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contest_dilated_random.py
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import math
import random
import sys
import datetime
import numpy as np
import tensorflow as tf
from PIL import Image
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics import f1_score
NUM_CLASSES = 7
class BatchColors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
def print_params(list_params):
print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++')
for i in range(1, len(sys.argv)):
print(list_params[i - 1] + '= ' + sys.argv[i])
print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++')
def select_batch(shuffle, batch_size, it, total_size):
batch = shuffle[it:min(it + batch_size, total_size)]
if min(it + batch_size, total_size) == total_size or total_size == it + batch_size:
shuffle = np.asarray(random.sample(range(total_size), total_size))
it = 0
if len(batch) < batch_size:
diff = batch_size - len(batch)
batch_c = shuffle[it:it + diff]
batch = np.concatenate((batch, batch_c))
it = diff
else:
it += batch_size
return shuffle, batch, it
def convert_class(value):
if value == 224 or value == 226:
c = 6
elif value == 104 or value == 105:
c = 0
elif value == 43:
c = 5
elif value == 76:
c = 2
elif value == 177 or value == 179:
c = 3
elif value == 148 or value == 150:
c = 1
elif value == 54:
c = 4
elif value == 0:
c = 7
else:
print(BatchColors.FAIL + "Pixel to class did not find combination!!! " + value + BatchColors.ENDC)
return -1
return c
def define_multinomial_probs(values, dif_prob=2):
interval_size = values[-1] - values[0] + 1
generalProb = 1.0 / float(interval_size)
maxProb = generalProb * dif_prob
probs = np.full(interval_size, (1.0 - maxProb * len(values)) / float(interval_size - len(values)))
for i in range(len(values)):
probs[values[i] - values[0]] = maxProb
return probs
def normalize_images(data, mean_full, std_full):
data[:, :, :, 0] = np.subtract(data[:, :, :, 0], mean_full[0])
data[:, :, :, 1] = np.subtract(data[:, :, :, 1], mean_full[1])
data[:, :, :, 2] = np.subtract(data[:, :, :, 2], mean_full[2])
data[:, :, :, 0] = np.divide(data[:, :, :, 0], std_full[0])
data[:, :, :, 1] = np.divide(data[:, :, :, 1], std_full[1])
data[:, :, :, 2] = np.divide(data[:, :, :, 2], std_full[2])
def compute_image_mean(data):
mean_full = np.mean(np.mean(np.mean(data, axis=0), axis=0), axis=0)
std_full = np.std(data, axis=0, ddof=1)[0, 0, :]
return np.asarray(mean_full), np.asarray(std_full)
def create_mean_and_std(data, class_distribution, crop_size):
all_patches = []
for i in range(len(class_distribution)):
cur_x = class_distribution[i][0]
cur_y = class_distribution[i][1]
patches = data[cur_x:cur_x + crop_size, cur_y:cur_y + crop_size, :]
if len(patches[0]) != crop_size or len(patches[1]) != crop_size:
print(BatchColors.FAIL + "Error! Current patch size: " + str(len(patches)) + "x" + \
str(len(patches[0])) + BatchColors.ENDC)
return
all_patches.append(patches)
return compute_image_mean(np.asarray(all_patches))
def read_pgm(f):
line = "#"
f.readline()[:-1]
while line[0] == "#":
line = f.readline()
w = int(line.split(" ")[0])
h = int(line.split(" ")[1])
val = [0] * (w * h)
max = f.readline()[:-1]
valid_regions = set()
index = 0
for line in f:
if line[-1] == "\n":
line = line[:-2]
vec = line.split(" ")
for i in vec:
val[index] = convert_class(int(i))
valid_regions.add(int(i))
index = index + 1
return np.reshape(np.asarray(val), (h, w))
def load_imgs_torch(f, is_train, mask_size):
if is_train is True:
img_data = np.empty([3989, 2830, 3], dtype=np.float32)
else:
img_data = np.empty([4386, 3769, 3], dtype=np.float32)
i = 0
try:
file_in = open(f)
except IOError:
print(BatchColors.FAIL + "Could not open file " + str(f) + BatchColors.ENDC)
for line in file_in:
if i == 7:
c, h, w = line.split(' ')
if i <= 16:
i = i + 1
continue
arr_chw = np.reshape(np.asarray(line.split(' '), dtype=np.float32), (int(c), int(h), int(w)))
arr_hcw = np.swapaxes(arr_chw, 0, 1)
arr_hwc = np.swapaxes(arr_hcw, 1, 2)
img_data[:, :, :] = arr_hwc
return img_data
def create_distributions_over_classes(labels, crop_size, stride_crop):
classes = [[[] for i in range(0)] for i in range(NUM_CLASSES)]
w, h = labels.shape
for i in range(0, w, stride_crop):
for j in range(0, h, stride_crop):
patch_class = labels[i:i + crop_size, j:j + crop_size]
if patch_class.shape == (crop_size, crop_size):
count = np.bincount(patch_class.astype(int).flatten())
if count[-1] == crop_size * crop_size:
continue
