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train.py
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train.py
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import argparse
import commentjson as json
import numpy as np
import os
import tinycudann as tcnn
from utils import utils
from utils.utils import debatch
import torch
from torch.nn import functional as F
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import pytorch_lightning as pl
#########################################################################################################
################################################ DATASET ################################################
#########################################################################################################
class BundleDataset(Dataset):
def __init__(self, args):
bundle = dict(np.load(args.bundle_path, allow_pickle=True))
utils.de_item(bundle)
if not args.no_raw:
raw_frames = torch.tensor(np.array([bundle[f'raw_{i}']['raw'] for i in range(bundle['num_raw_frames'])]).astype(np.int32))[None] # B,T,H,W
if args.no_shade_map or args.no_raw:
pass # no shade map needed
else:
shade_map = torch.tensor(np.load(os.path.join(os.getcwd(), "data/shade_map.npy")))[None,None,:,:] # 1,1,H,W, compensation for lens shading
raw_frames = raw_frames * shade_map
self.motion = bundle['motion']
if args.no_device_rotations:
self.frame_timestamps = torch.tensor(np.linspace(0,1, bundle['num_rgb_frames']))
self.motion_timestamps = torch.tensor(np.linspace(0,1, bundle['num_rgb_frames']))
self.quaternions = torch.tensor(np.repeat([[0,0,0,1.0]], bundle['num_rgb_frames'], axis=0)).float()
else:
self.frame_timestamps = torch.tensor([bundle[f'raw_{i}']['timestamp'] for i in range(bundle['num_rgb_frames'])])
self.motion_timestamps = torch.tensor(self.motion['timestamp'])
self.quaternions = torch.tensor(self.motion['quaternion']) # T',4, has different timestamps from frames
self.reference_quaternion = utils.multi_interp(self.frame_timestamps[0:1], self.motion_timestamps, self.quaternions) # quaternion at frame 0
self.reference_rotation = utils.convert_quaternions_to_rot(self.reference_quaternion)
self.processed_rgb_volume = torch.tensor(np.array([bundle[f'rgb_{i}']['rgb'] for i in range(bundle['num_rgb_frames'])]))
self.processed_rgb_volume = (self.processed_rgb_volume[:,:,:,:3].permute(0,3,1,2)).float() # remove alpha, make: T,C,H,W
self.processed_rgb_volume = self.processed_rgb_volume / self.processed_rgb_volume[0].max() # scale 0-1
intrinsics_ratio = 1.0
if args.no_phone_depth and not args.no_raw: # intrinsics from RGB, but img from RAW, rescale
intrinsics_ratio = bundle['raw_0']['height'] / bundle['rgb_0']['height']
elif not args.no_phone_depth and args.no_raw: # intrinsics from depth, img from processed RGB
intrinsics_ratio = bundle['rgb_0']['height'] / bundle['raw_0']['height']
if args.no_phone_depth:
self.intrinsics = torch.tensor(np.array([bundle[f'rgb_{i}']['intrinsics'] for i in range(bundle['num_rgb_frames'])])).float() # T,3,3
else:
self.intrinsics = torch.tensor(np.array([bundle[f'depth_{i}']['intrinsics'] for i in range(bundle['num_depth_frames'])]))
self.intrinsics[:,:3,:2] = self.intrinsics[:,:3,:2] * intrinsics_ratio
if args.no_raw: # use processed RGB
self.rgb_volume = (self.processed_rgb_volume).float()
self.rgb_volume = self.rgb_volume - self.rgb_volume.min()
self.rgb_volume = self.rgb_volume/self.rgb_volume.max()
else: # use minimally processed RAW
self.rgb_volume = (utils.raw_to_rgb(raw_frames)).float() # T,C,H,W
self.rgb_volume = self.rgb_volume - self.rgb_volume.min()
self.rgb_volume = self.rgb_volume/self.rgb_volume.max()
if args.dark: # cut off highlights for scaling (long-tail-distribution)
self.rgb_volume = self.rgb_volume/np.percentile(self.rgb_volume, 98)
self.rgb_volume = self.rgb_volume.clamp(0,1)
self.reference_intrinsics = self.intrinsics[0:1]
if args.no_phone_depth:
self.depth_volume = torch.zeros(bundle['num_rgb_frames'], 1, 64, 64, dtype=torch.float32) # placeholder depth
else:
self.depth_volume = torch.tensor(np.array([bundle[f'depth_{i}']['depth'] for i in range(bundle['num_depth_frames'])]))
self.depth_volume = 1/(self.depth_volume[:,:,:,None].permute(0,3,1,2)).float() # T,C,H,W; lidar has inverse depth
T,C,H,W = self.rgb_volume.shape
self.num_frames, self.img_channels, self.img_height, self.img_width = T,C,H,W
self.point_batch_size = args.point_batch_size
self.num_batches = args.num_batches
def __len__(self):
return self.num_batches # arbitrary as we continuously generate random samples
def __getitem__(self, idx):
# create uniform u,v between 0.025 and 0.975 to preserve edges
uv = torch.rand(self.point_batch_size, 2) * torch.tensor([[0.95,0.95]]) + torch.tensor([[0.025,0.025]])
