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_reshape_layer1(self):
dict1 = Reshape(name='reshape',
width=1,
height=2,
depth=3,
src_layers=[Dense(name='fc',
n=100)]).to_model_params()
self.assertTrue(self.sample_syntax['reshape1'] == dict1)
def test_model_crnn_bug(self):
model = Sequential(self.s, model_table='crnn')
model.add(InputLayer(3,256,16))
model.add(Reshape(height=16,width=256,depth=3))
model.add(Conv2d(64,3,3,stride=1,padding=1)) # size = 16x256x64
model.add(Pooling(2,2,2)) # size = 8x128x64
model.add(Conv2d(128,3,3,stride=1,padding=1)) # size = 8x128x128
model.add(Pooling(2,2,2)) # size = 4x64x128
model.add(Conv2d(256,3,3,stride=1,padding=1,act='IDENTITY')) # size = 4x64x256
model.add(BN(act='RELU')) # size = 4x64x256
model.add(Conv2d(256,3,3,stride=1,padding=1)) # size = 4x64x256
model.add(Pooling(1,2,stride_horizontal=1, stride_vertical=2))
#, padding=1)) # size = 2x64x256
#model.add(Pooling(1,2,stride=2,stride_horizontal=1, stride_vertical=2,)) # size = 2x64x256
model.add(Conv2d(512,3,3,stride=1,padding=1, act='IDENTITY')) # size = 2x64x512
model.add(BN(act='RELU'))
model.add(Conv2d(512,3,3,stride=1,padding=1)) # size = 2x64x512
model.add(Pooling(1,2,stride_horizontal=1, stride_vertical=2)) #, padding=1)) # size = 1x64x512
#model.add(Pooling(1,2,stride=2,stride_horizontal=1, stride_vertical=2,)) # size = 1x64x512
model.add(Conv2d(512,3,3,stride=1,padding=1, act='IDENTITY')) # size = 1x64x512
model.add(BN(act='RELU'))
model.add(Reshape(order='DWH',width=64, height=512, depth=1))
model.add(Recurrent(512,output_type='SAMELENGTH'))
model.add(OutputLayer(error='CTC'))
model.print_summary()
def test_formant_name_function(self):
ol = Reshape()
ol.format_name(block_num=1, local_count=7)
self.assertTrue(ol.name == 'Reshape1_7')
def test_reshape_layer2(self):
if not __dev__:
with self.assertRaises(DLPyError):
Reshape(not_a_parameter=1)
def _MBConvBlock(inputs, in_channels, out_channels, ksize, stride, expansion, se_ratio, stage_id, block_id,
noskip=False, activation_fn='relu'):
'''
Inverted Residual Block
Parameters
----------
inputs: input tensor
Speecify input tensor for block.
in_channels: integer
Specifies the number of input tensor's channel.
out_channels: integer
Specifies the number of output tensor's channel
ksize:
Specifies the kernel size of the convolution
stride: integer
Specifies the stride of the convolution
expansion: double
Specifies the expansion factor for the input layer.
se_ratio: double
Specifies the ratio to squeeze the input filters for squeeze-and-excitation block.
stage_id: integer
Specifies stage id for naming layers
block_id:
Specifies block id for naming layers
noskip: bool
Specifies whether the skip connection is used. By default, the skip connection is used.
activation_fn:
Specifies activation function
'''
# mobilenetv2 block is also known as inverted residual block, which consists of three convolutions:
# the first is 1*1 convolution for expansion
# the second is depthwise convolution
# the third is 1*1 convolution without any non-linearity for projection
x = inputs
prefix = 'stage_{}_block_{}'.format(stage_id, block_id)
n_groups = in_channels # for expansion=1, n_groups might be different from pointwise_filters
if expansion > 1:
# For MobileNet V2, expansion>1 when stage>0
n_groups = int(expansion * in_channels) ## update n_groups
x = Conv2d(n_groups, 1, include_bias=False, act='identity',
name=prefix + 'expand')(x)
x = BN(name=prefix + 'expand_BN', act='identity')(x)
# Depthwise convolution
x = GroupConv2d(n_groups, n_groups, ksize, stride=stride, act='identity',
include_bias=False, name=prefix + 'depthwise')(x)
x = BN(name=prefix + 'depthwise_BN', act=activation_fn)(x)
# Squeeze-Excitation
if 0 < se_ratio <= 1:
se_input = x # features to be squeezed
x = GlobalAveragePooling2D(name=prefix + "global_avg_pool")(x)
# Squeeze
channels_se = max(1, int(in_channels * se_ratio))
x = Conv2d(channels_se, 1, include_bias=True, act=activation_fn, name=prefix + 'squeeze')(x)
x = Conv2d(n_groups, 1, include_bias=True, act='sigmoid', name=prefix + 'excitation')(x)
x = Reshape(name=prefix + 'reshape', width=n_groups, height=1, depth=1)(x)
x = Scale(name=prefix + 'scale')([se_input, x]) # x = out*w
# Project
x = Conv2d(out_channels, 1, include_bias=False, act='identity', name=prefix + 'project')(x)
x = BN(name=prefix + 'project_BN', act='identity')(x) # identity activation on narrow tensor
# Prepare output for MBConv block
if in_channels == out_channels and stride == 1 and (not noskip):
# dropout can be added.
return Res(name=prefix + 'add_se_residual')([x, inputs])
else:
return x
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 test_model4(self):
try:
import onnx
from onnx import numpy_helper
except:
unittest.TestCase.skipTest(self, "onnx not found in the libraries")
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, act='IDENTITY'))
model1.add(Reshape(height=448, width=448, depth=2, act='RECTIFIER'))
model1.add(Conv2d(8, 7, act='identity', include_bias=False))
model1.add(BN(act='relu'))
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)
if self.data_dir_local is None:
unittest.TestCase.skipTest(
self,
"DLPY_DATA_DIR_LOCAL is not set in the environment variables")
#model1.deploy(self.data_dir_local, output_format='onnx')
import tempfile
tmp_dir_to_dump = tempfile.gettempdir()
model1.deploy(tmp_dir_to_dump, output_format='onnx')
import os
model_path = os.path.join(tmp_dir_to_dump, '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]))
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)