def compute_precision_and_recall(
output: Tensor,
labels: Tensor,
num_gt: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
"""
Arguments:
output(Tensor[N, K])
labels(Tensor[N, K])
num_gt(Tensor[K])
Returns:
prec(Tensor[N, K])
rec(Tensor[N, K])
"""
order = output.argsort(0, descending=True)
tp = labels[order,
torch.ones_like(order) * torch.arange(output.shape[1])]
fp = 1 - tp
tp = tp.cumsum(0)
fp = fp.cumsum(0)
prec = tp / (tp + fp)
rec = div(tp, labels.sum(0)) if num_gt is None \
else div(tp, num_gt)
return prec, rec
def compute_pr_for_each(
output: Tensor,
labels: Tensor,
num_gt: Optional[Union[int,
float]] = None) -> Tuple[Tensor, Tensor]:
"""
Arguments:
output(Tensor[N])
labels(Tensor[N]): Binary labels for each sample
num_gt(int or float): Number of ground truth instances
Returns:
prec(Tensor[N])
rec(Tensor[N])
"""
order = output.argsort(descending=True)
tp = labels[order]
fp = 1 - tp
tp = tp.cumsum(0)
fp = fp.cumsum(0)
prec = tp / (tp + fp)
rec = div(tp, labels.sum().item()) if num_gt is None \
else div(tp, num_gt)
return prec, rec
def _calculate(y: torch.Tensor, y_pred: torch.Tensor):
assert y.ndimension() == y_pred.ndimension() == 1 and len(y) == len(
y_pred), "y and y_pred must be 1 dimension data with same length."
assert y.unique().equal(
torch.tensor([0, 1], dtype=y.dtype, device=y.device)
), "y values must be 0 or 1, can not be all 0 or all 1."
n = len(y)
indexes = y_pred.argsort()
y = y[indexes].cpu().numpy()
y_pred = y_pred[indexes].cpu().numpy()
nneg = auc = tmp_pos = tmp_neg = 0.0
for i in range(n):
y_i = cast(float, y[i])
if i + 1 < n and y_pred[i] == y_pred[i + 1]:
tmp_pos += y_i
tmp_neg += 1 - y_i
continue
if tmp_pos + tmp_neg > 0:
tmp_pos += y_i
tmp_neg += 1 - y_i
nneg += tmp_neg
auc += tmp_pos * (nneg - tmp_neg / 2)
tmp_pos = tmp_neg = 0
continue
if y_i == 1:
auc += nneg
else:
nneg += 1
return auc / (nneg * (n - nneg))
def _calculate(y_pred: torch.Tensor, y: torch.Tensor) -> float:
if not (y.ndimension() == y_pred.ndimension() == 1 and len(y) == len(y_pred)):
raise AssertionError("y and y_pred must be 1 dimension data with same length.")
y_unique = y.unique()
if len(y_unique) == 1:
warnings.warn(f"y values can not be all {y_unique.item()}, skip AUC computation and return `Nan`.")
return float("nan")
if not y_unique.equal(torch.tensor([0, 1], dtype=y.dtype, device=y.device)):
warnings.warn(f"y values must be 0 or 1, but in {y_unique.tolist()}, skip AUC computation and return `Nan`.")
