def columnwise_clamp(
X: Tensor,
lower: Optional[Union[float, Tensor]] = None,
upper: Optional[Union[float, Tensor]] = None,
) -> Tensor:
r"""Clamp values of a Tensor in column-wise fashion (with support for t-batches).
This function is useful in conjunction with optimizers from the torch.optim
package, which don't natively handle constraints. If you apply this after
a gradient step you can be fancy and call it "projected gradient descent".
Args:
X: The `b x n x d` input tensor. If 2-dimensional, `b` is assumed to be 1.
lower: The column-wise lower bounds. If scalar, apply bound to all columns.
upper: The column-wise upper bounds. If scalar, apply bound to all columns.
Returns:
The clamped tensor.
"""
min_bounds = _expand_bounds(lower, X)
max_bounds = _expand_bounds(upper, X)
if min_bounds is not None and max_bounds is not None:
if torch.any(min_bounds > max_bounds):
raise ValueError("Minimum values must be <= maximum values")
Xout = X
if min_bounds is not None:
Xout = Xout.max(min_bounds)
if max_bounds is not None:
Xout = Xout.min(max_bounds)
return Xout
def _step(
self,
positive_predicates: List[str],
positive_observations: torch.FloatTensor,
negative_predicates_list: List[List[str]],
negative_observations_list: List[torch.FloatTensor],
) -> Tuple[torch.Tensor, Dict[str, Any]]:
""" Performs a forward pass of the model during training.
Used in both training_step and validation_step, this function accepts a set
of predicate names and performs a forward pass of the hypothesis generation
training routine. This involves generating negative samples for each
positive example and evaluating metrics that quantify the difference
between the two.
Args:
positive_predicates: List of string names associated with positive batch
positive_observations: Packed tensor containing info corresponding to
positive_predicates. Shape: <seq_len> X <batch_size> X <dim>
negative_predicates_list: List of predicate lists.
negative_predicates_list[i] corresponds to the i'th negative sample
batch. Each batch should be the same size as the positive batch.
negative_observations_list: List of packed tensors.
negative_observations_list[i] corresponds to the i'th negative sample.
Returns:
The first element is the loss tensor, used for back propagation. The
second element is a dict containing all extra metrics.
"""
# Do positive checks
assert isinstance(positive_observations, torch.Tensor), \
f"Err: positive_observations is {type(positive_observations)}"
assert len(positive_observations.shape) == 3
_, actual_batch_size, actual_dim = positive_observations.shape
assert len(positive_predicates) == actual_batch_size
assert self.hparams.positives_per_batch == actual_batch_size
assert self.hparams.dim == actual_dim
# no negative checks
assert len(negative_predicates_list) == len(negative_observations_list)
for n_preds, n_obs in zip(negative_predicates_list,
negative_observations_list):
assert isinstance(n_obs, torch.Tensor)
assert len(n_obs.shape) == 3
_, actual_batch_size, actual_dim = n_obs.shape
assert len(n_preds) == actual_batch_size
assert self.hparams.positives_per_batch == actual_batch_size
assert self.hparams.dim == actual_dim
positive_predictions = self.forward(positive_observations)
# We cannot tolerate an error on a positive sample
# An error occurs if any positive prediction is _not_ finite
# Note that `~` is bitwise "not" for our boolean matrix
if torch.any(~torch.isfinite(positive_predictions.detach().cpu())):
print(positive_predicates)
raise ValueError("Invalid positive sample")
partial_losses = []
for negative_predicates, negative_observations in zip(
negative_predicates_list, negative_observations_list):
negative_predictions = self.forward(negative_observations)
# We CAN tolerate an error on a negative sample
if torch.any(~torch.isfinite(negative_predictions.detach().cpu())):
# print debug info
print("ERROR: Encountered an issue with a negative predicate:")
print("Negative Predicate Scores:")
print(negative_predictions)
print("Negative Predicates")
print(negative_predicates)
else:
partial_losses.append(
self.loss_fn(
positive_predictions, negative_predictions,
positive_predictions.new_ones(
len(positive_predictions))))
assert len(
partial_losses) > 0, "Failure occurred on all negative batches."
# End of batch
loss = torch.mean(torch.stack(partial_losses))
return (
loss,
dict( # pbar metrics
))
def train_epoch(
train_loader: torch.utils.data.DataLoader, base_model: torch.nn.Module,
classification_layer: torch.nn.Module, forg_layer: torch.nn.Module,
epoch: int, optimizer: torch.optim.Optimizer,
lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
callback: Optional[VisdomLogger], device: torch.device, args: Any):
""" Trains the network for one epoch
Parameters
----------
train_loader: torch.utils.data.DataLoader
Iterable that loads the training set (x, y) tuples
base_model: torch.nn.Module
The model architecture that "extract features" from signatures
classification_layer: torch.nn.Module
The classification layer (from features to predictions of which user
wrote the signature)
forg_layer: torch.nn.Module
The forgery prediction layer (from features to predictions of whether
the signature is a forgery). Only used in args.forg = True
epoch: int
The current epoch (used for reporting)
optimizer: torch.optim.Optimizer
The optimizer (already initialized)
lr_scheduler: torch.optim.lr_scheduler._LRScheduler
The learning rate scheduler
callback: VisdomLogger (optional)
A callback to report the training progress
device: torch.device
The device (CPU or GPU) to use for training
args: Namespace
Extra arguments used for training:
args.forg: bool
Whether forgeries are being used for training
args.lamb: float
The weight used for the forgery loss (training with forgeries only)
Returns
-------
None
"""
step = 0
n_steps = len(train_loader)
for batch in train_loader:
x, y = batch[0], batch[1]
x = torch.tensor(x, dtype=torch.float).to(device)
y = torch.tensor(y, dtype=torch.long).to(device)
yforg = torch.tensor(batch[2], dtype=torch.float).to(device)
# Forward propagation
features = base_model(x)
if args.forg:
if args.loss_type == 'L1':
# Eq (3) in https://arxiv.org/abs/1705.05787
logits = classification_layer(features)
class_loss = F.cross_entropy(logits, y)
forg_logits = forg_layer(features).squeeze()
forg_loss = F.binary_cross_entropy_with_logits(
forg_logits, yforg)
loss = (1 - args.lamb) * class_loss
loss += args.lamb * forg_loss
else:
# Eq (4) in https://arxiv.org/abs/1705.05787
if torch.any(yforg == 0):
logits = classification_layer(features[yforg == 0])
class_loss = F.cross_entropy(logits, y[yforg == 0])
else:
class_loss = 0
forg_logits = forg_layer(features).squeeze()
forg_loss = F.binary_cross_entropy_with_logits(
forg_logits, yforg)
loss = (1 - args.lamb) * class_loss
loss += args.lamb * forg_loss
else:
# Eq (1) in https://arxiv.org/abs/1705.05787
logits = classification_layer(features)
loss = class_loss = F.cross_entropy(logits, y)
# Back propagation
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_value_(optimizer.param_groups[0]['params'],
10)
# Update weights
optimizer.step()
# Logging
if callback and step % 100 == 0:
iteration = epoch + (step / n_steps)
callback.scalar('class_loss', iteration, class_loss.detach())
pred = logits.argmax(1)
if args.loss_type == 'L1': acc = y.eq(pred).float().mean()
else: acc = y[yforg == 0].eq(pred[yforg == 0]).float().mean()
callback.scalar('train_acc', epoch + (step / n_steps),
acc.detach())
if args.forg:
forg_pred = forg_logits > 0
forg_acc = yforg.long().eq(forg_pred.long()).float().mean()
callback.scalar('forg_loss', iteration, forg_loss.detach())
callback.scalar('forg_acc', iteration, forg_acc.detach())
step += 1
lr_scheduler.step()
def infill_with_ilm(model,
special_tokens_to_ids,
x,
num_infills=1,
max_sequence_length=256,
nucleus=0.95):
_sep_id = special_tokens_to_ids['<|startofinfill|>']
_end_span_id = special_tokens_to_ids['<|endofinfill|>']
_special_ids = special_tokens_to_ids.values()
# Make sure example doesn't already ends with [sep]
if x[-1] == _sep_id:
x = x[:-1]
# Count number of blanks
blank_idxs = []
for i, tok_id in enumerate(x):
if tok_id in _special_ids:
blank_idxs.append(i)
k = len(blank_idxs)
if k == 0:
raise ValueError()
# Decode until we have that many blanks
with torch.no_grad():
device = next(model.parameters()).device
context = torch.tensor(x + [_sep_id], dtype=torch.long,
device=device).unsqueeze(0).repeat(
num_infills, 1)
terminated = []
while context.shape[0] > 0:
logits = model(context)[0][:, -1]
next_tokens = sample_from_logits(logits, nucleus=nucleus)
context = torch.cat((context, next_tokens), dim=1)
num_predicted_spans = (context == _end_span_id).long().sum(dim=1)
terminate_expected = num_predicted_spans >= k
terminate_toolong = torch.ones_like(context).long().sum(
dim=1) >= max_sequence_length
terminate = terminate_expected | terminate_toolong
if torch.any(terminate):
terminated_seqs = context[terminate, len(x) + 1:]
terminated.extend(
[list(s) for s in terminated_seqs.cpu().numpy()])
context = context[~terminate, :]
# Collect generated spans
generated_spans = []
for gen in terminated:
spans = []
while _end_span_id in gen:
spans.append(gen[:gen.index(_end_span_id)])
gen = gen[gen.index(_end_span_id) + 1:]
while len(spans) < k:
spans.append([])
generated_spans.append(spans)
# Insert into context
generated = []
for spans in generated_spans:
context = copy.deepcopy(x)
for i, j in enumerate(blank_idxs[::-1]):
del context[j]
context[j:j] = spans[k - 1 - i]
spans = [item for sublist in spans for item in sublist]
if spans not in generated:
generated.append(spans)
else:
pass
return generated
def main(config_path):
""" """
# load config
cfg = AttrDict.from_json_path(config_path)
# make outputs dir
out_path = os.path.join(cfg.path.output, cfg.exp_name)
os.makedirs(out_path, exist_ok=True)
# initialize seed
if cfg.seed != -1:
random.seed(cfg.seed)
np.random.seed(cfg.seed)
torch.manual_seed(cfg.seed)
torch.cuda.manual_seed(cfg.seed)
torch.cuda.manual_seed_all(cfg.seed)
torch.backends.cudnn.deterministic = True
# initialize logger
logger = initialize_logger(os.path.join(out_path, 'log.txt'))
logger.info(f"Experiment : {cfg.exp_name}")
# set device
if cfg.device:
cfg.device = torch.device(cfg.device)
else:
cfg.device = torch.device(f'cuda:0') if torch.cuda.is_available() else torch.device('cpu')
logger.info(f"Device set to {cfg.device}.")
