def standardExtract(generateNet=True):
'''
@brief Create a network wrapper that dumps dataset contents
@param generateNet IFF TRUE, a network prototxt will be made and printed
@returns Block(..) function that adds a data layer and dumping functions to a net
'''
def Block(net, datasetName, out_path='data'):
'''
@brief Add data a data layer and file outputs to a network
@param net Incomplete network definition
@param datasetName Name of the desired dataset (see Dataset.py)
@param out_path Output path for file dumps
'''
blobs = net.namedBlobs()
## Create data layer
dataset = Dataset.get(name=datasetName, phase='TEST')
img0, img1, flow_gt = dataset.flowLayer(net)
if not os.path.isdir(out_path):
os.makedirs(out_path)
## Write configuration file for viewer tool
f = open('%s/viewer.cfg' % (out_path), 'w')
f.write('2 2\n')
f.write('0 0 -img0.ppm\n')
f.write('1 0 -img1.ppm\n')
f.write('0 1 -gt.flo\n')
f.write('1 1 none\n')
f.close()
## Create network file outputs
net.writeImage(img0, folder=out_path, prefix='', suffix='-img0')
net.writeImage(img1, folder=out_path, prefix='', suffix='-img1')
net.writeFlow(flow_gt, folder=out_path, prefix='', suffix='-gt')
if generateNet:
net = Network()
dataset = str(param('dataset'))
if dataset is None:
raise Exception('please specify dataset=...')
Block(net, dataset)
print net.toProto()
return Block
def standardExtract(generateNet=True):
'''
@brief Create a network wrapper that dumps dataset contents
@param generateNet IFF TRUE, a network prototxt will be made and printed
@returns Block(..) function that adds a data layer and dumping functions to a net
'''
def Block(net, datasetName, out_path='data'):
'''
@brief Add data a data layer and file outputs to a network
@param net Incomplete network definition
@param datasetName Name of the desired dataset (see Dataset.py)
@param out_path Output path for file dumps
'''
blobs = net.namedBlobs()
## Create data layer
dataset = Dataset.get(name=datasetName, phase='TEST')
img0, img1, flow_gt = dataset.flowLayer(net)
if not os.path.isdir(out_path):
os.makedirs(out_path)
## Write configuration file for viewer tool
f = open('%s/viewer.cfg' % (out_path), 'w')
f.write('2 2\n')
f.write('0 0 -img0.ppm\n')
f.write('1 0 -img1.ppm\n')
f.write('0 1 -gt.flo\n')
f.write('1 1 none\n')
f.close()
## Create network file outputs
net.writeImage(img0, folder=out_path, prefix='', suffix='-img0')
net.writeImage(img1, folder=out_path, prefix='', suffix='-img1')
net.writeFlow(flow_gt, folder=out_path, prefix='', suffix='-gt')
if generateNet:
net = Network()
dataset = str(param('dataset'))
if dataset is None:
raise Exception('please specify dataset=...')
Block(net, dataset)
print net.toProto()
return Block
def standardSingleTest(DeployBlock, generateNet=True):
'''
@brief Create Testing-mode wrapper for a Deploy-mode net
@param DeployBlock Deploy-mode Block(..) function
@param generateNet IFF TRUE, a network prototxt will be made and printed
@returns Testing-mode Block(..) function that adds a data layer to a Deploy-mode
net
'''
def Block(net, datasetName, output, data_size, basename, prefix=None, use_augmentation_mean=True):
'''
@brief Add a data layer for dataset "datasetName" to a network
@param net Incomplete network definition
@param datasetName Name of the desired dataset (see Dataset.py)
@param output IFF TRUE, the network will write its input and output to disk
@param prefix Filename prefix for file outputs of "output" is TRUE
@param use_augmentation_mean IFF TRUE, data mean will be computed on the fly
'''
blobs = net.namedBlobs()