# t is time for all frames, looks like [0, 0,... 0, 1/41, 1/41, ..., 1/41, 2/41, 2/41, ..., 2/41, etc.]
t = torch.linspace(0,1,self.num_frames).repeat_interleave(uv.shape[0])[:,None] # num_frames * point_batch_size, 1
return self.sample_grid(uv, t, frame=0, sample_depth=True, sample_rgb=True, sample_processed_rgb=False)
def sample_grid(self, uv, t, frame, sample_depth=False, sample_rgb=False, sample_processed_rgb=False):
""" Return TUV grid, interpolated rotation, intrinsics, depth, rgb samples
"""
lidar_samples, rgb_samples, rgb_processed_samples = -1, -1, -1
# convert to frame times [0-1] -> (seconds)
t_frame = torch.tensor(np.interp(t, np.linspace(0,1,len(self.frame_timestamps)), self.frame_timestamps)).squeeze()
# grab linearly interpolated quaternions at those timestamps
quaternions = utils.multi_interp(t_frame, self.motion_timestamps, self.quaternions)
# grab linearly interpolated intrinsics at those timestamps
intrinsics = utils.multi_interp(t_frame, self.frame_timestamps, self.intrinsics.view(-1,9)).reshape(-1,3,3)
if sample_depth:
# grid_sample uses coordinates [-1,1] whereas MLP uses [0,1], hence rescaling
grid_uv = ((uv - 0.5) * 2)[None,:,None,:] # 1,point_batch_size,1,2
lidar_samples = F.grid_sample(self.depth_volume[frame:frame+1], grid_uv, mode="bilinear", padding_mode="border", align_corners=True)
lidar_samples = lidar_samples.squeeze()[:,None] # point_batch_size, C
if sample_rgb:
grid_uv = ((uv - 0.5) * 2)[None,:,None,:] # 1,point_batch_size,1,2
rgb_samples = F.grid_sample(self.rgb_volume[frame:frame+1], grid_uv, mode="bilinear", padding_mode="border", align_corners=True)
rgb_samples = rgb_samples.squeeze().permute(1,0) # point_batch_size, C
if sample_processed_rgb:
grid_uv = ((uv - 0.5) * 2)[None,:,None,:] # 1,point_batch_size,1,2
rgb_processed_samples = F.grid_sample(self.processed_rgb_volume[frame:frame+1], grid_uv, mode="bilinear", padding_mode="border", align_corners=True)
rgb_processed_samples = rgb_processed_samples.squeeze().permute(1,0) # point_batch_size, C
return t, uv, quaternions, intrinsics, lidar_samples, rgb_samples, rgb_processed_samples
#########################################################################################################
################################################ MODELS #################$###############################
#########################################################################################################
class PlaneModel(pl.LightningModule):
def __init__(self, depth):
super().__init__()
# ax + by + c
self.plane_coefs = torch.nn.Parameter(data=torch.tensor([1/10,1/10,depth/5]), requires_grad=True)
# increase effective learning rate of plane without custom lr scheduler
self.scale_factor = torch.nn.Parameter(data=torch.tensor([5.0,5.0,5.0]), requires_grad=False)
def forward(self, uv):
uv_homogenous = torch.cat((uv, torch.ones_like(uv[:,:1])), dim=1)
plane = uv_homogenous * self.plane_coefs * self.scale_factor
return torch.sum(plane, dim=1, keepdims=True)
class LearnedRotationModel(pl.LightningModule):
def __init__(self, args):
super().__init__()
self.args = args
self.rotation_betas = torch.nn.Parameter(data=torch.zeros(args.control_points_motion, 3, 1, dtype=torch.float32), requires_grad=True)
def forward(self, quaternions, t):
# use de casteljau algorithm for interpolation
rotation_deltas = utils.de_casteljau(self.rotation_betas, t)
rx, ry, rz = rotation_deltas[:,0], rotation_deltas[:,1], rotation_deltas[:,2]
r1 = torch.ones_like(rx)
# identity rotation eye(3) plus small rotational offsets
rotations = torch.stack([torch.stack([ r1, -rz, ry], dim=-1),
torch.stack([ rz, r1, -rx], dim=-1),
torch.stack([-ry, rx, r1], dim=-1)], dim=-1)
return rotations
class DeviceRotationModel(pl.LightningModule):
def __init__(self, args, reference_rotation):