#-------------------------------------------
# Make Dataset
#-------------------------------------------
data_info_df = pd.read_csv(os.path.join(cfg.path.data, 'ct_info.csv'), index_col=0)
dataset = public_SegICH_Dataset2D(data_info_df, cfg.path.data,
augmentation_transform=[getattr(tf, tf_name)(**tf_kwargs) for tf_name, tf_kwargs in cfg.data.augmentation.items()],
output_size=cfg.data.size, window=(cfg.data.win_center, cfg.data.win_width))
#-------------------------------------------
# Load FCDD Model
#-------------------------------------------
cfg_fcdd = AttrDict.from_json_path(cfg.fcdd_cfg_path)
fcdd_net = FCDD_CNN_VGG(in_shape=(cfg_fcdd.net.in_channels, 256, 256), bias=cfg_fcdd.net.bias)
loaded_state_dict = torch.load(cfg.fcdd_model_path, map_location=cfg.device)
fcdd_net.load_state_dict(loaded_state_dict)
fcdd_net = fcdd_net.to(cfg.device).eval()
logger.info(f"FCDD model succesfully loaded from {cfg.fcdd_model_path}")
# make FCDD object
fcdd = FCDD(fcdd_net, batch_size=cfg.batch_size, num_workers=cfg.num_workers,
device=cfg.device, print_progress=cfg.print_progress)
#-------------------------------------------
# Load Classifier Model
#-------------------------------------------
# Load Classifier
if cfg.classifier_model_path is not None:
cfg_classifier = AttrDict.from_json_path(os.path.join(cfg.classifier_model_path, 'config.json'))
classifier = getattr(rn, cfg_classifier.net.resnet)(num_classes=cfg_classifier.net.num_classes, input_channels=cfg_classifier.net.input_channels)
classifier_state_dict = torch.load(os.path.join(cfg.classifier_model_path, 'resnet_state_dict.pt'), map_location=cfg.device)
classifier.load_state_dict(classifier_state_dict)
classifier = classifier.to(cfg.device)
classifier.eval()
logger.info(f"ResNet classifier model succesfully loaded from {os.path.join(cfg.classifier_model_path, 'resnet_state_dict.pt')}")
#-------------------------------------------
# Generate Heat-Map for each slice
#-------------------------------------------
with torch.no_grad():
# make loader
loader = torch.utils.data.DataLoader(dataset, batch_size=cfg.batch_size, num_workers=cfg.num_workers,
shuffle=False, worker_init_fn=lambda _: np.random.seed())
fcdd_net.eval()
min_val, max_val = fcdd.get_min_max(loader, **cfg.heatmap_param)
# computing and saving heatmaps
out = dict(id=[], slice=[], label=[], ad_map_fn=[], ad_mask_fn=[],
TP=[], TN=[], FP=[], FN=[], AUC=[], classifier_pred=[])
for b, data in enumerate(loader):
im, mask, id, slice = data
im = im.to(cfg.device).float()
mask = mask.to(cfg.device).float()
# get heatmap
heatmap = fcdd.generate_heatmap(im, reception=cfg.heatmap_param.reception, std=cfg.heatmap_param.std,
cpu=cfg.heatmap_param.cpu)
# scaling
heatmap = ((heatmap - min_val) / (max_val - min_val)).clamp(0,1)
# Threshold
ad_mask = torch.where(heatmap >= cfg.heatmap_threshold, torch.ones_like(heatmap, device=heatmap.device),
torch.zeros_like(heatmap, device=heatmap.device))
# Compute CM
tn, fp, fn, tp = batch_binary_confusion_matrix(ad_mask, mask.to(heatmap.device))
# Save heatmaps/mask
map_fn, mask_fn = [], []
for i in range(im.shape[0]):
# Save AD Map
ad_map_fn = f"{id[i]}/{slice[i]}_map_anomalies.png"
save_path = os.path.join(out_path, 'pred/', ad_map_fn)
if not os.path.isdir(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path), exist_ok=True)
ad_map = heatmap[i].squeeze().cpu().numpy()
io.imsave(save_path, img_as_ubyte(ad_map), check_contrast=False)
# save ad_mask
ad_mask_fn = f"{id[i]}/{slice[i]}_anomalies.bmp"
save_path = os.path.join(out_path, 'pred/', ad_mask_fn)
io.imsave(save_path, img_as_ubyte(ad_mask[i].squeeze().cpu().numpy()), check_contrast=False)
map_fn.append(ad_map_fn)
mask_fn.append(ad_mask_fn)
# apply classifier ResNet-18
if cfg.classifier_model_path is not None:
pred_score = nn.functional.softmax(classifier(im), dim=1)[:,1] # take columns of softmax of positive class as score
clss_pred = torch.where(pred_score >= cfg.classification_threshold, torch.ones_like(pred_score, device=pred_score.device),
torch.zeros_like(pred_score, device=pred_score.device))
else:
clss_pred = [None]*im.shape[0]
# Save Values
out['id'] += id.cpu().tolist()
out['slice'] += slice.cpu().tolist()
out['label'] += mask.reshape(mask.shape[0], -1).max(dim=1)[0].cpu().tolist()
out['ad_map_fn'] += map_fn
out['ad_mask_fn'] += mask_fn
out['TN'] += tn.cpu().tolist()
out['FP'] += fp.cpu().tolist()
out['FN'] += fn.cpu().tolist()
out['TP'] += tp.cpu().tolist()
out['AUC'] += [roc_auc_score(mask[i].cpu().numpy().ravel(), heatmap[i].cpu().numpy().ravel()) if torch.any(mask[i]>0) else 'None' for i in range(im.shape[0])]
out['classifier_pred'] += clss_pred.cpu().tolist()
if cfg.print_progress:
print_progessbar(b, len(loader), Name='Heatmap Generation Batch', Size=100, erase=True)
# make df and save as csv
slice_df = pd.DataFrame(out)
volume_df = slice_df[['id', 'label', 'TP', 'TN', 'FP', 'FN']].groupby('id').agg({'label':'max', 'TP':'sum', 'TN':'sum', 'FP':'sum', 'FN':'sum'})
slice_df['Dice'] = (2*slice_df.TP + 1) / (2*slice_df.TP + slice_df.FP + slice_df.FN + 1)
volume_df['Dice'] = (2*volume_df.TP + 1) / (2*volume_df.TP + volume_df.FP + volume_df.FN + 1)
logger.info(f"Mean slice dice : {slice_df.Dice.mean(axis=0):.3f}")
logger.info(f"Mean volume dice : {volume_df.Dice.mean(axis=0):.3f}")
logger.info(f"Mean posiitve slice AUC {slice_df[slice_df.label == 1].AUC.mean(axis=0):.3f}")
# Save Scores and Config
slice_df.to_csv(os.path.join(out_path, 'slice_predictions.csv'))
logger.info(f"Slice prediction csv saved at {os.path.join(out_path, 'slice_predictions.csv')}")
volume_df.to_csv(os.path.join(out_path, 'volume_predictions.csv'))
logger.info(f"Volume prediction csv saved at {os.path.join(out_path, 'volume_predictions.csv')}")
cfg.device = str(cfg.device)
with open(os.path.join(out_path, 'config.json'), 'w') as f:
json.dump(cfg, f)
logger.info(f"Config file saved at {os.path.join(out_path, 'config.json')}")
def forward(self, *args): # pylint: disable=too-many-statements
LOG.debug('loss for %s', self.field_names)
x, t = args
assert len(x) == 1 + 2 * self.n_vectors + self.n_scales
x_intensity = x[0]
x_regs = x[1:1 + self.n_vectors]
x_spreads = x[1 + self.n_vectors:1 + 2 * self.n_vectors]
x_scales = []
if self.n_scales:
x_scales = x[1 + 2 * self.n_vectors:1 + 2 * self.n_vectors + self.n_scales]
if self.n_scales == 0:
t = t[:-self.n_vectors] # assume there are as many scales as vectors and remove them
assert len(t) == 1 + self.n_vectors + self.n_scales
target_intensity = t[0]
target_regs = t[1:1 + self.n_vectors]
target_scales = t[1 + self.n_vectors:]
bce_masks = (target_intensity[:, :-1] + target_intensity[:, -1:]) > 0.5
if not torch.any(bce_masks):
return None, None, None
batch_size = x_intensity.shape[0]
LOG.debug('batch size = %d', batch_size)
bce_x_intensity = x_intensity
bce_target_intensity = target_intensity[:, :-1]
if self.bce_blackout:
bce_x_intensity = bce_x_intensity[:, self.bce_blackout]
bce_masks = bce_masks[:, self.bce_blackout]
bce_target_intensity = bce_target_intensity[:, self.bce_blackout]
LOG.debug('BCE: x = %s, target = %s, mask = %s',
x_intensity.shape, bce_target_intensity.shape, bce_masks.shape)
bce_target = torch.masked_select(bce_target_intensity, bce_masks)
bce_weight = None
if self.background_weight != 1.0:
bce_weight = torch.ones_like(bce_target)
bce_weight[bce_target == 0] = self.background_weight
# print('TRIANGE loss', bce_x_intensity.shape, bce_masks.shape)
ce_loss = torch.nn.functional.binary_cross_entropy_with_logits(
torch.masked_select(bce_x_intensity, bce_masks),
bce_target,
weight=bce_weight,
)
reg_losses = [None for _ in target_regs]
reg_masks = target_intensity[:, :-1] > 0.5
if torch.any(reg_masks):
weight = None
if self.multiplicity_correction:
assert len(target_regs) == 2
lengths = torch.norm(target_regs[0] - target_regs[1], dim=2)
multiplicity = (lengths - 3.0) / self.independence_scale
multiplicity = torch.clamp(multiplicity, min=1.0)
multiplicity = torch.masked_select(multiplicity, reg_masks)
weight = 1.0 / multiplicity
reg_losses = []
for i, (x_reg, x_spread, target_reg) in enumerate(zip(x_regs, x_spreads, target_regs)):
if hasattr(self.regression_loss, 'scale'):
assert self.scales_to_kp is not None
self.regression_loss.scale = torch.masked_select(
torch.clamp(target_scales[i], 0.1, 1000.0), # pylint: disable=unsubscriptable-object
reg_masks,
)
reg_losses.append(self.regression_loss(
torch.masked_select(x_reg[:, :, 0], reg_masks),
torch.masked_select(x_reg[:, :, 1], reg_masks),
torch.masked_select(x_spread, reg_masks),
torch.masked_select(target_reg[:, :, 0], reg_masks),
torch.masked_select(target_reg[:, :, 1], reg_masks),
weight=(weight if weight is not None else 1.0) * 0.1,
) / 100.0 / batch_size)
scale_losses = []
if x_scales:
scale_losses = [
torch.nn.functional.l1_loss(
torch.masked_select(x_scale, torch.isnan(target_scale) == 0),
torch.masked_select(target_scale, torch.isnan(target_scale) == 0),
reduction='sum',
) / 1000.0 / batch_size
for x_scale, target_scale in zip(x_scales, target_scales)
]
margin_losses = [None for _ in target_regs] if self.margin else []
if self.margin and torch.any(reg_masks):
margin_losses = []
for i, (x_reg, target_reg) in enumerate(zip(x_regs, target_regs)):
margin_losses.append(quadrant_margin_loss(
torch.masked_select(x_reg[:, :, 0], reg_masks),
torch.masked_select(x_reg[:, :, 1], reg_masks),
torch.masked_select(target_reg[:, :, 0], reg_masks),
torch.masked_select(target_reg[:, :, 1], reg_masks),
torch.masked_select(target_reg[:, :, 2], reg_masks),
torch.masked_select(target_reg[:, :, 3], reg_masks),
torch.masked_select(target_reg[:, :, 4], reg_masks),
torch.masked_select(target_reg[:, :, 5], reg_masks),
) / 100.0 / batch_size)
return [ce_loss] + reg_losses + scale_losses + margin_losses
def forward(self, pyolos, targets, imgs_ts=None):
'''
:param pyolos: torch.Size([32, 7, 13, 13]) cls-3,box-4
:param targets:
:param imgs_ts:
:return:
'''
cfg = self.cfg
device = pyolos.device
batch, c, h, w = pyolos.shape
pyolos = pyolos.view(batch, c, -1).permute(0, 2, 1)
# cls-num_class, txywh-4, weight-1, gltrb-4
gdim = cfg.NUM_CLASSES + 4 + 1 + 4
gyolos = torch.empty((batch, h, w, gdim),
device=device) # 每批会整体更新这里不需要赋0
for i, target in enumerate(targets): # batch遍历
gboxes_ltrb_b = target['boxes'] # ltrb
glabels_b = target['labels']
gyolos[i] = fmatch4yolov1(
gboxes_ltrb_b=gboxes_ltrb_b,
glabels_b=glabels_b,
grid=h, # 7
gdim=gdim,
device=device,
img_ts=imgs_ts[i],
cfg=cfg,
use_conf=False)
'''可视化验证'''