classes[np.argmax(count[:-1])].append((i, j))
for i in range(len(classes)):
print("Class " + str(i + 1) + " has " + str(len(classes[i])) + " instances")
return classes[0] + classes[1] + classes[2] + classes[3] + classes[4] + classes[5] + classes[6]
def dynamically_create_patches(data, mask_data, crop_size, class_distribution, shuffle, is_train=False):
patches = []
classes = []
masks = []
for i in shuffle:
if i >= 2 * len(class_distribution):
cur_pos = i - 2 * len(class_distribution)
elif i >= len(class_distribution):
cur_pos = i - len(class_distribution)
else:
cur_pos = i
cur_x = class_distribution[cur_pos][0]
cur_y = class_distribution[cur_pos][1]
patch = data[cur_x:cur_x + crop_size, cur_y:cur_y + crop_size, :]
current_class = mask_data[cur_x:cur_x + crop_size, cur_y:cur_y + crop_size]
if len(patch) != crop_size and len(patch[0]) != crop_size:
patch = data[cur_x - (crop_size - len(patch)):cur_x + crop_size,
cur_y - (crop_size - len(patch[0])):cur_y + crop_size, :]
current_class = mask_data[cur_x - (crop_size - len(current_class)):cur_x + crop_size,
cur_y - (crop_size - len(current_class[0])):cur_y + crop_size]
elif len(patch) != crop_size:
patch = data[cur_x - (crop_size - len(patch)):cur_x + crop_size, cur_y:cur_y + crop_size, :]
current_class = mask_data[cur_x - (crop_size - len(current_class)):cur_x + crop_size, cur_y:cur_y + crop_size]
elif len(patch[0]) != crop_size:
patch = data[cur_x:cur_x + crop_size, cur_y - (crop_size - len(patch[0])):cur_y + crop_size, :]
current_class = mask_data[cur_x:cur_x + crop_size,
cur_y - (crop_size - len(current_class[0])):cur_y + crop_size]
if len(patch) != crop_size or len(patch[0]) != crop_size:
print(BatchColors.FAIL + "Error: Current patch size " + str(len(patch)) + " x " \
+ str(len(patch[0])) + BatchColors.ENDC)
print(cur_x, (crop_size - len(patch)), cur_x - (crop_size - len(patch)), cur_x + crop_size, cur_y, (
crop_size - len(patch[0])), cur_y - (crop_size - len(patch[0])), cur_y + crop_size)
return
if len(current_class) != crop_size or len(current_class[0]) != crop_size:
print(BatchColors.FAIL + "Error: Current class size " + str(len(current_class)) + " x " \
+ str(len(current_class[0])) + BatchColors.ENDC)
return
# MASKS
current_mask = np.copy(current_class)
current_mask[current_mask == 0] = 8
current_mask[current_mask == 7] = 0
current_mask = current_mask.astype(bool)
if i < len(class_distribution):
patches.append(patch)
classes.append(current_class)
masks.append(current_mask)
elif i < 2 * len(class_distribution):
patches.append(np.fliplr(patch))
classes.append(np.fliplr(current_class))
masks.append(np.fliplr(current_mask))
elif i >= 2 * len(class_distribution):
patches.append(np.flipud(patch))
classes.append(np.flipud(current_class))
masks.append(np.flipud(current_mask))
return np.asarray(patches), np.asarray(classes, dtype=np.int8), np.asarray(masks, dtype=np.bool)
def create_patches_per_map(data, mask_data, crop_size, stride_crop, index, batch_size):
patches = []
classes = []
masks = []
pos = []
h, w, c = data.shape
h_m, w_m = mask_data.shape
total_index_h = (int(((h - crop_size) / stride_crop)) + 1 if ((h - crop_size) % stride_crop) == 0 else int(
((h - crop_size) / stride_crop)) + 2)
total_index_w = (int(((w - crop_size) / stride_crop)) + 1 if ((w - crop_size) % stride_crop) == 0 else int(
((w - crop_size) / stride_crop)) + 2)
count = 0
# offset_h = int(((index*batch_size*stride_crop)/((w-crop_size)))*stride_crop)
# offset_h = int((index*batch_size)/(((h-crop_size)/stride_crop)+1))*stride_crop
# offset_w = int((index*batch_size)%(((w-crop_size)/stride_crop)+1))*stride_crop
offset_h = int((index * batch_size) / total_index_h) * stride_crop
offset_w = int((index * batch_size) % total_index_w) * stride_crop
first = True
# print total_index_h, total_index_w, offset_h, offset_w, index
for j in range(offset_h, total_index_h * stride_crop, stride_crop):
if first is False:
offset_w = 0
for k in range(offset_w, total_index_w * stride_crop, stride_crop):
if first is True:
first = False
cur_x = j
cur_y = k
patch = data[cur_x:cur_x + crop_size, cur_y:cur_y + crop_size, :]
if len(patch) != crop_size and len(patch[0]) != crop_size:
cur_x = cur_x - (crop_size - len(patch))
cur_y = cur_y - (crop_size - len(patch[0]))
patch = data[cur_x:cur_x + crop_size, cur_y:cur_y + crop_size, :]
elif len(patch) != crop_size:
cur_x = cur_x - (crop_size - len(patch))
patch = data[cur_x:cur_x + crop_size, cur_y:cur_y + crop_size, :]
elif len(patch[0]) != crop_size:
cur_y = cur_y - (crop_size - len(patch[0]))
patch = data[cur_x:cur_x + crop_size, cur_y:cur_y + crop_size, :]
if len(patch) != crop_size or len(patch[0]) != crop_size:
print("Error: Current patch size ", len(patch), len(patch[0]))
count += 1
patches.append(patch)
cur_mask_patch = mask_data[cur_x:cur_x + crop_size, cur_y:cur_y + crop_size]
classes.append(cur_mask_patch)
# MASKS
current_mask = np.copy(cur_mask_patch)
current_mask[current_mask == 0] = 8
current_mask[current_mask == 7] = 0
current_mask = current_mask.astype(bool)
masks.append(current_mask)
current_pos = np.zeros((2))
current_pos[0] = int(cur_x)
current_pos[1] = int(cur_y)
pos.append(current_pos)
if count == batch_size: # when completes current batch
return np.asarray(patches), np.asarray(classes, dtype=np.int8), np.asarray(masks, dtype=np.bool), pos
# when its not the total size of the batch
# print "--------- end without batch complete"
return np.asarray(patches), np.asarray(classes, dtype=np.int8), np.asarray(masks, dtype=np.bool), pos
def calc_cccuracy_by_crop(true, pred, mask, track_conf_matrix):
b, h, w = pred.shape
acc = 0
local_conf_matrix = np.zeros((NUM_CLASSES, NUM_CLASSES), dtype=np.uint32)
for i in range(b):
for j in range(h):
for k in range(w):
if mask[i, j, k] is True:
if true[i][j][k] == pred[i][j][k]:
acc = acc + 1
track_conf_matrix[true[i, j, k]][pred[i, j, k]] += 1
local_conf_matrix[true[i, j, k]][pred[i, j, k]] += 1
_sum = 0.0
for i in range(len(local_conf_matrix)):
_sum += (local_conf_matrix[i][i] / float(np.sum(local_conf_matrix[i])) if np.sum(local_conf_matrix[i]) != 0 else 0)
acc_norm = _sum / float(NUM_CLASSES)
return acc, acc_norm, local_conf_matrix
def select_best_patch_size(distribution_type, values, patch_acc_loss, patch_occur, is_loss_or_acc='acc',
patch_chosen_values=None,
debug=False):
patch_occur[np.where(patch_occur == 0)] = 1
patch_mean = patch_acc_loss / patch_occur
if is_loss_or_acc == 'acc':
argmax_acc = np.argmax(patch_mean)
if distribution_type == 'multi_fixed':
cur_patch_val = int(values[argmax_acc])
elif distribution_type == 'uniform' or distribution_type == 'multinomial':
cur_patch_val = values[0] + argmax_acc
if patch_chosen_values is not None:
patch_chosen_values[int(argmax_acc)] += 1
if debug is True:
print('patch_acc_loss', patch_acc_loss)
print('patch_occur', patch_occur)
print('patch_mean', patch_mean)
print('argmax_acc', argmax_acc)
print('specific', argmax_acc, patch_acc_loss[argmax_acc], patch_occur[argmax_acc], patch_mean[argmax_acc])
elif is_loss_or_acc == 'loss':
arg_sort_out = np.argsort(patch_mean)
if debug is True:
print('patch_acc_loss', patch_acc_loss)
print('patch_occur', patch_occur)
print('patch_mean', patch_mean)
print('arg_sort_out', arg_sort_out)
if distribution_type == 'multi_fixed':
for i in range(len(values)):
if patch_occur[arg_sort_out[i]] > 0:
cur_patch_val = int(values[arg_sort_out[i]]) # -1*(i+1)
if patch_chosen_values is not None:
patch_chosen_values[arg_sort_out[i]] += 1
if debug is True:
print('specific', arg_sort_out[i], patch_acc_loss[arg_sort_out[i]], patch_occur[
arg_sort_out[i]], patch_mean[arg_sort_out[i]])
break
elif distribution_type == 'uniform' or distribution_type == 'multinomial':
for i in range(values[-1] - values[0] + 1):
if patch_occur[arg_sort_out[i]] > 0:
cur_patch_val = values[0] + arg_sort_out[i]
if patch_chosen_values is not None:
patch_chosen_values[arg_sort_out[i]] += 1
if debug is True:
print('specific', arg_sort_out[i], patch_acc_loss[arg_sort_out[i]], patch_occur[
arg_sort_out[i]], patch_mean[arg_sort_out[i]])
break
if debug is True:
print('Current patch size ', cur_patch_val)
if patch_chosen_values is not None:
print('Distr of chosen sizes ', patch_chosen_values)
return cur_patch_val
def class_to_color(value):
if value == 0:
return (255, 0, 255)
elif value == 1:
return (0, 255, 0)
elif value == 2:
return (255, 0, 0)
elif value == 3:
return (0, 255, 255)
elif value == 4:
return (160, 32, 240)
elif value == 5:
return (46, 139, 87)
elif value == 6:
return (255, 255, 0)
else:
print(BatchColors.FAIL + "Class did not find: " + str(value) + BatchColors.ENDC)
def create_prediction_map(path, prob_img, mask, step):
img = Image.new("RGB", (3769, 4386), "black")
pixels = img.load()
for i in range(3769):
for j in range(4386):
if mask[j, i] != 7:
pixels[i, j] = class_to_color(int(prob_img[j, i]))
else:
prob_img[j, i] = 7
img.save(path + 'pred_map_' + str(step) + '.jpeg')
'''
TensorFlow
'''
def leaky_relu(x, alpha=0.1):
return tf.maximum(alpha * x, x)