return float("nan")
n = len(y)
indices = y_pred.argsort()
y = y[indices].cpu().numpy()
y_pred = y_pred[indices].cpu().numpy()
nneg = auc = tmp_pos = tmp_neg = 0.0
for i in range(n):
y_i = cast(float, y[i])
if i + 1 < n and y_pred[i] == y_pred[i + 1]:
tmp_pos += y_i
tmp_neg += 1 - y_i
continue
if tmp_pos + tmp_neg > 0:
tmp_pos += y_i
tmp_neg += 1 - y_i
nneg += tmp_neg
auc += tmp_pos * (nneg - tmp_neg / 2)
tmp_pos = tmp_neg = 0
continue
if y_i == 1:
auc += nneg
else:
nneg += 1
return auc / (nneg * (n - nneg))
def gp_posterior(ax,
x: torch.Tensor,
preds: MultivariateNormal,
ewma_alpha: float = 0.0,
label: Optional[str] = None,
sort=True,
fill_alpha=0.05,
**kwargs):
x = x.view(-1)
if sort:
# i = x.argsort(dim=-2)[:, 0]
i = x.argsort()
if i.equal(torch.arange(i.size(0))):
i = slice(None, None, None)
else:
i = slice(None, None, None)
x = n(x[i])
preds_mean = preds.mean.view(-1)
mean = ewma(n(preds_mean[i]), ewma_alpha)
line, *_ = ax.plot(x, mean, **kwargs)
if label is not None:
line.set_label(label)
C = line.get_color()
lower, upper = (p.view(-1) for p in preds.confidence_region())
lower = ewma(n(lower[i]), ewma_alpha)
upper = ewma(n(upper[i]), ewma_alpha)
ax.fill_between(x, lower, upper, alpha=fill_alpha, color=C)
ax.plot(x, lower, color=C, linewidth=0.5)
ax.plot(x, upper, color=C, linewidth=0.5)
def __call__(self, logits: torch.Tensor, labels: torch.Tensor, mask: torch.Tensor):
"""
logits and labels should be the same shape. Labels should be
an array of 0/1s to indicate if the document is relevant.
We don't need a mask here since we select nonzero labels and
masked entries in labels are never equal to 1 (Pedro is pretty sure)
"""
n_relevent = labels.sum().item()
if n_relevent == 0:
# None are relevent, no-op
return
preds = logits.argsort(dim=-1, descending=True)
# nonzeros occur where there are predictions to make
# (n_nonzero, 3)
# 3 = dims for batch, turn and fact
indices = labels.nonzero()
# TODO: This could be batched, but its a pain
all_ranks = []
# import ipdb; ipdb.set_trace()
for batch_idx, turn_idx, fact_idx in indices:
# List of predictions, first element is index
# of top ranked document, second of second-top, etc
inst_preds = preds[batch_idx, turn_idx]
rank = (inst_preds == fact_idx).nonzero().reshape(-1)
all_ranks.append(rank)
all_ranks = torch.cat(all_ranks)
# rank starts at zero from torch, += 1 for inversing it
reciprocal_ranks = 1 / (1 + all_ranks).float()
self._reciprocal_ranks.extend(reciprocal_ranks.cpu().numpy().tolist())
return reciprocal_ranks.mean()
def _erfinv(data: torch.Tensor, eps):
rank = data.argsort().argsort().float()
rank_scaled = (rank / rank.max() - 0.5) * 2
rank_scaled = rank_scaled.clamp(-1 + eps, 1 - eps)
tformed = rank_scaled.erfinv()
return tformed
def get_class_name(ranks: torch.Tensor):
CLASSNAMES = "imagenet1000_clsidx_to_labels.json"
classes = []
with open(os.path.join(file_path, CLASSNAMES), "r") as f:
for line in f.readlines():
raw = line.strip("{").strip("}")
key, value = raw.split(": ")
classes.append(value.strip("'").strip("',\n"))
sorted_ranks = ranks.argsort(descending=True)
return np.array(classes)[sorted_ranks]
def __init__(self, X: torch.Tensor, y: torch.Tensor) -> None:
assert X.dtype == torch.float
assert y.dtype == torch.long
assert len(X.shape) == 2
assert len(y.shape) == 1
assert X.shape[0] == y.shape[0]
self.sort_indices = y.argsort()
self.X = X[self.sort_indices, :]
self.y = y[self.sort_indices]
def manifold_dist_loss_relu_sum(model: nn.Module,
inputs: torch.Tensor,
train_distances: torch.Tensor,
manifold: RiemannianManifold,
margin=0.01,
discount_factor=0.9):
"""
See write up for details on this loss function -- encourages embeddings to preserve graph topology
Parameters:
model (nn.Module): model that takes in graph indices and outputs embeddings in output manifold space
inputs (torch.Tensor): LongTensor of shape [batch_size, num_samples+1] giving the indices of
the vertices to be trained with the first vertex in each element of the batch being the
main vertex and the others being samples
train_distances (torch.Tensor): floating point tensor of shape [batch_size, num_samples]
containing the training distances from the input vertex to the sampled vertices
manifold (RiemannianManifold): Manifold that model embeds vertices into
Returns:
pytorch scalar: Computed loss
"""
input_embeddings = model(inputs)
sample_vertices = input_embeddings.narrow(1, 1,
input_embeddings.size(1) - 1)
main_vertices = input_embeddings.narrow(1, 0, 1).expand_as(sample_vertices)
manifold_dists = manifold.dist(main_vertices, sample_vertices)
sorted_indices = train_distances.argsort(dim=-1)
manifold_dists_sorted = torch.gather(manifold_dists, -1, sorted_indices)
manifold_dists_sorted.add_(EPSILON).log_()
diff_matrix_shape = [
manifold_dists.size()[0],
manifold_dists.size()[1],
manifold_dists.size()[1]
]
row_expanded = manifold_dists_sorted.unsqueeze(2).expand(
*diff_matrix_shape)
column_expanded = manifold_dists_sorted.unsqueeze(1).expand(
*diff_matrix_shape)
diff_matrix = row_expanded - column_expanded + margin
train_dists_sorted = torch.gather(train_distances, -1, sorted_indices)
train_row_expanded = train_dists_sorted.unsqueeze(2).expand(
*diff_matrix_shape)
train_column_expanded = train_dists_sorted.unsqueeze(1).expand(
*diff_matrix_shape)
diff_matrix_train = train_row_expanded - train_column_expanded
masked_diff_matrix = torch.where(diff_matrix_train == 0, diff_matrix_train,
diff_matrix)
masked_diff_matrix.triu_()
relu_(masked_diff_matrix)
masked_diff_matrix = masked_diff_matrix.sum(-1)
loss = masked_diff_matrix.sum(-1).mean()
return loss
def mrr(predictions: torch.Tensor, ground_truth_idx: torch.Tensor) -> float:
"""Calculates mean reciprocal rank (MRR) for given predictions and ground truth values.
:param predictions: BxN tensor of prediction values where B is batch size and N number of classes. Predictions
must be sorted in class ids order
:param ground_truth_idx: Bx1 tensor with index of ground truth class
:return: Mean reciprocal rank score
"""
assert predictions.size(0) == ground_truth_idx.size(0)
indices = predictions.argsort()
return (indices == ground_truth_idx
).nonzero()[:, 1].float().add(1.0).sum().item()
def mapr(input: torch.Tensor, targs: torch.LongTensor, mapn: int):
"""
Compute the mean average precision
> map5 = partial(mapr, mapn=5)
"""
n = targs.shape[0] # number for samples
input = input.argsort(dim=-1, descending=True)[:, :mapn]
targs = targs.view(n, -1)
return ((input == targs).float() /
torch.arange(1, mapn + 1, device=input.device).float()).sum(
dim=-1).mean()
def _rank_data(data: Tensor) -> Tensor:
""" Calculate the rank for each element of a tensor. The rank refers to the indices of an element in the
corresponding sorted tensor (starting from 1). Duplicates of the same value will be assigned the mean of
their rank
Adopted from:
https://github.com/scipy/scipy/blob/v1.6.2/scipy/stats/stats.py#L4140-L4303
"""
n = data.numel()
rank = torch.empty_like(data)
idx = data.argsort()
rank[idx[:n]] = torch.arange(1, n + 1, dtype=data.dtype, device=data.device)
repeats = _find_repeats(data)
for r in repeats:
condition = rank == r
rank[condition] = rank[condition].mean()
return rank
def roc_curve(input: Tensor, targ: Tensor):
"Computes the receiver operator characteristic (ROC) curve by determining the true positive ratio (TPR) and false positive ratio (FPR) for various classification thresholds. Restricted binary classification tasks."
