def update_stats(self):
"""
Update the model with precise statistics. Users can manually call this method.
"""
if self._disabled:
return
def data_loader():
for num_iter in itertools.count(1):
if num_iter % 100 == 0:
self._logger.info(
"Running precise-BN ... {}/{} iterations.".format(
num_iter, self._num_iter))
# This way we can reuse the same iterator
try:
item = next(self._data_iter)
except StopIteration:
self._data_iter = iter(self._data_loader)
item = next(self._data_iter)
yield item
with EventStorage(): # capture events in a new storage to discard them
self._logger.info(
"Running precise-BN for {} iterations... ".format(
self._num_iter) +
"Note that this could produce different statistics every time."
)
update_bn_stats(self._model, data_loader(), self._num_iter)
def testScalarMismatchedPeriod(self):
with tempfile.TemporaryDirectory(
prefix="cvpods_tests") as dir, EventStorage() as storage:
json_file = os.path.join(dir, "test.json")
writer = JSONWriter(json_file)
for k in range(60):
if k % 17 == 0: # write in a differnt period
storage.put_scalar("key2", k, smoothing_hint=False)
storage.put_scalar("key", k, smoothing_hint=False)
if (k + 1) % 20 == 0:
writer.write()
storage.step()
writer.close()
with open(json_file) as f:
data = [json.loads(line) for line in f]
print([int(k.get("key2", 0)) for k in data])
print([int(k.get("key", 0)) for k in data])
print([int(k.get("iteration", 0)) for k in data])
self.assertTrue([int(k.get("key2", 0))
for k in data] == [17, 0, 34, 0, 51, 0])
self.assertTrue([int(k.get("key", 0))
for k in data] == [0, 19, 0, 39, 0, 59])
self.assertTrue([int(k["iteration"])
for k in data] == [17, 19, 34, 39, 51, 59])
def test_rroi_heads(self):
torch.manual_seed(121)
cfg = RCNNConfig()
cfg.MODEL.ANCHOR_GENERATOR.NAME = "RotatedAnchorGenerator"
# PROPOSAL_GENERATOR: "RRPN"
# ROI_HEADS: "RROIHeads"
# ROI_BOX_HEAD.NAME: "FastRCNNConvFCHead"
def build_box_head(cfg, input_shape):
return FastRCNNConvFCHead(cfg, input_shape)
cfg.build_box_head = build_box_head
cfg.MODEL.RESNETS.DEPTH = 50
cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2
cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1)
cfg.MODEL.RPN.HEAD_NAME = "StandardRPNHead"
cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignRotated"
cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5, 1)
backbone = build_backbone(cfg)
num_images = 2
images_tensor = torch.rand(num_images, 20, 30)
image_sizes = [(10, 10), (20, 30)]
images = ImageList(images_tensor, image_sizes)
num_channels = 1024
features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
image_shape = (15, 15)
gt_boxes0 = torch.tensor([[2, 2, 2, 2, 30], [4, 4, 4, 4, 0]], dtype=torch.float32)
gt_instance0 = Instances(image_shape)
gt_instance0.gt_boxes = RotatedBoxes(gt_boxes0)
gt_instance0.gt_classes = torch.tensor([2, 1])
gt_boxes1 = torch.tensor([[1.5, 5.5, 1, 3, 0], [8.5, 4, 3, 2, -50]], dtype=torch.float32)
gt_instance1 = Instances(image_shape)
gt_instance1.gt_boxes = RotatedBoxes(gt_boxes1)
gt_instance1.gt_classes = torch.tensor([1, 2])
gt_instances = [gt_instance0, gt_instance1]
# currently using DefaultAnchorGenerator in RRPN
proposal_generator = RRPN(cfg, backbone.output_shape())
roi_heads = RROIHeads(cfg, backbone.output_shape())
with EventStorage(): # capture events in a new storage to discard them
proposals, proposal_losses = proposal_generator(images, features, gt_instances)
_, detector_losses = roi_heads(images, features, proposals, gt_instances)
expected_losses = {
"loss_cls": torch.tensor(4.381618499755859),
"loss_box_reg": torch.tensor(0.0011829272843897343),
}
for name in expected_losses.keys():
err_msg = "detector_losses[{}] = {}, expected losses = {}".format(
name, detector_losses[name], expected_losses[name]
)
self.assertTrue(torch.allclose(detector_losses[name], expected_losses[name]), err_msg)
def testScalar(self):
with tempfile.TemporaryDirectory(
prefix="cvpods_tests") as dir, EventStorage() as storage:
json_file = os.path.join(dir, "test.json")
writer = JSONWriter(json_file)
for k in range(60):
storage.put_scalar("key", k, smoothing_hint=False)
if (k + 1) % 20 == 0:
writer.write()
storage.step()
writer.close()
with open(json_file) as f:
data = [json.loads(line) for line in f]
self.assertTrue([int(k["key"]) for k in data] == [19, 39, 59])
def test_roi_heads(self):
torch.manual_seed(121)
cfg = RCNNConfig()
# PROPOSAL_GENERATOR: "RPN"
# ROI_HEADS: "StandardROIHeads"
# ROI_BOX_HEAD: "FastRCNNConvFCHead"
cfg.MODEL.RESNETS.DEPTH = 50
cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2
cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5)
def build_box_head(cfg, input_shape):
return FastRCNNConvFCHead(cfg, input_shape)
cfg.build_box_head = build_box_head
backbone = build_backbone(cfg)
num_images = 2
images_tensor = torch.rand(num_images, 20, 30)
image_sizes = [(10, 10), (20, 30)]
images = ImageList(images_tensor, image_sizes)
num_channels = 1024
features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
image_shape = (15, 15)
gt_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
gt_instance0 = Instances(image_shape)
gt_instance0.gt_boxes = Boxes(gt_boxes0)
gt_instance0.gt_classes = torch.tensor([2, 1])
gt_boxes1 = torch.tensor([[1, 5, 2, 8], [7, 3, 10, 5]], dtype=torch.float32)
gt_instance1 = Instances(image_shape)
gt_instance1.gt_boxes = Boxes(gt_boxes1)
gt_instance1.gt_classes = torch.tensor([1, 2])
gt_instances = [gt_instance0, gt_instance1]
proposal_generator = RPN(cfg, backbone.output_shape())
roi_heads = StandardROIHeads(cfg, backbone.output_shape())
with EventStorage(): # capture events in a new storage to discard them
proposals, proposal_losses = proposal_generator(images, features, gt_instances)
_, detector_losses = roi_heads(images, features, proposals, gt_instances)
expected_losses = {
"loss_cls": torch.tensor(4.4236516953),
"loss_box_reg": torch.tensor(0.0091214813),
}
for name in expected_losses.keys():
self.assertTrue(torch.allclose(detector_losses[name], expected_losses[name]))
def test_fast_rcnn_empty_batch(self, device="cpu"):
cfg = RCNNConfig()
cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5)
box2box_transform = Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS)
logits = torch.randn(0, 100, requires_grad=True, device=device)
deltas = torch.randn(0, 4, requires_grad=True, device=device)
smooth_l1_beta = cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA
outputs = FastRCNNOutputs(
box2box_transform, logits, deltas, [], smooth_l1_beta
)
with EventStorage(): # capture events in a new storage to discard them
losses = outputs.losses()
for value in losses.values():
self.assertTrue(torch.allclose(value, torch.zeros_like(value)))
def test_fast_rcnn_rotated(self):
torch.manual_seed(132)
cfg = RCNNConfig()
cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5, 1)
box2box_transform = Box2BoxTransformRotated(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS)
box_head_output_size = 8
num_classes = 5
cls_agnostic_bbox_reg = False
box_predictor = FastRCNNOutputLayers(
box_head_output_size, num_classes, cls_agnostic_bbox_reg, box_dim=5
)
feature_pooled = torch.rand(2, box_head_output_size)
pred_class_logits, pred_proposal_deltas = box_predictor(feature_pooled)
image_shape = (10, 10)
proposal_boxes = torch.tensor(
[[2, 1.95, 2.4, 1.7, 0], [4.65, 5.25, 4.7, 5.5, 0]], dtype=torch.float32
)
gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32)
result = Instances(image_shape)
result.proposal_boxes = RotatedBoxes(proposal_boxes)
result.gt_boxes = RotatedBoxes(gt_boxes)
result.gt_classes = torch.tensor([1, 2])
proposals = []
proposals.append(result)
smooth_l1_beta = cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA
outputs = RotatedFastRCNNOutputs(
box2box_transform, pred_class_logits, pred_proposal_deltas, proposals, smooth_l1_beta
)
with EventStorage(): # capture events in a new storage to discard them
losses = outputs.losses()
