def test_model1(self):
model1 = Sequential(self.s, model_table='Simple_CNN1')
model1.add(InputLayer(3, 224, 224))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(Dense(16))
model1.add(OutputLayer(act='softmax', n=2))
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')
self.s.table.loadtable(caslib=caslib,
casout={'name': 'eee', 'replace': True},
path=path)
r = model1.fit(data='eee', inputs='_image_', target='_label_', lr=0.001)
if r.severity > 0:
for msg in r.messages:
print(msg)
self.assertTrue(r.severity <= 1)
if (caslib is not None) and tmp_caslib:
self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
def test_model23(self):
try:
import onnx
except:
unittest.TestCase.skipTest(self, "onnx not found in the libraries")
model1 = Sequential(self.s, model_table='Simple_CNN1')
model1.add(InputLayer(3, 224, 224))
model1.add(Conv2d(8, 7, act='identity', include_bias=False))
model1.add(BN(act='relu'))
model1.add(Pooling(2))
model1.add(Conv2d(8, 7, act='identity', include_bias=False))
model1.add(BN(act='relu'))
model1.add(Pooling(2))
model1.add(Dense(2))
model1.add(OutputLayer(act='softmax', n=2))
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
caslib, path = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')
self.s.table.loadtable(caslib=caslib,
casout={'name': 'eee', 'replace': True},
path=path)
r = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=1)
self.assertTrue(r.severity == 0)
model1.deploy(self.data_dir, output_format='onnx')
def test_CyclicLR(self):
model1 = Sequential(self.s, model_table = 'Simple_CNN1')
model1.add(InputLayer(3, 224, 224))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(Dense(16))
model1.add(OutputLayer(act = 'softmax', n = 2))
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
caslib, path, tmp_caslib = caslibify(self.s, path = self.data_dir + 'images.sashdat', task = 'load')
self.s.table.loadtable(caslib = caslib,
casout = {'name': 'eee', 'replace': True},
path = path)
lrs = CyclicLR(self.s, 'eee', 4, 1.0, 0.0000001, 0.01)
solver = VanillaSolver(lr_scheduler=lrs)
self.assertTrue(self.sample_syntax['CyclicLR'] == solver)
optimizer = Optimizer(algorithm = solver, log_level = 3, max_epochs = 4, mini_batch_size = 2)
r = model1.fit(data = 'eee', inputs = '_image_', target = '_label_', optimizer = optimizer, n_threads=2)
if r.severity > 0:
for msg in r.messages:
print(msg)
self.assertTrue(r.severity <= 1)
def test_model18(self):
model1 = Sequential(self.s, model_table='Simple_CNN1')
model1.add(InputLayer(3, 224, 224))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(Dense(16))
model1.add(OutputLayer(act='softmax', n=2))
if self.data_dir is None:
unittest.TestCase.skipTest(
self, "DLPY_DATA_DIR is not set in the environment variables")
caslib, path = caslibify(self.s,
path=self.data_dir + 'images.sashdat',
task='load')
self.s.table.loadtable(caslib=caslib,
casout={
'name': 'eee',
'replace': True
},
path=path)
r = model1.fit(data='eee',
inputs='_image_',
target='_label_',
max_epochs=1)
self.assertTrue(r.severity == 0)
model1.save_weights_csv(self.data_dir)
def test_model22(self):
model1 = Sequential(self.s, model_table='Simple_CNN1')
model1.add(InputLayer(3, 224, 224))
model1.add(Conv2d(8, 7))
pool1 = Pooling(2)
model1.add(pool1)
conv1 = Conv2d(1, 1, act='identity', src_layers=[pool1])
model1.add(conv1)
model1.add(Res(act='relu', src_layers=[conv1, pool1]))
model1.add(Pooling(2))
model1.add(Dense(2))
model1.add(OutputLayer(act='softmax', n=2))
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
caslib, path = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')
self.s.table.loadtable(caslib=caslib,
casout={'name': 'eee', 'replace': True},
path=path)
r = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=1)
self.assertTrue(r.severity == 0)
model1.deploy(self.data_dir, output_format='onnx')
def test_model12(self):
model1 = Sequential(self.s, model_table='Simple_CNN1')
model1.add(InputLayer(3, 224, 224))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(Dense(16))
model1.add(OutputLayer(act='softmax', n=2))
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')
self.s.table.loadtable(caslib=caslib,
casout={'name': 'eee', 'replace': True},
path=path)
r = model1.fit(data='eee', inputs='_image_', target='_label_', save_best_weights=True)
self.assertTrue(r.severity == 0)
r1 = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=3)
self.assertTrue(r1.severity == 0)
r2 = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=2, save_best_weights=True)
self.assertTrue(r2.severity == 0)
r3 = model1.predict(data='eee', use_best_weights=True)
self.assertTrue(r3.severity == 0)
if (caslib is not None) and tmp_caslib:
self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
def test_model15(self):
# test RECTIFIER activation for concat layer
try:
import onnx
except:
unittest.TestCase.skipTest(self, "onnx not found in the libraries")
model1 = Sequential(self.s, model_table='Simple_CNN1')
model1.add(InputLayer(3, 224, 224))
model1.add(Conv2d(8, 7))
pool1 = Pooling(2)
model1.add(pool1)
conv1 = Conv2d(1, 7, src_layers=[pool1])
conv2 = Conv2d(1, 7, src_layers=[pool1])
model1.add(conv1)
model1.add(conv2)
model1.add(Concat(act='RECTIFIER', src_layers=[conv1, conv2]))
model1.add(Pooling(2))
model1.add(Dense(2))
model1.add(OutputLayer(act='softmax', n=2))
