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_model13b(self):
model = Sequential(self.s, model_table='simple_cnn')
model.add(layer=InputLayer(n_channels=1, height=10, width=10))
model.add(layer=OutputLayer(n=10, full_connect=False))
self.assertTrue(model.summary.loc[1, 'Number of Parameters'] == (0, 0))
model1 = Sequential(self.s, model_table='simple_cnn')
model1.add(layer=InputLayer(n_channels=1, height=10, width=10))
model1.add(layer=OutputLayer(n=10, full_connect=True))
self.assertTrue(model1.summary.loc[1, 'Number of Parameters'] == (1000, 10))
model2 = Sequential(self.s, model_table='Simple_CNN')
model2.add(layer=InputLayer(n_channels=1, height=10, width=10))
model2.add(layer=OutputLayer(n=10, full_connect=True, include_bias=False))
self.assertTrue(model2.summary.loc[1, 'Number of Parameters'] == (1000, 0))
model3 = Sequential(self.s, model_table='Simple_CNN')
model3.add(layer=InputLayer(n_channels=1, height=10, width=10))
model3.add(layer=Conv2d(4, 3))
model3.add(layer=OutputLayer(n=10))
self.assertTrue(model3.summary.loc[2, 'Number of Parameters'] == (4000, 10))
model4 = Sequential(self.s, model_table='Simple_CNN')
model4.add(layer=InputLayer(n_channels=1, height=10, width=10))
model4.add(layer=Conv2d(4, 3))
model4.add(layer=OutputLayer(n=10, full_connect=False))
self.assertTrue(model4.summary.loc[2, 'Number of Parameters'] == (0, 0))
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_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_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_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_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 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 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_conv2d_layer1(self):
dict1 = Conv2d(name='convo1',
n_filters=10,
act='relu',
src_layers=[InputLayer(name='input1')
]).to_model_params()
self.assertTrue(self.sample_syntax['convo1'] == dict1)
def _conv_block(inputs, filters, alpha, kernel=3, stride=1):
"""
Adds an initial convolution layer (with batch normalization
inputs:
Input tensor
filters:
the dimensionality of the output space
alpha: controls the width of the network.
- If `alpha` < 1.0, proportionally decreases the number
of filters in each layer.
- If `alpha` > 1.0, proportionally increases the number
of filters in each layer.
- If `alpha` = 1, default number of filters from the paper
are used at each layer.
kernel:
specifying the width and height of the 2D convolution window.
strides:
the strides of the convolution
"""
filters = int(filters * alpha)
x = Conv2d(filters,
kernel,
act='identity',
include_bias=False,
stride=stride,
name='conv1')(inputs)
x = BN(name='conv1_bn', act='relu')(x)
return x, filters
def test_model22_1(self):
try:
import onnx
except:
unittest.TestCase.skipTest(self, "onnx not found in the libraries")
from onnx import numpy_helper
import numpy as np
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(Reshape(height=448, width=448, depth=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_local, output_format='onnx')
model_path = os.path.join(self.data_dir_local, 'Simple_CNN1.onnx')
m = onnx.load(model_path)
self.assertEqual(m.graph.node[1].op_type, 'Reshape')
init = numpy_helper.to_array(m.graph.initializer[1])
self.assertTrue(np.array_equal(init, [ -1, 2, 448, 448]))
def test_conv2d_layer_name_conflict(self):
if __dev__:
dict1 = Conv2d(n_filters=32, width=5, height=7, name='convo2',
stride_horizontal = 1, strideHorizontal=10,
include_bias=False, includeBias=True)
bias = dict1.num_bias
self.assertTrue(bias == 32)
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_conv2d_layer2(self):
dict1 = Conv2d(n_filters=32,
width=5,
height=7,
name='convo2',
src_layers=[InputLayer(name='input1')
]).to_model_params()
self.assertTrue(self.sample_syntax['convo2'] == dict1)
def test_conv2d_layer_name_format2(self):
if __dev__:
dict1 = Conv2d(n_filters=32,
width=5,
height=7,
name='convo2',
include_bias=False)
bias = dict1.num_bias
self.assertTrue(bias == 0)
def upsampling_bottleneck(x, in_depth, out_depth, projection_ratio=4):
'''
Defines the up-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:`BN`
'''
reduced_depth = int(in_depth // projection_ratio)
conv1 = Conv2d(reduced_depth,
1,
stride=1,
act='identity',
include_bias=False)(x)
bn1 = BN(act='relu')(conv1)
tconv1 = Conv2DTranspose(reduced_depth,
3,
stride=2,
padding=1,
output_padding=1,
act='identity',
include_bias=False)(bn1)
bn2 = BN(act='relu')(tconv1)
conv3 = Conv2d(out_depth, 1, stride=1, act='identity',
include_bias=False)(bn2)
bn3 = BN(act='relu')(conv3)
return bn3
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 _depthwise_conv_block(inputs,
n_groups,
pointwise_conv_filters,
alpha,
depth_multiplier=1,
stride=1,
block_id=1):
"""Adds a depthwise convolution block.
