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data_loader.py
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data_loader.py
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# Mostly based on the code written by Tinghui Zhou & Clement Godard:
# https://github.com/tinghuiz/SfMLearner/blob/master/data_loader.py
# https://github.com/mrharicot/monodepth/blob/master/monodepth_dataloader.py
from __future__ import division
import os
import random
import tensorflow as tf
import numpy as np
def read_npy_file(item):
data = np.load(item.decode())
return data.astype(np.uint8)
class DataLoader(object):
def __init__(self, opt=None,):
self.opt = opt
seed = 8964
tf.set_random_seed(seed)
random.seed(seed)
def load_train_batch(self):
"""Load a batch of training instances.
"""
opt = self.opt
# Load the list of training files into queues
#TODO
if opt.train_lite:
file_list = self.format_file_list(opt.dataset_dir, opt.filelist_dir, 'train_lite')
else:
file_list = self.format_file_list(opt.dataset_dir, opt.filelist_dir, 'train')
image_paths_queue = tf.train.string_input_producer(
file_list['image_file_list'], shuffle=False)
cam_paths_queue = tf.train.string_input_producer(
file_list['cam_file_list'], shuffle=False)
# Load camera intrinsics
cam_reader = tf.TextLineReader()
_, raw_cam_contents = cam_reader.read(cam_paths_queue)
rec_def = []
for i in range(9):
rec_def.append([1.])
raw_cam_vec = tf.decode_csv(raw_cam_contents,
record_defaults=rec_def)
raw_cam_vec = tf.stack(raw_cam_vec)
intrinsics = tf.reshape(raw_cam_vec, [3, 3])
# Load images
img_reader = tf.WholeFileReader()
_, image_contents = img_reader.read(image_paths_queue)
image_seq = tf.image.decode_jpeg(image_contents)
tgt_image, src_image_stack = \
self.unpack_image_sequence(
image_seq, opt.img_height, opt.img_width, opt.num_source)
#TODO Load Semantics
# See cityscape label defs in https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py#L62
# Also notice that deeplabv3+ uses `train_id` https://github.com/tensorflow/models/blob/69b016449ffc797421bf003d8b7fd8545db866d7/research/deeplab/datasets/build_cityscapes_data.py#L46
# Color maps are in https://github.com/tensorflow/models/blob/69b016449ffc797421bf003d8b7fd8545db866d7/research/deeplab/utils/get_dataset_colormap.py#L207
if opt.sem_assist:
sem_paths_queue = tf.train.string_input_producer(
file_list['sem_image_file_list'], shuffle=False)
sem_reader = tf.WholeFileReader()
sem_keys, sem_contents = sem_reader.read(sem_paths_queue)
if opt.load_from_raw:
sem_seq = tf.reshape(tf.decode_raw(sem_contents, tf.uint8), [1, opt.img_height, (opt.num_source+1) * opt.img_width])
else:
sem_seq = tf.py_func(read_npy_file, [sem_keys], [tf.uint8,])
#TODO Load Instances: we use COCO
# Two channels: class and id level. For id level we only use the edge
if opt.ins_assist:
ins_paths_queue = tf.train.string_input_producer(
file_list['ins_image_file_list'], shuffle=False)
ins_reader = tf.WholeFileReader()
ins_keys, ins_contents = ins_reader.read(ins_paths_queue)
if opt.load_from_raw:
ins_seq = tf.reshape(tf.decode_raw(ins_contents, tf.uint8), [1, opt.img_height, (opt.num_source+1) * opt.img_width, 2])
else:
ins_seq = tf.py_func(read_npy_file, [ins_keys], [tf.uint8,])
#TODO 1. SHUFFLE BATCH
# Form training batches
seed = random.randint(0, 2**31 - 1)
min_after_dequeue = 2048
capacity = min_after_dequeue + opt.num_threads * opt.batch_size
if opt.sem_assist and opt.ins_assist:
src_image_stack, tgt_image, intrinsics, sem_seq, ins_seq = tf.train.shuffle_batch(
[src_image_stack, tgt_image, intrinsics, sem_seq[0], ins_seq[0]],
opt.batch_size, capacity, min_after_dequeue, opt.num_threads, seed)
elif opt.sem_assist:
src_image_stack, tgt_image, intrinsics, sem_seq = tf.train.shuffle_batch(
[src_image_stack, tgt_image, intrinsics, sem_seq[0]],
opt.batch_size, capacity, min_after_dequeue, opt.num_threads, seed)
elif opt.ins_assist:
src_image_stack, tgt_image, intrinsics, ins_seq = tf.train.shuffle_batch(
[src_image_stack, tgt_image, intrinsics, ins_seq[0]],
opt.batch_size, capacity, min_after_dequeue, opt.num_threads, seed)
else:
src_image_stack, tgt_image, intrinsics = tf.train.shuffle_batch(
[src_image_stack, tgt_image, intrinsics],
opt.batch_size, capacity, min_after_dequeue, opt.num_threads, seed)
# semantic segmentation
tgt_sem=None
tgt_sem_map=None
tgt_sem_mask=None
tgt_sem_edge=None
src_sem_stack=None
src_sem_map_stack=None
src_sem_mask_stack=None
src_sem_edge_stack=None
# ins0 ~ instance level, but still class segmentation
tgt_ins0=None
tgt_ins0_map=None
tgt_ins0_edge=None
src_ins0_stack=None
src_ins0_map_stack=None
src_ins0_edge_stack=None
# ins1 ~ instance level, but this is id segmentation
tgt_ins1_edge=None
src_ins1_edge_stack=None
#TODO 2. TRAMSFORMATION AND UNPACKING
if opt.sem_assist:
#TODO get one-hot encoded sem_oh_seq (4,128,1248,19)X{0,1}
sem_oh_seq = tf.cast(tf.one_hot(sem_seq, on_value=1, depth = opt.sem_num_class), tf.uint8)
#TODO decouple tgt_sem (4,128,1248,19)X{0,1} src_sem_stack (4,128,1248,2*19)X{0,1}
tgt_sem, src_sem_stack = self.unpack_sem_sequence_batch_atom(sem_oh_seq, opt.sem_num_class)
#TODO get densemap sem_map_seq (4,128,1248,1)X{0,1,...,18}
sem_map_seq = tf.expand_dims(sem_seq,-1)
#TODO decouple tgt_sem_map (4,128,1248,1)X{0,1,...,18} src_sem_map_stack (4,128,1248,2*1)X{0,1,...,18}
tgt_sem_map, src_sem_map_stack = self.unpack_sem_sequence_batch_atom(sem_map_seq, 1)
if opt.sem_mask_explore:
#TODO get sem mask sem_mask_seq (4,128,1248,c) here we assume c=1
sem_mask_seq = self.get_sem_mask_batch(sem_seq)
#TODO decouple tgt_sem_mask (4,128,1248,c) src_sem_mask_stack (4,128,1248,2*c)
tgt_sem_mask, src_sem_mask_stack = self.unpack_sem_sequence_batch_atom(sem_mask_seq, 1)
if opt.sem_edge_explore:
#TODO get sem edge sem_edge_seq (4,128,1248,c) here we assume c=1
sem_edge_seq=self.get_sem_edge_batch(sem_seq)
#TODO decouple tgt_sem_edge (4,128,1248,c) src_sem_edge_stack (4,128,1248,2*c)
tgt_sem_edge, src_sem_edge_stack = self.unpack_sem_sequence_batch_atom(sem_edge_seq, 1)
if opt.ins_assist:
ins0_seq = ins_seq[:,:,:,0]
ins1_seq = ins_seq[:,:,:,1]
#TODO get one-hot ins0_oh_seq (4,128,1248,81)X{0,1}
ins0_oh_seq = tf.cast(tf.one_hot(ins0_seq, on_value=1, depth = opt.ins_num_class), tf.uint8)
#ins1_oh_seq = tf.cast(tf.one_hot(ins1_seq, on_value=1, depth = 255), tf.uint8)
#TODO decouple tgt_ins0 (4,128,1248,81)X{0,1} src_ins0_stack (4,128,1248,2*81)X{0,1}
tgt_ins0, src_ins0_stack = self.unpack_sem_sequence_batch_atom(ins0_oh_seq, opt.ins_num_class)
#tgt_ins1, src_ins1_stack = self.unpack_sem_sequence_batch_atom(ins1_oh_seq, opt.ins_num_class)
#TODO get densemap sem_ins0_seq (4,128,1248,1)X{0,1,...,80}
ins0_map_seq = ins_seq[:,:,:,:1]
ins1_map_seq = ins_seq[:,:,:,1:]
#TODO decouple tgt_ins0_map (4,128,1248,1)X{0,1,...,80} src_ins0_map_stack (4,128,1248,2*1)X{0,1,...,80}
tgt_ins0_map, src_ins0_map_stack = self.unpack_sem_sequence_batch_atom(ins0_map_seq, 1)