if self.data_dir is None:
unittest.TestCase.skipTest(
self, "DLPY_DATA_DIR is not set in the environment variables")
caslib, path, tmp_caslib = caslibify(self.s,
path=self.data_dir +
'images.sashdat',
task='load')
self.s.table.loadtable(caslib=caslib,
casout={
'name': 'eee',
'replace': True
},
path=path)
r = model1.fit(data='eee',
inputs='_image_',
target='_label_',
max_epochs=1)
self.assertTrue(r.severity == 0)
import tempfile
tmp_dir_to_dump = tempfile.gettempdir()
model1.deploy(tmp_dir_to_dump, output_format='onnx')
import os
os.remove(os.path.join(tmp_dir_to_dump, "Simple_CNN1.onnx"))
if (caslib is not None) and tmp_caslib:
self.s.retrieve('table.dropcaslib',
message_level='error',
caslib=caslib)
def test_model13(self):
# test dropout
try:
import onnx
except:
unittest.TestCase.skipTest(self, "onnx not found in the libraries")
model1 = Sequential(self.s, model_table='Simple_CNN1')
model1.add(InputLayer(3, 224, 224))
model1.add(
Conv2d(8, 7, act='IDENTITY', dropout=0.5, include_bias=False))
model1.add(BN(act='relu'))
model1.add(Pooling(2, pool='MEAN', dropout=0.5))
model1.add(
Conv2d(8, 7, act='IDENTITY', dropout=0.5, include_bias=False))
model1.add(BN(act='relu'))
model1.add(Pooling(2, pool='MEAN', dropout=0.5))
model1.add(Conv2d(8, 7, act='identity', include_bias=False))
model1.add(BN(act='relu'))
model1.add(Dense(16, act='IDENTITY', dropout=0.1))
model1.add(OutputLayer(act='softmax', n=2))
if self.data_dir is None:
unittest.TestCase.skipTest(
self, "DLPY_DATA_DIR is not set in the environment variables")
caslib, path, tmp_caslib = caslibify(self.s,
path=self.data_dir +
'images.sashdat',
task='load')
self.s.table.loadtable(caslib=caslib,
casout={
'name': 'eee',
'replace': True
},
path=path)
r = model1.fit(data='eee',
inputs='_image_',
target='_label_',
max_epochs=2)
self.assertTrue(r.severity == 0)
import tempfile
tmp_dir_to_dump = tempfile.gettempdir()
model1.deploy(tmp_dir_to_dump, output_format='onnx')
import os
os.remove(os.path.join(tmp_dir_to_dump, "Simple_CNN1.onnx"))
if (caslib is not None) and tmp_caslib:
self.s.retrieve('table.dropcaslib',
message_level='error',
caslib=caslib)
def test_stride(self):
model = Sequential(self.s, model_table = 'Simple_CNN_3classes_cropped')
model.add(InputLayer(1, width = 36, height = 144, #offsets = myimage.channel_means,
name = 'input1',
random_mutation = 'random',
random_flip = 'HV'))
model.add(Conv2d(64, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Conv2d(64, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Conv2d(64, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Pooling(height = 2, width = 2, stride_vertical = 2, stride_horizontal = 1, pool = 'max')) # 72, 36
model.add(Conv2d(128, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Conv2d(128, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Conv2d(128, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Pooling(height = 2, width = 2, stride_vertical = 2, stride_horizontal = 1, pool = 'max')) # 36*36
model.add(Conv2d(256, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Conv2d(256, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Conv2d(256, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Pooling(2, pool = 'max')) # 18 * 18
model.add(Conv2d(512, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Conv2d(512, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Conv2d(512, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Pooling(2, pool = 'max')) # 9 * 9
model.add(Conv2d(1024, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Conv2d(1024, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Conv2d(1024, 3, 3, include_bias = False, act = 'identity'))
model.add(BN(act = 'relu'))
model.add(Pooling(9))
model.add(Dense(256, dropout = 0.5))
model.add(OutputLayer(act = 'softmax', n = 3, name = 'output1'))
self.assertEqual(model.summary['Output Size'].values[-3], (1, 1, 1024))
model.print_summary()
# 2d print summary numerical check
self.assertEqual(model.summary.iloc[1, -1], 2985984)
def test_plot_ticks(self):
model1 = Sequential(self.s, model_table='Simple_CNN1')
model1.add(InputLayer(3, 224, 224))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(Dense(16))
model1.add(OutputLayer(act='softmax', n=2))
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')
self.s.table.loadtable(caslib=caslib,
casout={'name': 'eee', 'replace': True},
path=path)
r = model1.fit(data='eee', inputs='_image_', target='_label_', lr=0.001, max_epochs=5)
# Test default tick_frequency value of 1
ax = model1.plot_training_history()
self.assertEqual(len(ax.xaxis.majorTicks), model1.n_epochs)
# Test even
tick_frequency = 2
ax = model1.plot_training_history(tick_frequency=tick_frequency)
self.assertEqual(len(ax.xaxis.majorTicks), model1.n_epochs // tick_frequency + 1)
# Test odd
tick_frequency = 3
ax = model1.plot_training_history(tick_frequency=tick_frequency)
self.assertEqual(len(ax.xaxis.majorTicks), model1.n_epochs // tick_frequency + 1)
# Test max
tick_frequency = model1.n_epochs
ax = model1.plot_training_history(tick_frequency=tick_frequency)
self.assertEqual(len(ax.xaxis.majorTicks), model1.n_epochs // tick_frequency + 1)
# Test 0
tick_frequency = 0
ax = model1.plot_training_history(tick_frequency=tick_frequency)
self.assertEqual(len(ax.xaxis.majorTicks), model1.n_epochs)
if (caslib is not None) and tmp_caslib:
self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
def test_pool_layer2(self):
dict1 = Pooling(name='pool2',
width=3,
height=2,
src_layers=[Conv2d(n_filters=3,
name='conv')]).to_model_params()
self.assertTrue(self.sample_syntax['pool2'] == dict1)
def test_detection_layer1(self):
dict1 = Detection(name='detection',
predictions_per_grid=7,
iou_threshold=0.2,
detection_threshold=0.2,
src_layers=[Pooling(name='pool')]).to_model_params()
self.assertTrue(self.sample_syntax['detection1'] == dict1)
def test_build_gan_model_4(self):
if self.server_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR_SERVER is not set in the environment variables")
discriminator = Sequential(self.s)
discriminator.add(InputLayer(1, 28, 28))
discriminator.add(Conv2d(3, 3))
discriminator.add(Pooling(2))
discriminator.add(Conv2d(3, 3))
discriminator.add(Pooling(2))
discriminator.add(Dense(16))
discriminator.add(OutputLayer(n=1))
generator = Sequential(self.s)
generator.add(InputLayer(1, 100, 1))
generator.add(Dense(256, act='relu'))
generator.add(Dense(512, act='relu'))
generator.add(Dense(1024, act='relu'))
generator.add(Dense(28 * 28, act='tanh'))
generator.add(OutputLayer(act='softmax', n=2))
encoder = Sequential(self.s)
encoder.add(InputLayer(100, 1, 1))
encoder.add(Dense(256, act='relu'))
encoder.add(Dense(512, act='relu'))
encoder.add(Dense(1024, act='relu'))
encoder.add(Dense(100, act='tanh'))
encoder.add(OutputLayer(act='softmax', n=2))
gan_model = GANModel(generator, discriminator, encoder)
res = gan_model.models['generator'].print_summary()
print(res)
res = gan_model.models['discriminator'].print_summary()
print(res)
from dlpy.model import Optimizer, MomentumSolver, AdamSolver
solver = AdamSolver(lr_scheduler=StepLR(learning_rate=0.0001, step_size=4), clip_grad_max=100,
clip_grad_min=-100)
optimizer = Optimizer(algorithm=solver, mini_batch_size=8, log_level=2, max_epochs=4, reg_l2=0.0001)
res = gan_model.fit(optimizer, optimizer, self.server_dir + 'mnist_validate',
n_samples_generator=32, n_samples_discriminator=32, max_iter=2, n_threads=1,
damping_factor=0.5)
print(res)
def test_dense_layer2(self):
dict1 = Dense(name='dense',
n=10000,
init='xavier',
dropout=0.2,
include_bias=False,
src_layers=[Pooling(name='pool')]).to_model_params()
self.assertTrue(self.sample_syntax['fc2'] == dict1)
def test_model_crnn_bug(self):
model = Sequential(self.s, model_table='crnn')
model.add(InputLayer(3,256,16))
model.add(Reshape(height=16,width=256,depth=3))
model.add(Conv2d(64,3,3,stride=1,padding=1)) # size = 16x256x64
model.add(Pooling(2,2,2)) # size = 8x128x64
model.add(Conv2d(128,3,3,stride=1,padding=1)) # size = 8x128x128
model.add(Pooling(2,2,2)) # size = 4x64x128
model.add(Conv2d(256,3,3,stride=1,padding=1,act='IDENTITY')) # size = 4x64x256
model.add(BN(act='RELU')) # size = 4x64x256
model.add(Conv2d(256,3,3,stride=1,padding=1)) # size = 4x64x256
model.add(Pooling(1,2,stride_horizontal=1, stride_vertical=2))
#, padding=1)) # size = 2x64x256
#model.add(Pooling(1,2,stride=2,stride_horizontal=1, stride_vertical=2,)) # size = 2x64x256
model.add(Conv2d(512,3,3,stride=1,padding=1, act='IDENTITY')) # size = 2x64x512
model.add(BN(act='RELU'))
model.add(Conv2d(512,3,3,stride=1,padding=1)) # size = 2x64x512
model.add(Pooling(1,2,stride_horizontal=1, stride_vertical=2)) #, padding=1)) # size = 1x64x512
#model.add(Pooling(1,2,stride=2,stride_horizontal=1, stride_vertical=2,)) # size = 1x64x512
model.add(Conv2d(512,3,3,stride=1,padding=1, act='IDENTITY')) # size = 1x64x512
model.add(BN(act='RELU'))
model.add(Reshape(order='DWH',width=64, height=512, depth=1))
model.add(Recurrent(512,output_type='SAMELENGTH'))
model.add(OutputLayer(error='CTC'))
model.print_summary()
def test_model13a(self):
model = Sequential(self.s, model_table='simple_cnn')
model.add(InputLayer(3, 224, 224))
model.add(Conv2d(2, 3))
model.add(Pooling(2))
model.add(Dense(4))
model.add(OutputLayer(n=2))
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
model.save_to_table(self.data_dir)
def test_imagescaler2(self):
# test export model with imagescaler
try:
import onnx
except:
unittest.TestCase.skipTest(self, 'onnx not found')
if self.data_dir_local is None:
unittest.TestCase.skipTest(
self, 'DLPY_DATA_DIR_LOCAL is not set in '
'the environment variables')
model1 = Sequential(self.s, model_table='imagescaler2')
model1.add(
InputLayer(n_channels=3,
width=224,
height=224,
scale=1 / 255.,
offsets=[0.1, 0.2, 0.3]))
model1.add(Conv2d(8, 7))
model1.add(Pooling(2))
model1.add(OutputLayer(act='softmax', n=2))
caslib, path = caslibify(self.s,
path=self.data_dir + 'images.sashdat',
task='load')
self.s.table.loadtable(caslib=caslib,
casout={
'name': 'eee',
'replace': True
},
path=path)
r = model1.fit(data='eee',
inputs='_image_',
target='_label_',
max_epochs=1)
self.assertTrue(r.severity == 0)
from dlpy.model_conversion.write_onnx_model import sas_to_onnx
onnx_model = sas_to_onnx(model1.layers,
self.s.CASTable('imagescaler2'),
self.s.CASTable('imagescaler2_weights'))
self.assertAlmostEqual(onnx_model.graph.node[0].attribute[0].floats[0],
0.1)
self.assertAlmostEqual(onnx_model.graph.node[0].attribute[0].floats[1],
0.2)
self.assertAlmostEqual(onnx_model.graph.node[0].attribute[0].floats[2],
0.3)
self.assertAlmostEqual(onnx_model.graph.node[0].attribute[1].f,
1 / 255.)
