def forward(self, sents, sent_lengths):
'''
sents is (batch_size by padded_length)
when we evaluate sentence by sentence, you evaluate it with batch_size = 1, padded_length.
[[1, 2, 3, 4]] etc.
'''
batch_size = sents.size()[0]
sent_lengths = list(sent_lengths)
# We sort and then do pad packed sequence here.
descending_lengths = [x for x, _ in sorted(zip(sent_lengths, range(len(sent_lengths))), reverse=True)]
descending_indices = [x for _, x in sorted(zip(sent_lengths, range(len(sent_lengths))), reverse=True)]
descending_lengths = torch.tensor(descending_lengths)
descending_indices = torch.tensor(descending_indices).to(device)
descending_sents = torch.index_select(sents, torch.tensor(0), descending_indices)
# get embedding
embed = self.embedding(descending_sents)
# pack padded sequence
embed = torch.nn.utils.rnn.pack_padded_sequence(embed, descending_lengths, batch_first=True)
# fprop though RNN
self.hidden = self.init_hidden(batch_size)
rnn_out, self.hidden = self.gru(embed, self.hidden)
pdb.set_trace()
rnn_out, _ = torch.nn.utils.rnn.pad_packed_sequence(rnn_out, batch_first=True)
# rnn_out is 32 by 72 by 256
# change the order back
change_it_back = [x for _, x in sorted(zip(descending_indices, range(len(descending_indices))))]
self.hidden = torch.index_select(self.hidden, 1, torch.LongTensor(change_it_back).to(device))
rnn_out = torch.index_select(rnn_out, 0, torch.LongTensor(change_it_back).to(device))
return rnn_out, self.hidden
def get_triplet_loss(image_a_pred, image_b_pred, matches_a, matches_b, non_matches_a, non_matches_b, alpha):
"""
Computes the loss function
\sum_{triplets} ||D(I_a, u_a, I_b, u_{b,match})||_2^2 - ||D(I_a, u_a, I_b, u_{b,non-match)||_2^2 + alpha
"""
num_matches = matches_a.size()[0]
num_non_matches = non_matches_a.size()[0]
multiplier = num_non_matches / num_matches
## non_matches_a is already replicated up to be the right size
## non_matches_b is also that side
## matches_a is just a smaller version of non_matches_a
## matches_b is the only thing that needs to be replicated up in size
matches_b_long = torch.t(matches_b.repeat(multiplier, 1)).contiguous().view(-1)
matches_a_descriptors = torch.index_select(image_a_pred, 1, non_matches_a)
matches_b_descriptors = torch.index_select(image_b_pred, 1, matches_b_long)
non_matches_b_descriptors = torch.index_select(image_b_pred, 1, non_matches_b)
triplet_losses = (matches_a_descriptors - matches_b_descriptors).pow(2) - (matches_a_descriptors - non_matches_b_descriptors).pow(2) + alpha
triplet_loss = 1.0 / num_non_matches * torch.clamp(triplet_losses, min=0).sum()
return triplet_loss
def forward(cls, ctx, indices, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq,
sparse=False):
ctx.padding_idx = padding_idx
ctx.scale_grad_by_freq = scale_grad_by_freq
ctx._indices = None
ctx.sparse = sparse
assert indices.dim() <= 2
assert not ctx.needs_input_grad[0], "Embedding doesn't " \
"compute the gradient w.r.t. the indices"
ctx._backend = type2backend[type(weight)]
ctx._weight_size = weight.size()
if not indices.is_contiguous():
ctx._indices = indices.contiguous()