# Note: the expected losses are slightly different even if
# the boxes are essentially the same as in the FastRCNNOutput test, because
# bbox_pred in FastRCNNOutputLayers have different Linear layers/initialization
# between the two cases.
expected_losses = {
"loss_cls": torch.tensor(1.7920907736),
"loss_box_reg": torch.tensor(4.0410838127),
}
for name in expected_losses.keys():
assert torch.allclose(losses[name], expected_losses[name])
def test_fast_rcnn(self):
torch.manual_seed(132)
cfg = RCNNConfig()
cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5)
box2box_transform = Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS)
box_head_output_size = 8
num_classes = 5
cls_agnostic_bbox_reg = False
box_predictor = FastRCNNOutputLayers(
box_head_output_size, num_classes, cls_agnostic_bbox_reg, box_dim=4
)
feature_pooled = torch.rand(2, box_head_output_size)
pred_class_logits, pred_proposal_deltas = box_predictor(feature_pooled)
image_shape = (10, 10)
proposal_boxes = torch.tensor([[0.8, 1.1, 3.2, 2.8], [2.3, 2.5, 7, 8]], dtype=torch.float32)
gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
result = Instances(image_shape)
result.proposal_boxes = Boxes(proposal_boxes)
result.gt_boxes = Boxes(gt_boxes)
result.gt_classes = torch.tensor([1, 2])
proposals = []
proposals.append(result)
smooth_l1_beta = cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA
outputs = FastRCNNOutputs(
box2box_transform, pred_class_logits, pred_proposal_deltas, proposals, smooth_l1_beta
)
with EventStorage(): # capture events in a new storage to discard them
losses = outputs.losses()
expected_losses = {
"loss_cls": torch.tensor(1.7951188087),
"loss_box_reg": torch.tensor(4.0357131958),
}
for name in expected_losses.keys():
assert torch.allclose(losses[name], expected_losses[name])
def train(self, start_iter: int, max_iter: int, max_epoch):
"""
Args:
start_iter, max_iter (int): See docs above
"""
logger = logging.getLogger(__name__)
logger.info("Starting training from iteration {}".format(start_iter))
self.iter = self.start_iter = start_iter
self.max_iter = max_iter
self.max_epoch = max_epoch
with EventStorage(start_iter) as self.storage:
try:
self.before_train()
for self.iter in range(start_iter, self.max_iter):
self.before_step()
self.run_step()
self.after_step()
except Exception:
logger.exception("Exception during training:")
finally:
self.after_train()
def do_train(cfg, model, resume=False):
model.train()
optimizer = build_optimizer(cfg, model)
scheduler = build_lr_scheduler(cfg, optimizer)
checkpointer = DefaultCheckpointer(
model, cfg.OUTPUT_DIR, optimizer=optimizer, scheduler=scheduler
)
start_iter = (
checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1
)
max_iter = cfg.SOLVER.MAX_ITER
periodic_checkpointer = PeriodicCheckpointer(
checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_iter=max_iter
)
writers = (
[
CommonMetricPrinter(max_iter),
JSONWriter(os.path.join(cfg.OUTPUT_DIR, "metrics.json")),
TensorboardXWriter(cfg.OUTPUT_DIR),
]
if comm.is_main_process()
else []
)
# compared to "train_net.py", we do not support accurate timing and
# precise BN here, because they are not trivial to implement
data_loader = build_train_loader(cfg)
logger.info("Starting training from iteration {}".format(start_iter))
with EventStorage(start_iter) as storage:
for data, iteration in zip(data_loader, range(start_iter, max_iter)):
iteration = iteration + 1
storage.step()
loss_dict = model(data)
losses = sum(loss for loss in loss_dict.values())
assert torch.isfinite(losses).all(), loss_dict
loss_dict_reduced = {k: v.item() for k, v in comm.reduce_dict(loss_dict).items()}
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
if comm.is_main_process():
storage.put_scalars(total_loss=losses_reduced, **loss_dict_reduced)
optimizer.zero_grad()
losses.backward()
optimizer.step()
storage.put_scalar("lr", optimizer.param_groups[0]["lr"], smoothing_hint=False)
scheduler.step()
if (
cfg.TEST.EVAL_PERIOD > 0
