def test_model21(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, 7, src_layers=[pool1])
conv2 = Conv2d(1, 7, src_layers=[pool1])
model1.add(conv1)
model1.add(conv2)
model1.add(Concat(act='identity', 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 = 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 onnx_extract_concat(graph, node, layers):
'''
Construct concat 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:`Concat`
'''
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]
return Concat(name=node.name, act='identity', src_layers=src)
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 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 YoloV2_MultiSize(conn,
anchors,
model_table='YoloV2-MultiSize',
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):
'''
Generates a deep learning model with the Yolov2 architecture.
The model is same as Yolov2 proposed in original paper. In addition to
Yolov2, the model adds a passthrough layer that brings feature from an
earlier layer to lower resolution 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 224 416
model.add(Conv2d(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'))
# conv2 112 208
model.add(Conv2d(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'))
# conv3 56 104
model.add(
Conv2d(128, width=3, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv4 56 104
model.add(Conv2d(64, width=1, act='identity', include_bias=False,
stride=1))
model.add(BN(act=act))
# conv5 56 104
model.add(
Conv2d(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'))
# conv6 28 52
model.add(
Conv2d(256, width=3, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv7 28 52
model.add(
Conv2d(128, width=1, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv8 28 52
model.add(
Conv2d(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'))
# conv9 14 26
model.add(
Conv2d(512, width=3, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv10 14 26
model.add(
Conv2d(256, width=1, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv11 14 26
model.add(
Conv2d(512, width=3, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv12 14 26
model.add(
Conv2d(256, width=1, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv13 14 26
model.add(
Conv2d(512, width=3, act='identity', include_bias=False, stride=1))
pointLayer1 = BN(act=act, name='BN5_13')
model.add(pointLayer1)
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
# conv14 7 13
model.add(
Conv2d(1024, width=3, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv15 7 13
model.add(
Conv2d(512, width=1, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv16 7 13
model.add(
Conv2d(1024, width=3, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv17 7 13
model.add(
Conv2d(512, width=1, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv18 7 13
model.add(
Conv2d(1024, width=3, act='identity', include_bias=False, stride=1))
model.add(BN(act=act))
# conv19 7 13
model.add(
Conv2d(1024, width=3, act='identity', include_bias=False, stride=1))
model.add(BN(act=act, name='BN6_19'))
# conv20 7 13
model.add(
Conv2d(1024, width=3, act='identity', include_bias=False, stride=1))
pointLayer2 = BN(act=act, name='BN6_20')
model.add(pointLayer2)
# conv21 7 26 * 26 * 512 -> 26 * 26 * 64
model.add(
Conv2d(64,
width=1,
act='identity',
include_bias=False,
stride=1,
src_layers=[pointLayer1]))
model.add(BN(act=act))
# reshape 26 * 26 * 64 -> 13 * 13 * 256
pointLayer3 = Reshape(act='identity',
width=grid_number,
height=grid_number,
depth=256,
name='reshape1')
model.add(pointLayer3)
# concat
model.add(Concat(act='identity', src_layers=[pointLayer2, pointLayer3]))
# conv22 7 13
model.add(
Conv2d(1024, 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
def DenseNet121(conn,
model_table='DENSENET121',
n_classes=1000,
conv_channel=64,
growth_rate=32,
n_cells=[6, 12, 24, 16],
n_channels=3,
reduction=0.5,
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 DenseNet121 architecture.
Parameters
----------
conn : CAS
Specifies the connection of the CAS connection.
model_table : string
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
conv_channel : int, optional
Specifies the number of filters of the first convolution layer.
Default: 64
growth_rate : int, optional
Specifies the growth rate of convolution layers.
Default: 32
n_cells : int array length=4, optional
Specifies the number of dense connection for each DenseNet block.
Default: [6, 12, 24, 16]
reduction : double, optional
Specifies the factor of transition blocks.
