def main(csv=True, coords=None, gps=False): # pylint: disable=unused-argument
if coords is None:
coords = np.array([[ -1.353887, 50.965639],
[ -1.386731, 50.935744],
[ -1.401907, 50.925657],
[ -1.368191, 50.914509],
[ -1.354101, 50.90988 ],
[ -1.34853 , 50.902636],
[ -0.964885, 50.81373 ]])
n = coords.shape[0]
print('%s: Loading nnet...' % time.ctime())
# For the validation and test data, we'll just hold the entire dataset in
# one constant node.
#test_data_node = tf.constant(data)
test_data_node = tf.placeholder( tf.float32, shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
test_data_node2 = tf.placeholder( tf.float32, shape=(n%BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
test_centre_node = tf.placeholder( tf.float32, shape=(BATCH_SIZE, SINGLE_SIZE, SINGLE_SIZE, NUM_CHANNELS))
test_centre_node2 = tf.placeholder( tf.float32, shape=(n%BATCH_SIZE, SINGLE_SIZE, SINGLE_SIZE, NUM_CHANNELS))
# The variables below hold all the trainable weights. They are passed an
# initial value which will be assigned when when we call:
# {tf.initialize_all_variables().run()}
depth1=SINGLE_SIZE*SINGLE_SIZE*NUM_CHANNELS
conv1_weights = tf.constant( # shape: [SINGLE_SIZE, SINGLE_SIZE, NUM_CHANNELS, depth1]
np.reshape(np.diag([1 for _ in range(depth1)]).astype('float32'),[SINGLE_SIZE, SINGLE_SIZE, NUM_CHANNELS, depth1]))
#centre_weights =tf.constant( # shape: [IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS, depth1]
# get_centre(depth1))
#conv1_biases = tf.constant(np.zeros([depth1], dtype='float32'))
# conv2_weights = tf.Variable(tf.concat(3,[tf.ones([1,1,depth1,1])*2*(.5-i%2) for i in range(2*depth1)]))
conv2_weights = tf.concat(3,[tf.ones([1,1,depth1,1]),-tf.ones([1,1,depth1,1])])
kernel_weights = tf.ones([SINGLE_SIZE, SINGLE_SIZE, NUM_CHANNELS, 1]) # gaussian or linear kernel to use instead avg_pool
def model(data, centre_data, train=False):
"""The Model definition."""
# 1. extract features from centre and every (other) patch in the same way (first attempt: identity)
conv1 = tf.nn.conv2d(data, conv1_weights, strides=[1, PIXELperLABEL, PIXELperLABEL, 1], padding='VALID')
centre = tf.nn.conv2d(centre_data, conv1_weights, strides=[1, PIXELperLABEL, PIXELperLABEL, 1], padding='VALID')
square_dim = conv1.get_shape().as_list()[2]
rep_centre = tf.concat(1, [tf.concat(2,[centre for _ in range(square_dim)]) for _ in range(square_dim)])
layer1 = tf.add(conv1, -rep_centre)
# 2. compute absolute values of the differences (there must be a better way ...)
conv2 = tf.nn.depthwise_conv2d(layer1, conv2_weights, strides = [1,1,1,1], padding='SAME')
# conv2 = tf.nn.conv2d(relu1, conv2_weights, strides=[1, 1, 1, depth1], padding='VALID')
conv2_shape = conv2.get_shape().as_list()
layer2 = tf.reshape(conv2,[conv2_shape[0], conv2_shape[1] * conv2_shape[2] *conv2_shape[3]/2, 2, 1])
pool1 = tf.nn.max_pool(layer2, ksize=[1, 1, 2, 1], strides=[1, 1, 2, 1], padding='SAME')
pool1_shape = pool1.get_shape().as_list()
layer3 = tf.reshape(pool1,[pool1_shape[0], pool1_shape[1]/depth1, depth1, 1])
# 3. aggregate the absolute pixel differences (first attempt: average)
out_pool = tf.nn.avg_pool(layer3, ksize=[1, 1, depth1, 1], strides=[1, 1, depth1, 1], padding='SAME')
out_shape = out_pool.get_shape().as_list()
print('Dimensions of network Tensors: [minibatch size, ..dims.. , channels]')
print(data.get_shape().as_list(), '->',
conv1.get_shape().as_list(), '->', layer1.get_shape().as_list(), '->',
conv2.get_shape().as_list(), '->', layer2.get_shape().as_list(), '->',
pool1.get_shape().as_list(), '->', layer3.get_shape().as_list(), '->',
out_shape)
return tf.reshape(out_pool, [out_shape[0], out_shape[1]])
