def assertNotEqual(self, x, y, prec=None, message=''):
if prec is None:
prec = self.precision
x, y = self.unwrapVariables(x, y)
if torch.is_tensor(x) and torch.is_tensor(y):
if x.size() != y.size():
super(TestCase, self).assertNotEqual(x.size(), y.size())
self.assertGreater(x.numel(), 0)
y = y.type_as(x)
y = y.cuda(device=x.get_device()) if x.is_cuda else y.cpu()
nan_mask = x != x
if torch.equal(nan_mask, y != y):
diff = x - y
if diff.is_signed():
diff = diff.abs()
diff[nan_mask] = 0
max_err = diff.max()
self.assertGreaterEqual(max_err, prec, message)
elif type(x) == str and type(y) == str:
super(TestCase, self).assertNotEqual(x, y)
elif is_iterable(x) and is_iterable(y):
super(TestCase, self).assertNotEqual(x, y)
else:
try:
self.assertGreaterEqual(abs(x - y), prec, message)
return
except (TypeError, AssertionError):
pass
super(TestCase, self).assertNotEqual(x, y, message)
def add(self, output, target):
if torch.is_tensor(output):
output = output.cpu().squeeze().numpy()
if torch.is_tensor(target):
target = target.cpu().squeeze().numpy()
elif isinstance(target, numbers.Number):
target = np.asarray([target])
if np.ndim(output) == 1:
output = output[np.newaxis]
else:
assert np.ndim(output) == 2, \
'wrong output size (1D or 2D expected)'
assert np.ndim(target) == 1, \
'target and output do not match'
assert target.shape[0] == output.shape[0], \
'target and output do not match'
topk = self.topk
maxk = int(topk[-1]) # seems like Python3 wants int and not np.int64
no = output.shape[0]
pred = torch.from_numpy(output).topk(maxk, 1, True, True)[1].numpy()
correct = pred == target[:, np.newaxis].repeat(pred.shape[1], 1)
for k in topk:
self.sum[k] += no - correct[:, 0:k].sum()
self.n += no
def __init__(
self,
model: Model,
best_f: Union[float, Tensor],
sampler: Optional[MCSampler] = None,
objective: Optional[MCAcquisitionObjective] = None,
tau: float = 1e-3,
) -> None:
r"""q-Probability of Improvement.
Args:
model: A fitted model.
best_f: The best (feasible) function value observed so far (assumed
noiseless).
sampler: The sampler used to draw base samples. Defaults to
`SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True)`
objective: The MCAcquisitionObjective under which the samples are
evaluated. Defaults to `IdentityMCObjective()`.
tau: The temperature parameter used in the sigmoid approximation
of the step function. Smaller values yield more accurate
approximations of the function, but result in gradients
estimates with higher variance.
"""
super().__init__(model=model, sampler=sampler, objective=objective)
if not torch.is_tensor(best_f):
best_f = torch.tensor(float(best_f))
self.register_buffer("best_f", best_f)
if not torch.is_tensor(tau):
tau = torch.tensor(float(tau))
self.register_buffer("tau", tau)
def assertEqual(self, x, y, prec=None, message='', allow_inf=False):
if isinstance(prec, str) and message == '':
message = prec
prec = None
if prec is None:
prec = self.precision
x, y = self.unwrapVariables(x, y)
if isinstance(x, torch.Tensor) and isinstance(y, Number):
self.assertEqual(x.item(), y, prec, message, allow_inf)
elif isinstance(y, torch.Tensor) and isinstance(x, Number):
self.assertEqual(x, y.item(), prec, message, allow_inf)
elif torch.is_tensor(x) and torch.is_tensor(y):
def assertTensorsEqual(a, b):
super(TestCase, self).assertEqual(a.size(), b.size(), message)
if a.numel() > 0:
b = b.type_as(a)
b = b.cuda(device=a.get_device()) if a.is_cuda else b.cpu()
# check that NaNs are in the same locations
nan_mask = a != a
self.assertTrue(torch.equal(nan_mask, b != b), message)
diff = a - b
diff[nan_mask] = 0
# TODO: implement abs on CharTensor
if diff.is_signed() and 'CharTensor' not in diff.type():
diff = diff.abs()
max_err = diff.max()
self.assertLessEqual(max_err, prec, message)
super(TestCase, self).assertEqual(x.is_sparse, y.is_sparse, message)
if x.is_sparse:
x = self.safeCoalesce(x)
y = self.safeCoalesce(y)
assertTensorsEqual(x._indices(), y._indices())
assertTensorsEqual(x._values(), y._values())
else:
assertTensorsEqual(x, y)
elif isinstance(x, string_classes) and isinstance(y, string_classes):
super(TestCase, self).assertEqual(x, y, message)
elif type(x) == set and type(y) == set:
super(TestCase, self).assertEqual(x, y, message)
elif is_iterable(x) and is_iterable(y):
super(TestCase, self).assertEqual(len(x), len(y), message)
for x_, y_ in zip(x, y):
self.assertEqual(x_, y_, prec, message)
elif isinstance(x, bool) and isinstance(y, bool):
super(TestCase, self).assertEqual(x, y, message)
elif isinstance(x, Number) and isinstance(y, Number):
if abs(x) == float('inf') or abs(y) == float('inf'):
if allow_inf:
super(TestCase, self).assertEqual(x, y, message)
else:
self.fail("Expected finite numeric values - x={}, y={}".format(x, y))
return
super(TestCase, self).assertLessEqual(abs(x - y), prec, message)
else:
super(TestCase, self).assertEqual(x, y, message)
def recursiveCopy(t1, t2):
if isinstance(t2, list):
t1 = t1 if isinstance(t1, list) else [t1]
for i, _ in enumerate(t2):
t1[i], t2[i] = recursiveCopy(t1[i], t2[i])
elif torch.is_tensor(t2):
t1 = t1 if torch.is_tensor(t1) else t2.new()
t1.resize_as_(t2).copy_(t2)
else:
raise RuntimeError("expecting nested tensors or tables. Got " +
type(t1).__name__ + " and " + type(t2).__name__ + " instead")
return t1, t2
def recursiveAdd(t1, val=1, t2=None):
if t2 is None:
t2 = val
val = 1
if isinstance(t2, list):
t1 = t1 if isinstance(t1, list) else [t1]
for i, _ in enumerate(t2):
t1[i], t2[i] = recursiveAdd(t1[i], val, t2[i])
elif torch.is_tensor(t1) and torch.is_tensor(t2):
t1.add_(val, t2)
else:
raise RuntimeError("expecting nested tensors or tables. Got " +
type(t1).__name__ + " and " + type(t2).__name__ + " instead")
return t1, t2
def recursiveResizeAs(t1, t2):
if isinstance(t2, list):
t1 = t1 if isinstance(t1, list) else [t1]
if len(t1) < len(t2):
t1 += [None] * (len(t2) - len(t1))
for i, _ in enumerate(t2):
t1[i], t2[i] = recursiveResizeAs(t1[i], t2[i])
t1 = t1[:len(t2)]
elif torch.is_tensor(t2):
t1 = t1 if torch.is_tensor(t1) else t2.new()
t1.resize_as_(t2)
else:
raise RuntimeError("Expecting nested tensors or tables. Got " +
type(t1).__name__ + " and " + type(t2).__name__ + "instead")
return t1, t2
def default_collate(batch):
"Puts each data field into a tensor with outer dimension batch size"
if torch.is_tensor(batch[0]):
out = None
if _use_shared_memory:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = batch[0].storage()._new_shared(numel)
out = batch[0].new(storage)
return torch.stack(batch, 0, out=out)
elif type(batch[0]).__module__ == 'numpy':
elem = batch[0]
if type(elem).__name__ == 'ndarray':
return torch.stack([torch.from_numpy(b) for b in batch], 0)
if elem.shape == (): # scalars
py_type = float if elem.dtype.name.startswith('float') else int
return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
elif isinstance(batch[0], int):
return torch.LongTensor(batch)
elif isinstance(batch[0], float):
return torch.DoubleTensor(batch)
elif isinstance(batch[0], string_classes):
return batch
elif isinstance(batch[0], collections.Mapping):
return {key: default_collate([d[key] for d in batch]) for key in batch[0]}
elif isinstance(batch[0], collections.Sequence):
transposed = zip(*batch)
return [default_collate(samples) for samples in transposed]
raise TypeError(("batch must contain tensors, numbers, dicts or lists; found {}"
.format(type(batch[0]))))
def convert_cuda(self, model, input):
cuda_model = model.cuda()
# input might be nested - we want to move everything to GPU
cuda_input = function._nested_map(
lambda o: isinstance(o, Variable) or torch.is_tensor(o),
lambda o: o.cuda())(input)
return cuda_model, cuda_input
def updateOutput_patch(*args):
input = args[0]
while not torch.is_tensor(input):
input = input[0]
obj._backend = type2backend[type(input)]
obj.updateOutput = updateOutput_orig
return obj.updateOutput(*args)
def _expand_bounds(
bounds: Optional[Union[float, Tensor]], X: Tensor
) -> Optional[Tensor]:
r"""Expands a tensor representing bounds.
Expand the dimension of bounds if necessary such that the last dimension of
bounds is the same as the last dimension of `X`.
Args:
bounds: a bound (either upper or lower) of each column (last dimension)
of `X`. If this is a single float, then all columns have the same bound.
X: `... x d` tensor
Returns:
A tensor of bounds expanded to be compatible with the size of `X` if
bounds is not None, and None if bounds is None
"""
if bounds is not None:
if not torch.is_tensor(bounds):
bounds = torch.tensor(bounds)
if len(bounds.shape) == 0:
ebounds = bounds.expand(1, X.shape[-1])
elif len(bounds.shape) == 1:
ebounds = bounds.view(1, -1)
else:
ebounds = bounds
if ebounds.shape[1] != X.shape[-1]:
raise RuntimeError(
"Bounds must either be a single value or the same dimension as X"
)
return ebounds.to(dtype=X.dtype, device=X.device)
else:
return None
def _unpack(self, value):
if isinstance(value, Variable):
return value.data
elif torch.is_tensor(value):
return value
else:
return type(value)(self._unpack(v) for v in value)
def map_tensor_sizes(sizes):
if isinstance(sizes, list):
return [map_tensor_sizes(s) for s in sizes]
elif torch.is_tensor(sizes):
return sizes.double()
else:
return torch.randn(*sizes)
def _unpack_input(self, input):
if isinstance(input, Variable):
return input.data
elif torch.is_tensor(input):
return input
else:
return type(input)(self._unpack_input(i) for i in input)
def __init__(self, module):
super(DistributedDataParallel, self).__init__()
self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
self.module = module
param_list = [param for param in self.module.state_dict().values() if torch.is_tensor(param)]
if dist._backend == dist.dist_backend.NCCL:
for param in param_list:
assert param.is_cuda, "NCCL backend only supports model parameters to be on GPU."
#broadcast parameters
flat_dist_call(param_list, dist.broadcast, (0,) )
#all reduce gradient hook
def allreduce_params():
if(self.needs_reduction):
self.needs_reduction = False
else:
return
grads = [param.grad.data for param in self.module.parameters() if param.grad is not None]
flat_dist_call(grads, dist.all_reduce)
for param in list(self.module.parameters()):
def allreduce_hook(*unused):
torch.autograd.Variable._execution_engine.queue_callback(allreduce_params)
if param.requires_grad:
param.register_hook(allreduce_hook)
def _validate_log_prob_arg(self, value):
"""
Argument validation for `log_prob` methods. The rightmost dimensions
of a value to be scored via `log_prob` must agree with the distribution's
batch and event shapes.
Args:
value (Tensor or Variable): the tensor whose log probability is to be
computed by the `log_prob` method.
Raises
ValueError: when the rightmost dimensions of `value` do not match the
distribution's batch and event shapes.
"""
if not (torch.is_tensor(value) or isinstance(value, Variable)):
raise ValueError('The value argument to log_prob must be a Tensor or Variable instance.')
event_dim_start = len(value.size()) - len(self._event_shape)
if value.size()[event_dim_start:] != self._event_shape:
raise ValueError('The right-most size of value must match event_shape: {} vs {}.'.
format(value.size(), self._event_shape))
actual_shape = value.size()
expected_shape = self._batch_shape + self._event_shape
for i, j in zip(reversed(actual_shape), reversed(expected_shape)):
if i != 1 and j != 1 and i != j:
raise ValueError('Value is not broadcastable with batch_shape+event_shape: {} vs {}.'.
format(actual_shape, expected_shape))
def eval_accuracies(pred_s, target_s, pred_e, target_e):
"""An unofficial evalutation helper.
Compute exact start/end/complete match accuracies for a batch.