indices = ctx._indices
else:
ctx.save_for_backward(indices)
output = weight.new()
if max_norm is not None:
cls._renorm(ctx, indices, weight, max_norm, norm_type)
if indices.dim() == 1:
output = torch.index_select(weight, 0, indices)
else:
output = torch.index_select(weight, 0, indices.view(-1))
output = output.view(indices.size(0), indices.size(1), weight.size(1))
return output
def get_loss(self, image_a_pred, image_b_pred, mask_a, mask_b):
loss = 0
# get the nonzero indices
mask_a_indices_flat = torch.nonzero(mask_a)
mask_b_indices_flat = torch.nonzero(mask_b)
if len(mask_a_indices_flat) == 0:
return Variable(torch.cuda.LongTensor([0]), requires_grad=True)
if len(mask_b_indices_flat) == 0:
return Variable(torch.cuda.LongTensor([0]), requires_grad=True)
# take 5000 random pixel samples of the object, using the mask
num_samples = 10000
rand_numbers_a = (torch.rand(num_samples)*len(mask_a_indices_flat)).cuda()
rand_indices_a = Variable(torch.floor(rand_numbers_a).type(torch.cuda.LongTensor), requires_grad=False)
randomized_mask_a_indices_flat = torch.index_select(mask_a_indices_flat, 0, rand_indices_a).squeeze(1)
rand_numbers_b = (torch.rand(num_samples)*len(mask_b_indices_flat)).cuda()
rand_indices_b = Variable(torch.floor(rand_numbers_b).type(torch.cuda.LongTensor), requires_grad=False)
randomized_mask_b_indices_flat = torch.index_select(mask_b_indices_flat, 0, rand_indices_b).squeeze(1)
# index into the image and get descriptors
M_margin = 0.5 # margin parameter
random_img_a_object_descriptors = torch.index_select(image_a_pred, 1, randomized_mask_a_indices_flat)
random_img_b_object_descriptors = torch.index_select(image_b_pred, 1, randomized_mask_b_indices_flat)
pixel_wise_loss = (random_img_a_object_descriptors - random_img_b_object_descriptors).pow(2).sum(dim=2)
pixel_wise_loss = torch.add(pixel_wise_loss, -2*M_margin)
zeros_vec = torch.zeros_like(pixel_wise_loss)
loss += torch.max(zeros_vec, pixel_wise_loss).sum()
return loss
def dual_OT_model(self, Xs_batch, i_t):
batch_size = i_t.shape[0]
u_batch = self.u(Xs_batch)
v_batch = torch.index_select(self.v, dim=0, index=i_t)
Xt_batch = torch.index_select(self.Xt, dim=0, index=i_t)
return self.dual_OT_batch_loss(batch_size=batch_size, u_batch=u_batch, v_batch=v_batch, Xs_batch=Xs_batch, Xt_batch=Xt_batch)
def get_loss_original(self, image_a_pred, image_b_pred, matches_a,
matches_b, non_matches_a, non_matches_b,
M_margin=0.5, non_match_loss_weight=1.0):
# this is pegged to it's implemenation at sha 87abdb63bb5b99d9632f5c4360b5f6f1cf54245f
"""
Computes the loss function
DCN = Dense Correspondence Network
num_images = number of images in this batch
num_matches = number of matches
num_non_matches = number of non-matches
W = image width
H = image height
D = descriptor dimension
match_loss = 1/num_matches \sum_{num_matches} ||descriptor_a - descriptor_b||_2^2
non_match_loss = 1/num_non_matches \sum_{num_non_matches} max(0, M_margin - ||descriptor_a - descriptor_b||_2^2 )
loss = match_loss + non_match_loss
:param image_a_pred: Output of DCN network on image A.