# if cfg.IS_VISUAL:
# # conf-1, cls-1, box-4, weight-1
# gyolo_test = gyolos[i].clone() # torch.Size([32, 13, 13, 9])
# gyolo_test = gyolo_test.view(-1, gdim)
# gconf_one = gyolo_test[:, 0]
# mask_pos = gconf_one == 1 # [169]
#
# # torch.Size([169, 4])
# gtxywh = gyolo_test[:, 1 + cfg.NUM_CLASSES:1 + cfg.NUM_CLASSES + 4]
#
# # 这里是修复是 xy
# _xy_grid = gtxywh[:, :2] + f_mershgrid(h, w, is_rowcol=False).to(device)
# hw_ts = torch.tensor((h, w), device=device)
# gtxywh[:, :2] = torch.true_divide(_xy_grid, hw_ts)
# gtxywh = gtxywh[mask_pos]
# gtxywh[:, 2:4] = torch.exp(gtxywh[:, 2:]) / cfg.IMAGE_SIZE[0]
#
# from f_tools.pic.enhance.f_data_pretreatment4pil import f_recover_normalization4ts
# img_ts = f_recover_normalization4ts(imgs_ts[i])
# from torchvision.transforms import functional as transformsF
# img_pil = transformsF.to_pil_image(img_ts).convert('RGB')
# import numpy as np
# img_np = np.array(img_pil)
# f_show_od_np4plt(img_np, gboxes_ltrb=gboxes_ltrb_b.cpu()
# , pboxes_ltrb=xywh2ltrb(gtxywh.cpu()), is_recover_size=True,
# grids=(h, w))
# [32, 13, 13, 7] -> torch.Size([32, 169, 12])
gyolos = gyolos.view(batch, -1, gdim) # h*w
gcls = gyolos[:, :, 0:cfg.NUM_CLASSES] # torch.Size([5, 169])
mask_pos_3d = gcls > 0 # torch.Size([32, 169, 3])
mask_neg_3d = gcls == 0
# [32, 169, 3] -> [32, 169]
mask_pos_2d = torch.any(mask_pos_3d, dim=-1)
# mask_pos = gconf == 1 # yolo1 gt 写死是1
nums_pos = (mask_pos_2d.sum(-1).to(
torch.float)).clamp(min=torch.finfo(torch.float16).eps)
pyolos_pos = pyolos[
mask_pos_2d] # torch.Size([32, 169, 13]) -> [nn, 13]
gyolos_pos = gyolos[
mask_pos_2d] # torch.Size([32, 169, 13]) -> [nn, 13]
''' ---------------- 类别损失 ---------------- '''
# cls-num_class, txywh-4, weight-1, gltrb-4
pcls_sigmoid = pyolos[:, :, 0:cfg.NUM_CLASSES].sigmoid()
gcls = gyolos[:, :, 0:cfg.NUM_CLASSES] # torch.Size([32, 169, 3])
# 正反比 1:169*3
# _loss_val = x_bce(pcls_sigmoid, gcls, reduction="none") # torch.Size([46, 3])
# l_cls_pos = ((_loss_val * mask_pos_3d).sum(-1).sum(-1) / nums_pos).mean()
# l_cls_neg = ((_loss_val * mask_neg_3d).sum(-1).sum(-1) / nums_pos).mean()
# ------------ conf-mse ------------''' 666666
# _loss_val = F.mse_loss(pconf_sigmoid, gconf, reduction="none") # 用MSE效果更好
# _loss_val = x_bce(pconf_sigmoid, gconf, reduction="none")
# l_conf_pos = ((_loss_val * mask_pos_3d).sum(-1) / nums_pos).mean() * cfg.LOSS_WEIGHT[0]
# l_conf_neg = ((_loss_val * mask_neg_3d).sum(-1) / nums_pos).mean() * cfg.LOSS_WEIGHT[1]
# ------------ conf_ohem ap26_26 ------------'''
# _loss_val = x_bce(pconf_sigmoid, gconf)
# mask_ignore = torch.logical_not(torch.logical_or(mask_pos, mask_neg))
# mask_neg_hard = f_ohem(_loss_val, nums_pos * 3, mask_pos=mask_pos, mash_ignore=mask_ignore)
# l_conf_pos = ((_loss_val * mask_pos).sum(-1) / nums_pos).mean() * 3 # 正例越多反例越多
# l_conf_neg = ((_loss_val * mask_neg_hard).sum(-1) / nums_pos).mean() * 3
# ------------ focalloss ------------
l_pos, l_neg = focalloss(pcls_sigmoid,
gcls,
mask_pos=mask_pos_2d,
is_debug=True,
alpha=0.75)
l_cls_pos = (l_pos.sum(-1).sum(-1) / nums_pos).mean() * 30
l_cls_neg = (l_neg.sum(-1).sum(-1) / nums_pos).mean() * 30
''' ----------------回归损失 xy采用bce wh采用mes----------------- '''
# ------------ mse+bce ------------ 666666
# conf-1, cls-num_class, txywh-4, weight-1, gltrb-4
# ptxty_sigmoid_pos = pyolos_pos[:, cfg.NUM_CLASSES:cfg.NUM_CLASSES + 2].sigmoid() # 这个需要归一化
# ptwth_pos = pyolos_pos[:, cfg.NUM_CLASSES + 2:cfg.NUM_CLASSES + 4]
#
# # cls-num_class, txywh-4, weight-1, gltrb-4
# # id = cfg.NUM_CLASSES + 4 +1 -1
# weight_pos = gyolos_pos[:, cfg.NUM_CLASSES + 4] # torch.Size([32, 845])
# gtxty_pos = gyolos_pos[:, cfg.NUM_CLASSES:cfg.NUM_CLASSES + 2] # [nn]
# gtwth_pos = gyolos_pos[:, cfg.NUM_CLASSES + 2:cfg.NUM_CLASSES + 4]
#
# _loss_val = x_bce(ptxty_sigmoid_pos, gtxty_pos, reduction="none")
# l_txty = (_loss_val.sum(-1) * weight_pos).mean()
# _loss_val = F.mse_loss(ptwth_pos, gtwth_pos, reduction="none")
# l_twth = (_loss_val.sum(-1) * weight_pos).mean()
# ------------ iou损失 ------------
# 解码pxywh 计算预测与 GT 的 iou 作为 gconf
ptxywh_pos = pyolos[:, :, cfg.NUM_CLASSES:cfg.NUM_CLASSES + 4]
# 这个是批量解码 3D 故解码出来再筛选
zltrb_pos = boxes_decode4yolo1(ptxywh_pos, h, h, cfg)[mask_pos_2d]
gltrb_pos = gyolos_pos[:, cfg.NUM_CLASSES + 4 + 1:cfg.NUM_CLASSES + 4 +
1 + 4]
iou_zg = bbox_iou4one_2d(zltrb_pos, gltrb_pos, is_ciou=True)
l_reg = (1 - iou_zg).mean()
''' ---------------- loss完成 ----------------- '''
# loss_total = l_cls_pos + l_cls_neg + l_txty + l_twth
loss_total = l_cls_pos + l_cls_neg + l_reg
log_dict = {}
log_dict['l_total'] = loss_total.item()
log_dict['l_cls_pos'] = l_cls_pos.item()
log_dict['l_cls_neg'] = l_cls_neg.item()
log_dict['l_reg'] = l_reg.item()
# log_dict['l_xy'] = l_txty.item()
# log_dict['l_wh'] = l_twth.item()
log_dict['p_max'] = pcls_sigmoid.max().item()
log_dict['p_min'] = pcls_sigmoid.min().item()
log_dict['p_mean'] = pcls_sigmoid.mean().item()
return loss_total, log_dict
def forward(self, # forward接受所有可能用到的参数
support_seqs, support_imgs, support_lens, support_labels,
query_seqs, query_imgs, query_lens, query_labels,
epoch=None, metric='euc', return_embeddings=False):
embedded_support_seqs, embedded_query_seqs, \
embedded_support_imgs, embedded_query_imgs = self.embed(support_seqs, query_seqs,
support_lens, query_lens,
support_imgs, query_imgs)
k, n, qk = self.TaskParams.k, self.TaskParams.n, self.TaskParams.qk
# 直接使用seq和img的raw output进行fuse
support_fused_features = self._fuse(embedded_support_seqs, embedded_support_imgs, fuse_dim=1)
query_fused_features = self._fuse(embedded_query_seqs, embedded_query_imgs, fuse_dim=1)
dim = support_fused_features.size(1)
nClusters = n # 初始类簇的数量等于类数量
nInitialClusters = nClusters
# 此处设定batch=1
support_labels = support_labels.unsqueeze(0)
query_labels = query_labels.unsqueeze(0)
support_fused_features = support_fused_features.view(n * k, -1).unsqueeze(0)
query_fused_features = query_fused_features.view(qk, -1).unsqueeze(0)
# create probabilities for points
# _, idx = np.unique(batch.y_train.squeeze().data.cpu().numpy(), return_inverse=True)
prob_support = one_hot(support_labels, nClusters).cuda() # 将属于类簇的概率初始化为标签的one-hot
# make initial radii for labeled clusters
bsize = support_fused_features.size()[0]
radii = t.ones(bsize, nClusters).cuda() # * t.exp(self.Sigma) # 初始半径由log_sigma_l初始化(该参数可学习)
if self.Sigma is not None:
radii *= t.exp(self.Sigma)
cluster_labels = t.arange(0, nClusters).cuda().long()
# compute initial prototypes from labeled examples
# 由于初始时，共有类别个类簇，而且类簇的分配系数是one-hot，因此初始类簇就是类中心
# shape: [batch, cluster, dim]
protos = self._compute_protos(support_fused_features, prob_support)
# estimate lamda
# lamda = self.estimate_lambda(protos.data, False)
# loop for a given number of clustering steps
for ii in range(self.NumClusterSteps):
# protos = protos.data
# iterate over labeled examples to reassign first
for i, ex in enumerate(support_fused_features[0]):
# 找到样本label对应的cluster的index
idxs = t.nonzero(support_labels[0, i] == cluster_labels)[0] # TODO: 取0？
#****************************************************************************
# 计算与标签对应的类簇的距离(由于其他不对应的类簇的距离都是正无穷，求min时直接可忽略)
# distances = self._compute_distances(protos[:, idxs, :], ex.data)
# if t.min(distances) > lamda:
#****************************************************************************
distances = self._compute_distances(protos,ex)
# 如果发现离自己最近的cluster不是自己的类的cluster，就直接增加一个cluster
if not t.any(t.min(distances,dim=1).indices==idxs).item():
nClusters, protos, radii = self._add_cluster(nClusters, protos, radii,
cluster_type='labeled', ex=ex.data)
cluster_labels = t.cat([cluster_labels, support_labels[0, [i]].data], dim=0) # 将样本标签设定为类簇标签
# perform partial reassignment based on newly created labeled clusters
if nClusters > nInitialClusters:
support_targets = support_labels.data[0, :, None] == cluster_labels # 找到每个样本实际对应的类簇（每一行是每个样本对应的类簇bool）
prob_support = assign_cluster_radii_limited(protos, support_fused_features, radii,
support_targets) # 样本属于每个类簇的概率
nTrainClusters = nClusters
protos = protos.cuda()
protos = self._compute_protos(support_fused_features, prob_support)
protos, radii, cluster_labels = self.delete_empty_clusters(protos, prob_support, radii, cluster_labels)
# 计算query的类簇logits
logits = compute_logits_radii(protos, query_fused_features, radii, use_sigma=self.Sigma is not None).squeeze()
# convert class targets into indicators for supports in each class
labels = query_labels # batch.y_test.data
labels[labels >= nInitialClusters] = -1
support_targets = labels[0, :, None] == cluster_labels # 寻找查询集样本的标签对应的类簇
loss = self.loss(logits, support_targets,
cluster_labels) # support_targets: 查询样本标签对应的类簇指示; suppott_labels: 类簇的标签
# map support predictions back into classes to check accuracy
_, support_preds = t.max(logits.data, dim=1)
y_pred = cluster_labels[support_preds]
return {
"logits": None,
"loss": loss,
"predicts": y_pred
}
def decode_beam_batch(self,
bodies,
beam_size=3,
max_output_length=100,
sample=False,
show_each_beam=False):
if self.mode != 'eval':
print("BEWARE. Model is not in eval mode.")
self.eval() ## << Surely you are not training with beam decode?
batch_size = len(bodies)
N = batch_size * beam_size
inputs = self.preprocess_input(bodies)
next_words = torch.LongTensor([self.tokenizer.start_id] * N).to(
self.device).unsqueeze(1)
build_up = None
scores = torch.zeros((N)).to(self.device)
one_every_k = torch.FloatTensor([1] + [0] * (beam_size - 1)).repeat(
batch_size * beam_size).to(self.device)
# Sometimes, we process the same input, as we run it once as a sampled, and once as an argmax, in which case we should reuse the computation
_, input_past = self.model(input_ids=inputs, past_key_values=None)
input_past = [
torch.repeat_interleave(p, repeats=beam_size, dim=1)
for p in input_past
]
past = input_past
while build_up is None or (
build_up.shape[1] < max_output_length and
not all([self.tokenizer.end_id in build
for build in build_up])):
logits, past = self.model(input_ids=next_words,
past_key_values=past)
probs = torch.nn.functional.softmax(logits, dim=2).squeeze(1)
logprobs = torch.nn.functional.log_softmax(logits, dim=2)
if sample:
all_selects = torch.multinomial(probs, beam_size).unsqueeze(1)
else:
_, all_selects = torch.topk(logprobs, k=beam_size, dim=2)
if build_up is not None:
not_finished = (1 -
torch.any(build_up == self.tokenizer.end_id,
dim=1).float()).to(self.device)
else:
not_finished = torch.ones_like(scores,
dtype=torch.float,
device=self.device)
expanded_not_finished = torch.repeat_interleave(not_finished,
repeats=beam_size)
expanded_score = torch.repeat_interleave(
scores,
repeats=beam_size) # This should be batch_size * beam_size²
added_score = logprobs[
torch.repeat_interleave(torch.arange(N), repeats=beam_size), 0,
all_selects.view(-1)]
expanded_score += (expanded_not_finished * added_score)
# We don't want you to select from finished beams
expanded_score -= (1 - expanded_not_finished) * (
1 - one_every_k) * 1000.0
batched_scores = expanded_score.view(batch_size, -1)
if build_up is None:
choices = torch.arange(beam_size,
device=self.device).repeat(batch_size)
batched_choices = choices.view(batch_size, beam_size)
else:
_, batched_choices = torch.topk(
batched_scores, k=beam_size,
dim=1) # Going from k² choices per element to k choices.
