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train_depth_estimation.py
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train_depth_estimation.py
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'''
Author: Xingtong Liu, Maia Stiber, Jindan Huang, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, and Mathias Unberath
Copyright (C) 2020 Johns Hopkins University - All Rights Reserved
You may use, distribute and modify this code under the
terms of the GNU GENERAL PUBLIC LICENSE Version 3 license for non-commercial usage.
You should have received a copy of the GNU GENERAL PUBLIC LICENSE Version 3 license with
this file. If not, please write to: [email protected] or [email protected]
'''
import tqdm
import cv2
import numpy as np
from pathlib import Path
import torchsummary
import math
import torch
import random
from tensorboardX import SummaryWriter
import argparse
import datetime
import json
import os
# Local
import models
import losses
import utils
import dataset
def main():
cv2.destroyAllWindows()
parser = argparse.ArgumentParser(
description='Probabilistic depth training with dense descriptor',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--adjacent_range', nargs='+', type=int, required=True,
help='frame interval range for sampling two frames')
parser.add_argument('--image_downsampling', type=float, default=4.0,
help='image downsampling rate to speed up training and reduce overfitting')
parser.add_argument('--network_downsampling', type=int, default=64,
help='network downsampling rate from input to bottleneck layer')
parser.add_argument('--input_size', nargs='+', type=int, default=None,
help='input size for architecture summary')
parser.add_argument('--batch_size', type=int, default=8, help='batch size for training and testing')
parser.add_argument('--num_workers', type=int, default=8, help='number of workers for data loader')
parser.add_argument('--slp_weight', type=float, default=1.0, help='weight for sparse log prob loss')
parser.add_argument('--dcl_weight', type=float, default=0.5, help='weight for depth consistency loss')
parser.add_argument('--sfl_weight', type=float, default=100.0, help='weight for sparse flow loss')
parser.add_argument('--dl_weight', type=float, default=20.0, help='weight for descriptor loss')
parser.add_argument('--lr_range', nargs='+', type=float, default=[1.0e-4, 1.0e-3],
help='cyclic lr range (min, max)')
parser.add_argument('--inlier_percentage', type=float, default=0.998,
help='percentage of inliers of SfM point clouds (to prune some outliers)')
parser.add_argument('--display_interval', type=int, default=50, help='iteration interval for image display')
parser.add_argument('--save_interval', type=int, default=2, help='interval for saving model')
parser.add_argument('--visible_interval', type=int, default=5,
help='range for propagating point visibility information')
parser.add_argument('--training_patient_id', nargs='+', type=int, required=True, help='id of the training patients')
parser.add_argument('--load_intermediate_data', action='store_true',
help='whether or not to load pre-compute data')
parser.add_argument('--load_trained_model', action='store_true',
help='whether to load pre-trained model')
parser.add_argument('--trained_model_path', type=str, default=None, help='path to the trained model')
parser.add_argument('--num_epoch', type=int, required=True, help='number of epochs in total')
parser.add_argument('--architecture_summary', action='store_true', help='summarize the network architecture')
parser.add_argument('--display_architecture', action='store_true', help='summarize the network architecture')
parser.add_argument('--data_root', type=str, required=True, help='path to the training data')
parser.add_argument('--log_root', type=str, required=True, help='root of the training logs')
parser.add_argument('--precompute_root', type=str, required=True, help='root of the pre-compute data')
parser.add_argument('--num_iter', type=int, default=1000,
help='number of iterations per epoch')
parser.add_argument('--descriptor_model_path', type=str, required=True,