:type image_a_pred: torch.Variable(torch.FloatTensor) shape [1, W * H, D]
:param image_b_pred: same as image_a_pred
:type image_b_pred:
:param matches_a: torch.Variable(torch.LongTensor) has shape [num_matches,], a (u,v) pair is mapped
to (u,v) ---> image_width * v + u, this matches the shape of one dimension of image_a_pred
:type matches_a: torch.Variable(torch.FloatTensor)
:param matches_b: same as matches_b
:type matches_b:
:param non_matches_a: torch.Variable(torch.FloatTensor) has shape [num_non_matches,], a (u,v) pair is mapped
to (u,v) ---> image_width * v + u, this matches the shape of image_a_pred
:type non_matches_a: torch.Variable(torch.FloatTensor)
:param non_matches_b: same as non_matches_a
:type non_matches_b:
:return: loss, match_loss, non_match_loss
:rtype: torch.Variable(torch.FloatTensor) each of shape torch.Size([1])
"""
num_matches = matches_a.size()[0]
num_non_matches = non_matches_a.size()[0]
matches_a_descriptors = torch.index_select(image_a_pred, 1, matches_a)
matches_b_descriptors = torch.index_select(image_b_pred, 1, matches_b)
match_loss = 1.0/num_matches * (matches_a_descriptors - matches_b_descriptors).pow(2).sum()
# add loss via non_matches
non_matches_a_descriptors = torch.index_select(image_a_pred, 1, non_matches_a)
non_matches_b_descriptors = torch.index_select(image_b_pred, 1, non_matches_b)
pixel_wise_loss = (non_matches_a_descriptors - non_matches_b_descriptors).pow(2).sum(dim=2)
pixel_wise_loss = torch.add(torch.neg(pixel_wise_loss), M_margin)
zeros_vec = torch.zeros_like(pixel_wise_loss)
non_match_loss = non_match_loss_weight * 1.0/num_non_matches * torch.max(zeros_vec, pixel_wise_loss).sum()
loss = match_loss + non_match_loss
return loss, match_loss, non_match_loss
def forward(self, input, output, input_lens=None, output_lens=None, lookup=None, **kwargs):
h0 = self.h0.expand(1, input.size(0), self.hidden_dim).contiguous()
c0 = self.c0.expand(1, input.size(0), self.hidden_dim).contiguous()
input_encoded, input_h, input_c = self.encoder(input, h0, c0, lens=input_lens)
if lookup:
input_h = th.index_select(input_h, 1, lookup)
input_c = th.index_select(input_c, 1, lookup)
transfer_h, transfer_c = self.transfer(input_h, input_c, **kwargs)
log_probs, _, _ = self.decoder(output, transfer_h, transfer_c, lens=output_lens)
return log_probs
def updateOutput(self, input):
self.renorm(input)
input = self._makeInputContiguous(input)
if input.dim() == 1:
torch.index_select(self.weight, 0, input, out=self.output)
elif input.dim() == 2:
torch.index_select(self.weight, 0, input.view(-1), out=self.output)
self.output = self.output.view(input.size(0), input.size(1), self.weight.size(1))
else:
raise RuntimeError("input must be a vector or matrix")
return self.output
def forward(self, base_feat, im_info, gt_boxes, num_boxes):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, dim=1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
return rois, self.rpn_loss_cls, self.rpn_loss_box
def barycentric_mapping_loss_model(self, neuralNet, Xs_batch, i_t):
self.u.eval()
self.v.requires_grad_(False)
u_batch = self.u(Xs_batch)
v_batch = torch.index_select(self.v, dim=0, index=i_t)
Xt_batch = torch.index_select(self.Xt, dim=0, index=i_t)
fXs_batch = neuralNet(Xs_batch)
return self.barycentric_model_batch_loss(u_batch, v_batch, Xs_batch, Xt_batch, fXs_batch)
def random_sample_from_masked_image_torch(img_mask, num_samples):
"""
:param img_mask: Numpy array [H,W] or torch.Tensor with shape [H,W]
:type img_mask:
:param num_samples: an integer
:type num_samples:
:return: tuple of torch.LongTensor in (u,v) format. Each torch.LongTensor has shape
[num_samples]
:rtype:
"""
image_height, image_width = img_mask.shape
if isinstance(img_mask, np.ndarray):
img_mask_torch = torch.from_numpy(img_mask).float()
else:
img_mask_torch = img_mask
# This code would randomly subsample from the mask
mask = img_mask_torch.view(image_width*image_height,1).squeeze(1)
mask_indices_flat = torch.nonzero(mask)
if len(mask_indices_flat) == 0:
return (None, None)
rand_numbers = torch.rand(num_samples)*len(mask_indices_flat)
rand_indices = torch.floor(rand_numbers).long()
uv_vec_flattened = torch.index_select(mask_indices_flat, 0, rand_indices).squeeze(1)
uv_vec = utils.flattened_pixel_locations_to_u_v(uv_vec_flattened, image_width)
return uv_vec
def next(self):
if self.next_i+self.batch_size > len(self.data):
raise StopIteration()
else:
x_idx = self.x_idx[self.next_i:self.next_i+self.batch_size]
self.next_i += self.batch_size
labels = {k: torch.index_select(self.labels[k], 0, x_idx) for k in self.labels}
x = self.select_data(x_idx)
inputs = {}
sizes = {}
for k,v in labels.items():
possibilities = [self.label_idxs[k][v[i].item()] for i in range(len(x_idx))]
sizes[k] = [len(X) for X in possibilities]
input_idx = [np.random.choice(X, size=self.k_shot[k]) for X in possibilities]
_inputs = [
self.select_data(torch.LongTensor([I[j] for I in input_idx]))
for j in range(self.k_shot[k])]
if self.mode == "tensor":
inputs[k] = torch.cat([x.unsqueeze(1) for x in _inputs], dim=1)
elif self.mode == "list":
inputs[k] = [[_inputs[j][i] for j in range(self.k_shot[k])]
for i in range(len(_inputs[0]))]
batch = VHEBatch(target=x, inputs=inputs, sizes=sizes)
for transform in self.transforms:
batch = transform.apply(batch)
return batch
def forward(self, x):
x = torch.index_select(x, 1, Variable(self.index))
x = self.norm(x)
x = self.relu(x)
x = self.conv(x)
x = ShuffleLayer(x, self.groups)
return x
def model():
p_latent = pyro.param("p1", Variable(torch.Tensor([[0.7], [0.3]])))
p_obs = pyro.param("p2", Variable(torch.Tensor([[0.9], [0.1]])))
latents = [Variable(torch.ones(1, 1))]
observes = []
for t in range(self.model_steps):
latents.append(
pyro.sample("latent_{}".format(str(t)),
Bernoulli(torch.index_select(p_latent, 0, latents[-1].view(-1).long()))))
observes.append(
pyro.observe("observe_{}".format(str(t)),
Bernoulli(torch.index_select(p_obs, 0, latents[-1].view(-1).long())),
self.data[t]))
return torch.sum(torch.cat(latents))
def deprocess_img(img):
# BGR to RGB
idx = torch.LongTensor([2, 1, 0])
img = torch.index_select(img, 0, idx)
# [-1,1] to [0,1]
img = img.add_(1).div_(2)
return img
def nms(boxes, scores, overlap=0.5, top_k=100):
keep = scores.new(scores.size(0)).zero_().long()
if boxes.numel() == 0: return keep
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
area = torch.mul(x2 - x1, y2 - y1)
v, idx = scores.sort(0) # sort in ascending order
idx = idx[-top_k:] # indices of the top-k largest vals
xx1 = boxes.new()
yy1 = boxes.new()
xx2 = boxes.new()
yy2 = boxes.new()
w = boxes.new()
h = boxes.new()
count = 0
while idx.numel() > 0:
i = idx[-1] # index of current largest val
keep[count] = i
count += 1
if idx.size(0) == 1: break
idx = idx[:-1] # remove kept element from view
# load bboxes of next highest vals
torch.index_select(x1, 0, idx, out=xx1)
torch.index_select(y1, 0, idx, out=yy1)
torch.index_select(x2, 0, idx, out=xx2)
torch.index_select(y2, 0, idx, out=yy2)
# store element-wise max with next highest score
xx1 = torch.clamp(xx1, min=x1[i])
yy1 = torch.clamp(yy1, min=y1[i])
xx2 = torch.clamp(xx2, max=x2[i])
yy2 = torch.clamp(yy2, max=y2[i])
w.resize_as_(xx2)
h.resize_as_(yy2)
w = xx2 - xx1
h = yy2 - yy1
# check sizes of xx1 and xx2.. after each iteration
w = torch.clamp(w, min=0.0)
h = torch.clamp(h, min=0.0)
inter = w*h
# IoU = i / (area(a) + area(b) - i)
rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
union = (rem_areas - inter) + area[i]
IoU = inter/union # store result in iou
# keep only elements with an IoU <= overlap
idx = idx[IoU.le(overlap)]
return keep, count
def log_sum_exp(vecs):
n = len(vecs.size())
if n == 1:
vecs = vecs.view(1, -1)
_, idx = torch.max(vecs, 1)
max_score = torch.index_select(vecs, 1, idx.view(-1))
ret = max_score + torch.log(torch.sum(torch.exp(vecs - max_score.expand_as(vecs))))
if n == 1:
return ret.view(-1)
return ret
def crop1d(x,cutoff,dim):
'''Crops tensor x by cutoff elements from the beginning and the end along dimension dim.