batched_tracks = batched_choices / beam_size
tracks = beam_size * torch.repeat_interleave(
torch.arange(batch_size), repeats=beam_size).to(
self.device) + batched_tracks.view(-1)
tracks = list(tracks)
selected_scores = batched_scores[torch.repeat_interleave(
torch.arange(batch_size), repeats=beam_size),
batched_choices.view(-1)]
# Figure out the kept words to be added to the build-up
per_batch_selects = all_selects.view(batch_size, -1)
next_words = per_batch_selects[torch.repeat_interleave(
torch.arange(batch_size), repeats=beam_size),
batched_choices.view(-1)]
next_words = next_words.unsqueeze(1)
# [BOOKKEEPING] Going from k² to k options at each time means we have to swap all the caches around: past, build-up
if build_up is not None:
#######
if show_each_beam is True:
print(build_up)
building_output = []
for beams in build_up:
out_ = [
self.tokenizer.decode(beam.tolist()) + "END"
for beam in beams
]
out_ = [S[:S.index("END")] for S in out_]
building_output.append(out_)
for output, score in zip(building_output, selected_scores):
print("".join(output), "|%.2f" % float(score))
build_up = build_up[tracks, :]
past = [p[:, tracks, :] for p in past]
# Update the latest scores, and the current_build
if build_up is None:
build_up = next_words
else:
build_up = torch.cat((build_up, next_words), dim=1)
scores = selected_scores.view(-1)
batched_build_up = build_up.view(batch_size, beam_size, -1)
batched_scores = scores.view(batch_size, -1)
# torch.cuda.empty_cache()
outputs = []
for beams in batched_build_up:
out_beams = [
self.tokenizer.decode(beam.tolist()) + "END" for beam in beams
]
out_beams = [S[:S.index("END")] for S in out_beams]
outputs.append(out_beams)
return outputs, batched_scores.tolist()
def decode_batch(self,
bodies,
special_append=None,
max_output_length=100,
sample=False,
return_scores=False,
return_logprobs=False,
input_past=None):
N = len(bodies)
current = torch.LongTensor([self.tokenizer.start_id] * N).to(
self.device).unsqueeze(1)
build_up = None
scores = torch.zeros((N)).to(self.device)
total_logprobs = []
# Sometimes, we process the same input, as we run it once as a sampled, and once as an argmax, in which case we should reuse the computation
if input_past is None:
inputs = self.preprocess_input(bodies, special_append)
_, input_past = self.model(input_ids=inputs, past_key_values=None)
past = input_past
while build_up is None or (
build_up.shape[1] < max_output_length and
not all([self.tokenizer.end_id in build
for build in build_up])):
logits, past = self.model(input_ids=current, past_key_values=past)
probs = torch.nn.functional.softmax(logits, dim=2).squeeze(1)
logprobs = torch.nn.functional.log_softmax(logits, dim=2)
if sample:
current = torch.multinomial(probs, 1)
else:
current = torch.argmax(logprobs, dim=2)
if build_up is None:
build_up = current
else:
build_up = torch.cat((build_up, current), dim=1)
if return_logprobs:
selected_logprobs = logprobs[torch.arange(N), 0,
current.squeeze()].unsqueeze(1)
total_logprobs.append(selected_logprobs)
not_finished = (1 - torch.any(build_up == self.tokenizer.end_id,
dim=1).float()).to(self.device)
scores += not_finished * logprobs[torch.arange(N), :,
current.squeeze(1)].squeeze()
end_id = self.tokenizer.end_id
build_up = [build.tolist() for build in build_up]
end_indices = [
max_output_length +
1 if end_id not in build else build.index(end_id)
for build in build_up
]
outputs = [self.tokenizer.decode(build) + "END" for build in build_up]
outputs = [S[:S.index("END")] for S in outputs]
if return_logprobs:
return outputs, torch.cat(total_logprobs,
dim=1), build_up, input_past, end_indices
elif return_scores:
return outputs, scores.tolist()
else:
return outputs, end_indices
def __beaver_protocol(op, x, y, *args, **kwargs):
"""Performs Beaver protocol for additively secret-shared tensors x and y
1. Obtain uniformly random sharings [a],[b] and [c] = [a * b]
2. Additively hide [x] and [y] with appropriately sized [a] and [b]
3. Open ([epsilon] = [x] - [a]) and ([delta] = [y] - [b])
4. Return [z] = [c] + (epsilon * [b]) + ([a] * delta) + (epsilon * delta)
"""
assert op in {
"mul",
"matmul",
"conv1d",
"conv2d",
"conv_transpose1d",
"conv_transpose2d",
}
if x.device != y.device:
raise ValueError(
f"x lives on device {x.device} but y on device {y.device}")
provider = crypten.mpc.get_default_provider()
a, b, c = provider.generate_additive_triple(x.size(),
y.size(),
op,
device=x.device,
*args,
**kwargs)
from .arithmetic import ArithmeticSharedTensor
if crypten.mpc.config.active_security:
"""
Reference: "Multiparty Computation from Somewhat Homomorphic Encryption"
Link: https://eprint.iacr.org/2011/535.pdf
"""
f, g, h = provider.generate_additive_triple(x.size(),
y.size(),
op,
device=x.device,
*args,
**kwargs)
t = ArithmeticSharedTensor.PRSS(a.size(), device=x.device)
t_plain_text = t.get_plain_text()
rho = (t_plain_text * a - f).get_plain_text()
sigma = (b - g).get_plain_text()
triples_check = t_plain_text * c - h - sigma * f - rho * g - rho * sigma
triples_check = triples_check.get_plain_text()
if torch.any(triples_check != 0):
raise ValueError("Beaver Triples verification failed!")
# Vectorized reveal to reduce rounds of communication
epsilon, delta = ArithmeticSharedTensor.reveal_batch([x - a, y - b])
# z = c + (a * delta) + (epsilon * b) + epsilon * delta
c._tensor += getattr(torch, op)(epsilon, b._tensor, *args, **kwargs)
c._tensor += getattr(torch, op)(a._tensor, delta, *args, **kwargs)
c += getattr(torch, op)(epsilon, delta, *args, **kwargs)
return c
def initialize_q_batch_nonneg(X: Tensor,
Y: Tensor,
n: int,
eta: float = 1.0,
alpha: float = 1e-4) -> Tensor:
r"""Heuristic for selecting initial conditions for non-neg. acquisition functions.
This function is similar to `initialize_q_batch`, but designed specifically
for acquisition functions that are non-negative and possibly zero over
large areas of the feature space (e.g. qEI). All samples for which
`Y < alpha * max(Y)` will be ignored (assuming that `Y` contains at least
one positive value).
Args:
X: A `b x q x d` tensor of `b` samples of `q`-batches from a `d`-dim.
feature space. Typically, these are generated using qMC.
Y: A tensor of `b` outcomes associated with the samples. Typically, this
is the value of the batch acquisition function to be maximized.
n: The number of initial condition to be generated. Must be less than `b`.
eta: Temperature parameter for weighting samples.
alpha: The threshold (as a fraction of the maximum observed value) under
which to ignore samples. All input samples for which
`Y < alpha * max(Y)` will be ignored.
Returns:
A `n x q x d` tensor of `n` `q`-batch initial conditions.
Example:
# To get `n=10` starting points of q-batch size `q=3` for model with `d=6`:
>>> qEI = qExpectedImprovement(model, best_f=0.2)
>>> Xrnd = torch.rand(500, 3, 6)
>>> Xinit = initialize_q_batch(Xrnd, qEI(Xrnd), 10)
"""
n_samples = X.shape[0]
if n > n_samples:
raise RuntimeError(
"n cannot be larger than the number of provided samples")
elif n == n_samples:
return X
max_val, max_idx = torch.max(Y, dim=0)
if torch.any(max_val <= 0):
warnings.warn(
"All acquisition values for raw sampled points are nonpositive, so "
"initial conditions are being selected randomly.",
BadInitialCandidatesWarning,
)
return X[torch.randperm(n=n_samples, device=X.device)][:n]
# make sure there are at least `n` points with positive acquisition values
pos = Y > 0
num_pos = pos.sum().item()
if num_pos < n:
# select all positive points and then fill remaining quota with randomly
# selected points
remaining_indices = (~pos).nonzero().view(-1)
rand_indices = torch.randperm(remaining_indices.shape[0],
device=Y.device)
sampled_remaining_indices = remaining_indices[rand_indices[:n -
num_pos]]
pos[sampled_remaining_indices] = 1
return X[pos]
# select points within alpha of max_val, iteratively decreasing alpha by a
# factor of 10 as necessary
alpha_pos = Y >= alpha * max_val
while alpha_pos.sum() < n:
alpha = 0.1 * alpha
alpha_pos = Y >= alpha * max_val
alpha_pos_idcs = torch.arange(len(Y), device=Y.device)[alpha_pos]
weights = torch.exp(eta * (Y[alpha_pos] / max_val - 1))
idcs = alpha_pos_idcs[torch.multinomial(weights, n)]
if max_idx not in idcs:
idcs[-1] = max_idx
return X[idcs]
def forward(
ctx,
representation_tree,
dtype,
device,
matrix_shape,
batch_shape=torch.Size(),
inv_quad=False,
logdet=False,
probe_vectors=None,
probe_vector_norms=None,
*args,
):
"""
*args - The arguments representing the PSD matrix A (or batch of PSD matrices A)
If self.inv_quad is true, the first entry in *args is inv_quad_rhs (Tensor)
- the RHS of the matrix solves.
Returns:
- (Scalar) The inverse quadratic form (or None, if self.inv_quad is False)
- (Scalar) The log determinant (or None, self.if logdet is False)
"""
if not (inv_quad or logdet):
raise RuntimeError(
"Either inv_quad or logdet must be true (or both)")
ctx.representation_tree = representation_tree
ctx.dtype = dtype
ctx.device = device
ctx.matrix_shape = matrix_shape
ctx.batch_shape = batch_shape
ctx.inv_quad = inv_quad
ctx.logdet = logdet
matrix_args = None
inv_quad_rhs = None
if ctx.inv_quad:
matrix_args = args[1:]
inv_quad_rhs = args[0]
else:
matrix_args = args
# Get closure for matmul
lazy_tsr = ctx.representation_tree(*matrix_args)
with torch.no_grad():
preconditioner, precond_lt, logdet_correction = lazy_tsr._preconditioner(
)
ctx.preconditioner = preconditioner
if (probe_vectors is None or probe_vector_norms is None) and logdet:
num_random_probes = settings.num_trace_samples.value()
if preconditioner is None:
if settings.deterministic_probes.on():
warnings.warn(
"Deterministic probes will currently work only if you aren't training multiple independent"
" models simultaneously.",
UserWarning,
)
if settings.deterministic_probes.probe_vectors is None:
probe_vectors = torch.empty(matrix_shape[-1],
num_random_probes,
dtype=dtype,
device=device)
probe_vectors.bernoulli_().mul_(2).add_(-1)
settings.deterministic_probes.probe_vectors = probe_vectors
else:
probe_vectors = settings.deterministic_probes.probe_vectors
else:
probe_vectors = torch.empty(matrix_shape[-1],
num_random_probes,
dtype=dtype,
device=device)
probe_vectors.bernoulli_().mul_(2).add_(-1)
probe_vector_norms = torch.norm(probe_vectors,
2,
dim=-2,
keepdim=True)
if batch_shape is not None:
probe_vectors = probe_vectors.expand(
*batch_shape, matrix_shape[-1], num_random_probes)
probe_vector_norms = probe_vector_norms.expand(
*batch_shape, 1, num_random_probes)
else: # When preconditioning, probe vectors must be drawn from N(0, P)
if settings.deterministic_probes.on():
# NOTE: calling precond_lt.root_decomposition() is expensive
# because it requires Lanczos
# We don't have any other choice for when we want to use deterministic probes, however
if precond_lt.size()[-2:] == torch.Size([1, 1]):
covar_root = precond_lt.evaluate().sqrt()
else:
covar_root = precond_lt.root_decomposition().root
warnings.warn(
"Deterministic probes will currently work only if you aren't training multiple independent"
" models simultaneously.",
UserWarning,
)
base_samples = settings.deterministic_probes.probe_vectors
if base_samples is None or covar_root.size(
-1) != base_samples.size(-2):
base_samples = torch.randn(
*precond_lt.batch_shape,
covar_root.size(-1),
num_random_probes,
dtype=precond_lt.dtype,
device=precond_lt.device,
)
settings.deterministic_probes.probe_vectors = base_samples
probe_vectors = covar_root.matmul(base_samples).permute(
-1, *range(precond_lt.dim() - 1))
else:
probe_vectors = precond_lt.zero_mean_mvn_samples(
num_random_probes)
probe_vectors = probe_vectors.unsqueeze(-2).transpose(
0, -2).squeeze(0).transpose(-2, -1).contiguous()
probe_vector_norms = torch.norm(probe_vectors,
p=2,
dim=-2,
keepdim=True)
probe_vectors = probe_vectors.div(probe_vector_norms)
ctx.probe_vectors = probe_vectors
ctx.probe_vector_norms = probe_vector_norms