def _variable_on_cpu(name, shape, ini):
with tf.device('/cpu:0'):
var = tf.get_variable(name, shape, initializer=ini, dtype=tf.float32)
return var
def _variable_with_weight_decay(name, shape, ini, weight_decay):
var = _variable_on_cpu(name, shape, ini)
# tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32)
# tf.contrib.layers.xavier_initializer(dtype=tf.float32))
# tf.truncated_normal_initializer(stddev=stddev, dtype=tf.float32))
# orthogonal_initializer()
if weight_decay is not None:
try:
weight_decay = tf.mul(tf.nn.l2_loss(var), weight_decay, name='weight_loss')
except:
weight_decay = tf.multiply(tf.nn.l2_loss(var), weight_decay, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var
def _fc_layer(input, layer_shape, weight_decay, name, activation=None):
with tf.variable_scope(name):
weights = _variable_with_weight_decay('weights', shape=layer_shape,
ini=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', layer_shape[-1], tf.constant_initializer(0.1))
fc = tf.matmul(input, weights)
fc = tf.add(fc, biases)
if activation == 'relu':
fc = tf.nn.relu(fc, name=name)
return fc
def _squeeze_excitation_layer(input_data, weight_decay, ratio, layer_name):
with tf.name_scope(layer_name) :
squeeze = tf.reduce_mean(input_data, axis=[1,2])
out_dim = squeeze.get_shape().as_list()[1]
# print(squeeze.get_shape().as_list(), out_dim)
excitation = _fc_layer(squeeze, [out_dim, out_dim/ratio], weight_decay, layer_name+'_fc1')
excitation = tf.nn.relu(excitation)
excitation = _fc_layer(excitation, [out_dim/ratio, out_dim], weight_decay, name=layer_name+'_fc2')
excitation = tf.nn.sigmoid(excitation)
excitation = tf.reshape(excitation, [-1, 1, 1, out_dim])
scale = input_data * excitation
return scale
def _batch_norm(input_data, is_training, scope=None):
# Note: is_training is tf.placeholder(tf.bool) type
return tf.cond(is_training,
lambda: tf.contrib.layers.batch_norm(input_data, is_training=True, center=False, updates_collections=None,
scope=scope),
lambda: tf.contrib.layers.batch_norm(input_data, is_training=False, center=False,
updates_collections=None, scope=scope, reuse=True)
)
def _conv_layer(input_data, layer_shape, name, weight_decay, is_training, rate=1, strides=None, pad='SAME',
activation='relu', batch_norm=True, has_activation=True, is_normal_conv=False):
if strides is None:
strides = [1, 1, 1, 1]
with tf.variable_scope(name) as scope:
weights = _variable_with_weight_decay('weights', shape=layer_shape,
ini=tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', layer_shape[-1], tf.constant_initializer(0.1))
if is_normal_conv is False:
conv_op = tf.nn.atrous_conv2d(input_data, weights, rate=rate, padding=pad)
else:
conv_op = tf.nn.conv2d(input_data, weights, strides=strides, padding=pad)
conv_act = tf.nn.bias_add(conv_op, biases)
if batch_norm is True:
conv_act = _batch_norm(conv_act, is_training, scope=scope)
if has_activation is True:
if activation is 'relu':
conv_act = tf.nn.relu(conv_act, name=name)
else:
conv_act = leaky_relu(conv_act)
return conv_act
def _squeeze_conv_layer(input_data, in_dim, out_dim, k_dim, kernel_size, name, weight_decay, is_training, rate=1,
strides=None, pad='SAME', activation='relu', batch_norm=True, has_activation=True,
is_normal_conv=False):
conv1 = _conv_layer(input_data, [1, 1, in_dim, k_dim], name + '_s1', weight_decay, is_training, rate, strides, pad,
activation, batch_norm, has_activation, is_normal_conv)
conv2_1 = _conv_layer(conv1, [1, 1, k_dim, out_dim/2], name + '_s2_1', weight_decay, is_training, rate, strides,
pad, activation, batch_norm, has_activation, is_normal_conv)
conv2_2 = _conv_layer(conv1, [kernel_size, kernel_size, k_dim, out_dim/2], name + '_s2_2', weight_decay,
is_training, rate, strides, pad, activation, batch_norm, has_activation, is_normal_conv)
try:
out = tf.concat([conv2_1, conv2_2], 3)
except:
out = tf.concat(concat_dim=3, values=[conv2_1, conv2_2])
return out
def _max_pool(input_data, kernel, strides, name, pad='SAME', debug=False):
pool = tf.nn.max_pool(input_data, ksize=kernel, strides=strides, padding=pad, name=name)
if debug:
pool = tf.Print(pool, [tf.shape(pool)], message='Shape of %s' % name)
return pool
def dilated_icpr_old(x, dropout, is_training, weight_decay, cropSz):
# Reshape input_data picture
x = tf.reshape(x, shape=[-1, cropSz, cropSz, 3]) ## default: 25x25
# print x.get_shape()
conv1 = _conv_layer(x, [5, 5, 3, 64], "conv1", weight_decay, is_training, rate=1)
