def test_extract_clustering1(self):
model = Sequential(self.s, model_table = 'Simple_CNN')
model.add(InputLayer(3, 48, 96, scale = 1.0 / 255, random_mutation = 'none'))
model.add(Split(16))
model.add(Clustering(n_clusters=10))
model_extracted = Model.from_table(self.s.CASTable(model.model_table['name']))
model_extracted.compile()
def test_extract_embeddingloss1(self):
model = Sequential(self.s, model_table = 'Simple_CNN')
model.add(InputLayer(3, 48, 96, scale = 1.0 / 255, random_mutation = 'none'))
model.add(Dense(16))
model.add(EmbeddingLoss(margin=10))
model_extracted = Model.from_table(self.s.CASTable(model.model_table['name']))
model_extracted.compile()
self.assertTrue(model_extracted.layers[-1].config['margin'] == 10)
def test_extract_segmentation1(self):
model = Sequential(self.s, model_table = 'Simple_CNN')
model.add(InputLayer(3, 48, 96, scale = 1.0 / 255, random_mutation = 'none'))
model.add(Dense(16))
model.add(Segmentation(act='AUTO', target_scale=10))
model_extracted = Model.from_table(self.s.CASTable(model.model_table['name']))
model_extracted.compile()
self.assertTrue(model_extracted.layers[-1].config['act'] == 'AUTO')
self.assertTrue(model_extracted.layers[-1].config['target_scale'] == 10)
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 InceptionV3(conn,
model_table='InceptionV3',
n_classes=1000,
n_channels=3,
width=299,
height=299,
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 Inceptionv3 architecture with batch normalization layers.
Parameters
----------
conn : CAS
Specifies the CAS connection object.
model_table : string, optional
Specifies the name of CAS table to store the model in.
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: 299
height : int, optional
Specifies the height of the input layer.
Default: 299
scale : double, optional
Specifies a scaling factor to be applied to each pixel intensity values.
Default: 1.0
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 from ImageNet data set
Default: False
pre_trained_weights_file : string, optional
Specifies the file name for the pretained weights.
Must be a fully qualified file name of SAS-compatible file (*.caffemodel.h5)
Note: Required when pre_train_weight=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_train_weight` is `False`
:class:`Model`
If `pre_train_weight` is `True`
References
----------
https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.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))
# 299 x 299 x 3
model.add(
Conv2d(n_filters=32,
width=3,
height=3,
stride=2,
act='identity',
include_bias=False,
padding=0))
model.add(BN(act='relu'))
# 149 x 149 x 32
model.add(
Conv2d(n_filters=32,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False,
padding=0))
model.add(BN(act='relu'))
# 147 x 147 x 32
model.add(
Conv2d(n_filters=64,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
# 147 x 147 x 64
model.add(Pooling(width=3, height=3, stride=2, pool='max', padding=0))
# 73 x 73 x 64
model.add(
Conv2d(n_filters=80,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
padding=0))
model.add(BN(act='relu'))
# 73 x 73 x 80
model.add(
Conv2d(n_filters=192,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False,
padding=0))
model.add(BN(act='relu'))
# 71 x 71 x 192
pool2 = Pooling(width=3, height=3, stride=2, pool='max', padding=0)
model.add(pool2)
# mixed 0: output 35 x 35 x 256
# branch1x1
model.add(
Conv2d(n_filters=64,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[pool2]))
branch1x1 = BN(act='relu')
model.add(branch1x1)
# branch5x5
model.add(
Conv2d(n_filters=48,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[pool2]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=64,
width=5,
height=5,
stride=1,
act='identity',
include_bias=False))
branch5x5 = BN(act='relu')
model.add(branch5x5)
# branch3x3dbl
model.add(
Conv2d(n_filters=64,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[pool2]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=96,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=96,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False))
branch3x3dbl = BN(act='relu')
model.add(branch3x3dbl)
# branch_pool
model.add(
Pooling(width=3,
height=3,
stride=1,
pool='average',
src_layers=[pool2]))
model.add(
Conv2d(n_filters=32,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False))
branch_pool = BN(act='relu')
model.add(branch_pool)
# mixed0 concat
concat = Concat(
act='identity',
src_layers=[branch1x1, branch5x5, branch3x3dbl, branch_pool])
model.add(concat)
# mixed 1: output 35 x 35 x 288
# branch1x1
model.add(
Conv2d(n_filters=64,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
branch1x1 = BN(act='relu')
model.add(branch1x1)
# branch5x5
model.add(
Conv2d(n_filters=48,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=64,
width=5,
height=5,
stride=1,
act='identity',
include_bias=False))
branch5x5 = BN(act='relu')
model.add(branch5x5)
# branch3x3dbl
model.add(