super().__init__()
self.args = args
self.reference_rotation = reference_rotation
self.rotation_betas = torch.nn.Parameter(data=torch.zeros(args.control_points_motion, 3, 1, dtype=torch.float32), requires_grad=True)
def forward(self, quaternions, t):
rotations = torch.inverse(self.reference_rotation) @ utils.convert_quaternions_to_rot(quaternions) # from gyro
rotation_deltas = utils.de_casteljau(self.rotation_betas, t)
rx, ry, rz = rotation_deltas[:,0], rotation_deltas[:,1], rotation_deltas[:,2]
r0 = torch.zeros_like(rx)
rotation_offsets = torch.stack([torch.stack([ r0, -rz, ry], dim=-1),
torch.stack([ rz, r0, -rx], dim=-1),
torch.stack([-ry, rx, r0], dim=-1)], dim=-1)
return rotations + self.args.rotation_weight * rotation_offsets
class TranslationModel(pl.LightningModule):
def __init__(self, args):
super().__init__()
self.args = args
self.translation_betas = torch.nn.Parameter(data=torch.zeros(args.control_points_motion, 3, 1, dtype=torch.float32), requires_grad=True)
def forward(self, t):
return self.args.translation_weight * utils.de_casteljau(self.translation_betas, t)
class IntrinsicsModel(pl.LightningModule):
def __init__(self, args, reference_intrinsics):
super().__init__()
self.args = args
self.intrinsic_betas = torch.nn.Parameter(data=torch.zeros(args.control_points_intrinsics, 1, 1, dtype=torch.float32), requires_grad=True)
self.focal = torch.nn.Parameter(data=torch.tensor([reference_intrinsics[0,0,0]]), requires_grad=True)
self.cy = reference_intrinsics[0,2,0]
self.cx = reference_intrinsics[0,2,1]
def forward(self, t):
f_deltas = utils.de_casteljau(self.intrinsic_betas, t)
cy = self.cy * torch.ones_like(t)
cx = self.cx * torch.ones_like(t)
f = (self.focal * torch.ones_like(t)) + f_deltas
f0 = torch.zeros_like(t)
f1 = torch.ones_like(t)
intrinsics = torch.stack([torch.stack([f, f0, cy], dim=-1),
torch.stack([f0, f, cx], dim=-1),
torch.stack([f0, f0, f1], dim=-1)], dim=-1)
return intrinsics.squeeze(dim=1)
#########################################################################################################
################################################ NETWORK ################################################
#########################################################################################################
class BundleMLP(pl.LightningModule):
def __init__(self, args):
super().__init__()
# load network configs
with open(args.config_path_depth) as config_depth:
config_depth = json.load(config_depth)
with open(args.config_path_rgb) as config_rgb:
config_rgb = json.load(config_rgb)
self.args = args
self.encoding_depth = tcnn.Encoding(n_input_dims=2, encoding_config=config_depth["encoding"])
self.network_depth = tcnn.Network(n_input_dims=self.encoding_depth.n_output_dims, n_output_dims=1, network_config=config_depth["network"])
self.encoding_rgb = tcnn.Encoding(n_input_dims=2, encoding_config=config_rgb["encoding"])
self.network_rgb = tcnn.Network(n_input_dims=self.encoding_rgb.n_output_dims, n_output_dims=3, network_config=config_rgb["network"])
self.model_translation = TranslationModel(args)
self.model_plane = PlaneModel(depth=1.0)
self.mask = torch.ones(self.encoding_depth.n_output_dims, dtype=torch.float32)
self.save_hyperparameters()
bundle = BundleDataset(args)
self.bundle = bundle
self.rgb_volume = bundle.rgb_volume
self.processed_rgb_volume = bundle.processed_rgb_volume
self.reference_intrinsics = bundle.reference_intrinsics
self.reference_rotation = bundle.reference_rotation
if args.no_device_rotations: # learn rotations from scratch
self.model_rotation = LearnedRotationModel(args)
else: # use gyro data
self.model_rotation = DeviceRotationModel(args, self.reference_rotation)
self.model_intrinsics = IntrinsicsModel(args, self.reference_intrinsics)