## Make data layer
#dataset = Dataset.get(name=datasetName, phase='TEST')
#img0, img1, flow_gt = dataset.flowLayer(net)
img0 = net.addInput(1, 3, data_size[0], data_size[1])
img1 = net.addInput(1, 3, data_size[0], data_size[1])
flow_gt = net.zeros(1, 2, data_size[0], data_size[1])
## Connect data to Deploy-mode net
flow_pred = DeployBlock(net, img0, img1, flow_gt,
data_size[1], data_size[0], None, use_augmentation_mean)
## Output network input and output
if output:
if prefix:
out_path = 'output_%s_%s' % (prefix, datasetName)
else:
out_path = 'output_%s' % datasetName
if not os.path.isdir(out_path):
os.makedirs(out_path)
## Write configuration file for viewer tool
f = open('%s/viewer.cfg' % out_path, 'w')
f.write('3 2\n')
f.write('0 0 -img0.ppm\n')
f.write('1 0 -img1.ppm\n')
f.write('2 0 EPE(-flow.flo,-gt.flo)\n')
f.write('0 1 -flow.flo\n')
f.write('1 1 -gt.flo\n')
f.write('2 1 DIFF(-flow.flo,-gt.flo)\n')
f.close()
## Create network file outputs
net.writeImage(img0, folder=out_path, filename=(os.path.join(out_path,basename+'-imgL.ppm')))
net.writeImage(img1, folder=out_path, filename=(os.path.join(out_path,basename+'-imgR.ppm')))
net.writeFloat(flow_pred, folder=out_path, filename=(os.path.join(out_path,basename+'-flow.float3')))
if generateNet:
net = Network()
dataset = str(param('dataset'))
if dataset is None:
raise Exception('please specify dataset=...')
use_augmentation_mean = bool(param('use_augmentation_mean', default=True))
output = bool(param('output', default=False))
prefix = str(param('prefix', default=None))
basename = str(param('basename', default=''))
height = int(param('height', default=-1))
width = int(param('width', default=-1))
assert use_augmentation_mean # Must be used because we don't have mean color
Block(net,
dataset,
output,
(height, width),
basename,
prefix,
use_augmentation_mean)
print net.toProto()
return Block
def standardDeploy(NetworkBlock, generateNet=True):
'''
@brief Create Deploy-mode wrapper for a raw net
@param NetworkBlock Block(..) function of a raw network
@param generateNet IFF TRUE, a network prototxt will be made and printed
@returns Deploy-mode Block(..) function that adds data preprocessing and results postprocessing to a raw network
'''
def Block(net, img0, img1, flow_gt,
width, height, mean_color, augmentation_mean=True):
'''
@brief Add data preprocessing to a network and connect data inputs
@param net Incomplete network definition
@param img0 Optical flow: First image
@param img1 Optical flow: Second image
@param flow_gt Optical flow: Flow groundtruth
@param width Input width (pixels)
@param height Input height (pixels)
@param mean_color Data mean to be subtracted from data if "augmentation_mean" is FALSE
@param augmentation_mean IFF TRUE, data mean will be computed on the fly
@returns Optical flow prediction layer of the network "net"
'''
blobs = net.namedBlobs()
## Connect inputs
blobs.img0 = img0
blobs.img1 = img1
blobs.flow_gt = flow_gt
## Rescale input images to [0,1]
blobs.img0s = net.imageToRange01(blobs.img0)
blobs.img1s = net.imageToRange01(blobs.img1)
## Subtract given mean or connect mean computation layer
if augmentation_mean:
blobs.img0_nomean = net.subtractAugmentationMean(blobs.img0s, name="img0s_aug", width=width, height=height)
blobs.img1_nomean = net.subtractAugmentationMean(blobs.img1s, name="img1s_aug", width=width, height=height)
else:
blobs.img0_nomean = net.subtractMean(blobs.img0s, mean_color)
blobs.img1_nomean = net.subtractMean(blobs.img1s, mean_color)
## Resample input data (needs to be 64-pixels aligned)
divisor = 64.
