def setup_graph():
graph_params = {}
graph_params['graph'] = tf.Graph()
with graph_params['graph'].as_default():
model_params = model.params()
graph_params['target_image'] = tf.placeholder(
tf.float32,
shape=(1, model.IMG_HEIGHT, model.IMG_WIDTH, model.IMG_CHANNELS))
logits = model.cnn(graph_params['target_image'],
model_params,
keep_prob=1.0)
graph_params['pred'] = tf.nn.softmax(logits)
graph_params['saver'] = tf.train.Saver()
return graph_params
def main():
if len(sys.argv) > 1:
test_image_fn = sys.argv[1]
if not os.path.exists(test_image_fn):
print("Not found:", test_image_fn)
sys.exit(-1)
else:
# Select a test image from a test directory
test_dirs = [
os.path.join(common.CROPPED_AUG_IMAGE_DIR, class_name, 'test')
for class_name in common.CLASS_NAME
]
test_dir = np.random.choice(test_dirs)
test_images_fn = [test_image for test_image in os.listdir(test_dir)]
test_image_fn = np.random.choice(test_images_fn, 1)[0]
test_image_fn = os.path.join(test_dir, test_image_fn)
print("Test image:", test_image_fn)
# Open and resize a test image
if common.CNN_IN_CH == 1:
test_image_org = skimage.io.imread(test_image_fn, as_grey=True)
test_image_org = test_image_org.reshape(
common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH, common.CNN_IN_CH)
else:
test_image_org = skimage.io.imread(test_image_fn)
if test_image_org.shape != (common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH):
test_image_org = imresize(
test_image_org, (common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH),
interp='bicubic')
test_image_org = preprocess.scaling(test_image_org)
test_image = test_image_org.reshape(
(1, common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH)).astype(np.float32)
# Training model
graph = tf.Graph()
with graph.as_default():
# Weights and biases
model_params = model.params()
# restore weights
f = "weights.npz"
if os.path.exists(f):
initial_weights = load_initial_weights(f)
else:
initial_weights = None
if initial_weights is not None:
assert len(initial_weights) == len(model_params)
assign_ops = [
w.assign(v) for w, v in zip(model_params, initial_weights)
]
# A placeholder for a test image
tf_test_image = tf.constant(test_image)
# model
logits = model.cnn(tf_test_image, model_params, keep_prob=1.0)
test_pred = tf.nn.softmax(logits)
# Restore ops
saver = tf.train.Saver()
# Recognize a brand logo of test image
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
if initial_weights is not None:
session.run(assign_ops)
print('initialized by pre-learned weights')
elif os.path.exists("models"):
save_path = "models/deep_logo_model"
saver.restore(session, save_path)
print('Model restored')
else:
print('initialized')
pred = session.run([test_pred])
print("Class name:", common.CLASS_NAME[np.argmax(pred)])
print("Probability:", np.max(pred))
def main():
if len(sys.argv) > 1:
test_image_fn = sys.argv[1]
if not os.path.exists(test_image_fn):
print("Not found:", test_image_fn)
sys.exit(-1)
else:
# Select a test image from a test directory
test_dirs = [
os.path.join(common.CROPPED_AUG_IMAGE_DIR, class_name, 'test')
for class_name in common.CLASS_NAME
]
test_dir = np.random.choice(test_dirs)
test_images_fn = [test_image for test_image in os.listdir(test_dir)]
test_image_fn = np.random.choice(test_images_fn, 1)[0]
test_image_fn = os.path.join(test_dir, test_image_fn)
print("Test image:", test_image_fn)
# Open and resize a test image
if common.CNN_IN_CH == 1:
test_image_org = skimage.io.imread(test_image_fn, as_grey=True)
test_image_org = test_image_org.reshape(common.CNN_IN_HEIGHT,
common.CNN_IN_WIDTH,
common.CNN_IN_CH)
else:
test_image_org = skimage.io.imread(test_image_fn)
if test_image_org.shape != (common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH):
test_image_org = imresize(test_image_org,
(common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH),
interp='bicubic')
test_image_org = preprocess.scaling(test_image_org)
test_image = test_image_org.reshape(
(1, common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH)).astype(np.float32)
# Training model
graph = tf.Graph()
with graph.as_default():
# Weights and biases
model_params = model.params()
# restore weights
f = "weights.npz"
if os.path.exists(f):
initial_weights = load_initial_weights(f)