if ctx.logdet and not ctx.probe_vectors.numel():
raise RuntimeError(
"Probe vectors were not supplied for logdet computation")
# Collect terms for LinearCG
# We use LinearCG for both matrix solves and for stochastically estimating the log det
rhs_list = []
num_random_probes = 0
num_inv_quad_solves = 0
# RHS for logdet
if ctx.logdet:
rhs_list.append(ctx.probe_vectors)
num_random_probes = ctx.probe_vectors.size(-1)
# RHS for inv_quad
ctx.is_vector = False
if ctx.inv_quad:
if inv_quad_rhs.ndimension() == 1:
inv_quad_rhs = inv_quad_rhs.unsqueeze(-1)
ctx.is_vector = True
rhs_list.append(inv_quad_rhs)
num_inv_quad_solves = inv_quad_rhs.size(-1)
# Perform solves (for inv_quad) and tridiagonalization (for estimating logdet)
rhs = torch.cat(rhs_list, -1)
t_mat = None
if ctx.logdet and settings.skip_logdet_forward.off():
solves, t_mat = lazy_tsr._solve(rhs,
preconditioner,
num_tridiag=num_random_probes)
else:
solves = lazy_tsr._solve(rhs, preconditioner, num_tridiag=0)
# Final values to return
logdet_term = torch.zeros(lazy_tsr.batch_shape,
dtype=ctx.dtype,
device=ctx.device)
inv_quad_term = torch.zeros(lazy_tsr.batch_shape,
dtype=ctx.dtype,
device=ctx.device)
# Compute logdet from tridiagonalization
if ctx.logdet and settings.skip_logdet_forward.off():
if torch.any(torch.isnan(t_mat)).item():
logdet_term = torch.tensor(float("nan"),
dtype=ctx.dtype,
device=ctx.device)
else:
if ctx.batch_shape is None:
t_mat = t_mat.unsqueeze(1)
eigenvalues, eigenvectors = lanczos_tridiag_to_diag(t_mat)
slq = StochasticLQ()
(logdet_term, ) = slq.evaluate(ctx.matrix_shape, eigenvalues,
eigenvectors,
[lambda x: x.log()])
# Add correction
if logdet_correction is not None:
logdet_term = logdet_term + logdet_correction
# Extract inv_quad solves from all the solves
if ctx.inv_quad:
inv_quad_solves = solves.narrow(-1, num_random_probes,
num_inv_quad_solves)
inv_quad_term = (inv_quad_solves * inv_quad_rhs).sum(-2)
ctx.num_random_probes = num_random_probes
ctx.num_inv_quad_solves = num_inv_quad_solves
to_save = list(matrix_args) + [solves]
ctx.save_for_backward(*to_save)
if settings.memory_efficient.off():
ctx._lazy_tsr = lazy_tsr
return inv_quad_term, logdet_term
def sinc_inv(x):
usetaylor = (x.abs()<thresh)
texpand = 1+(1/6)*x**2 +(7/360)*x**4
assert not torch.any(torch.isinf(texpand)|torch.isnan(texpand)),'sincinv texpand inf'+torch.any(torch.isinf(texpand))
return torch.where(usetaylor,texpand,x/x.sin())
def _test_no_pad_and_pad(self, no_pad_features, pad_features):
tokenizer = BertTokenizer(self.vocab_file)
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
batch = data_collator(no_pad_features)
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
batch = data_collator(pad_features)
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
data_collator = DataCollatorForLanguageModeling(tokenizer,
mlm=False,
pad_to_multiple_of=8)
batch = data_collator(no_pad_features)
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
batch = data_collator(pad_features)
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
tokenizer._pad_token = None
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
with self.assertRaises(ValueError):
# Expect error due to padding token missing
data_collator(pad_features)
set_seed(42) # For reproducibility
tokenizer = BertTokenizer(self.vocab_file)
data_collator = DataCollatorForLanguageModeling(tokenizer)
batch = data_collator(no_pad_features)
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
self.assertTrue(torch.any(masked_tokens))
self.assertTrue(
all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
batch = data_collator(pad_features)
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
self.assertTrue(torch.any(masked_tokens))
self.assertTrue(
all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
data_collator = DataCollatorForLanguageModeling(tokenizer,
pad_to_multiple_of=8)
batch = data_collator(no_pad_features)
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
self.assertTrue(torch.any(masked_tokens))
self.assertTrue(
all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
batch = data_collator(pad_features)
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
self.assertTrue(torch.any(masked_tokens))
self.assertTrue(
all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
def sinkhorn_knopp(a,
b,
C,
reg=1e-1,
maxIter=1000,
stopThr=1e-9,
verbose=True,
log=True,
warm_start=None,
eval_freq=10,
print_freq=200,
**kwargs):
"""
Solve the entropic regularization optimal transport
The input should be PyTorch tensors
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,C>_F + reg\cdot\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
\gamma\geq 0
where :
- C is the (ns,nt) metric cost matrix
- :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
- a and b are target and source measures (sum to 1)
The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [1].
Parameters
----------
a : torch.tensor (na,)
samples measure in the target domain
b : torch.tensor (nb,)
samples in the source domain
C : torch.tensor (na,nb)
loss matrix
reg : float
Regularization term > 0
maxIter : int, optional
Max number of iterations
stopThr : float, optional
Stop threshol on error ( > 0 )
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
gamma : (na x nb) torch.tensor
Optimal transportation matrix for the given parameters
log : dict
log dictionary return only if log==True in parameters
References
----------
[1] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013
See Also
--------
"""
device = a.device
na, nb = C.shape
assert na >= 1 and nb >= 1, 'C needs to be 2d'
assert na == a.shape[0] and nb == b.shape[
0], "Shape of a or b does't match that of C"
assert reg > 0, 'reg should be greater than 0'
assert a.min() >= 0. and b.min() >= 0., 'Elements in a or b less than 0'
# unnecessary check for our special case
if log:
log = {'err': []}
if warm_start is not None:
u = warm_start['u']
v = warm_start['v']
else:
u = torch.ones(na, dtype=a.dtype).to(device) / na
v = torch.ones(nb, dtype=b.dtype).to(device) / nb
K = torch.empty(C.shape, dtype=C.dtype).to(device)
torch.div(C, -reg, out=K)
torch.exp(K, out=K)
b_hat = torch.empty(b.shape, dtype=C.dtype).to(device)
it = 1
err = 1
# allocate memory beforehand
KTu = torch.empty(v.shape, dtype=v.dtype).to(device)
Kv = torch.empty(u.shape, dtype=u.dtype).to(device)
while (err > stopThr and it <= maxIter):
upre, vpre = u, v
torch.matmul(u, K, out=KTu)
v = torch.div(b, KTu + M_EPS)
torch.matmul(K, v, out=Kv)
u = torch.div(a, Kv + M_EPS)
if torch.any(torch.isnan(u)) or torch.any(torch.isnan(v)) or \
torch.any(torch.isinf(u)) or torch.any(torch.isinf(v)):
print('Warning: numerical errors at iteration', it)
u, v = upre, vpre
break
if log and it % eval_freq == 0:
# we can speed up the process by checking for the error only all
# the eval_freq iterations
# below is equivalent to:
# b_hat = torch.sum(u.reshape(-1, 1) * K * v.reshape(1, -1), 0)
# but with more memory efficient
b_hat = torch.matmul(u, K) * v
err = (b - b_hat).pow(2).sum().item()
# err = (b - b_hat).abs().sum().item()
log['err'].append(err)
if verbose and it % print_freq == 0:
print('iteration {:5d}, constraint error {:5e}'.format(it, err))
it += 1
if log:
log['u'] = u
log['v'] = v
log['alpha'] = reg * torch.log(u + M_EPS)
log['beta'] = reg * torch.log(v + M_EPS)
# transport plan
P = u.reshape(-1, 1) * K * v.reshape(1, -1)
if log:
return P, log
else:
return P
for gid in gids: # 每一张图
mask_gid = datas_dt[:, 0] == gid
datas_dt_gid = datas_dt[mask_gid]
datas_gt_gid = datas_gt[datas_gt[:, 0] == gid]
ious = calc_iou4ts(datas_dt_gid[:, 3:7], datas_gt_gid[:, 2:6])
# print(ious)
for i in range(len(datas_gt_gid)):
cls = datas_gt_gid[i][1]
# clses.add(int(cls.item()))
# 类别cls-1d
mask_cls = datas_dt_gid[:, 1] == cls
mask_nomatch = datas_dt_gid[:, -1] != 1
mask_iou = ious[:, i] > iou_std
_mask = torch.logical_and(mask_cls, mask_nomatch)
_mask = torch.logical_and(_mask, mask_iou)
if torch.any(_mask):
# 分数
index_score = datas_dt_gid[:, 2][_mask].max(0)[1]
# datas_dt_gid[index_score, -1] = 1
dim0 = torch.where(mask_gid)
datas_dt[dim0[0][index_score], -1] = 1
if not debug:
continue
for c in clses: # 每个图片的每个类的 tp情况 debug
num_gt = (datas_gt_gid[:, 1] == c).sum()
mask_gid_cls = datas_dt_gid[:, 1] == c
tp = (torch.logical_and(mask_gid_cls, datas_dt_gid[:, -1] == 1)).sum()
fp = mask_gid_cls.sum() - tp
fn = num_gt - tp
print('gid=%s, cls=%s, tp=%s, fp=%s, fn=%s' %
def generate_batch(self,
batch,
gt_prefix=1,
enc_state=None,
whole_policy=False,
special_actions=None,
temperature=1,
acpt_range=None):
# This is to ensure we can stop at EOS for stateful models
assert batch.size == 1
# Run the model
policy, p_mean, p_logstd = self.model(batch.encoder_intent,
batch.encoder_price,
batch.encoder_pmask,
batch.encoder_dianum)
# print('policy is:', policy)
# Get embeddings of target
# tgt_emb = self.model.encoder.embeddings(batch.target_intent)
# tgt_emb = torch.cat([tgt_emb, batch.target_price], )
# policy.sub_(policy.max(1, keepdim=True)[0].expand(policy.size(0), policy.size(1)))
# policy[batch.policy_mask == 0] = -100
policy.sub_(policy.max(1, keepdim=True)[0].expand(-1, policy.size(1)))
policy = policy * temperature
# mask = batch.policy_mask
# policy[mask == 0] = -100.
# print(batch.policy_mask)
# Avoid policy equal to zero ( + 1e-6 )
p_exp = (policy.exp() + 1e-6).mul(batch.policy_mask)
# p_exp = (policy.exp() + 1e-6)
# if torch.sum(p_exp).item() == 0:
# p_exp += torch.ones_like(p_exp).mul(batch.policy_mask)
policy = p_exp / (torch.sum(p_exp, keepdim=True, dim=1))
if torch.any(policy < 0) or torch.any(torch.isnan(policy)):
print('lots of errors: ', p_exp, batch.policy_mask)
intent = torch.multinomial(policy, 1).squeeze(1) # (batch_size,)
# Use Normal distribution with constant variance as policy on price
# price = p_mean + p_logstd.exp()*0.1 * torch.randn_like(p_mean)
price = p_mean + (1.1 - temperature) * torch.randn_like(p_mean)
# price = p_mean
# print(torch.cat([price.view(-1,1), p_mean.view(-1,1), p_logstd.view(-1,1)], dim=1))
# price = price + LFSampler.var_for_price * torch.randn_like(price).abs()
# Use rule for Supervised learning agent
if acpt_range is not None:
assert len(acpt_range) == 2
# Check if last action is offer
if batch.encoder_intent[0, -1] in self.offer:
offer_price = batch.encoder_price[0, 0, -1].item()
policy = policy * 0
if (acpt_range[0] <= offer_price) and (offer_price <=
acpt_range[1]):
# Accept
act_idx = self.acc_or_rej[0]
else:
act_idx = self.acc_or_rej[1]
intent[0] = act_idx
policy[0, act_idx] = 1
# TODO: Not correct, for multiple data.
if intent not in self.price_actions:
price = None
# print('gen output: ',policy, price)
ret = {
"intent": intent,
"price": price,
"policy": policy,
"price_mean": p_mean,
"price_logstd": p_logstd,
}
ret["batch"] = batch
# ret["policies"] = policies
# ret["probability"] = probs
return ret
def assign_one_hot_gt_indices(self,
is_bbox_in_gt_core,
is_bbox_in_gt_shadow,
gt_priority=None):
"""Assign only one gt index to each prior box.
Gts with large gt_priority are more likely to be assigned.
Args:
is_bbox_in_gt_core (Tensor): Bool tensor indicating the bbox center
is in the core area of a gt (e.g. 0-0.2).
Shape: (num_prior, num_gt).
is_bbox_in_gt_shadow (Tensor): Bool tensor indicating the bbox
center is in the shadowed area of a gt (e.g. 0.2-0.5).
Shape: (num_prior, num_gt).
gt_priority (Tensor): Priorities of gts. The gt with a higher
priority is more likely to be assigned to the bbox when the bbox
match with multiple gts. Shape: (num_gt, ).
Returns:
tuple: Returns (assigned_gt_inds, shadowed_gt_inds).
- assigned_gt_inds: The assigned gt index of each prior bbox \
(i.e. index from 1 to num_gts). Shape: (num_prior, ).
- shadowed_gt_inds: shadowed gt indices. It is a tensor of \
shape (num_ignore, 2) with first column being the \
shadowed prior bbox indices and the second column the \
shadowed gt indices (1-based).