help='path to the trained feature matching model')
# parser.add_argument('--architecture_summary', action='store_true', help='Summarize the model architecture')
args = parser.parse_args()
# Fix randomness for reproducibility
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
torch.manual_seed(10085)
np.random.seed(10085)
random.seed(10085)
date = datetime.datetime.now()
log_root = Path(args.log_root) / "depth_train_{}_{}_{}_{}".format(
date.month,
date.day,
date.hour,
date.minute)
if not log_root.exists():
log_root.mkdir(parents=True)
if not Path(args.precompute_root).exists():
Path(args.precompute_root).mkdir(parents=True)
with open(str(log_root / 'commandline_args'), 'w') as f:
f.write("script: {}".format(str(os.path.realpath(__file__))))
json.dump(args.__dict__, f, indent=2)
writer = SummaryWriter(logdir=str(log_root))
print("Tensorboard visualization at {}".format(str(log_root)))
# Get color image filenames
train_filenames = utils.get_color_file_names_by_bag(Path(args.data_root),
id_list=args.training_patient_id)
folder_list = utils.get_parent_folder_names(Path(args.data_root), id_list=args.training_patient_id)
# Build training and validation dataset
train_dataset = dataset.DepthDataset(image_file_names=train_filenames,
folder_list=folder_list,
adjacent_range=args.adjacent_range,
image_downsampling=args.image_downsampling,
network_downsampling=args.network_downsampling,
inlier_percentage=args.inlier_percentage,
load_intermediate_data=args.load_intermediate_data,
intermediate_data_root=Path(args.precompute_root),
visible_interval=args.visible_interval,
num_pre_workers=args.num_workers,
num_iter=args.num_iter,
phase="Train")
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers)
depth_estimation_model = models.FCDenseNetStd(
in_channels=3, down_blocks=(4, 4, 4, 4, 4),
up_blocks=(4, 4, 4, 4, 4), bottleneck_layers=4,
growth_rate=12, out_chans_first_conv=48)
# Initialize the depth estimation network with Kaiming He initialization
utils.init_net(depth_estimation_model, type="kaiming", mode="fan_in", activation_mode="relu",
distribution="normal")
# Multi-GPU running
depth_estimation_model = torch.nn.DataParallel(depth_estimation_model)
# Summary network architecture
if args.architecture_summary:
torchsummary.summary(depth_estimation_model, input_size=(3, args.input_size[0], args.input_size[1]))
# Optimizer
optimizer = torch.optim.SGD(depth_estimation_model.parameters(), lr=args.lr_range[1], momentum=0.9)
lr_scheduler = models.CyclicLR(optimizer, base_lr=args.lr_range[0], max_lr=args.lr_range[1])
# Custom layers
depth_scaling_layer = models.DepthMeanStdScalingLayer()
depth_warping_layer = models.DepthWarpingLayer()
feature_warping_layer = models.FeatureWarpingLayer()
flow_from_depth_layer = models.FlowfromDepthLayer()
# Loss functions
sparse_log_prob_loss = losses.SparseLogProbLoss()
dense_log_prob_loss = losses.DenseLogProbLoss()
sparse_masked_l1_loss = losses.NormalizedSparseMaskedL1Loss()
# Load previous student model and so on
if args.load_trained_model:
if Path(args.trained_model_path).exists():
print("Loading {:s} ...".format(str(args.trained_model_path)))
state = torch.load(str(args.trained_model_path), encoding="latin1")
step = state['step']
epoch = state['epoch']
depth_estimation_model.load_state_dict(state["model"])
print('Restored model, epoch {}, step {}'.format(epoch, step))
else:
print("No pre-trained model detected")
raise OSError
else:
epoch = 0
step = 0
descriptor_model = models.FCDenseNetFeature(
in_channels=3, down_blocks=(3, 3, 3, 3, 3),
up_blocks=(3, 3, 3, 3, 3), bottleneck_layers=4,
growth_rate=10, out_chans_first_conv=16, feature_length=128)
# Initialize the depth estimation network with Kaiming He initialization
descriptor_model = utils.init_net(descriptor_model, type="kaiming", mode="fan_in",
activation_mode="relu",
distribution="normal")
# Multi-GPU running
descriptor_model = torch.nn.DataParallel(descriptor_model)
descriptor_model.eval()
if Path(args.descriptor_model_path).exists():
print("Loading {:s} ...".format(str(args.descriptor_model_path)))