inputs:
Input tensor
n_groups : int
number of groups
pointwise_conv_filters:
the dimensionality of the output space
alpha: controls the width of the network.
- If `alpha` < 1.0, proportionally decreases the number
of filters in each layer.
- If `alpha` > 1.0, proportionally increases the number
of filters in each layer.
- If `alpha` = 1, default number of filters from the paper
are used at each layer.
depth_multiplier:
The number of depthwise convolution output channels
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution
block_id: Integer, a unique identification designating
the block number.
"""
pointwise_conv_filters = int(pointwise_conv_filters * alpha)
x = GroupConv2d(n_groups * depth_multiplier,
n_groups,
3,
stride=stride,
act='identity',
include_bias=False,
name='conv_dw_%d' % block_id)(inputs)
x = BN(name='conv_dw_%d_bn' % block_id, act='relu')(x)
x = Conv2d(pointwise_conv_filters,
1,
act='identity',
include_bias=False,
stride=1,
name='conv_pw_%d' % block_id)(x)
x = BN(name='conv_pw_%d_bn' % block_id, act='relu')(x)
return x, pointwise_conv_filters
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 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 MobileNetV2_ONNX(conn,
model_file,
n_classes=1000,
width=224,
height=224,
offsets=(255 * 0.406, 255 * 0.456, 255 * 0.485),
norm_stds=(255 * 0.225, 255 * 0.224, 255 * 0.229),
random_flip=None,
random_crop=None,
random_mutation=None,
include_top=False):
"""
Generates a deep learning model with the MobileNetV2_ONNX architecture.
The model architecture and pre-trained weights is generated from MobileNetV2 ONNX trained on ImageNet dataset.
The model file and the weights file can be downloaded from https://support.sas.com/documentation/prod-p/vdmml/zip/.
To learn more information about the model and pre-processing.
Please go to the websites: https://github.com/onnx/models/tree/master/vision/classification/mobilenet.
Parameters
----------
conn : CAS
Specifies the CAS connection object.
model_file : string
Specifies the absolute server-side path of the model table file.
The model table file can be downloaded from https://support.sas.com/documentation/prod-p/vdmml/zip/.
n_classes : int, optional
Specifies the number of classes.
Default: 1000
width : int, optional
Specifies the width of the input layer.
Default: 224
height : int, optional
Specifies the height of the input layer.
Default: 224
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.
The channel order is BGR.
Default: (255*0.406, 255*0.456, 255*0.485)
norm_stds : double or iter-of-doubles, optional
Specifies a standard deviation for each channel in the input data.
The final input data is normalized with specified means and standard deviations.
The channel order is BGR.
Default: (255*0.225, 255*0.224, 255*0.229)
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'
random_mutation : string, optional
Specifies how to apply data augmentations/mutations to the data in the input layer.
Valid Values: 'none', 'random'
include_top : bool, optional
Specifies whether to include pre-trained weights of the top layers (i.e., the FC layers)
Default: False
"""
parameters = locals()
input_parameters = get_layer_options(input_layer_options, parameters)
# load model and model weights
model = Model.from_sashdat(conn, path=model_file)
# check if a user points to a correct model.
if model.summary.shape[0] != 120:
raise DLPyError(
"The model file doesn't point to a valid MobileNetV2_ONNX model. "
"Please check the SASHDAT file.")