# wassname: fix this by making LongTensor([0]=>device)
targ = targ == 1
desc_score_indices = torch.flip(input.argsort(-1), [-1])
input = input[desc_score_indices]
targ = targ[desc_score_indices]
d = input[1:] - input[:-1]
distinct_value_indices = torch.nonzero(d).transpose(0, 1)[0]
threshold_idxs = torch.cat(
(distinct_value_indices, LongTensor([len(targ) - 1]).to(targ.device)))
tps = torch.cumsum(targ * 1, dim=-1)[threshold_idxs]
fps = 1 + threshold_idxs - tps
if tps[0] != 0 or fps[0] != 0:
zer = torch.zeros(1, dtype=fps.dtype, device=fps.device)
fps = torch.cat((zer, fps))
tps = torch.cat((zer, tps))
fpr, tpr = fps.float() / fps[-1], tps.float() / tps[-1]
return fpr, tpr
def _rank_data(data: Tensor) -> Tensor:
"""Calculate the rank for each element of a tensor. The rank refers to the indices of an element in the
corresponding sorted tensor (starting from 1). Duplicates of the same value will be assigned the mean of their
rank.
Adopted from: `Rank of element tensor`_
"""
n = data.numel()
rank = torch.empty_like(data)
idx = data.argsort()
rank[idx[:n]] = torch.arange(1,
n + 1,
dtype=data.dtype,
device=data.device)
repeats = _find_repeats(data)
for r in repeats:
condition = data == r
rank[condition] = rank[condition].mean()
return rank
def unique1d(tensor: torch.Tensor, return_index: bool = False):
"""Port of np.unique to PyTorch with `return_index` functionality"""
assert len(tensor.shape) == 1
optional_indices = return_index
if optional_indices:
perm = tensor.argsort()
aux = tensor[perm]
else:
tensor.sort_()
aux = tensor
mask = torch.zeros(aux.shape)
mask[:1] = 1
mask[1:] = aux[1:] != aux[:-1]
ret = (aux[mask.byte()], )
if return_index:
ret += (perm[mask.byte()], )
return ret
def __call__(self,
predictions: torch.Tensor,
gold_labels: torch.Tensor,
mask: Optional[torch.Tensor] = None) -> None:
predictions, gold_labels, mask = \
self.detach_tensors(predictions, gold_labels, mask)
num_classes = predictions.size(-1)
predictions = predictions.view((-1, num_classes))
gold_labels = gold_labels.view(-1).long()
predicted_ids = predictions.argsort(-1, descending=True)
correct = predicted_ids.eq(gold_labels.unsqueeze(-1)).float()
reciprocals = torch.arange(1, num_classes + 1,
device=correct.device).float().reciprocal()
reciprocal_ranks = torch.matmul(correct, reciprocals)
if mask is not None:
self.summed_reciprocal_ranks += reciprocal_ranks[mask].sum().item()
self.total_count += mask.sum().item()
else:
self.summed_reciprocal_ranks += reciprocal_ranks.sum().item()
self.total_count += gold_labels.numel()
def _label_ranking_loss_update(
preds: Tensor,
target: Tensor,
sample_weight: Optional[Tensor] = None
) -> Tuple[Tensor, int, Optional[Tensor]]:
"""Accumulate state for label ranking loss.
