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Fix monodepth2 tutorials (#1459)
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* init

* comments dataset part

Co-authored-by: Kuang <[email protected]>
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@KuangHaofei
KuangHaofei and Kuang authored Sep 25, 2020
1 parent cf767a1 commit c109792
Showing 1 changed file with 79 additions and 70 deletions.
149 changes: 79 additions & 70 deletions docs/tutorials/depth/train_monodepth2.py
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# disk speed. For example, it takes around 2 hours on an AWS EC2 instance with EBS.
#
# We provide self-supervised depth estimation datasets in :class:`gluoncv.data`.
# For example, we can easily get the KITTI RAW Stereo dataset:
import os
from gluoncv.data.kitti import readlines, dict_batchify_fn

train_filenames = os.path.join(
os.path.expanduser("~"), '.mxnet/datasets/kitti/splits/eigen_full/train_files.txt')
train_filenames = readlines(train_filenames)
train_dataset = gluoncv.data.KITTIRAWDataset(
filenames=train_filenames, height=192, width=640,
frame_idxs=[0, -1, 1, "s"], num_scales=4, is_train=True, img_ext='.png')
print('Training images:', len(train_dataset))
# set batch_size = 12 for toy example
batch_size = 12
train_loader = gluon.data.DataLoader(
train_dataset, batch_size=batch_size, shuffle=True, batchify_fn=dict_batchify_fn,
num_workers=12, pin_memory=True, last_batch='discard')
#
# For example, we can easily get the KITTI RAW Stereo dataset::
#
# import os
# from gluoncv.data.kitti import readlines, dict_batchify_fn
#
# train_filenames = os.path.join(
# os.path.expanduser("~"), '.mxnet/datasets/kitti/splits/eigen_full/train_files.txt')
# train_filenames = readlines(train_filenames)
# train_dataset = gluoncv.data.KITTIRAWDataset(
# filenames=train_filenames, height=192, width=640,
# frame_idxs=[0, -1, 1, "s"], num_scales=4, is_train=True, img_ext='.png')
# print('Training images:', len(train_dataset))
# # set batch_size = 12 for toy example
# batch_size = 12
# train_loader = gluon.data.DataLoader(
# train_dataset, batch_size=batch_size, shuffle=True, batchify_fn=dict_batchify_fn,
# num_workers=12, pin_memory=True, last_batch='discard')

##############################################################################
# Here, the ``frame_idxs`` argument is used to decide the input frame. It is a list and the first element
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# We follow the standard data augmentation routine to transform the input image.
# Here, we just use RandomFlip with 50% probability for input images.
#
# Random pick one example for visualization:
import random
from datetime import datetime
random.seed(datetime.now())
idx = random.randint(0, len(train_dataset))

data = train_dataset[idx]
input_img = data[("color", 0, 0)]
input_stereo_img = data[("color", 's', 0)]
input_gt = data['depth_gt']

input_img = np.transpose((input_img.asnumpy() * 255).astype(np.uint8), (1, 2, 0))
input_stereo_img = np.transpose((input_stereo_img.asnumpy() * 255).astype(np.uint8), (1, 2, 0))
input_gt = np.transpose((input_gt.asnumpy()).astype(np.uint8), (1, 2, 0))

from PIL import Image
input_img = Image.fromarray(input_img)
input_stereo_img = Image.fromarray(input_stereo_img)
input_gt = Image.fromarray(input_gt[:, :, 0])

