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simple_cnn_network_6.py
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simple_cnn_network_6.py
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import vgg
import tensorflow as tf
from silknet import *
from silknet import LoadInterface
from interface import implements
from silknet import FolderDataReader
import cv2
slim = tf.contrib.slim
class TrainDataLoader(implements(LoadInterface)):
def __init__(self, image_height, image_width, use_second_frame_instead_of_flow):
self.image_width = image_width
self.image_height = image_height
self.use_second_frame_instead_of_flow = use_second_frame_instead_of_flow
def load_map(self, path, w, h):
map = np.fromfile(path, dtype=np.float32)
print(map)
return np.reshape(map, (h, w))
def load_datum(self, full_path):
indices_x = []
indices_y = []
print(full_path)
image = cv2.imread(os.path.join(full_path, 'frame_1.jpg'))
if self.use_second_frame_instead_of_flow:
second_image = cv2.imread(os.path.join(full_path, 'flow.jpg'))
else:
second_image = cv2.imread(os.path.join(full_path, 'frame_2.jpg'))
loaded_image_height, loaded_image_width, _ = np.shape(image)
density_map = self.load_map(os.path.join(full_path, 'density.dat'), loaded_image_width, loaded_image_height)
sum = np.sum(density_map)
image = cv2.resize(image, (self.image_height * 3, self.image_width * 3))
second_image = cv2.resize(second_image, (self.image_height * 3, self.image_width * 3))
density_map = cv2.resize(density_map, (int(self.image_height * 3 / 4), int(self.image_width * 3 / 4)))
sum_2 = np.sum(density_map)
density_map = (sum / sum_2) * density_map
input = np.concatenate((image, second_image), axis=2)
h = self.image_height
w = self.image_width
h2 = int(h / 4)
w2 = int(w / 4)
inputs = []
density_maps = []
print(np.shape(density_map))
print(h2, w2)
for i in range(3):
for j in range(3):
inputs.append(input[i * h : (i + 1) * h, j * w : (j + 1) * w, :])
density_maps.append(density_map[i * h2 : (i + 1) * h2, j * w2 : (j + 1) * w2])
indices_x.append(i)
indices_y.append(j)
datum = dict()
datum['inputs'] = inputs
datum['density_maps'] = density_maps
datum['indices_x'] = indices_x
datum['indices_y'] = indices_y
return datum
class SimpleCnnNetworkWithFlow:
def __init__(self):
config = cp.ConfigParser()
config.read('settings.ini')
self.image_width = int(config['network_6']['image_width'])
self.image_height = int(config['network_6']['image_height'])
self.learning_rate = float(config['network_6']['learning_rate'])
self.from_scratch = int(config['network_6']['from_scratch']) == 1
self.EPOCHS = int(config['network_6']['epochs'])
self.full_model_path = str(config['network_6']['model_path'])
self.data_path = str(config['network_6']['train_data_path'])
self.test_data_path = str(config['network_6']['test_data_path'])
self.density_map_width = int(self.image_width / 4)
self.density_map_height = int(self.image_height / 4)
self.saver_vgg = None
self.saver_all = None
self.use_second_frame_instead_of_flow = int(config['network_6']['use_two_frames']) == 1
self.keep_secondary_image_separate = int(config['network_6']['keep_initial_layers_separate']) == 1
if self.use_second_frame_instead_of_flow:
self.full_model_path = 'models/model_full_with_two_frames.ckpt'
else:
self.full_model_path = 'models/model_full_with_flow.ckpt'
if self.keep_secondary_image_separate:
self.full_model_path = 'models/model_full_separate.ckpt'
self.from_scratch = True