Conv2d(n_filters=64,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=96,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=96,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False))
branch3x3dbl = BN(act='relu')
model.add(branch3x3dbl)
# branch_pool
model.add(
Pooling(width=3,
height=3,
stride=1,
pool='average',
src_layers=[concat]))
model.add(
Conv2d(n_filters=64,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False))
branch_pool = BN(act='relu')
model.add(branch_pool)
# mixed1 concat
concat = Concat(
act='identity',
src_layers=[branch1x1, branch5x5, branch3x3dbl, branch_pool])
model.add(concat)
# mixed 2: output 35 x 35 x 288
# branch1x1
model.add(
Conv2d(n_filters=64,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
branch1x1 = BN(act='relu')
model.add(branch1x1)
# branch5x5
model.add(
Conv2d(n_filters=48,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=64,
width=5,
height=5,
stride=1,
act='identity',
include_bias=False))
branch5x5 = BN(act='relu')
model.add(branch5x5)
# branch3x3dbl
model.add(
Conv2d(n_filters=64,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=96,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=96,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False))
branch3x3dbl = BN(act='relu')
model.add(branch3x3dbl)
# branch_pool
model.add(
Pooling(width=3,
height=3,
stride=1,
pool='average',
src_layers=[concat]))
model.add(
Conv2d(n_filters=64,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False))
branch_pool = BN(act='relu')
model.add(branch_pool)
# mixed2 concat
concat = Concat(
act='identity',
src_layers=[branch1x1, branch5x5, branch3x3dbl, branch_pool])
model.add(concat)
# mixed 3: output 17 x 17 x 768
# branch3x3
model.add(
Conv2d(n_filters=384,
width=3,
height=3,
stride=2,
act='identity',
include_bias=False,
padding=0,
src_layers=[concat]))
branch3x3 = BN(act='relu')
model.add(branch3x3)
# branch3x3dbl
model.add(
Conv2d(n_filters=64,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=96,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=96,
width=3,
height=3,
stride=2,
act='identity',
include_bias=False,
padding=0))
branch3x3dbl = BN(act='relu')
model.add(branch3x3dbl)
# branch_pool
branch_pool = Pooling(width=3,
height=3,
stride=2,
pool='max',
padding=0,
src_layers=[concat])
model.add(branch_pool)
# mixed3 concat
concat = Concat(act='identity',
src_layers=[branch3x3, branch3x3dbl, branch_pool])
model.add(concat)
# mixed 4: output 17 x 17 x 768
# branch1x1
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
branch1x1 = BN(act='relu')
model.add(branch1x1)
# branch7x7
model.add(
Conv2d(n_filters=128,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=128,
width=7,
height=1,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=1,
height=7,
stride=1,
act='identity',
include_bias=False))
branch7x7 = BN(act='relu')
model.add(branch7x7)
# branch7x7dbl
model.add(
Conv2d(n_filters=128,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=128,
width=1,
height=7,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=128,
width=7,
height=1,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=128,
width=1,
height=7,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=7,
height=1,
stride=1,
act='identity',
include_bias=False))
branch7x7dbl = BN(act='relu')
model.add(branch7x7dbl)
# branch_pool
model.add(
Pooling(width=3,
height=3,
stride=1,
pool='average',
src_layers=[concat]))
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False))
branch_pool = BN(act='relu')
model.add(branch_pool)
# mixed4 concat
concat = Concat(
act='identity',
src_layers=[branch1x1, branch7x7, branch7x7dbl, branch_pool])
model.add(concat)
# mixed 5, 6: output 17 x 17 x 768
for i in range(2):
# branch1x1
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
branch1x1 = BN(act='relu')
model.add(branch1x1)
# branch7x7
model.add(
Conv2d(n_filters=160,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=160,
width=7,
height=1,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=1,
height=7,
stride=1,
act='identity',
include_bias=False))
branch7x7 = BN(act='relu')
model.add(branch7x7)
# branch7x7dbl
model.add(
Conv2d(n_filters=160,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=160,
width=1,
height=7,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=160,
width=7,
height=1,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=160,
width=1,
height=7,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=7,
height=1,
stride=1,
act='identity',
include_bias=False))
branch7x7dbl = BN(act='relu')
model.add(branch7x7dbl)
# branch_pool
model.add(
Pooling(width=3,
height=3,
stride=1,
pool='average',
src_layers=[concat]))
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False))
branch_pool = BN(act='relu')
model.add(branch_pool)
# concat
concat = Concat(
act='identity',
src_layers=[branch1x1, branch7x7, branch7x7dbl, branch_pool])
model.add(concat)
# mixed 7: output 17 x 17 x 768
# branch1x1
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