"""
# Convert 1D tensors to lists of lists (compatibility)
if torch.is_tensor(target_s):
target_s = [[e] for e in target_s]
target_e = [[e] for e in target_e]
# Compute accuracies from targets
batch_size = len(pred_s)
start = utils.AverageMeter()
end = utils.AverageMeter()
em = utils.AverageMeter()
for i in range(batch_size):
# Start matches
if pred_s[i] in target_s[i]:
start.update(1)
else:
start.update(0)
# End matches
if pred_e[i] in target_e[i]:
end.update(1)
else:
end.update(0)
# Both start and end match
if any([1 for _s, _e in zip(target_s[i], target_e[i])
if _s == pred_s[i] and _e == pred_e[i]]):
em.update(1)
else:
em.update(0)
return start.avg * 100, end.avg * 100, em.avg * 100
def recursiveType(param, type, tensorCache={}):
from .Criterion import Criterion
from .Module import Module
if isinstance(param, list):
for i, p in enumerate(param):
param[i] = recursiveType(p, type, tensorCache)
elif isinstance(param, Module) or isinstance(param, Criterion):
param.type(type, tensorCache)
elif torch.is_tensor(param):
if torch.typename(param) != type:
key = param._cdata
if key in tensorCache:
newparam = tensorCache[key]
else:
newparam = torch.Tensor().type(type)
storageType = type.replace('Tensor', 'Storage')
param_storage = param.storage()
if param_storage:
storage_key = param_storage._cdata
if storage_key not in tensorCache:
tensorCache[storage_key] = torch._import_dotted_name(
storageType)(param_storage.size()).copy_(param_storage)
newparam.set_(
tensorCache[storage_key],
param.storage_offset(),
param.size(),
param.stride()
)
tensorCache[key] = newparam
param = newparam
return param
def collate_fn_cat(batch):
"Puts each data field into a tensor with outer dimension batch size"
if torch.is_tensor(batch[0]):
out = None
return torch.cat(batch, 0, out=out)
elif type(batch[0]).__module__ == 'numpy':
elem = batch[0]
if type(elem).__name__ == 'ndarray':
return torch.cat([torch.from_numpy(b) for b in batch], 0)
if elem.shape == (): # scalars
py_type = float if elem.dtype.name.startswith('float') else int
return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
elif isinstance(batch[0], int):
return torch.LongTensor(batch)
elif isinstance(batch[0], float):
return torch.DoubleTensor(batch)
elif isinstance(batch[0], string_classes):
return batch
elif isinstance(batch[0], collections.Mapping):
return {key: collate_fn_cat([d[key] for d in batch]) for key in batch[0]}
elif isinstance(batch[0], collections.Sequence):
transposed = zip(*batch)
return [collate_fn_cat(samples) for samples in transposed]
raise TypeError(("batch must contain tensors, numbers, dicts or lists; found {}"
.format(type(batch[0]))))
def safeCoalesce(self, t):
tc = t.coalesce()
value_map = {}
for idx, val in zip(t._indices().t(), t._values()):
idx_tup = tuple(idx)
if idx_tup in value_map:
value_map[idx_tup] += val
else:
value_map[idx_tup] = val.clone() if torch.is_tensor(val) else val
new_indices = sorted(list(value_map.keys()))
new_values = [value_map[idx] for idx in new_indices]
if t._values().ndimension() < 2:
new_values = t._values().new(new_values)
else:
new_values = torch.stack(new_values)
new_indices = t._indices().new(new_indices).t()
tg = t.new(new_indices, new_values, t.size())
self.assertEqual(tc._indices(), tg._indices())
self.assertEqual(tc._values(), tg._values())
return tg
def _warn_if_nan(name, value):
if torch.is_tensor(value):
value = value.item()
if torch_isnan(value):
warnings.warn("Encountered NAN log_prob_sum at site '{}'".format(name))
if torch_isinf(value) and value > 0:
warnings.warn("Encountered +inf log_prob_sum at site '{}'".format(name))
def torch_backward(x):
"""
Like ``x.backward()`` for a :class:`~torch.Tensor`, but also accepts
numbers (a no-op if given a number).
"""
if torch.is_tensor(x):
x.backward()
def _make_grads(outputs, grads, user_create_graph):
if user_create_graph is not None:
create_graph = user_create_graph
else:
create_graph = any(isinstance(grad, Variable) and not grad.volatile
for grad in grads)
new_grads = []
for out, grad in zip(outputs, grads):
if isinstance(grad, Variable):
new_grads.append(grad)
elif torch.is_tensor(grad):
new_grads.append(Variable(grad, volatile=not create_graph))
elif grad is None:
if out.requires_grad:
if out.numel() != 1:
raise RuntimeError("grad can be implicitly created only for scalar outputs")
data = out.data
new_grads.append(
Variable(data.new().resize_as_(data).fill_(1), volatile=not create_graph))
else:
new_grads.append(None)
else:
raise TypeError("gradients can be either Tensors, Variables or None, but got " +
type(grad).__name__)
return tuple(new_grads), create_graph
def __call__(self, pic):
"""
Args:
pic (Tensor or numpy.ndarray): Image to be converted to PIL.Image.
Returns:
PIL.Image: Image converted to PIL.Image.
"""
npimg = pic
mode = None
if isinstance(pic, torch.FloatTensor):
pic = pic.mul(255).byte()
if torch.is_tensor(pic):
npimg = np.transpose(pic.numpy(), (1, 2, 0))
assert isinstance(npimg, np.ndarray), 'pic should be Tensor or ndarray'
if npimg.shape[2] == 1:
npimg = npimg[:, :, 0]
if npimg.dtype == np.uint8:
mode = 'L'
if npimg.dtype == np.int16:
mode = 'I;16'
if npimg.dtype == np.int32:
mode = 'I'
elif npimg.dtype == np.float32:
mode = 'F'
else:
if npimg.dtype == np.uint8:
mode = 'RGB'
assert mode is not None, '{} is not supported'.format(npimg.dtype)
return Image.fromarray(npimg, mode=mode)
def __len__(self):
if isinstance(self.data, dict):
return len(list(self.data.values())[0])
elif isinstance(self.data, list):
return len(self.data[0])
elif torch.is_tensor(self.data) or isinstance(self.data, np.ndarray):
return len(self.data)
def load_model(self):
if len(glob.glob(os.path.join(args.save_dir, args.corpus) + '-selector-*.pth')) == 0:
return
if args.load_iter is None:
f_list = glob.glob(os.path.join(args.save_dir, args.corpus) + '-selector-*.pth')
iter_list = [int(i.split('-')[-1].split('.')[0]) for i in f_list]
start_iter = sorted(iter_list)[-1]
else:
start_iter = args.load_iter
name = args.corpus + '-selector-{}.pth'.format(start_iter)
model_file_path = os.path.join(args.save_dir, name)
print("loading model", model_file_path)
if opt.device == torch.device('cuda'):
state = torch.load(model_file_path)
else:
state = torch.load(model_file_path, map_location=opt.device)
self._epoch = state['epoch']
self._iter = state['iter']
self.running_avg_loss = state['current_loss']
self.min_loss = state['min_loss']
self.model.sentence_selector.load_state_dict(state['selector_state_dict'])
if not args.is_coverage:
self.optimizer.load_state_dict(state['optimizer'])
if opt.device == torch.device('cuda'):
for state in list(self.optimizer.state.values()):
for k, v in list(state.items()):
if torch.is_tensor(v):
state[k] = v.cuda()
def to_torch(ndarray):
if type(ndarray).__module__ == 'numpy':
return torch.from_numpy(ndarray)
elif not torch.is_tensor(ndarray):
raise ValueError("Cannot convert {} to torch tensor"
.format(type(ndarray)))
return ndarray
def _compute_dice_elbo(model_trace, guide_trace):
# y depends on x iff ordering[x] <= ordering[y]
# TODO refine this coarse dependency ordering.
ordering = {name: frozenset(f for f in site["cond_indep_stack"] if f.vectorized)
for trace in (model_trace, guide_trace)
for name, site in trace.nodes.items()
if site["type"] == "sample"}
costs = {}
for name, site in model_trace.nodes.items():
if site["type"] == "sample":
_dict_iadd(costs, ordering[name], site["log_prob"])
for name, site in guide_trace.nodes.items():
if site["type"] == "sample":
_dict_iadd(costs, ordering[name], -site["log_prob"])
dice = Dice(guide_trace, ordering)
elbo = 0.0
for ordinal, cost in costs.items():
dice_prob = dice.in_context(cost.shape, ordinal)
mask = dice_prob > 0
if torch.is_tensor(mask) and not mask.all():
dice_prob = dice_prob[mask]
cost = cost[mask]
# TODO use score_parts.entropy_term to "stick the landing"
elbo = elbo + (dice_prob * cost).sum()
return elbo
def to_numpy(tensor):
if torch.is_tensor(tensor):
return tensor.cpu().numpy()
elif type(tensor).__module__ != 'numpy':
raise ValueError("Cannot convert {} to numpy array"
.format(type(tensor)))
return tensor
def add(self, output, target):
"""
Args:
output (Tensor): NxK tensor that for each of the N examples
indicates the probability of the example belonging to each of
the K classes, according to the model. The probabilities should
sum to one over all classes
target (Tensor): binary NxK tensort that encodes which of the K
classes are associated with the N-th input
(eg: a row [0, 1, 0, 1] indicates that the example is
associated with classes 2 and 4)
weight (optional, Tensor): Nx1 tensor representing the weight for
each example (each weight > 0)
"""
if not torch.is_tensor(output):
output = torch.from_numpy(output)
if not torch.is_tensor(target):
target = torch.from_numpy(target)
if output.dim() == 1:
output = output.view(-1, 1)
else:
assert output.dim() == 2, \
'wrong output size (should be 1D or 2D with one column \
per class)'
if target.dim() == 1:
target = target.view(-1, 1)
else:
assert target.dim() == 2, \
'wrong target size (should be 1D or 2D with one column \
per class)'
if self.scores.numel() > 0:
assert target.size(1) == self.targets.size(1), \
'dimensions for output should match previously added examples.'
# make sure storage is of sufficient size
if self.scores.storage().size() < self.scores.numel() + output.numel():
new_size = math.ceil(self.scores.storage().size() * 1.5)
self.scores.storage().resize_(int(new_size + output.numel()))
self.targets.storage().resize_(int(new_size + output.numel()))
# store scores and targets
offset = self.scores.size(0) if self.scores.dim() > 0 else 0
self.scores.resize_(offset + output.size(0), output.size(1))
self.targets.resize_(offset + target.size(0), target.size(1))
self.scores.narrow(0, offset, output.size(0)).copy_(output)
self.targets.narrow(0, offset, target.size(0)).copy_(target)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
sample = {'x': self.x[idx], 'y': self.y[idx]}
return sample
def update(self, mask, current, cost, parent=None, score=None, compute_unique_device=None, remaining_capacity=None, last_action=None, potential_info=None, batch_ids=0):
# We will group all the entries in the beam by the mask (for each batch_id)
device = mask[0].device
mask_cols = [col for col_group in mask for col in col_group] # Flatten all columns in the mask
if torch.is_tensor(batch_ids) and len(batch_ids) > 0 and (batch_ids != batch_ids[0]).any():
# Prepend batch id to mask_cols so we get unique masks per graph
mask_cols.insert(0, batch_ids)
group_idx, mask_idx_per_group = unique_inverse(mask_cols, return_index=True, device=compute_unique_device)
# Get back all results to the current device
mask = [msk.to(device) for msk in mask]
group_idx = group_idx.to(device)
mask_idx_per_group = mask_idx_per_group.to(device)
mask_idx = None # We don't need as mask_idx unless we sort by the mask 'lazily'
current_csr = None # We don't have a current csr representation unless we sort by current
current_counts = None
if self.sort_by is not None:
if self.sort_by == 'current':
assert self.batch_size == 1, "Sorting by current not compatible with batch ids"
current, argsort = torch.sort(current)
# If we sort by current, since there are only very few current we also store a compact 'csr' representation
current_csr = coo_to_csr(current, minlength=self.num_nodes)
current_counts = current_csr[1:] - current_csr[:-1]
elif self.sort_by == 'group_idx':
group_idx, argsort = torch.sort(group_idx)
else:
assert False, "Unknown sort by"
parent = torch.gather(parent, 0, argsort) if parent is not None else None
current = current if self.sort_by == 'current' else torch.gather(current, 0, argsort)
group_idx = group_idx if self.sort_by == 'group_idx' else torch.gather(group_idx, 0, argsort)
cost = torch.gather(cost, 0, argsort)
if score is not None:
score = torch.gather(score, 0, argsort)
if remaining_capacity is not None:
remaining_capacity = torch.gather(remaining_capacity, 0, argsort)
if last_action is not None:
last_action = torch.gather(last_action, 0, argsort)
if potential_info is not None:
potential_info = tuple(p_info.index_select(0, argsort) for p_info in potential_info)
if self.batch_size > 1:
assert self.sort_by == 'group_idx'
# When sorting by group_idx should already be sorted by batch_id
batch_ids = torch.gather(batch_ids, 0, argsort)
# If mask_idx is None, then it means the mask is aligned with the sorting
if self.gather_mask == 'eager':
mask = [col_group.index_select(-1, argsort) for col_group in mask]
elif self.gather_mask == 'lazy':
# Lazy, will get the mask in order when needed
# We want all entries of the same group to point to the same mask_idx so we can 'abuse' mask_idx as
# group idx as well
mask_idx = torch.gather(mask_idx_per_group, 0, group_idx)
else:
assert False, "Unknown gather mask option"
self.mask = mask
self.mask_idx = mask_idx
self.group_idx = group_idx
self.current = current
self.current_csr = current_csr
self.current_counts = current_counts
self.cost = cost
self.score = score
self.remaining_capacity = remaining_capacity
self.last_action = last_action if last_action is not None else current # For TSP, last action is current
self.potential_info = potential_info
self.parent = parent
self.batch_ids = batch_ids
self.size = cost.size(0)
def assert_equal(obj1, obj2):
try:
import torch
if torch.is_tensor(obj1) and torch.is_tensor(obj2):
assert torch.equal(obj1, obj2)
return
except ImportError:
pass
module_numpy = (type(obj1).__module__ == np.__name__ or
type(obj2).__module__ == np.__name__)
if module_numpy:
empty_shape = ((hasattr(obj1, "shape") and obj1.shape == ()) or
(hasattr(obj2, "shape") and obj2.shape == ()))
if empty_shape:
# This is a special case because currently np.testing.assert_equal
# fails because we do not properly handle different numerical
# types.
assert obj1 == obj2, ("Objects {} and {} are "
"different.".format(obj1, obj2))
else:
np.testing.assert_equal(obj1, obj2)
elif hasattr(obj1, "__dict__") and hasattr(obj2, "__dict__"):
special_keys = ["_pytype_"]
assert (set(list(obj1.__dict__.keys()) + special_keys) ==
set(list(obj2.__dict__.keys()) + special_keys)), ("Objects {} "
"and {} are "
"different."