def sample_volume(self, uv, volume, frame=None):
""" Grid sample from 2D image volume at coordinates (u,v)
If frame=None, sample from all frames, else single frame
"""
pbs = self.bundle.point_batch_size
grid_uv = ((uv - 0.5) * 2)
if frame is None:
grid_uv = grid_uv.reshape(self.bundle.num_frames, pbs, 1, -1) # frames, pbs, 1, 2
rgb_samples = F.grid_sample(volume, grid_uv, mode="bilinear", padding_mode="border", align_corners=True)
rgb_samples = rgb_samples.squeeze().permute(0,2,1).reshape(pbs * self.bundle.num_frames, -1)
else:
grid_uv = grid_uv[None,:,None,:] # frames, pbs, 1, 2
rgb_samples = F.grid_sample(volume[frame:frame+1], grid_uv, mode="bilinear", padding_mode="border", align_corners=True)
rgb_samples = rgb_samples.squeeze().permute(1,0)
return rgb_samples
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.args.lr, betas=(0.9, 0.99), eps=1e-15, weight_decay=0)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=self.args.gamma)
return [optimizer], [scheduler]
def forward(self, t, uv, quaternions, lidar_samples, rgb_samples):
""" Forward model pass, estimate motion, implicit depth + image.
"""
translation = self.model_translation(t)
rotation = self.model_rotation(quaternions, t)
uv_depth = self.encoding_depth(uv)
uv_rgb = self.encoding_rgb(uv)
mask = self.mask.to(self.device)[None,:]
if self.args.allow_negative_depth: # no ReLUs, no clamps, depth can go hog-wild
plane = self.model_plane(uv)
depth = (plane - self.network_depth(uv_depth * mask))
else: # clamp depth between 0.01 and 10, depth offset must be *in front* of plane
plane = F.relu(self.model_plane(uv)).clamp(0.01, 10.0)
depth = (plane - F.relu(self.network_depth(uv_depth * mask))).clamp(0.01, 10)
if self.args.fixed_image: # just sample static reference frame
rgb = rgb_samples
else: # sample from RGB MLP
rgb = F.relu(0.5 + self.network_rgb(uv_rgb)).float()
return rgb, depth, plane, rotation, translation
def reproject(self, t, uv, depth, rotation, translation, intrinsics):
""" Reproject uv coordinates to new refererence frame
"""
if self.args.no_intrinsics: # use learned model
intrinsics = self.model_intrinsics(t)
reference_intrinsics = self.model_intrinsics(torch.zeros_like(t)[0:1])
else: # used stored intrinsics
reference_intrinsics = self.reference_intrinsics
uvz = torch.cat((uv, depth), dim=1)
xyz = utils.uvz_to_xyz(uvz, reference_intrinsics, img_width=self.bundle.img_width, img_height=self.bundle.img_height)
xyz = (torch.inverse(rotation) @ xyz[:,:,None])[:,:,0] + translation # project to query
uvz_reprojected = utils.xyz_to_uvz(xyz, intrinsics, img_width=self.bundle.img_width, img_height=self.bundle.img_height)
tuv_reprojected = torch.cat((t, uvz_reprojected[:,0:2]), dim=1)
return tuv_reprojected
def training_step(self, train_batch, batch_idx):
N = self.bundle.num_frames
pbs = self.args.point_batch_size
t, uv, quaternions, intrinsics, lidar_samples, rgb_samples, _ = train_batch # collapse batch + point dimensions
t, uv, quaternions, intrinsics, lidar_samples, rgb_samples = debatch(t), debatch(uv), debatch(quaternions), debatch(intrinsics), debatch(lidar_samples), debatch(rgb_samples)
rgb, depth, plane, rotation, translation = self.forward(t, uv, quaternions, lidar_samples, rgb_samples)
uv, rgb, depth, plane = uv.repeat(N,1), rgb.repeat(N,1), depth.repeat(N,1), plane.repeat(N,1)
tuv_plane_reprojected = self.reproject(t, uv, plane, rotation, translation, intrinsics)
tuv_depth_reprojected = self.reproject(t, uv, depth, rotation, translation, intrinsics)
rgb_plane_reprojected = self.sample_volume(tuv_plane_reprojected[:,1:], self.rgb_volume) # sample all timesteps with u,v
rgb_depth_reprojected = self.sample_volume(tuv_depth_reprojected[:,1:], self.rgb_volume)
loss = 0.0
# overall depth loss
depth_rgb_loss = ((rgb/(rgb.detach() + 0.001)) - (rgb_depth_reprojected/(rgb.detach() + 0.001))) ** 2