and iteration % cfg.TEST.EVAL_PERIOD == 0
and iteration != max_iter
):
do_test(cfg, model)
# Compared to "train_net.py", the test results are not dumped to EventStorage
comm.synchronize()
if iteration - start_iter > 5 and (iteration % 20 == 0 or iteration == max_iter):
for writer in writers:
writer.write()
periodic_checkpointer.step(iteration)
def test_rpn(self):
torch.manual_seed(121)
cfg = RCNNConfig()
# PROPOSAL_GENERATOR: "RPN"
# ANCHOR_GENERATOR: "DefaultAnchorGenerator"
cfg.MODEL.RESNETS.DEPTH = 50
cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1)
backbone = build_backbone(cfg)
proposal_generator = RPN(cfg, backbone.output_shape())
num_images = 2
images_tensor = torch.rand(num_images, 20, 30)
image_sizes = [(10, 10), (20, 30)]
images = ImageList(images_tensor, image_sizes)
image_shape = (15, 15)
num_channels = 1024
features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
gt_instances = Instances(image_shape)
gt_instances.gt_boxes = Boxes(gt_boxes)
with EventStorage(): # capture events in a new storage to discard them
proposals, proposal_losses = proposal_generator(
images, features, [gt_instances[0], gt_instances[1]]
)
expected_losses = {
"loss_rpn_cls": torch.tensor(0.0804563984),
"loss_rpn_loc": torch.tensor(0.0990132466),
}
for name in expected_losses.keys():
err_msg = "proposal_losses[{}] = {}, expected losses = {}".format(
name, proposal_losses[name], expected_losses[name]
)
self.assertTrue(torch.allclose(proposal_losses[name], expected_losses[name]), err_msg)
expected_proposal_boxes = [
Boxes(torch.tensor([[0, 0, 10, 10], [7.3365392685, 0, 10, 10]])),
Boxes(
torch.tensor(
[
[0, 0, 30, 20],
[0, 0, 16.7862777710, 13.1362524033],
[0, 0, 30, 13.3173446655],
[0, 0, 10.8602609634, 20],
[7.7165775299, 0, 27.3875980377, 20],
]
)
),
]
expected_objectness_logits = [
torch.tensor([0.1225359365, -0.0133192837]),
torch.tensor([0.1415634006, 0.0989848152, 0.0565387346, -0.0072308783, -0.0428492837]),
]
for proposal, expected_proposal_box, im_size, expected_objectness_logit in zip(
proposals, expected_proposal_boxes, image_sizes, expected_objectness_logits
):
self.assertEqual(len(proposal), len(expected_proposal_box))
self.assertEqual(proposal.image_size, im_size)
self.assertTrue(
torch.allclose(proposal.proposal_boxes.tensor, expected_proposal_box.tensor)
)
self.assertTrue(torch.allclose(proposal.objectness_logits, expected_objectness_logit))
def test_rrpn(self):
torch.manual_seed(121)
cfg = RCNNConfig()
# PROPOSAL_GENERATOR: "RRPN"
# ANCHOR_GENERATOR: "RotatedAnchorGenerator"
cfg.MODEL.RESNETS.DEPTH = 50
cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]]
cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1]]
cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [[0, 60]]
cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1)
# cfg.MODEL.RPN.HEAD_NAME = "StandardRPNHead"
backbone = build_backbone(cfg)
# currently using DefaultAnchorGenerator in RRPN
proposal_generator = RRPN(cfg, backbone.output_shape())
num_images = 2
images_tensor = torch.rand(num_images, 20, 30)
image_sizes = [(10, 10), (20, 30)]
images = ImageList(images_tensor, image_sizes)
image_shape = (15, 15)
num_channels = 1024
features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32)
gt_instances = Instances(image_shape)
gt_instances.gt_boxes = RotatedBoxes(gt_boxes)
with EventStorage(): # capture events in a new storage to discard them
proposals, proposal_losses = proposal_generator(
images, features, [gt_instances[0], gt_instances[1]]
)
expected_losses = {
"loss_rpn_cls": torch.tensor(0.043263837695121765),
"loss_rpn_loc": torch.tensor(0.14432406425476074),
}
for name in expected_losses.keys():
err_msg = "proposal_losses[{}] = {}, expected losses = {}".format(
name, proposal_losses[name], expected_losses[name]
)
self.assertTrue(torch.allclose(proposal_losses[name], expected_losses[name]), err_msg)
expected_proposal_boxes = [
RotatedBoxes(
torch.tensor(
[
[0.60189795, 1.24095452, 61.98131943, 18.03621292, -4.07244873],