tgt_ins1_map, src_ins1_map_stack = self.unpack_sem_sequence_batch_atom(ins1_map_seq, 1)
if opt.ins0_edge_explore:
#TODO get edge ins0_edge_seq (4,128,1248,c) here we assume c=1
ins0_edge_seq=self.get_sem_edge_batch(ins0_seq)
#TODO decouple tgt_ins0_edge (4,128,1248,c) src_ins0_edge_stack (4,128,1248,2*c)
tgt_ins0_edge, src_ins0_edge_stack = self.unpack_sem_sequence_batch_atom(ins0_edge_seq, 1)
if opt.ins1_edge_explore:
#TODO get edge ins1_edge_seq (4,128,1248,c) here we assume c=1
ins1_edge_seq=self.get_sem_edge_batch(ins1_seq)
#TODO decouple tgt_ins1_edge (4,128,1248,c) src_ins1_edge_stack (4,128,1248,2*c)
tgt_ins1_edge, src_ins1_edge_stack = self.unpack_sem_sequence_batch_atom(ins1_edge_seq, 1)
#TODO 3. DATA AUGMENTATION
image_all = tf.concat([tgt_image, src_image_stack], axis=3)
image_all, intrinsics, aug_params = self.data_augmentation(
image_all, intrinsics, opt.img_height, opt.img_width) #TODO changed API
if opt.sem_assist:
##TODO Do the same data augmentation for semantic segmentations
tgt_sem, src_sem_stack = self.data_aug(tgt_sem, src_sem_stack, aug_params, "bilinear")
tgt_sem_map, src_sem_map_stack = self.data_aug(tgt_sem_map, src_sem_map_stack, aug_params, "neighbor")
if self.opt.sem_mask_explore:
tgt_sem_mask, src_sem_mask_stack = \
self.data_aug(tgt_sem_mask, src_sem_mask_stack, aug_params, "bilinear")
if self.opt.sem_edge_explore:
tgt_sem_edge, src_sem_edge_stack = \
self.data_aug(tgt_sem_edge, src_sem_edge_stack, aug_params, "bilinear")
#TODO maybe transfer needs this settings self.data_aug(tgt_sem_edge, src_sem_edge_stack, aug_params, "neighbor")
if opt.ins_assist:
##TODO Do the same data augmentation for instance segmentations
tgt_ins0, src_ins0_stack = self.data_aug(tgt_ins0, src_ins0_stack, aug_params, "bilinear")
#tgt_ins1, src_ins1_stack = self.data_aug(tgt_ins1, src_ins1_stack, aug_params, "bilinear")
tgt_ins0_map, src_ins0_map_stack = self.data_aug(tgt_ins0_map, src_ins0_map_stack, aug_params, "neighbor")
#tgt_ins1_map, src_ins1_map_stack = self.data_aug(tgt_ins1_map, src_ins1_map_stack, aug_params, "neighbor")
if self.opt.ins0_edge_explore:
tgt_ins0_edge, src_ins0_edge_stack = \
self.data_aug(tgt_ins0_edge, src_ins0_edge_stack, aug_params, "bilinear")
#TODO maybe transfer needs this settings self.data_aug(tgt_ins0_edge, src_ins0_edge_stack, aug_params, "neighbor")
if self.opt.ins1_edge_explore:
tgt_ins1_edge, src_ins1_edge_stack = \
self.data_aug(tgt_ins1_edge, src_ins1_edge_stack, aug_params, "bilinear")
#TODO maybe transfer needs this settings self.data_aug(tgt_ins1_edge, src_ins1_edge_stack, aug_params, "neighbor")
# 4. RETURN
# image_channels=3*opt.seq_length
tgt_image = image_all[:, :, :, :3]
src_image_stack = image_all[:, :, :, 3:] #3:image_channels]
intrinsics = self.get_multi_scale_intrinsics(intrinsics, opt.num_scales)
# if opt.sem_assist and opt.ins_assist:
return tgt_image, src_image_stack, intrinsics, \
[tgt_sem, tgt_sem_map, tgt_sem_mask, tgt_sem_edge], \
[src_sem_stack, src_sem_map_stack, src_sem_mask_stack, src_sem_edge_stack], \
[tgt_ins0, tgt_ins0_map, tgt_ins0_edge, tgt_ins1_edge], \
[src_ins0_stack, src_ins0_map_stack, src_ins0_edge_stack, src_ins1_edge_stack]
def data_aug(self, tgt_sem, src_sem_stack, aug_params, f_str):
if f_str=="bilinear":
resize_f=tf.image.resize_bilinear
elif f_str=="neighbor":
resize_f=tf.image.resize_nearest_neighbor
else:
print("dont know this resizing method, exit...")