depth_rgb_loss = depth_rgb_loss.mean(dim=1, keepdims=True) # mean over RGB channels
loss += depth_rgb_loss.mean()
# plane-only loss
plane_rgb_loss = ((rgb/(rgb.detach() + 0.001)) - (rgb_plane_reprojected/(rgb.detach() + 0.001))) ** 2
plane_rgb_loss = plane_rgb_loss.mean(dim=1, keepdims=True) # mean over RGB channels
# weighted plane loss
plane_depth_loss = (depth/plane - 1) ** 2
weighted_plane_depth_loss = plane_rgb_loss/(depth_rgb_loss + 0.001) * plane_depth_loss
loss += self.args.plane_weight * weighted_plane_depth_loss.mean()
self.log('loss', loss)
return loss
def make_grid(self, height, width, u_lims, v_lims):
""" Create (u,v) meshgrid with size (height,width) extent (u_lims, v_lims)
"""
u = torch.linspace(u_lims[0], u_lims[1], width)
v = torch.linspace(v_lims[0], v_lims[1], height)
u_grid, v_grid = torch.meshgrid([u, v], indexing="xy") # u/v grid
return torch.stack((u_grid.flatten(), v_grid.flatten())).t()
def generate_imgs(self, frame, height=960, width=720, u_lims=[0,1], v_lims=[0,1]):
""" Produce reference images and depth maps for tensorboard/visualization
"""
device = self.device
uv = self.make_grid(height, width, u_lims, v_lims)
t = torch.tensor(frame/(self.bundle.num_frames - 1)).repeat(uv.shape[0])[:,None] # num_points, 1
batch = self.bundle.sample_grid(uv, t, frame, sample_depth=True, sample_rgb=True, sample_processed_rgb=True)
batch = [elem.to(device) for elem in batch]
t, uv, quaternions, intrinsics, lidar_samples, rgb_samples, rgb_processed_samples = batch
rgb_raw = rgb_samples.reshape(height, width, 3).permute(2,0,1) # channel first
rgb_processed = rgb_processed_samples.reshape(height, width, 3).permute(2,0,1) # channel first
depth_lidar = lidar_samples.reshape(height, width)
depth_lidar_img = utils.colorize_tensor(depth_lidar, vmin=lidar_samples.min(), vmax=lidar_samples.max(), cmap="RdYlBu")
return rgb_raw, rgb_processed, depth_lidar, depth_lidar_img
def generate_outputs(self, frame, height=960, width=720, u_lims=[0,1], v_lims=[0,1]):
""" Use forward model to sample implicit image I(u,v), depth D(u,v) and raw/processed images
at reprojected u,v, coordinates. Results should be aligned (sampled at (u',v'))
"""
device = self.device
uv = self.make_grid(height, width, u_lims, v_lims)
t = torch.tensor(frame/(self.bundle.num_frames - 1)).repeat(uv.shape[0])[:,None] # num_points, 1
batch = self.bundle.sample_grid(uv, t, frame, sample_depth=True, sample_rgb=True, sample_processed_rgb=True)
batch = [elem.to(device) for elem in batch]
t, uv, quaternions, intrinsics, lidar_samples, rgb_samples, rgb_processed_samples = batch
with torch.no_grad():
rgb, depth, plane, rotation, translation = self.forward(t, uv, quaternions, lidar_samples, rgb_samples)
tuv_reprojected = self.reproject(t, uv, depth, rotation, translation, intrinsics)
rgb_raw = self.sample_volume(tuv_reprojected[:,1:], self.rgb_volume, frame=frame)
rgb_processed = self.sample_volume(tuv_reprojected[:,1:], self.processed_rgb_volume, frame=frame)
rgb = rgb.reshape(height, width, 3).permute(2,0,1) # channel first
rgb_raw = rgb_raw.reshape(height, width, 3).permute(2,0,1) # channel first
rgb_processed = rgb_processed.reshape(height, width, 3).permute(2,0,1)
depth = depth.reshape(height, width)
depth_img = utils.colorize_tensor(depth, vmin=0, vmax=depth.max(), cmap="RdYlBu")
return rgb, rgb_raw, rgb_processed, depth, depth_img
#########################################################################################################
############################################### VALIDATION ##############################################
#########################################################################################################
class ValidationCallback(pl.Callback):