def downsampling_bottleneck(x, in_depth, out_depth, projection_ratio=4):
'''
Defines the down-sampling bottleneck of ENet
Parameters
----------
x : class:`Layer'
Previous layer to this block
in_depth : int
Depth of the layer fed into this block
out_depth : int
Depth of the output layer of this block
projection_ratio : int, optional
Used to calculate the reduced_depth for intermediate convolution layers
Default: 4
Returns
-------
:class:`Res`
'''
reduced_depth = int(in_depth // projection_ratio)
conv1 = Conv2d(reduced_depth,
3,
stride=2,
padding=1,
act='identity',
include_bias=False)(x)
bn1 = BN(act='relu')(conv1)
conv2 = Conv2d(reduced_depth,
3,
stride=1,
act='identity',
include_bias=False)(bn1)
bn2 = BN(act='relu')(conv2)
conv3 = Conv2d(out_depth, 1, stride=1, act='identity',
include_bias=False)(bn2)
bn3 = BN(act='relu')(conv3)
pool1 = Pooling(2, stride=2)(x)
conv4 = Conv2d(out_depth, 1, stride=1, act='identity',
include_bias=False)(pool1)
bn4 = BN(act='relu')(conv4)
res = Res()([bn3, bn4])
return res
def setUp(self):
swat.reset_option()
swat.options.cas.print_messages = False
swat.options.interactive_mode = False
self.s = swat.CAS(HOST, PORT, USER, PASSWD, protocol=PROTOCOL)
if type(self).server_type is None:
# Set once per class and have every test use it. No need to change between tests.
type(self).server_type = tm.get_cas_host_type(self.s)
self.srcLib = tm.get_casout_lib(self.server_type)
# Define the model
model = Sequential(self.s, model_table='test_model')
model.add(InputLayer(3, 224, 224, offsets=(0, 0, 0)))
model.add(Conv2d(8, 7))
model.add(Pooling(2))
model.add(Conv2d(8, 7))
model.add(Pooling(2))
model.add(Dense(16))
model.add(OutputLayer(act='softmax', n=2))
self.model = model
def initial_block(inp):
'''
Defines the initial block of ENet
Parameters
----------
inp : class:`InputLayer`
Input layer
Returns
-------
:class:`Concat`
'''
x = Conv2d(13, 3, stride=2, padding=1, act='identity',
include_bias=False)(inp)
x_bn = BN(act='relu')(x)
y = Pooling(2)(inp)
merge = Concat()([x_bn, y])
return merge
def onnx_extract_globalpool(graph, node, layers):
'''
Construct global pool layer from ONNX op
Parameters
----------
graph : ONNX GraphProto
Specifies a GraphProto object.
node : ONNX NodeProto
Specifies a NodeProto object.
layers : list of Layers
Specifies the existing layers of a model.
Returns
-------
:class:`Pooling`
'''
previous = onnx_find_previous_compute_layer(graph, node)
if not previous:
src_names = [find_input_layer_name(graph)]
else:
src_names = [p.name for p in previous]
src = [get_dlpy_layer(layers, i) for i in src_names]
# check the shape of the input to pool op
_, C, height, width = onnx_get_shape(graph, node.input[0])
return Pooling(width=width,
height=height,
stride=1,
name=node.name,
padding=0,
pool='AVERAGE',
src_layers=src)
def test_scale_layer1(self):
dict1 = Scale(name='scale',
src_layers=[Pooling(name='pool')]).to_model_params()
self.assertTrue(self.sample_syntax['scale1'] == dict1)
def test_output_layer1(self):
dict1 = OutputLayer(name='output',
n=100,
src_layers=[Pooling(name='pool')
]).to_model_params()
self.assertTrue(self.sample_syntax['output1'] == dict1)
def test_pool_layer4(self):
if not __dev__:
with self.assertRaises(DLPyError):
Pooling(not_a_parameter=1)
def test_dense_layer1(self):
dict1 = Dense(name='dense', n=10,
src_layers=[Pooling(name='pool')]).to_model_params()
self.assertTrue(self.sample_syntax['fc1'] == dict1)
def onnx_extract_pool(graph, node, layers, pool='MAX'):
'''
Construct pool layer from ONNX op
Parameters
----------
graph : ONNX GraphProto
Specifies a GraphProto object.
node : ONNX NodeProto
Specifies a NodeProto object.
layers : list of Layers
Specifies the existing layers of a model.
pool : str, optional
Specifies the type of pooling.
