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import torch.nn as nn
class PointNetCmapEncoder(nn.Module):
def __init__(self, layers_size=[4, 64, 128, 512]):
super(PointNetCmapEncoder, self).__init__()
self.layers_size = layers_size
self.conv_layers = nn.ModuleList()
self.bn_layers = nn.ModuleList()
self.activate_func = nn.ReLU()
for i in range(len(layers_size) - 1):
self.conv_layers.append(nn.Conv1d(layers_size[i], layers_size[i + 1], 1))
self.bn_layers.append(nn.BatchNorm1d(layers_size[i + 1]))
nn.init.xavier_normal_(self.conv_layers[-1].weight)
def forward(self, x):
# input: B * N * 4
# output: B * latent_size
x = x.transpose(1, 2)
for i in range(len(self.conv_layers) - 1):
x = self.conv_layers[i](x)
x = self.bn_layers[i](x)
x = self.activate_func(x)
x = self.bn_layers[-1](self.conv_layers[-1](x))
x = torch.max(x, 2, keepdim=True)[0]
x = x.view(-1, self.layers_size[-1])
return x
class PointNetCmapDecoder(nn.Module):
def __init__(
self,
global_feat_size=512,
latent_size=128,
pointwise_layers_size=[3, 64, 64],
global_layers_size=[64, 128, 512],
decoder_layers_size=[64 + 512 + 128, 512, 64, 64, 1],
):
super(PointNetCmapDecoder, self).__init__()
assert global_feat_size == global_layers_size[-1]
assert (
decoder_layers_size[0]
== latent_size + global_feat_size + pointwise_layers_size[-1]
)
self.global_feat_size = global_feat_size
self.latent_size = latent_size
self.pointwise_layers_size = pointwise_layers_size
self.global_layers_size = global_layers_size
self.decoder_layers_size = decoder_layers_size
self.pointwise_conv_layers = nn.ModuleList()
self.pointwise_bn_layers = nn.ModuleList()
self.global_conv_layers = nn.ModuleList()
self.global_bn_layers = nn.ModuleList()
self.activate_func = nn.ReLU()
for i in range(len(pointwise_layers_size) - 1):
self.pointwise_conv_layers.append(
nn.Conv1d(pointwise_layers_size[i], pointwise_layers_size[i + 1], 1)
)
self.pointwise_bn_layers.append(
nn.BatchNorm1d(pointwise_layers_size[i + 1])
)
nn.init.xavier_normal_(self.pointwise_conv_layers[-1].weight)
for i in range(len(global_layers_size) - 1):
self.global_conv_layers.append(
nn.Conv1d(global_layers_size[i], global_layers_size[i + 1], 1)
)
self.global_bn_layers.append(nn.BatchNorm1d(global_layers_size[i + 1]))
nn.init.xavier_normal_(self.global_conv_layers[-1].weight)
self.decoder_conv_layers = nn.ModuleList()
self.decoder_bn_layers = nn.ModuleList()
self.sigmoid = nn.Sigmoid()
for i in range(len(decoder_layers_size) - 1):
self.decoder_conv_layers.append(
nn.Conv1d(decoder_layers_size[i], decoder_layers_size[i + 1], 1)
)
self.decoder_bn_layers.append(nn.BatchNorm1d(decoder_layers_size[i + 1]))
nn.init.xavier_normal_(self.decoder_conv_layers[-1].weight)
# self.h2_decoder_conv_layers = nn.ModuleList()
# self.h2_decoder_bn_layers = nn.ModuleList()
# for i in range(len(decoder_layers_size) - 1):
# self.h2_decoder_conv_layers.append(
# nn.Conv1d(decoder_layers_size[i], decoder_layers_size[i + 1], 1)
# )
# self.h2_decoder_bn_layers.append(nn.BatchNorm1d(decoder_layers_size[i + 1]))
# nn.init.xavier_normal_(self.h2_decoder_conv_layers[-1].weight)
def forward(self, x, z_latent_code):
"""
:param x: B x N x 3