# extract input layer config
model_table_df = conn.CASTable(**model.model_table).to_frame()
input_layer_df = model_table_df[model_table_df['_DLLayerID_'] == 0]
input_layer = extract_input_layer(input_layer_df)
input_layer_config = input_layer.config
# update input layer config
input_layer_config.update(input_parameters)
# update the layer list
model.layers[0] = InputLayer(**input_layer_config,
name=model.layers[0].name)
# warning if model weights doesn't exist
if not conn.tableexists(model.model_weights.name).exists:
weights_file_path = os.path.join(os.path.dirname(model_file),
model.model_name + '_weights.sashdat')
print('WARNING: Model weights is not attached '
'since system cannot find a weights file located at {}'.format(
weights_file_path))
if include_top:
if n_classes != 1000:
raise DLPyError(
"If include_top is enabled, n_classes has to be 1000.")
else:
# since the output layer is non fully connected layer,
# we need to modify the convolution right before the output. The number of filter is set to n_classes.
conv_layer_df = model_table_df[model_table_df['_DLLayerID_'] == 118]
conv_layer = extract_conv_layer(conv_layer_df)
conv_layer_config = conv_layer.config
# update input layer config
conv_layer_config.update({'n_filters': n_classes})
# update the layer list
model.layers[-2] = Conv2d(**conv_layer_config,
name=model.layers[-2].name,
src_layers=model.layers[-3])
# overwrite n_classes in output layer
out_layer_df = model_table_df[model_table_df['_DLLayerID_'] == 119]
out_layer = extract_output_layer(out_layer_df)
out_layer_config = out_layer.config
# update input layer config
out_layer_config.update({'n': n_classes})
# update the layer list
model.layers[-1] = OutputLayer(**out_layer_config,
name=model.layers[-1].name,
src_layers=model.layers[-2])
# remove top weights
model.model_weights.append_where('_LayerID_<118')
model._retrieve_('table.partition',
table=model.model_weights,
casout=dict(replace=True,
name=model.model_weights.name))
model.set_weights(model.model_weights.name)
# recompile the whole network according to the new layer list
model.compile()
return model
def _inverted_res_block(inputs, in_channels, expansion, stride, alpha,
filters, block_id):
"""
Inverted Residual Block
Parameters
----------
inputs:
Input tensor
in_channels:
Specifies the number of input tensor's channel
expansion:
expansion factor always applied to the input size.
stride:
the strides of the convolution
alpha:
width multiplier.
filters:
the dimensionality of the output space.
block_id:
block id used for naming layers
"""
pointwise_conv_filters = int(filters * alpha)
pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
x = inputs
prefix = 'block_{}_'.format(block_id)
n_groups = in_channels
if block_id:
# Expand
n_groups = expansion * in_channels
x = Conv2d(expansion * in_channels,
1,
include_bias=False,
act='identity',
name=prefix + 'expand')(x)
x = BN(name=prefix + 'expand_BN', act='identity')(x)
else:
prefix = 'expanded_conv_'
# Depthwise
x = GroupConv2d(n_groups,
n_groups,
3,
stride=stride,
act='identity',
include_bias=False,
name=prefix + 'depthwise')(x)
x = BN(name=prefix + 'depthwise_BN', act='relu')(x)
# Project
x = Conv2d(pointwise_filters,
1,
include_bias=False,
act='identity',
name=prefix + 'project')(x)
x = BN(name=prefix + 'project_BN',
act='identity')(x) # identity activation on narrow tensor
if in_channels == pointwise_filters and stride == 1:
return Res(name=prefix + 'add')([inputs, x]), pointwise_filters
return x, pointwise_filters
def MobileNetV2(conn,
model_table='MobileNetV2',
n_classes=1000,
n_channels=3,
width=224,
height=224,
norm_stds=(255 * 0.229, 255 * 0.224, 255 * 0.225),
offsets=(255 * 0.485, 255 * 0.456, 255 * 0.406),
random_flip=None,
random_crop=None,
random_mutation=None,
alpha=1):
'''
Generates a deep learning model with the MobileNetV2 architecture.