input_img.save("input_img.png")
input_stereo_img.save("input_stereo_img.png")
input_gt.save("input_gt.png")
# Random pick one example for visualization::
#
# import random
# from datetime import datetime
# random.seed(datetime.now())
# idx = random.randint(0, len(train_dataset))
#
# data = train_dataset[idx]
# input_img = data[("color", 0, 0)]
# input_stereo_img = data[("color", 's', 0)]
# input_gt = data['depth_gt']
#
# input_img = np.transpose((input_img.asnumpy() * 255).astype(np.uint8), (1, 2, 0))
# input_stereo_img = np.transpose((input_stereo_img.asnumpy() * 255).astype(np.uint8), (1, 2, 0))
# input_gt = np.transpose((input_gt.asnumpy()).astype(np.uint8), (1, 2, 0))
#
# from PIL import Image
# input_img = Image.fromarray(input_img)
# input_stereo_img = Image.fromarray(input_stereo_img)
# input_gt = Image.fromarray(input_gt[:, :, 0])
#
# input_img.save("input_img.png")
# input_stereo_img.save("input_stereo_img.png")
# input_gt.save("input_gt.png")

##############################################################################
# Plot the stereo image pairs and ground truth of the left image
from matplotlib import pyplot as plt

input_img = Image.open('input_img.png').convert('RGB')
input_stereo_img = Image.open('input_stereo_img.png').convert('RGB')
input_gt = Image.open('input_gt.png')

fig = plt.figure()
# subplot 1 for left image
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.75)
fig.add_subplot(3, 1, 1)
plt.title("left image")
plt.imshow(input_img)
# subplot 2 for right images
fig.add_subplot(3, 1, 2)
plt.title("right image")
plt.imshow(input_stereo_img)
# subplot 3 for the ground truth
fig.add_subplot(3, 1, 3)
plt.title("ground truth of left input (the reprojection of LiDAR data)")
plt.imshow(input_gt)
# display
plt.show()
# Plot the stereo image pairs and ground truth of the left image::
#
# from matplotlib import pyplot as plt
#
# input_img = Image.open('input_img.png').convert('RGB')
# input_stereo_img = Image.open('input_stereo_img.png').convert('RGB')
# input_gt = Image.open('input_gt.png')
#
# fig = plt.figure()
# # subplot 1 for left image
# plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.75)
# fig.add_subplot(3, 1, 1)
# plt.title("left image")
# plt.imshow(input_img)
# # subplot 2 for right images
# fig.add_subplot(3, 1, 2)
# plt.title("right image")
# plt.imshow(input_stereo_img)
# # subplot 3 for the ground truth
# fig.add_subplot(3, 1, 3)
# plt.title("ground truth of left input (the reprojection of LiDAR data)")
# plt.imshow(input_gt)
# # display
# plt.show()

##############################################################################
# The Dataloader will provide a dictionary which includes raw images, augmented images, camera intrinsics,
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# We use a 'step' learning rate scheduler for Monodepth2 training, provided in :class:`gluoncv.utils.LRScheduler`.
# We use a learning rate of 10−4 for the first 15 epochs which is then dropped to 10−5 for the remainder.
#
lr_scheduler = gluoncv.utils.LRSequential([
gluoncv.utils.LRScheduler(
'step', base_lr=1e-4, nepochs=20, iters_per_epoch=len(train_dataset), step_epoch=[15])
])
optimizer_params = {'lr_scheduler': lr_scheduler,
'learning_rate': 1e-4}
# The example of optimization is defined as::
#
# lr_scheduler = gluoncv.utils.LRSequential([
# gluoncv.utils.LRScheduler(
# 'step', base_lr=1e-4, nepochs=20, iters_per_epoch=len(train_dataset), step_epoch=[15])
# ])
# optimizer_params = {'lr_scheduler': lr_scheduler,
# 'learning_rate': 1e-4}

##############################################################################
# - Create Adam solver
depth_optimizer = gluon.Trainer(model.collect_params(), 'adam', optimizer_params)
pose_optimizer = gluon.Trainer(posenet.collect_params(), 'adam', optimizer_params)
#
# The example for depth & pose optimizer are defined as::
#
# depth_optimizer = gluon.Trainer(model.collect_params(), 'adam', optimizer_params)
# pose_optimizer = gluon.Trainer(posenet.collect_params(), 'adam', optimizer_params)

##############################################################################
# The training loop
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