self.data_path = '/home/srq/Datasets/CUHK Crowd/Dataset_Ready/Train'
self.test_data_path = '/home/srq/Datasets/CUHK Crowd/Dataset_Ready/Test'
self.EPOCHS = 50
def get_r1(self, x):
with slim.arg_scope(vgg.vgg_arg_scope()):
net = slim.conv2d(x, 16, [9, 9], scope='r1_c1')
net = slim.max_pool2d(net, [2, 2], scope='r1_p1')
net = slim.conv2d(net, 32, [7, 7], scope='r1_c2')
net = slim.max_pool2d(net, [2, 2], scope='r1_p2')
net = slim.conv2d(net, 16, [7, 7], scope='r1_c3')
net = slim.conv2d(net, 8, [7, 7], scope='r1_c4')
net = slim.conv2d(net, 1, [1, 1], scope='r1_c5')
return net
def get_r1_2(self, x1, x2):
with slim.arg_scope(vgg.vgg_arg_scope()):
net1 = slim.conv2d(x1, 16, [9, 9], scope='r1_c1_1')
net1 = slim.max_pool2d(net1, [2, 2], scope='r1_p1_1')
net1 = slim.conv2d(net1, 32, [7, 7], scope='r1_c2_1')
net1 = slim.max_pool2d(net1, [2, 2], scope='r1_p2_1')
net2 = slim.conv2d(x2, 16, [9, 9], scope='r1_c1_2')
net2 = slim.max_pool2d(net2, [2, 2], scope='r1_p1_2')
net2 = slim.conv2d(net2, 32, [7, 7], scope='r1_c2_2')
net2 = slim.max_pool2d(net2, [2, 2], scope='r1_p2_2')
net = tf.concat([net1, net2], axis=3)
net = slim.conv2d(net, 16, [7, 7], scope='r1_c3')
net = slim.conv2d(net, 8, [7, 7], scope='r1_c4')
net = slim.conv2d(net, 1, [1, 1], scope='r1_c5')
return net
# def get_r1(self, x):
# with slim.arg_scope(vgg.vgg_arg_scope()):
# net = slim.conv2d(x, 24, [5, 5], scope='r3_c1')
# net = slim.max_pool2d(net, [2, 2], scope='r3_p1')
# net = slim.conv2d(net, 48, [3, 3], scope='r3_c2')
# net = slim.max_pool2d(net, [2, 2], scope='r3_p2')
# net = slim.conv2d(net, 24, [3, 3], scope='r3_c3')
# net = slim.conv2d(net, 12, [3, 3], scope='r3_c4')
# net = slim.conv2d(net, 1, [1, 1], scope='r3_c5')
# return net
#
# def get_r1_2(self, x1, x2):
# with slim.arg_scope(vgg.vgg_arg_scope()):
# net1 = slim.conv2d(x1, 24, [5, 5], scope='r1_c1_1')
# net1 = slim.max_pool2d(net1, [2, 2], scope='r1_p1_1')
# net1 = slim.conv2d(net1, 48, [3, 3], scope='r1_c2_1')
# net1 = slim.max_pool2d(net1, [2, 2], scope='r1_p2_1')
#
# net2 = slim.conv2d(x2, 24, [5, 5], scope='r1_c1_2')
# net2 = slim.max_pool2d(net2, [2, 2], scope='r1_p1_2')
# net2 = slim.conv2d(net2, 48, [3, 3], scope='r1_c2_2')
# net2 = slim.max_pool2d(net2, [2, 2], scope='r1_p2_2')
#
# net = tf.concat([net1, net2], axis=3)
#
# net = slim.conv2d(net, 24, [3, 3], scope='r3_c3')
# net = slim.conv2d(net, 12, [3, 3], scope='r3_c4')
# net = slim.conv2d(net, 1, [1, 1], scope='r3_c5')
#
# return net
def construct_graphs(self):
if self.keep_secondary_image_separate:
regressor_input_1 = self.regressor_input_1 = tf.placeholder("float32",
shape=[1, self.image_height, self.image_width, 3])
regressor_input_2 = self.regressor_input_2 = tf.placeholder("float32",
shape=[1, self.image_height, self.image_width, 3])
else:
regressor_input = self.regressor_input = tf.placeholder("float32", shape=[1, self.image_height, self.image_width, 6])
regressor_output_ground_truth = self.regressor_output_ground_truth = tf.placeholder("float32",
shape=[1, self.density_map_height, self.density_map_width])
with slim.arg_scope(vgg.vgg_arg_scope()):
if self.keep_secondary_image_separate:
r1_output = self.get_r1_2(regressor_input_1, regressor_input_2)
else:
r1_output = self.get_r1(regressor_input)
self.cost_r1 = tf.reduce_sum(tf.pow(
tf.subtract(tf.squeeze(r1_output), tf.scalar_mul(1000, tf.squeeze(regressor_output_ground_truth))), 2))
self.sum_r1 = tf.scalar_mul(0.001, tf.reduce_sum(r1_output))