branch1x1 = BN(act='relu')
model.add(branch1x1)
# branch7x7
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=7,
height=1,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=1,
height=7,
stride=1,
act='identity',
include_bias=False))
branch7x7 = BN(act='relu')
model.add(branch7x7)
# branch7x7dbl
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=1,
height=7,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=7,
height=1,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=1,
height=7,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=7,
height=1,
stride=1,
act='identity',
include_bias=False))
branch7x7dbl = BN(act='relu')
model.add(branch7x7dbl)
# branch_pool
model.add(
Pooling(width=3,
height=3,
stride=1,
pool='average',
src_layers=[concat]))
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False))
branch_pool = BN(act='relu')
model.add(branch_pool)
# mixed7 concat
concat = Concat(
act='identity',
src_layers=[branch1x1, branch7x7, branch7x7dbl, branch_pool])
model.add(concat)
# mixed 8: output 8 x 8 x 1280
# branch3x3
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=320,
width=3,
height=3,
stride=2,
act='identity',
include_bias=False,
padding=0))
branch3x3 = BN(act='relu')
model.add(branch3x3)
# branch7x7x3
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=7,
height=1,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=1,
height=7,
stride=1,
act='identity',
include_bias=False))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=192,
width=3,
height=3,
stride=2,
act='identity',
include_bias=False,
padding=0))
branch7x7x3 = BN(act='relu')
model.add(branch7x7x3)
# branch_pool
branch_pool = Pooling(width=3,
height=3,
stride=2,
pool='max',
padding=0,
src_layers=[concat])
model.add(branch_pool)
# mixed8 concat
concat = Concat(act='identity',
src_layers=[branch3x3, branch7x7x3, branch_pool])
model.add(concat)
# mixed 9, 10: output 8 x 8 x 2048
for i in range(2):
# branch1x1
model.add(
Conv2d(n_filters=320,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
branch1x1 = BN(act='relu')
model.add(branch1x1)
# branch3x3
model.add(
Conv2d(n_filters=384,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
branch3x3 = BN(act='relu')
model.add(branch3x3)
model.add(
Conv2d(n_filters=384,
width=3,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[branch3x3]))
branch3x3_1 = BN(act='relu')
model.add(branch3x3_1)
model.add(
Conv2d(n_filters=384,
width=1,
height=3,
stride=1,
act='identity',
include_bias=False,
src_layers=[branch3x3]))
branch3x3_2 = BN(act='relu')
model.add(branch3x3_2)
branch3x3 = Concat(act='identity',
src_layers=[branch3x3_1, branch3x3_2])
model.add(branch3x3)
# branch3x3dbl
model.add(
Conv2d(n_filters=448,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[concat]))
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=384,
width=3,
height=3,
stride=1,
act='identity',
include_bias=False))
branch3x3dbl = BN(act='relu')
model.add(branch3x3dbl)
model.add(
Conv2d(n_filters=384,
width=3,
height=1,
stride=1,
act='identity',
include_bias=False,
src_layers=[branch3x3dbl]))
branch3x3dbl_1 = BN(act='relu')
model.add(branch3x3dbl_1)
model.add(
Conv2d(n_filters=384,
width=1,
height=3,
stride=1,
act='identity',
include_bias=False,
src_layers=[branch3x3dbl]))
branch3x3dbl_2 = BN(act='relu')
model.add(branch3x3dbl_2)
branch3x3dbl = Concat(act='identity',
src_layers=[branch3x3dbl_1, branch3x3dbl_2])
model.add(branch3x3dbl)
# branch_pool
model.add(
Pooling(width=3,
height=3,
stride=1,
pool='average',
src_layers=[concat]))
model.add(
Conv2d(n_filters=192,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False))
branch_pool = BN(act='relu')
model.add(branch_pool)
# concat
concat = Concat(
act='identity',
src_layers=[branch1x1, branch3x3, branch3x3dbl, branch_pool])
model.add(concat)
# calculate dimensions for global average pooling
w = max((width - 75) // 32 + 1, 1)
h = max((height - 75) // 32 + 1, 1)
# global average pooling
model.add(
Pooling(width=w,
height=h,
stride=1,
pool='average',
padding=0,
src_layers=[concat]))
# output layer
model.add(OutputLayer(n=n_classes))
return model
else:
if pre_trained_weights_file is None:
raise ValueError(
'\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_train_weight_file using '
'the fully qualified server side path.')
print('NOTE: Scale is set to 1/127.5, and offsets 1 to '
'match Keras preprocessing.')
model_cas = model_inceptionv3.InceptionV3_Model(
s=conn,
model_table=model_table,
n_channels=n_channels,
width=width,
height=height,
random_crop=random_crop,
offsets=[1, 1, 1],
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_<218'))
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='predictions')
model._retrieve_('deeplearn.addlayer',
model=model_table,
name='predictions',
layer=dict(type='output',
n=n_classes,
act='softmax'),
srcLayers=['avg_pool'])
model = Model.from_table(conn.CASTable(model_table))
return model