:param z_latent_code: B x latent_size
:return:
"""
bs = x.shape[0]
npts = x.shape[1]
pointwise_feature = x.transpose(1, 2)
for i in range(len(self.pointwise_conv_layers) - 1):
pointwise_feature = self.pointwise_conv_layers[i](pointwise_feature)
pointwise_feature = self.pointwise_bn_layers[i](pointwise_feature)
pointwise_feature = self.activate_func(pointwise_feature)
pointwise_feature = self.pointwise_bn_layers[-1](
self.pointwise_conv_layers[-1](pointwise_feature)
)
global_feature = pointwise_feature.clone()
for i in range(len(self.global_conv_layers) - 1):
global_feature = self.global_conv_layers[i](global_feature)
global_feature = self.global_bn_layers[i](global_feature)
global_feature = self.activate_func(global_feature)
global_feature = self.global_bn_layers[-1](
self.global_conv_layers[-1](global_feature)
)
global_feature = torch.max(global_feature, 2, keepdim=True)[0]
global_feature = global_feature.view(bs, self.global_feat_size)
global_feature = torch.cat([global_feature, z_latent_code], dim=1)
global_feature = global_feature.view(
bs, self.global_feat_size + self.latent_size, 1
).repeat(1, 1, npts)
pointwise_feature = torch.cat([pointwise_feature, global_feature], dim=1)
# pointwise_feature_h2 = pointwise_feature.clone()
for i in range(len(self.decoder_conv_layers) - 1):
pointwise_feature = self.decoder_conv_layers[i](pointwise_feature)
pointwise_feature = self.decoder_bn_layers[i](pointwise_feature)
pointwise_feature = self.activate_func(pointwise_feature)
pointwise_feature = self.decoder_bn_layers[-1](
self.decoder_conv_layers[-1](pointwise_feature)
)
# for i in range(len(self.h2_decoder_conv_layers) - 1):
# pointwise_feature_h2 = self.h2_decoder_conv_layers[i](pointwise_feature_h2)
# pointwise_feature_h2 = self.h2_decoder_bn_layers[i](pointwise_feature_h2)
# pointwise_feature_h2 = self.activate_func(pointwise_feature_h2)
# pointwise_feature_h2 = self.h2_decoder_bn_layers[-1](
# self.h2_decoder_conv_layers[-1](pointwise_feature_h2)
# )
### pointwise_feature shape B x out_size x N
# pointwise_feature = (
# self.sigmoid(pointwise_feature).view(bs, npts, -1).squeeze(-1)
# )
# Keep this without sigmoid, since we might do additional transforms
# return pointwise_feature.view(bs, npts, -1).squeeze(-1)
return pointwise_feature # shape (bs, -1, npts)
# output = torch.cat((pointwise_feature, pointwise_feature_h2), dim=1).view(bs, npts, -1)
# return output
# return self.sigmoid(output)
class GcsCVAE(nn.Module):
def __init__(
self,
latent_size=128,
encoder_layers_size=[5, 64, 128, 512],
decoder_global_feat_size=512,
decoder_pointwise_layers_size=[3, 64, 64],
decoder_global_layers_size=[64, 128, 512],
decoder_decoder_layers_size=[64 + 512 + 128, 512, 64, 64, 1],
num_coarse_pts=2048,
uv_layers_size=None,
):
# NOTE:
# encoder_layers_size[0] is 5 instead of 4 since our input is (obj_pc, obj_uv_coords)
super(GcsCVAE, self).__init__()
self.num_coarse = num_coarse_pts
self.latent_size = latent_size
self.cmap_encoder = PointNetCmapEncoder(layers_size=encoder_layers_size)
self.cmap_decoder = PointNetCmapDecoder(
latent_size=latent_size,
global_feat_size=decoder_global_feat_size,