else:
initial_weights = None
if initial_weights is not None:
assert len(initial_weights) == len(model_params)
assign_ops = [
w.assign(v) for w, v in zip(model_params, initial_weights)
]
# A placeholder for a test image
tf_test_image = tf.constant(test_image)
# model
logits = model.cnn(tf_test_image, model_params, keep_prob=1.0)
test_pred = tf.nn.softmax(logits)
# Restore ops
saver = tf.train.Saver()
# Recognize a brand logo of test image
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
if initial_weights is not None:
session.run(assign_ops)
print('initialized by pre-learned weights')
elif os.path.exists("models"):
save_path = "models/deep_logo_model"
saver.restore(session, save_path)
print('Model restored')
else:
print('initialized')
pred = session.run([test_pred])
print("Class name:", common.CLASS_NAME[np.argmax(pred)])
print("Probability:", np.max(pred))
def main():
if len(sys.argv) > 1:
f = np.load(sys.argv[1])
# f.files has unordered keys ['arr_8', 'arr_9', 'arr_6'...]
# Sorting keys by value of numbers
initial_weights = [
f[n] for n in sorted(f.files, key=lambda s: int(s[4:]))
]
else:
initial_weights = None
# read input data
dataset, labels = read_data()
train_dataset, train_labels = reformat(dataset[0], labels[0])
valid_dataset, valid_labels = reformat(dataset[1], labels[1])
test_dataset, test_labels = reformat(dataset[2], labels[2])
print('Training set', train_dataset.shape, train_labels.shape)
print('Valid set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
# Training model
graph = tf.Graph()
with graph.as_default():
# Weights and biases
model_params = model.params()
# Initial weights
if initial_weights is not None:
assert len(model_params) == len(initial_weights)
assign_ops = [
w.assign(v) for w, v in zip(model_params, initial_weights)
]
# Input data
tf_train_dataset = tf.placeholder(
tf.float32,
shape=(FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width,
FLAGS.num_channels))
tf_train_labels = tf.placeholder(tf.float32,
shape=(FLAGS.batch_size,
model.NUM_CLASSES))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
# Training computation
logits = model.cnn(tf_train_dataset, model_params, keep_prob=0.5)
with tf.name_scope('loss'):
loss = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=tf_train_labels))
tf.summary.scalar('loss', loss)
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(loss)
# Predictions for the training, validation, and test data
train_prediction = tf.nn.softmax(logits)
valid_prediction = tf.nn.softmax(
model.cnn(tf_valid_dataset, model_params, keep_prob=1.0))
test_prediction = tf.nn.softmax(
model.cnn(tf_test_dataset, model_params, keep_prob=1.0))
# Merge all summaries
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.train_dir + '/train')
# Add ops to save and restore all the variables
saver = tf.train.Saver()
# Do training
cpu_count = 1
config = tf.ConfigProto(intra_op_parallelism_threads=cpu_count,
inter_op_parallelism_threads=cpu_count,
allow_soft_placement=True,
device_count={"CPU": cpu_count})
with tf.Session(graph=graph, config=config) as session:
tf.global_variables_initializer().run()
if initial_weights is not None:
session.run(assign_ops)
print('initialized by pre-learned values')
else:
print('initialized')
for step in range(FLAGS.max_steps):
offset = (step * FLAGS.batch_size) % (train_labels.shape[0] -
FLAGS.batch_size)
batch_data = train_dataset[offset:(offset +
FLAGS.batch_size), :, :, :]
batch_labels = train_labels[offset:(offset + FLAGS.batch_size), :]
feed_dict = {
tf_train_dataset: batch_data,
tf_train_labels: batch_labels
}
try:
_, l, predictions = session.run(
[optimizer, loss, train_prediction], feed_dict=feed_dict)
if step % 50 == 0:
summary, _ = session.run([merged, optimizer],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
print('Minibatch loss at step %d: %f' % (step, l))
print('Minibatch accuracy: %.1f%%' %
accuracy(predictions, batch_labels))
print('Validation accuracy: %.1f%%' %
accuracy(valid_prediction.eval(), valid_labels))
except KeyboardInterrupt:
last_weights = [p.eval() for p in model_params]
np.savez("weights.npz", *last_weights)
return last_weights
print('Test accuracy: %.1f%%' %
accuracy(test_prediction.eval(), test_labels))
