From fcd8f2d7fc8cd37ff27033e0d9154bcf1b6b47e3 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 1 Sep 2024 17:57:31 -0700 Subject: [PATCH] Refactor evaluation script for improved code organization and performance --- benchmarks/Visualization.ipynb | 87 +++++++++++++++++++++++++++++++--- benchmarks/openx.py | 23 +++++---- evaluation.sh | 21 ++++++++ fog_x/loader/hdf5.py | 81 ++++++++++++++++++++++++------- 4 files changed, 179 insertions(+), 33 deletions(-) create mode 100755 evaluation.sh diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb index 8d13351..58049c8 100644 --- a/benchmarks/Visualization.ipynb +++ b/benchmarks/Visualization.ipynb @@ -2,18 +2,93 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "f7a8ba59-fd57-46b6-bca7-870a6f014290", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import pandas as pd\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", "\n", - "# Load the data\n", - "df = pd.read_csv('trajectory_results.csv')" + "# Read the CSV file\n", + "df = pd.read_csv('../format_comparison_results.csv')\n", + "\n", + "# Define colors for each format\n", + "colors = {'VLA': 'blue', 'HDF5': 'green', 'LEROBOT': 'red', 'RLDS': 'purple'}\n", + "\n", + "# Get unique datasets and batch sizes\n", + "datasets = df['Dataset'].unique()\n", + "batch_sizes = df['BatchSize'].unique()\n", + "\n", + "# Set the width of each bar\n", + "bar_width = 1\n", + "\n", + "# Create a figure for each dataset\n", + "for dataset in datasets:\n", + " plt.figure(figsize=(12, 6))\n", + " \n", + " dataset_df = df[df['Dataset'] == dataset]\n", + " \n", + " # Create the grouped bar plot\n", + " for i, format in enumerate(colors.keys()):\n", + " data = dataset_df[dataset_df['Format'] == format]\n", + " plt.bar(data['BatchSize'] + i*bar_width, data['AverageLoadingTime(s)'], \n", + " width=bar_width, color=colors[format], label=format)\n", + "\n", + " # Customize the plot\n", + " plt.xlabel('Batch Size')\n", + " plt.ylabel('Average Loading Time (s)')\n", + " plt.title(f'Comparison of Loading Times for Different Formats - {dataset}')\n", + " plt.legend()\n", + " plt.xticks(batch_sizes + bar_width*1.5, batch_sizes)\n", + "\n", + " # Add a grid for better readability\n", + " plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "\n", + " # Show the plot\n", + " plt.tight_layout()\n", + " plt.show()" ] }, { diff --git a/benchmarks/openx.py b/benchmarks/openx.py index a809ff2..f97aa0d 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -22,8 +22,9 @@ "berkeley_autolab_ur5", "bridge", ] -DEFAULT_DATASET_NAMES = ["bridge"] -CACHE_DIR = "/tmp/fog_x/cache/" +# DEFAULT_DATASET_NAMES = ["bridge"] +# CACHE_DIR = "/tmp/fog_x/cache/" +CACHE_DIR = "/mnt/data/fog_x/cache/" DEFAULT_LOG_FREQUENCY = 20 # suppress tensorflow warnings @@ -117,6 +118,7 @@ def write_result(self, format_name, elapsed_time, index): "Format": format_name, "AverageTrajectorySize(MB)": self.measure_average_trajectory_size(), "LoadingTime(s)": elapsed_time, + "AverageLoadingTime(s)": elapsed_time / (index + 1), "Index": index, "BatchSize": self.batch_size, } @@ -141,11 +143,11 @@ def measure_random_loading_time(self): elapsed_time = time.time() - start_time self.write_result( - f"{self.dataset_type.upper()}-RandomLoad", elapsed_time, batch_num + f"{self.dataset_type.upper()}", elapsed_time, batch_num ) if batch_num % self.log_frequency == 0: - logger.debug( - f"{self.dataset_type.upper()}-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s" + logger.info( + f"{self.dataset_type.upper()} - Loaded {batch_num} random {self.batch_size} batches from {self.dataset_name}, Time: {elapsed_time:.2f} s, Average Time: {elapsed_time / (batch_num + 1):.2f} s" ) return time.time() - start_time @@ -333,13 +335,16 @@ def evaluation(args): new_results.append( { "Dataset": dataset_name, - "Format": f"{handler.dataset_type.upper()}-RandomLoad", + "Format": f"{handler.dataset_type.upper()}", "AverageTrajectorySize(MB)": avg_traj_size, "LoadingTime(s)": random_load_time, + "AverageLoadingTime(s)": random_load_time / (args.num_batches + 1), + "Index": args.num_batches, + "BatchSize": args.batch_size, } ) logger.debug( - f"{handler.dataset_type.upper()}-RandomLoad - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {random_load_time:.2f} s" + f"{handler.dataset_type.upper()} - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {random_load_time:.2f} s" ) # Combine existing and new results @@ -376,11 +381,11 @@ def evaluation(args): parser.add_argument( "--num_batches", type=int, - default=1, + default=1000, help="Number of batches to load for each loader.", ) parser.add_argument( - "--batch_size", type=int, default=8, help="Batch size for loaders." + "--batch_size", type=int, default=16, help="Batch size for loaders." ) args = parser.parse_args() diff --git a/evaluation.sh b/evaluation.sh new file mode 100755 index 0000000..2145a98 --- /dev/null +++ b/evaluation.sh @@ -0,0 +1,21 @@ +# ask for sudo access +sudo echo "Use sudo access for clearning cache" + +rm *.csv + +# Define a list of batch sizes to iterate through +batch_sizes=(1 8 16 32) +# batch_sizes=(1 2) + +num_batches=10 + +# Iterate through each batch size +for batch_size in "${batch_sizes[@]}" +do + echo "Running benchmarks with batch size: $batch_size" + + python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size +done \ No newline at end of file diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py index 1faf24f..14743e7 100644 --- a/fog_x/loader/hdf5.py +++ b/fog_x/loader/hdf5.py @@ -5,6 +5,10 @@ import glob import h5py import asyncio +import random +import multiprocessing as mp +import time +import logging # flatten the data such that all data starts with root level tree (observation and action) def _flatten(data, parent_key='', sep='/'): @@ -27,33 +31,64 @@ def recursively_read_hdf5_group(group): class HDF5Loader(BaseLoader): - def __init__(self, path, batch_size=1): + def __init__(self, path, batch_size=1, buffer_size=100, num_workers=4): super(HDF5Loader, self).__init__(path) - self.index = 0 self.files = glob.glob(self.path, recursive=True) self.batch_size = batch_size - async def _read_hdf5_async(self, data_path): - return await asyncio.to_thread(self._read_hdf5, data_path) - - async def get_batch(self): - tasks = [] - for _ in range(self.batch_size): - if self.index < len(self.files): - file_path = self.files[self.index] - self.index += 1 - tasks.append(self._read_hdf5_async(file_path)) - else: + self.buffer_size = buffer_size + self.buffer = mp.Queue(maxsize=buffer_size) + self.num_workers = num_workers + self.processes = [] + random.shuffle(self.files) + self._start_workers() + + def _worker(self): + while True: + if not self.files: + logging.info("Worker finished") + break + file_path = random.choice(self.files) + data = self._read_hdf5(file_path) + self.buffer.put(data) + + def _start_workers(self): + for _ in range(self.num_workers): + p = mp.Process(target=self._worker) + p.start() + logging.debug(f"Started worker {p.pid}") + self.processes.append(p) + + def get_batch(self): + batch = [] + timeout = 5 + start_time = time.time() + + while len(batch) < self.batch_size: + if time.time() - start_time > timeout: + logging.warning(f"Timeout reached while getting batch. Batch size: {len(batch)}") break - return await asyncio.gather(*tasks) + + try: + item = self.buffer.get(timeout=1) + batch.append(item) + except mp.queues.Empty: + if all(not p.is_alive() for p in self.processes) and self.buffer.empty(): + if len(batch) == 0: + return None + else: + break + + return batch def __next__(self): - if self.index >= len(self.files): - self.index = 0 + batch = self.get_batch() + if batch is None: + random.shuffle(self.files) + self._start_workers() raise StopIteration - return asyncio.run(self.get_batch()) + return batch def _read_hdf5(self, data_path): - with h5py.File(data_path, "r") as f: data_unflattened = recursively_read_hdf5_group(f) @@ -69,6 +104,16 @@ def __iter__(self): def __len__(self): return len(self.files) + def peek(self): + if self.buffer.empty(): + return None + return self.buffer.get() + + def __del__(self): + for p in self.processes: + p.terminate() + p.join() + class HDF5IterableDataset(IterableDataset): def __init__(self, path, batch_size=1): self.hdf5_loader = HDF5Loader(path, batch_size)