/test"
testing_percentage = 100
validation_percentage = 0
category = 'testing'
image_lists = retrain.create_image_lists(image_dir, testing_percentage,
validation_percentage)
class_count = len(image_lists.keys())
print(class_count)
total_start = time.time()
ground_truths = []
filenames = []
all_files_df = pd.DataFrame(
columns=['image_name', 'ground_truth', "predecit_label"])
for label_index, label_name in enumerate(image_lists.keys()):
for image_index, image_name in enumerate(
image_lists[label_name][category]):
image_name = retrain.get_image_path(image_lists, label_name,
image_index, image_dir,
category)
ground_truth = np.zeros([1, class_count], dtype=np.float32)
ground_truth[0, label_index] = 1.0
ground_truths.append(ground_truth)
filenames.append(image_name)
ground_truth_argmax = np.argmax(ground_truth, axis=1)
ground_truth_argmax = np.squeeze(ground_truth_argmax)
all_files_df = all_files_df.append(
[{
'image_name': image_name,
'ground_truth': ground_truth_argmax
}],
ignore_index=True)
#all_files_df.to_csv("ground_truth.csv")
if os.path.exists("./data"):
print("data is exist, please delete it!")
exit()
#shutil.rmtree("./data")
#os.makedirs("./data")
#sio.savemat('./data/truth.mat',{"truth": ground_truths})
cf = 0.875
predictions = []
i = 0
start = time.time()
with tf.Session(graph=graph) as sess:
read_tensor_from_jpg_image_file_op = read_tensor_from_jpg_image_file(
input_height=224, input_width=224)
for filename in filenames:
t = sess.run(read_tensor_from_jpg_image_file_op,
feed_dict={"fnamejpg:0": filename})
t = np.expand_dims(t, axis=0)
#print(t.shape)
# feed_dict={image_buffer_input: t}
# ft = final_tensor.eval(feed_dict, sess)
pre = sess.run(output_operation.outputs[0],
{input_operation.outputs[0]: t})
predictions.append(pre)
#i = i + 1
#print(i)
predictions = np.array(predictions)
predictions = np.squeeze(predictions)
ground_truths = np.array(ground_truths)
ground_truths = np.squeeze(ground_truths)
print(predictions.shape)
print(ground_truths.shape)
with tf.Session(graph=graph) as sess:
ground_truth_input = tf.placeholder(tf.float32, [None, 10],
name='GroundTruthInput')
fts = tf.placeholder(tf.float32, [None, 10], name='fts')
accuracy, _ = retrain.add_evaluation_step(fts, ground_truth_input)
feed_dict = {fts: predictions, ground_truth_input: ground_truths}
#accuracies.append(accuracy.eval(feed_dict, sess))
ret = accuracy.eval(feed_dict, sess)
for index, row in all_files_df.iterrows():
row['predecit_label'] = np.squeeze(
np.argmax(predictions[index, :], axis=0))
all_files_df.to_csv("ground_truth.csv")
print('Ensemble Accuracy: %g' % ret)
stop = time.time()
#print(str((stop-start)/len(ftg))+' seconds.')
#sio.savemat('./data/feature.mat',{"feature": ftg})
total_stop = time.time()
print("total time is " + str((total_stop - total_start)) + ' seconds.')