def __init__(self):
super().__init__()
def on_train_epoch_start(self, trainer, model):
args = model.args
coef = ((model.current_epoch/model.args.max_epochs) * args.mask_k_max) + ((1 - model.current_epoch/model.args.max_epochs) * args.mask_k_min)
model.mask = torch.sigmoid(torch.linspace(args.mask_k_max, coef, len(model.mask)))
print("Mask mean:", model.mask.mean())
# let plane train on its own for 10 epochs
if model.current_epoch == 10:
# start training depth
model.encoding_depth.requires_grad_(True)
model.encoding_depth.train(True)
model.network_depth.requires_grad_(True)
model.network_depth.train(True)
if args.no_intrinsics:
model.model_intrinsics.requires_grad_(True)
model.model_intrinsics.train(True)
for i, frame in enumerate([0]): # can sample more frames
rgb, rgb_raw, rgb_processed, depth, depth_img = model.generate_outputs(frame)
model.logger.experiment.add_image(f'pred/{i}_rgb', rgb, global_step=trainer.global_step)
model.logger.experiment.add_image(f'pred/{i}_raw', rgb_raw, global_step=trainer.global_step)
model.logger.experiment.add_image(f'pred/{i}_processed', rgb_processed, global_step=trainer.global_step)
model.logger.experiment.add_image(f'pred/{i}_depth', depth_img, global_step=trainer.global_step)
if model.args.save_video: # save the evolution of the model
if i == 0: # save first frame
np.save(f"video/{model.args.name}/{model.current_epoch}_depth.npy", depth.detach().cpu().numpy())
np.save(f"video/{model.args.name}/{model.current_epoch}_rgb.npy", rgb.detach().cpu().numpy())
# zoomed images
# rgb, rgb_raw, rgb_processed, depth, depth_img, depth_lidar, depth_lidar_img = model.generate_imgs(frame, u_lims=[0.4,0.6], v_lims=[0.4,0.6])
# model.logger.experiment.add_image(f'pred/{i}_rgb_zoom', rgb, global_step=trainer.global_step)
# model.logger.experiment.add_image(f'pred/{i}_depth_zoom', depth_img, global_step=trainer.global_step)
def on_train_start(self, trainer, model):
pl.seed_everything(42)
# pl doesn't put non-parameters on the right device
model.rgb_volume = model.rgb_volume.to(model.device)
model.processed_rgb_volume = model.processed_rgb_volume.to(model.device)
model.reference_intrinsics = model.reference_intrinsics.to(model.device)
if not model.args.no_device_rotations:
model.model_rotation.reference_rotation = model.model_rotation.reference_rotation.to(model.device)
model.model_intrinsics.focal = model.model_intrinsics.focal.to(model.device)
model.logger.experiment.add_text("args", str(model.args))
rgb_raw, rgb_processed, depth_lidar, depth_lidar_img = model.generate_imgs(0)
model.logger.experiment.add_image('gt/lidar', depth_lidar_img, global_step=trainer.global_step)
for i, frame in enumerate([0]):
rgb_raw, rgb_processed, depth_lidar, depth_lidar_img = model.generate_imgs(frame)
model.logger.experiment.add_image(f'gt/{i}_rgb_raw', rgb_raw, global_step=trainer.global_step)
model.logger.experiment.add_image(f'gt/{i}_rgb_processed', rgb_processed, global_step=trainer.global_step)
# zoomed images
# rgb, rgb_raw, rgb_processed, depth, depth_img, depth_lidar, depth_lidar_img = model.generate_imgs(frame, u_lims=[0.4,0.6], v_lims=[0.4,0.6])
# model.logger.experiment.add_image(f'gt/{i}_rgb_raw_zoom', rgb_raw, global_step=trainer.global_step)
# model.logger.experiment.add_image(f'gt/{i}_rgb_processed_zoom', rgb_processed, global_step=trainer.global_step)
if model.args.save_video:
os.makedirs(f"video/{model.args.name}", exist_ok=True)
def on_train_end(self, trainer, model):
checkpoint_dir = os.path.join("checkpoints", args.name, "last.ckpt")
trainer.save_checkpoint(checkpoint_dir)
if __name__ == "__main__":
# argparse
parser = argparse.ArgumentParser()
# data
parser.add_argument('--point_batch_size', type=int, default=1024, help="Number of points to sample per dataloader index.")