exit()
# 1. fetch params
out_hw, yxhw = aug_params
offset_y,offset_x,out_h,out_w = yxhw
# 2. random scaling
tgt_sem = resize_f(tgt_sem, out_hw)
src_sem_stack = resize_f(src_sem_stack, out_hw)
# 3. random cropping
tgt_sem = tf.image.crop_to_bounding_box(
tgt_sem, offset_y, offset_x, out_h, out_w)
src_sem_stack = tf.image.crop_to_bounding_box(
src_sem_stack, offset_y, offset_x, out_h, out_w)
# 4. cast
if self.opt.data_aug_cast:
tgt_sem = tf.cast(tgt_sem, dtype=tf.uint8)
src_sem_stack=tf.cast(src_sem_stack, dtype=tf.uint8)
return tgt_sem, src_sem_stack
def make_intrinsics_matrix(self, fx, fy, cx, cy):
# Assumes batch input
batch_size = fx.get_shape().as_list()[0]
zeros = tf.zeros_like(fx)
r1 = tf.stack([fx, zeros, cx], axis=1)
r2 = tf.stack([zeros, fy, cy], axis=1)
r3 = tf.constant([0.,0.,1.], shape=[1, 3])
r3 = tf.tile(r3, [batch_size, 1])
intrinsics = tf.stack([r1, r2, r3], axis=1)
return intrinsics
#TODO change the api parameters, add one to the return value to make sure segmentation aug the save way
def data_augmentation(self, im, intrinsics, out_h, out_w):
# Random scaling
def random_scaling(im, intrinsics):
batch_size, in_h, in_w, _ = im.get_shape().as_list()
scaling = tf.random_uniform([2], 1, 1.15)
x_scaling = scaling[0]
y_scaling = scaling[1]
out_h = tf.cast(in_h * y_scaling, dtype=tf.int32)
out_w = tf.cast(in_w * x_scaling, dtype=tf.int32)
im = tf.image.resize_area(im, [out_h, out_w])
fx = intrinsics[:,0,0] * x_scaling
fy = intrinsics[:,1,1] * y_scaling
cx = intrinsics[:,0,2] * x_scaling
cy = intrinsics[:,1,2] * y_scaling
intrinsics = self.make_intrinsics_matrix(fx, fy, cx, cy)
return im, intrinsics, [out_h,out_w]
# Random cropping
def random_cropping(im, intrinsics, out_h, out_w):
batch_size, in_h, in_w, _ = tf.unstack(tf.shape(im))
offset_y = tf.random_uniform([1], 0, in_h - out_h + 1, dtype=tf.int32)[0]
offset_x = tf.random_uniform([1], 0, in_w - out_w + 1, dtype=tf.int32)[0]
im = tf.image.crop_to_bounding_box(
im, offset_y, offset_x, out_h, out_w)
fx = intrinsics[:,0,0]
fy = intrinsics[:,1,1]
cx = intrinsics[:,0,2] - tf.cast(offset_x, dtype=tf.float32)
cy = intrinsics[:,1,2] - tf.cast(offset_y, dtype=tf.float32)
intrinsics = self.make_intrinsics_matrix(fx, fy, cx, cy)
return im, intrinsics, [offset_y, offset_x,out_h,out_w]
# Random coloring
def random_coloring(im):
batch_size, in_h, in_w, in_c = im.get_shape().as_list()
im_f = tf.image.convert_image_dtype(im, tf.float32)
# randomly shift gamma
random_gamma = tf.random_uniform([], 0.8, 1.2)
im_aug = im_f ** random_gamma
# randomly shift brightness
random_brightness = tf.random_uniform([], 0.5, 2.0)
im_aug = im_aug * random_brightness
# randomly shift color
random_colors = tf.random_uniform([in_c], 0.8, 1.2)
white = tf.ones([batch_size, in_h, in_w])
color_image = tf.stack([white * random_colors[i] for i in range(in_c)], axis=3)
im_aug *= color_image
# saturate
im_aug = tf.clip_by_value(im_aug, 0, 1)