# pool1 = _max_pool(conv1, kernel=[1, 2, 2, 1], strides=[1, 2, 2, 1], name='pool1')
# conv2 = _conv_layer(conv1, [5,5,64,64], 'conv2', weight_decay, is_training, rate=1)
# pool2 = _max_pool(conv2, kernel=[1, 2, 2, 1], strides=[1, 2, 2, 1], name='pool2')
conv3 = _conv_layer(conv1, [4, 4, 64, 128], 'conv3', weight_decay, is_training, rate=2)
# pool3 = _max_pool(conv3, kernel=[1, 2, 2, 1], strides=[1, 1, 1, 1], name='pool3')
# conv4 = _conv_layer(conv3, [4,4,128,128], "conv4", weight_decay, is_training, rate=2)
conv5 = _conv_layer(conv3, [3, 3, 128, 256], "conv5", weight_decay, is_training, rate=4)
# conv6 = _conv_layer(conv5, [3,3,256,256], "conv6", weight_decay, is_training, rate=4)
with tf.variable_scope('conv_classifier') as scope:
kernel = _variable_with_weight_decay('weights', shape=[1, 1, 256, NUM_CLASSES],
ini=tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(conv5, kernel, [1, 1, 1, 1], padding='SAME')
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
return conv_classifier
def dilated_grsl_old(x, dropout, is_training, weight_decay, cropSz):
# Reshape input_data picture
x = tf.reshape(x, shape=[-1, cropSz, cropSz, 3]) ## default: 25x25
x = tf.Print(x, [tf.shape(x)], message='Shape of x')
conv1 = _conv_layer(x, [5, 5, 3, 64], "conv1", weight_decay, is_training, rate=1, activation='lrelu')
pool1 = _max_pool(conv1, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool1')
conv2 = _conv_layer(pool1, [5, 5, 64, 64], 'conv2', weight_decay, is_training, rate=2, activation='lrelu')
pool2 = _max_pool(conv2, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool2')
conv3 = _conv_layer(pool2, [4, 4, 64, 128], 'conv3', weight_decay, is_training, rate=3, activation='lrelu')
pool3 = _max_pool(conv3, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool3')
conv4 = _conv_layer(pool3, [4, 4, 128, 128], "conv4", weight_decay, is_training, rate=4, activation='lrelu')
pool4 = _max_pool(conv4, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool4')
conv5 = _conv_layer(pool4, [3, 3, 128, 256], "conv5", weight_decay, is_training, rate=5, activation='lrelu')
pool5 = _max_pool(conv5, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool5')
conv6 = _conv_layer(pool5, [3, 3, 256, 256], "conv6", weight_decay, is_training, rate=6, activation='lrelu')
pool6 = _max_pool(conv6, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool6')
with tf.variable_scope('conv_classifier') as scope:
kernel = _variable_with_weight_decay('weights', shape=[1, 1, 256, NUM_CLASSES],
ini=tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(pool6, kernel, [1, 1, 1, 1], padding='SAME')
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
return conv_classifier
'''
New networks
'''
def dilated_icpr_rate6(x, dropout, is_training, weight_decay, crop_size):
# Reshape input_data picture
x = tf.reshape(x, shape=[-1, crop_size, crop_size, 3]) # default: 25x25
conv1 = _conv_layer(x, [5, 5, 3, 64], "conv1", weight_decay, is_training, rate=1)
conv2 = _conv_layer(conv1, [5, 5, 64, 64], 'conv2', weight_decay, is_training, rate=2)
conv3 = _conv_layer(conv2, [4, 4, 64, 128], "conv3", weight_decay, is_training, rate=3)
conv4 = _conv_layer(conv3, [4, 4, 128, 128], "conv4", weight_decay, is_training, rate=4)
conv5 = _conv_layer(conv4, [3, 3, 128, 256], "conv5", weight_decay, is_training, rate=5)
conv6 = _conv_layer(conv5, [3, 3, 256, 256], "conv6", weight_decay, is_training, rate=6)
with tf.variable_scope('conv_classifier') as scope:
kernel = _variable_with_weight_decay('weights', shape=[1, 1, 256, NUM_CLASSES],
ini=tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(conv6, kernel, [1, 1, 1, 1], padding='SAME')
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
return conv_classifier
def dilated_icpr_rate6_nodilation(x, dropout, is_training, weight_decay, crop_size, batch_norm=True):
# Reshape input_data picture
x = tf.reshape(x, shape=[-1, crop_size, crop_size, 3]) # default: 25x25
conv1 = _conv_layer(x, [5, 5, 3, 64], "conv1", weight_decay, is_training,
batch_norm=batch_norm, is_normal_conv=True)
conv2 = _conv_layer(conv1, [5, 5, 64, 64], 'conv2', weight_decay, is_training,
batch_norm=batch_norm, is_normal_conv=True)
conv3 = _conv_layer(conv2, [4, 4, 64, 128], "conv3", weight_decay, is_training,
batch_norm=batch_norm, is_normal_conv=True)
conv4 = _conv_layer(conv3, [4, 4, 128, 128], "conv4", weight_decay, is_training,
batch_norm=batch_norm, is_normal_conv=True)
conv5 = _conv_layer(conv4, [3, 3, 128, 256], "conv5", weight_decay, is_training,
batch_norm=batch_norm, is_normal_conv=True)