Default: MAX
Returns
-------
:class:`Pooling`
'''
previous = onnx_find_previous_compute_layer(graph, node)
if not previous:
src_names = [find_input_layer_name(graph)]
else:
src_names = [p.name for p in previous]
src = [get_dlpy_layer(layers, i) for i in src_names]
height = None
padding = None
padding_height = None
padding_width = None
stride = None
stride_horizontal = None
stride_vertical = None
width = None
# if padding is not present, default to 0
is_padding = False
for attr in node.attribute:
if attr.name == 'kernel_shape':
height, width = attr.ints
elif attr.name == 'strides':
stride_vertical, stride_horizontal = attr.ints
# only specify one of stride and stride_horizontal
if stride_horizontal == stride_vertical:
stride = stride_horizontal
stride_horizontal = None
stride_vertical = None
elif attr.name == 'auto_pad':
is_padding = True
attr_s = attr.s.decode('utf8')
if attr_s == 'SAME_UPPER' or attr_s == 'SAME_LOWER':
continue
elif attr_s == 'NOTSET':
continue
else: # 'VALID'
padding = 0
elif attr.name == 'pads':
is_padding = True
padding_height, padding_width, p_h2, p_w2 = attr.ints
if padding_height != p_h2 or padding_width != p_w2:
print('WARNING: Unequal padding not supported for ' +
node.name + ' Setting auto padding instead.')
if padding_height == 0 and p_h2 != 0:
padding_height = None
else:
padding_height = max(padding_height, p_h2)
if padding_width == 0 and p_w2 != 0:
padding_width = None
else:
padding_width = max(padding_width, p_w2)
if not is_padding:
padding = 0
return Pooling(width=width,
height=height,
stride=stride,
name=node.name,
stride_horizontal=stride_horizontal,
stride_vertical=stride_vertical,
padding=padding,
padding_width=padding_width,
padding_height=padding_height,
pool=pool,
src_layers=src)
def VGG19(conn,
model_table='VGG19',
n_classes=1000,
n_channels=3,
width=224,
height=224,
scale=1,
random_flip=None,
random_crop=None,
offsets=(103.939, 116.779, 123.68),
pre_trained_weights=False,
pre_trained_weights_file=None,
include_top=False,
random_mutation=None):
'''
Generates a deep learning model with the VGG19 architecture.
Parameters
----------
conn : CAS
Specifies the CAS connection object.
model_table : string, optional
Specifies the name of CAS table to store the model.
n_classes : int, optional
Specifies the number of classes. If None is assigned, the model will
automatically detect the number of classes based on the training set.
Default: 1000
n_channels : int, optional
Specifies the number of the channels (i.e., depth) of the input layer.
Default: 3
width : int, optional
Specifies the width of the input layer.
Default: 224
height : int, optional
Specifies the height of the input layer.
Default: 224
scale : double, optional
Specifies a scaling factor to be applied to each pixel intensity values.
Default: 1
random_flip : string, optional
Specifies how to flip the data in the input layer when image data is
used. Approximately half of the input data is subject to flipping.
Valid Values: 'h', 'hv', 'v', 'none'
random_crop : string, optional
Specifies how to crop the data in the input layer when image data is
used. Images are cropped to the values that are specified in the width
and height parameters. Only the images with one or both dimensions
that are larger than those sizes are cropped.
Valid Values: 'none', 'unique', 'randomresized', 'resizethencrop'
offsets : double or iter-of-doubles, optional
Specifies an offset for each channel in the input data. The final input
data is set after applying scaling and subtracting the specified offsets.
Default: (103.939, 116.779, 123.68)
pre_trained_weights : bool, optional
Specifies whether to use the pre-trained weights trained on the ImageNet data set.
Default: False
pre_trained_weights_file : string, optional
Specifies the file name for the pre-trained weights.
Must be a fully qualified file name of SAS-compatible file (e.g., *.caffemodel.h5)
Note: Required when pre_trained_weights=True.
include_top : bool, optional
Specifies whether to include pre-trained weights of the top layers (i.e., the FC layers).
Default: False
random_mutation : string, optional
Specifies how to apply data augmentations/mutations to the data in the input layer.
Valid Values: 'none', 'random'
Returns
-------
:class:`Sequential`
If `pre_trained_weights` is False
:class:`Model`
If `pre_trained_weights` is True
References
----------
https://arxiv.org/pdf/1409.1556.pdf
'''
conn.retrieve('loadactionset',
_messagelevel='error',
actionset='deeplearn')
# get all the parms passed in
parameters = locals()
if not pre_trained_weights:
model = Sequential(conn=conn, model_table=model_table)
# get the input parameters
input_parameters = get_layer_options(input_layer_options, parameters)
model.add(InputLayer(**input_parameters))
model.add(Conv2d(n_filters=64, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=64, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=128, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=128, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=256, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=256, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=256, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=256, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Dense(n=4096, dropout=0.5))
model.add(Dense(n=4096, dropout=0.5))
model.add(OutputLayer(n=n_classes))
return model
else:
if pre_trained_weights_file is None:
raise DLPyError(
'\nThe pre-trained weights file is not specified.\n'
'Please follow the steps below to attach the pre-trained weights:\n'
'1. Go to the website https://support.sas.com/documentation/prod-p/vdmml/zip/ '
'and download the associated weight file.\n'
'2. Upload the *.h5 file to '
'a server side directory which the CAS session has access to.\n'
'3. Specify the pre_trained_weights_file using the fully qualified server side path.'
)
model_cas = model_vgg19.VGG19_Model(s=conn,
model_table=model_table,
n_channels=n_channels,
width=width,
height=height,
random_crop=random_crop,
offsets=offsets,
random_flip=random_flip,
random_mutation=random_mutation)
if include_top:
if n_classes != 1000:
warnings.warn(
'If include_top = True, n_classes will be set to 1000.',
RuntimeWarning)
model = Model.from_table(model_cas)
model.load_weights(path=pre_trained_weights_file, labels=True)
return model
else:
model = Model.from_table(model_cas, display_note=False)
model.load_weights(path=pre_trained_weights_file)
weight_table_options = model.model_weights.to_table_params()
weight_table_options.update(dict(where='_LayerID_<22'))
model._retrieve_('table.partition',
table=weight_table_options,
casout=dict(
replace=True,
**model.model_weights.to_table_params()))
model._retrieve_('deeplearn.removelayer',
model=model_table,
name='fc8')
model._retrieve_('deeplearn.addlayer',
model=model_table,
name='fc8',
layer=dict(type='output',
n=n_classes,
act='softmax'),
srcLayers=['fc7'])
model = Model.from_table(conn.CASTable(model_table))
return model
def VGG11(conn,
model_table='VGG11',
n_classes=1000,
n_channels=3,
width=224,
height=224,
scale=1,
random_flip=None,
random_crop=None,
offsets=(103.939, 116.779, 123.68),
random_mutation=None):
'''
Generates a deep learning model with the VGG11 architecture.