temp_width = ceil(width/divisor) * divisor
temp_height = ceil(height/divisor) * divisor
rescale_coeff_x = width / temp_width
rescale_coeff_y = height / temp_height
blobs.img0_nomean_resize = net.resample(blobs.img0_nomean,
width=temp_width,
height=temp_height,
type='LINEAR',
antialias=True)
blobs.img1_nomean_resize = net.resample(blobs.img1_nomean,
width=temp_width,
height=temp_height,
type='LINEAR',
antialias=True)
## Use NEAREST here, since KITTI groundtruth is sparse
blobs.flow_gt_resize = net.resample(blobs.flow_gt,
width=temp_width,
height=temp_height,
type='NEAREST',
antialias=True)
## Connect data preprocessing layers to raw net
from net import Block as Network
prediction = NetworkBlock(net,
blobs.img0_nomean_resize,
blobs.img1_nomean_resize,
blobs.flow_gt_resize)
## Resample net output to input resolution
blobs.predict_flow_resize = net.resample(prediction,
width=width,
height=height,
reference=None,
type='LINEAR',
antialias=True)
blobs.predict_flow_final = net.scale(blobs.predict_flow_resize,
(rescale_coeff_x, rescale_coeff_y))
## Connect L1 flow loss layer
epe_loss = Layers.L1Loss(net, (blobs.flow_gt, blobs.predict_flow_final),
nout=1, loss_weight=(1,), name='flow_epe',
l2_per_location=True,
normalize_by_num_entries=True, epsilon=0)
epe_loss.setName('flow_epe')
epe_loss.enableOutput()
return blobs.predict_flow_final
if generateNet:
net = Network()
Block(net)
print net.toProto()
return Block
def standardTest(DeployBlock, generateNet=True):
'''
@brief Create Testing-mode wrapper for a Deploy-mode net
@param DeployBlock Deploy-mode Block(..) function
@param generateNet IFF TRUE, a network prototxt will be made and printed
@returns Testing-mode Block(..) function that adds a data layer to a Deploy-mode
net
'''
def Block(net, datasetName, output, prefix=None, use_augmentation_mean=True):
'''
@brief Add a data layer for dataset "datasetName" to a network
@param net Incomplete network definition
@param datasetName Name of the desired dataset (see Dataset.py)
@param output IFF TRUE, the network will write its input and output to disk
@param prefix Filename prefix for file outputs of "output" is TRUE
@param use_augmentation_mean IFF TRUE, data mean will be computed on the fly
'''
blobs = net.namedBlobs()
## Make data layer
dataset = Dataset.get(name=datasetName, phase='TEST')
img0, img1, flow_gt = dataset.flowLayer(net)
## Connect data to Deploy-mode net
flow_pred = DeployBlock(net, img0, img1, flow_gt,
dataset.width(), dataset.height(),
dataset.meanColors(), use_augmentation_mean)
## Output network input and output
if output:
if prefix:
out_path = 'output_%s_%s' % (prefix, datasetName)
else:
out_path = 'output_%s' % datasetName
if not os.path.isdir(out_path):
os.makedirs(out_path)
## Write configuration file for viewer tool
f = open('%s/viewer.cfg' % out_path, 'w')
f.write('3 2\n')
f.write('0 0 -img0.ppm\n')
f.write('1 0 -img1.ppm\n')
f.write('2 0 EPE(-flow.flo,-gt.flo)\n')
f.write('0 1 -flow.flo\n')
f.write('1 1 -gt.flo\n')
f.write('2 1 DIFF(-flow.flo,-gt.flo)\n')
f.close()
## Create network file outputs
net.writeImage(img0, folder=out_path, prefix='', suffix='-img0')
net.writeImage(img1, folder=out_path, prefix='', suffix='-img1')
net.writeFlow(flow_pred, folder=out_path, prefix='', suffix='-flow')
net.writeFlow(flow_gt, folder=out_path, prefix='', suffix='-gt')
if generateNet:
net = Network()
dataset = str(param('dataset'))
if dataset is None:
raise Exception('please specify dataset=...')