state = torch.load(str(args.descriptor_model_path))
descriptor_model.load_state_dict(state["model"])
else:
print("No pre-trained descriptor model detected")
raise OSError
for cur_epoch in range(epoch, args.num_epoch + 1):
# Set the seed correlated to cur_epoch for reproducibility
torch.manual_seed(10086 + cur_epoch)
np.random.seed(10086 + cur_epoch)
random.seed(10086 + cur_epoch)
depth_estimation_model.train()
if cur_epoch <= 10:
dcl_weight = 0.0
dl_weight = 0.0
slp_weight = 0.0
else:
dcl_weight = args.dcl_weight
dl_weight = args.dl_weight
slp_weight = args.slp_weight
# Update progress bar
tq = tqdm.tqdm(total=len(train_loader) * args.batch_size)
for batch, (
colors_1, colors_2, sparse_depths_1, sparse_depths_2,
sparse_depth_masks_1, sparse_depth_masks_2,
sparse_flows_1, sparse_flows_2, sparse_flow_masks_1, sparse_flow_masks_2, boundaries, shrink_boundaries,
rotations_1_wrt_2,
rotations_2_wrt_1, translations_1_wrt_2, translations_2_wrt_1, intrinsics, folders, file_names) in \
enumerate(train_loader):
# Update learning rate
lr_scheduler.batch_step(batch_iteration=step)
tq.set_description('Epoch {}, lr {}'.format(cur_epoch, lr_scheduler.get_lr()))
with torch.no_grad():
colors_1 = colors_1.cuda()
colors_2 = colors_2.cuda()
sparse_depths_1 = sparse_depths_1.cuda()
sparse_depths_2 = sparse_depths_2.cuda()
sparse_depth_masks_1 = sparse_depth_masks_1.cuda()
sparse_depth_masks_2 = sparse_depth_masks_2.cuda()
sparse_flows_1 = sparse_flows_1.cuda()
sparse_flows_2 = sparse_flows_2.cuda()
sparse_flow_masks_1 = sparse_flow_masks_1.cuda()
sparse_flow_masks_2 = sparse_flow_masks_2.cuda()
boundaries = boundaries.cuda()
shrink_boundaries = shrink_boundaries.cuda()
rotations_1_wrt_2 = rotations_1_wrt_2.cuda()
rotations_2_wrt_1 = rotations_2_wrt_1.cuda()
translations_1_wrt_2 = translations_1_wrt_2.cuda()
translations_2_wrt_1 = translations_2_wrt_1.cuda()
intrinsics = intrinsics.cuda()
colors_1 = boundaries * colors_1
colors_2 = boundaries * colors_2
# Generate dense descriptor map
feature_maps_1 = descriptor_model(colors_1)
feature_maps_2 = descriptor_model(colors_2)
feature_maps_1 = boundaries * feature_maps_1
feature_maps_2 = boundaries * feature_maps_2
# Predicted depth
predicted_mean_depth_maps_1, predicted_std_depth_maps_1 = depth_estimation_model(colors_1)
predicted_mean_depth_maps_2, predicted_std_depth_maps_2 = depth_estimation_model(colors_2)
print("y3mmashy000000")
scaled_mean_depth_maps_1, scaled_std_depth_maps_1 = depth_scaling_layer(
[predicted_mean_depth_maps_1, predicted_std_depth_maps_1,
sparse_depths_1, sparse_depth_masks_1])
scaled_mean_depth_maps_2, scaled_std_depth_maps_2 = depth_scaling_layer(
[predicted_mean_depth_maps_2, predicted_std_depth_maps_2,
sparse_depths_2, sparse_depth_masks_2])
# Feature consistency loss
warped_feature_maps_2_to_1, shrink_intersect_masks_1 = feature_warping_layer(
[scaled_mean_depth_maps_1, feature_maps_2, shrink_boundaries, translations_1_wrt_2,
rotations_1_wrt_2, intrinsics])
warped_feature_maps_1_to_2, shrink_intersect_masks_2 = feature_warping_layer(
[scaled_mean_depth_maps_2, feature_maps_1, shrink_boundaries, translations_2_wrt_1,
rotations_2_wrt_1, intrinsics])
descriptor_loss_1 = torch.mean((torch.sum(
shrink_intersect_masks_1 * (feature_maps_1 - warped_feature_maps_2_to_1) ** 2, dim=(1, 2, 3))) / (
torch.sum(shrink_intersect_masks_1, dim=(1, 2, 3)) + 1.0e-8))
descriptor_loss_2 = torch.mean((torch.sum(
shrink_intersect_masks_2 * (feature_maps_2 - warped_feature_maps_1_to_2) ** 2, dim=(1, 2, 3))) / (
torch.sum(shrink_intersect_masks_2, dim=(1, 2, 3)) + 1.0e-8))
dl_loss = dl_weight * (0.5 * descriptor_loss_1 + 0.5 * descriptor_loss_2)
# Sparse log prob loss
sd_loss = slp_weight * (0.5 * sparse_log_prob_loss([scaled_mean_depth_maps_1, scaled_std_depth_maps_1,
sparse_depths_1, sparse_depth_masks_1]) + 0.5 *
sparse_log_prob_loss([scaled_mean_depth_maps_2, scaled_std_depth_maps_2,
sparse_depths_2, sparse_depth_masks_2]))
warped_mean_depth_maps_2_to_1, intersect_masks_1 = depth_warping_layer(
[scaled_mean_depth_maps_1, scaled_mean_depth_maps_2, boundaries, translations_1_wrt_2,