Args:
preds: tensor with predictions
target: tensor with ground truth labels
sample_weight: optional tensor with weight for each sample
"""
_check_ranking_input(preds, target, sample_weight)
n_preds, n_labels = preds.shape
relevant = target == 1
n_relevant = relevant.sum(dim=1)
# Ignore instances where number of true labels is 0 or n_labels
mask = (n_relevant > 0) & (n_relevant < n_labels)
preds = preds[mask]
relevant = relevant[mask]
n_relevant = n_relevant[mask]
# Nothing is relevant
if len(preds) == 0:
return torch.tensor(0.0, device=preds.device), 1, sample_weight
inverse = preds.argsort(dim=1).argsort(dim=1)
per_label_loss = ((n_labels - inverse) * relevant).to(torch.float32)
correction = 0.5 * n_relevant * (n_relevant + 1)
denom = n_relevant * (n_labels - n_relevant)
loss = (per_label_loss.sum(dim=1) - correction) / denom
if isinstance(sample_weight, Tensor):
loss *= sample_weight[mask]
sample_weight = sample_weight.sum()
return loss.sum(), n_preds, sample_weight
def scatter_split(src: torch.Tensor, indexes: torch.Tensor) -> List[torch.Tensor]:
sorted_src = src[indexes.argsort()]
indexes_count = torch.unique(indexes, return_counts=True)[1]
return torch.split(sorted_src, indexes_count.tolist(), dim=0)
def _get_ranks(x: torch.Tensor) -> torch.Tensor:
tmp = x.argsort()
ranks = torch.zeros_like(tmp)
ranks[tmp] = torch.arange(len(x), device=x.device)
return ranks
def _get_ranks(self, x: torch.Tensor) -> torch.Tensor:
tmp = x.argsort()
ranks = torch.zeros_like(tmp)
ranks[tmp] = torch.arange(x.size(0), device=ranks.device)
return ranks
def get_index(logit: torch.Tensor):
return logit.argsort(dim=0)
def _get_ranks(x: torch.Tensor) -> torch.Tensor:
argsort = x.argsort()
ranks = torch.zeros_like(argsort, device=x.device)
ranks[argsort] = torch.arange(len(x), device=x.device)
return ranks
def evaluate(distmat: torch.Tensor,
qpids: torch.Tensor,
gpids: torch.Tensor,
qcamids: torch.Tensor,
gcamids: torch.Tensor,
max_rank=50):
"""
Evaluate match # by rank
:param distmat:
:param qpids:
:param gpids:
:param qcamids:
:param gcamids:
:param max_rank:
:return:
all_cmc: percentage of queries matched until each rank r
ap_list: average precision for each query image (mean for mAP)
inp_list: inp score for each query image (mean for mINP)
"""
device = distmat.device
q, q = distmat.shape
if q < max_rank:
max_rank = q
print(
"Note: number of gallery samples is quite small, got {}".format(q))
indices = distmat.argsort(dim=1)
matches = gpids[indices].eq(qpids.reshape(-1, 1)).int()
order = indices
remove = torch.logical_and(gpids[order].eq(qpids.reshape(-1, 1)),
gcamids[order].eq(qcamids.reshape(-1, 1)))
keep = remove.logical_not()
kept = keep.cumsum(dim=1)
q, g = len(qpids), len(gpids)
valid_matches = matches * keep
valid_query = valid_matches.sum(dim=1).gt(
0) # at least one matchable (== matched) gallery image
assert (valid_query.all()) # reid dataset queries should all be valid
assert (valid_matches.sum() != 0
) # error: all query identities do not appear in gallery
final_rank_positions = (valid_matches *
torch.arange(1, g + 1, device=device)).argmax(
dim=1)
final_rank_valid = kept[torch.arange(q, device=device),
final_rank_positions]
all_INP = valid_matches.sum(dim=1).float() / final_rank_valid.float()
# `kept` is analogous to index within only-valid instances
cum_precision = valid_matches.cumsum(dim=1).float() / kept.float()
cum_precision[cum_precision.isnan()] = 1
all_AP = (cum_precision *
valid_matches).sum(dim=1) / valid_matches.sum(dim=1)
# Compute CMC (need to go query-by-query) (assume that at least 10% are valid)
buffer = 10
keep = keep[:, :max_rank * buffer]
matches = matches[:, :max_rank * buffer]
all_cmc = []
for i in range(q):
mc = matches[i][keep[i]][:50]
if len(mc) < max_rank:
raise AssertionError(
"Not enough matching galleries. Consider higher `buffer` value."
