def format_kv_recursive_flat(handler, kvs):
if kvs is None or kvs == '':
return '<pre>N/A</pre>'
if not isinstance(kvs, dict):
kvs = json.loads(kvs)
return '<pre>' + kvformat(
{k: v
for k, v in dict_deep_kv(kvs) if not '__' in k}) + '</pre>'
def initialize(self):
self.client.initialize()
logger.info('Client started.')
logger.info(' Name: {}'.format(self.name))
logger.info(' Identifier: {}'.format(self.identifier))
logger.info(' Conn info: {}'.format(self.conn_info))
logger.info(' Server name: {}'.format(self.get_server_name()))
logger.info(' Server identifier: {}'.format(
self.get_server_identifier()))
logger.info(' Server signaature: {}'.format(self.get_signature()))
configs = self.get_configs()
if configs is not None:
logger.info(' Server configs:\n' + kvformat(configs, indent=2))
def validate_epoch(epoch,
model,
val_dataloader,
meters,
meter_prefix='validation'):
end = time.time()
visualized = 0
vis = HTMLTableVisualizer(args.vis_dir, 'NSCL Execution Visualization')
vis.begin_html()
try:
with tqdm_pbar(total=len(val_dataloader)) as pbar:
for feed_dict in val_dataloader:
if args.use_gpu:
if not args.gpu_parallel:
feed_dict = async_copy_to(feed_dict, 0)
data_time = time.time() - end
end = time.time()
output_dict = model(feed_dict)
monitors = {
meter_prefix + '/' + k: v
for k, v in as_float(output_dict['monitors']).items()
}
step_time = time.time() - end
end = time.time()
n = feed_dict['image'].size(0)
meters.update(monitors, n=n)
meters.update({'time/data': data_time, 'time/step': step_time})
feed_dict = GView(as_detached(as_cpu(feed_dict)))
output_dict = GView(as_detached(as_cpu(output_dict)))
for i in range(n):
with vis.table(
'Visualize #{} Metainfo'.format(visualized), [
HTMLTableColumnDesc('id', 'QID', 'text',
{'width': '50px'}),
HTMLTableColumnDesc('image', 'Image', 'figure',
{'width': '400px'}),
HTMLTableColumnDesc('qa', 'QA', 'text',
{'width': '200px'}),
HTMLTableColumnDesc('p', 'Program', 'code',
{'width': '200px'})
]):
image_filename = osp.join(args.data_image_root,
feed_dict.image_filename[i])
image = Image.open(image_filename)
fig, ax = vis_bboxes(image,
feed_dict.objects_raw[i],
'object',
add_text=False)
_ = ax.set_title('object bounding box annotations')
QA_string = """
<p><b>Q</b>: {}</p>
<p><b>A</b>: {}</p>
""".format(feed_dict.question_raw[i],
feed_dict.answer[i])
P_string = '\n'.join(
[repr(x) for x in feed_dict.program_seq[i]])
vis.row(id=i, image=fig, qa=QA_string, p=P_string)
plt.close()
with vis.table(
'Visualize #{} Metainfo'.format(visualized), [
HTMLTableColumnDesc('id', 'QID', 'text',
{'width': '50px'}),
HTMLTableColumnDesc('image', 'Image', 'figure',
{'width': '400px'}),
HTMLTableColumnDesc('mask', 'Mask', 'figure',
{'width': '700px'})
]):
image_filename = osp.join(args.data_image_root,
feed_dict.image_filename[i])
image = Image.open(image_filename)
fig, ax = vis_bboxes(image,
feed_dict.objects_raw[i],
'object',
add_text=False)
_ = ax.set_title('object bounding box annotations')
if not args.show_mask:
montage = fig
else:
num_slots = output_dict['monet/m'].shape[1]
monet_fig = [
[
tensor2im(output_dict['monet/m'][i, k])
for k in range(num_slots)
],
[
tensor2im(output_dict['monet/x'][i, k])
for k in range(num_slots)
],
[
tensor2im(output_dict['monet/xm'][i, k])
for k in range(num_slots)
],
[tensor2im(output_dict['monet/x_input'][i])] +
[
tensor2im(output_dict['monet/x_tilde'][i])
for k in range(num_slots - 1)
]
]
montage = montage_fig(monet_fig)
vis.row(id=i, image=fig, mask=montage)
plt.close()
with vis.table('Visualize #{} Trace'.format(visualized), [
HTMLTableColumnDesc('id', 'Step', 'text',
{'width': '50px'}),
HTMLTableColumnDesc('image', 'Image', 'figure',
{'width': '600px'}),
HTMLTableColumnDesc('p', 'operation', 'text',
{'width': '200px'}),
HTMLTableColumnDesc('r', 'result', 'code',
{'width': '200px'})
]):
# TODO(Jiayuan Mao @ 11/20): support output_dict.programs.
for j, (prog, buf) in enumerate(
zip(feed_dict.program_seq[i],
output_dict.buffers[i])):
if j != len(feed_dict.program_seq[i]) - 1 and (
buf > 0
).long().sum().item() > 0 and buf.size(
0) == feed_dict.objects_raw[i].shape[0]:
this_objects = feed_dict.objects_raw[i][
torch.nonzero(buf > 0)[:, 0].numpy()]
fig, ax = vis_bboxes(image,
this_objects,
'object',
add_text=False)
else:
fig, ax = vis_bboxes(image, [],
'object',
add_text=False)
vis.row(id=j, image=fig, p=repr(prog), r=repr(buf))
plt.close()
visualized += 1
if visualized > args.nr_visualize:
raise StopIteration()
pbar.set_description(
meters.format_simple(
'Epoch {} (validation)'.format(epoch), {
k: v
for k, v in meters.val.items()
if k.startswith('validation') and k.count('/') <= 1
},
compressed=True))
pbar.update()
end = time.time()
except StopIteration:
pass
from jacinle.utils.meta import dict_deep_kv
from jacinle.utils.printing import kvformat
with vis.table('Info', [
HTMLTableColumnDesc('name', 'Name', 'code', {}),
HTMLTableColumnDesc('info', 'KV', 'code', {})
]):
vis.row(name='args', info=kvformat(args.__dict__, max_key_len=32))
vis.row(name='configs',
info=kvformat(dict(dict_deep_kv(configs)), max_key_len=32))
vis.end_html()
logger.info(
'Happy Holiday! You can find your result at "http://monday.csail.mit.edu/xiuming'
+ osp.realpath(args.vis_dir) + '".')
def dumps_kv(value):
return kvformat(value)
def format(self, sep=': ', end='\n'):
return kvformat(dict(dict_deep_kv(self)), sep=sep, end=end)
def format_kv(handler, kvs):
if kvs is None or kvs == '':
return '<pre>N/A</pre>'
if not isinstance(kvs, dict):
kvs = json.loads(kvs)
return '<pre>' + kvformat(kvs) + '</pre>'
def format_args(args):
return kvformat(args.__dict__)