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train.py
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# Import the converted model's class
import numpy as np
import random
import tensorflow as tf
from posenet import GoogLeNet as PoseNet
import cv2
from tqdm import tqdm
batch_size = 75
max_iterations = 30000
# Set this path to your dataset directory
directory = 'path_to_datasets/KingsCollege/'
dataset = 'dataset_train.txt'
class datasource(object):
def __init__(self, images, poses):
self.images = images
self.poses = poses
def centeredCrop(img, output_side_length):
height, width, depth = img.shape
new_height = output_side_length
new_width = output_side_length
if height > width:
new_height = output_side_length * height / width
else:
new_width = output_side_length * width / height
height_offset = (new_height - output_side_length) / 2
width_offset = (new_width - output_side_length) / 2
cropped_img = img[height_offset:height_offset + output_side_length,
width_offset:width_offset + output_side_length]
return cropped_img
def preprocess(images):
images_out = [] #final result
#Resize and crop and compute mean!
images_cropped = []
for i in tqdm(range(len(images))):
X = cv2.imread(images[i])
X = cv2.resize(X, (455, 256))
X = centeredCrop(X, 224)
images_cropped.append(X)
#compute images mean
N = 0
mean = np.zeros((1, 3, 224, 224))
for X in tqdm(images_cropped):
X = np.transpose(X,(2,0,1))
mean[0][0] += X[:,:,0]
mean[0][1] += X[:,:,1]
mean[0][2] += X[:,:,2]
N += 1
mean[0] /= N
#Subtract mean from all images
for X in tqdm(images_cropped):
X = np.transpose(X,(2,0,1))
X = X - mean
X = np.squeeze(X)
X = np.transpose(X, (1,2,0))
images_out.append(X)
return images_out
def get_data():
poses = []
images = []
with open(directory+dataset) as f:
next(f) # skip the 3 header lines
next(f)
next(f)
for line in f:
fname, p0,p1,p2,p3,p4,p5,p6 = line.split()
p0 = float(p0)
p1 = float(p1)
p2 = float(p2)
p3 = float(p3)
p4 = float(p4)
p5 = float(p5)
p6 = float(p6)
poses.append((p0,p1,p2,p3,p4,p5,p6))
images.append(directory+fname)
images = preprocess(images)
return datasource(images, poses)
def gen_data(source):
while True:
indices = range(len(source.images))
random.shuffle(indices)
for i in indices:
image = source.images[i]
pose_x = source.poses[i][0:3]
pose_q = source.poses[i][3:7]
yield image, pose_x, pose_q
def gen_data_batch(source):
data_gen = gen_data(source)
while True:
image_batch = []
pose_x_batch = []
pose_q_batch = []
for _ in range(batch_size):
image, pose_x, pose_q = next(data_gen)
image_batch.append(image)
pose_x_batch.append(pose_x)
pose_q_batch.append(pose_q)
yield np.array(image_batch), np.array(pose_x_batch), np.array(pose_q_batch)
def main():
images = tf.placeholder(tf.float32, [batch_size, 224, 224, 3])
poses_x = tf.placeholder(tf.float32, [batch_size, 3])
poses_q = tf.placeholder(tf.float32, [batch_size, 4])
datasource = get_data()
net = PoseNet({'data': images})
p1_x = net.layers['cls1_fc_pose_xyz']
p1_q = net.layers['cls1_fc_pose_wpqr']
p2_x = net.layers['cls2_fc_pose_xyz']
p2_q = net.layers['cls2_fc_pose_wpqr']
p3_x = net.layers['cls3_fc_pose_xyz']
p3_q = net.layers['cls3_fc_pose_wpqr']
l1_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_x, poses_x)))) * 0.3
l1_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_q, poses_q)))) * 150
l2_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_x, poses_x)))) * 0.3
l2_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_q, poses_q)))) * 150
l3_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_x, poses_x)))) * 1
l3_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_q, poses_q)))) * 500
loss = l1_x + l1_q + l2_x + l2_q + l3_x + l3_q
opt = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.9, beta2=0.999, epsilon=0.00000001, use_locking=False, name='Adam').minimize(loss)
# Set GPU options
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6833)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
outputFile = "PoseNet.ckpt"
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Load the data
sess.run(init)
net.load('posenet.npy', sess)
data_gen = gen_data_batch(datasource)
for i in range(max_iterations):
np_images, np_poses_x, np_poses_q = next(data_gen)
feed = {images: np_images, poses_x: np_poses_x, poses_q: np_poses_q}
sess.run(opt, feed_dict=feed)
np_loss = sess.run(loss, feed_dict=feed)
if i % 20 == 0:
print("iteration: " + str(i) + "\n\t" + "Loss is: " + str(np_loss))
if i % 5000 == 0:
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)
if __name__ == '__main__':
main()