Default: 0.5
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/1608.06993.pdf
'''
conn.retrieve('loadactionset',
_messagelevel='error',
actionset='deeplearn')
# get all the parms passed in
parameters = locals()
n_blocks = len(n_cells)
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))
# Top layers
model.add(
Conv2d(conv_channel,
width=7,
act='identity',
include_bias=False,
stride=2))
model.add(BN(act='relu'))
src_layer = Pooling(width=3, height=3, stride=2, padding=1, pool='max')
model.add(src_layer)
for i in range(n_blocks):
for _ in range(n_cells[i]):
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=growth_rate * 4,
width=1,
act='identity',
stride=1,
include_bias=False))
model.add(BN(act='relu'))
src_layer2 = Conv2d(n_filters=growth_rate,
width=3,
act='identity',
stride=1,
include_bias=False)
model.add(src_layer2)
src_layer = Concat(act='identity',
src_layers=[src_layer, src_layer2])
model.add(src_layer)
conv_channel += growth_rate
if i != (n_blocks - 1):
# transition block
conv_channel = int(conv_channel * reduction)
model.add(BN(act='relu'))
model.add(
Conv2d(n_filters=conv_channel,
width=1,
act='identity',
stride=1,
include_bias=False))
src_layer = Pooling(width=2, height=2, stride=2, pool='mean')
model.add(src_layer)
model.add(BN(act='identity'))
# Bottom Layers
model.add(GlobalAveragePooling2D())
model.add(OutputLayer(act='softmax', n=n_classes))
return model
def _shuffle_unit(inputs,
in_channels,
out_channels,
groups,
bottleneck_ratio,
strides=2,
stage=1,
block=1):
"""
create a shuffle unit
Parameters
----------
inputs:
Input tensor of with `channels_last` data format
in_channels:
number of input channels
out_channels:
number of output channels
strides:
An integer or tuple/list of 2 integers,
groups:
number of groups per channel
bottleneck_ratio: float
bottleneck ratio implies the ratio of bottleneck channels to output channels.
stage:
stage number
block:
block number
"""
prefix = 'stage%d/block%d' % (stage, block)
# if strides >= 2:
# out_channels -= in_channels
# default: 1/4 of the output channel of a ShuffleNet Unit
bottleneck_channels = int(out_channels * bottleneck_ratio)
groups = (1 if stage == 2 and block == 1 else groups)
# x = _group_conv(inputs, in_channels, out_channels = bottleneck_channels,
# groups = (1 if stage == 2 and block == 1 else groups),
# name = '%s/1x1_gconv_1' % prefix)
x = GroupConv2d(bottleneck_channels,
n_groups=(1 if stage == 2 and block == 1 else groups),
act='identity',
width=1,
height=1,
stride=1,
include_bias=False,
name='%s/1x1_gconv_1' % prefix)(inputs)
x = BN(act='relu', name='%s/bn_gconv_1' % prefix)(x)
x = ChannelShuffle(n_groups=groups,
name='%s/channel_shuffle' % prefix)(x)
# depthwise convolutioin
x = GroupConv2d(x.shape[-1],
n_groups=x.shape[-1],
width=3,
height=3,
include_bias=False,
stride=strides,
act='identity',
name='%s/1x1_dwconv_1' % prefix)(x)
x = BN(act=block_act, name='%s/bn_dwconv_1' % prefix)(x)
out_channels = out_channels if strides == 1 else out_channels - in_channels
x = GroupConv2d(out_channels,
n_groups=groups,
width=1,
height=1,
stride=1,
act='identity',
include_bias=False,
name='%s/1x1_gconv_2' % prefix)(x)
x = BN(act=block_act, name='%s/bn_gconv_2' % prefix)(x)
if strides < 2:
ret = Res(act='relu', name='%s/add' % prefix)([x, inputs])
else:
avg = Pooling(width=3,
height=3,
stride=2,
pool='mean',
name='%s/avg_pool' % prefix)(inputs)
ret = Concat(act='relu', name='%s/concat' % prefix)([x, avg])
return ret
def UNet(conn,
model_table='UNet',
n_classes=2,
n_channels=1,
width=256,
height=256,
scale=1.0 / 255,
norm_stds=None,
offsets=None,
random_mutation=None,
init=None,
bn_after_convolutions=False,
random_flip=None,
random_crop=None):
'''
Generates a deep learning model with the U-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.
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: 256
height : int, optional
Specifies the height of the input layer.
Default: 256
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
bn_after_convolutions : Boolean
If set to True, a batch normalization layer is added after each convolution layer.