# Create a local session to run this computation.
with tf.Session() as s:
# Run all the initializers to prepare the trainable parameters.
tf.initialize_all_variables().run()
num_batches = np.floor(n/BATCH_SIZE).astype('int16')
print('%s: Initialized! (Loading data in %d minibatches))' % (time.ctime(), num_batches+1))
if gps:
from get_data.map_coverage import MercatorProjection, G_Point, G_LatLng
from get_data.labels_GPS import labels_GPS, labels_suspect, labels_GPS_list
suspect = np.zeros((0,2))
else:
suspect = np.zeros((0,LABEL_SIZE**2))
for step in range(num_batches):
offset = (step * BATCH_SIZE)
batch_data = load_dataset(coords=coords[offset:(offset + BATCH_SIZE), :])
feed_dict = {test_data_node: batch_data, test_centre_node: get_centre(batch_data)}
# Run the graph and fetch some of the nodes.
test_predictions = s.run(model(test_data_node, test_centre_node), feed_dict=feed_dict)
print('.'),
if gps:
suspect = np.concatenate([suspect, labels_GPS_list(labels= np.array(test_predictions[:,:,1]), coords=coords[offset:(offset + BATCH_SIZE), :], pixels= PIXELperLABEL, zoom=ZOOM_LEVEL)], axis=0 )
else:
suspect = np.concatenate([suspect, np.array(test_predictions[:,:,1])], axis=0)
if (step+1)%10==0:
print('%s: Processing batch %d out of %d' %(time.ctime(), step, num_batches+1))
np.savetxt('tmp_images/suspect_tmp'+time.ctime()[3:10]+'.csv', suspect, fmt='%.6f', delimiter=', ')
if n%BATCH_SIZE > 0 :
offset = num_batches * BATCH_SIZE
batch_data = load_dataset(coords=coords[offset:n, :])
feed_dict = {test_data_node2: batch_data, test_centre_node2: get_centre(batch_data)}
# Run the graph and fetch some of the nodes.
test_predictions = s.run(model(test_data_node2, test_centre_node2), feed_dict=feed_dict)
if gps:
suspect = np.concatenate([suspect, labels_GPS_list(labels= np.array(test_predictions[:,:,1]), coords=coords[offset:n, :], pixels= PIXELperLABEL, zoom=ZOOM_LEVEL) ], axis=0 )
else:
suspect = np.concatenate([suspect, np.array(test_predictions)], axis=0)
# Finally save the result!
print('%s: Result with %d rows and %d columns. ' % (time.ctime(), suspect.shape[0], suspect.shape[1]))
if csv:
np.savetxt('tmp_images/centre_heat.csv', suspect, fmt='%.6f', delimiter=', ')
else: return(suspect)
#np.savetxt('tmp_images/ids.csv', data_matrix, fmt='%.6f', delimiter=', ')
s.close()
def main(csv=True, coords=None, gps=False): # pylint: disable=unused-argument
if coords is None:
coords = np.genfromtxt('tmp_images/assemble_ids.csv', delimiter=',', skip_header= False)[:,1:3]
n = coords.shape[0]
print('%s: Loading nnet...' % time.ctime())
# For the validation and test data, we'll just hold the entire dataset in
# one constant node.
#test_data_node = tf.constant(data)
test_data_node = tf.placeholder(
tf.float32, shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
test_data_node2 = tf.placeholder(
tf.float32, shape=(n%BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
# The variables below hold all the trainable weights. They are passed an
# initial value which will be assigned when when we call:
# {tf.initialize_all_variables().run()}
depth1=32
conv1_weights = tf.Variable(
tf.truncated_normal([5, 5, NUM_CHANNELS, depth1], # 5x5 filter, depth 32.