.format(
obj1,
obj2))
try:
# Workaround to make comparison of OrderedDicts work on Python 2.7
if obj1 == obj2:
return
except Exception:
pass
if obj1.__dict__ == {}:
print("WARNING: Empty dict in ", obj1)
for key in obj1.__dict__.keys():
if key not in special_keys:
assert_equal(obj1.__dict__[key], obj2.__dict__[key])
elif type(obj1) is dict or type(obj2) is dict:
assert_equal(obj1.keys(), obj2.keys())
for key in obj1.keys():
assert_equal(obj1[key], obj2[key])
elif type(obj1) is list or type(obj2) is list:
assert len(obj1) == len(obj2), ("Objects {} and {} are lists with "
"different lengths."
.format(obj1, obj2))
for i in range(len(obj1)):
assert_equal(obj1[i], obj2[i])
elif type(obj1) is tuple or type(obj2) is tuple:
assert len(obj1) == len(obj2), ("Objects {} and {} are tuples with "
"different lengths."
.format(obj1, obj2))
for i in range(len(obj1)):
assert_equal(obj1[i], obj2[i])
elif (pa.lib.is_named_tuple(type(obj1)) or
pa.lib.is_named_tuple(type(obj2))):
assert len(obj1) == len(obj2), ("Objects {} and {} are named tuples "
"with different lengths."
.format(obj1, obj2))
for i in range(len(obj1)):
assert_equal(obj1[i], obj2[i])
else:
assert obj1 == obj2, ("Objects {} and {} are different."
.format(obj1, obj2))
def optimizer_to_device(op, device):
for state in op.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.to(device)
def _is_tensor_image(img):
return torch.is_tensor(img) and img.ndimension() == 3
def debug_memory():
import collections, gc, torch
tensors = collections.Counter((str(o.device), o.dtype, tuple(o.shape))
for o in gc.get_objects()
if torch.is_tensor(o))
def __call__(self,
vertices: Tensor,
faces: Union[Tensor, Array],
focal_length: Union[Tensor, Array],
camera_translation: Union[Tensor, Array],
camera_center: Union[Tensor, Array],
bg_imgs: Array,
render_bg: bool = True,
deg: float = 0,
return_with_alpha: bool = False,
body_color: List[float] = None,
**kwargs):
'''
Parameters
----------
vertices: BxVx3, torch.Tensor
The torch Tensor that contains the current vertices to be drawn
faces: Fx3, np.array
The faces of the meshes to be drawn. Right now only support a
batch of meshes with the same topology
focal_length: B, torch.Tensor
The focal length used by the perspective camera
camera_translation: Bx3, torch.Tensor
The translation of the camera estimated by the network
camera_center: Bx2, torch.Tensor
The center of the camera in pixels
bg_imgs: np.ndarray
Optional background images used for overlays
render_bg: bool, optional
Render on top of the background image
deg: float, optional
Degrees to rotate the mesh around itself. Used to render the
same mesh from multiple viewpoints. Defaults to 0 degrees
return_with_alpha: bool, optional
Whether to return the rendered image with an alpha channel.
Default value is False.
body_color: list, optional
The color used to render the image.
'''
if torch.is_tensor(vertices):
vertices = vertices.detach().cpu().numpy()
if torch.is_tensor(faces):
faces = faces.detach().cpu().numpy()
if torch.is_tensor(focal_length):
focal_length = focal_length.detach().cpu().numpy()
if torch.is_tensor(camera_translation):
camera_translation = camera_translation.detach().cpu().numpy()
if torch.is_tensor(camera_center):
camera_center = camera_center.detach().cpu().numpy()
batch_size = vertices.shape[0]
output_imgs = []
for bidx in range(batch_size):
if body_color is None:
body_color = COLORS['N']
_, H, W = bg_imgs[bidx].shape
# Update the renderer's viewport
self.renderer.viewport_height = H
self.renderer.viewport_width = W
self.update_camera(
focal_length=focal_length[bidx],
translation=camera_translation[bidx],
center=camera_center[bidx],
)
self.update_mesh(vertices[bidx],
faces,
body_color=body_color,
deg=deg)
flags = (pyrender.RenderFlags.RGBA
| pyrender.RenderFlags.SKIP_CULL_FACES)
color, depth = self.renderer.render(self.scene, flags=flags)
color = np.transpose(color, [2, 0, 1]).astype(np.float32) / 255.0
color = np.clip(color, 0, 1)
if render_bg:
if return_with_alpha:
valid_mask = (color[3] > 0)[np.newaxis]
if bg_imgs[bidx].shape[0] < 4:
curr_bg_img = np.concatenate([
bg_imgs[bidx],
np.ones_like(bg_imgs[bidx, [0], :, :])
],
axis=0)
else:
curr_bg_img = bg_imgs[bidx]
output_img = (color * valid_mask +
(1 - valid_mask) * curr_bg_img)
output_imgs.append(np.clip(output_img, 0, 1))
else:
valid_mask = (color[3] > 0)[np.newaxis]
output_img = (color[:-1] * valid_mask +
(1 - valid_mask) * bg_imgs[bidx])
output_imgs.append(np.clip(output_img, 0, 1))
else:
if return_with_alpha:
output_imgs.append(color)
else:
output_imgs.append(color[:-1])
return np.stack(output_imgs, axis=0)
def run_epic(echoes,
reverse_echoes=None,
voxshift=None,
extrapolate=True,
lam=1,
sigma=1,
max_iter=(10, 32),
tol=1e-5,
verbose=False):
"""Run EPIC on pre-loaded tensors.
Parameters
----------
echoes : (N, *spatial) tensor
Echoes acquired with bipolar readout, Readout direction should be last.
reverse_echoes : (N, *spatial)
Echoes acquired with reverse bipolar readout. Else: synthesized.
voxshift : (*spatial) tensor
Voxel shift map used to deform towards even (0, 2, ...) echoes.
Its inverse is used to deform towards odd (1, 3, ...) echoes.
extrapolate : bool
Extrapolate first/last echo when reverse_echoes is None.
Otherwise, only use interpolated echoes.
lam : [list of] float
Regularization factor (per echo)
sigma : float
Noise standard deviation
max_iter : [pair of] int
Maximum number of RLS and CG iterations
tol : float
Tolerance for early stopping
verbose : int,
Verbosity level
Returns
-------
echoes : (N, *spatial) tensor
Undistorted + denoised echoes
"""
if reverse_echoes is False:
return run_epic_noreverse(echoes, voxshift, lam, sigma, max_iter, tol,
verbose)
ne = len(echoes) # number of echoes
nv = echoes.shape[1:].numel() # number of voxels
nd = echoes.dim() - 1 # number of dimensions
# synthesize echoes
synth = not torch.is_tensor(reverse_echoes)
if synth:
neg = synthesize_neg(echoes[0::2])
pos = synthesize_pos(echoes[1::2])
reverse_echoes = torch.stack([x for y in zip(pos, neg) for x in y])
del pos, neg
else:
extrapolate = True
# initialize denoised echoes
fit = (echoes + reverse_echoes).div_(2)
if not extrapolate:
fit[0] = echoes[0]
fit[-1] = echoes[-1]
fwd_fit = torch.zeros_like(fit)
bwd_fit = torch.zeros_like(fit)
# prepare voxel shift maps
if voxshift is not None:
ivoxshift = add_identity_1d(-voxshift)
voxshift = add_identity_1d(voxshift)
else:
ivoxshift = None
# prepare parameters
max_iter, sub_iter = py.make_list(max_iter, 2)
tol, sub_tol = py.make_list(tol, 2)
lam = [l / ne for l in py.make_list(lam, ne)]
isigma2 = 1 / (sigma * sigma)
# compute hessian once and for all
if voxshift is not None:
one = torch.ones_like(voxshift)[None]
if extrapolate:
h = push1d(pull1d(one, voxshift), voxshift)
h += push1d(pull1d(one, ivoxshift), ivoxshift)
weight_ = lambda x: x.mul_(0.5)
halfweight_ = lambda x: x.mul_(math.sqrt(0.5))
else:
h = torch.zeros_like(fit)
h[:-1] += push1d(pull1d(one, voxshift), voxshift)
h[1:] += push1d(pull1d(one, ivoxshift), ivoxshift)
weight_ = lambda x: x[1:-1].mul_(0.5)
halfweight_ = lambda x: x[1:-1].mul_(math.sqrt(0.5))
del one
weight_(h)
else:
h = fit.new_ones([ne] + [1] * nd)
if extrapolate:
h *= 2
weight_ = lambda x: x.mul_(0.5)
halfweight_ = lambda x: x.mul_(math.sqrt(0.5))
else:
h[1:-1] *= 2
weight_ = lambda x: x[1:-1].mul_(0.5)
halfweight_ = lambda x: x[1:-1].mul_(math.sqrt(0.5))
weight_(h)
h *= isigma2
loss = float('inf')
for n_iter in range(max_iter):
# update weights
w, jtv = membrane_weights(fit, factor=lam, return_sum=True)
# gradient of likelihood (forward)
pull_forward(fit, voxshift, ivoxshift, out=fwd_fit)
fwd_fit.sub_(echoes)
halfweight_(fwd_fit)
ll = ssq(fwd_fit)
halfweight_(fwd_fit)
push_forward(fwd_fit, voxshift, ivoxshift, out=fwd_fit)
# gradient of likelihood (reversed)
pull_backward(fit, voxshift, ivoxshift, extrapolate, out=bwd_fit)
if extrapolate:
bwd_fit.sub_(reverse_echoes)
else:
bwd_fit[1:-1].sub_(reverse_echoes[1:-1])
halfweight_(bwd_fit)
ll += ssq(bwd_fit)
halfweight_(bwd_fit)
push_backward(bwd_fit, voxshift, ivoxshift, extrapolate, out=bwd_fit)
g = fwd_fit.add_(bwd_fit).mul_(isigma2)
ll *= 0.5 * isigma2
# gradient of prior
g += regulariser(fit, membrane=1, factor=lam, weights=w)
# solve
fit -= solve_field(h,
g,
w,
membrane=1,
factor=lam,
max_iter=sub_iter,
tolerance=sub_tol)
# track objective
ll, jtv = ll.item() / (ne * nv), jtv.item() / (ne * nv)
loss, loss_prev = ll + jtv, loss
if n_iter:
gain = (loss_prev - loss) / max((loss_max - loss), 1e-8)
else:
gain = float('inf')
loss_max = loss
if verbose:
end = '\n' if verbose > 1 else '\r'
print(
f'{n_iter+1:02d} | {ll:12.6g} + {jtv:12.6g} = {loss:12.6g} '
f'| gain = {gain:12.6g}',
end=end)
if gain < tol:
break
if verbose == 1:
print('')
return fit
def epic(echoes,
reverse_echoes=True,
fieldmap=None,
extrapolate=False,
bandwidth=1,
polarity=1,
readout=-1,
slicewise=False,
lam=1e2,
max_iter=(10, 32),
tol=1e-5,
verbose=False,
device=None):
"""Edge-Preserving B0 inhomogeneity correction (EPIC)
References
----------
.. "A new distortion correction approach for multi-contrast MRI",
Divya Varadarajan, et al. ISMRM (2020)
Parameters
----------
echoes : list[file_like] or (N, *spatial) tensor,
Echoes acquired with a bipolar readout.
reverse_echoes : bool or list[file_like] or (N, *spatial) tensor
Echoes acquired with reverse bipolar readout. If True: synthesized.