parser.add_argument('--num_batches', type=int, default=256, help="Number of training batches.")
parser.add_argument('--no_shade_map', action='store_true', help="Don't use shade map, useful for low-light captures.")
parser.add_argument('--no_raw', action='store_true', help="No RAW data available, use RGB volume instead.")
parser.add_argument('--no_device_rotations', action='store_true', help="Learn rotations from scratch, useful if no gyro data available.")
parser.add_argument('--no_intrinsics', action='store_true', help="Learn camera intrinsics from scratch, useful if no camera intrinsics available.")
parser.add_argument('--no_phone_depth', action='store_true', help="No phone depth data in bundle.")
parser.add_argument('--allow_negative_depth', action='store_true', help="Allow negative depth solutions, useful for weird or digitally stabilized data.")
parser.add_argument('--dark', action='store_true', help="Low-light capture, automatically also turns off shade map.")
# model
parser.add_argument('--control_points_motion', type=int, default=21, help="Spline control points for translation/rotation model.")
parser.add_argument('--control_points_intrinsics', type=int, default=4, help="Spline control points for intrinsics model.")
parser.add_argument('--config_path_depth', type=str, default="config/config_depth.json", help="Depth model config.")
parser.add_argument('--config_path_rgb', type=str, default="config/config_rgb.json", help="RGB model config.")
parser.add_argument('--plane_weight', type=float, default=1e-4, help="Depth regularization.")
parser.add_argument('--rotation_weight', type=float, default=1e-1, help="Scale learned rotation.")
parser.add_argument('--translation_weight', type=float, default=1e-1, help="Scale learned translation.")
parser.add_argument('--mask_k_min', type=float, default=-100, help="Mask weight evolution parameter.")
parser.add_argument('--mask_k_max', type=float, default=100, help="Mask weight evolution parameter.")
parser.add_argument('--fixed_image', action='store_true', help="Fix I(u,v) to be the zero-th frame during training.")
# training
parser.add_argument('--bundle_path', type=str, required=True, help="Path to frame_bundle.npz")
parser.add_argument('--name', type=str, required=True, help="Experiment name for logs and checkpoints.")
parser.add_argument('--max_epochs', type=int, default=100, help="Number of training epochs.")
parser.add_argument('--gamma', type=float, default=0.98, help="Learning rate decay gamma.")
parser.add_argument('--lr', type=float, default=1e-4, help="Learning rate.")
parser.add_argument('--save_video', action='store_true', help="Store training outputs at each epoch for visualization.")
args = parser.parse_args()
if args.dark:
args.no_shade_map = True
print(args)
# dataset
bundle_dataset = BundleDataset(args)
train_loader = DataLoader(bundle_dataset, batch_size=1, num_workers=os.cpu_count(), shuffle=True, pin_memory=True)
# model
model = BundleMLP(args)
# let plane train on its own at the start
model.network_depth.requires_grad_(False)
model.encoding_depth.requires_grad_(False)
model.model_intrinsics.requires_grad_(False)
# training
# checkpoint_callback = pl.callbacks.ModelCheckpoint(dirpath=os.path.join("checkpoints", args.name), save_top_k=1, save_last=True, monitor="loss")
lr_callback = pl.callbacks.LearningRateMonitor()
logger = pl.loggers.TensorBoardLogger(save_dir=os.getcwd(), version=args.name, name="lightning_logs")
validation_callback = ValidationCallback()
trainer = pl.Trainer(accelerator="auto", strategy="auto", max_epochs=args.max_epochs,
logger=logger, callbacks=[validation_callback, lr_callback], enable_checkpointing=False)
trainer.fit(model, train_loader)