self.optimizer_r1 = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.cost_r1)
self.saver_all = tf.train.Saver()
def run_training(self):
init = tf.global_variables_initializer()
dataset = FolderDataReader(self.data_path, TrainDataLoader(self.image_height, self.image_width, self.use_second_frame_instead_of_flow))
dataset.init()
with tf.Session() as sess:
sess.run(init)
if not self.from_scratch:
self.saver_all.restore(sess, self.full_model_path)
# ======================================== Pre-training start ==================================================
iteration = 0
print("========== Starting Pre-Training =========")
while True:
if dataset.get_next_epoch() == self.EPOCHS:
break
datum, epoch, id = dataset.next_element()
inputs = datum['inputs']
density_maps = datum['density_maps']
assert(len(inputs) == 9 and len(density_maps) == 9)
for i in range(9):
if self.keep_secondary_image_separate:
image1 = (inputs[i])[:, :, 0:3]
image2 = (inputs[i])[:, :, 3:6]
else:
image = inputs[i]
density_map = density_maps[i]
sum_gt = np.sum(density_map)
# SGD backprop through all of these0
if self.keep_secondary_image_separate:
c1, s1, o1 = sess.run([self.cost_r1, self.sum_r1, self.optimizer_r1],
feed_dict={self.regressor_input_1: [image1],
self.regressor_input_2: [image2],
self.regressor_output_ground_truth: [density_map]})
else:
c1, s1, o1 = sess.run([self.cost_r1, self.sum_r1, self.optimizer_r1],
feed_dict={self.regressor_input: [image],
self.regressor_output_ground_truth: [density_map]})
print("\tEpoch", epoch, "Iteration", iteration, "Patch", i+1)
print("\tCost R1", c1, "Original sum:", sum_gt, "Predicted sum", s1)
iteration += 1
self.saver_all.save(sess, self.full_model_path)
# print("========== Pre-Training Complete =========")
dataset.halt()
def run_tests(self):
init = tf.global_variables_initializer()
dataset = FolderDataReader(self.test_data_path, TrainDataLoader(self.image_height, self.image_width, self.use_second_frame_instead_of_flow))
dataset.init()
total_examples = 0
total_absolute_error = 0
total_square_error = 0
total_gt_sum = 0
with tf.Session() as sess:
sess.run(init)
self.saver_all.restore(sess, self.full_model_path)
iteration = 0
while True:
if dataset.get_next_epoch() == 1:
break
datum, epoch, id = dataset.next_element()
inputs = datum['inputs']
density_maps = datum['density_maps']
assert(len(inputs) == 9 and len(density_maps) == 9)
sum_gt_total = 0
sum_predicted_total = 0
for i in range(9):
if self.keep_secondary_image_separate:
image1 = (inputs[i])[:, :, 0:3]
image2 = (inputs[i])[:, :, 3:6]
else:
image = inputs[i]
density_map = density_maps[i]
sum_gt = np.sum(density_map)
sum_gt_total += sum_gt
# SGD backprop through all of these0
if self.keep_secondary_image_separate:
sum_predicted = sess.run([self.sum_r1], feed_dict={self.regressor_input_1: [image1], self.regressor_input_2: [image2]})
else:
sum_predicted = sess.run([self.sum_r1], feed_dict={self.regressor_input: [image]})
sum_predicted_total += sum_predicted[0]
total_absolute_error += abs(sum_predicted_total - sum_gt_total)
total_square_error += pow(abs(sum_predicted_total - sum_gt_total), 2)
total_gt_sum += sum_gt_total
iteration += 1
total_examples += 1
dataset.halt()
mean_absolute_error = total_absolute_error / total_examples
mean_squared_error = np.sqrt(total_square_error) / total_examples
mean_people_per_image = total_gt_sum / total_examples
print("MAE", mean_absolute_error)
print("MSE", mean_squared_error)
print("Mean people per image", mean_people_per_image)