fieldmap : file_like or (*spatial) tensor, Fieldmap or voxel shift map
extrapolate : bool, Extrapolate first/last echo when reverse_echoes is None
bandwidth : float, Bandwidth of the input echoes, in Hz/pixel
polarity : +1 or -1, Readout polarity of the first echo
readout : int, Index of the readout dimension
slicewise : bool or int, Run the algorithm slicewise. If int, chunk size.
lam : [list of] float, Regularization factor (per echo)
max_iter : [pair of] int, Maximum number of RLS and CG iterations
tol : float, Tolerance for early stopping
verbose : int, Verbosity level
device : {'cpu', 'cuda'} or torch.device
Returns
-------
echoes : (N, *spatial) tensor
Undistorted + denoised echoes
"""
device = torch.device('cuda' if device == 'gpu' else device)
backend = dict(dtype=torch.float32, device=device)
echoes = map_files(echoes)
reverse_echoes = map_files(reverse_echoes)
fieldmap = map_files(fieldmap, nobatch=True)
ndim = len(echoes.shape) - 1
# estimate noise variance + scale regularization
noise, tissue = 1, []
for echo in echoes:
noise1, tissue1 = estimate_noise(load(echo, **backend))
noise *= noise1['sd']
tissue.append(tissue1['mean'])
noise = noise**(1 / len(echoes))
lam = py.make_list(lam, len(echoes))
lam = [l / mu for l, mu in zip(lam, tissue)]
# ensure readout dimension is last
readout = readout - ndim if readout > 0 else readout
echoes = movedim(echoes, readout, -1)
fieldmap = movedim(fieldmap, readout, -1)
reverse_echoes = movedim(reverse_echoes, readout, -1)
if slicewise:
# --- loop over slices -----------------------------------------
# ensure slice direction is second to last
dz = -2 if echoes.shape[-2] < echoes.shape[-3] else -3
echoes = movedim(echoes, dz, -2)
fieldmap = movedim(fieldmap, dz, -2)
reverse_echoes = movedim(reverse_echoes, dz, -2)
nz = echoes.shape[-2]
# allocate output
if torch.is_tensor(echoes):
out_backend = dict(dtype=echoes.dtype, device=echoes.device)
else:
out_backend = dict(dtype=torch.float32, device='cpu')
fit = torch.zeros(echoes.shape, **out_backend)
# prepare runner
slicewise = int(slicewise)
run_slicewise = RunSlicewise(slicewise, echoes, reverse_echoes,
fieldmap, bandwidth, polarity, lam, noise,
extrapolate, max_iter, tol, backend,
out_backend, verbose)
# parallel process
with multiprocessing.Pool(torch.get_num_threads()) as pool:
slices = pool.imap_unordered(run_slicewise,
range(0, nz, slicewise))
for chunk in slices:
chunk, fitchunk = chunk
fit[..., chunk, :] = fitchunk
# unpermute slice
fit = movedim(fit, -2, dz)
else:
# --- process full volume --------------------------------------
# load data
echoes = load(echoes, **backend)
fieldmap = load(fieldmap, **backend)
reverse_echoes = load(reverse_echoes, **backend)
# rescale fieldmap
if fieldmap is not None:
fieldmap = fieldmap / bandwidth
if polarity < 0:
fieldmap = -fieldmap
# run EPIC
fit = run_epic(echoes,
reverse_echoes,
fieldmap,
extrapolate=extrapolate,
lam=lam,
sigma=noise,
max_iter=max_iter,
tol=tol,
verbose=verbose)
# unpermute readout
fit = movedim(fit, -1, readout)
return fit
def main(args):
ts = time.strftime('%Y-%b-%d-%H:%M:%S', time.gmtime())
splits = ['train', 'valid'] + (['test'] if args.test else [])
datasets = OrderedDict()
for split in splits:
datasets[split] = PTB(data_dir=args.data_dir,
split=split,
create_data=args.create_data,
max_sequence_length=args.max_sequence_length,
min_occ=args.min_occ)
model = SentenceVAE(vocab_size=datasets['train'].vocab_size,
sos_idx=datasets['train'].sos_idx,
eos_idx=datasets['train'].eos_idx,
pad_idx=datasets['train'].pad_idx,
unk_idx=datasets['train'].unk_idx,
max_sequence_length=args.max_sequence_length,
embedding_size=args.embedding_size,
rnn_type=args.rnn_type,
hidden_size=args.hidden_size,
word_dropout=args.word_dropout,
embedding_dropout=args.embedding_dropout,
latent_size=args.latent_size,
num_layers=args.num_layers,
bidirectional=args.bidirectional)
if torch.cuda.is_available():
model = model.cuda()
print(model)
if args.tensorboard_logging:
writer = SummaryWriter(
os.path.join(args.logdir, expierment_name(args, ts)))
writer.add_text("model", str(model))
writer.add_text("args", str(args))
writer.add_text("ts", ts)
save_model_path = os.path.join(args.save_model_path, ts)
os.makedirs(save_model_path)
def kl_anneal_function(anneal_function, step, k, x0):
if anneal_function == 'logistic':
return float(1 / (1 + np.exp(-k * (step - x0))))
elif anneal_function == 'linear':
return min(1, step / x0)
NLL = torch.nn.NLLLoss(ignore_index=datasets['train'].pad_idx,
reduction='sum')
def loss_fn(logp, target, length, mean, logv, anneal_function, step, k,
x0):
# cut-off unnecessary padding from target, and flatten
target = target[:, :torch.max(length).item()].contiguous().view(-1)
logp = logp.view(-1, logp.size(2))
# Negative Log Likelihood
NLL_loss = NLL(logp, target)
# KL Divergence
KL_loss = -0.5 * torch.sum(1 + logv - mean.pow(2) - logv.exp())
KL_weight = kl_anneal_function(anneal_function, step, k, x0)
return NLL_loss, KL_loss, KL_weight
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
tensor = torch.cuda.FloatTensor if torch.cuda.is_available(
) else torch.Tensor
step = 0
for epoch in range(args.epochs):
for split in splits:
data_loader = DataLoader(dataset=datasets[split],
batch_size=args.batch_size,
shuffle=split == 'train',
num_workers=cpu_count(),
pin_memory=torch.cuda.is_available())
tracker = defaultdict(tensor)
# Enable/Disable Dropout
if split == 'train':
model.train()
else:
model.eval()
for iteration, batch in enumerate(data_loader):
batch_size = batch['input'].size(0)
for k, v in batch.items():
if torch.is_tensor(v):
batch[k] = to_var(v)
# Forward pass
logp, mean, logv, z = model(batch['input'], batch['length'])
# loss calculation
NLL_loss, KL_loss, KL_weight = loss_fn(logp, batch['target'],
batch['length'], mean,
logv,
args.anneal_function,
step, args.k, args.x0)
loss = (NLL_loss + KL_weight * KL_loss) / batch_size
# backward + optimization
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
step += 1
# bookkeepeing
tracker['ELBO'] = torch.cat(
(tracker['ELBO'], loss.data.view(1, -1)), dim=0)
if args.tensorboard_logging:
writer.add_scalar("%s/ELBO" % split.upper(), loss.item(),
epoch * len(data_loader) + iteration)
writer.add_scalar("%s/NLL Loss" % split.upper(),
NLL_loss.item() / batch_size,
epoch * len(data_loader) + iteration)
writer.add_scalar("%s/KL Loss" % split.upper(),
KL_loss.item() / batch_size,
epoch * len(data_loader) + iteration)
writer.add_scalar("%s/KL Weight" % split.upper(),
KL_weight,
epoch * len(data_loader) + iteration)
if iteration % args.print_every == 0 or iteration + 1 == len(
data_loader):
print(
"%s Batch %04d/%i, Loss %9.4f, NLL-Loss %9.4f, KL-Loss %9.4f, KL-Weight %6.3f"
% (split.upper(), iteration, len(data_loader) - 1,
loss.item(), NLL_loss.item() / batch_size,
KL_loss.item() / batch_size, KL_weight))
if split == 'valid':
if 'target_sents' not in tracker:
tracker['target_sents'] = list()
tracker['target_sents'] += idx2word(
batch['target'].data,
i2w=datasets['train'].get_i2w(),
pad_idx=datasets['train'].pad_idx)
tracker['z'] = torch.cat((tracker['z'], z.data), dim=0)
print("%s Epoch %02d/%i, Mean ELBO %9.4f" %
(split.upper(), epoch, args.epochs, tracker['ELBO'].mean()))
if args.tensorboard_logging:
writer.add_scalar("%s-Epoch/ELBO" % split.upper(),
torch.mean(tracker['ELBO']), epoch)
# save a dump of all sentences and the encoded latent space
if split == 'valid':
dump = {
'target_sents': tracker['target_sents'],
'z': tracker['z'].tolist()
}
if not os.path.exists(os.path.join('dumps', ts)):
os.makedirs('dumps/' + ts)
with open(
os.path.join('dumps/' + ts +
'/valid_E%i.json' % epoch),
'w') as dump_file:
json.dump(dump, dump_file)
# save checkpoint
if split == 'train':
checkpoint_path = os.path.join(save_model_path,
"E%i.pytorch" % epoch)
torch.save(model.state_dict(), checkpoint_path)
print("Model saved at %s" % checkpoint_path)
load_model_path = save_path + '/model_checkpoint_' + str(args.continue_from) + '.pth'
package = torch.load(load_model_path, map_location=lambda storage, loc: storage)
model = DeepSpeech.load_model_package(package)
labels = DeepSpeech.get_labels(model)
parameters = model.parameters()
if args.optimizer == 'SGD':
optimizer = torch.optim.SGD(parameters, lr=args.lr, momentum=args.momentum, nesterov=True)
elif args.optimizer == 'Adam':
optimizer = torch.optim.Adam(parameters, lr=args.lr)
optimizer.load_state_dict(package['optim_dict'])
# Temporary fix for pytorch #2830 & #1442 while pull request #3658 in not incorporated in a release
# TODO : remove when a new release of pytorch include pull request #3658
if args.cuda:
for state in optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
start_epoch = int(package.get('epoch', 1)) - 1 # Index start at 0 for training
start_iter = package.get('iteration', None)
if start_iter is None:
start_epoch += 1 # We saved model after epoch finished, start at the next epoch.
start_iter = 0
else:
start_iter += 1
avg_loss = int(package.get('avg_loss', 0))
loss_results, per_results = package['loss_results'], package['per_results']
#best_per = package['best_per']
else:
raise ValueError('shoud give integer to continue_from')
if args.cuda:
def test_log_works_in_train_callback(tmpdir):
"""
Tests that log can be called within callback
"""
os.environ['PL_DEV_DEBUG'] = '1'
class TestCallback(callbacks.Callback):
# helpers
count = 1
choices = [False, True]
# used to compute expected values
callback_funcs_called = collections.defaultdict(list)
funcs_called_count = collections.defaultdict(int)
funcs_attr = {}
def make_logging(self, pl_module: pl.LightningModule, func_name, func_idx,
on_steps=[], on_epochs=[], prob_bars=[]):
self.funcs_called_count[func_name] += 1
for idx, (on_step, on_epoch, prog_bar) in enumerate(list(itertools.product(*[on_steps, on_epochs, prob_bars]))):
# run logging
custom_func_name = f"{func_idx}_{idx}_{func_name}"
pl_module.log(custom_func_name, self.count * func_idx, on_step=on_step,
on_epoch=on_epoch, prog_bar=prog_bar)
# catch information for verification
# on on_train_start is outside the main loop. Won't be called
if func_name == "on_train_start":
self.callback_funcs_called[func_name].append([self.count * func_idx])
# Saved only values from second epoch, so we can compute its mean or latest.
if pl_module.trainer.current_epoch == 1:
self.callback_funcs_called[func_name].append([self.count * func_idx])
forked = on_step and on_epoch
self.funcs_attr[custom_func_name] = {
"on_step": on_step,
"on_epoch": on_epoch,
"prog_bar": prog_bar,
"forked": forked,
"func_name": func_name}
if on_step and on_epoch:
self.funcs_attr[f"{custom_func_name}_step"] = {
"on_step": True,
"on_epoch": False,
"prog_bar": prog_bar,
"forked": False,
"func_name": func_name}
self.funcs_attr[f"{custom_func_name}_epoch"] = {
"on_step": False,
"on_epoch": True,
"prog_bar": prog_bar,
"forked": False,
"func_name": func_name}
def on_train_start(self, trainer, pl_module):
self.make_logging(pl_module, 'on_train_start', 1, on_steps=self.choices,
on_epochs=self.choices, prob_bars=self.choices)
def on_epoch_start(self, trainer, pl_module):
self.make_logging(pl_module, 'on_epoch_start', 2, on_steps=self.choices,
on_epochs=self.choices, prob_bars=self.choices)
def on_train_epoch_start(self, trainer, pl_module):
self.make_logging(pl_module, 'on_train_epoch_start', 3, on_steps=self.choices,
on_epochs=self.choices, prob_bars=self.choices)
def on_batch_start(self, trainer, pl_module):
self.make_logging(pl_module, 'on_batch_start', 4, on_steps=self.choices,
on_epochs=self.choices, prob_bars=self.choices)
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
self.make_logging(pl_module, 'on_train_batch_start', 5, on_steps=self.choices,
on_epochs=self.choices, prob_bars=self.choices)
def on_batch_end(self, trainer, pl_module):
self.make_logging(pl_module, 'on_batch_end', 6, on_steps=self.choices,
on_epochs=self.choices, prob_bars=self.choices)
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
self.make_logging(pl_module, 'on_train_batch_end', 7, on_steps=self.choices,
on_epochs=self.choices, prob_bars=self.choices)