stddev=0.1,
seed=SEED))
conv1_biases = tf.Variable(tf.zeros([depth1]))
depth2=64
conv2_weights = tf.Variable(
tf.truncated_normal([5, 5, depth1, depth2],
stddev=0.1,
seed=SEED))
conv2_biases = tf.Variable(tf.constant(0.1, shape=[depth2]))
depth3=256#*koef
hidden2_size = SINGLE_SIZE/4 #((IMAGE_SIZE-4)/2-4)/2
fc1_weights = tf.Variable( # fully connected, depth 512. ! but input nodes kept in the shape of square ! for future assembly into larger image
tf.truncated_normal([hidden2_size, hidden2_size, depth2, depth3],
stddev=0.1,
seed=SEED))
fc1_biases = tf.Variable(tf.constant(0.1, shape=[depth3]))
fc2_weights = tf.Variable(
tf.truncated_normal([1, 1, depth3, NUM_LABELS],
stddev=0.1,
seed=SEED))
fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))
# We will replicate the model structure for the training subgraph, as well
# as the evaluation subgraphs, while sharing the trainable parameters.
def model(data, train=False):
"""The Model definition."""
# 2D convolution, with 'SAME' padding (i.e. the output feature map has
# the same size as the input). Note that {strides} is a 4D array whose
# shape matches the data layout: [image index, y, x, depth].
conv1 = tf.nn.conv2d(data,
conv1_weights,
strides=[1, 1, 1, 1],
padding='SAME')
# Bias and rectified linear non-linearity.
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))
# Max pooling. The kernel size spec {ksize} also follows the layout of
# the data. Here we have a pooling window of 2, and a stride of 2.
pool1 = tf.nn.max_pool(relu1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
conv2 = tf.nn.conv2d(pool1,
conv2_weights,
strides=[1, 1, 1, 1],
padding='SAME')
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))
pool2 = tf.nn.max_pool(relu2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
# Fully connected layer on pretrained patches 24x24 for single caravan sites.
hidden_pool = tf.nn.conv2d(pool2,
fc1_weights,
strides=[1, 1, 1, 1],
padding='VALID')
hidden = tf.nn.relu(tf.nn.bias_add(hidden_pool, fc1_biases))
# Add a 50% dropout during training only. Dropout also scales
# activations such that no rescaling is needed at evaluation time.
if train:
hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
single_pool = tf.nn.conv2d(hidden,
fc2_weights,
strides=[1, 1, 1, 1],
padding='SAME')
out_pool = tf.nn.bias_add(single_pool, fc2_biases)
out_shape = out_pool.get_shape().as_list()
reshape = tf.reshape(out_pool,
[out_shape[0] * out_shape[1] * out_shape[2] , out_shape[3]])
if train:
print('Dimensions of network Tensors: [minibatch size, ..dims.. , channels]')
print(data.get_shape().as_list(), '->',
conv1.get_shape().as_list(), '->', pool1.get_shape().as_list(), '->',
conv2.get_shape().as_list(), '->', pool2.get_shape().as_list(), '->',
hidden.get_shape().as_list(), '->', single_pool.get_shape().as_list(), '->',
out_shape)
#return tf.nn.bias_add(reshape, assembly_biases)
out_softmax = tf.nn.softmax(reshape)
return tf.reshape(out_softmax,[out_shape[0], out_shape[1] * out_shape[2], out_shape[3]])
# Create a local session to run this computation.
with tf.Session() as s:
# Run all the initializers to prepare the trainable parameters.
tf.initialize_all_variables().run()
saver = tf.train.Saver({'conv1_weights':conv1_weights, 'conv1_biases':conv1_biases,
'conv2_weights':conv2_weights, 'conv2_biases':conv2_biases,
'fc1_weights':fc1_weights, 'fc1_biases':fc1_biases,
'fc2_weights':fc2_weights, 'fc2_biases':fc2_biases})
# Load pretrained parameters for single 24*24 patches.
saver.restore(s, NET_FILE)
num_batches = np.floor(n/BATCH_SIZE).astype('int16')
print('%s: Initialized! (Loading data in %d minibatches))' % (time.ctime(), num_batches+1))
if gps:
from get_data.map_coverage import MercatorProjection, G_Point, G_LatLng
from get_data.labels_GPS import labels_GPS, labels_suspect, labels_GPS_list
suspect = np.zeros((0,2))
else:
suspect = np.zeros((0,LABEL_SIZE**2))
for step in range(num_batches):
offset = (step * BATCH_SIZE)
batch_data = load_dataset(coords=coords[offset:(offset + BATCH_SIZE), :])
