Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
@@ -0,0 +1,167 @@
|
||||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import argparse
|
||||
import json
|
||||
from collections import defaultdict
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import seaborn as sns
|
||||
|
||||
|
||||
def cal_train_time(log_dicts, args):
|
||||
for i, log_dict in enumerate(log_dicts):
|
||||
print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}')
|
||||
all_times = []
|
||||
for epoch in log_dict.keys():
|
||||
if args.include_outliers:
|
||||
all_times.append(log_dict[epoch]['time'])
|
||||
else:
|
||||
all_times.append(log_dict[epoch]['time'][1:])
|
||||
all_times = np.array(all_times)
|
||||
epoch_ave_time = all_times.mean(-1)
|
||||
slowest_epoch = epoch_ave_time.argmax()
|
||||
fastest_epoch = epoch_ave_time.argmin()
|
||||
std_over_epoch = epoch_ave_time.std()
|
||||
print(f'slowest epoch {slowest_epoch + 1}, '
|
||||
f'average time is {epoch_ave_time[slowest_epoch]:.4f}')
|
||||
print(f'fastest epoch {fastest_epoch + 1}, '
|
||||
f'average time is {epoch_ave_time[fastest_epoch]:.4f}')
|
||||
print(f'time std over epochs is {std_over_epoch:.4f}')
|
||||
print(f'average iter time: {np.mean(all_times):.4f} s/iter')
|
||||
print()
|
||||
|
||||
|
||||
def plot_curve(log_dicts, args):
|
||||
if args.backend is not None:
|
||||
plt.switch_backend(args.backend)
|
||||
sns.set_style(args.style)
|
||||
# if legend is None, use {filename}_{key} as legend
|
||||
legend = args.legend
|
||||
if legend is None:
|
||||
legend = []
|
||||
for json_log in args.json_logs:
|
||||
for metric in args.keys:
|
||||
legend.append(f'{json_log}_{metric}')
|
||||
assert len(legend) == (len(args.json_logs) * len(args.keys))
|
||||
metrics = args.keys
|
||||
|
||||
num_metrics = len(metrics)
|
||||
for i, log_dict in enumerate(log_dicts):
|
||||
epochs = list(log_dict.keys())
|
||||
for j, metric in enumerate(metrics):
|
||||
print(f'plot curve of {args.json_logs[i]}, metric is {metric}')
|
||||
if metric not in log_dict[epochs[0]]:
|
||||
raise KeyError(
|
||||
f'{args.json_logs[i]} does not contain metric {metric}')
|
||||
xs = []
|
||||
ys = []
|
||||
num_iters_per_epoch = log_dict[epochs[0]]['iter'][-1]
|
||||
for epoch in epochs:
|
||||
iters = log_dict[epoch]['iter']
|
||||
if log_dict[epoch]['mode'][-1] == 'val':
|
||||
iters = iters[:-1]
|
||||
xs.append(np.array(iters) + (epoch - 1) * num_iters_per_epoch)
|
||||
ys.append(np.array(log_dict[epoch][metric][:len(iters)]))
|
||||
xs = np.concatenate(xs)
|
||||
ys = np.concatenate(ys)
|
||||
plt.xlabel('iter')
|
||||
plt.plot(xs, ys, label=legend[i * num_metrics + j], linewidth=0.5)
|
||||
plt.legend()
|
||||
if args.title is not None:
|
||||
plt.title(args.title)
|
||||
if args.out is None:
|
||||
plt.show()
|
||||
else:
|
||||
print(f'save curve to: {args.out}')
|
||||
plt.savefig(args.out)
|
||||
plt.cla()
|
||||
|
||||
|
||||
def add_plot_parser(subparsers):
|
||||
parser_plt = subparsers.add_parser(
|
||||
'plot_curve', help='parser for plotting curves')
|
||||
parser_plt.add_argument(
|
||||
'json_logs',
|
||||
type=str,
|
||||
nargs='+',
|
||||
help='path of train log in json format')
|
||||
parser_plt.add_argument(
|
||||
'--keys',
|
||||
type=str,
|
||||
nargs='+',
|
||||
default=['top1_acc'],
|
||||
help='the metric that you want to plot')
|
||||
parser_plt.add_argument('--title', type=str, help='title of figure')
|
||||
parser_plt.add_argument(
|
||||
'--legend',
|
||||
type=str,
|
||||
nargs='+',
|
||||
default=None,
|
||||
help='legend of each plot')
|
||||
parser_plt.add_argument(
|
||||
'--backend', type=str, default=None, help='backend of plt')
|
||||
parser_plt.add_argument(
|
||||
'--style', type=str, default='dark', help='style of plt')
|
||||
parser_plt.add_argument('--out', type=str, default=None)
|
||||
|
||||
|
||||
def add_time_parser(subparsers):
|
||||
parser_time = subparsers.add_parser(
|
||||
'cal_train_time',
|
||||
help='parser for computing the average time per training iteration')
|
||||
parser_time.add_argument(
|
||||
'json_logs',
|
||||
type=str,
|
||||
nargs='+',
|
||||
help='path of train log in json format')
|
||||
parser_time.add_argument(
|
||||
'--include-outliers',
|
||||
action='store_true',
|
||||
help='include the first value of every epoch when computing '
|
||||
'the average time')
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description='Analyze Json Log')
|
||||
# currently only support plot curve and calculate average train time
|
||||
subparsers = parser.add_subparsers(dest='task', help='task parser')
|
||||
add_plot_parser(subparsers)
|
||||
add_time_parser(subparsers)
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def load_json_logs(json_logs):
|
||||
# load and convert json_logs to log_dict, key is epoch, value is a sub dict
|
||||
# keys of sub dict is different metrics, e.g. memory, top1_acc
|
||||
# value of sub dict is a list of corresponding values of all iterations
|
||||
log_dicts = [dict() for _ in json_logs]
|
||||
for json_log, log_dict in zip(json_logs, log_dicts):
|
||||
with open(json_log, 'r') as log_file:
|
||||
for line in log_file:
|
||||
log = json.loads(line.strip())
|
||||
# skip lines without `epoch` field
|
||||
if 'epoch' not in log:
|
||||
continue
|
||||
epoch = log.pop('epoch')
|
||||
if epoch not in log_dict:
|
||||
log_dict[epoch] = defaultdict(list)
|
||||
for k, v in log.items():
|
||||
log_dict[epoch][k].append(v)
|
||||
return log_dicts
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
json_logs = args.json_logs
|
||||
for json_log in json_logs:
|
||||
assert json_log.endswith('.json')
|
||||
|
||||
log_dicts = load_json_logs(json_logs)
|
||||
|
||||
eval(args.task)(log_dicts, args)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user