rotations_1_wrt_2,
intrinsics])
warped_mean_depth_maps_1_to_2, intersect_masks_2 = depth_warping_layer(
[scaled_mean_depth_maps_2, scaled_mean_depth_maps_1, boundaries, translations_2_wrt_1,
rotations_2_wrt_1,
intrinsics])
# Depth consistency loss
dc_loss = dcl_weight * (0.5 * dense_log_prob_loss([scaled_mean_depth_maps_1, scaled_std_depth_maps_1,
warped_mean_depth_maps_2_to_1, intersect_masks_1]) +
0.5 * dense_log_prob_loss([scaled_mean_depth_maps_2, scaled_std_depth_maps_2,
warped_mean_depth_maps_1_to_2, intersect_masks_2]))
flows_from_depth_1 = flow_from_depth_layer(
[scaled_mean_depth_maps_1, boundaries, translations_1_wrt_2, rotations_1_wrt_2,
intrinsics])
flows_from_depth_2 = flow_from_depth_layer(
[scaled_mean_depth_maps_2, boundaries, translations_2_wrt_1, rotations_2_wrt_1,
intrinsics])
sparse_flow_masks_1 = sparse_flow_masks_1 * boundaries
sparse_flow_masks_2 = sparse_flow_masks_2 * boundaries
sparse_flows_1 = sparse_flows_1 * boundaries
sparse_flows_2 = sparse_flows_2 * boundaries
flows_from_depth_1 = flows_from_depth_1 * boundaries
flows_from_depth_2 = flows_from_depth_2 * boundaries
sf_loss = args.sfl_weight * 0.5 * (sparse_masked_l1_loss(
[sparse_flows_1, flows_from_depth_1, sparse_flow_masks_1]) + sparse_masked_l1_loss(
[sparse_flows_2, flows_from_depth_2, sparse_flow_masks_2]))
# Overall Loss
loss = sd_loss + dc_loss + sf_loss + dl_loss
# Display depth and color at TensorboardX
if batch % args.display_interval == 0:
display_list_1 = \
utils.display_color_mean_std_depth_sparse_flow_dense_flow(colors_1,
scaled_mean_depth_maps_1 * boundaries,
scaled_std_depth_maps_1 * boundaries,
sparse_flows_1,
flows_from_depth_1)
display_list_2 = \
utils.display_color_mean_std_depth_sparse_flow_dense_flow(colors_2,
scaled_mean_depth_maps_2 * boundaries,
scaled_std_depth_maps_2 * boundaries,
sparse_flows_2,
flows_from_depth_2)
utils.stack_and_display(phase="Train",
title="Results (c1, d1, sd1, sf1, df1, c2, d2, sd2, sf2, df2)",
step=step, writer=writer,
image_list=display_list_1 + display_list_2)
# Handle nan/inf cases
if math.isnan(loss.item()) or math.isinf(loss.item()):
optimizer.zero_grad()
loss.backward()
optimizer.zero_grad()
continue
else:
optimizer.zero_grad()
loss.backward()
# Prevent one sample from having too much impact on the training
torch.nn.utils.clip_grad_norm_(depth_estimation_model.parameters(), 10.0)
optimizer.step()
if batch == 0:
mean_loss = loss.item()
mean_sd_loss = sd_loss.item()
mean_dc_loss = dc_loss.item()
mean_sf_loss = sf_loss.item()
mean_dl_loss = dl_loss.item()
else:
mean_loss = (mean_loss * batch + loss.item()) / (batch + 1.0)
mean_sd_loss = (mean_sd_loss * batch + sd_loss.item()) / (batch + 1.0)
mean_dc_loss = (mean_dc_loss * batch + dc_loss.item()) / (batch + 1.0)
mean_sf_loss = (mean_sf_loss * batch + sf_loss.item()) / (batch + 1.0)
mean_dl_loss = (mean_dl_loss * batch + dl_loss.item()) / (batch + 1.0)
step += 1
tq.update(colors_1.shape[0])
tq.set_postfix(loss='avg: {:.3f}, cur: {:.3f}'.format(mean_loss, loss.item()),
sd_loss='avg: {:.3f}, cur: {:.3f}'.format(mean_sd_loss,
sd_loss.item()),
dc_loss='avg: {:.3f}, cur: {:.3f}'.format(mean_dc_loss,
dc_loss.item()),
sf_loss='avg: {:.3f}, cur: {:.3f}'.format(mean_sf_loss,
sf_loss.item()),
dl_loss='avg: {:.3f}, cur: {:.3f}'.format(mean_dl_loss,
dl_loss.item())
)
# TensorboardX
writer.add_scalars('Training', {'overall': mean_loss,
'slp loss': mean_sd_loss,
'dcl loss': mean_dc_loss,
'sfl loss': mean_sf_loss,
'dl loss': mean_dl_loss
}, step)
tq.close()
print("before .pt 0000000000000000000000000000\n")
if cur_epoch % args.save_interval == 0:
writer.export_scalars_to_json(
str(log_root / ("all_scalars_" + str(cur_epoch) + ".json")))
model_path_epoch = log_root / 'checkpoint_model_epoch_{}_{}_{}_{}_{}_{}.pt'.format(cur_epoch,
mean_loss,
mean_sd_loss,
mean_dc_loss,
mean_sf_loss,
mean_dl_loss)
utils.save_model(model=depth_estimation_model, optimizer=optimizer,
epoch=cur_epoch + 1, step=step, model_path=model_path_epoch,
validation_loss=mean_sf_loss)
writer.close()
if __name__ == '__main__':
main()