Parameters
----------
conn : CAS
Specifies the CAS connection object.
model_table : string, optional
Specifies the name of CAS table to store the model.
n_classes : int, optional
Specifies the number of classes. If None is assigned, the model will
automatically detect the number of classes based on the training set.
Default: 1000
n_channels : int, optional
Specifies the number of the channels (i.e., depth) of the input layer.
Default: 3
width : int, optional
Specifies the width of the input layer.
Default: 224
height : int, optional
Specifies the height of the input layer.
Default: 224
scale : double, optional
Specifies a scaling factor to be applied to each pixel intensity values.
Default: 1
random_flip : string, optional
Specifies how to flip the data in the input layer when image data is
used. Approximately half of the input data is subject to flipping.
Valid Values: 'h', 'hv', 'v', 'none'
random_crop : string, optional
Specifies how to crop the data in the input layer when image data is
used. Images are cropped to the values that are specified in the width
and height parameters. Only the images with one or both dimensions
that are larger than those sizes are cropped.
Valid Values: 'none', 'unique', 'randomresized', 'resizethencrop'
offsets : double or iter-of-doubles, optional
Specifies an offset for each channel in the input data. The final
input data is set after applying scaling and subtracting the
specified offsets.
Default: (103.939, 116.779, 123.68)
random_mutation : string, optional
Specifies how to apply data augmentations/mutations to the data in the input layer.
Valid Values: 'none', 'random'
Returns
-------
:class:`Sequential`
References
----------
https://arxiv.org/pdf/1409.1556.pdf
'''
conn.retrieve('loadactionset',
_messagelevel='error',
actionset='deeplearn')
# get all the parms passed in
parameters = locals()
model = Sequential(conn=conn, model_table=model_table)
# get the input parameters
input_parameters = get_layer_options(input_layer_options, parameters)
model.add(InputLayer(**input_parameters))
model.add(Conv2d(n_filters=64, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=128, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=256, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=256, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Dense(n=4096, dropout=0.5))
model.add(Dense(n=4096, dropout=0.5))
model.add(OutputLayer(n=n_classes))
return model
def YoloV1(conn,
model_table='YoloV1',
n_channels=3,
width=448,
height=448,
scale=1.0 / 255,
random_mutation=None,
act='leaky',
dropout=0,
act_detection='AUTO',
softmax_for_class_prob=True,
coord_type='YOLO',
max_label_per_image=30,
max_boxes=30,
n_classes=20,
predictions_per_grid=2,
do_sqrt=True,
grid_number=7,
coord_scale=None,
object_scale=None,
prediction_not_a_object_scale=None,
class_scale=None,
detection_threshold=None,
iou_threshold=None,
random_boxes=False,
random_flip=None,
random_crop=None):
'''
Generates a deep learning model with the Yolo V1 architecture.
Parameters
----------
conn : CAS
Specifies the connection of the CAS connection.
model_table : string, optional
Specifies the name of CAS table to store the model.
n_channels : int, optional
Specifies the number of the channels (i.e., depth) of the input layer.
Default: 3
width : int, optional
Specifies the width of the input layer.
Default: 448
height : int, optional
Specifies the height of the input layer.
Default: 448
scale : double, optional
Specifies a scaling factor to be applied to each pixel intensity values.
Default: 1.0 / 255
random_mutation : string, optional
Specifies how to apply data augmentations/mutations to the data in
the input layer.
Valid Values: 'none', 'random'
act: String, optional
Specifies the activation function to be used in the convolutional layer
layers and the final convolution layer.
Default: 'leaky'
dropout: double, optional
Specifies the drop out rate.
Default: 0
act_detection : string, optional
Specifies the activation function for the detection layer.
Valid Values: AUTO, IDENTITY, LOGISTIC, SIGMOID, TANH, RECTIFIER, RELU, SOFPLUS, ELU, LEAKY, FCMP
Default: AUTO
softmax_for_class_prob : bool, optional
Specifies whether to perform Softmax on class probability per
predicted object.
Default: True
coord_type : string, optional
Specifies the format of how to represent bounding boxes. For example,
a bounding box can be represented with the x and y locations of the
top-left point as well as width and height of the rectangle.
This format is the 'rect' format. We also support coco and yolo formats.
Valid Values: 'rect', 'yolo', 'coco'
Default: 'yolo'
max_label_per_image : int, optional
Specifies the maximum number of labels per image in the training.
Default: 30
max_boxes : int, optional
Specifies the maximum number of overall predictions allowed in the
detection layer.
Default: 30
n_classes : int, optional
Specifies the number of classes. If None is assigned, the model will
automatically detect the number of classes based on the training set.
Default: 20
predictions_per_grid : int, optional
Specifies the amount of predictions will be done per grid.
Default: 2
do_sqrt : bool, optional
Specifies whether to apply the SQRT function to width and height of
the object for the cost function.
Default: True
grid_number : int, optional
Specifies the amount of cells to be analyzed for an image. For example,
if the value is 5, then the image will be divided into a 5 x 5 grid.
