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ArucoDataset.py
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import numpy as np
import time
import h5py
import torch
import torch.utils.data as data
import sys
from random import shuffle
import os
import matplotlib.pyplot as plt
class ArucoDataset(data.Dataset):
def __init__(self, data_folder_dir, require_one, ignore_list, stride=10, max_len=-1):
self.max_len = max_len
self.runs = os.walk(os.path.join(data_folder_dir, 'processed_h5py')).next()[1]
shuffle(self.runs) # shuffle each epoch to allow shuffle False
self.run_files = []
# Initialize List of Files
self.invisible = []
self.visible = []
self.total_length = 0
self.full_length = 0
run_num = 0
for run in self.runs:
run_num += 1
segs_in_run = os.walk(os.path.join(data_folder_dir, 'processed_h5py', run)).next()[1]
shuffle(segs_in_run) # shuffle on each epoch to allow shuffle False
run_labels = h5py.File(
os.path.join(data_folder_dir,
'processed_h5py',
run,
'run_labels.h5py'),
'r')
# Ignore invalid runs
ignored = False
for ignore in ignore_list:
if ignore in run_labels and run_labels[ignore][0]:
ignored = True
break
if ignored:
continue
ignored = len(require_one) > 0
for require in require_one:
if require in run_labels and run_labels[require][0]:
ignored = False
break
if ignored:
continue
print 'Loading Run {}/{}'.format(run_num, len(self.runs))
for seg in segs_in_run:
images = h5py.File(
os.path.join(
data_folder_dir,
'processed_h5py',
run,
seg,
'images.h5py'),
'r')
metadata = h5py.File(
os.path.join(data_folder_dir,
'processed_h5py',
run,
seg,
'metadata.h5py'),
'r')
length = len(images['left'])
self.run_files.append({'images': images, 'metadata': metadata, 'run_labels' : run_labels})
self.visible.append(self.total_length) # visible indicies
# invisible is not actually used at all, but is extremely useful
# for debugging indexing problems and gives very little slowdown
self.invisible.append(self.full_length + 7) # actual indicies mapped
self.total_length += 4 * (length - 7)
self.full_length += length
# Create row gradient
self.row_gradient = torch.FloatTensor(94, 168)
for row in range(94):
self.row_gradient[row, :] = row / 93.
# Create col gradient
self.col_gradient = torch.FloatTensor(94, 168)
for col in range(168):
self.col_gradient[:, col] = col / 167.
self.stride = stride
self.aruco_idx_to_key = ['cwdirect', 'ccwdirect', 'cwfollow', 'ccwfollow']
def __getitem__(self, index):
run_idx, t = self.create_map(index)
camera_t = t // 4
aruco_idx = t % 4
aruco_key = self.aruco_idx_to_key[aruco_idx]
list_camera_input = []
list_camera_input.append(
torch.from_numpy(
self.run_files[
run_idx]['images']['left'][camera_t - 7]))
for delta_time in range(6, -1, -1):
list_camera_input.append(
torch.from_numpy(
self.run_files[
run_idx]['images']['left'][camera_t - delta_time,:,:,1:2]))
list_camera_input.append(
torch.from_numpy(
self.run_files[
run_idx]['images']['right'][camera_t - 1,:,:,1:2]))
list_camera_input.append(
torch.from_numpy(
self.run_files[
run_idx]['images']['right'][camera_t,:,:,1:2]))
camera_data = torch.cat(list_camera_input, 2)
camera_data = camera_data.float() / 255. - 0.5
camera_data = torch.transpose(camera_data, 0, 2)
camera_data = torch.transpose(camera_data, 1, 2)
final_camera_data = torch.FloatTensor(14, 94, 168)
final_camera_data[0:12, :, :] = camera_data
final_camera_data[12, :, :] = self.row_gradient
final_camera_data[13, :, :] = self.col_gradient
# Get behavioral mode
metadata_raw = self.run_files[run_idx]['run_labels']
metadata = torch.FloatTensor(20, 11, 20)
metadata[:] = 0.
if aruco_idx < 2: # Direct
metadata[2, :, :] = 1.
else: # Follow
metadata[1, :, :] = 1.
if aruco_idx % 2 == 0: # Clockwise
metadata[5, :, :] = 1.
else: # Counterclockwise
metadata[6, :, :] = 1.
# Get Ground Truth
steer = []
motor = []
steer.append(float(self.run_files[run_idx]['metadata'][aruco_key][0]))
for i in range(0, self.stride * 9, self.stride):
steer.append(0.)
motor.append(float(self.run_files[run_idx]['metadata']['motor'][0]))
for i in range(0, self.stride * 29, self.stride):
motor.append(0.)
final_ground_truth = torch.FloatTensor(steer + motor) / 99.
mask = torch.FloatTensor([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, # ONLY VALIDATE ON ONE STEERING AND MOTOR
1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
return final_camera_data, metadata, final_ground_truth, mask
def __len__(self):
if self.max_len == -1:
return self.total_length
return min(self.total_length, self.max_len)
def create_map(self, global_index):
for idx, length in enumerate(self.visible[::-1]):
if global_index >= length:
return len(self.visible) - idx - 1, global_index - length + 7
if __name__ == '__main__':
train_dataset = Dataset('/hostroot/data/dataset/bair_car_data_new_28April2017', [], [])
train_data_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=500,
shuffle=False, pin_memory=False)
start = time.time()
for cam, meta, truth, mask in train_data_loader:
cur = time.time()
print(500./(cur - start))
start = cur
pass