def test_bicubic_resize_name_shape(self):
scope = 'unittest'
model_config = DeconvModuleConfig()
TEST_MODULE_NAME = 'bicubic_resize'
with tf.name_scope(name=scope):
input_width = 2
input_height = 2
input_shape = [1, input_height, input_width, 1]
unpool_rate = 2
expected_output_shape = [
input_shape[0], input_shape[1] * unpool_rate,
input_shape[2] * unpool_rate, input_shape[3]
]
inputs = create_test_input(batchsize=input_shape[0],
heightsize=input_shape[1],
widthsize=input_shape[2],
channelnum=input_shape[3])
y_unpool2, midpoint = get_deconv_module(
inputs=inputs,
unpool_rate=unpool_rate,
module_type=TEST_MODULE_NAME,
model_config=model_config,
scope=scope)
expected_midpoint = ModuleEndpointName(
conv_type=TEST_MODULE_NAME,
input_shape=input_shape,
output_shape=expected_output_shape)
print('------------------------------------------------')
print('[tfTest] run test_bicubic_resize_name_shape()')
print('[tfTest] midpoint name and shape')
print('[tfTest] unpool rate = %s' % unpool_rate)
self.assertItemsEqual(midpoint.keys(), expected_midpoint.name_list)
for name, shape in six.iteritems(expected_midpoint.shape_dict):
print('%s : shape = %s' % (name, shape))
self.assertListEqual(midpoint[name].get_shape().as_list(),
shape)
def test_nearest_neighbor_unpool(self):
scope = 'unittest'
TEST_MODULE_NAME = 'nearest_neighbor_unpool'
input_width = 2
input_height = 2
input_shape = [1, input_height, input_width, 1]
module_graph = tf.Graph()
with module_graph.as_default():
x = tf.to_float([[0, 1], [2, 3]])
x = tf.reshape(x, shape=input_shape)
y_unpool2_test1_expected = tf.to_float([[0, 0, 1, 1], [0, 0, 1, 1],
[2, 2, 3, 3], [2, 2, 3,
3]])
y_unpool2_test1_expected = tf.reshape(
y_unpool2_test1_expected,
shape=[1, input_height * 2, input_width * 2, 1])
y_unpool3_test1_expected = tf.to_float([[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[2, 2, 2, 3, 3, 3],
[2, 2, 2, 3, 3, 3],
[2, 2, 2, 3, 3, 3]])
y_unpool3_test1_expected = tf.reshape(
y_unpool3_test1_expected,
shape=[1, input_height * 3, input_width * 3, 1])
y_unpool2_test1, end_point_test1 = get_deconv_module(
inputs=x,
unpool_rate=2,
module_type=TEST_MODULE_NAME,
layer_index=0,
scope=scope)
y_unpool3_test1, end_point_test1 = get_deconv_module(
inputs=x,
unpool_rate=3,
module_type=TEST_MODULE_NAME,
layer_index=1,
scope=scope)
# tensorboard graph summary =============
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
tb_logdir_path = getcwd() + '/tf_logs'
tb_logdir = "{}/run-{}/".format(tb_logdir_path, now)
if not tf.gfile.Exists(tb_logdir_path):
tf.gfile.MakeDirs(tb_logdir_path)
# summary
tb_summary_writer = tf.summary.FileWriter(logdir=tb_logdir)
tb_summary_writer.add_graph(module_graph)
tb_summary_writer.close()
with self.test_session(graph=module_graph) as sess:
print('--------------------------------------------')
print('[tfTest] run test_nearest_neighbor_unpool()')
sess.run(tf.global_variables_initializer())
self.assertAllClose(y_unpool2_test1.eval(),
y_unpool2_test1_expected.eval())
self.assertAllClose(y_unpool3_test1.eval(),
y_unpool3_test1_expected.eval())
# print ('[test1] Result of unpool rate2 = %s' % y_unpool2_test1.eval())
# print ('[test1] Expected of unpool rate2 = %s' % y_unpool2_test1_expected.eval())
# print ('[test1] Result of unpool rate3 = %s' % y_unpool3_test1.eval())
# print ('[test1] Expected of unpool rate3 = %s' % y_unpool3_test1_expected.eval())
print('[test1] input shape of x = %s' % x.get_shape().as_list())
print('[test1] output shape of y_unpool2 = %s' %
y_unpool2_test1.get_shape().as_list())
print('[test1] output shape of y_unpool3 = %s' %
y_unpool3_test1.get_shape().as_list())
print('------------------------------------------------')
def test_nearest_neighbor_unpool(self):
scope = 'unittest'
TEST_MODULE_NAME = 'nearest_neighbor_unpool'