conv6 = _conv_layer(conv5, [3, 3, 256, 256], "conv6", weight_decay, is_training,
batch_norm=batch_norm, is_normal_conv=True)
with tf.variable_scope('conv_classifier') as scope:
kernel = _variable_with_weight_decay('weights', shape=[1, 1, 256, NUM_CLASSES],
ini=tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(conv6, kernel, [1, 1, 1, 1], padding='SAME')
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
return conv_classifier
def dilated_icpr_rate6_densely(x, dropout, is_training, weight_decay, crop_size):
# Reshape input_data picture
x = tf.reshape(x, shape=[-1, crop_size, crop_size, 3]) # default: 25x25
conv1 = _conv_layer(x, [5, 5, 3, 32], "conv1", weight_decay, is_training, rate=1)
conv2 = _conv_layer(conv1, [5, 5, 32, 32], 'conv2', weight_decay, is_training, rate=2)
try:
c1 = tf.concat([conv1, conv2], 3) # c1 = 32+32 = 64
except:
c1 = tf.concat(concat_dim=3, values=[conv1, conv2])
conv3 = _conv_layer(c1, [4, 4, 64, 64], "conv3", weight_decay, is_training, rate=3)
try:
c2 = tf.concat([c1, conv3], 3) # c2 = 64+64 = 128
except:
c2 = tf.concat(concat_dim=3, values=[c1, conv3])
conv4 = _conv_layer(c2, [4, 4, 128, 64], "conv4", weight_decay, is_training, rate=4)
try:
c3 = tf.concat([c2, conv4], 3) # c3 = 128+64 = 192
except:
c3 = tf.concat(concat_dim=3, values=[c2, conv4])
conv5 = _conv_layer(c3, [3, 3, 192, 128], "conv5", weight_decay, is_training, rate=5)
try:
c4 = tf.concat([c3, conv5], 3) # c4 = 192+128 = 320
except:
c4 = tf.concat(concat_dim=3, values=[c3, conv5])
conv6 = _conv_layer(c4, [3, 3, 320, 128], "conv6", weight_decay, is_training, rate=6)
try:
c5 = tf.concat([c4, conv6], 3) # c5 = 320+256 = 448
except:
c5 = tf.concat(concat_dim=3, values=[c4, conv6])
with tf.variable_scope('conv_classifier') as scope:
kernel = _variable_with_weight_decay('weights', shape=[1, 1, 448, NUM_CLASSES],
ini=tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(c5, kernel, [1, 1, 1, 1], padding='SAME')
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
return conv_classifier
def dilated_grsl(x, dropout, is_training, weight_decay, crop_size):
# Reshape input_data picture
x = tf.reshape(x, shape=[-1, crop_size, crop_size, 3]) # default: 25x25
conv1 = _conv_layer(x, [5, 5, 3, 64], "conv1", weight_decay, is_training, rate=1, activation='lrelu')
pool1 = _max_pool(conv1, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool1')
conv2 = _conv_layer(pool1, [5, 5, 64, 64], 'conv2', weight_decay, is_training, rate=2, activation='lrelu')
pool2 = _max_pool(conv2, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool2')
conv3 = _conv_layer(pool2, [4, 4, 64, 128], 'conv3', weight_decay, is_training, rate=3, activation='lrelu')
pool3 = _max_pool(conv3, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool3')
conv4 = _conv_layer(pool3, [4, 4, 128, 128], "conv4", weight_decay, is_training, rate=4, activation='lrelu')
pool4 = _max_pool(conv4, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool4')
conv5 = _conv_layer(pool4, [3, 3, 128, 256], "conv5", weight_decay, is_training, rate=5, activation='lrelu')
pool5 = _max_pool(conv5, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool5')
conv6 = _conv_layer(pool5, [3, 3, 256, 256], "conv6", weight_decay, is_training, rate=6, activation='lrelu')
pool6 = _max_pool(conv6, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool6')
with tf.variable_scope('conv_classifier') as scope:
kernel = _variable_with_weight_decay('weights', shape=[1, 1, 256, NUM_CLASSES],
ini=tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(pool6, kernel, [1, 1, 1, 1], padding='SAME')
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
return conv_classifier
def dilated_grsl_rate8(x, dropout, is_training, weight_decay, crop_size):
# Reshape input_data picture
x = tf.reshape(x, shape=[-1, crop_size, crop_size, 3]) # default: 25x25
conv1 = _conv_layer(x, [5, 5, 3, 64], "conv1", weight_decay, is_training, rate=1, activation='lrelu')
pool1 = _max_pool(conv1, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool1')
conv2 = _conv_layer(pool1, [5, 5, 64, 64], 'conv2', weight_decay, is_training, rate=2, activation='lrelu')
pool2 = _max_pool(conv2, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool2')
conv3 = _conv_layer(pool2, [4, 4, 64, 128], 'conv3', weight_decay, is_training, rate=3, activation='lrelu')
pool3 = _max_pool(conv3, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool3')
conv4 = _conv_layer(pool3, [4, 4, 128, 128], "conv4", weight_decay, is_training, rate=4, activation='lrelu')