# used to make sure aggregation works fine.
# we should obtain func[value * c for c in range(1, max_epochs * limit_train_batches)])
# with func = np.mean if on_epoch else func = np.max
self.count += 1
def on_epoch_end(self, trainer, pl_module):
self.make_logging(pl_module, 'on_epoch_end', 8, on_steps=[False],
on_epochs=self.choices, prob_bars=self.choices)
def on_train_epoch_end(self, trainer, pl_module, outputs):
self.make_logging(pl_module, 'on_train_epoch_end', 9, on_steps=[False],
on_epochs=self.choices, prob_bars=self.choices)
class TestModel(BoringModel):
manual_loss = []
def training_step(self, batch, batch_idx):
output = self.layer(batch)
loss = self.loss(batch, output)
self.manual_loss.append(loss)
self.log('train_loss', loss)
return {"loss": loss}
max_epochs = 2
limit_train_batches = 2
model = TestModel()
test_callback = TestCallback()
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=limit_train_batches,
limit_val_batches=0,
limit_test_batches=0,
val_check_interval=0.,
num_sanity_val_steps=0,
max_epochs=max_epochs,
callbacks=[test_callback]
)
trainer.fit(model)
assert test_callback.funcs_called_count["on_train_start"] == 1
assert test_callback.funcs_called_count["on_epoch_start"] == 2
assert test_callback.funcs_called_count["on_train_epoch_start"] == 2
assert test_callback.funcs_called_count["on_batch_start"] == 4
assert test_callback.funcs_called_count["on_train_batch_start"] == 4
assert test_callback.funcs_called_count["on_batch_end"] == 4
assert test_callback.funcs_called_count["on_train_batch_end"] == 4
assert test_callback.funcs_called_count["on_epoch_end"] == 2
assert test_callback.funcs_called_count["on_train_epoch_end"] == 2
# Make sure the func_name exists within callback_metrics. If not, we missed some
callback_metrics_keys = [*trainer.callback_metrics.keys()]
for func_name in test_callback.callback_funcs_called.keys():
is_in = False
for callback_metrics_key in callback_metrics_keys:
if func_name in callback_metrics_key:
is_in = True
assert is_in, (func_name, callback_metrics_keys)
# function used to describe expected return logic
def get_expected_output(func_attr, original_values):
if func_attr["on_epoch"] and not func_attr["on_step"]:
# Apply mean on values
expected_output = np.mean(original_values)
else:
# Keep the latest value
expected_output = np.max(original_values)
return expected_output
# Make sure the func_name output equals the average from all logged values when on_epoch true
# pop extra keys
trainer.callback_metrics.pop("debug_epoch")
assert trainer.logged_metrics["train_loss"] == model.manual_loss[-1]
assert trainer.callback_metrics["train_loss"] == model.manual_loss[-1]
trainer.callback_metrics.pop("train_loss")
for func_name, output_value in trainer.callback_metrics.items():
if torch.is_tensor(output_value):
output_value = output_value.item()
# get creation attr
func_attr = test_callback.funcs_attr[func_name]
# retrived orginal logged values
original_values = test_callback.callback_funcs_called[func_attr["func_name"]]
# compute expected output and compare to actual one
expected_output = get_expected_output(func_attr, original_values)
assert float(output_value) == float(expected_output)
for func_name, func_attr in test_callback.funcs_attr.items():
if func_attr["prog_bar"] and (func_attr["on_step"] or func_attr["on_epoch"]) and not func_attr["forked"]:
assert func_name in trainer.logger_connector.progress_bar_metrics
else:
assert func_name not in trainer.logger_connector.progress_bar_metrics
def fit_se_gn(cov, sqdist, max_iter=10000, tol=1e-8, verbose=False):
"""Fit the amplitude and length-scale of a squared-exponential kernel
This function minimises the Frobenius norm of the difference
between the experimental and fitted covariance matrices
(i.e., it is a least-squares between the elements of the matrices).
It performs a non-linear least-squares fit that uses the robust
Hessian from Balbastre et al. (2021).
Parameters
----------
cov : (*batch, vox, vox)
Log of the empirical covariance matrix
sqdist : tuple[int] or (vox, vox) tensor
If a tensor -> it is the pre-computed squared distance map
If a tuple -> it is the shape and we build the distance map
max_iter : int, default=100
tol : float, default=1e-5
verbose : {0, 1, 2}, default=0
Returns
-------
sig : (*batch,) tensor
Amplitude of the kernel
lam : (*batch,) tensor
Length-scale of the kernel
References
----------
..[1] "Model-based multi-parameter mapping"
Yael Balbastre, Mikael Brudfors, Michaela Azzarito,
Christian Lambert, Martina F. Callaghan and John Ashburner
Preprint, 2021
"""
cov = torch.as_tensor(cov).clone()
backend = utils.backend(cov)
if not torch.is_tensor(sqdist):
shape = sqdist
sqdist = dist_map(shape, **backend)
else:
sqdist = sqdist.to(**backend).clone()
sqdist = sqdist.flatten()
# exponential fit
a = cov.diagonal(0, -1, -2).abs().mean(-1).log()
cov = cov.reshape([-1, py.prod(sqdist.shape)])
b = torch.ones_like(a).div_(-2)
ll0 = None
ll1 = None
for it in range(max_iter):
# compute objective
e = sqdist.mul(b[:, None]).add_(a[:, None]).exp_()
ll = (e - cov).square().sum() * 0.5
if ll0 is None:
ll0 = ll
gain = constants.inf
else:
gain = (ll1 - ll) / ll0
ll1 = ll
if verbose:
end = '\n' if verbose > 1 else '\r'
print(
f'{it+1:3d} | ll = {ll:12.6g} | gain = {gain:12.6g} '
f'| a = {a.mean():12.6g} | b = {b.mean():12.6g}',
end=end)
if it == 0:
print('')
if abs(gain) < tol:
break
# compute gradient
r = (e - cov).abs_().mul_(sqdist + 1)
ed = e * sqdist
ha = (e.square() + e * r).sum(-1)
hb = (ed.square() + ed * r).sum(-1)
hab = (e * ed).sum(-1)
h = torch.stack([ha, hb, hab], -1)
del ha, hb, hab, ed, r
ga = e * (e - cov)
gb = (sqdist * ga).sum(-1)
ga = ga.sum(-1)
g = torch.stack([ga, gb], -1)
del ga, gb
# udpate
h[..., :2] += 1e-3
delta = linalg.sym_solve(h, g)
del g, h
a -= delta[..., 0]
b -= delta[..., 1]
del delta
if verbose == 1:
print('')
lam = b.reciprocal_().mul_(-0.5).sqrt_()
sig = a.div_(2).exp_()
return sig, lam
def type_as(a, b):
if torch.is_tensor(a) and torch.is_tensor(b):
return a.to(b)
else:
return a
def pipeline( # noqa: C901
*,
# 1. Dataset
dataset: Union[None, str, Dataset, Type[Dataset]] = None,
dataset_kwargs: Optional[Mapping[str, Any]] = None,
training: Hint[CoreTriplesFactory] = None,
testing: Hint[CoreTriplesFactory] = None,
validation: Hint[CoreTriplesFactory] = None,
evaluation_entity_whitelist: Optional[Collection[str]] = None,
evaluation_relation_whitelist: Optional[Collection[str]] = None,
# 2. Model
model: Union[None, str, Model, Type[Model]] = None,
model_kwargs: Optional[Mapping[str, Any]] = None,
interaction: Union[None, str, Interaction, Type[Interaction]] = None,
interaction_kwargs: Optional[Mapping[str, Any]] = None,
dimensions: Union[None, int, Mapping[str, int]] = None,
# 3. Loss
loss: HintType[Loss] = None,
loss_kwargs: Optional[Mapping[str, Any]] = None,
# 4. Regularizer
regularizer: HintType[Regularizer] = None,
regularizer_kwargs: Optional[Mapping[str, Any]] = None,
# 5. Optimizer
optimizer: HintType[Optimizer] = None,
optimizer_kwargs: Optional[Mapping[str, Any]] = None,
clear_optimizer: bool = True,
# 6. Training Loop
training_loop: HintType[TrainingLoop] = None,
training_loop_kwargs: Optional[Mapping[str, Any]] = None,
negative_sampler: HintType[NegativeSampler] = None,
negative_sampler_kwargs: Optional[Mapping[str, Any]] = None,
# 7. Training (ronaldo style)
training_kwargs: Optional[Mapping[str, Any]] = None,
stopper: HintType[Stopper] = None,
stopper_kwargs: Optional[Mapping[str, Any]] = None,
# 8. Evaluation
evaluator: HintType[Evaluator] = None,
evaluator_kwargs: Optional[Mapping[str, Any]] = None,
evaluation_kwargs: Optional[Mapping[str, Any]] = None,
# 9. Tracking
result_tracker: HintType[ResultTracker] = None,
result_tracker_kwargs: Optional[Mapping[str, Any]] = None,
# Misc
metadata: Optional[Dict[str, Any]] = None,
device: Hint[torch.device] = None,
random_seed: Optional[int] = None,
use_testing_data: bool = True,
evaluation_fallback: bool = False,
filter_validation_when_testing: bool = True,
) -> PipelineResult:
"""Train and evaluate a model.
:param dataset:
The name of the dataset (a key from :data:`pykeen.datasets.datasets`) or the :class:`pykeen.datasets.Dataset`
instance. Alternatively, the training triples factory (``training``), testing triples factory (``testing``),
and validation triples factory (``validation``; optional) can be specified.
:param dataset_kwargs:
The keyword arguments passed to the dataset upon instantiation
:param training:
A triples factory with training instances or path to the training file if a a dataset was not specified
:param testing:
A triples factory with training instances or path to the test file if a dataset was not specified
:param validation:
A triples factory with validation instances or path to the validation file if a dataset was not specified
:param evaluation_entity_whitelist:
Optional restriction of evaluation to triples containing *only* these entities. Useful if the downstream task
is only interested in certain entities, but the relational patterns with other entities improve the entity
embedding quality.
:param evaluation_relation_whitelist:
Optional restriction of evaluation to triples containing *only* these relations. Useful if the downstream task
is only interested in certain relation, but the relational patterns with other relations improve the entity
embedding quality.
:param model:
The name of the model, subclass of :class:`pykeen.models.Model`, or an instance of
:class:`pykeen.models.Model`. Can be given as None if the ``interaction`` keyword is used.
:param model_kwargs:
Keyword arguments to pass to the model class on instantiation
:param interaction: The name of the interaction class, a subclass of :class:`pykeen.nn.modules.Interaction`,
or an instance of :class:`pykeen.nn.modules.Interaction`. Can not be given when there is also a model.
:param interaction_kwargs:
Keyword arguments to pass during instantiation of the interaction class. Only use with ``interaction``.
:param dimensions:
Dimensions to assign to the embeddings of the interaction. Only use with ``interaction``.
:param loss:
The name of the loss or the loss class.
:param loss_kwargs:
Keyword arguments to pass to the loss on instantiation
:param regularizer:
The name of the regularizer or the regularizer class.
:param regularizer_kwargs:
Keyword arguments to pass to the regularizer on instantiation
:param optimizer:
The name of the optimizer or the optimizer class. Defaults to :class:`torch.optim.Adagrad`.
:param optimizer_kwargs:
Keyword arguments to pass to the optimizer on instantiation
:param clear_optimizer:
Whether to delete the optimizer instance after training. As the optimizer might have additional memory
consumption due to e.g. moments in Adam, this is the default option. If you want to continue training, you
should set it to False, as the optimizer's internal parameter will get lost otherwise.
:param training_loop:
The name of the training loop's training approach (``'slcwa'`` or ``'lcwa'``) or the training loop class.
Defaults to :class:`pykeen.training.SLCWATrainingLoop`.
:param training_loop_kwargs:
Keyword arguments to pass to the training loop on instantiation
:param negative_sampler:
The name of the negative sampler (``'basic'`` or ``'bernoulli'``) or the negative sampler class.
Only allowed when training with sLCWA.
Defaults to :class:`pykeen.sampling.BasicNegativeSampler`.
:param negative_sampler_kwargs:
Keyword arguments to pass to the negative sampler class on instantiation
:param training_kwargs:
Keyword arguments to pass to the training loop's train function on call
:param stopper:
What kind of stopping to use. Default to no stopping, can be set to 'early'.
:param stopper_kwargs:
Keyword arguments to pass to the stopper upon instantiation.
:param evaluator:
The name of the evaluator or an evaluator class. Defaults to :class:`pykeen.evaluation.RankBasedEvaluator`.
