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test_model.py
executable file
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test_model.py
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#!/usr/bin/env python
# Martin Kersner, m.kersner@gmail.com
# 2016/03/18
from __future__ import print_function
caffe_root = 'code/'
import sys
sys.path.insert(0, caffe_root + 'python')
import os
import caffe
import numpy as np
from skimage.io import imread
from py_img_seg_eval.eval_segm import *
from utils import get_id_classes, create_lut
from segmenter import Segmenter
from ProgressBar import *
def main():
base_dir = 'exper/voc12'
gpu_id, model_name, iteration_num, phase, subset_dataset = process_arguments(sys.argv)
if phase == 1:
model_path = os.path.join(base_dir, 'model', model_name, 'train_iter_{}.caffemodel')
elif phase == 2:
model_path = os.path.join(base_dir, 'model', model_name, 'train2_iter_{}.caffemodel')
if subset_dataset:
net_path = os.path.join(model_name, 'deploy4.prototxt')
class_names = ['bird', 'bottle', 'chair'] # CHANGE
class_ids = get_id_classes(class_names)
file_names = load_test_data(os.path.join(base_dir, 'list_subset/val_id.txt'))
images_path = os.path.join(base_dir, 'data/images_orig')
labels_path = os.path.join(base_dir, 'data/labels_sub_orig')
else:
net_path = os.path.join(model_name, 'deploy21.prototxt')
class_ids = range(1,21)
file_names = load_test_data(os.path.join(base_dir, 'list/val_id.txt'))
images_path = os.path.join(base_dir, 'data/images_orig')
labels_path = os.path.join(base_dir, 'data/labels_orig')
lut = create_lut(class_ids)
images, labels = create_full_paths(file_names, images_path, labels_path)
test_net(net_path, model_path.format(iteration_num), images, labels, lut, gpu_id)
def load_test_data(file_name='test.txt'):
file_names = []
with open(file_name, 'rb') as f:
for fn in f:
file_names.append(fn.strip())
return file_names
def create_full_paths(file_names, image_dir, label_dir, image_ext='.jpg', label_ext='.png'):
image_paths = []
label_paths = []
for file_name in file_names:
image_paths.append(os.path.join(image_dir, file_name+image_ext))
label_paths.append(os.path.join(label_dir, file_name+label_ext))
return image_paths, label_paths
def preprocess_image(file_name, mean_vec, height=505, width=505):
image = imread(file_name).astype(np.float32)
im = image[:,:,::-1]
im = im - mean_vec
cur_h, cur_w, cur_c = im.shape
pad_h = height - cur_h
pad_w = width - cur_w
im = np.pad(im, pad_width=((0, pad_h), (0, pad_w), (0, 0)), mode = 'constant', constant_values = 0)
return im, cur_h, cur_w
def test_net(net_path, model_path, images, labels, lut, gpu_id):
net = Segmenter(net_path, model_path, gpu_id)
mean_vec = np.array([103.939, 116.779, 123.68], dtype=np.float32)
reshaped_mean_vec = mean_vec.reshape(1, 1, 3);
pa_list = []
ma_list = []
m_IU_list = []
fw_IU_list = []
pb = ProgressBar(len(images))
for img_path, label_path in zip(images, labels):
im, cur_h, cur_w = preprocess_image(img_path, reshaped_mean_vec)
label = imread(label_path)
label = lut[label]
segmentation = net.predict([im])
pred = segmentation[0:cur_h, 0:cur_w]
pa = pixel_accuracy(pred, label)
ma = mean_accuracy(pred, label)
m_IU = mean_IU(pred, label)
fw_IU = frequency_weighted_IU(pred, label)
pa_list.append(pa)
ma_list.append(ma)
m_IU_list.append(m_IU)
fw_IU_list.append(fw_IU)
pb.print_progress()
print("pixel_accuracy: " + str(np.mean(pa_list)))
print("mean_accuracy: " + str(np.mean(ma_list)))
print("mean_IU: " + str(np.mean(m_IU_list)))
print("frequency_weighted: " + str(np.mean(fw_IU_list)))
def process_arguments(argv):
if len(argv) != 6 or (int(argv[4]) != 1 and int(argv[4]) != 2) or (int(argv[5]) != 0 and int(argv[5]) != 1):
help()
gpu_id = int(argv[1])
model_name = argv[2]
iteration_num = argv[3]
phase = int(argv[4])
subset_dataset = bool(int(argv[5]))
return gpu_id, model_name, iteration_num, phase, subset_dataset
def help():
print('Usage: python test_model.py GPU_ID MODE_NAME ITERATION_NUM PHASE SUBSET_DATASET\n'
'GPU_ID denotes gpu ID that should be employed for test.\n'
'MODEL_NAME denotes name of model that should be tested (e.g. DeepLab-LargeFOV or DeepLab-LargeFOV-Semi-Bbox-Fixed).\n'
'ITERATION_NUM denotes iteration number of model which will be tested.\n'
'PHASE denotes training phase (either 1 or 2) that should be tested.\n'
'SUBSET_DATASET determines whether subset of whole dataset should be used (value 1) or whole dataset will be exploited (value 0).'
, file=sys.stderr)
exit()
if __name__ == '__main__':
main()