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small tweaks

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Justin committed 2023-03-27 04:15:58 +00:00
1 parent 6ec2920da3
commit 724c27481c
3 files changed
+15 -9
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nerfstudio/configs/method_configs.py
+2 -2
View File
@@ -107,8 +107,8 @@ method_configs["lerf"] = TrainerConfig(
),
optimizers={
"proposal_networks": {
"optimizer": RAdamOptimizerConfig(lr=1e-2, eps=1e-15),
"scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-3, max_steps=30000),
"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
"scheduler": None,
},
"fields": {
"optimizer": RAdamOptimizerConfig(lr=1e-2, eps=1e-15),
nerfstudio/fields/lerf_field.py
+7 -4
View File
@@ -27,10 +27,13 @@ class LERFField(Field):
super().__init__()
self.spatial_distortion = spatial_distortion
self.clip_encs = torch.nn.ModuleList([
LERFField._get_encoding( 16, 128, 12, indim=3),
LERFField._get_encoding(128, 512, 12, indim=3)
])
self.clip_encs = torch.nn.ModuleList(
[
# LERFField._get_encoding(16, 512, 16, indim=3)
LERFField._get_encoding(16, 128, 10, indim=3),
LERFField._get_encoding(128, 1024, 13, indim=3),
]
)
tot_out_dims = sum([e.n_output_dims for e in self.clip_encs])
self.clip_net = tcnn.Network(
nerfstudio/models/lerf.py
+6 -3
View File
@@ -42,7 +42,6 @@ class LERFModel(NerfactoModel):
self.network = self.kwargs["network"]
self.lerf_field = LERFField(clip_n_dims=self.network.clip_n_dims)
# self.lerf_sampler = PDFSampler(num_samples=self.config.num_lerf_samples, histogram_padding=0.00)
def get_max_across(self, ray_samples, weights, hashgrid_field, scales_shape, preset_scales=None):
# TODO smoothen this out
@@ -106,8 +105,12 @@ class LERFModel(NerfactoModel):
lerf_field_outputs = self.lerf_field.get_outputs(lerf_samples, clip_scales)
if self.training:
outputs['clip'] = self.renderer_clip(embeds=lerf_field_outputs[FieldHeadNames.CLIP], weights=lerf_weights.detach())
outputs['dino'] = self.renderer_mean(embeds=lerf_field_outputs[FieldHeadNames.DINO], weights=lerf_weights.detach())
outputs["clip"] = self.renderer_clip(
embeds=lerf_field_outputs[FieldHeadNames.CLIP], weights=lerf_weights.detach()
)
outputs["dino"] = self.renderer_mean(
embeds=lerf_field_outputs[FieldHeadNames.DINO], weights=lerf_weights.detach()
)
if not self.training:
max_across, best_scales = self.get_max_across(