def test_midpoint_name_shape(self):
ch_in_num = 256
ch_out_num = 256
model_config = ConvModuleConfig()
scope = 'unittest'
stride = 2
kernel_size = 3
# test input shape
input_shape = [None, 64, 64, ch_in_num]
# expected output shape
expected_output_height = input_shape[1] / stride
expected_output_width = input_shape[2] / stride
expected_output_shape = [
input_shape[0], expected_output_height, expected_output_width,
ch_out_num
]
inputs = create_test_input(batchsize=input_shape[0],
heightsize=input_shape[1],
widthsize=input_shape[2],
channelnum=input_shape[3])
module_output, mid_points = get_module(ch_in=inputs,
ch_out_num=ch_out_num,
model_config=model_config,
stride=stride,
kernel_size=kernel_size,
conv_type=TEST_MODULE_NAME,
scope=scope)
expected_midpoint = ModuleEndpointName(
conv_type=TEST_MODULE_NAME,
input_shape=input_shape,
output_shape=expected_output_shape)
print('------------------------------------------------')
print('[tfTest] run test_midpoint_name_shape()')
print('[tfTest] midpoint name and shape')
self.assertItemsEqual(mid_points.keys(), expected_midpoint.name_list)
for name, shape in six.iteritems(expected_midpoint.shape_dict):
print('%s : shape = %s' % (name, shape))
self.assertListEqual(mid_points[name].get_shape().as_list(), shape)
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_endpoint_name_shape(self):
ch_in_num = 3
ch_out_num = 256
model_config = ModelTestConfig()
scope = 'unittest'
stride = 2
kernel_size = 3
# test input shape
input_shape = [None,64,64,ch_in_num]
# expected output shape
expected_output_height = input_shape[1]/stride
expected_output_width = input_shape[2]/stride
expected_output_shape = [input_shape[0],expected_output_height,expected_output_width,ch_out_num]
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,end_points = get_module(ch_in=inputs,
ch_out_num=ch_out_num,
model_config=model_config,
stride=stride,
kernel_size=kernel_size,
conv_type=TEST_MODULE_NAME,
scope=scope)
expected_output_name = 'unittest0/'+TEST_MODULE_NAME+'_out'
print('------------------------------------------------')
print ('[tfTest] run test_endpoint_name_shape()')
print ('[tfTest] module name = %s' % TEST_MODULE_NAME)
print ('[tfTest] model output name = %s' % end_points.keys()[-1])
print ('[tfTest] expected output name = %s' % expected_output_name)
print ('[tfTest] model output shape = %s' % module_output.get_shape().as_list())
print ('[tfTest] expected output shape = %s' % expected_output_shape)
# check name of the module output
self.assertTrue(expected_output_name in end_points)
# check shape of the module output
self.assertListEqual(module_output.get_shape().as_list(),expected_output_shape)
print('------------------------------------------------')
print ('[tfTest] write pbfile')
self.assertTrue(expected_output_name in end_points)
# 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'
graph = tf.get_default_graph()
with self.test_session(graph=module_graph) as sess:
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)
def test_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
'''
ch_in_num = 3
ch_out_num = 256
model_config = ModelTestConfig()
scope = 'unittest'
stride = 2
kernel_size = 3
# test input shape
input_shape = [None, 64, 64, ch_in_num]
# test batch size
batch_size = 1
# expected output shape
expected_output_height = input_shape[1]/stride
expected_output_width = input_shape[2]/stride
# which generate a tf.placeholder
inputs = create_test_input(None,
heightsize=input_shape[1],
widthsize=input_shape[2],
channelnum=input_shape[3])
module_output, mid_points = get_module(ch_in=inputs,
ch_out_num=ch_out_num,
model_config=model_config,
stride=stride,
kernel_size=kernel_size,
conv_type=TEST_MODULE_NAME,
scope=scope)
input_shape[0] = batch_size
expected_output_shape = [input_shape[0], expected_output_height, expected_output_width, ch_out_num]
expected_prefix = 'unittest0/'+TEST_MODULE_NAME
self.assertTrue(module_output.op.name.startswith(expected_prefix))
self.assertListEqual(module_output.get_shape().as_list(),
[None,expected_output_height,expected_output_width,ch_out_num])
# which generate a sample image using np.arange()
print('------------------------------------------------')
print ('[tfTest] run test_unknown_batchsize_shape()')
images = create_test_input( batchsize=input_shape[0],
heightsize=input_shape[1],
widthsize=input_shape[2],
channelnum=input_shape[3])
with self.test_session() 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)
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_endpoint_name_shape(self):
ch_in_num = 256
ch_out_num = 256
model_config = ConvModuleConfig()
scope = 'unittest'
stride = 2
kernel_size = 3
# test input shape
input_shape = [1, 64, 64, ch_in_num]
# expected output shape
expected_output_height = input_shape[1] / stride
expected_output_width = input_shape[2] / stride
expected_output_shape = [
input_shape[0], expected_output_height, expected_output_width,
ch_out_num
]
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, end_points = get_module(ch_in=inputs,
ch_out_num=ch_out_num,
model_config=model_config,
stride=stride,
kernel_size=kernel_size,
conv_type=TEST_MODULE_NAME,
scope=scope)
init = tf.global_variables_initializer()
ckpt_saver = tf.train.Saver(tf.global_variables())
expected_output_name = 'unittest0/' + TEST_MODULE_NAME + '_out'
print('------------------------------------------------')
print('[tfTest] run test_endpoint_name_shape()')
print('[tfTest] module name = %s' % TEST_MODULE_NAME)
print('[tfTest] model output name = %s' % end_points.keys()[-1])
print('[tfTest] expected output name = %s' % expected_output_name)
print('[tfTest] model output shape = %s' %
module_output.get_shape().as_list())
print('[tfTest] expected output shape = %s' % expected_output_shape)
# check name of the module output
self.assertTrue(expected_output_name in end_points)
# check shape of the module output
self.assertListEqual(module_output.get_shape().as_list(),
expected_output_shape)
self.assertTrue(expected_output_name in end_points)
# 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:
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)
# frozen graph generation
convert_to_frozen_pb(module_name=TEST_MODULE_NAME,
pbsavedir=pbsavedir,
pbfilename=pbfilename,
ckptfilename=ckptfilename,
output_node_name=output_node_name,
input_shape=input_shape)
# # check tflite compatibility
print('------------------------------------------------')
print('[tfTest] convert to tflite')
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)
print('[tfTest] tflite conversion successful')
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)
