def _build_model(self):
# Encoder takes a 3D point cloud as an input.
# Note that a linear layer is applied to the global feature vector
self.encoder = ShapeEncoder(in_channels=self.hparams.in_channels, code_size=self.hparams.code_size,
dense=self.hparams.dense_encoder)
self.decoder = ShapeDecoder(pnt_code_size=self.hparams.in_channels + 2 * self.hparams.code_size,
out_channels=self.hparams.out_channels, num_convl=self.hparams.decoder_convl)
class F2PEncoderDecoderTBased(CompletionLightningModel):
def _build_model(self):
# Encoder takes a 3D point cloud as an input.
# Note that a linear layer is applied to the global feature vector
self.template = Template(self.hparams.in_channels, self.hparams.dev)
self.encoder = ShapeEncoder(in_channels=self.hparams.in_channels, code_size=self.hparams.code_size,
dense=self.hparams.dense_encoder)
self.decoder = ShapeDecoder(pnt_code_size=self.hparams.in_channels + self.hparams.code_size,
out_channels=self.hparams.out_channels, num_convl=self.hparams.decoder_convl)
self.regressor = Regressor(code_size=self.hparams.code_size)
def _init_model(self):
self.encoder.init_weights()
self.decoder.init_weights()
# TODO: (optionally) non default weight init of regressor
@staticmethod
def add_model_specific_args(parent_parser):
p = HyperOptArgumentParser(parents=parent_parser, add_help=False, conflict_handler='resolve')
p.add_argument('--dense_encoder', default=False, type=bool)
p.add_argument('--code_size', default=512, type=int)
p.add_argument('--out_channels', default=3, type=int)
p.add_argument('--decoder_convl', default=3, type=int)
if not parent_parser: # Name clash with parent
p.add_argument('--in_channels', default=3, type=int)
return p
def forward(self, input_dict):
part = input_dict['gt_part']
full = input_dict['tp']
gt = input_dict['gt']
# part, full, gt [bs x nv x in_channels]
bs = part.size(0)
nv = part.size(1)
part_code = self.encoder(part) # [b x code_size]
full_code = self.encoder(full) # [b x code_size]
gt_code = self.encoder(gt) # [b x code_size]
comp_code = self.regressor(torch.cat((part_code, full_code), 1).contiguous())
output_dict = {'comp_code': comp_code, 'gt_code': gt_code}
part_code = part_code.unsqueeze(1).expand(bs, nv, self.hparams.code_size) # [b x nv x code_size]
full_code = full_code.unsqueeze(1).expand(bs, nv, self.hparams.code_size) # [b x nv x code_size]
comp_code = comp_code.unsqueeze(1).expand(bs, nv, self.hparams.code_size) # [b x nv x code_size]
gt_code = gt_code.unsqueeze(1).expand(bs, nv, self.hparams.code_size) # [b x nv x code_size]
# all reconsturction (also completion are achieved by FIXED template deformation)
template = self.template.get_template().expand(bs, nv, self.hparams.in_channels)
full_rec = self.decoder(
torch.cat((template, full_code), 2).contiguous()) # decoder input: [b x nv x (in_channels + code_size)]
part_rec = self.decoder(
torch.cat((template, part_code), 2).contiguous()) # decoder input: [b x nv x (in_channels + code_size)]
gt_rec = self.decoder(
torch.cat((template, gt_code), 2).contiguous()) # decoder input: [b x nv x (in_channels + code_size)]
completion = self.decoder(
torch.cat((template, comp_code), 2).contiguous()) # decoder input: [b x nv x (in_channels + code_size)]
output_dict.update({'completion_xyz': completion, 'full_rec': full_rec, 'part_rec': part_rec, 'gt_rec': gt_rec})
return output_dict
class F2PEncoderDecoderSkeptic(CompletionLightningModel):
def _build_model(self):