[15.64940453, 1.69624567, 59.59749603, 16.34339333, 2.62692475],
[-3.02982378, -2.69752932, 67.90952301, 59.62455750, 59.97010040],
[16.71863365, 1.98309708, 35.61507797, 32.81484985, 62.92267227],
[0.49432933, -7.92979717, 67.77606201, 62.93098450, -1.85656738],
[8.00880814, 1.36017394, 121.81007385, 32.74150467, 50.44297409],
[16.44299889, -4.82221127, 63.39775848, 61.22503662, 54.12270737],
[5.00000000, 5.00000000, 10.00000000, 10.00000000, -0.76943970],
[17.64130402, -0.98095351, 61.40377808, 16.28918839, 55.53118134],
[0.13016054, 4.60568953, 35.80157471, 32.30180359, 62.52872086],
[-4.26460743, 0.39604485, 124.30079651, 31.84611320, -1.58203125],
[7.52815342, -0.91636634, 62.39784622, 15.45565224, 60.79549789],
]
)
),
RotatedBoxes(
torch.tensor(
[
[0.07734215, 0.81635046, 65.33510590, 17.34688377, -1.51821899],
[-3.41833067, -3.11320257, 64.17595673, 60.55617905, 58.27033234],
[20.67383385, -6.16561556, 63.60531998, 62.52315903, 54.85546494],
[15.00000000, 10.00000000, 30.00000000, 20.00000000, -0.18218994],
[9.22646523, -6.84775209, 62.09895706, 65.46472931, -2.74307251],
[15.00000000, 4.93451595, 30.00000000, 9.86903191, -0.60272217],
[8.88342094, 2.65560246, 120.95362854, 32.45022202, 55.75970078],
[16.39088631, 2.33887148, 34.78761292, 35.61492920, 60.81977463],
[9.78298569, 10.00000000, 19.56597137, 20.00000000, -0.86660767],
[1.28576660, 5.49873352, 34.93610382, 33.22600174, 60.51599884],
[17.58912468, -1.63270092, 62.96052551, 16.45713997, 52.91245270],
[5.64749718, -1.90428460, 62.37649155, 16.19474792, 61.09543991],
[0.82255805, 2.34931135, 118.83985901, 32.83671188, 56.50753784],
[-5.33874989, 1.64404404, 125.28501892, 33.35424042, -2.80731201],
]
)
),
]
expected_objectness_logits = [
torch.tensor(
[
0.10111768,
0.09112845,
0.08466332,
0.07589971,
0.06650183,
0.06350251,
0.04299347,
0.01864817,
0.00986163,
0.00078543,
-0.04573630,
-0.04799230,
]
),
torch.tensor(
[
0.11373727,
0.09377633,
0.05281663,
0.05143715,
0.04040275,
0.03250912,
0.01307789,
0.01177734,
0.00038105,
-0.00540255,
-0.01194804,
-0.01461012,
-0.03061717,
-0.03599222,
]
),
]
torch.set_printoptions(precision=8, sci_mode=False)
for proposal, expected_proposal_box, im_size, expected_objectness_logit in zip(
proposals, expected_proposal_boxes, image_sizes, expected_objectness_logits
):
self.assertEqual(len(proposal), len(expected_proposal_box))
self.assertEqual(proposal.image_size, im_size)
# It seems that there's some randomness in the result across different machines:
# This test can be run on a local machine for 100 times with exactly the same result,
# However, a different machine might produce slightly different results,
# thus the atol here.
err_msg = "computed proposal boxes = {}, expected {}".format(
proposal.proposal_boxes.tensor, expected_proposal_box.tensor
)
self.assertTrue(
torch.allclose(
proposal.proposal_boxes.tensor, expected_proposal_box.tensor, atol=1e-5
),
err_msg,
)
err_msg = "computed objectness logits = {}, expected {}".format(
proposal.objectness_logits, expected_objectness_logit
)
self.assertTrue(
torch.allclose(proposal.objectness_logits, expected_objectness_logit, atol=1e-5),
err_msg,
)
if xylabels[1]: plt.ylabel(xylabels[1])
ax.set_title(prefix)
if not label:
label = range(len(tensor_list))
ax.hist(array_list, bincount, histtype='bar', label=label)
ax.legend()
if __name__ == "__main__":
_dict = dict(VISDOM=dict(
HOST="192.168.1.1",
PORT="8082",
TURN_ON=True,
ENV_PREFIX='test',
), )
import torch
from easydict import EasyDict
cfg = EasyDict(_dict)
from cvpods.utils import (CommonMetricPrinter, JSONWriter, PathManager,
TensorboardXWriter, collect_env_info, comm,
seed_all_rng, setup_logger, VisdomWriter)
from cvpods.utils import EventStorage
with EventStorage() as storage:
hist = PltHistogram()
hist.mode = 'data'
visdom_writer = VisdomWriter(cfg.VISDOM.HOST, cfg.VISDOM.PORT, 1, [],
cfg.VISDOM.ENV_PREFIX)
storage.put_image('histo', hist(torch.rand((1000, ))))
visdom_writer.write()