im_aug = tf.image.convert_image_dtype(im_aug, tf.uint8)
return im_aug
im, intrinsics, out_hw = random_scaling(im, intrinsics)
im, intrinsics, yxhw = random_cropping(im, intrinsics, out_h, out_w)
im = tf.cast(im, dtype=tf.uint8)
do_augment = tf.random_uniform([], 0, 1)
im = tf.cond(do_augment > 0.5, lambda: random_coloring(im), lambda: im)
return im, intrinsics, [out_hw, yxhw]
def format_file_list(self, data_root, filelist_root, split):
with open(filelist_root + '/%s.txt' % split, 'r') as f:
frames = f.readlines()
subfolders = [x.split(' ')[0] for x in frames]
frame_ids = [x.split(' ')[1][:-1] for x in frames]
image_file_list = [os.path.join(data_root, subfolders[i],
frame_ids[i] + '.jpg') for i in range(len(frames))]
cam_file_list = [os.path.join(data_root, subfolders[i],
frame_ids[i] + '_cam.txt') for i in range(len(frames))]
#TODO add sem file list
if self.opt.sem_assist:
if self.opt.load_from_raw:
sem_file_list = [os.path.join(data_root, subfolders[i],
frame_ids[i] + '.raw') for i in range(len(frames))]
else:
sem_file_list = [os.path.join(data_root, subfolders[i],
frame_ids[i] + '.npy') for i in range(len(frames))]
#TODO add ins file list
if self.opt.ins_assist:
if self.opt.load_from_raw:
ins_file_list = [os.path.join(self.opt.ins_train_kitti_dir, subfolders[i],
frame_ids[i] + '_instance_new.raw') for i in range(len(frames))]
else:
ins_file_list = [os.path.join(self.opt.ins_train_kitti_dir, subfolders[i],
frame_ids[i] + '_instance_new.npy') for i in range(len(frames))]
all_list = {}
all_list['image_file_list'] = image_file_list
all_list['cam_file_list'] = cam_file_list
#TODO add sem image path
if self.opt.sem_assist:
all_list['sem_image_file_list']=sem_file_list
#TODO add ins_image_path
if self.opt.ins_assist:
all_list["ins_image_file_list"]=ins_file_list
return all_list
def unpack_image_sequence(self, image_seq, img_height, img_width, num_source):
# Assuming the center image is the target frame
tgt_start_idx = int(img_width * (num_source//2))
tgt_image = tf.slice(image_seq,
[0, tgt_start_idx, 0],
[-1, img_width, -1])
# Source frames before the target frame
src_image_1 = tf.slice(image_seq,
[0, 0, 0],
[-1, int(img_width * (num_source//2)), -1])
# Source frames after the target frame
src_image_2 = tf.slice(image_seq,
[0, int(tgt_start_idx + img_width), 0],
[-1, int(img_width * (num_source//2)), -1])
src_image_seq = tf.concat([src_image_1, src_image_2], axis=1)
# Stack source frames along the color channels (i.e. [H, W, N*3])
src_image_stack = tf.concat([tf.slice(src_image_seq,
[0, i*img_width, 0],
[-1, img_width, -1])
for i in range(num_source)], axis=2)
src_image_stack.set_shape([img_height,
img_width,
num_source * 3])
tgt_image.set_shape([img_height, img_width, 3])
return tgt_image, src_image_stack
#TODO unpack semantic
# Shape (batch_size, sem_height, sem_width*(num_source+1), num_channel)
# Return tgt and src things
# They are in (bs, h, w, ch) and (bs, h, w, ch*num_source)
def unpack_sem_sequence_batch_atom(self, sem_seq, num_channel):