pool4 = _max_pool(conv4, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool4')
conv5 = _conv_layer(pool4, [3, 3, 128, 192], "conv5", weight_decay, is_training, rate=5, activation='lrelu')
pool5 = _max_pool(conv5, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool5')
conv6 = _conv_layer(pool5, [3, 3, 192, 192], "conv6", weight_decay, is_training, rate=6, activation='lrelu')
pool6 = _max_pool(conv6, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool6')
conv7 = _conv_layer(pool6, [3, 3, 192, 256], "conv7", weight_decay, is_training, rate=7, activation='lrelu')
pool7 = _max_pool(conv7, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool7')
conv8 = _conv_layer(pool7, [3, 3, 256, 256], "conv8", weight_decay, is_training, rate=8, activation='lrelu')
pool8 = _max_pool(conv8, kernel=[1, 3, 3, 1], strides=[1, 1, 1, 1], name='pool8')
with tf.variable_scope('conv_classifier') as scope:
kernel = _variable_with_weight_decay('weights', shape=[1, 1, 256, NUM_CLASSES],
ini=tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(pool8, kernel, [1, 1, 1, 1], padding='SAME')
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
return conv_classifier
def dilated_icpr_rate6_SE(x, dropout, is_training, weight_decay, crop_size):
# Reshape input_data picture
x = tf.reshape(x, shape=[-1, crop_size, crop_size, 3]) # default: 25x25
conv1 = _conv_layer(x, [5, 5, 3, 64], "conv1", weight_decay, is_training, rate=1)
conv2 = _conv_layer(conv1, [5, 5, 64, 64], 'conv2', weight_decay, is_training, rate=2)
se1 = _squeeze_excitation_layer(conv2, weight_decay, ratio=4, layer_name='se1')
conv3 = _conv_layer(se1, [4, 4, 64, 128], "conv3", weight_decay, is_training, rate=3)
conv4 = _conv_layer(conv3, [4, 4, 128, 128], "conv4", weight_decay, is_training, rate=4)
se2 = _squeeze_excitation_layer(conv4, weight_decay, ratio=4, layer_name='se2')
conv5 = _conv_layer(se2, [3, 3, 128, 256], "conv5", weight_decay, is_training, rate=5)
conv6 = _conv_layer(conv5, [3, 3, 256, 256], "conv6", weight_decay, is_training, rate=6)
se3 = _squeeze_excitation_layer(conv6, weight_decay, ratio=4, layer_name='se3')
with tf.variable_scope('conv_classifier') as scope:
kernel = _variable_with_weight_decay('weights', shape=[1, 1, 256, NUM_CLASSES],
ini=tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(se3, kernel, [1, 1, 1, 1], padding='SAME')
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
return conv_classifier
def dilated_icpr_rate6_squeeze(x, dropout, is_training, weight_decay, crop_size):
# Reshape input_data picture
x = tf.reshape(x, shape=[-1, crop_size, crop_size, 3]) # default: 25x25
conv1 = _conv_layer(x, [5, 5, 3, 64], "conv1", weight_decay, is_training, rate=1)
conv2 = _squeeze_conv_layer(conv1, 64, 64, 32, 5, 'conv2', weight_decay, is_training, rate=2)
conv3 = _squeeze_conv_layer(conv2, 64, 128, 64, 4, "conv3", weight_decay, is_training, rate=3)
conv4 = _squeeze_conv_layer(conv3, 128, 128, 64, 4, "conv4", weight_decay, is_training, rate=4)
conv5 = _squeeze_conv_layer(conv4, 128, 256, 64, 3, "conv5", weight_decay, is_training, rate=5)
conv6 = _squeeze_conv_layer(conv5, 256, 256, 128, 3, "conv6", weight_decay, is_training, rate=6)
with tf.variable_scope('conv_classifier') as scope:
kernel = _variable_with_weight_decay('weights', shape=[1, 1, 256, NUM_CLASSES],
ini=tf.contrib.layers.xavier_initializer_conv2d(dtype=tf.float32),
weight_decay=weight_decay)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(conv6, kernel, [1, 1, 1, 1], padding='SAME')
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
return conv_classifier
def loss_def(_logits, _labels, _mask):
# _logits = tf.Print(_logits, [tf.shape(_logits)], message='1 Shape of _logits ')
# _labels = tf.Print(_labels, [tf.shape(_labels)], message='1 Shape of _labels ')
# Calculate the average cross entropy loss across the batch
mask = tf.cast(tf.reshape(_mask, [-1]), tf.bool)
logits = tf.boolean_mask(tf.reshape(_logits, [-1, NUM_CLASSES]), mask)
labels = tf.boolean_mask(tf.cast(tf.reshape(_labels, [-1]), tf.int64), mask)
# logits = tf.Print(logits, [tf.shape(logits)], message='2 Shape of logits ')
# labels = tf.Print(labels, [tf.shape(labels)], message='2 Shape of labels ')
# Calculate the average cross entropy loss across the batch.
# labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels,
name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# The total loss is defined as the cross entropy loss plus all of the weight decay terms (L2 loss).
