def test_omniglot():
viz = Visdom(port=8000, env='main')
splits = load_omniglot()
n_way, n_support, n_query = 3, 5, 7
# task = omniglot_task(splits['train'], n_way=3, n_support=5, n_query=7)
batch_size = 2
for i, batch in enumerate(
taskbatch(omniglot_task,
batch_size=batch_size,
n_task=batch_size,
split_dict=splits['val'],
n_way=n_way,
n_support=n_support,
n_query=n_query)):
for i_task in range(batch_size):
x_train = batch['x_train'][i_task]
x_test = batch['x_test'][i_task]
y_train = batch['y_train'][i_task]
y_test = batch['y_test'][i_task]
viz.images(tensor=np.transpose(x_train, (0, 3, 1, 2)),
nrow=n_support)
viz.text(f'y_train: {y_train}')
viz.images(tensor=np.transpose(x_test, (0, 3, 1, 2)),
nrow=n_query)
viz.text(f'y_test: {y_test}')
viz.save(viz.get_env_list())
def test_circle():
viz = Visdom(port=8000, env='circle')
for i in range(10):
task = circle_task(n_way=3, n_support=5, n_query=7)
viz.scatter(X=task['x_train'],
Y=np.argmax(task['y_train'], axis=1) + 1,
opts=dict(title=f'task {i}: train'))
viz.scatter(X=task['x_test'],
Y=np.argmax(task['y_test'], axis=1) + 1,
opts=dict(title=f'task {i}: test'))
viz.save(viz.get_env_list())
for i in range(1, args.n_inner_step + 1):
p = inner_get_params(state)
g = grad_loss(p, x1, y1)
state = inner_opt_update(i, g, state)
p = inner_get_params(state)
predictions = f(p, xrange_inputs)
plotter.line(win_name='inference',
Y=predictions,
X=xrange_inputs,
name=f'{i}-step predictions',
update='append')
p_lin = inner_get_params(state_lin)
g_lin = grad_loss_lin(p_lin, x1, y1)
state_lin = inner_opt_update(i, g_lin, state_lin)
p_lin = inner_get_params(state_lin)
predictions_lin = f_lin(p_lin, xrange_inputs)
plotter.line(win_name='inference_lin',
Y=predictions_lin,
X=xrange_inputs,
name=f'{i}-step predictions',
update='append')
# serialize
np_dir = os.path.join(args.log_dir, 'np')
os.makedirs(np_dir, exist_ok=True)
onp.save(file=os.path.join(np_dir, f'log'), arr=log)
# serialize visdom envs
viz.save(viz.get_env_list())
class VisdomPlotter:
"""
A Visdom based plotter, to plot aggregated metrics.
How to use:
------------
(1) Start the server with:
python -m visdom.server
(2) Then, in your browser, you can go to:
http://localhost:8097
"""
def __init__(self, experiment_env, server='http://localhost', port=8097):
self.server = server
self.port = port
self.viz = Visdom(
server=server,
port=port) # Connect to Visdom server on server / port
if not self.start_visdom_server():
raise ValueError('Failed to launch Visdom server at %r:%r' %
(server, port))
if experiment_env in self.viz.get_env_list():
self.viz.delete_env(
experiment_env) # Clear previous runs with same id
self.experiment_env = experiment_env
self.plots = {}
def start_visdom_server(self):
is_visdom_server_connected = self.viz.check_connection(
timeout_seconds=1) # Ping if it's already on..
if not is_visdom_server_connected:
interpreter_path = sys.executable
os.system(interpreter_path + ' -m visdom.server &')
is_visdom_server_connected = self.viz.check_connection(
timeout_seconds=35)
return is_visdom_server_connected
def plot_single_metric(self, metric, line_id, title, epoch, value):
if metric not in self.plots:
self.plots[metric] = self.viz.line(X=np.array([epoch, epoch]),
Y=np.array([value, value]),
env=self.experiment_env,
opts=dict(legend=[line_id],
title=title,
xlabel='Epochs',
ylabel=metric))
else:
self.viz.line(X=np.array([epoch]),
Y=np.array([value]),
env=self.experiment_env,
win=self.plots[metric],
name=line_id,
update='append')
def plot_confusion_matrix(self, metric, matrix, label_classes):
if metric not in self.plots:
self.plots[metric] = self.viz.heatmap(
X=matrix,
env=self.experiment_env,
opts=dict(columnnames=label_classes, rownames=label_classes))
else:
self.viz.heatmap(X=matrix,
env=self.experiment_env,
win=self.plots[metric],
opts=dict(columnnames=label_classes,
rownames=label_classes))
def plot_images(self, images_bchw):
self.viz.images(images_bchw)
def plot_aggregated_metrics(self, metrics, epoch):
for metric in metrics.metrics:
title = metrics.metric_to_title[metric]
value = metrics[epoch][metric]
if metric == 'confusion_matrix':
label_classes = metrics.label_classes
self.plot_confusion_matrix(metric, value, label_classes)
else:
if hasattr(value, 'shape') and value.size > 1:
for idx, dim_val in enumerate(value):
line_id = metrics.label_classes[idx]
self.plot_single_metric(metric, line_id, title, epoch,
dim_val)
else:
line_id = metrics.data_type
self.plot_single_metric(metric, line_id, title, epoch,
value)
assert len(value['content']['data']) == 1, "content has multiple items, how to handle?"
x = value['content']['data'][0]['x']
y = value['content']['data'][0]['y']
dfs[value['title']] = pd.DataFrame({'x': x, 'y': y})
return dfs
def dataframes2csv(dfs):
for k, v in dfs.items():
v.to_csv(k + ".csv", index=False)
if __name__ == "__main__":
# build the visdom object
viz = Visdom(server=args.visdom_url, port=args.visdom_port, use_incoming_socket=False)
# handle the --ls case
if args.ls is not None:
env_list = [e for e in viz.get_env_list() if args.ls in e]
for env in env_list:
print(env)
exit(0)
# grab the dataframes
dfs = vis2dataframe(viz, args.env_base_name, args.feature_name)
# write the dataframes to csv
dataframes2csv(dfs)