# Encoder takes a 3D point cloud as an input.
# Note that a linear layer is applied to the global feature vector
self.template = Template(self.hparams.in_channels, self.hparams.dev)
self.encoder = ShapeEncoder(in_channels=self.hparams.in_channels, code_size=self.hparams.code_size,
dense=self.hparams.dense_encoder)
self.comp_decoder = ShapeDecoder(pnt_code_size=self.hparams.in_channels + 2 * self.hparams.code_size,
out_channels=self.hparams.out_channels,
num_convl=self.hparams.comp_decoder_convl)
self.rec_decoder = ShapeDecoder(pnt_code_size=self.hparams.in_channels + self.hparams.code_size,
out_channels=self.hparams.out_channels,
num_convl=self.hparams.rec_decoder_convl)
def _init_model(self):
self.encoder.init_weights()
self.comp_decoder.init_weights()
self.rec_decoder.init_weights()
@staticmethod
def add_model_specific_args(parent_parser):
p = HyperOptArgumentParser(parents=parent_parser, add_help=False, conflict_handler='resolve')
p.add_argument('--dense_encoder', default=False, type=bool)
p.add_argument('--code_size', default=512, type=int)
p.add_argument('--out_channels', default=3, type=int)
p.add_argument('--comp_decoder_convl', default=5, type=int)
p.add_argument('--rec_decoder_convl', default=3, type=int)
if not parent_parser: # Name clash with parent
p.add_argument('--in_channels', default=3, type=int)
return p
def forward(self, input_dict):
part = input_dict['gt_part']
full = input_dict['tp']
gt = input_dict['gt']
# part, full [bs x nv x in_channels]
bs = part.size(0)
nv = part.size(1)
part_code = self.encoder(part) # [b x code_size]
full_code = self.encoder(full) # [b x code_size]
gt_code = self.encoder(gt) # [b x code_size]
part_code = part_code.unsqueeze(1).expand(bs, nv, self.hparams.code_size) # [b x nv x code_size]
full_code = full_code.unsqueeze(1).expand(bs, nv, self.hparams.code_size) # [b x nv x code_size]
gt_code = gt_code.unsqueeze(1).expand(bs, nv, self.hparams.code_size) # [b x nv x code_size]
completion = self.comp_decoder(torch.cat((full, part_code, full_code), 2).contiguous())
template = self.template.get_template().expand(bs, nv, self.hparams.in_channels)
full_rec = self.rec_decoder(torch.cat((template, full_code), 2).contiguous())
part_rec = self.rec_decoder(torch.cat((template, part_code), 2).contiguous())
gt_rec = self.rec_decoder(torch.cat((template, gt_code), 2).contiguous())
return {'completion_xyz': completion, 'full_rec': full_rec, 'part_rec': part_rec, 'gt_rec': gt_rec}
class F2PEncoderDecoderRealistic(CompletionLightningModel):
def _build_model(self):
# Encoder takes a 3D point cloud as an input.
# Note that a linear layer is applied to the global feature vector
self.encoder_full = ShapeEncoder(in_channels=self.hparams.in_channels,
code_size=self.hparams.code_size,
dense=self.hparams.dense_encoder)
self.encoder_part = ShapeEncoder(in_channels=3,
code_size=self.hparams.code_size,
dense=self.hparams.dense_encoder)
self.decoder = ShapeDecoder(pnt_code_size=self.hparams.in_channels +
2 * self.hparams.code_size,
out_channels=self.hparams.out_channels,
num_convl=self.hparams.decoder_convl)
def _init_model(self):
self.encoder_full.init_weights()
self.encoder_part.init_weights()
self.decoder.init_weights()
@staticmethod
def add_model_specific_args(parent_parser):
p = HyperOptArgumentParser(parents=parent_parser,
add_help=False,
conflict_handler='resolve')
p.add_argument('--dense_encoder', default=False, type=bool)
p.add_argument('--code_size', default=512, type=int)
p.add_argument('--out_channels', default=3, type=int)
p.add_argument('--decoder_convl', default=5, type=int)
if not parent_parser: # Name clash with parent
p.add_argument('--in_channels', default=3, type=int)
return p
def forward(self, input_dict):
part = input_dict['gt_part']
full = input_dict['tp']
# part, full [bs x nv x in_channels]
bs = part.size(0)
nv = part.size(1)
part_code = self.encoder_part(part[:, :, :3]) # [b x code_size]
full_code = self.encoder_full(full) # [b x code_size]
part_code = part_code.unsqueeze(1).expand(
bs, nv, self.hparams.code_size) # [b x nv x code_size]
full_code = full_code.unsqueeze(1).expand(
bs, nv, self.hparams.code_size) # [b x nv x code_size]
y = torch.cat((full, part_code, full_code),
2).contiguous() # [b x nv x (in_channels + 2*code_size)]
y = self.decoder(y)
return {'completion_xyz': y}
def _build_model(self):