Default: 7
coord_scale : float, optional
Specifies the weight for the cost function in the detection layer,
when objects exist in the grid.
object_scale : float, optional
Specifies the weight for object detected for the cost function in
the detection layer.
prediction_not_a_object_scale : float, optional
Specifies the weight for the cost function in the detection layer,
when objects do not exist in the grid.
class_scale : float, optional
Specifies the weight for the class of object detected for the cost
function in the detection layer.
detection_threshold : float, optional
Specifies the threshold for object detection.
iou_threshold : float, optional
Specifies the IOU Threshold of maximum suppression in object detection.
random_boxes : bool, optional
Randomizing boxes when loading the bounding box information.
Default: False
random_flip : string, optional
Specifies how to flip the data in the input layer when image data is
used. Approximately half of the input data is subject to flipping.
Valid Values: 'h', 'hv', 'v', 'none'
random_crop : string, optional
Specifies how to crop the data in the input layer when image data is
used. Images are cropped to the values that are specified in the width
and height parameters. Only the images with one or both dimensions
that are larger than those sizes are cropped.
Valid Values: 'none', 'unique', 'randomresized', 'resizethencrop'
Returns
-------
:class:`Sequential`
References
----------
https://arxiv.org/pdf/1506.02640.pdf
'''
model = Sequential(conn=conn, model_table=model_table)
parameters = locals()
input_parameters = get_layer_options(input_layer_options, parameters)
model.add(InputLayer(**input_parameters))
# conv1 448
model.add(Conv2d(32, width=3, act=act, include_bias=False, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv2 224
model.add(Conv2d(64, width=3, act=act, include_bias=False, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv3 112
model.add(Conv2d(128, width=3, act=act, include_bias=False, stride=1))
# conv4 112
model.add(Conv2d(64, width=1, act=act, include_bias=False, stride=1))
# conv5 112
model.add(Conv2d(128, width=3, act=act, include_bias=False, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv6 56
model.add(Conv2d(256, width=3, act=act, include_bias=False, stride=1))
# conv7 56
model.add(Conv2d(128, width=1, act=act, include_bias=False, stride=1))
# conv8 56
model.add(Conv2d(256, width=3, act=act, include_bias=False, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv9 28
model.add(Conv2d(512, width=3, act=act, include_bias=False, stride=1))
# conv10 28
model.add(Conv2d(256, width=1, act=act, include_bias=False, stride=1))
# conv11 28
model.add(Conv2d(512, width=3, act=act, include_bias=False, stride=1))
# conv12 28
model.add(Conv2d(256, width=1, act=act, include_bias=False, stride=1))
# conv13 28
model.add(Conv2d(512, width=3, act=act, include_bias=False, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv14 14
model.add(Conv2d(1024, width=3, act=act, include_bias=False, stride=1))
# conv15 14
model.add(Conv2d(512, width=1, act=act, include_bias=False, stride=1))
# conv16 14
model.add(Conv2d(1024, width=3, act=act, include_bias=False, stride=1))
# conv17 14
model.add(Conv2d(512, width=1, act=act, include_bias=False, stride=1))
# conv18 14
model.add(Conv2d(1024, width=3, act=act, include_bias=False, stride=1))
# conv19 14
model.add(Conv2d(1024, width=3, act=act, include_bias=False, stride=1))
# conv20 7
model.add(Conv2d(1024, width=3, act=act, include_bias=False, stride=2))
# conv21 7
model.add(Conv2d(1024, width=3, act=act, include_bias=False, stride=1))
# conv22 7
model.add(Conv2d(1024, width=3, act=act, include_bias=False, stride=1))
# conv23 7
model.add(
Conv2d(256,
width=3,
act=act,
include_bias=False,
stride=1,
dropout=dropout))
model.add(
Dense(n=(n_classes + (5 * predictions_per_grid)) * grid_number *
grid_number,
act='identity'))
model.add(
Detection(act=act_detection,
detection_model_type='yolov1',
softmax_for_class_prob=softmax_for_class_prob,
coord_type=coord_type,
class_number=n_classes,
grid_number=grid_number,
predictions_per_grid=predictions_per_grid,
do_sqrt=do_sqrt,
coord_scale=coord_scale,
object_scale=object_scale,
prediction_not_a_object_scale=prediction_not_a_object_scale,
class_scale=class_scale,
detection_threshold=detection_threshold,
iou_threshold=iou_threshold,
random_boxes=random_boxes,
max_label_per_image=max_label_per_image,
max_boxes=max_boxes))
return model
def Tiny_YoloV2(conn,
anchors,
model_table='Tiny-Yolov2',
n_channels=3,
width=416,
height=416,
scale=1.0 / 255,
random_mutation=None,
act='leaky',
act_detection='AUTO',
softmax_for_class_prob=True,
coord_type='YOLO',
max_label_per_image=30,
max_boxes=30,
n_classes=20,
predictions_per_grid=5,
do_sqrt=True,
grid_number=13,
coord_scale=None,
object_scale=None,
prediction_not_a_object_scale=None,
class_scale=None,
detection_threshold=None,
iou_threshold=None,
random_boxes=False,
match_anchor_size=None,
num_to_force_coord=None,
random_flip=None,
random_crop=None):
'''
Generate a deep learning model with the Tiny Yolov2 architecture.
Tiny Yolov2 is a very small model of Yolov2, so that it includes fewer
numbers of convolutional layer and batch normalization layer.
Parameters
----------
conn : CAS
Specifies the connection of the CAS connection.
anchors : list
Specifies the anchor box values.
model_table : string, optional
Specifies the name of CAS table to store the model.
n_channels : int, optional
Specifies the number of the channels (i.e., depth) of the input layer.
Default: 3
width : int, optional
Specifies the width of the input layer.
Default: 416
height : int, optional
Specifies the height of the input layer.