Example:
x=torch.FloatTensor([1,2,3,4,5,6,7,8]).cuda(1)
crop1d(x,2,0) '''
idx = torch.arange(cutoff, x.shape[dim]-cutoff).long()
if x.is_cuda:
dev = x.get_device()
idx = idx.cuda(dev)
return torch.index_select(x,dim,idx)
def flip(x,dim):
'''Flip tensor along dimension dim.
Example:
A=torch.FloatTensor([[1,2],[3,4]]).cuda()
A,flip(A,1)'''
inv_idx = torch.arange(x.shape[dim]-1, -1, -1).long()
if x.is_cuda:
dev = x.get_device()
inv_idx = inv_idx.cuda(dev)
return torch.index_select(x,dim,inv_idx)
def non_match_descriptor_loss(image_a_pred, image_b_pred, non_matches_a, non_matches_b, M=0.5, invert=False):
"""
Computes the max(0, M - D(I_a,I_b,u_a,u_b))^2 term
This is effectively: "a and b should be AT LEAST M away from each other"
With invert=True, this is: "a and b should be AT MOST M away from each other"
:param image_a_pred: Output of DCN network on image A.
:type image_a_pred: torch.Variable(torch.FloatTensor) shape [1, W * H, D]
:param image_b_pred: same as image_a_pred
:type image_b_pred:
:param non_matches_a: torch.Variable(torch.FloatTensor) has shape [num_non_matches,], a (u,v) pair is mapped
to (u,v) ---> image_width * v + u, this matches the shape of image_a_pred
:type non_matches_a: torch.Variable(torch.FloatTensor)
:param non_matches_b: same as non_matches_a
:param M: the margin
:type M: float
:return: torch.FloatTensor with shape torch.Shape([num_non_matches])
:rtype:
"""
non_matches_a_descriptors = torch.index_select(image_a_pred, 1, non_matches_a).squeeze()
non_matches_b_descriptors = torch.index_select(image_b_pred, 1, non_matches_b).squeeze()
# crazily enough, if there is only one element to index_select into
# above, then the first dimension is collapsed down, and we end up
# with shape [D,], where we want [1,D]
# this unsqueeze fixes that case
if len(non_matches_a) == 1:
non_matches_a_descriptors = non_matches_a_descriptors.unsqueeze(0)
non_matches_b_descriptors = non_matches_b_descriptors.unsqueeze(0)
norm_degree = 2
non_match_loss = (non_matches_a_descriptors - non_matches_b_descriptors).norm(norm_degree, 1)
if not invert:
non_match_loss = torch.clamp(M - non_match_loss, min=0).pow(2)
else:
non_match_loss = torch.clamp(non_match_loss - M, min=0).pow(2)
hard_negative_idxs = torch.nonzero(non_match_loss)
num_hard_negatives = len(hard_negative_idxs)
return non_match_loss, num_hard_negatives, non_matches_a_descriptors, non_matches_b_descriptors
def updateOutput(self, input):
self.output.set_(self.network.forward([input, self.partition]))
if self.bias is not None:
self.output.add_(torch.index_select(self.bias, 1, self.partition).expand_as(self.output))
if self.addBuffer is None:
self.addBuffer = input.new()
if self.addBuffer.nelement() != input.size(0):
self.addBuffer.resize_(input.size(0)).fill_(1)
return self.output
def prune_matches_if_occluded(foreground_mask_numpy, background_matches_pair):
"""
Checks if any of the matches have been occluded.