sem_height = self.opt.img_height
sem_width = self.opt.img_width
num_source = self.opt.num_source
batch_size =self.opt.batch_size
# Assuming the center sem image is the target frame
tgt_start_idx = int(sem_width * (num_source//2))
tgt_sem = sem_seq[:, :, tgt_start_idx: tgt_start_idx + sem_width, :]
# Source frames before the target frame
src_sem_1 = sem_seq[:, :, :tgt_start_idx, :]
# Source frames after the target frame
src_sem_2 = sem_seq[:, :, tgt_start_idx+sem_width:, :]
# Still get a fat tensor, but only src related
src_sem_seq = tf.concat([src_sem_1, src_sem_2], axis=2)
# Then we get a thick tensor stacking source along channels (i.e. [batchsize, H, W, N*K])
src_sem_stack = tf.concat([src_sem_seq[:, :, i*sem_width:(i+1)*sem_width, :]
for i in range(num_source)], axis=3)
# Set the shape
tgt_sem.set_shape([batch_size, sem_height, sem_width, num_channel])
src_sem_stack.set_shape([batch_size, sem_height, sem_width, num_source * num_channel])
return tgt_sem, src_sem_stack
def get_sem_mask_batch(self, sem_seq): #4*128*(416*3=1248) X {0,1,..,18} => 4*128*1248*c
if self.opt.sem_mask_pattern=="foreground":
return tf.expand_dims(tf.cast(sem_seq>10,tf.uint8),-1)
else:
return tf.expand_dims(tf.cast(sem_seq<=10,tf.uint8),-1)
def get_sem_edge_batch(self, sem_seq): #4*128*(416*3=1248) X {0,1,..,18} => 4*128*1248*c
def some_f(seg):
x = tf.pad(1-tf.cast(tf.equal(seg[:, :, :-1], seg[:, :, 1:]), dtype=tf.uint8), [[0, 0], [0, 0], [0, 1]])
y = tf.pad(1-tf.cast(tf.equal(seg[:, :-1, :], seg[:, 1:, :]), dtype=tf.uint8), [[0, 0], [0, 1], [0, 0]])
return x + y
width=self.opt.img_width
num_source=self.opt.num_source
tgt_start = int(width * (num_source//2))
tf_seg0 = sem_seq[:,:,tgt_start:tgt_start+width]
tf_seg1 = sem_seq[:,:,:tgt_start]
tf_seg2 = sem_seq[:,:,tgt_start + width:]
tf_edge0=some_f(tf_seg0)
tf_edge1=some_f(tf_seg1)
tf_edge2=some_f(tf_seg2)
return tf.expand_dims(tf.concat([tf_edge0, tf_edge1, tf_edge2], axis=2),-1)
#TODO tensorflow shuffle batch based on our condition
def tf_shuffle_batch(self, shuffle_list, batch_size, capacity, min_after_dequeue, num_threads, seed):
src_image_stack=tgt_image=intrinsics=src_sem_stack=tgt_sem=src_sem_mask_stack=tgt_sem_mask=src_sem_edge_stack=tgt_sem_edge = None
return src_image_stack, tgt_image, intrinsics, src_sem_stack, tgt_sem, src_sem_mask_stack, tgt_sem_mask, src_sem_edge_stack, tgt_sem_edge
def get_multi_scale_intrinsics(self, intrinsics, num_scales):
intrinsics_mscale = []
# Scale the intrinsics accordingly for each scale
for s in range(num_scales):
fx = intrinsics[:,0,0]/(2 ** s)
fy = intrinsics[:,1,1]/(2 ** s)
cx = intrinsics[:,0,2]/(2 ** s)
cy = intrinsics[:,1,2]/(2 ** s)
intrinsics_mscale.append(
self.make_intrinsics_matrix(fx, fy, cx, cy))
intrinsics_mscale = tf.stack(intrinsics_mscale, axis=1)
return intrinsics_mscale