Default: 416
scale : double, optional
Specifies a scaling factor to be applied to each pixel intensity values.
Default: 1.0 / 255
random_mutation : string, optional
Specifies how to apply data augmentations/mutations to the data in the
input layer.
Valid Values: 'none', 'random'
act : string, optional
Specifies the activation function for the batch normalization layers.
Default: 'leaky'
act_detection : string, optional
Specifies the activation function for the detection layer.
Valid Values: AUTO, IDENTITY, LOGISTIC, SIGMOID, TANH, RECTIFIER, RELU, SOFPLUS, ELU, LEAKY, FCMP
Default: AUTO
softmax_for_class_prob : bool, optional
Specifies whether to perform Softmax on class probability per
predicted object.
Default: True
coord_type : string, optional
Specifies the format of how to represent bounding boxes. For example,
a bounding box can be represented with the x and y locations of the
top-left point as well as width and height of the rectangle.
This format is the 'rect' format. We also support coco and yolo formats.
Valid Values: 'rect', 'yolo', 'coco'
Default: 'yolo'
max_label_per_image : int, optional
Specifies the maximum number of labels per image in the training.
Default: 30
max_boxes : int, optional
Specifies the maximum number of overall predictions allowed in the
detection layer.
Default: 30
n_classes : int, optional
Specifies the number of classes. If None is assigned, the model will
automatically detect the number of classes based on the training set.
Default: 20
predictions_per_grid : int, optional
Specifies the amount of predictions will be done per grid.
Default: 5
do_sqrt : bool, optional
Specifies whether to apply the SQRT function to width and height of
the object for the cost function.
Default: True
grid_number : int, optional
Specifies the amount of cells to be analyzed for an image. For example,
if the value is 5, then the image will be divided into a 5 x 5 grid.
Default: 13
coord_scale : float, optional
Specifies the weight for the cost function in the detection layer,
when objects exist in the grid.
object_scale : float, optional
Specifies the weight for object detected for the cost function in
the detection layer.
prediction_not_a_object_scale : float, optional
Specifies the weight for the cost function in the detection layer,
when objects do not exist in the grid.
class_scale : float, optional
Specifies the weight for the class of object detected for the cost
function in the detection layer.
detection_threshold : float, optional
Specifies the threshold for object detection.
iou_threshold : float, optional
Specifies the IOU Threshold of maximum suppression in object detection.
random_boxes : bool, optional
Randomizing boxes when loading the bounding box information.
Default: False
match_anchor_size : bool, optional
Whether to force the predicted box match the anchor boxes in sizes for all predictions
num_to_force_coord : int, optional
The number of leading chunk of images in training when the algorithm forces predicted objects
in each grid to be equal to the anchor box sizes, and located at the grid center
random_flip : string, optional
Specifies how to flip the data in the input layer when image data is
used. Approximately half of the input data is subject to flipping.
Valid Values: 'h', 'hv', 'v', 'none'
random_crop : string, optional
Specifies how to crop the data in the input layer when image data is
used. Images are cropped to the values that are specified in the width
and height parameters. Only the images with one or both dimensions
that are larger than those sizes are cropped.
Valid Values: 'none', 'unique', 'randomresized', 'resizethencrop'
Returns
-------
:class:`Sequential`
References
----------
https://arxiv.org/pdf/1612.08242.pdf
'''
model = Sequential(conn=conn, model_table=model_table)
parameters = locals()
input_parameters = get_layer_options(input_layer_options, parameters)
model.add(InputLayer(**input_parameters))
# conv1 416 448
model.add(
Conv2d(n_filters=16,
width=3,
act='identity',
include_bias=False,
stride=1))
model.add(BN(act=act))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv2 208 224
model.add(
Conv2d(n_filters=32,
width=3,
act='identity',
include_bias=False,
stride=1))
model.add(BN(act=act))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv3 104 112
model.add(
Conv2d(n_filters=64,
width=3,
act='identity',
include_bias=False,
stride=1))
model.add(BN(act=act))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv4 52 56
model.add(
Conv2d(n_filters=128,
width=3,
act='identity',
include_bias=False,
stride=1))
model.add(BN(act=act))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv5 26 28
model.add(
Conv2d(n_filters=256,
width=3,
act='identity',
include_bias=False,
stride=1))
model.add(BN(act=act))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv6 13 14
model.add(
Conv2d(n_filters=512,
width=3,
act='identity',
include_bias=False,
stride=1))
model.add(BN(act=act))
model.add(Pooling(width=2, height=2, stride=1, pool='max'))
# conv7 13
model.add(
Conv2d(n_filters=1024,
width=3,
act='identity',
include_bias=False,
stride=1))
model.add(BN(act=act))
# conv8 13
model.add(
Conv2d(n_filters=512,
width=3,
act='identity',
include_bias=False,
stride=1))
model.add(BN(act=act))
model.add(
Conv2d((n_classes + 5) * predictions_per_grid,
width=1,
act='identity',
include_bias=False,
stride=1))
model.add(
Detection(act=act_detection,
detection_model_type='yolov2',
anchors=anchors,
softmax_for_class_prob=softmax_for_class_prob,
coord_type=coord_type,
class_number=n_classes,
grid_number=grid_number,
predictions_per_grid=predictions_per_grid,
do_sqrt=do_sqrt,
coord_scale=coord_scale,
object_scale=object_scale,
prediction_not_a_object_scale=prediction_not_a_object_scale,
class_scale=class_scale,
detection_threshold=detection_threshold,
iou_threshold=iou_threshold,
random_boxes=random_boxes,
max_label_per_image=max_label_per_image,
max_boxes=max_boxes,
match_anchor_size=match_anchor_size,
num_to_force_coord=num_to_force_coord))
return model