feed_dict = {test_data_node: batch_data}
# Run the graph and fetch some of the nodes.
test_predictions = s.run(model(test_data_node), feed_dict=feed_dict)
print('.'),
if gps:
suspect = np.concatenate([suspect, labels_GPS_list(labels= np.array(test_predictions[:,:,1]), coords=coords[offset:(offset + BATCH_SIZE), :], pixels= PIXELperLABEL, zoom=ZOOM_LEVEL)], axis=0 )
else:
suspect = np.concatenate([suspect, np.array(test_predictions[:,:,1])], axis=0)
if (step+1)%10==0:
print('%s: Processing batch %d out of %d' %(time.ctime(), step, num_batches+1))
np.savetxt('tmp_images/suspect_all'+time.ctime()[3:10]+'.csv', suspect, fmt='%.6f', delimiter=', ')
if n%BATCH_SIZE > 0 :
offset = num_batches * BATCH_SIZE
batch_data = load_dataset(coords=coords[offset:n, :])
feed_dict = {test_data_node2: batch_data}
# Run the graph and fetch some of the nodes.
test_predictions = s.run(model(test_data_node2), feed_dict=feed_dict)
if gps:
suspect = np.concatenate([suspect, labels_GPS_list(labels= np.array(test_predictions[:,:,1]), coords=coords[offset:n, :], pixels= PIXELperLABEL, zoom=ZOOM_LEVEL) ], axis=0 )
else:
suspect = np.concatenate([suspect, np.array(test_predictions[:,:,1])], axis=0)
# Finally save the result!
print('%s: Result with %d rows and %d columns. ' % (time.ctime(), suspect.shape[0], suspect.shape[1]))
if csv:
np.savetxt('tmp_images/assemble1_susp.csv', suspect, fmt='%.6f', delimiter=', ')
else: return(suspect)
#np.savetxt('tmp_images/ids.csv', data_matrix, fmt='%.6f', delimiter=', ')
s.close()
def main(csv=True, coords=None, gps=False): # pylint: disable=unused-argument
if coords is None:
coords = np.genfromtxt('tmp_images/assemble_ids.csv',
delimiter=',',
skip_header=False)[:, 1:3]
n = coords.shape[0]
print('%s: Loading nnet...' % time.ctime())
# For the validation and test data, we'll just hold the entire dataset in
# one constant node.
#test_data_node = tf.constant(data)
test_data_node = tf.placeholder(tf.float32,
shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE,
NUM_CHANNELS))
test_data_node2 = tf.placeholder(tf.float32,
shape=(n % BATCH_SIZE, IMAGE_SIZE,
IMAGE_SIZE, NUM_CHANNELS))
# The variables below hold all the trainable weights. They are passed an
# initial value which will be assigned when when we call:
# {tf.initialize_all_variables().run()}
depth1 = 32
conv1_weights = tf.Variable(
tf.truncated_normal(
[5, 5, NUM_CHANNELS, depth1], # 5x5 filter, depth 32.
stddev=0.1,
seed=SEED))
conv1_biases = tf.Variable(tf.zeros([depth1]))
depth2 = 64
conv2_weights = tf.Variable(
tf.truncated_normal([5, 5, depth1, depth2], stddev=0.1, seed=SEED))
conv2_biases = tf.Variable(tf.constant(0.1, shape=[depth2]))
depth3 = 256 #*koef
hidden2_size = SINGLE_SIZE / 4 #((IMAGE_SIZE-4)/2-4)/2
fc1_weights = tf.Variable( # fully connected, depth 512. ! but input nodes kept in the shape of square ! for future assembly into larger image
tf.truncated_normal([hidden2_size, hidden2_size, depth2, depth3],
stddev=0.1,
seed=SEED))
fc1_biases = tf.Variable(tf.constant(0.1, shape=[depth3]))
fc2_weights = tf.Variable(
tf.truncated_normal([1, 1, depth3, NUM_LABELS], stddev=0.1, seed=SEED))
fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))
# We will replicate the model structure for the training subgraph, as well
# as the evaluation subgraphs, while sharing the trainable parameters.
def model(data, train=False):
"""The Model definition."""