"""
num_bboxes, num_gts = is_bbox_in_gt_core.shape
if gt_priority is None:
gt_priority = torch.arange(num_gts,
device=is_bbox_in_gt_core.device)
assert gt_priority.size(0) == num_gts
# The bigger gt_priority, the more preferable to be assigned
# The assigned inds are by default 0 (background)
assigned_gt_inds = is_bbox_in_gt_core.new_zeros((num_bboxes, ),
dtype=torch.long)
# Shadowed bboxes are assigned to be background. But the corresponding
# label is ignored during loss calculation, which is done through
# shadowed_gt_inds
shadowed_gt_inds = torch.nonzero(is_bbox_in_gt_shadow, as_tuple=False)
if is_bbox_in_gt_core.sum() == 0: # No gt match
shadowed_gt_inds[:, 1] += 1 # 1-based. For consistency issue
return assigned_gt_inds, shadowed_gt_inds
# The priority of each prior box and gt pair. If one prior box is
# matched bo multiple gts. Only the pair with the highest priority
# is saved
pair_priority = is_bbox_in_gt_core.new_full((num_bboxes, num_gts),
-1,
dtype=torch.long)
# Each bbox could match with multiple gts.
# The following codes deal with this situation
# Matched bboxes (to any gt). Shape: (num_pos_anchor, )
inds_of_match = torch.any(is_bbox_in_gt_core, dim=1)
# The matched gt index of each positive bbox. Length >= num_pos_anchor
# , since one bbox could match multiple gts
matched_bbox_gt_inds = torch.nonzero(is_bbox_in_gt_core,
as_tuple=False)[:, 1]
# Assign priority to each bbox-gt pair.
pair_priority[is_bbox_in_gt_core] = gt_priority[matched_bbox_gt_inds]
_, argmax_priority = pair_priority[inds_of_match].max(dim=1)
assigned_gt_inds[inds_of_match] = argmax_priority + 1 # 1-based
# Zero-out the assigned anchor box to filter the shadowed gt indices
is_bbox_in_gt_core[inds_of_match, argmax_priority] = 0
# Concat the shadowed indices due to overlapping with that out side of
# effective scale. shape: (total_num_ignore, 2)
shadowed_gt_inds = torch.cat(
(shadowed_gt_inds, torch.nonzero(is_bbox_in_gt_core,
as_tuple=False)),
dim=0)
# `is_bbox_in_gt_core` should be changed back to keep arguments intact.
is_bbox_in_gt_core[inds_of_match, argmax_priority] = 1
# 1-based shadowed gt indices, to be consistent with `assigned_gt_inds`
shadowed_gt_inds[:, 1] += 1
return assigned_gt_inds, shadowed_gt_inds
def forward(
self,
tokens_p: TextFieldTensors,
tokens_h: TextFieldTensors,
g_p: SparseAdjacencyFieldTensors,
g_h: SparseAdjacencyFieldTensors,
label: torch.Tensor = None,
return_attention: bool = False,
) -> Dict[str, torch.Tensor]:
"""
GMN for NLI
let B be batch size
let N be p length
let M be h length
input :
tokens_p in shape [*] (B, N)
g_p["edge_index"]
ouput : tensor dict
"""
# Shape: (batch_size, num_tokens, embedding_dim)
# embedder will take out the desired entry, for ex: ["tokens"]
embedded_p = self.embedder(tokens_p)
embedded_h = self.embedder(tokens_h)
# Shape: (num_batch_edges, edge_embedding_dim)
# can use pass through if type is the information required
# this is general for any kind of edge representation to GP2Vencoder
# https://docs.allennlp.org/master/api/modules/token_embedders/embedding/
# inplace will change input!!!
g_p_embedded = deepcopy(g_p)
g_h_embedded = deepcopy(g_h)
g_p_embedded["edge_attr"] = self.edge_embedder(g_p["edge_attr"])
g_h_embedded["edge_attr"] = self.edge_embedder(g_h["edge_attr"])
assert (not torch.any(torch.isnan(embedded_p)))
assert (not torch.any(torch.isnan(embedded_h)))
# Shape: (batch_size, num_tokens, projected_dim)
embedded_p = self.projector(embedded_p)
embedded_h = self.projector(embedded_h)
assert (not torch.any(torch.isnan(embedded_p)))
assert (not torch.any(torch.isnan(embedded_h)))
# Shape:
# node_attr : (num_tokens, embedding_dim)
# batch_id : (num_tokens)
sparse_p = tensor_op.dense2sparse(
embedded_p,
tokens_p["tokens"]["mask"]) #need to overload indexer for this
sparse_h = tensor_op.dense2sparse(embedded_h,
tokens_h["tokens"]["mask"])
assert (not torch.any(torch.isnan(sparse_p["data"])))
assert (not torch.any(torch.isnan(sparse_h["data"])))
# Shape: (batch_size, classifier_in_dim)
if return_attention:
cls_vector, attention_dict = self.encoder(
sparse_p,
sparse_h,
g_p_embedded,
g_h_embedded,
return_attention=return_attention)
else:
cls_vector = self.encoder(sparse_p,
sparse_h,
g_p_embedded,
g_h_embedded,
return_attention=return_attention)
# Shape: (batch_size, num_labels)
logits = self.classifier(cls_vector)
assert (not torch.any(torch.isnan(cls_vector)))
# Shape: (batch_size, num_labels)
probs = torch.nn.functional.softmax(logits, dim=1)
# Shape: TensorDict
output = {'probs': probs}
if label is not None:
#print(logits.size(), label.size())
self.accuracy(logits, label)
# the two value can be kind of different for numerical isse IMO
self.entropy(logits, label)
output['loss'] = torch.nn.functional.cross_entropy(logits, label)
if return_attention is True:
output['attentions'] = attention_dict
return output
def greedy_cos_idf(ref_embedding, ref_masks, ref_idf,
hyp_embedding, hyp_masks, hyp_idf,
all_layers=False):
"""
Compute greedy matching based on cosine similarity.
Args:
- :param: `ref_embedding` (torch.Tensor):
embeddings of reference sentences, BxKxd,
B: batch size, K: longest length, d: bert dimenison
- :param: `ref_lens` (list of int): list of reference sentence length.
- :param: `ref_masks` (torch.LongTensor): BxKxK, BERT attention mask for
reference sentences.
- :param: `ref_idf` (torch.Tensor): BxK, idf score of each word
piece in the reference setence
- :param: `hyp_embedding` (torch.Tensor):
embeddings of candidate sentences, BxKxd,
B: batch size, K: longest length, d: bert dimenison
- :param: `hyp_lens` (list of int): list of candidate sentence length.
- :param: `hyp_masks` (torch.LongTensor): BxKxK, BERT attention mask for
candidate sentences.
- :param: `hyp_idf` (torch.Tensor): BxK, idf score of each word
piece in the candidate setence
"""
ref_embedding.div_(torch.norm(ref_embedding, dim=-1).unsqueeze(-1))
hyp_embedding.div_(torch.norm(hyp_embedding, dim=-1).unsqueeze(-1))
if all_layers:
B, _, L, D = hyp_embedding.size()
hyp_embedding = hyp_embedding.transpose(1, 2).transpose(0, 1)\
.contiguous().view(L*B, hyp_embedding.size(1), D)
ref_embedding = ref_embedding.transpose(1, 2).transpose(0, 1)\
.contiguous().view(L*B, ref_embedding.size(1), D)
batch_size = ref_embedding.size(0)
sim = torch.bmm(hyp_embedding, ref_embedding.transpose(1, 2))
masks = torch.bmm(hyp_masks.unsqueeze(2).float(), ref_masks.unsqueeze(1).float())
if all_layers:
masks = masks.unsqueeze(0).expand(L, -1, -1, -1)\
.contiguous().view_as(sim)
else:
masks = masks.expand(batch_size, -1, -1)\
.contiguous().view_as(sim)
masks = masks.float().to(sim.device)
sim = sim * masks
word_precision = sim.max(dim=2)[0]
word_recall = sim.max(dim=1)[0]
hyp_idf.div_(hyp_idf.sum(dim=1, keepdim=True))
ref_idf.div_(ref_idf.sum(dim=1, keepdim=True))
precision_scale = hyp_idf.to(word_precision.device)
recall_scale = ref_idf.to(word_recall.device)
if all_layers:
precision_scale = precision_scale.unsqueeze(0)\
.expand(L, B, -1).contiguous().view_as(word_precision)
recall_scale = recall_scale.unsqueeze(0)\
.expand(L, B, -1).contiguous().view_as(word_recall)
P = (word_precision * precision_scale).sum(dim=1)
R = (word_recall * recall_scale).sum(dim=1)
F = 2 * P * R / (P + R)
hyp_zero_mask = hyp_masks.sum(dim=1).eq(2)
ref_zero_mask = ref_masks.sum(dim=1).eq(2)
if all_layers:
P = P.view(L, B)
R = R.view(L, B)
F = F.view(L, B)
if torch.any(hyp_zero_mask):
print("Warning: Empty candidate sentence; Setting precision to be 0.", file=sys.stderr)
P = P.masked_fill(hyp_zero_mask, 0.)
if torch.any(ref_zero_mask):
print("Warning: Empty candidate sentence; Setting recall to be 0.", file=sys.stderr)
R = R.masked_fill(ref_zero_mask, 0.)
F = F.masked_fill(torch.isnan(F), 0.)
return P, R, F
def nucleus_sampling(model, batch, batch_size, threshold=0.9, use_packed=True):
model.eval()
text_vecs = batch['text_vecs'].to(current_device)
if use_packed:
encoded = model.encoder(text_vecs,
batch['text_lens'],
use_packed=batch['use_packed'])
else:
encoded = model.encoder(text_vecs)
encoder_output, encoder_hidden, attention_mask = encoded
# 1 is __start__
starts = torch.Tensor(
[1]).long().to(model.decoder.embedding.weight.device).expand(
batch_size, 1).long() # expand to batch size
decoder_hidden = encoder_hidden
# greedy decoding here
preds = [starts]
scores = []
# track if each sample in the mini batch is finished
# if all finished, stop predicting
finish_mask = torch.Tensor([0] * batch_size).byte().to(
model.decoder.embedding.weight.device)
xs = starts
_attn_w_log = []
for ts in range(100):
decoder_output, decoder_hidden, attn_w_log = model.decoder(
xs, decoder_hidden,
encoded) # decoder_output: [batch, time, vocab]
_probs = torch.softmax(decoder_output, dim=-1)[0][0]
_sorted_probs, _sorted_indices = torch.sort(_probs, descending=True)
cumulative_probs = torch.cumsum(_sorted_probs, dim=-1)
selected_probs = cumulative_probs < threshold
selected_probs[1:] = selected_probs[:-1].clone()
selected_probs[0] = True
_sorted_probs[~selected_probs] = 0
P = _sorted_probs.sum()
_sorted_probs /= P
chosen_index = torch.multinomial(_sorted_probs, 1)
_preds = _sorted_indices[chosen_index]
_scores = torch.log(_probs[_preds])
_preds = _preds.unsqueeze(0)
preds.append(_preds)
_attn_w_log.append(attn_w_log)
scores.append(_scores.view(-1) * (finish_mask == 0).float())
finish_mask += (_preds == 2).byte().view(-1)
if not (torch.any(~finish_mask.bool())):
break
xs = _preds
preds = torch.cat(preds, dim=-1)
return preds, torch.sum(torch.Tensor(scores))
def forward(self, x, t): # pylint: disable=arguments-differ
assert len(x) == 1 + 2 * self.n_vectors + self.n_scales
x_intensity = x[0]
x_regs = x[1:1 + self.n_vectors]
x_spreads = x[1 + self.n_vectors:1 + 2 * self.n_vectors]
x_scales = []
if self.n_scales:
x_scales = x[1 + 2 * self.n_vectors:1 + 2 * self.n_vectors +
self.n_scales]
assert len(t) == 1 + self.n_vectors + 1
target_intensity = t[0]
target_regs = t[1:1 + self.n_vectors]
target_scale = t[-1]
bce_masks = (target_intensity[:, :-1] + target_intensity[:, -1:]) > 0.5
if not torch.any(bce_masks):
return None, None, None
batch_size = x_intensity.shape[0]
LOG.debug('batch size = %d', batch_size)
bce_x_intensity = x_intensity
bce_target_intensity = target_intensity[:, :-1]
if self.bce_blackout:
bce_x_intensity = bce_x_intensity[:, self.bce_blackout]
bce_masks = bce_masks[:, self.bce_blackout]
bce_target_intensity = bce_target_intensity[:, self.bce_blackout]
LOG.debug('BCE: target = %s, mask = %s', bce_target_intensity.shape,
bce_masks.shape)
bce_target = torch.masked_select(bce_target_intensity, bce_masks)
bce_weight = None
if self.background_weight != 1.0:
bce_weight = torch.ones_like(bce_target)
bce_weight[bce_target == 0] = self.background_weight
ce_loss = torch.nn.functional.binary_cross_entropy_with_logits(
torch.masked_select(bce_x_intensity, bce_masks),
bce_target,
weight=bce_weight,
)
reg_losses = [None for _ in target_regs]
reg_masks = target_intensity[:, :-1] > 0.5
if torch.any(reg_masks):
weight = None
if self.multiplicity_correction:
assert len(target_regs) == 2
lengths = torch.norm(target_regs[0] - target_regs[1], dim=2)
multiplicity = (lengths - 3.0) / self.independence_scale
multiplicity = torch.clamp(multiplicity, min=1.0)
multiplicity = torch.masked_select(multiplicity, reg_masks)
weight = 1.0 / multiplicity
reg_losses = []
for i, (x_reg, x_spread, target_reg) in enumerate(
zip(x_regs, x_spreads, target_regs)):
if hasattr(self.regression_loss, 'scale'):
assert self.scales_to_kp is not None
self.regression_loss.scale = torch.masked_select(
torch.clamp(target_scale * self.scales_to_kp[i], 0.1,
1000.0), # pylint: disable=unsubscriptable-object
reg_masks,
)
reg_losses.append(
self.regression_loss(
torch.masked_select(x_reg[:, :, 0], reg_masks),
torch.masked_select(x_reg[:, :, 1], reg_masks),
torch.masked_select(x_spread, reg_masks),
torch.masked_select(target_reg[:, :, 0], reg_masks),
torch.masked_select(target_reg[:, :, 1], reg_masks),
weight=weight,
) / 1000.0 / batch_size)
scale_losses = []
if x_scales:
scale_losses = [
torch.nn.functional.l1_loss(
torch.masked_select(x_scale, reg_masks),
torch.masked_select(target_scale * scale_to_kp, reg_masks),
reduction='sum',
) / 1000.0 / batch_size
for x_scale, scale_to_kp in zip(x_scales, self.scales_to_kp)
]
return [ce_loss] + reg_losses + scale_losses
def train_with_pruning_callback(
tmpdir,
parameters_to_prune=False,
use_global_unstructured=False,
pruning_fn="l1_unstructured",
use_lottery_ticket_hypothesis=False,
strategy=None,
accelerator="cpu",
devices=1,
):
model = TestModel()
# Weights are random. None is 0
assert torch.all(model.layer.mlp_2.weight != 0)
pruning_kwargs = {
"pruning_fn": pruning_fn,
"amount": 0.3,
"use_global_unstructured": use_global_unstructured,
"use_lottery_ticket_hypothesis": use_lottery_ticket_hypothesis,
"verbose": 1,
}
if parameters_to_prune:
pruning_kwargs["parameters_to_prune"] = [(model.layer.mlp_1, "weight"),
(model.layer.mlp_2, "weight")]
else:
if isinstance(pruning_fn, str) and pruning_fn.endswith("_structured"):
pruning_kwargs["parameter_names"] = ["weight"]
else:
pruning_kwargs["parameter_names"] = ["weight", "bias"]
if isinstance(pruning_fn, str) and pruning_fn.endswith("_structured"):
pruning_kwargs["pruning_dim"] = 0
if pruning_fn == "ln_structured":
pruning_kwargs["pruning_norm"] = 1
# Misconfiguration checks
if isinstance(pruning_fn, str) and pruning_fn.endswith(
"_structured") and use_global_unstructured:
with pytest.raises(
MisconfigurationException,
match="is supported with `use_global_unstructured=True`"):
ModelPruning(**pruning_kwargs)
return
if ModelPruning._is_pruning_method(
pruning_fn) and not use_global_unstructured:
with pytest.raises(MisconfigurationException,
match="currently only supported with"):
ModelPruning(**pruning_kwargs)
return
pruning = ModelPruning(**pruning_kwargs)
trainer = Trainer(
default_root_dir=tmpdir,
enable_progress_bar=False,
enable_model_summary=False,
enable_checkpointing=False,
logger=False,
limit_train_batches=10,
limit_val_batches=2,
max_epochs=10,
strategy=strategy,
accelerator=accelerator,
devices=devices,
callbacks=pruning,
)
trainer.fit(model)
trainer.test(model)
if not strategy:
# Check some have been pruned
assert torch.any(model.layer.mlp_2.weight == 0)
def _compute_perturbation( # pylint: disable=W0221
self, x: "torch.Tensor", y: "torch.Tensor", mask: Optional["torch.Tensor"]
) -> "torch.Tensor":
"""
Compute perturbations.