:param evaluator_kwargs:
Keyword arguments to pass to the evaluator on instantiation
:param evaluation_kwargs:
Keyword arguments to pass to the evaluator's evaluate function on call
:param result_tracker:
The ResultsTracker class or name
:param result_tracker_kwargs:
The keyword arguments passed to the results tracker on instantiation
:param metadata:
A JSON dictionary to store with the experiment
:param use_testing_data:
If true, use the testing triples. Otherwise, use the validation triples. Defaults to true - use testing triples.
:param device: The device or device name to run on. If none is given, the device will be looked up with
:func:`pykeen.utils.resolve_device`.
:param random_seed: The random seed to use. If none is specified, one will be assigned before any code
is run for reproducibility purposes. In the returned :class:`PipelineResult` instance, it can be accessed
through :data:`PipelineResult.random_seed`.
:param evaluation_fallback:
If true, in cases where the evaluation failed using the GPU it will fall back to using a smaller batch size or
in the last instance evaluate on the CPU, if even the smallest possible batch size is too big for the GPU.
:param filter_validation_when_testing:
If true, during the evaluating of the test dataset, validation triples are added to the set of known positive
triples, which are filtered out when performing filtered evaluation following the approach described by
[bordes2013]_. This should be explicitly set to false only in the scenario that you are training a single
model using the pipeline and evaluating with the testing set, but never using the validation set for
optimization at all. This is a very atypical scenario, so it is left as true by default to promote
comparability to previous publications.
:returns: A pipeline result package.
:raises ValueError:
If a negative sampler is specified with LCWA
:raises TypeError:
If an invalid argument type is given for ``evaluation_kwargs["additional_filter_triples"]``
"""
if training_kwargs is None:
training_kwargs = {}
training_kwargs = dict(training_kwargs)
# To allow resuming training from a checkpoint when using a pipeline, the pipeline needs to obtain the
# used random_seed to ensure reproducible results
checkpoint_name = training_kwargs.get('checkpoint_name')
if checkpoint_name is not None:
checkpoint_directory = pathlib.Path(
training_kwargs.get('checkpoint_directory', PYKEEN_CHECKPOINTS))
checkpoint_directory.mkdir(parents=True, exist_ok=True)
checkpoint_path = checkpoint_directory / checkpoint_name
if checkpoint_path.is_file():
checkpoint_dict = torch.load(checkpoint_path)
_random_seed = checkpoint_dict['random_seed']
logger.info('loaded random seed %s from checkpoint.', _random_seed)
# We have to set clear optimizer to False since training should be continued
clear_optimizer = False
else:
logger.info(
f"=> no training loop checkpoint file found at '{checkpoint_path}'. Creating a new file."
)
if random_seed is None:
_random_seed = random_non_negative_int()
logger.warning(
f'No random seed is specified. Setting to {_random_seed}.')
else:
_random_seed = random_seed
elif random_seed is None:
_random_seed = random_non_negative_int()
logger.warning(
f'No random seed is specified. Setting to {_random_seed}.')
else:
_random_seed = random_seed # random seed given successfully
set_random_seed(_random_seed)
_result_tracker = tracker_resolver.make(result_tracker,
result_tracker_kwargs)
if not metadata:
metadata = {}
title = metadata.get('title')
# Start tracking
_result_tracker.start_run(run_name=title)
_device: torch.device = resolve_device(device)
dataset_instance: Dataset = get_dataset(
dataset=dataset,
dataset_kwargs=dataset_kwargs,
training=training,
testing=testing,
validation=validation,
)
if dataset is not None:
_result_tracker.log_params(
dict(dataset=dataset_instance.get_normalized_name()))
else: # means that dataset was defined by triples factories
_result_tracker.log_params(
dict(
dataset=USER_DEFINED_CODE,
training=training
if isinstance(training, str) else USER_DEFINED_CODE,
testing=testing
if isinstance(training, str) else USER_DEFINED_CODE,
validation=validation
if isinstance(training, str) else USER_DEFINED_CODE,
))
training, testing, validation = dataset_instance.training, dataset_instance.testing, dataset_instance.validation
# evaluation restriction to a subset of entities/relations
if any(f is not None for f in (evaluation_entity_whitelist,
evaluation_relation_whitelist)):
testing = testing.new_with_restriction(
entities=evaluation_entity_whitelist,
relations=evaluation_relation_whitelist,
)
if validation is not None:
validation = validation.new_with_restriction(
entities=evaluation_entity_whitelist,
relations=evaluation_relation_whitelist,
)
model_instance: Model
if model is not None and interaction is not None:
raise ValueError('can not pass both a model and interaction')
elif model is None and interaction is None:
raise ValueError('must pass one of model or interaction')
elif interaction is not None:
if dimensions is None:
raise ValueError('missing dimensions')
model = make_model_cls(
interaction=interaction,
dimensions=dimensions,
interaction_kwargs=interaction_kwargs,
)
if isinstance(model, Model):
model_instance = model
# TODO should training be reset?
# TODO should kwargs for loss and regularizer be checked and raised for?
else:
model_instance = _build_model_helper(
model=model,
model_kwargs=model_kwargs,
loss=loss,
loss_kwargs=loss_kwargs,
regularizer=regularizer,
regularizer_kwargs=regularizer_kwargs,
_device=_device,
_random_seed=_random_seed,
training_triples_factory=training,
)
# Log model parameters
_result_tracker.log_params(
params=dict(cls=model_instance.__class__.__name__,
kwargs=model_kwargs),
prefix='model',
)
optimizer_instance = optimizer_resolver.make(
optimizer,
optimizer_kwargs,
params=model_instance.get_grad_params(),
)
_result_tracker.log_params(
params=dict(cls=optimizer_instance.__class__.__name__,
kwargs=optimizer_kwargs),
prefix='optimizer',
)
training_loop_cls = training_loop_resolver.lookup(training_loop)
if training_loop_kwargs is None:
training_loop_kwargs = {}
if negative_sampler is None:
negative_sampler_cls = None
training_loop_instance = training_loop_cls(
model=model_instance,
triples_factory=training,
optimizer=optimizer_instance,
**training_loop_kwargs,
)
elif not issubclass(training_loop_cls, SLCWATrainingLoop):
raise ValueError('Can not specify negative sampler with LCWA')
else:
negative_sampler_cls = negative_sampler_resolver.lookup(
negative_sampler)
_result_tracker.log_params(
params=dict(cls=negative_sampler_cls.__name__,
kwargs=negative_sampler_kwargs),
prefix='negative_sampler',
)
training_loop_instance = SLCWATrainingLoop(
model=model_instance,
triples_factory=training,
optimizer=optimizer_instance,
negative_sampler=negative_sampler_cls,
negative_sampler_kwargs=negative_sampler_kwargs,
**training_loop_kwargs,
)
_result_tracker.log_params(
params=dict(cls=training_loop_instance.__class__.__name__),
prefix='training_loop',
)
if evaluator_kwargs is None:
evaluator_kwargs = {}
evaluator_kwargs = dict(evaluator_kwargs)
evaluator_instance: Evaluator = evaluator_resolver.make(
evaluator, evaluator_kwargs)
if evaluation_kwargs is None:
evaluation_kwargs = {}
evaluation_kwargs = dict(evaluation_kwargs)
# Stopping
if 'stopper' in training_kwargs and stopper is not None:
raise ValueError('Specified stopper in training_kwargs and as stopper')
if 'stopper' in training_kwargs:
stopper = training_kwargs.pop('stopper')
if stopper_kwargs is None:
stopper_kwargs = {}
stopper_kwargs = dict(stopper_kwargs)
# Load the evaluation batch size for the stopper, if it has been set
_evaluation_batch_size = evaluation_kwargs.get('batch_size')
if _evaluation_batch_size is not None:
stopper_kwargs.setdefault('evaluation_batch_size',
_evaluation_batch_size)
stopper_instance: Stopper = stopper_resolver.make(
stopper,
model=model_instance,
evaluator=evaluator_instance,
training_triples_factory=training,
evaluation_triples_factory=validation,
result_tracker=_result_tracker,
**stopper_kwargs,
)
training_kwargs.setdefault('num_epochs', 5)
training_kwargs.setdefault('batch_size', 256)
_result_tracker.log_params(params=training_kwargs, prefix='training')
# Add logging for debugging
logging.debug("Run Pipeline based on following config:")
if dataset is not None:
logging.debug(f"dataset: {dataset}")
logging.debug(f"dataset_kwargs: {dataset_kwargs}")
else:
logging.debug('training: %s', training)
logging.debug('testing: %s', testing)
if validation:
logging.debug('validation: %s', validation)
logging.debug(f"model: {model_instance}")
logging.debug(f"model_kwargs: {model_kwargs}")
logging.debug(f"loss: {model_instance.loss}")
logging.debug(f"loss_kwargs: {loss_kwargs}")
logging.debug(f"regularizer: {regularizer}")
logging.debug(f"regularizer_kwargs: {regularizer_kwargs}")
logging.debug(f"optimizer: {optimizer}")
logging.debug(f"optimizer_kwargs: {optimizer_kwargs}")
logging.debug(f"training_loop: {training_loop_instance}")
if negative_sampler_cls is not None:
logging.debug(f"negative_sampler: {negative_sampler_cls}")
logging.debug(f"_negative_sampler_kwargs: {negative_sampler_kwargs}")
logging.debug(f"_training_kwargs: {training_kwargs}")
logging.debug(f"stopper: {stopper_instance}")
logging.debug(f"stopper_kwargs: {stopper_kwargs}")
logging.debug(f"evaluator: {evaluator}")
logging.debug(f"evaluator_kwargs: {evaluator_kwargs}")
# Train like Cristiano Ronaldo
training_start_time = time.time()
losses = training_loop_instance.train(
triples_factory=training,
stopper=stopper_instance,
result_tracker=_result_tracker,
clear_optimizer=clear_optimizer,
**training_kwargs,
)
assert losses is not None # losses is only none if it's doing search mode
training_end_time = time.time() - training_start_time
if use_testing_data:
mapped_triples = testing.mapped_triples
elif validation is None:
raise ValueError('no validation triples available')
else:
mapped_triples = validation.mapped_triples
# Build up a list of triples if we want to be in the filtered setting
if evaluator_instance.filtered:
additional_filter_triples: List[MappedTriples] = [
training.mapped_triples,
]
# If the user gave custom "additional_filter_triples"
popped_additional_filter_triples = evaluation_kwargs.pop(
'additional_filter_triples', [])
if isinstance(popped_additional_filter_triples, (list, tuple)):
additional_filter_triples.extend(popped_additional_filter_triples)
elif torch.is_tensor(
popped_additional_filter_triples): # a single MappedTriple
additional_filter_triples.append(popped_additional_filter_triples)
else:
raise TypeError(
f'Invalid type for `evaluation_kwargs["additional_filter_triples"]`:'
f' {type(popped_additional_filter_triples)}', )
# Determine whether the validation triples should also be filtered while performing test evaluation
if (use_testing_data and filter_validation_when_testing
and validation is not None):
if isinstance(stopper, EarlyStopper):
logging.info(
"When evaluating the test dataset after running the pipeline with early stopping, the validation"
" triples are added to the set of known positive triples which are filtered out when performing"
" filtered evaluation following the approach described by (Bordes et al., 2013).",
)
else:
logging.info(
"When evaluating the test dataset, validation triples are added to the set of known positive"
" triples which are filtered out when performing filtered evaluation following the approach"
" described by (Bordes et al., 2013).", )
additional_filter_triples.append(validation.mapped_triples)
# TODO consider implications of duplicates
evaluation_kwargs[
'additional_filter_triples'] = additional_filter_triples
# Evaluate
# Reuse optimal evaluation parameters from training if available, only if the validation triples are used again
if evaluator_instance.batch_size is not None or evaluator_instance.slice_size is not None and not use_testing_data:
evaluation_kwargs['batch_size'] = evaluator_instance.batch_size
evaluation_kwargs['slice_size'] = evaluator_instance.slice_size
# Add logging about evaluator for debugging
logging.debug("Evaluation will be run with following parameters:")
logging.debug(f"evaluation_kwargs: {evaluation_kwargs}")
evaluate_start_time = time.time()
metric_results: MetricResults = _safe_evaluate(
model=model_instance,
mapped_triples=mapped_triples,
evaluator=evaluator_instance,
evaluation_kwargs=evaluation_kwargs,
evaluation_fallback=evaluation_fallback,
)
evaluate_end_time = time.time() - evaluate_start_time
_result_tracker.log_metrics(
metrics=metric_results.to_dict(),
step=training_kwargs.get('num_epochs'),
)
_result_tracker.end_run()