# 2D convolution, with 'SAME' padding (i.e. the output feature map has
# the same size as the input). Note that {strides} is a 4D array whose
# shape matches the data layout: [image index, y, x, depth].
conv1 = tf.nn.conv2d(data,
conv1_weights,
strides=[1, 1, 1, 1],
padding='SAME')
# Bias and rectified linear non-linearity.
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))
# Max pooling. The kernel size spec {ksize} also follows the layout of
# the data. Here we have a pooling window of 2, and a stride of 2.
pool1 = tf.nn.max_pool(relu1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
conv2 = tf.nn.conv2d(pool1,
conv2_weights,
strides=[1, 1, 1, 1],
padding='SAME')
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))
pool2 = tf.nn.max_pool(relu2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
# Fully connected layer on pretrained patches 24x24 for single caravan sites.
hidden_pool = tf.nn.conv2d(pool2,
fc1_weights,
strides=[1, 1, 1, 1],
padding='VALID')
hidden = tf.nn.relu(tf.nn.bias_add(hidden_pool, fc1_biases))
# Add a 50% dropout during training only. Dropout also scales
# activations such that no rescaling is needed at evaluation time.
if train:
hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
single_pool = tf.nn.conv2d(hidden,
fc2_weights,
strides=[1, 1, 1, 1],
padding='SAME')
out_pool = tf.nn.bias_add(single_pool, fc2_biases)
out_shape = out_pool.get_shape().as_list()
reshape = tf.reshape(
out_pool,
[out_shape[0] * out_shape[1] * out_shape[2], out_shape[3]])
if train:
print(
'Dimensions of network Tensors: [minibatch size, ..dims.. , channels]'
)
print(data.get_shape().as_list(), '->',
conv1.get_shape().as_list(), '->',
pool1.get_shape().as_list(), '->',
conv2.get_shape().as_list(), '->',
pool2.get_shape().as_list(), '->',
hidden.get_shape().as_list(), '->',
single_pool.get_shape().as_list(), '->', out_shape)
#return tf.nn.bias_add(reshape, assembly_biases)
out_softmax = tf.nn.softmax(reshape)
return tf.reshape(
out_softmax,
[out_shape[0], out_shape[1] * out_shape[2], out_shape[3]])
# Create a local session to run this computation.
with tf.Session() as s:
# Run all the initializers to prepare the trainable parameters.
tf.initialize_all_variables().run()
saver = tf.train.Saver({
'conv1_weights': conv1_weights,
'conv1_biases': conv1_biases,
'conv2_weights': conv2_weights,
'conv2_biases': conv2_biases,
'fc1_weights': fc1_weights,
'fc1_biases': fc1_biases,
'fc2_weights': fc2_weights,
'fc2_biases': fc2_biases
})
# Load pretrained parameters for single 24*24 patches.
saver.restore(s, NET_FILE)
num_batches = np.floor(n / BATCH_SIZE).astype('int16')
print('%s: Initialized! (Loading data in %d minibatches))' %
(time.ctime(), num_batches + 1))
if gps:
from get_data.map_coverage import MercatorProjection, G_Point, G_LatLng
from get_data.labels_GPS import labels_GPS, labels_suspect, labels_GPS_list
suspect = np.zeros((0, 2))
else:
suspect = np.zeros((0, LABEL_SIZE**2))
for step in range(num_batches):
offset = (step * BATCH_SIZE)
batch_data = load_dataset(coords=coords[offset:(offset +
BATCH_SIZE), :])
feed_dict = {test_data_node: batch_data}
# Run the graph and fetch some of the nodes.
test_predictions = s.run(model(test_data_node),
feed_dict=feed_dict)
print('.'),
if gps:
suspect = np.concatenate([
suspect,
labels_GPS_list(
labels=np.array(test_predictions[:, :, 1]),
coords=coords[offset:(offset + BATCH_SIZE), :],
pixels=PIXELperLABEL,
zoom=ZOOM_LEVEL)
],
axis=0)
else:
suspect = np.concatenate(
[suspect, np.array(test_predictions[:, :, 1])], axis=0)
if (step + 1) % 10 == 0:
print('%s: Processing batch %d out of %d' %
(time.ctime(), step, num_batches + 1))
np.savetxt('tmp_images/suspect_all' + time.ctime()[3:10] +
'.csv',
suspect,
fmt='%.6f',
delimiter=', ')
if n % BATCH_SIZE > 0:
offset = num_batches * BATCH_SIZE
batch_data = load_dataset(coords=coords[offset:n, :])