pointwise_layers_size=decoder_pointwise_layers_size,
global_layers_size=decoder_global_layers_size,
decoder_layers_size=decoder_decoder_layers_size,
)
self.encoder_z_means = nn.Linear(encoder_layers_size[-1], latent_size)
self.encoder_z_logvars = nn.Linear(encoder_layers_size[-1], latent_size)
self.pred_uv = False
if uv_layers_size:
# uv_layers_size = [64, 64, 1]
self.pred_uv = True
num_layers = len(uv_layers_size) - 1
self._pred_u_layers = nn.ModuleList()
self._pred_v_layers = nn.ModuleList()
for i in range(num_layers):
curr_size, next_size = uv_layers_size[i], uv_layers_size[i + 1]
self._pred_u_layers.append(nn.Conv1d(curr_size, next_size, 1))
nn.init.xavier_normal_(self._pred_u_layers[-1].weight)
self._pred_u_layers.append(nn.BatchNorm1d(next_size))
self._pred_v_layers.append(nn.Conv1d(curr_size, next_size, 1))
nn.init.xavier_normal_(self._pred_v_layers[-1].weight)
self._pred_v_layers.append(nn.BatchNorm1d(next_size))
if i < num_layers:
self._pred_u_layers.append(nn.ReLU())
self._pred_v_layers.append(nn.ReLU())
self.pred_u_net = nn.Sequential(*self._pred_u_layers)
self.pred_v_net = nn.Sequential(*self._pred_v_layers)
def forward(self, obj_pts, gcs_gt):
"""
:param obj_pts: B, N, 3
:param gcs_gt: B, N, 2
:return:
"""
bs = obj_pts.shape[0]
npts = obj_pts.shape[1]
obj_cmap = torch.cat(
(
obj_pts,
gcs_gt.unsqueeze(-1) if gcs_gt.ndim == 2 else gcs_gt,
),
dim=-1,
)
means, logvars = self.forward_encoder(object_cmap=obj_cmap)
z_latent_code = self.reparameterize(means=means, logvars=logvars)
cmap_values = self.forward_decoder(obj_pts, z_latent_code).view(bs, npts, -1)
return obj_pts, cmap_values, means, logvars, z_latent_code
def predict(self, object_pts):
"""
Test time prediction of contact maps from randomly sampled latent vectors on a given input
Input:
object_pts: (B, N, 3) tensor
Returns:
gcs_values: (B, N, 2) tensor of contact map values for each batched obj pc
"""
bsize = object_pts.shape[0]
z_samples = torch.randn(
bsize, self.latent_size, device=object_pts.device
).float()
return self.inference(object_pts, z_samples)
def inference(self, object_pts, z_latent_code):
"""
:param object_pts: B x N x 3
:param z_latent_code: B x latent_size
:return:
"""
cmap_values = self.forward_decoder(object_pts, z_latent_code)
return cmap_values
def reparameterize(self, means, logvars):
std = torch.exp(0.5 * logvars)
eps = torch.randn_like(std)
return means + eps * std
def forward_encoder(self, object_cmap):
cmap_feat = self.cmap_encoder(object_cmap)
means = self.encoder_z_means(cmap_feat)
logvars = self.encoder_z_logvars(cmap_feat)
return means, logvars
def forward_decoder(self, object_pts, z_latent_code):
"""
:param object_pts: B x N x 3
:param z_latent_code: B x latent_size
:return:
"""
cmap_values = self.cmap_decoder(object_pts, z_latent_code)
bs = cmap_values.shape[0]
npts = cmap_values.shape[-1]
if self.pred_uv:
u = self.pred_u_net(cmap_values).view(bs, npts, -1)
v = self.pred_v_net(cmap_values).view(bs, npts, -1)
# u = torch.sigmoid(u)
# v = torch.sigmoid(v)
output = torch.cat([u, v], dim=-1).view(bs, npts, -1)
return output
else:
return cmap_values.view(bs, npts, -1)
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