return PipelineResult(
random_seed=_random_seed,
model=model_instance,
training=training,
training_loop=training_loop_instance,
losses=losses,
stopper=stopper_instance,
metric_results=metric_results,
metadata=metadata,
train_seconds=training_end_time,
evaluate_seconds=evaluate_end_time,
)
def __getitem__(self, index):
if self.num_blocks is None:
res = super(BlockLazyTensor, self).__getitem__(index)
return res
# Cases for when there's an inner batch
else:
batch_index = index if not isinstance(index, tuple) else index[0]
first_tensor_index_dim = None
# Keeping all batch dimensions - recursion base case
if isinstance(batch_index, slice) and batch_index == slice(
None, None, None):
res = super(BlockLazyTensor, self).__getitem__(index)
return res
# Construct a new lazy tensor
# Get rid of sum_batch_index if we're choosing one batch tensor
if isinstance(batch_index, int):
batch_index = slice(batch_index * self.num_blocks,
(batch_index + 1) * self.num_blocks, None)
num_blocks = None
# Keep sum_batch_index, because we still have an inner batch
elif isinstance(batch_index, slice):
start, stop, step = batch_index.indices(self.size(0))
batch_index = slice(start * self.num_blocks,
stop * self.num_blocks, step)
num_blocks = self.num_blocks
# Keep sum_batch_index, because we still have an inner batch
# Also keep track that there has been tensor indexing
elif torch.is_tensor(batch_index):
block_index = torch.arange(0,
self.num_blocks,
dtype=torch.long,
device=self.device)
batch_index = (batch_index.unsqueeze(1).mul(self.num_blocks) +
block_index.unsqueeze(0)).view(-1)
num_blocks = self.num_blocks
first_tensor_index_dim = 0
else:
raise RuntimeError("Unknown batch index type")
# Now construct a new sum batch lazy tensor, and recurse
components = tuple(component[batch_index]
for component in self._args)
new_var = self.__class__(*components, num_blocks=num_blocks)
# If the index was only on the batch index, we're done
if not isinstance(index, tuple) or len(index) == 1:
return new_var
# Else - recurse
else:
left_index = index[1]
right_index = index[2] if len(index) >= 3 else slice(
None, None, None)
# Normal case if we're indexing the LT with ints or slices
# Also squeeze dimensions if we're indexing with tensors
squeeze_left = False
squeeze_right = False
if isinstance(left_index, int):
left_index = slice(left_index, left_index + 1, None)
squeeze_left = True
elif torch.is_tensor(left_index):
squeeze_left = True
if isinstance(right_index, int):
right_index = slice(right_index, right_index + 1, None)
squeeze_right = True
elif torch.is_tensor(right_index):
squeeze_right = True
if torch.is_tensor(left_index) and torch.is_tensor(
right_index):
if left_index.numel() != right_index.numel():
raise RuntimeError(
"Expected the tensor indices to be the same size: got {} and {}"
.format(left_index.numel(), right_index.numel()))
if new_var.ndimension() == 2:
return new_var._get_indices(left_index, right_index)
else:
batch_index = torch.arange(0,
new_var.size(0),
dtype=torch.long,
device=self.device)
if first_tensor_index_dim is not None:
if batch_index.numel() != left_index.numel():
raise RuntimeError(
"Expected the tensor indices to be the same size: got {}, {} and {}"
.format(batch_index.numel(),
left_index.numel(),
right_index.numel()))
return new_var._batch_get_indices(
batch_index, left_index, right_index)
else:
batch_size = batch_index.numel()
row_col_size = left_index.numel()
batch_index = batch_index.unsqueeze(1).repeat(
1, row_col_size).view(-1)
left_index = left_index.unsqueeze(1).repeat(
batch_size, 1).view(-1)
right_index = right_index.unsqueeze(1).repeat(
batch_size, 1).view(-1)
res = new_var._batch_get_indices(
batch_index, left_index, right_index)
return res.view(batch_size, row_col_size)
# Normal case: we have to do some processing on eithe rthe rows or columns
res = new_var._getitem_nonbatch(left_index, right_index,
first_tensor_index_dim)
if (squeeze_left or squeeze_right) and isinstance(
res, LazyTensor):
res = res.evaluate()
if squeeze_left:
res = res.squeeze(-2)
if squeeze_right:
res = res.squeeze(-1)
return res
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
return self.data[idx]
def _set_noise(self, value):
if not torch.is_tensor(value):
value = torch.tensor(value)
self.initialize(raw_noise=self._inv_param_transform(value))
def is_xla_tensor(tensor):
return torch.is_tensor(tensor) and tensor.device.type == "xla"
def make_grid(tensor, nrow=8, padding=2,
normalize=False, range=None, scale_each=False, pad_value=0):
"""Make a grid of images.
Args:
tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)
or a list of images all of the same size.
nrow (int, optional): Number of images displayed in each row of the grid.
The Final grid size is (B / nrow, nrow). Default is 8.
padding (int, optional): amount of padding. Default is 2.
normalize (bool, optional): If True, shift the image to the range (0, 1),
by subtracting the minimum and dividing by the maximum pixel value.
range (tuple, optional): tuple (min, max) where min and max are numbers,
then these numbers are used to normalize the image. By default, min and max
are computed from the tensor.
scale_each (bool, optional): If True, scale each image in the batch of
images separately rather than the (min, max) over all images.
pad_value (float, optional): Value for the padded pixels.
Example:
See this notebook `here <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`_
"""
if not (torch.is_tensor(tensor) or
(isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
raise TypeError('tensor or list of tensors expected, got {}'.format(type(tensor)))
# if list of tensors, convert to a 4D mini-batch Tensor
if isinstance(tensor, list):
tensor = torch.stack(tensor, dim=0)
if tensor.dim() == 2: # single image H x W
tensor = tensor.view(1, tensor.size(0), tensor.size(1))
if tensor.dim() == 3: # single image
if tensor.size(0) == 1: # if single-channel, convert to 3-channel
tensor = torch.cat((tensor, tensor, tensor), 0)
return tensor
if tensor.dim() == 4 and tensor.size(1) == 1: # single-channel images
tensor = torch.cat((tensor, tensor, tensor), 1)
if normalize is True:
tensor = tensor.clone() # avoid modifying tensor in-place
if range is not None:
assert isinstance(range, tuple), \
"range has to be a tuple (min, max) if specified. min and max are numbers"
def norm_ip(img, min, max):
img.clamp_(min=min, max=max)
img.add_(-min).div_(max - min)
def norm_range(t, range):
if range is not None:
norm_ip(t, range[0], range[1])
else:
norm_ip(t, t.min(), t.max())
if scale_each is True:
for t in tensor: # loop over mini-batch dimension
norm_range(t, range)
else:
norm_range(tensor, range)
# make the mini-batch of images into a grid
nmaps = tensor.size(0)
xmaps = min(nrow, nmaps)
ymaps = int(math.ceil(float(nmaps) / xmaps))
height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding)
grid = tensor.new(3, height * ymaps + padding, width * xmaps + padding).fill_(pad_value)
k = 0
for y in irange(ymaps):
for x in irange(xmaps):
if k >= nmaps:
break
grid.narrow(1, y * height + padding, height - padding)\
.narrow(2, x * width + padding, width - padding)\
.copy_(tensor[k])
k = k + 1
return grid
def is_tensor(v):
if type(v).__module__.startswith('torch'):
import torch
return torch.is_tensor(v)
return False
def __to_torch(self, *args):
return (a if a is None or torch.is_tensor(a) else torch.tensor(
a, device=self.config['device'], dtype=torch.float32)
for a in args)
def __init__(self, dataset_dir):
self._dataset_dir = dataset_dir
# files = [name for name in os.listdir(self._dataset_dir)]
files = sorted(
glob(os.path.join(self._dataset_dir, 'pyprob_traces_sorted_*')))
if len(files) > 0:
self._sorted_on_disk = True
else:
self._sorted_on_disk = False
files = sorted(
glob(os.path.join(self._dataset_dir, 'pyprob_traces_*')))
if len(files) == 0:
raise RuntimeError(
'Cannot find any data set files at {}'.format(dataset_dir))
datasets = []
for file in files:
try:
dataset = OfflineDatasetFile(file)
datasets.append(dataset)
except Exception as e:
print(e)
warnings.warn(
'Dataset file potentially corrupt, omitting: {}'.format(
file))
super().__init__(datasets)
print('OfflineDataset at: {}'.format(self._dataset_dir))
print('Num. traces : {:,}'.format(len(self)))
print('Sorted on disk : {}'.format(self._sorted_on_disk))
if self._sorted_on_disk:
self._sorted_indices = list(range(len(self)))
else:
file_name = os.path.join(self._dataset_dir, 'pyprob_hashes')
try:
hashes_file = shelve.open(file_name, 'r')
hashes_exist = 'hashes' in hashes_file
hashes_file.close()
except:
hashes_exist = False
if hashes_exist:
print('Using pre-computed hashes in: {}'.format(file_name))
hashes_file = shelve.open(file_name, 'r')
self._hashes = hashes_file['hashes']
self._sorted_indices = hashes_file['sorted_indices']
hashes_file.close()
if torch.is_tensor(self._hashes):
self._hashes = self._hashes.cpu().numpy()
if len(self._sorted_indices) != len(self):
raise RuntimeError(
'Length of pre-computed hashes ({}) and length of offline dataset ({}) do not match. Dataset files have been altered. Delete and re-generate pre-computed hash file: {}'
.format(len(self._sorted_indices), len(self),
file_name))
else:
print('No pre-computed hashes found, generating: {}'.format(
file_name))
hashes_file = shelve.open(file_name, 'c')
hashes, sorted_indices = self._compute_hashes()
hashes_file['hashes'] = hashes
hashes_file['sorted_indices'] = sorted_indices
hashes_file.close()
self._sorted_indices = sorted_indices
self._hashes = hashes
print('Num. trace types : {:,}'.format(len(set(self._hashes))))
hashes_and_counts = OrderedDict(
sorted(Counter(self._hashes).items()))
print('Trace hash\tCount')
for hash, count in hashes_and_counts.items():
print('{:.8f}\t{}'.format(hash, count))
print()
def _set_period_length(self, value):
if not torch.is_tensor(value):
value = torch.as_tensor(value).to(self.raw_period_length)
self.initialize(raw_period_length=self.raw_period_length_constraint.
inverse_transform(value))
def roi_tanh_circular_restore(warped_images: torch.Tensor,
rois: torch.Tensor,
image_width: int,
image_height: int,
angular_offsets: Union[float,
torch.Tensor] = 0.0,
interpolation: str = 'bilinear',
padding: str = 'zeros',
keep_aspect_ratio: bool = False) -> torch.Tensor:
warped_height, warped_width = warped_images.size()[-2:]
roi_centers = (rois[:, 2:4] + rois[:, :2]) / 2.0
rois_radii = (rois[:, 2:4] - rois[:, :2]) / math.pi**0.5
grids = torch.zeros(warped_images.size()[:1] +
(image_height, image_width, 2),
dtype=warped_images.dtype,
device=warped_images.device)
dest_x_indices = torch.arange(image_width,
dtype=warped_images.dtype,
device=warped_images.device)
dest_y_indices = torch.arange(image_height,
dtype=warped_images.dtype,
device=warped_images.device)
dest_indices = torch.cat(
(dest_x_indices.unsqueeze(0).expand(
(image_height, image_width)).unsqueeze(-1),
dest_y_indices.unsqueeze(-1).expand(
(image_height, image_width)).unsqueeze(-1)), -1)
if torch.is_tensor(angular_offsets):
cos_offsets, sin_offsets = angular_offsets.cos(), angular_offsets.sin()
else:
cos_offsets = [math.cos(angular_offsets)] * grids.size()[0]
sin_offsets = [math.sin(angular_offsets)] * grids.size()[0]
for roi_center, roi_radii, grid, cos_offset, sin_offset in zip(
roi_centers, rois_radii, grids, cos_offsets, sin_offsets):
normalised_dest_indices = dest_indices - roi_center
normalised_dest_indices[..., 0], normalised_dest_indices[..., 1] = (
cos_offset * normalised_dest_indices[..., 0] +
sin_offset * normalised_dest_indices[..., 1],
cos_offset * normalised_dest_indices[..., 1] -
sin_offset * normalised_dest_indices[..., 0])
if keep_aspect_ratio:
radii = normalised_dest_indices.norm(dim=-1)
orientation_x = normalised_dest_indices[...,
0] / radii.clamp(min=1e-9)
orientation_y = normalised_dest_indices[...,
1] / radii.clamp(min=1e-9)
radii *= torch.sqrt(roi_radii[1]**2 * orientation_x**2 +
roi_radii[0]**2 *
orientation_y**2) / roi_radii[0] / roi_radii[1]
else:
normalised_dest_indices /= roi_radii
radii = normalised_dest_indices.norm(dim=-1)
orientation_x = normalised_dest_indices[...,
0] / radii.clamp(min=1e-9)
orientation_y = normalised_dest_indices[...,
1] / radii.clamp(min=1e-9)
warped_radii = torch.tanh(radii)
grid[...,
0] = ((orientation_x * warped_radii + 1.0) * warped_width / 2.0 -
0.5) / (warped_width - 1.0) * 2.0 - 1.0
grid[...,
1] = ((orientation_y * warped_radii + 1.0) * warped_height / 2.0 -
0.5) / (warped_height - 1.0) * 2.0 - 1.0
return tf.grid_sample(warped_images,
grids,
mode=interpolation,
padding_mode=padding,
align_corners=True)
def train_bert(net, data_iter, lr, num_epochs, batch_size, tgt_vocab, device):
"""Train a model for sequence to sequence."""
