File size: 4,405 Bytes
93dd1fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | from __future__ import print_function # For Python 2.7 compatibility with print()
import h5py
import os
import numpy as np
import glob
import zlib
# Paths
SPLIT = "training"
INPUT_DIR = "task_ABC_D/{}/".format(SPLIT)
OUTPUT_DIR = "h5_task_ABC_D/{}/".format(SPLIT)
PROGRESS_FILE = "train_progress.txt"
TOTAL_FILES_PER_H5 = int(1e5) # Ensure integer
# Create output directory if it doesn't exist
try:
os.makedirs(OUTPUT_DIR)
except OSError:
if not os.path.isdir(OUTPUT_DIR):
raise
def get_start_index():
"""Reads the last saved index from progress file, or returns 0 if not found."""
if os.path.exists(PROGRESS_FILE):
with open(PROGRESS_FILE, "r") as f:
return int(f.read().strip())
return 0
def save_progress(index):
"""Saves the current index to the progress file."""
with open(PROGRESS_FILE, "w") as f:
f.write(str(index))
def delete_progress_file():
"""Deletes the progress file after successful completion."""
if os.path.exists(PROGRESS_FILE):
os.remove(PROGRESS_FILE)
def get_scene_no(filepath):
"""Extracts the scene number from the filepath."""
filename = os.path.basename(filepath)
# Assuming filename like "calvin_scene_000.npz", extract "000"
return filename.split('_')[-1].split('.')[0]
def process_npz_files():
"""Processes .npz files and stores them in HDF5 format."""
sorted_files = sorted(glob.glob(os.path.join(INPUT_DIR, "*.npz")))
start_idx = get_start_index()
current_h5 = None
# Initialize current_h5 based on the batch containing start_idx
if start_idx < len(sorted_files):
# Calculate the starting index of the batch
batch_start = (start_idx // TOTAL_FILES_PER_H5) * TOTAL_FILES_PER_H5
# Get the scene number from the file at the batch start
scene_no = get_scene_no(sorted_files[batch_start])
current_h5_file = os.path.join(OUTPUT_DIR, "{}_{}.h5".format(SPLIT, scene_no))
current_h5 = h5py.File(current_h5_file, "a")
print("Resuming with HDF5 file: {}".format(current_h5_file))
for i in range(start_idx, len(sorted_files)):
filepath = sorted_files[i]
scene_no = get_scene_no(filepath)
# Create a new HDF5 file when starting a new batch
if i % TOTAL_FILES_PER_H5 == 0:
if current_h5 is not None:
current_h5.close()
current_h5_file = os.path.join(OUTPUT_DIR, "{}_{}.h5".format(SPLIT, scene_no))
current_h5 = h5py.File(current_h5_file, "a")
print("Processing scene: {}".format(scene_no))
# Process and save .npz file into HDF5
save_npz_to_h5(filepath, current_h5)
# Save progress at every iteration
save_progress(i)
# Close last opened HDF5 file
if current_h5 is not None:
current_h5.close()
# Remove progress file after successful completion
delete_progress_file() # Uncomment if desired
print("Processing completed!")
def save_npz_to_h5(filepath, h5_file):
"""Loads an .npz file and stores its contents in an HDF5 file."""
# Use the full filename without extension as the group name for uniqueness
group_name = os.path.basename(filepath).split('.')[0]
# If the group already exists, delete it to overwrite
if group_name in h5_file:
del h5_file[group_name]
print("Overwriting existing group: {}".format(group_name))
file_group = h5_file.create_group(group_name)
npz_data = np.load(filepath)
data_dict = {}
# Try to load all keys; if any fail, skip the file
try:
for key in npz_data.files:
data_dict[key] = npz_data[key] # Attempt to decompress and load the data
except zlib.error as e:
print("Error decompressing data in file '{}': {}".format(filepath, e))
npz_data.close()
# Delete the empty group since loading failed
del h5_file[group_name]
# Log the skipped file
with open("skipped_files.log", "a") as log_file:
log_file.write("{}\n".format(filepath))
return # Skip this file entirely
# If we get here, all data loaded successfully; add datasets to the existing group
for key, data in data_dict.items():
file_group.create_dataset(key, data=data, compression="lzf")
npz_data.close()
if __name__ == "__main__":
process_npz_files() |