"""
测试使用retrain.py重训后的inception_v3模型
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import numpy as np
import tensorflow as tf
import os
def load_graph(model_file):
graph = tf.Graph()
graph_def = tf.compat.v1.GraphDef()
with open(model_file, "rb") as f:
graph_def.ParseFromString(f.read())
with graph.as_default():
tf.import_graph_def(graph_def)
return graph
def read_tensor_from_image_file(file_name,
input_height=299,
input_width=299,
input_mean=0,
input_std=255):
input_name = "file_reader"
output_name = "normalized"
file_reader = tf.io.read_file(file_name, input_name)
if file_name.endswith(".png"):
image_reader = tf.image.decode_png(
file_reader, channels=3, name="png_reader")
elif file_name.endswith(".gif"):
image_reader = tf.squeeze(
tf.image.decode_gif(file_reader, name="gif_reader"))
elif file_name.endswith(".bmp"):
image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader")
else:
image_reader = tf.image.decode_jpeg(
file_reader, channels=3, name="jpeg_reader")
float_caster = tf.cast(image_reader, tf.float32)
dims_expander = tf.expand_dims(float_caster, 0)
resized = tf.compat.v1.image.resize_bilinear(dims_expander, [input_height, input_width])
normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
sess = tf.compat.v1.Session()
result = sess.run(normalized)
return result
def load_labels(label_file):
label = []
proto_as_ascii_lines = tf.io.gfile.GFile(label_file).readlines()
for l in proto_as_ascii_lines:
label.append(l.rstrip())
return label
if __name__ == "__main__":
file_name = " "
image_path = './test_images'
model_file = './tmp/output_graph/output_graph.pb'
label_file = "./tmp/output_labels.txt"
input_height = 299
input_width = 299
input_mean = 0
input_std = 255
input_layer = "Placeholder"
output_layer = "final_result"
parser = argparse.ArgumentParser()
parser.add_argument("--image", help="image to be processed")
parser.add_argument("--graph", help="graph/model to be executed")
parser.add_argument("--labels", help="name of file containing labels")
parser.add_argument("--input_height", type=int, help="input height")
parser.add_argument("--input_width", type=int, help="input width")
parser.add_argument("--input_mean", type=int, help="input mean")
parser.add_argument("--input_std", type=int, help="input std")
parser.add_argument("--input_layer", help="name of input layer")
parser.add_argument("--output_layer", help="name of output layer")
args = parser.parse_args()
graph = load_graph(model_file)
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.compat.v1.Session(graph=graph) as sess:
for root, dirs, files in os.walk(image_path):
for file in files:
if file.endswith('.png') or file.endswith('.gif') or file.endswith('.bmp') \
or file.endswith('.jpg') or file.endswith('.gif'):
file_name = os.path.join(image_path, 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)
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
top_k = results.argsort()[-5:][::-1]
labels = load_labels(label_file)
predictions_list = []
for i in top_k:
predictions_list.append([labels[i], results[i]])
print(file_name, '\n', predictions_list, '\n')