def make_graph(self, data, include_losses=True):
pred_config = nd.PredConfig()
pred_config.add(
nd.PredConfigId(
type='disp',
perspective='L',
channels=1,
scale=self._scale,
))
pred_dispL_t_1 = data.disp.L
pred_flow_fwd = data.flow[0].fwd
pred_occ_fwd = data.occ[0].fwd
pred_dispL_t1_warped = nd.ops.warp(pred_dispL_t_1, pred_flow_fwd)
pred_config[0].mod_func = lambda x: self.interpolator(
pred=x, prev_disp=pred_dispL_t1_warped)
inp = nd.ops.concat(data.img.L, nd.ops.scale(pred_dispL_t1_warped,
0.05), pred_occ_fwd)
with nd.Scope('refine_disp', learn=True, **self.scope_args()):
arch = Architecture_S(
num_outputs=pred_config.total_channels(),
disassembling_function=pred_config.disassemble,
loss_function=None,
conv_upsample=self._conv_upsample,
exit_after=0)
out = arch.make_graph(inp, edge_features=data.img.L)
return out
def make_graph(self, data, include_losses=False):
# hypNet
pred_config = nd.PredConfig()
pred_config.add(nd.PredConfigId(type='flow_hyp', dir='fwd', offset=0, channels=2, scale=self._scale, array_length=self._num_hypotheses))
pred_config.add(nd.PredConfigId(type='iul_b_hyp_log', dir='fwd', offset=0, channels=2, scale=self._scale, array_length=self._num_hypotheses, mod_func=self._log_sigmoid))
nd.log('pred_config:')
nd.log(pred_config)
with nd.Scope('hypNet', shared_batchnorm=False, correlation_leaky_relu=True, **self.scope_args()):
arch = Architecture_C(
num_outputs=pred_config.total_channels(),
disassembling_function=pred_config.disassemble,
conv_upsample=True,
loss_function= None,
channel_factor=self._channel_factor,
feature_channels=self._feature_channels
)
out_hyp = arch.make_graph(data.img[0], data.img[1])
# mergeNet
pred_config = nd.PredConfig()
pred_config.add(nd.PredConfigId(type='flow', dir='fwd', offset=0, channels=2, scale=self._scale, dist=1))
pred_config.add(nd.PredConfigId(type='iul_b_log', dir='fwd', offset=0, channels=2, scale=self._scale, dist=1, mod_func=self.iul_b_log_sigmoid))
nd.log('pred_config:')
nd.log(pred_config)
hyps = [nd.ops.resample(hyp, reference=data.img[0], antialias=False, type='LINEAR') for hyp in [out_hyp.final.flow_hyp[0].fwd[i] for i in range(self._num_hypotheses)]]
uncertainties = [nd.ops.resample(unc, reference=data.img[0], antialias=False, type='LINEAR') for unc in [out_hyp.final.iul_b_hyp_log[0].fwd[i] for i in range(self._num_hypotheses)]]
img_warped = [nd.ops.warp(data.img[1], hyp) for hyp in hyps]
with nd.Scope('mergeNet', shared_batchnorm=False, **self.scope_args()):
input = nd.ops.concat([data.img[0]] + [data.img[1]] + hyps + uncertainties + img_warped)
arch = Architecture_S(
num_outputs=pred_config.total_channels(),
disassembling_function=pred_config.disassemble,
conv_upsample=True,
loss_function= None,
channel_factor=self._channel_factor
)
out_merge = arch.make_graph(input)
return out_merge
def make_graph(self, data, include_losses=True):
pred_config = nd.PredConfig()
pred_config.add(
nd.PredConfigId(type='flow',
dir='fwd',
offset=0,
channels=2,
scale=self._scale))
pred_config.add(
nd.PredConfigId(type='occ',
dir='fwd',
offset=0,
channels=2,
scale=self._scale))
nd.log('pred_config:')
nd.log(pred_config)
#### Net 1 ####
with nd.Scope('net1', learn=False, **self.scope_args()):
arch1 = Architecture_C(
num_outputs=pred_config.total_channels(),
disassembling_function=pred_config.disassemble,
loss_function=None,
conv_upsample=self._conv_upsample)
out1 = arch1.make_graph(data.img[0], data.img[1])
#### Net 2 ####
flow_fwd = out1.final.flow[0].fwd
upsampled_flow_fwd = nd.ops.differentiable_resample(
flow_fwd, reference=data.img[0])
warped = nd.ops.warp(data.img[1], upsampled_flow_fwd)
# prepare data for second net
occ_fwd = self.resample_occ(out1.final.occ[0].fwd, data.img[0])
input2 = nd.ops.concat(data.img[0], data.img[1],
nd.ops.scale(upsampled_flow_fwd, 0.05), warped,
occ_fwd)
pred_config[0].mod_func = lambda x: nd.ops.add(
x,
nd.ops.resample(
flow_fwd, reference=x, type='LINEAR', antialias=False))