The implementation is revised based on
https://github.com/keras-team/keras-applications/blob/master/keras_applications/mobilenet_v2.py
Parameters
----------
conn : CAS
Specifies the CAS connection object.
model_table : string or dict or CAS table, optional
Specifies the CAS table to store the deep learning 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
norm_stds : double or iter-of-doubles, optional
Specifies a standard deviation for each channel in the input data.
The final input data is normalized with specified means and standard deviations.
Default: (255 * 0.229, 255 * 0.224, 255 * 0.225)
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: (255*0.485, 255*0.456, 255*0.406)
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'
random_mutation : string, optional
Specifies how to apply data augmentations/mutations to the data in the input layer.
Valid Values: 'none', 'random'
alpha : int, optional
Specifies the width multiplier in the MobileNet paper
Default: 1
alpha : int, optional
Returns
-------
:class:`Model`
References
----------
https://arxiv.org/abs/1801.04381
'''
def _make_divisible(v, divisor, min_value=None):
# make number of channel divisible
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
def _inverted_res_block(inputs, in_channels, expansion, stride, alpha,
filters, block_id):
"""
Inverted Residual Block
Parameters
----------
inputs:
Input tensor
in_channels:
Specifies the number of input tensor's channel
expansion:
expansion factor always applied to the input size.
stride:
the strides of the convolution
alpha:
width multiplier.
filters:
the dimensionality of the output space.
block_id:
block id used for naming layers
"""
pointwise_conv_filters = int(filters * alpha)
pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
x = inputs
prefix = 'block_{}_'.format(block_id)
n_groups = in_channels
if block_id:
# Expand
n_groups = expansion * in_channels
x = Conv2d(expansion * in_channels,
1,
include_bias=False,
act='identity',
name=prefix + 'expand')(x)
x = BN(name=prefix + 'expand_BN', act='identity')(x)
else:
prefix = 'expanded_conv_'
# Depthwise
x = GroupConv2d(n_groups,
n_groups,
3,
stride=stride,
act='identity',
include_bias=False,
name=prefix + 'depthwise')(x)
x = BN(name=prefix + 'depthwise_BN', act='relu')(x)
# Project
x = Conv2d(pointwise_filters,
1,
include_bias=False,
act='identity',
name=prefix + 'project')(x)
x = BN(name=prefix + 'project_BN',
act='identity')(x) # identity activation on narrow tensor
if in_channels == pointwise_filters and stride == 1:
return Res(name=prefix + 'add')([inputs, x]), pointwise_filters
return x, pointwise_filters
parameters = locals()
input_parameters = get_layer_options(input_layer_options, parameters)
inp = Input(**input_parameters, name='data')