feed_dict = {test_data_node2: batch_data}
# Run the graph and fetch some of the nodes.
test_predictions = s.run(model(test_data_node2),
feed_dict=feed_dict)
if gps:
suspect = np.concatenate([
suspect,
labels_GPS_list(labels=np.array(test_predictions[:, :, 1]),
coords=coords[offset:n, :],
pixels=PIXELperLABEL,
zoom=ZOOM_LEVEL)
],
axis=0)
else:
suspect = np.concatenate(
[suspect, np.array(test_predictions[:, :, 1])], axis=0)
# Finally save the result!
print('%s: Result with %d rows and %d columns. ' %
(time.ctime(), suspect.shape[0], suspect.shape[1]))
if csv:
np.savetxt('tmp_images/assemble1_susp.csv',
suspect,
fmt='%.6f',
delimiter=', ')
else:
return (suspect)
#np.savetxt('tmp_images/ids.csv', data_matrix, fmt='%.6f', delimiter=', ')
s.close()
def main(csv=True, coords=None, gps=False): # pylint: disable=unused-argument
if coords is None:
coords = np.array([[-1.353887, 50.965639], [-1.386731, 50.935744],
[-1.401907, 50.925657], [-1.368191, 50.914509],
[-1.354101, 50.90988], [-1.34853, 50.902636],
[-0.964885, 50.81373]])
n = coords.shape[0]
print('%s: Loading nnet...' % time.ctime())
# For the validation and test data, we'll just hold the entire dataset in
# one constant node.
#test_data_node = tf.constant(data)
test_data_node = tf.placeholder(tf.float32,
shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE,
NUM_CHANNELS))
test_data_node2 = tf.placeholder(tf.float32,
shape=(n % BATCH_SIZE, IMAGE_SIZE,
IMAGE_SIZE, NUM_CHANNELS))
test_centre_node = tf.placeholder(tf.float32,
shape=(BATCH_SIZE, SINGLE_SIZE,
SINGLE_SIZE, NUM_CHANNELS))
test_centre_node2 = tf.placeholder(tf.float32,
shape=(n % BATCH_SIZE, SINGLE_SIZE,
SINGLE_SIZE, NUM_CHANNELS))
# The variables below hold all the trainable weights. They are passed an
# initial value which will be assigned when when we call:
# {tf.initialize_all_variables().run()}
depth1 = SINGLE_SIZE * SINGLE_SIZE * NUM_CHANNELS
conv1_weights = tf.constant( # shape: [SINGLE_SIZE, SINGLE_SIZE, NUM_CHANNELS, depth1]
np.reshape(
np.diag([1 for _ in range(depth1)]).astype('float32'),
[SINGLE_SIZE, SINGLE_SIZE, NUM_CHANNELS, depth1]))
#centre_weights =tf.constant( # shape: [IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS, depth1]
# get_centre(depth1))
#conv1_biases = tf.constant(np.zeros([depth1], dtype='float32'))
# conv2_weights = tf.Variable(tf.concat(3,[tf.ones([1,1,depth1,1])*2*(.5-i%2) for i in range(2*depth1)]))
conv2_weights = tf.concat(
3, [tf.ones([1, 1, depth1, 1]), -tf.ones([1, 1, depth1, 1])])
kernel_weights = tf.ones(
[SINGLE_SIZE, SINGLE_SIZE, NUM_CHANNELS,
1]) # gaussian or linear kernel to use instead avg_pool
def model(data, centre_data, train=False):
"""The Model definition."""
# 1. extract features from centre and every (other) patch in the same way (first attempt: identity)
conv1 = tf.nn.conv2d(data,
conv1_weights,
strides=[1, PIXELperLABEL, PIXELperLABEL, 1],
padding='VALID')
centre = tf.nn.conv2d(centre_data,
conv1_weights,
strides=[1, PIXELperLABEL, PIXELperLABEL, 1],
padding='VALID')
square_dim = conv1.get_shape().as_list()[2]
rep_centre = tf.concat(1, [
tf.concat(2, [centre for _ in range(square_dim)])
for _ in range(square_dim)
])
layer1 = tf.add(conv1, -rep_centre)
