fcn module

inference.py

@@ -0,0 +1,51 @@
+from __future__ import print_function
+
+import argparse
+import os
+import sys
+import time
+from PIL import Image
+import tensorflow as tf
+import numpy as np
+from scipy import misc
+
+from model import FCN8s
+from tools import *
+
+save_dir = './output/'
+model_path = './model/fcn_iter_160000.npy'
+
+def get_arguments():
+    parser = argparse.ArgumentParser(description="Reproduced PSPNet")
+    parser.add_argument("--img-path", type=str, default='',
+                        help="Path to the RGB image file.",
+                        required=True)
+    parser.add_argument("--save-dir", type=str, default=save_dir,
+                        help="Path to save output.")
+
+    return parser.parse_args()
+
+def main():
+    args = get_arguments()
+
+    model = FCN8s()
+    model.read_input(args.img_path)
+
+    # Init tf Session
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    sess = tf.Session(config=config)
+    init = tf.global_variables_initializer()
+
+    sess.run(init)
+
+    model.load(model_path, sess)
+
+    preds = model.forward(sess)
+
+    if not os.path.exists(args.save_dir):
+        os.makedirs(args.save_dir)
+    misc.imsave(args.save_dir + model.filename, preds[0])
+
+if __name__ == '__main__':
+    main()

model.py

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+from network import Network
+from tools import *
+import tensorflow as tf
+import os
+
+class FCN8s(Network):
+    def __init__(self, is_training=False, num_classes=151, input_size=[384, 384]):
+        self.input_size = input_size
+
+        self.x = tf.placeholder(dtype=tf.float32, shape=[None, None, 3])
+        self.img_tf = preprocess(self.x, self.input_size)
+
+        super().__init__({'data': self.img_tf}, num_classes, is_training)
+
+    def setup(self, is_training, num_classes):
+        (self.feed('data')
+             .zero_padding(paddings=100, name='padding1')
+             .conv(3, 3, 64, 1, 1, name='conv1_1')
+             .zero_padding(paddings=1, name='padding2')
+             .conv(3, 3, 64, 1, 1, name='conv1_2')
+             .max_pool(2, 2, 2, 2, name='pool1')
+             .zero_padding(paddings=1, name='padding3')
+             .conv(3, 3, 128, 1, 1, name='conv2_1')
+             .zero_padding(paddings=1, name='padding4')
+             .conv(3, 3, 128, 1, 1, name='conv2_2')
+             .zero_padding(paddings=1, name='padding5')
+             .max_pool(2, 2, 2, 2, name='pool2')
+             .zero_padding(paddings=1, name='padding6')
+             .conv(3, 3, 256, 1, 1, name='conv3_1')
+             .zero_padding(paddings=1, name='padding7')
+             .conv(3, 3, 256, 1, 1, name='conv3_2')
+             .zero_padding(paddings=1, name='padding8')
+             .conv(3, 3, 256, 1, 1, name='conv3_3')
+             .max_pool(2, 2, 2, 2, name='pool3')
+             .scale(0.00001, name='scale_pool3')
+             .conv(1, 1, num_classes, 1, 1, name='score_pool3'))
+
+        (self.feed('pool3')
+             .zero_padding(paddings=1, name='padding9')
+             .conv(3, 3, 512, 1, 1, name='conv4_1')
+             .zero_padding(paddings=1, name='padding10')
+             .conv(3, 3, 512, 1, 1, name='conv4_2')
+             .zero_padding(paddings=1, name='padding11')
+             .conv(3, 3, 512, 1, 1, name='conv4_3')
+             .zero_padding(paddings=1, name='padding12')
+             .max_pool(2, 2, 2, 2, name='pool4')
+             .scale(0.01, name='scale_pool4')
+             .conv(1, 1, num_classes, 1, 1, name='score_pool4'))
+
+        (self.feed('pool4')
+             .zero_padding(paddings=1, name='padding13')
+             .conv(3, 3, 512, 1, 1, name='conv5_1')
+             .zero_padding(paddings=1, name='padding14')
+             .conv(3, 3, 512, 1, 1, name='conv5_2')
+             .zero_padding(paddings=1, name='padding15')
+             .conv(3, 3, 512, 1, 1, name='conv5_3')
+             .zero_padding(paddings=1, name='padding16')
+             .max_pool(2, 2, 2, 2, name='pool5')
+             .conv(7, 7, 4096, 1, 1, name='fc6')
+             .conv(1, 1, 4096, 1, 1, name='fc7')
+             .conv(1, 1, num_classes, 1, 1, name='score_fr')
+             .deconv(4, 4, num_classes, 2, 2, name='upscore2'))
+
+        (self.feed('upscore2', 'score_pool4')
+             .crop(5, name='score_pool4c'))
+
+        (self.feed('upscore2', 'score_pool4c')
+             .add(name='fuse_pool4')
+             .deconv(4, 4, num_classes, 2, 2, name='upscore_pool4'))
+
+        (self.feed('upscore_pool4', 'score_pool3')
+             .crop(9, name='score_pool3c'))
+
+        (self.feed('upscore_pool4', 'score_pool3c')
+             .add(name='fuse_pool3')
+             .deconv(16, 16, num_classes, 8, 8, name='upscore8'))
+
+        (self.feed('data', 'upscore8')
+             .crop(31, name='score'))
+
+        score = self.layers['score']
+        score = tf.argmax(score, dimension=3)
+        self.pred = decode_labels(score, self.input_size, num_classes)
+
+    def read_input(self, img_path):
+        self.img, self.filename = load_img(img_path)
+
+    def forward(self, sess):
+        return sess.run(self.pred, feed_dict={self.x: self.img})