# compared with mobilenetv1, v2 introduces inverted residual structure.
# and Non-linearities in narrow layers are removed.
# inverted residual block does three convolutins: first is 1*1 convolution, second is depthwise convolution,
# third is 1*1 convolution but without any non-linearity
first_block_filters = _make_divisible(32 * alpha, 8)
x = Conv2d(first_block_filters,
3,
stride=2,
include_bias=False,
name='Conv1',
act='identity')(inp)
x = BN(name='bn_Conv1', act='relu')(x)
x, n_channels = _inverted_res_block(x,
first_block_filters,
filters=16,
alpha=alpha,
stride=1,
expansion=1,
block_id=0)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=24,
alpha=alpha,
stride=2,
expansion=6,
block_id=1)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=24,
alpha=alpha,
stride=1,
expansion=6,
block_id=2)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=32,
alpha=alpha,
stride=2,
expansion=6,
block_id=3)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=32,
alpha=alpha,
stride=1,
expansion=6,
block_id=4)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=32,
alpha=alpha,
stride=1,
expansion=6,
block_id=5)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=64,
alpha=alpha,
stride=2,
expansion=6,
block_id=6)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=64,
alpha=alpha,
stride=1,
expansion=6,
block_id=7)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=64,
alpha=alpha,
stride=1,
expansion=6,
block_id=8)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=64,
alpha=alpha,
stride=1,
expansion=6,
block_id=9)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=96,
alpha=alpha,
stride=1,
expansion=6,
block_id=10)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=96,
alpha=alpha,
stride=1,
expansion=6,
block_id=11)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=96,
alpha=alpha,
stride=1,
expansion=6,
block_id=12)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=160,
alpha=alpha,
stride=2,
expansion=6,
block_id=13)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=160,
alpha=alpha,
stride=1,
expansion=6,
block_id=14)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=160,
alpha=alpha,
stride=1,
expansion=6,
block_id=15)
x, n_channels = _inverted_res_block(x,
n_channels,
filters=320,
alpha=alpha,
stride=1,
expansion=6,
block_id=16)
# no alpha applied to last conv as stated in the paper:
# if the width multiplier is greater than 1 we increase the number of output channels
if alpha > 1.0:
last_block_filters = _make_divisible(1280 * alpha, 8)
else:
last_block_filters = 1280
x = Conv2d(last_block_filters,
1,
include_bias=False,
name='Conv_1',
act='identity')(x)
x = BN(name='Conv_1_bn', act='relu')(x)
x = GlobalAveragePooling2D(name="Global_avg_pool")(x)
x = OutputLayer(n=n_classes)(x)
model = Model(conn, inp, x, model_table)
model.compile()
return model
def ENet(conn,
model_table='ENet',
n_classes=2,
n_channels=3,
width=512,
height=512,
scale=1.0 / 255,
norm_stds=None,
offsets=None,
random_mutation=None,
init=None,
random_flip=None,
random_crop=None,
output_image_type=None,
output_image_prob=False):
'''
Generates a deep learning model with the E-Net 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.
Default: ENet
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: 2
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: 512
height : int, optional
Specifies the height of the input layer.
Default: 512
scale : double, optional
Specifies a scaling factor to be applied to each pixel intensity values.
Default: 1.0/255
norm_stds : double or iter-of-doubles, optional
Specifies a standard deviation for each channel in the input data.
The final input data is normalized with specified means and standard deviations.
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.
random_mutation : string, optional
Specifies how to apply data augmentations/mutations to the data in the
input layer.
Valid Values: 'none', 'random'
init : str
Specifies the initialization scheme for convolution layers.
Valid Values: XAVIER, UNIFORM, NORMAL, CAUCHY, XAVIER1, XAVIER2, MSRA, MSRA1, MSRA2
Default: None
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'
output_image_type: string, optional
Specifies the output image type of this layer.
possible values: [ WIDE, PNG, BASE64 ]
default: WIDE
output_image_prob: bool, options
Does not include probabilities if doing classification (default).
Returns
-------
:class:`Sequential`
References
----------
https://arxiv.org/abs/1606.02147
'''
parameters = locals()
input_parameters = get_layer_options(input_layer_options, parameters)
inp = Input(**input_parameters, name='InputLayer_1')
# initial
x = initial_block(inp)
# stage one
x = downsampling_bottleneck(x, 16, 64)
for i in range(4):
x = regular_bottleneck(x, 64, 64)
# stage two
x = downsampling_bottleneck(x, 64, 128)
for i in range(2):
x = regular_bottleneck(x, 128, 128)
x = regular_bottleneck(x, 128, 128)
# stage three
for i in range(2):
x = regular_bottleneck(x, 128, 128)
x = regular_bottleneck(x, 128, 128)
# stage four
x = upsampling_bottleneck(x, 128, 64)
for i in range(2):
x = regular_bottleneck(x, 64, 64)
# stage five
x = upsampling_bottleneck(x, 64, 16)
x = regular_bottleneck(x, 16, 16)
x = upsampling_bottleneck(x, 16, 16)
conv = Conv2d(n_classes, 3, act='relu')(x)
seg = Segmentation(name='Segmentation_1',
output_image_type=output_image_type,
output_image_prob=output_image_prob)(conv)
model = Model(conn, inputs=inp, outputs=seg)
model.compile()
return model