)
cmc = mc[:max_rank].cumsum(dim=0)
# E.g., 0 1 x x x x ... to 0 1 1 1 1 1 ...
cmc[cmc > 1] = 1
all_cmc.append(cmc)
all_cmc = torch.stack(all_cmc).float()
all_cmc = all_cmc.sum(dim=0) / valid_query.float().sum()
# mAP = all_AP[valid_query].mean()
# mINP = all_INP[valid_query].mean()
return all_cmc, all_AP, all_INP
def evaluate(distmat: torch.Tensor,
q_pids: torch.Tensor,
g_pids: torch.Tensor,
q_camids: torch.Tensor,
g_camids: torch.Tensor,
max_rank=50,
device=None):
"""
Torch implementation of evaluate. Slower on CPU.
:param distmat:
:param q_pids:
:param g_pids:
:param q_camids:
:param g_camids:
:param max_rank:
:return:
"""
distmat = torch.as_tensor(distmat, device=device)
q_pids = torch.as_tensor(q_pids, device=device)
g_pids = torch.as_tensor(g_pids, device=device)
q_camids = torch.as_tensor(q_camids, device=device)
g_camids = torch.as_tensor(g_camids, device=device)
num_q, num_g = distmat.shape
if num_g < max_rank:
max_rank = num_g
print("Note: number of gallery samples is quite small, got {}".format(
num_g))
indices = distmat.argsort(dim=1)
matches = (g_pids[indices] == q_pids.reshape(-1, 1)).int()
order = indices
remove = (g_pids[order] == q_pids.reshape(
-1, 1)) & (g_camids[order] == q_camids.reshape(-1, 1))
keep = remove == False
kept = keep.cumsum(dim=1)
q, g = len(q_pids), len(g_pids)
valid_matches = matches * keep
valid_query = (valid_matches.sum(dim=1) > 0
) # at least one matchable (== matched) gallery image
assert (valid_matches.sum() != 0
) # error: all query identities do not appear in gallery
final_rank_positions = (valid_matches *
torch.arange(1, g + 1, device=device)).argmax(
dim=1)
final_rank_valid = kept[torch.arange(q, device=device),
final_rank_positions]
all_INP = valid_matches.sum(dim=1).float() / final_rank_valid.float()
# `kept` is analogous to index within only-valid instances
cum_precision = valid_matches.cumsum(dim=1).float() / kept.float()
cum_precision[cum_precision.isnan()] = 1
all_AP = (cum_precision *
valid_matches).sum(dim=1) / valid_matches.sum(dim=1)
# Compute CMC (need to go query-by-query) (assume that at least 10% are valid)
buffer = 10
keep = keep[:, :max_rank * buffer]
matches = matches[:, :max_rank * buffer]
all_cmc = []
for i in range(q):
mc = matches[i][keep[i]][:50]
if len(mc) < max_rank:
raise AssertionError(
"Not enough matching galleries. Consider higher `buffer` value."
)
cmc = mc[:max_rank].cumsum(dim=0)
# E.g., 0 1 x x x x ... to 0 1 1 1 1 1 ...
cmc[cmc > 1] = 1
all_cmc.append(cmc)
all_cmc = torch.stack(all_cmc).float()
all_cmc = all_cmc.sum(dim=0) / valid_query.float().sum()
mAP = all_AP[valid_query].mean()
mINP = all_INP[valid_query].mean()
return all_cmc.cpu().numpy(), mAP.item(), mINP.item()