use_augmentation_mean = bool(param('use_augmentation_mean', default=True))
output = bool(param('output', default=False))
prefix = str(param('prefix', default=None))
Block(net,
dataset,
output,
prefix,
use_augmentation_mean)
print net.toProto()
return Block
def standardSingleTest(DeployBlock, generateNet=True):
'''
@brief Create Testing-mode wrapper for a Deploy-mode net
@param DeployBlock Deploy-mode Block(..) function
@param generateNet IFF TRUE, a network prototxt will be made and printed
@returns Testing-mode Block(..) function that adds a data layer to a Deploy-mode
net
'''
def Block(net,
datasetName,
output,
data_size,
basename,
prefix=None,
use_augmentation_mean=True):
'''
@brief Add a data layer for dataset "datasetName" to a network
@param net Incomplete network definition
@param datasetName Name of the desired dataset (see Dataset.py)
@param output IFF TRUE, the network will write its input and output to disk
@param prefix Filename prefix for file outputs of "output" is TRUE
@param use_augmentation_mean IFF TRUE, data mean will be computed on the fly
'''
blobs = net.namedBlobs()
## Make data layer
#dataset = Dataset.get(name=datasetName, phase='TEST')
#img0, img1, flow_gt = dataset.flowLayer(net)
img0 = net.addInput(1, 3, data_size[0], data_size[1])
img1 = net.addInput(1, 3, data_size[0], data_size[1])
flow_gt = net.zeros(1, 2, data_size[0], data_size[1])
## Connect data to Deploy-mode net
flow_pred = DeployBlock(net, img0, img1, flow_gt, data_size[1],
data_size[0], None, use_augmentation_mean)
## Output network input and output
if output:
if prefix:
out_path = 'output_%s_%s' % (prefix, datasetName)
else:
out_path = 'output_%s' % datasetName
if not os.path.isdir(out_path):
os.makedirs(out_path)
## Write configuration file for viewer tool
f = open('%s/viewer.cfg' % out_path, 'w')
f.write('3 2\n')
f.write('0 0 -img0.ppm\n')
f.write('1 0 -img1.ppm\n')
f.write('2 0 EPE(-flow.flo,-gt.flo)\n')
f.write('0 1 -flow.flo\n')
f.write('1 1 -gt.flo\n')
f.write('2 1 DIFF(-flow.flo,-gt.flo)\n')
f.close()
## Create network file outputs
net.writeImage(img0,
folder=out_path,
filename=(os.path.join(out_path,
basename + '-imgL.ppm')))
net.writeImage(img1,
folder=out_path,
filename=(os.path.join(out_path,
basename + '-imgR.ppm')))
net.writeFloat(flow_pred,
folder=out_path,
filename=(os.path.join(out_path,
basename + '-flow.float3')))
if generateNet:
net = Network()
dataset = str(param('dataset'))
if dataset is None:
raise Exception('please specify dataset=...')
use_augmentation_mean = bool(
param('use_augmentation_mean', default=True))
output = bool(param('output', default=False))
prefix = str(param('prefix', default=None))
basename = str(param('basename', default=''))
height = int(param('height', default=-1))
width = int(param('width', default=-1))
assert use_augmentation_mean # Must be used because we don't have mean color
Block(net, dataset, output, (height, width), basename, prefix,
use_augmentation_mean)
print net.toProto()
return Block
def standardDeploy(NetworkBlock, generateNet=True):
'''
@brief Create Deploy-mode wrapper for a raw net
@param NetworkBlock Block(..) function of a raw network
@param generateNet IFF TRUE, a network prototxt will be made and printed
@returns Deploy-mode Block(..) function that adds data preprocessing and results postprocessing to a raw network
'''
def Block(net,
img0,
img1,
flow_gt,
width,
height,
mean_color,
augmentation_mean=True):
'''
@brief Add data preprocessing to a network and connect data inputs
@param net Incomplete network definition
@param img0 Optical flow: First image
@param img1 Optical flow: Second image
@param flow_gt Optical flow: Flow groundtruth
@param width Input width (pixels)
@param height Input height (pixels)
@param mean_color Data mean to be subtracted from data if "augmentation_mean" is FALSE
@param augmentation_mean IFF TRUE, data mean will be computed on the fly
@returns Optical flow prediction layer of the network "net"
'''
blobs = net.namedBlobs()
## Connect inputs
blobs.img0 = img0
blobs.img1 = img1
blobs.flow_gt = flow_gt
## Rescale input images to [0,1]
blobs.img0s = net.imageToRange01(blobs.img0)
blobs.img1s = net.imageToRange01(blobs.img1)
## Subtract given mean or connect mean computation layer
if augmentation_mean:
blobs.img0_nomean = net.subtractAugmentationMean(blobs.img0s,
name="img0s_aug",
width=width,
height=height)
blobs.img1_nomean = net.subtractAugmentationMean(blobs.img1s,
name="img1s_aug",
width=width,
height=height)
else:
blobs.img0_nomean = net.subtractMean(blobs.img0s, mean_color)
blobs.img1_nomean = net.subtractMean(blobs.img1s, mean_color)
## Resample input data (needs to be 64-pixels aligned)
divisor = 64.