If yes, prunes them from the list of matches.
NOTE:
- background_matches is a tuple
- the first element of the tuple HAS to be the one that we are actually checking for occlusions
- the second element of the tuple must also get pruned
:param foreground_mask_numpy: The mask of the foreground image
:type foreground_mask_numpy: numpy 2d array of shape (H,W)
:param background_matches: a tuple of torch Tensors, each of length n, i.e:
(u_pixel_positions, v_pixel_positions)
Where each of the elements of the tuple are torch Tensors of length n
Note: only support torch.LongTensors
"""
background_matches_a = background_matches_pair[0]
background_matches_b = background_matches_pair[1]
idxs_to_keep = []
# this is slow but works
for i in range(len(background_matches_a[0])):
u = background_matches_a[0][i]
v = background_matches_a[1][i]
if foreground_mask_numpy[v,u] == 0:
idxs_to_keep.append(i)
if len(idxs_to_keep) == 0:
return (None, None)
idxs_to_keep = torch.LongTensor(idxs_to_keep)
background_matches_a = (torch.index_select(background_matches_a[0], 0, idxs_to_keep), torch.index_select(background_matches_a[1], 0, idxs_to_keep))
background_matches_b = (torch.index_select(background_matches_b[0], 0, idxs_to_keep), torch.index_select(background_matches_b[1], 0, idxs_to_keep))
return (background_matches_a, background_matches_b)
def reverse_sequences_torch(mini_batch, seq_lengths):
reversed_mini_batch = mini_batch.new_zeros(mini_batch.size())
for b in range(mini_batch.size(0)):
T = seq_lengths[b]
time_slice = np.arange(T - 1, -1, -1)
time_slice = torch.cuda.LongTensor(time_slice) if 'cuda' in mini_batch.data.type() \
else torch.LongTensor(time_slice)
reversed_sequence = torch.index_select(mini_batch[b, :, :], 0, time_slice)
reversed_mini_batch[b, 0:T, :] = reversed_sequence
return reversed_mini_batch
def indice_pooling(x, indices):
#out = x.contiguous().transpose(1,2)
#out = out.contiguous().view(-1,x.size(1))
#indices = indices.unsqueeze(2).repeat(1,1,x.size(2))
#print('indices:',indices.size())
#print('x:',x.size())
out=torch.cat([torch.index_select(x_, 0, i).unsqueeze(0) for x_, i in zip(x, indices)])
#print('out:',out)
#out = x.gather(dim=2, index=indices)
#out = out.view(x.size(0),x.size(2),-1)
#out = out.transpose(1,2)
return out
def __call__(self, batch):
image_batch, theta_batch = batch['image'], batch['theta']
if self.use_cuda:
image_batch = image_batch.cuda()
theta_batch = theta_batch.cuda()
b, c, h, w = image_batch.size()
# generate symmetrically padded image for bigger sampling region
image_batch = self.symmetricImagePad(image_batch,self.padding_factor)
# convert to variables
image_batch = Variable(image_batch,requires_grad=False)
theta_batch = Variable(theta_batch,requires_grad=False)
# get cropped image
cropped_image_batch = self.rescalingTnf(image_batch=image_batch,
theta_batch=None,
padding_factor=self.padding_factor,
crop_factor=self.crop_factor) # Identity is used as no theta given
# get transformed image
warped_image_batch = self.geometricTnf(image_batch=image_batch,
theta_batch=theta_batch,
padding_factor=self.padding_factor,
crop_factor=self.crop_factor) # Identity is used as no theta given