input_width = 2
input_height = 2
input_shape = [1, input_height, input_width, 1]
x = tf.to_float([[0, 1], [2, 3]])
x = tf.reshape(x, shape=input_shape)
y_unpool2_test1_expected = tf.to_float([[0, 0, 1, 1], [0, 0, 1, 1],
[2, 2, 3, 3], [2, 2, 3, 3]])
y_unpool2_test1_expected = tf.reshape(
y_unpool2_test1_expected,
shape=[1, input_height * 2, input_width * 2, 1])
y_unpool3_test1_expected = tf.to_float([[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[2, 2, 2, 3, 3, 3],
[2, 2, 2, 3, 3, 3],
[2, 2, 2, 3, 3, 3]])
y_unpool3_test1_expected = tf.reshape(
y_unpool3_test1_expected,
shape=[1, input_height * 3, input_width * 3, 1])
y_unpool2_test1, end_point_test1 = get_deconv_module(
inputs=x,
unpool_rate=2,
module_type=TEST_MODULE_NAME,
layer_index=0,
scope=scope)
y_unpool3_test1, end_point_test1 = get_deconv_module(
inputs=x,
unpool_rate=3,
module_type=TEST_MODULE_NAME,
layer_index=1,
scope=scope)
with self.test_session() as sess:
print('--------------------------------------------')
print('[tfTest] run test_nearest_neighbor_unpool()')
sess.run(tf.global_variables_initializer())
self.assertAllClose(y_unpool2_test1.eval(),
y_unpool2_test1_expected.eval())
self.assertAllClose(y_unpool3_test1.eval(),
y_unpool3_test1_expected.eval())
# print ('[test1] Result of unpool rate2 = %s' % y_unpool2_test1.eval())
# print ('[test1] Expected of unpool rate2 = %s' % y_unpool2_test1_expected.eval())
# print ('[test1] Result of unpool rate3 = %s' % y_unpool3_test1.eval())
# print ('[test1] Expected of unpool rate3 = %s' % y_unpool3_test1_expected.eval())
print('[test1] input shape of x = %s' % x.get_shape().as_list())
print('[test1] output shape of y_unpool2 = %s' %
y_unpool2_test1.get_shape().as_list())
print('[test1] output shape of y_unpool3 = %s' %
y_unpool3_test1.get_shape().as_list())
def test_transconv_unknown_batchsize_shape(self):
'''
this func check the below test case:
- when a module is built without specifying batch_norm size,
check whether the model output has a proper batch_size given by an input
'''
scope = 'unittest'
model_config = DeconvModuleConfig()
TEST_MODULE_NAME = 'conv2dtrans_unpool'
batch_size = 1
input_width = 2
input_height = 2
input_shape = [None, input_height, input_width, 1]
unpool_rate = 3
module_graph = tf.Graph()
with module_graph.as_default():
inputs = create_test_input(batchsize=input_shape[0],
heightsize=input_shape[1],
widthsize=input_shape[2],
channelnum=input_shape[3])
module_output, midpoint = get_deconv_module(
inputs=inputs,
unpool_rate=unpool_rate,
module_type=TEST_MODULE_NAME,
model_config=model_config,
scope=scope)
init = tf.global_variables_initializer()
ckpt_saver = tf.train.Saver(tf.global_variables())
expected_prefix = scope + '0'
self.assertTrue(module_output.op.name.startswith(expected_prefix))
self.assertListEqual(module_output.get_shape().as_list(), [
None, input_shape[1] * unpool_rate,
input_shape[2] * unpool_rate, input_shape[3]
])
input_shape[0] = batch_size
expected_output_shape = [
input_shape[0], input_shape[1] * unpool_rate,
input_shape[2] * unpool_rate, input_shape[3]
]
# which generate a sample image using np.arange()
print('------------------------------------------------')
print('[tfTest] run test_transconv_unknown_batchsize_shape()')
print('[tfTest] unpool rate = %s' % unpool_rate)
images = create_test_input(batchsize=input_shape[0],
heightsize=input_shape[1],
widthsize=input_shape[2],
channelnum=input_shape[3])
# tensorboard graph summary =============
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
tb_logdir_path = getcwd() + '/tf_logs'
tb_logdir = "{}/run-{}/".format(tb_logdir_path, now)
if not tf.gfile.Exists(tb_logdir_path):
tf.gfile.MakeDirs(tb_logdir_path)
# summary
tb_summary_writer = tf.summary.FileWriter(logdir=tb_logdir)
tb_summary_writer.add_graph(module_graph)
