I have successfully trained my model on a custom dataset with 4 classes of size 480x640, with an xception65 encoder, using Deeplab. I am getting decent results on the validation set whenever I use the vis.py script: EvalImageA_ckpt, EvalImageB_ckpt. However, I am not getting the same results on the same images when I freeze the model.
I froze the model using export_model.py and successfully outputted a frozen_model.pb file. However, when I run inferences using this pb file, the outputs are always 0 (i.e. everything is classified as "background") on the same exact images I provided links to above. Everything is black!
I believe this to be an issue with how I am exporting or loading the model, and not necessarily with the model itself because the performance on the images is different between running the vis.py script and my custom code for inference. Perhaps I am not loading the graph or initializing the variables correctly. Or perhaps I'm not saving the weights correctly in the first place. Any help would be greatly appreciated!
Below I provide my code for inference:
from deeplab.utils import get_dataset_colormap
from PIL import Image
import tensorflow as tf
import time
import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
import glob
# tensorflow arguments
flags = tf.app.flags # flag object for setup
FLAGS = flags.FLAGS # object to access initialized flags
flags.DEFINE_string('frozen', None,
'The path/to/frozen.pb file.')
def _load_graph(frozen):
print('Loading model `deeplabv3_graph` into memory from',frozen)
with tf.gfile.GFile(frozen, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(
graph_def,
input_map=None,
return_elements=None,
name="",
op_dict=None,
producer_op_list=None
)
return graph
def _run_inferences(sess, image, title):
batch_seg_map = sess.run('SemanticPredictions:0',
feed_dict={'ImageTensor:0': [np.asarray(image)]})
semantic_prediction = get_dataset_colormap.label_to_color_image(batch_seg_map[0],
dataset=get_dataset_colormap.__PRDL3_V1).astype(np.uint8)
plt.imshow(semantic_prediction)
plt.axis('off')
plt.title(title)
plt.show()
def main(argv):
# initialize model
frozen = os.path.normpath(FLAGS.frozen)
assert os.path.isfile(frozen)
graph = _load_graph(frozen)
# open graph resource and begin inference in-loop
with tf.Session(graph=graph) as sess:
for img_path in glob.glob('*.png'):
img = Image.open(img_path).convert('RGB')
_run_inferences(sess, img, img_path)
if __name__ == '__main__':
flags.mark_flag_as_required('frozen')
tf.app.run() # call the main() function
And below is my code for exporting the model, using the provided export_model.py script.
python export_model.py \
--logtostderr \
--atrous_rates=6 \
--atrous_rates=12 \
--atrous_rates=18 \
--output_stride=16 \
--checkpoint_path="/path/to/.../model.ckpt-32245" \
--export_path="/path/to/.../frozen_4_11_19.pb" \
--model_variant="xception_65" \
--num_classes=4 \
--crop_size=481 \
--crop_size=641 \
--inference_scales=1.0
答案 0 :(得分：0)
我也在努力推断我的推理结果。但就我而言，使用导出的模型时，我的结果令人非常满意，只是它们的准确性不如我的可视化结果。
这是我的脚本，它是一个基于演示的脚本。希望对您有帮助
import os
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
from PIL import Image
import time
import cv2
from tqdm import tqdm
import tensorflow as tf
# Needed to show segmentation colormap labels
from deeplab.utils import get_dataset_colormap
from deeplab.utils import labels_alstom
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('model_dir', None, 'Where the model is')
flags.DEFINE_string('image_dir', None, 'Where the image is')
flags.DEFINE_string('save_dir', None, 'Dir for saving results')
flags.DEFINE_string('image_name', None, 'Image name')
class DeepLabModel(object):
"""Class to load deeplab model and run inference."""
INPUT_TENSOR_NAME = 'ImageTensor:0'
OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'
INPUT_SIZE = 513
def __init__(self, model_dir):
"""Creates and loads pretrained deeplab model."""
self.graph = tf.Graph()
graph_def = None
# Extract frozen graph from tar archive.
model_filename = FLAGS.model_dir
with tf.gfile.FastGFile(model_filename, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
if graph_def is None:
raise RuntimeError('Cannot find inference graph in tar archive.')
with self.graph.as_default():
tf.import_graph_def(graph_def, name='')
self.sess = tf.Session(graph=self.graph)
def run(self, image):
"""Runs inference on a single image.
Args:
image: A PIL.Image object, raw input image.
Returns:
resized_image: RGB image resized from original input image.
seg_map: Segmentation map of `resized_image`.
"""
width, height = image.size
resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)
target_size = (int(resize_ratio * width), int(resize_ratio * height))
resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)
print('Image resized')
start_time = time.time()
batch_seg_map = self.sess.run(
self.OUTPUT_TENSOR_NAME,
feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]})
print('Image processing finished')
print('Elapsed time : ' + str(time.time() - start_time))
seg_map = batch_seg_map[0]
return resized_image, seg_map
model = DeepLabModel(FLAGS.model_dir)
print('Model created successfully')
def vis_segmentation(image, seg_map):
seg_image = get_dataset_colormap.label_to_color_image(
seg_map, get_dataset_colormap.get_alstom_name()).astype(np.uint8)
return seg_image
def run_demo_image(image_path):
try:
print(image_path)
orignal_im = Image.open(image_path)
except IOError:
print ('Failed to read image from %s.' % image_path)
return
print ('running deeplab on image...')
resized_im, seg_map = model.run(orignal_im)
return vis_segmentation(resized_im, seg_map)
IMAGE_DIR = FLAGS.image_dir
files = os.listdir(FLAGS.image_dir)
for f in tqdm(files):
prediction = run_demo_image(IMAGE_DIR+f)
Image.fromarray(prediction).save(FLAGS.save_dir+'prediction_'+f)
答案 1 :(得分：0)
使用以下标志运行导出模型脚本
python deeplab/export_model.py \
--logtostderr \
--model_variant="xception_65" \
--atrous_rates=6 \
--atrous_rates=12 \
--atrous_rates=18 \
--output_stride=16 \
--num_classes=2 \
--decoder_output_stride=4 \
--crop_size=<<crop width>> \
--crop_size=<<crop height>> \
--dataset="shoes" \
--checkpoint_path="<<Checkpoint path>>" \
--export_path="<<Output frozen graph path>>" \