def flowWarp(img, flow):
import caffe
width = img.shape[1]
height = img.shape[0]
print 'processing (%dx%d)' % (width, height)
defFile = tempFilename('.prototxt')
preprocessFile('/home/ilge/hackathon2/common/prototmp/apply_flow.prototmp', defFile, {'WIDTH': width, 'HEIGHT': height})
caffe.set_logging_disabled()
caffe.set_mode_gpu()
net = caffe.Net(
defFile,
caffe.TEST
)
os.remove(defFile)
print 'network forward pass'
img_input = img[np.newaxis, :, :, :].transpose(0, 3, 1, 2)
flow_input = flow[np.newaxis, :, :, :].transpose(0, 3, 1, 2)
net.blobs['image'].reshape(*img_input.shape)
net.blobs['image'].data[...] = img_input
net.blobs['flow'].reshape(*flow_input.shape)
net.blobs['flow'].data[...] = flow_input
net.forward()
output = net.blobs['output'].data[...].transpose(0, 2, 3, 1).squeeze()
return output
def flowWarp(img, flow):
import caffe
width = img.shape[1]
height = img.shape[0]
print 'processing (%dx%d)' % (width, height)
defFile = tempFilename('.prototxt')
preprocessFile('/home/ilge/hackathon2/common/prototmp/apply_flow.prototmp',
defFile, {
'WIDTH': width,
'HEIGHT': height
})
caffe.set_logging_disabled()
caffe.set_mode_gpu()
net = caffe.Net(defFile, caffe.TEST)
os.remove(defFile)
print 'network forward pass'
img_input = img[np.newaxis, :, :, :].transpose(0, 3, 1, 2)
flow_input = flow[np.newaxis, :, :, :].transpose(0, 3, 1, 2)
net.blobs['image'].reshape(*img_input.shape)
net.blobs['image'].data[...] = img_input
net.blobs['flow'].reshape(*flow_input.shape)
net.blobs['flow'].data[...] = flow_input
net.forward()
output = net.blobs['output'].data[...].transpose(0, 2, 3, 1).squeeze()
return output
def caffeNet(modelFile=None, prototmp=None, inputs={}, phase=None, logging=False):
if phase is None: phase=caffe.TEST
if prototmp is not None:
modelFile = tempFilename('.prototmp')
open(modelFile,'w').write(prototmp)
if not logging: caffe.set_logging_disabled()
caffe.set_mode_gpu()
net = caffe.Net(
modelFile,
phase
)
if prototmp is not None:
os.remove(modelFile)
for name, value in inputs.iteritems():
value = value[np.newaxis, :, :, :].transpose(0, 3, 1, 2)
net.blobs[name].reshape(*value.shape)
net.blobs[name].data[...] = value
return net