# Encoder takes a 3D point cloud as an input.
# Note that a linear layer is applied to the global feature vector
self.template = Template(self.hparams.in_channels, self.hparams.dev)
self.encoder = ShapeEncoder(in_channels=self.hparams.in_channels, code_size=self.hparams.code_size,
dense=self.hparams.dense_encoder)
self.comp_decoder = ShapeDecoder(pnt_code_size=self.hparams.in_channels + self.hparams.code_size,
out_channels=self.hparams.out_channels,
num_convl=self.hparams.comp_decoder_convl)
self.rec_decoder = ShapeDecoder(pnt_code_size=self.hparams.in_channels + self.hparams.code_size,
out_channels=self.hparams.out_channels,
num_convl=self.hparams.rec_decoder_convl)
self.regressor = Regressor(code_size=self.hparams.code_size)
def _build_model(self):
self.encoder = ShapeEncoderDGCNN(in_channels=self.hparams.in_channels,
code_size=self.hparams.code_size,
k=20,
device=self.hparams.dev)
self.decoder = ShapeDecoder(pnt_code_size=self.hparams.in_channels +
2 * self.hparams.code_size,
out_channels=self.hparams.out_channels,
num_convl=self.hparams.decoder_convl)
class F2PEncoderDecoder(CompletionLightningModel):
def _build_model(self):
# Encoder takes a 3D point cloud as an input.
# Note that a linear layer is applied to the global feature vector
self.encoder = ShapeEncoder(in_channels=self.hparams.in_channels,
code_size=self.hparams.code_size,
dense=self.hparams.dense_encoder)
self.decoder = ShapeDecoder(
pnt_code_size=self.hparams.code_size_in_shape + 2 * self.hparams.
code_size, #self.hparams.in_channels --> 100 latent space dimension
out_channels=self.hparams.out_channels,
num_convl=self.hparams.decoder_convl)
def _init_model(self):
self.encoder.init_weights()
self.decoder.init_weights()
@staticmethod
def add_model_specific_args(parent_parser):
p = HyperOptArgumentParser(parents=parent_parser,
add_help=False,
conflict_handler='resolve')
p.add_argument('--dense_encoder', default=False, type=bool)
p.add_argument('--code_size', default=512, type=int)
p.add_argument('--code_size_in_shape', default=30, type=int)
p.add_argument('--out_channels', default=3, type=int)
p.add_argument('--decoder_convl', default=5, type=int)
if not parent_parser: # Name clash with parent
p.add_argument('--in_channels', default=3, type=int)
return p
def forward(self, input_dict):
part = input_dict['gt_part'] if 'gt_part' in input_dict else input_dict[
'gt_noise'] # TODO - Generalize this
full = input_dict['tp']
# part, full [bs x nv x in_channels]
bs = part.size(0)
nv = part.size(1)
part_code = self.encoder(part) # [b x code_size]
full_code = self.encoder(full) # [b x code_size]
part_code = part_code.unsqueeze(1).expand(
bs, nv, self.hparams.code_size) # [b x nv x code_size]
full_code = full_code.unsqueeze(1).expand(
bs, nv, self.hparams.code_size) # [b x nv x code_size]
latent_in_shape = torch.randn(bs,
nv,
self.hparams.code_size_in_shape,
device=self.hparams.dev,
dtype=getattr(torch,
UNIVERSAL_PRECISION),
requires_grad=False)
#y = torch.cat((full, part_code, full_code), 2).contiguous() # [b x nv x (in_channels + 2*code_size)]
y = torch.cat(
(latent_in_shape, part_code, full_code),
2).contiguous() # [b x nv x (code_size_in_shape + 2*code_size)]
y = self.decoder(y)
return {'completion_xyz': y}