temp_width = ceil(width / divisor) * divisor
temp_height = ceil(height / divisor) * divisor
rescale_coeff_x = width / temp_width
rescale_coeff_y = height / temp_height
blobs.img0_nomean_resize = net.resample(blobs.img0_nomean,
width=temp_width,
height=temp_height,
type='LINEAR',
antialias=True)
blobs.img1_nomean_resize = net.resample(blobs.img1_nomean,
width=temp_width,
height=temp_height,
type='LINEAR',
antialias=True)
## Use NEAREST here, since KITTI groundtruth is sparse
blobs.flow_gt_resize = net.resample(blobs.flow_gt,
width=temp_width,
height=temp_height,
type='NEAREST',
antialias=True)
## Connect data preprocessing layers to raw net
from net import Block as Network
prediction = NetworkBlock(net, blobs.img0_nomean_resize,
blobs.img1_nomean_resize,
blobs.flow_gt_resize)
## Resample net output to input resolution
blobs.predict_flow_resize = net.resample(prediction,
width=width,
height=height,
reference=None,
type='LINEAR',
antialias=True)
blobs.predict_flow_final = net.scale(
blobs.predict_flow_resize, (rescale_coeff_x, rescale_coeff_y))
## Connect L1 flow loss layer
epe_loss = Layers.L1Loss(net,
(blobs.flow_gt, blobs.predict_flow_final),
nout=1,
loss_weight=(1, ),
name='flow_epe',
l2_per_location=True,
normalize_by_num_entries=True,
epsilon=0)
epe_loss.setName('flow_epe')
epe_loss.enableOutput()
return blobs.predict_flow_final
if generateNet:
net = Network()
Block(net)
print net.toProto()
return Block
def standardTest(DeployBlock, generateNet=True):
'''
@brief Create Testing-mode wrapper for a Deploy-mode net
@param DeployBlock Deploy-mode Block(..) function
@param generateNet IFF TRUE, a network prototxt will be made and printed
@returns Testing-mode Block(..) function that adds a data layer to a Deploy-mode
net
'''
def Block(net,
datasetName,
output,
prefix=None,
use_augmentation_mean=True):
'''
@brief Add a data layer for dataset "datasetName" to a network
@param net Incomplete network definition
@param datasetName Name of the desired dataset (see Dataset.py)
@param output IFF TRUE, the network will write its input and output to disk
@param prefix Filename prefix for file outputs of "output" is TRUE
@param use_augmentation_mean IFF TRUE, data mean will be computed on the fly
'''
blobs = net.namedBlobs()
## Make data layer
dataset = Dataset.get(name=datasetName, phase='TEST')
img0, img1, flow_gt = dataset.flowLayer(net)
## Connect data to Deploy-mode net
flow_pred = DeployBlock(net, img0, img1, flow_gt, dataset.width(),
dataset.height(), dataset.meanColors(),
use_augmentation_mean)
## Output network input and output
if output:
if prefix:
out_path = 'output_%s_%s' % (prefix, datasetName)
else:
out_path = 'output_%s' % datasetName
if not os.path.isdir(out_path):
os.makedirs(out_path)
## Write configuration file for viewer tool
f = open('%s/viewer.cfg' % out_path, 'w')
f.write('3 2\n')
f.write('0 0 -img0.ppm\n')
f.write('1 0 -img1.ppm\n')
f.write('2 0 EPE(-flow.flo,-gt.flo)\n')
f.write('0 1 -flow.flo\n')
f.write('1 1 -gt.flo\n')
f.write('2 1 DIFF(-flow.flo,-gt.flo)\n')
f.close()
## Create network file outputs
net.writeImage(img0, folder=out_path, prefix='', suffix='-img0')
net.writeImage(img1, folder=out_path, prefix='', suffix='-img1')
net.writeFlow(flow_pred,
folder=out_path,
prefix='',
suffix='-flow')
net.writeFlow(flow_gt, folder=out_path, prefix='', suffix='-gt')
if generateNet:
net = Network()
dataset = str(param('dataset'))
if dataset is None:
raise Exception('please specify dataset=...')
use_augmentation_mean = bool(
param('use_augmentation_mean', default=True))
output = bool(param('output', default=False))
prefix = str(param('prefix', default=None))
Block(net, dataset, output, prefix, use_augmentation_mean)
print net.toProto()
return Block