:param x: Current adversarial examples.
:param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
(nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
(explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
:param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
features for which the mask is zero will not be adversarially perturbed.
:return: Perturbations.
"""
import torch # lgtm [py/repeated-import]
# Pick a small scalar to avoid division by 0
tol = 10e-8
# Get gradient wrt loss; invert it if attack is targeted
grad = self.estimator.loss_gradient(x=x, y=y) * (1 - 2 * int(self.targeted))
# Write summary
if self.summary_writer is not None:
self.summary_writer.add_scalar(
"gradients/norm-L1/batch-{}".format(self._batch_id),
np.linalg.norm(grad.flatten(), ord=1),
global_step=self._i_max_iter,
)
self.summary_writer.add_scalar(
"gradients/norm-L2/batch-{}".format(self._batch_id),
np.linalg.norm(grad.flatten(), ord=2),
global_step=self._i_max_iter,
)
self.summary_writer.add_scalar(
"gradients/norm-Linf/batch-{}".format(self._batch_id),
np.linalg.norm(grad.flatten(), ord=np.inf),
global_step=self._i_max_iter,
)
if hasattr(self.estimator, "compute_losses"):
losses = self.estimator.compute_losses(x=x, y=y)
for key, value in losses.items():
self.summary_writer.add_scalar(
"loss/{}/batch-{}".format(key, self._batch_id),
np.mean(value.detach().cpu().numpy()),
global_step=self._i_max_iter,
)
# Check for nan before normalisation an replace with 0
if torch.any(grad.isnan()):
logger.warning("Elements of the loss gradient are NaN and have been replaced with 0.0.")
grad[grad.isnan()] = 0.0
# Apply mask
if mask is not None:
grad = torch.where(mask == 0.0, torch.tensor(0.0).to(self.estimator.device), grad)
# Apply norm bound
if self.norm in ["inf", np.inf]:
grad = grad.sign()
elif self.norm == 1:
ind = tuple(range(1, len(x.shape)))
grad = grad / (torch.sum(grad.abs(), dim=ind, keepdims=True) + tol) # type: ignore
elif self.norm == 2:
ind = tuple(range(1, len(x.shape)))
grad = grad / (torch.sqrt(torch.sum(grad * grad, axis=ind, keepdims=True)) + tol) # type: ignore
assert x.shape == grad.shape
return grad
def run(self):
# ---- Subscribe ----
with self.neuron:
# ---- Weights ----
self.row = self.neuron.metagraph.row
# --- Run state ---
self.global_step = 0
self.best_train_loss = math.inf
# --- Loop forever ---
for self.epoch in range(self.config.miner.n_epochs):
try:
# ---- Serve ----
self.neuron.axon.serve(self.model)
# ---- Train Model ----
self.train()
self.scheduler.step()
# If model has borked for some reason, we need to make sure it doesn't emit weights
# Instead, reload into previous version of model
if torch.any(
torch.isnan(
torch.cat([
param.view(-1)
for param in self.model.parameters()
]))):
self.model, self.optimizer = self.model_toolbox.load_model(
self.config)
continue
# ---- Emit row-weights ----
self.neuron.metagraph.set_weights(
self.row, wait_for_inclusion=True
) # Sets my row-weights on the chain.
# ---- Sync metagraph ----
self.neuron.metagraph.sync(
) # Pulls the latest metagraph state (with my update.)
self.row = self.neuron.metagraph.row
# --- Epoch logs ----
print(self.neuron.axon.__full_str__())
print(self.neuron.dendrite.__full_str__())
print(self.neuron.metagraph)
# ---- Update Tensorboard ----
self.neuron.dendrite.__to_tensorboard__(
self.tensorboard, self.global_step)
self.neuron.metagraph.__to_tensorboard__(
self.tensorboard, self.global_step)
self.neuron.axon.__to_tensorboard__(
self.tensorboard, self.global_step)
# ---- Save best loss and model ----
if self.training_loss and self.epoch % 10 == 0:
if self.training_loss < self.best_train_loss:
self.best_train_loss = self.training_loss # update best train loss
self.model_toolbox.save_model(
self.config.miner.full_path, {
'epoch':
self.epoch,
'model_state_dict':
self.model.state_dict(),
'loss':
self.best_train_loss,
'optimizer_state_dict':
self.optimizer.state_dict(),
})
self.tensorboard.add_scalar(
'Neuron/Train_loss', self.training_loss,
self.global_step)
# --- Catch Errors ----
except Exception as e:
logger.error('Exception in training script with error: {}',
e)
logger.info(traceback.print_exc())
logger.info('Continuing to train.')
time.sleep(1)
def main():
summary = SummaryWriter('./log')
tr_set, dv_set, feat_dim, msg = load_dataset(args.njobs, args.gpu, args.pin_memory,
config['hparas']['curriculum'] > 0,
**config['data'])
verbose(msg)
# model select
print('Model initializing\n')
net = torch.nn.DataParallel(
AttentionModel(120, hidden_size=args.hidden_size, dropout_p=args.dropout_p, use_attn=args.attn_use,
stacked_encoder=args.stacked_encoder, attn_len=args.attn_len))
# net = AttentionModel(257, 112, dropout_p = args.dropout_p, use_attn = arg0s.attn_use)
net = net.cuda()
print(net)
optimizer = optim.Adam(net.parameters(), lr=args.learning_rate)
scheduler = ExponentialLR(optimizer, 0.5)
# check point load
# Check point load
print('Trying Checkpoint Load\n')
ckpt_dir = 'ckpt_dir'
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
best_loss = 200000.
ckpt_path = os.path.join(ckpt_dir, args.ck_name)
if os.path.exists(ckpt_path):
ckpt = torch.load(ckpt_path)
try:
net.load_state_dict(ckpt['model'])
optimizer.load_state_dict(ckpt['optimizer'])
best_loss = ckpt['best_loss']
print('checkpoint is loaded !')
print('current best loss : %.4f' % best_loss)
except RuntimeError as e:
print('wrong checkpoint\n')
else:
print('checkpoint not exist!')
print('current best loss : %.4f' % best_loss)
print('Training Start!')
# train
iteration = 0
train_losses = []
test_losses = []
for epoch in range(args.num_epochs):
n = 0
avg_loss = 0
net.train()
for input in tqdm(tr_set):
tr_noisy_set, feat_dim = load_noisy_dataset("train", input[0], args.njobs,
args.gpu,
args.pin_memory,
config['hparas']['curriculum'] > 0,
**config['data_noisy'])
for input_noisy in tr_noisy_set:
train_clean_feat, feat_len = fetch_data(input)
train_noisy_feat, feat_len = fetch_data(input_noisy)
iteration += 1
# feed data
train_mixed_feat, attn_weight = net(train_noisy_feat)
if train_mixed_feat.shape == train_clean_feat.shape:
loss = F.mse_loss(train_mixed_feat, train_clean_feat, True)
if torch.any(torch.isnan(loss)):
torch.save(
{'clean_mag': train_clean_feat, 'noisy_mag': train_noisy_feat, 'out_mag': train_mixed_feat},
'nan_mag')
raise ('loss is NaN')
avg_loss += loss.item()
n += 1
# gradient optimizer
optimizer.zero_grad()
loss.backward()
# update weight
optimizer.step()
avg_loss /= n
print('result:')
print('[epoch: {}, iteration: {}] avg_loss : {:.4f}'.format(epoch, iteration, avg_loss))
summary.add_scalar('Train Loss', avg_loss, iteration)
train_losses.append(avg_loss)
if (len(train_losses) > 2) and (train_losses[-2] < avg_loss):
print("Learning rate Decay")
scheduler.step()
# test phase
n = 0
avg_test_loss = 0
net.eval()
with torch.no_grad():
for input in tqdm(dv_set):
dv_noisy_set, feat_dim = load_noisy_dataset("dev", input[0], args.njobs,
args.gpu,
args.pin_memory,
config['hparas']['curriculum'] > 0,
**config['data_noisy'])
for input_noisy in dv_noisy_set:
test_clean_feat = input[1].to(device='cuda')
test_noisy_feat = input_noisy[1].to(device='cuda')
test_mixed_feat, logits_attn_weight = net(test_noisy_feat)
if test_mixed_feat.shape == test_clean_feat.shape:
test_loss = F.mse_loss(test_mixed_feat, test_clean_feat, True)
avg_test_loss += test_loss.item()
n += 1
avg_test_loss /= n
test_losses.append(avg_test_loss)
summary.add_scalar('Test Loss', avg_test_loss, iteration)
print('[epoch: {}, iteration: {}] test loss : {:.4f} '.format(epoch, iteration, avg_test_loss))
if avg_test_loss < best_loss:
best_loss = avg_test_loss
# Note: optimizer also has states ! don't forget to save them as well.
ckpt = {'model': net.state_dict(),
'optimizer': optimizer.state_dict(),
'best_loss': best_loss}
torch.save(ckpt, ckpt_path)
print('checkpoint is saved !')