tb_summary_writer.close()
with self.test_session(graph=module_graph) as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(module_output, {inputs: images.eval()})
self.assertListEqual(list(output.shape), expected_output_shape)
print('[TfTest] output shape = %s' % list(output.shape))
print('[TfTest] expected_output_shape = %s' %
expected_output_shape)
# tflite compatibility check
expected_output_name = 'unittest0/' + TEST_MODULE_NAME + '_out'
output_node_name = 'unittest0/' + TEST_MODULE_NAME + '/' + expected_output_name
pbsavedir, pbfilename, ckptfilename = \
save_pb_ckpt(module_name=TEST_MODULE_NAME,
init=init,
sess=sess,
ckpt_saver=ckpt_saver)
print('------------------------------------------------')
tflitedir = getcwd() + '/tflite_files/'
if not tf.gfile.Exists(tflitedir):
tf.gfile.MakeDirs(tflitedir)
tflitefilename = TEST_MODULE_NAME + '.tflite'
toco = tf.contrib.lite.TocoConverter.from_session(
sess=sess,
input_tensors=[inputs],
output_tensors=[module_output])
tflite_model = toco.convert()
open(tflitedir + '/' + tflitefilename, 'wb').write(tflite_model)
def test_transconv_unknown_batchsize_shape(self):
'''
this func check the below test case:
- when a module is built without specifying batch_norm size,
check whether the model output has a proper batch_size given by an input
'''
scope = 'unitest'
model_config = ModelTestConfig()
TEST_MODULE_NAME = 'conv2dtrans_unpool'
batch_size = 1
input_width = 2
input_height = 2
input_shape = [None, input_height, input_width, 1]
unpool_rate = 3
module_graph = tf.Graph()
with module_graph.as_default():
inputs = create_test_input(batchsize=input_shape[0],
heightsize=input_shape[1],
widthsize=input_shape[2],
channelnum=input_shape[3])
module_output, midpoint = get_deconv_module(
inputs=inputs,
unpool_rate=unpool_rate,
module_type=TEST_MODULE_NAME,
model_config=model_config,
scope=scope)
expected_prefix = scope
self.assertTrue(module_output.op.name.startswith(expected_prefix))
self.assertListEqual(module_output.get_shape().as_list(), [
None, input_shape[1] * unpool_rate,
input_shape[2] * unpool_rate, input_shape[3]
])
input_shape[0] = batch_size
expected_output_shape = [
input_shape[0], input_shape[1] * unpool_rate,
input_shape[2] * unpool_rate, input_shape[3]
]
# which generate a sample image using np.arange()
print('------------------------------------------------')
print('[tfTest] run test_transconv_unknown_batchsize_shape()')
print('[tfTest] unpool rate = %s' % unpool_rate)
images = create_test_input(batchsize=input_shape[0],
heightsize=input_shape[1],
widthsize=input_shape[2],
channelnum=input_shape[3])
# tensorboard graph summary =============
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
tb_logdir_path = getcwd() + '/tf_logs'
tb_logdir = "{}/run-{}/".format(tb_logdir_path, now)
if not tf.gfile.Exists(tb_logdir_path):
tf.gfile.MakeDirs(tb_logdir_path)
# summary
tb_summary_writer = tf.summary.FileWriter(logdir=tb_logdir)
tb_summary_writer.add_graph(module_graph)
tb_summary_writer.close()
# write pbfile of graph_def
savedir = getcwd() + '/pbfiles'
if not tf.gfile.Exists(savedir):
tf.gfile.MakeDirs(savedir)
pbfilename = TEST_MODULE_NAME + '.pb'
pbtxtfilename = TEST_MODULE_NAME + '.pbtxt'
with self.test_session(graph=module_graph) as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(module_output, {inputs: images.eval()})
self.assertListEqual(list(output.shape), expected_output_shape)
print('[TfTest] output shape = %s' % list(output.shape))
print('[TfTest] expected_output_shape = %s' %
expected_output_shape)
print("TF graph_def is saved in pb at %s." % savedir + pbfilename)
tf.train.write_graph(graph_or_graph_def=sess.graph_def,
logdir=savedir,
name=pbfilename)
tf.train.write_graph(graph_or_graph_def=sess.graph_def,
logdir=savedir,
name=pbtxtfilename,
as_text=True)