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/1505.04597
'''
parameters = locals()
input_parameters = get_layer_options(input_layer_options, parameters)
inp = Input(**input_parameters, name='data')
act_conv = 'relu'
bias_conv = True
if bn_after_convolutions:
act_conv = 'identity'
bias_conv = False
# The model follows UNet paper architecture. The network down-samples by performing max pooling with stride=2
conv1 = Conv2d(64, 3, act=act_conv, init=init, include_bias=bias_conv)(inp)
conv1 = BN(act='relu')(conv1) if bn_after_convolutions else conv1
conv1 = Conv2d(64, 3, act=act_conv, init=init,
include_bias=bias_conv)(conv1)
conv1 = BN(act='relu')(conv1) if bn_after_convolutions else conv1
pool1 = Pooling(2)(conv1)
conv2 = Conv2d(128, 3, act=act_conv, init=init,
include_bias=bias_conv)(pool1)
conv2 = BN(act='relu')(conv2) if bn_after_convolutions else conv2
conv2 = Conv2d(128, 3, act=act_conv, init=init,
include_bias=bias_conv)(conv2)
conv2 = BN(act='relu')(conv2) if bn_after_convolutions else conv2
pool2 = Pooling(2)(conv2)
conv3 = Conv2d(256, 3, act=act_conv, init=init,
include_bias=bias_conv)(pool2)
conv3 = BN(act='relu')(conv3) if bn_after_convolutions else conv3
conv3 = Conv2d(256, 3, act=act_conv, init=init,
include_bias=bias_conv)(conv3)
conv3 = BN(act='relu')(conv3) if bn_after_convolutions else conv3
pool3 = Pooling(2)(conv3)
conv4 = Conv2d(512, 3, act=act_conv, init=init,
include_bias=bias_conv)(pool3)
conv4 = BN(act='relu')(conv4) if bn_after_convolutions else conv4
conv4 = Conv2d(512, 3, act=act_conv, init=init,
include_bias=bias_conv)(conv4)
conv4 = BN(act='relu')(conv4) if bn_after_convolutions else conv4
pool4 = Pooling(2)(conv4)
conv5 = Conv2d(1024, 3, act=act_conv, init=init,
include_bias=bias_conv)(pool4)
conv5 = BN(act='relu')(conv5) if bn_after_convolutions else conv5
conv5 = Conv2d(1024, 3, act=act_conv, init=init,
include_bias=bias_conv)(conv5)
conv5 = BN(act='relu')(conv5) if bn_after_convolutions else conv5
# the minimum is 1/2^4 of the original image size
# Our implementation applies Transpose convolution to upsample feature maps.
tconv6 = Conv2DTranspose(512,
3,
stride=2,
act='relu',
padding=1,
output_size=conv4.shape,
init=init)(conv5) # 64
# concatenation layers to combine encoder and decoder features
merge6 = Concat()([conv4, tconv6])
conv6 = Conv2d(512, 3, act=act_conv, init=init,
include_bias=bias_conv)(merge6)
conv6 = BN(act='relu')(conv6) if bn_after_convolutions else conv6
conv6 = Conv2d(512, 3, act=act_conv, init=init,
include_bias=bias_conv)(conv6)
conv6 = BN(act='relu')(conv6) if bn_after_convolutions else conv6
tconv7 = Conv2DTranspose(256,
3,
stride=2,
act='relu',
padding=1,
output_size=conv3.shape,
init=init)(conv6) # 128
merge7 = Concat()([conv3, tconv7])
conv7 = Conv2d(256, 3, act=act_conv, init=init,
include_bias=bias_conv)(merge7)
conv7 = BN(act='relu')(conv7) if bn_after_convolutions else conv7
conv7 = Conv2d(256, 3, act=act_conv, init=init,
include_bias=bias_conv)(conv7)
conv7 = BN(act='relu')(conv7) if bn_after_convolutions else conv7
tconv8 = Conv2DTranspose(128,
stride=2,
act='relu',
padding=1,
output_size=conv2.shape,
init=init)(conv7) # 256
merge8 = Concat()([conv2, tconv8])
conv8 = Conv2d(128, 3, act=act_conv, init=init,
include_bias=bias_conv)(merge8)
conv8 = BN(act='relu')(conv8) if bn_after_convolutions else conv8
conv8 = Conv2d(128, 3, act=act_conv, init=init,
include_bias=bias_conv)(conv8)
conv8 = BN(act='relu')(conv8) if bn_after_convolutions else conv8
tconv9 = Conv2DTranspose(64,
stride=2,
act='relu',
padding=1,
output_size=conv1.shape,
init=init)(conv8) # 512
merge9 = Concat()([conv1, tconv9])
conv9 = Conv2d(64, 3, act=act_conv, init=init,
include_bias=bias_conv)(merge9)
conv9 = BN(act='relu')(conv9) if bn_after_convolutions else conv9
conv9 = Conv2d(64, 3, act=act_conv, init=init,
include_bias=bias_conv)(conv9)
conv9 = BN(act='relu')(conv9) if bn_after_convolutions else conv9
conv9 = Conv2d(n_classes, 3, act='relu', init=init)(conv9)
seg1 = Segmentation(name='Segmentation_1')(conv9)
model = Model(conn, inputs=inp, outputs=seg1, model_table=model_table)
model.compile()
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