#### END CODE HERE ####
# Make sure shapes are correct
assert tuple(
fake_image_and_labels.shape) == (len(real),
fake.detach().shape[1] +
image_one_hot_labels.shape[1], 28,
28)
assert tuple(
real_image_and_labels.shape) == (len(real), real.shape[1] +
image_one_hot_labels.shape[1], 28,
28)
# Make sure that enough predictions were made
assert len(disc_real_pred) == len(real)
# Make sure that the inputs are different
assert torch.any(fake_image_and_labels != real_image_and_labels)
# Shapes must match
assert tuple(fake_image_and_labels.shape) == tuple(
real_image_and_labels.shape)
assert tuple(disc_fake_pred.shape) == tuple(disc_real_pred.shape)
disc_fake_loss = criterion(disc_fake_pred,
torch.zeros_like(disc_fake_pred))
disc_real_loss = criterion(disc_real_pred,
torch.ones_like(disc_real_pred))
disc_loss = (disc_fake_loss + disc_real_loss) / 2
disc_loss.backward(retain_graph=True)
disc_opt.step()
# Keep track of the average discriminator loss
discriminator_losses += [disc_loss.item()]
def test_retrieval(model, criterion, transforms_cuda, device, epoch, args):
accuracy = [AverageMeter(),AverageMeter(),AverageMeter(),AverageMeter()]
model.eval()
def tr(x):
B = x.size(0); assert B == 1
test_sample = x.size(2)//(args.seq_len*args.num_seq)
return transforms_cuda(x)\
.view(3,test_sample,args.num_seq,args.seq_len,args.img_dim,args.img_dim).permute(1,2,0,3,4,5)
with torch.no_grad():
transform = transforms.Compose([
A.CenterCrop(size=(224,224)),
A.Scale(size=(args.img_dim,args.img_dim)),
A.ColorJitter(0.2, 0.2, 0.2, 0.1, p=0.3, consistent=True),
A.ToTensor()])
if args.dataset == 'ucf101':
d_class = UCF101LMDB
elif args.dataset == 'ucf101-f':
d_class = UCF101Flow_LMDB
elif args.dataset == 'hmdb51':
d_class = HMDB51LMDB
elif args.dataset == 'hmdb51-f':
d_class = HMDB51Flow_LMDB
train_dataset = d_class(mode='train',
transform=transform,
num_frames=args.num_seq*args.seq_len,
ds=args.ds,
which_split=1,
return_label=True,
return_path=True)
print('train dataset size: %d' % len(train_dataset))
test_dataset = d_class(mode='test',
transform=transform,
num_frames=args.num_seq*args.seq_len,
ds=args.ds,
which_split=1,
return_label=True,
return_path=True)
print('test dataset size: %d' % len(test_dataset))
train_sampler = data.SequentialSampler(train_dataset)
test_sampler = data.SequentialSampler(test_dataset)
train_loader = data.DataLoader(train_dataset,
batch_size=1,
sampler=train_sampler,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
test_loader = data.DataLoader(test_dataset,
batch_size=1,
sampler=test_sampler,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.dirname is None:
dirname = 'feature'
else:
dirname = args.dirname
if os.path.exists(os.path.join(os.path.dirname(args.test), dirname, '%s_test_feature.pth.tar' % args.dataset)):
test_feature = torch.load(os.path.join(os.path.dirname(args.test), dirname, '%s_test_feature.pth.tar' % args.dataset)).to(device)
test_label = torch.load(os.path.join(os.path.dirname(args.test), dirname, '%s_test_label.pth.tar' % args.dataset)).to(device)
else:
try: os.makedirs(os.path.join(os.path.dirname(args.test), dirname))
except: pass
print('Computing test set feature ... ')
test_feature = None
test_label = []
test_vname = []
sample_id = 0
for idx, (input_seq, target) in tqdm(enumerate(test_loader), total=len(test_loader)):
B = 1
input_seq = input_seq.to(device, non_blocking=True)
input_seq = tr(input_seq)
current_target, vname = target
current_target = current_target.to(device, non_blocking=True)
test_sample = input_seq.size(0)
input_seq = input_seq.squeeze(1)
logit, feature = model(input_seq)
if test_feature is None:
test_feature = torch.zeros(len(test_dataset), feature.size(-1), device=feature.device)
test_feature[sample_id,:] = feature.mean(0)
test_label.append(current_target)
test_vname.append(vname)
sample_id += 1
print(test_feature.size())
# test_feature = torch.stack(test_feature, dim=0)
test_label = torch.cat(test_label, dim=0)
torch.save(test_feature, os.path.join(os.path.dirname(args.test), dirname, '%s_test_feature.pth.tar' % args.dataset))
torch.save(test_label, os.path.join(os.path.dirname(args.test), dirname, '%s_test_label.pth.tar' % args.dataset))
with open(os.path.join(os.path.dirname(args.test), dirname, '%s_test_vname.pkl' % args.dataset), 'wb') as fp:
pickle.dump(test_vname, fp)
if os.path.exists(os.path.join(os.path.dirname(args.test), dirname, '%s_train_feature.pth.tar' % args.dataset)):
train_feature = torch.load(os.path.join(os.path.dirname(args.test), dirname, '%s_train_feature.pth.tar' % args.dataset)).to(device)
train_label = torch.load(os.path.join(os.path.dirname(args.test), dirname, '%s_train_label.pth.tar' % args.dataset)).to(device)
else:
print('Computing train set feature ... ')
train_feature = None
train_label = []
train_vname = []
sample_id = 0
for idx, (input_seq, target) in tqdm(enumerate(train_loader), total=len(train_loader)):
B = 1
input_seq = input_seq.to(device, non_blocking=True)
input_seq = tr(input_seq)
current_target, vname = target
current_target = current_target.to(device, non_blocking=True)
test_sample = input_seq.size(0)
input_seq = input_seq.squeeze(1)
logit, feature = model(input_seq)
if train_feature is None:
train_feature = torch.zeros(len(train_dataset), feature.size(-1), device=feature.device)
train_feature[sample_id,:] = feature.mean(0)
# train_feature[sample_id,:] = feature[:,-1,:].mean(0)
train_label.append(current_target)
train_vname.append(vname)
sample_id += 1
# train_feature = torch.stack(train_feature, dim=0)
print(train_feature.size())
train_label = torch.cat(train_label, dim=0)
torch.save(train_feature, os.path.join(os.path.dirname(args.test), dirname, '%s_train_feature.pth.tar' % args.dataset))
torch.save(train_label, os.path.join(os.path.dirname(args.test), dirname, '%s_train_label.pth.tar' % args.dataset))
with open(os.path.join(os.path.dirname(args.test), dirname, '%s_train_vname.pkl' % args.dataset), 'wb') as fp:
pickle.dump(train_vname, fp)
ks = [1,5,10,20,50]
NN_acc = []
# centering
test_feature = test_feature - test_feature.mean(dim=0, keepdim=True)
train_feature = train_feature - train_feature.mean(dim=0, keepdim=True)
# normalize
test_feature = F.normalize(test_feature, p=2, dim=1)
train_feature = F.normalize(train_feature, p=2, dim=1)
# dot product
sim = test_feature.matmul(train_feature.t())
torch.save(sim, os.path.join(os.path.dirname(args.test), dirname, '%s_sim.pth.tar' % args.dataset))
for k in ks:
topkval, topkidx = torch.topk(sim, k, dim=1)
acc = torch.any(train_label[topkidx] == test_label.unsqueeze(1), dim=1).float().mean().item()
NN_acc.append(acc)
print('%dNN acc = %.4f' % (k, acc))
args.logger.log('NN-Retrieval on %s:' % args.dataset)
for k,acc in zip(ks, NN_acc):
args.logger.log('\t%dNN acc = %.4f' % (k, acc))
with open(os.path.join(os.path.dirname(args.test), dirname, '%s_test_vname.pkl' % args.dataset), 'rb') as fp:
test_vname = pickle.load(fp)
with open(os.path.join(os.path.dirname(args.test), dirname, '%s_train_vname.pkl' % args.dataset), 'rb') as fp:
train_vname = pickle.load(fp)
sys.exit(0)
def infer(
self,
phonemes: torch.LongTensor,
combine_strategy: str = "concat",
max_len: int = 1024,
stop_threshold: float = 0.25,
verbose: bool = False,
stop_at_stop_token: bool = True,
) -> Tuple[torch.Tensor, torch.LongTensor]:
"""
Run inference loop to generate a new spectrogram
:param phonemes: of shape (batch size, phonemes length)
:param combine_strategy: "concat" to concatenate outputs from consecutive iterations,
"replace" to replace previous output using output from current iteration
:param max_len: maximum length of generated spectrogram
:param stop_threshold: value in (-1 , 1) above which sigmoid(stop_pred)
is considered to indicate end of predicted sequence
:param verbose: if true, prints progress info every 10 steps (useful for cpu)
:param stop_at_stop_token: if true, ignores the stop threshold and stops only at max_len
:return: tuple (spectrogram, stop_idx) where
spectrogram shape = (batch size, num_mel_coeffs, spectrogram length)
stop_idx shape = (batch size), contains end index for each spectrogram in batch
"""
assert combine_strategy in {"concat", "replace"}
assert -1. < stop_threshold < 1.
batch_size = phonemes.shape[0]
zeros = torch.zeros((batch_size, 1, self.num_mel_coeffs))
spectrogram = zeros.clone()
stop = torch.zeros(batch_size, dtype=torch.long)
while not torch.all(stop > 0):
iteration = spectrogram.shape[1]
still_running = stop == 0
if verbose and iteration % 10 == 0:
number_of_running_samples = sum(still_running)
print(f"reached {iteration=}, {number_of_running_samples=}...")
_, generated, stop_pred, _ = self.forward(phonemes, spectrogram)
stop_pred = stop_pred.view(
-1, generated.shape[1]) # view as (batch, len)
if combine_strategy == "concat":
generated_slice = generated[:, -1, :].view(
batch_size, 1, self.num_mel_coeffs)
spectrogram = torch.cat([spectrogram, generated_slice], dim=1)
if stop_at_stop_token:
stops_now = torch.sigmoid(stop_pred[:,
-1]) > stop_threshold
still_running_stops_now = still_running * stops_now
stop = torch.where(
still_running_stops_now,
(stops_now.to(dtype=torch.long) * iteration) + 1, stop)
elif combine_strategy == "replace":
spectrogram = torch.cat([zeros, generated], dim=1)
if stop_at_stop_token:
stops_now = torch.any(
torch.sigmoid(stop_pred) > stop_threshold, dim=1)
still_running_stops_now = stops_now * still_running
stop = torch.where(still_running_stops_now,
torch.argmax(stop_pred, dim=1) + 1,
stop)
if max(spectrogram.shape) > max_len:
if verbose:
print(f"stopped at {max_len=}")
break
stop_at_end = torch.ones_like(stop, dtype=torch.long) * max_len
stop: torch.LongTensor = torch.where(stop == 0, stop_at_end, stop)
spectrogram: torch.Tensor = spectrogram.transpose(1, 2)
return spectrogram[:, :, 1:], stop
def forward(self, backbone_outputs, match_coords, is_train):
# Enumerate network over pyramids.
fpn_outputs = []
targets = []
classifications = []
pyramid_output = None
num_layers = len(backbone_outputs)
if is_train:
target_coords = [ME.utils.batched_coordinates([match[i][0] for match in match_coords])
for i in range(num_layers)]
ambiguous_coords = [ME.utils.batched_coordinates([match[i][1] for match in match_coords])
for i in range(num_layers)]
for layer_idx in reversed(range(num_layers)):
conv_feat_layer = self.get_layer('conv_feat', layer_idx)
conv_cls_layer = self.get_layer('conv_cls', layer_idx)
conv_up_layer = self.get_layer('conv_up', layer_idx)
# Current feature
curr_feat = backbone_outputs[layer_idx]
# Add previous layer output
if pyramid_output is not None:
assert pyramid_output.tensor_stride == curr_feat.tensor_stride
curr_feat = curr_feat + pyramid_output
# Two branches: upsample and fpn feature and classification
# 1. FPN feature & classification
fpn_feat = conv_feat_layer(curr_feat)
feat_cls = conv_cls_layer(fpn_feat)
pred_prob = F.softmax(feat_cls.F, 1)[:, 1]
# target calculation
target = None
if is_train:
target = torch.zeros(len(fpn_feat), dtype=torch.long)
pos_ins = utils.map_coordinates(fpn_feat, torch.cat(ambiguous_coords[:layer_idx + 1]),
force_stride=True)[0]
target[pos_ins] = self.config.ignore_label
pos_ins = utils.map_coordinates(fpn_feat, torch.cat(target_coords[:layer_idx + 1]),
force_stride=True)[0]
target[pos_ins] = 1
# Get keep labels
keep = (pred_prob > self.config.sfpn_min_confidence).cpu()
if is_train: # Force put GT labels within keep
keep |= target == 1
if torch.any(keep):
# Prune and upsample
pyramid_output = conv_up_layer(self.pruning(curr_feat, keep))
# Generate final feature for current level
final_pruned = self.pruning(fpn_feat, keep)
else:
pyramid_output = None
final_pruned = None
# Post processing
classifications.insert(0, feat_cls)
targets.insert(0, target)
fpn_outputs.insert(0, final_pruned)
return fpn_outputs, targets, classifications