# 2. compute absolute values of the differences (there must be a better way ...)
conv2 = tf.nn.depthwise_conv2d(layer1,
conv2_weights,
strides=[1, 1, 1, 1],
padding='SAME')
# conv2 = tf.nn.conv2d(relu1, conv2_weights, strides=[1, 1, 1, depth1], padding='VALID')
conv2_shape = conv2.get_shape().as_list()
layer2 = tf.reshape(conv2, [
conv2_shape[0],
conv2_shape[1] * conv2_shape[2] * conv2_shape[3] / 2, 2, 1
])
pool1 = tf.nn.max_pool(layer2,
ksize=[1, 1, 2, 1],
strides=[1, 1, 2, 1],
padding='SAME')
pool1_shape = pool1.get_shape().as_list()
layer3 = tf.reshape(
pool1, [pool1_shape[0], pool1_shape[1] / depth1, depth1, 1])
# 3. aggregate the absolute pixel differences (first attempt: average)
out_pool = tf.nn.avg_pool(layer3,
ksize=[1, 1, depth1, 1],
strides=[1, 1, depth1, 1],
padding='SAME')
out_shape = out_pool.get_shape().as_list()
print(
'Dimensions of network Tensors: [minibatch size, ..dims.. , channels]'
)
print(data.get_shape().as_list(), '->',
conv1.get_shape().as_list(), '->',
layer1.get_shape().as_list(), '->',
conv2.get_shape().as_list(), '->',
layer2.get_shape().as_list(), '->',
pool1.get_shape().as_list(), '->',
layer3.get_shape().as_list(), '->', out_shape)
return tf.reshape(out_pool, [out_shape[0], out_shape[1]])
# Create a local session to run this computation.
with tf.Session() as s:
# Run all the initializers to prepare the trainable parameters.
tf.initialize_all_variables().run()
num_batches = np.floor(n / BATCH_SIZE).astype('int16')
print('%s: Initialized! (Loading data in %d minibatches))' %
(time.ctime(), num_batches + 1))
if gps:
from get_data.map_coverage import MercatorProjection, G_Point, G_LatLng
from get_data.labels_GPS import labels_GPS, labels_suspect, labels_GPS_list
suspect = np.zeros((0, 2))
else:
suspect = np.zeros((0, LABEL_SIZE**2))
for step in range(num_batches):
offset = (step * BATCH_SIZE)
batch_data = load_dataset(coords=coords[offset:(offset +
BATCH_SIZE), :])
feed_dict = {
test_data_node: batch_data,
test_centre_node: get_centre(batch_data)
}
# Run the graph and fetch some of the nodes.
test_predictions = s.run(model(test_data_node, test_centre_node),
feed_dict=feed_dict)
print('.'),
if gps:
suspect = np.concatenate([
suspect,
labels_GPS_list(
labels=np.array(test_predictions[:, :, 1]),
coords=coords[offset:(offset + BATCH_SIZE), :],
pixels=PIXELperLABEL,
zoom=ZOOM_LEVEL)
],
axis=0)
else:
suspect = np.concatenate(
[suspect, np.array(test_predictions[:, :, 1])], axis=0)
if (step + 1) % 10 == 0:
print('%s: Processing batch %d out of %d' %
(time.ctime(), step, num_batches + 1))
np.savetxt('tmp_images/suspect_tmp' + time.ctime()[3:10] +
'.csv',
suspect,
fmt='%.6f',
delimiter=', ')
if n % BATCH_SIZE > 0:
offset = num_batches * BATCH_SIZE
batch_data = load_dataset(coords=coords[offset:n, :])
feed_dict = {
test_data_node2: batch_data,
test_centre_node2: get_centre(batch_data)
}
# Run the graph and fetch some of the nodes.
test_predictions = s.run(model(test_data_node2, test_centre_node2),
feed_dict=feed_dict)
if gps:
suspect = np.concatenate([
suspect,
labels_GPS_list(labels=np.array(test_predictions[:, :, 1]),
coords=coords[offset:n, :],
pixels=PIXELperLABEL,
zoom=ZOOM_LEVEL)
],
axis=0)
else:
suspect = np.concatenate(
[suspect, np.array(test_predictions)], axis=0)
# Finally save the result!
print('%s: Result with %d rows and %d columns. ' %
(time.ctime(), suspect.shape[0], suspect.shape[1]))
if csv:
np.savetxt('tmp_images/centre_heat.csv',
suspect,
fmt='%.6f',
delimiter=', ')
else:
return (suspect)
#np.savetxt('tmp_images/ids.csv', data_matrix, fmt='%.6f', delimiter=', ')
s.close()