pred_config[1].mod_func = lambda x: nd.ops.add(
x,
nd.ops.resample(
occ_fwd, reference=x, type='LINEAR', antialias=False))
with nd.Scope('net2', learn=False, **self.scope_args()):
arch2 = Architecture_S(
num_outputs=pred_config.total_channels(),
disassembling_function=pred_config.disassemble,
loss_function=None,
conv_upsample=self._conv_upsample)
out2 = arch2.make_graph(input2)
#### Net 3 ####
flow_fwd = out2.final.flow[0].fwd
upsampled_flow_fwd = nd.ops.differentiable_resample(
flow_fwd, reference=data.img[0])
warped = nd.ops.warp(data.img[1], upsampled_flow_fwd)
# prepare data for third net
occ_fwd = self.resample_occ(out2.final.occ[0].fwd, data.img[0])
input3 = nd.ops.concat(data.img[0], data.img[1],
nd.ops.scale(upsampled_flow_fwd, 0.05), warped,
occ_fwd)
pred_config.add(
nd.PredConfigId(type='mb',
dir='fwd',
offset=0,
channels=2,
scale=self._scale))
pred_config[0].mod_func = lambda x: nd.ops.add(
x,
nd.ops.resample(
flow_fwd, reference=x, type='LINEAR', antialias=False))
pred_config[1].mod_func = lambda x: nd.ops.add(
x,
nd.ops.resample(
occ_fwd, reference=x, type='LINEAR', antialias=False))
with nd.Scope('net3', learn=True, **self.scope_args()):
arch3 = Architecture_S(
num_outputs=pred_config.total_channels(),
disassembling_function=pred_config.disassemble,
loss_function=None,
conv_upsample=self._conv_upsample,
exit_after=0,
)
out3 = arch3.make_graph(input3, edge_features=data.img[0])
return out3
def make_graph(self, data, include_losses=True):
pred_config = nd.PredConfig()
pred_config.add(
nd.PredConfigId(type='disp',
perspective='L',
channels=1,
scale=self._scale,
mod_func=lambda b: nd.ops.neg_relu(b)))
pred_config.add(
nd.PredConfigId(
type='occ',
perspective='L',
channels=2,
scale=self._scale,
))
with nd.Scope('net1', learn=False, **self.scope_args()):
arch1 = Architecture_C(
num_outputs=pred_config.total_channels(),
disassembling_function=pred_config.disassemble,
loss_function=None,
conv_upsample=self._conv_upsample,
channel_factor=0.375)
out1 = arch1.make_graph(data.img.L,
data.img.R,
use_1D_corr=True,
single_direction=0)
disp1 = out1.final.disp.L
occ1 = self.resample_occ(out1.final.occ.L, data.img.L)
upsampled_disp1 = nd.ops.differentiable_resample(disp1,
reference=data.img.L)
pred_config[0].mod_func = lambda x: nd.ops.add(
x,
nd.ops.resample(disp1, reference=x, type='LINEAR', antialias=False)
)
pred_config[1].mod_func = lambda x: nd.ops.add(
x,
nd.ops.resample(occ1, reference=x, type='LINEAR', antialias=False))
warped = nd.ops.warp(data.img.R, nd.ops.disp_to_flow(upsampled_disp1))
input2 = nd.ops.concat(data.img.L, data.img.R,
nd.ops.scale(upsampled_disp1, 0.05), warped,
occ1)
with nd.Scope('net2', learn=False, **self.scope_args()):
arch2 = Architecture_S(
num_outputs=pred_config.total_channels(),
disassembling_function=pred_config.disassemble,
loss_function=None,
conv_upsample=self._conv_upsample,
channel_factor=0.375)
out2 = arch2.make_graph(input2)
## Net 3
disp2 = out2.final.disp.L
occ2 = self.resample_occ(out2.final.occ.L, data.img.L)
upsampled_disp2 = nd.ops.differentiable_resample(disp2,
reference=data.img.L)
pred_config.add(
nd.PredConfigId(type='db',
perspective='L',
channels=2,
scale=self._scale))
pred_config[0].mod_func = lambda x: nd.ops.add(
x,
nd.ops.resample(disp2, reference=x, type='LINEAR', antialias=False)
)
pred_config[1].mod_func = lambda x: nd.ops.add(
x,
nd.ops.resample(occ2, reference=x, type='LINEAR', antialias=False))
warped = nd.ops.warp(data.img.R, nd.ops.disp_to_flow(upsampled_disp2))
input3 = nd.ops.concat(data.img.L, data.img.R,
nd.ops.scale(upsampled_disp2, 0.05), warped,
occ2)
with nd.Scope('net3', learn=True, **self.scope_args()):
arch3 = Architecture_S(
num_outputs=pred_config.total_channels(),
disassembling_function=pred_config.disassemble,
loss_function=None,
conv_upsample=self._conv_upsample,
exit_after=0,
channel_factor=0.375)
out3 = arch3.make_graph(input3, edge_features=data.img.L)
return out3