def xavier_init_weights(m):
if type(m) == nn.Linear:
torch.nn.init.xavier_uniform_(m.weight)
if type(m) == nn.GRU:
for param in m._flat_weights_names:
if "weight" in param:
torch.nn.init.xavier_uniform_(m._parameters[param])
# net.apply(xavier_init_weights)
try:
checkpoint_prefix = os.path.join("model_data/model_bert.pt")
checkpoint = torch.load(checkpoint_prefix)
net.load_state_dict(checkpoint['model_state_dict'])
net.to(device)
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
optimizer.load_state_dict(checkpoint['optimizer'])
for state in optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.to(device)
except Exception as e:
net.apply(xavier_init_weights)
net.to(device)
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
print("Can not load the model with error:", e)
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
loss = am.MaskedSoftmaxCELoss()
net.train()
animator = am.Animator(xlabel='epoch', ylabel='loss',
xlim=[1, num_epochs*batch_size])
checkpoint_prefix = os.path.join("model_data/model_bert.pt")
# ratio = 100 / len(data_iter)
# print("ratio=", ratio)
num_trained = 0
for epoch in range(num_epochs):
timer = Utility.Timer()
metric = am.Accumulator(2) # Sum of training loss, no. of tokens
# print("epoch ...", epoch)
for i, batch in enumerate(data_iter):
# if random.random() < (1 - ratio * 1.5):
# continue
num_trained += 1
optimizer.zero_grad()
X, X_valid_len, Y, Y_valid_len = [x.to(device) for x in batch]
bos = torch.tensor([tgt_vocab['<bos>']] * Y.shape[0],
device=device).reshape(-1, 1)
dec_input = torch.cat([bos, Y[:, :-1]], 1) # Teacher forcing
Y_hat, _ = net(X, dec_input, X_valid_len)
l = loss(Y_hat, Y, Y_valid_len)
l.sum().backward() # Make the loss scalar for `backward`
# Utility.grad_clipping(net, 1)
num_tokens = Y_valid_len.sum()
optimizer.step()
with torch.no_grad():
metric.add(l.sum(), num_tokens)
# if (i + 1) % 100 == 0:
# print(" batch>>>", i)
if (num_trained + 1) % 100 == 0:
animator.add(num_trained + 1, (metric[0] / metric[1],))
# print(f'epoch = {epoch}, loss = {metric[0] / metric[1]:.3f}')
torch.save({'model_state_dict': net.state_dict(), "optimizer": optimizer.state_dict()},checkpoint_prefix)
# if (epoch + 1) % 10 == 0:
# animator.add(epoch + 1, (metric[0] / metric[1],))
# # print(f'epoch = {epoch}, loss = {metric[0] / metric[1]:.3f}')
# torch.save({'model_state_dict': net.state_dict(), "optimizer": optimizer.state_dict()},checkpoint_prefix)
# sys.stdout.flush()
print(f'loss {metric[0] / metric[1]:.3f}, {metric[1] / timer.stop():.1f} '
f'tokens/sec on {str(device)}')
def roi_tanh_polar_warp(images: torch.Tensor,
rois: torch.Tensor,
target_width: int,
target_height: int,
angular_offsets: Union[float, torch.Tensor] = 0.0,
interpolation: str = 'bilinear',
padding: str = 'zeros',
keep_aspect_ratio: bool = False) -> torch.Tensor:
image_height, image_width = images.size()[-2:]
roi_centers = (rois[:, 2:4] + rois[:, :2]) / 2.0
rois_radii = (rois[:, 2:4] - rois[:, :2]) / math.pi**0.5
grids = torch.zeros(images.size()[:1] + (target_height, target_width, 2),
dtype=images.dtype,
device=images.device)
warped_radii = arctanh(
torch.arange(0.0,
1.0,
1.0 / target_width,
dtype=grids.dtype,
device=grids.device)).unsqueeze(0).expand(
(target_height, target_width))
thetas = torch.arange(0.0,
2.0 * math.pi,
2.0 * math.pi / target_height,
dtype=grids.dtype,
device=grids.device).unsqueeze(-1).expand(
(target_height, target_width))
orientation_x = torch.cos(thetas)
orientation_y = torch.sin(thetas)
if not keep_aspect_ratio:
orientation_x *= warped_radii
orientation_y *= warped_radii
if torch.is_tensor(angular_offsets):
cos_offsets, sin_offsets = angular_offsets.cos(), angular_offsets.sin()
else:
cos_offsets = [math.cos(angular_offsets)] * grids.size()[0]
sin_offsets = [math.sin(angular_offsets)] * grids.size()[0]
for roi_center, roi_radii, grid, cos_offset, sin_offset in zip(
roi_centers, rois_radii, grids, cos_offsets, sin_offsets):
if keep_aspect_ratio:
src_radii = warped_radii * (
roi_radii[0] * roi_radii[1] /
torch.sqrt(roi_radii[1]**2 * orientation_x**2 +
roi_radii[0]**2 * orientation_y**2))
warped_x_indices = src_radii * orientation_x
warped_y_indices = src_radii * orientation_y
else:
warped_x_indices = roi_radii[0] * orientation_x
warped_y_indices = roi_radii[1] * orientation_y
src_x_indices, src_y_indices = (cos_offset * warped_x_indices -
sin_offset * warped_y_indices,
cos_offset * warped_y_indices +
sin_offset * warped_x_indices)
grid[..., 0] = (roi_center[0] + src_x_indices) / (image_width -
1.0) * 2.0 - 1.0
grid[..., 1] = (roi_center[1] + src_y_indices) / (image_height -
1.0) * 2.0 - 1.0
return tf.grid_sample(images,
grids,
mode=interpolation,
padding_mode=padding,
align_corners=True)
def roi_tanh_circular_warp(images: torch.Tensor,
rois: torch.Tensor,
target_width: int,
target_height: int,
angular_offsets: Union[float, torch.Tensor] = 0.0,
interpolation: str = 'bilinear',
padding: str = 'zeros',
keep_aspect_ratio: bool = False) -> torch.Tensor:
image_height, image_width = images.size()[-2:]
roi_centers = (rois[:, 2:4] + rois[:, :2]) / 2.0
rois_radii = (rois[:, 2:4] - rois[:, :2]) / math.pi**0.5
grids = torch.zeros(images.size()[:1] + (target_height, target_width, 2),
dtype=images.dtype,
device=images.device)
normalised_dest_x_indices = torch.arange(
-1.0, 1.0, 2.0 / target_width, dtype=grids.dtype,
device=grids.device) + 1.0 / target_width
normalised_dest_y_indices = torch.arange(
-1.0, 1.0, 2.0 / target_height, dtype=grids.dtype,
device=grids.device) + 1.0 / target_height
normalised_dest_indices = torch.cat(
(normalised_dest_x_indices.unsqueeze(0).expand(
(target_height, target_width)).unsqueeze(-1),
normalised_dest_y_indices.unsqueeze(-1).expand(
(target_height, target_width)).unsqueeze(-1)), -1)
radii = normalised_dest_indices.norm(dim=-1)
orientation_x = normalised_dest_indices[..., 0] / radii.clamp(min=1e-9)
orientation_y = normalised_dest_indices[..., 1] / radii.clamp(min=1e-9)
if torch.is_tensor(angular_offsets):
cos_offsets, sin_offsets = angular_offsets.cos(), angular_offsets.sin()
else:
cos_offsets = [math.cos(angular_offsets)] * grids.size()[0]
sin_offsets = [math.sin(angular_offsets)] * grids.size()[0]
warped_radii = arctanh(radii)
warped_x_indices = warped_radii * orientation_x
warped_y_indices = warped_radii * orientation_y
for roi_center, roi_radii, grid, cos_offset, sin_offset in zip(
roi_centers, rois_radii, grids, cos_offsets, sin_offsets):
if keep_aspect_ratio:
src_radii = warped_radii * (
roi_radii[0] * roi_radii[1] /
torch.sqrt(roi_radii[1]**2 * orientation_x**2 +
roi_radii[0]**2 * orientation_y**2))
src_x_indices, src_y_indices = src_radii * orientation_x, src_radii * orientation_y
else:
src_x_indices, src_y_indices = roi_radii[
0] * warped_x_indices, roi_radii[1] * warped_y_indices
src_x_indices, src_y_indices = (cos_offset * src_x_indices -
sin_offset * src_y_indices,
cos_offset * src_y_indices +
sin_offset * src_x_indices)
grid[..., 0] = (roi_center[0] + src_x_indices) / (image_width -
1.0) * 2.0 - 1.0
grid[..., 1] = (roi_center[1] + src_y_indices) / (image_height -
1.0) * 2.0 - 1.0
return tf.grid_sample(images,
grids,
mode=interpolation,
padding_mode=padding,
align_corners=True)
def __call__(self,
vertices,
faces,
intrinsics,
bg_imgs=None,
deg=0,
return_with_alpha=False,
**kwargs):
''' Returns a B3xHxW batch of mesh overlays
'''
if torch.is_tensor(vertices):
vertices = vertices.detach().cpu().numpy()
if torch.is_tensor(intrinsics):
intrinsics = intrinsics.detach().cpu().numpy()
batch_size = vertices.shape[0]
body_color = COLORS['GT']
output_imgs = []
for bidx in range(batch_size):
if bg_imgs is not None:
_, H, W = bg_imgs[bidx].shape
# Update the renderer's viewport
self.renderer.viewport_height = H
self.renderer.viewport_width = W
self.update_camera(intrinsics[bidx])
self.update_mesh(vertices[bidx],
faces,
body_color=body_color,
deg=deg)
flags = (pyrender.RenderFlags.RGBA
| pyrender.RenderFlags.SKIP_CULL_FACES)
color, depth = self.renderer.render(self.scene, flags=flags)
color = np.transpose(color, [2, 0, 1]).astype(np.float32) / 255.0
color = np.clip(color, 0, 1)
if bg_imgs is None:
if return_with_alpha:
output_imgs.append(color)
else:
output_imgs.append(color[:-1])
else:
if return_with_alpha:
valid_mask = (color[3] > 0)[np.newaxis]
if bg_imgs[bidx].shape[0] < 4:
curr_bg_img = np.concatenate([
bg_imgs[bidx],
np.ones_like(bg_imgs[bidx, [0], :, :])
],
axis=0)
else:
curr_bg_img = bg_imgs[bidx]
output_img = (color * valid_mask +
(1 - valid_mask) * curr_bg_img)
output_imgs.append(np.clip(output_img, 0, 1))
else:
valid_mask = (color[3] > 0)[np.newaxis]
output_img = (color[:-1] * valid_mask +
(1 - valid_mask) * bg_imgs[bidx])
output_imgs.append(np.clip(output_img, 0, 1))
return np.stack(output_imgs, axis=0)
def add(self, output, target):
"""
Args:
output (Tensor): NxK tensor that for each of the N examples
indicates the probability of the super_video belonging to each of
the K classes, according to the model. The probabilities should
sum to one over all classes
target (Tensor): binary NxK tensort that encodes which of the K
classes are associated with the N-th input
(eg: a row [0, 1, 0, 1] indicates that the super_video is
associated with classes 2 and 4)
weight (optional, Tensor): Nx1 tensor representing the weight for
each super_video (each weight > 0)
"""
if not torch.is_tensor(output):
output = torch.from_numpy(output)
if not torch.is_tensor(target):
target = torch.from_numpy(target)
if output.dim() == 1:
output = output.view(-1, 1)
else:
assert output.dim() == 2, \
'wrong output size (should be 1D or 2D with one column \
per class)'
if target.dim() == 1:
target = target.view(-1, 1)
else:
assert target.dim() == 2, \
'wrong target size (should be 1D or 2D with one column \
per class)'
if self.scores.numel() > 0:
assert target.size(1) == self.targets.size(1), \
'dimensions for output should match previously added examples.'
# make sure storage is of sufficient size
if self.scores.storage().size() < self.scores.numel() + output.numel():
new_size = math.ceil(self.scores.storage().size() * 1.5)
self.scores.storage().resize_(int(new_size + output.numel()))
self.targets.storage().resize_(int(new_size + output.numel()))
# store scores and targets
offset = self.scores.size(0) if self.scores.dim() > 0 else 0
self.scores.resize_(offset + output.size(0), output.size(1))
self.targets.resize_(offset + target.size(0), target.size(1))
self.scores.narrow(0, offset, output.size(0)).copy_(output)
self.targets.narrow(0, offset, target.size(0)).copy_(target)
# Idx of correct preds
B, C = target.size()
list_idx_correct_preds = []
for idx in range(B):
correct_preds = True
for j in range(C):
# does not have the same sign so bad preds -> break
target_idx_j = -1 if target[idx, j] == 0 else 1
if target_idx_j * output[idx, j] < 0:
correct_preds = False
break
# good preds! if finished the loop
if correct_preds:
list_idx_correct_preds.append(idx)
return list_idx_correct_preds