return tf.add_n(tf.get_collection('losses'), name='total_loss')
def test(sess, test_data, test_mask_data, mean_full, std_full, batch_size, x, y, mask, crop, keep_prob, is_training,
pred_up, logits, step, crop_size, path):
stride_crop = int(math.floor(crop_size / 2.0))
h, w = test_mask_data.shape
instaces_stride_h = (int(((h - crop_size) / stride_crop)) + 1 if ((h - crop_size) % stride_crop) == 0 else int(
((h - crop_size) / stride_crop)) + 2)
instaces_stride_w = (int(((w - crop_size) / stride_crop)) + 1 if ((w - crop_size) % stride_crop) == 0 else int(
((w - crop_size) / stride_crop)) + 2)
instaces_stride = instaces_stride_h * instaces_stride_w
prob_im = np.zeros([h, w, NUM_CLASSES], dtype=np.float32)
occur_im = np.zeros([h, w, NUM_CLASSES], dtype=np.uint32)
for i in range(0, (
(instaces_stride / batch_size) + 1 if instaces_stride % batch_size != 0 else (instaces_stride / batch_size))):
test_patches, test_classes, test_masks, pos = create_patches_per_map(test_data, test_mask_data, crop_size,
stride_crop, i, batch_size)
normalize_images(test_patches, mean_full, std_full)
bx = np.reshape(test_patches, (-1, crop_size * crop_size * 3))
by = np.reshape(test_classes, (-1, crop_size * crop_size * 1))
batch_mask = np.reshape(test_masks, (-1, crop_size * crop_size * 1))
_pred_up, _logits = sess.run([pred_up, logits],
feed_dict={x: bx, y: by, mask: batch_mask, crop: crop_size, keep_prob: 1.,
is_training: False})
for j in range(len(_logits)):
prob_im[int(pos[j][0]):int(pos[j][0]) + crop_size, int(pos[j][1]):int(pos[j][1]) + crop_size, :] += \
_logits[j, :, :, :]
occur_im[int(pos[j][0]):int(pos[j][0]) + crop_size, int(pos[j][1]):int(pos[j][1]) + crop_size, :] += 1
# index += 1
occur_im[np.where(occur_im == 0)] = 1
# np.save(output_path + 'prob_map' + str(testingInstances) + '.npy', prob_im/occur_im.astype(float))
prob_im_argmax = np.argmax(prob_im / occur_im.astype(float), axis=2)
# create_prediction_map(path, prob_im_argmax, test_mask_data, step)
cm_test_per_map = np.zeros((NUM_CLASSES, NUM_CLASSES), dtype=np.uint32)
for t in range(h):
for r in range(w):
if int(test_mask_data[t, r]) != 7:
cm_test_per_map[int(test_mask_data[t, r])][int(prob_im_argmax[t, r])] += 1
_sum = 0.0
total = 0
for i in range(len(cm_test_per_map)):
_sum += (cm_test_per_map[i][i] / float(np.sum(cm_test_per_map[i])) if np.sum(cm_test_per_map[i]) != 0 else 0)
total += cm_test_per_map[i][i]
cur_kappa = cohen_kappa_score(test_mask_data.flatten(), prob_im_argmax.flatten(), labels=[0, 1, 2, 3, 4, 5, 6])
cur_f1 = f1_score(test_mask_data.flatten(), prob_im_argmax.flatten(), labels=[0, 1, 2, 3, 4, 5, 6],
average='macro')
print("---- Iter " + str(step) + " -- Time " + str(datetime.datetime.now().time()) +
" -- Test: Overall Accuracy= " + str(total) +
" Overall Accuracy= " + "{:.6f}".format(total / float(np.sum(cm_test_per_map))) +
" Normalized Accuracy= " + "{:.6f}".format(_sum / float(NUM_CLASSES)) +
" F1 Score= " + "{:.4f}".format(cur_f1) +
" Kappa= " + "{:.4f}".format(cur_kappa) +
" Confusion Matrix= " + np.array_str(cm_test_per_map).replace("\n", "")
)
def train(training_data, training_mask_data, test_data, test_mask_data, class_distribution,
mean_full, std_full, output_path, current_model,
lr_initial, weight_decay, batch_size, niter, net_type,
distribution_type, update_type,
patch_acc_loss, patch_occur, patch_chosen_values, probs, values):
total_length = len(class_distribution)
display_step = 50
###################
epoch_number = 1000 # int(len(training_classes)/batch_size) # 1 epoch = images / batch
val_inteval = 1000 # int(len(training_classes)/batch_size)
###################
# Network Parameters
dropout = 0.5 # Dropout, probability to keep units
crop = tf.placeholder(tf.int32)
x = tf.placeholder(tf.float32, [None, None])
y = tf.placeholder(tf.float32, [None, None])
mask = tf.placeholder(tf.bool, [None, None])
keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)
is_training = tf.placeholder(tf.bool, [], name='is_training')
global_step = tf.Variable(0, name='global_step', trainable=False)
# ConvNets
if net_type == 'dilated_icpr_old':
logits = dilated_icpr_old(x, keep_prob, is_training, weight_decay, crop)
elif net_type == 'dilated_grsl_old':
logits = dilated_grsl_old(x, keep_prob, is_training, weight_decay, crop)
elif net_type == 'dilated_grsl':