# Save the variables to disk.
save_dir = "models"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir, "deep_logo_model")
saved = saver.save(session, save_path)
print("Model saved in file: %s" % saved)
def main():
if len(sys.argv) > 1:
f = np.load(sys.argv[1])
# f.files has unordered keys ['arr_8', 'arr_9', 'arr_6'...]
# Sorting keys by value of numbers
initial_weights = [
f[n] for n in sorted(f.files, key=lambda s: int(s[4:]))
]
else:
initial_weights = None
# read input data
dataset, labels = read_data()
train_dataset, train_labels = reformat(dataset[0], labels[0])
valid_dataset, valid_labels = reformat(dataset[1], labels[1])
test_dataset, test_labels = reformat(dataset[2], labels[2])
print('Training set', train_dataset.shape, train_labels.shape)
print('Valid set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
# Training model
graph = tf.Graph()
with graph.as_default():
# Weights and biases
model_params = model.params()
# Initial weights
if initial_weights is not None:
assert len(model_params) == len(initial_weights)
assign_ops = [
w.assign(v) for w, v in zip(model_params, initial_weights)
]
# Input data
tf_train_dataset = tf.placeholder(
tf.float32,
shape=(FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width,
FLAGS.num_channels))
tf_train_labels = tf.placeholder(
tf.float32, shape=(FLAGS.batch_size, model.NUM_CLASSES))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
# Training computation
logits = model.cnn(tf_train_dataset, model_params, keep_prob=0.5)
with tf.name_scope('loss'):
loss = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=tf_train_labels))
tf.summary.scalar('loss', loss)
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(loss)
# Predictions for the training, validation, and test data
train_prediction = tf.nn.softmax(logits)
valid_prediction = tf.nn.softmax(
model.cnn(tf_valid_dataset, model_params, keep_prob=1.0))
test_prediction = tf.nn.softmax(
model.cnn(tf_test_dataset, model_params, keep_prob=1.0))
# Merge all summaries
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.train_dir + '/train')
# Add ops to save and restore all the variables
saver = tf.train.Saver()
# Do training
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
if initial_weights is not None:
session.run(assign_ops)
print('initialized by pre-learned values')
else:
print('initialized')
for step in range(FLAGS.max_steps):
offset = (step * FLAGS.batch_size) % (
train_labels.shape[0] - FLAGS.batch_size)
batch_data = train_dataset[offset:(offset + FLAGS.batch_size
), :, :, :]
batch_labels = train_labels[offset:(offset + FLAGS.batch_size), :]
feed_dict = {
tf_train_dataset: batch_data,
tf_train_labels: batch_labels
}
try:
_, l, predictions = session.run(
[optimizer, loss, train_prediction], feed_dict=feed_dict)
if step % 50 == 0:
summary, _ = session.run(
[merged, optimizer], feed_dict=feed_dict)
train_writer.add_summary(summary, step)
print('Minibatch loss at step %d: %f' % (step, l))
print('Minibatch accuracy: %.1f%%' % accuracy(
predictions, batch_labels))
print('Validation accuracy: %.1f%%' % accuracy(
valid_prediction.eval(), valid_labels))
except KeyboardInterrupt:
last_weights = [p.eval() for p in model_params]
np.savez("weights.npz", *last_weights)
return last_weights
print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(),
test_labels))
# Save the variables to disk.
save_dir = "models"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir, "deep_logo_model")
saved = saver.save(session, save_path)
print("Model saved in file: %s" % saved)