if self.supervision=='strong':
return {'source_image': cropped_image_batch, 'target_image': warped_image_batch, 'theta_GT': theta_batch}
elif self.supervision=='weak':
pos_batch_idx = torch.LongTensor(range(int(b/2)))
neg_batch_idx = torch.LongTensor(range(int(b/2),b))
if self.use_cuda:
pos_batch_idx = pos_batch_idx.cuda()
neg_batch_idx = neg_batch_idx.cuda()
source_image = torch.cat((torch.index_select(cropped_image_batch,0,pos_batch_idx),
torch.index_select(cropped_image_batch,0,pos_batch_idx)),0)
target_image = torch.cat((torch.index_select(warped_image_batch,0,pos_batch_idx),
torch.index_select(cropped_image_batch,0,neg_batch_idx)),0)
return {'source_image': source_image, 'target_image': target_image, 'theta_GT': theta_batch}
def forward(self, sents, sent_lengths):
'''
sents is (batch_size by padded_length)
when we evaluate sentence by sentence, you evaluate it with batch_size = 1, padded_length.
[[1, 2, 3, 4]] etc.
'''
batch_size = sents.size()[0]
sent_lengths = list(sent_lengths)
# We sort and then do pad packed sequence here.
descending_lengths = [x for x, _ in sorted(zip(sent_lengths, range(len(sent_lengths))), reverse=True)]
descending_indices = [x for _, x in sorted(zip(sent_lengths, range(len(sent_lengths))), reverse=True)]
descending_lengths = torch.tensor(descending_lengths)
descending_indices = torch.tensor(descending_indices).to(device)
descending_sents = torch.index_select(sents, torch.tensor(0), descending_indices)
# get embedding
embed = self.embedding(descending_sents)
# pack padded sequence
embed = torch.nn.utils.rnn.pack_padded_sequence(embed, descending_lengths, batch_first=True)
# fprop though RNN
self.hidden = self.init_hidden(batch_size)
rnn_out, self.hidden = self.gru(embed, self.hidden)
rnn_out, _ = torch.nn.utils.rnn.pad_packed_sequence(rnn_out, batch_first=True)
# rnn_out is 32 by 72 by 256
# change the order back
change_it_back = [x for _, x in sorted(zip(descending_indices, range(len(descending_indices))))]
self.hidden = torch.index_select(self.hidden, 1, torch.LongTensor(change_it_back).to(device))
rnn_out = torch.index_select(rnn_out, 0, torch.LongTensor(change_it_back).to(device))
# self.hidden is 4 by 8 by 256
# let's only use the top-most layer for the encoder output
# so we want to return 8 by 512
hidden_top = torch.cat((self.hidden[2], self.hidden[3]), dim=1)
hidden_bottom = torch.cat((self.hidden[0], self.hidden[1]), dim=1)
self.hidden = torch.stack((hidden_top, hidden_bottom))
return rnn_out, self.hidden
def expand_z_where(z_where):
# Take a batch of three-vectors, and massages them into a batch of
# 2x3 matrices with elements like so:
# [s,x,y] -> [[s,0,x],
# [0,s,y]]
n = z_where.size(0)
out = torch.cat((ng_zeros([1, 1]).type_as(z_where).expand(n, 1), z_where), 1)
ix = Variable(expansion_indices)
if z_where.is_cuda:
ix = ix.cuda()
out = torch.index_select(out, 1, ix)
out = out.view(n, 2, 3)
return out
def forward(ctx, input, weight):
assert input.dim() == 2
assert weight.dim() == 2
ctx._weight_size = weight.size()
# repeat each row by number of embeddings
input = input.view(input.size(0), 1, -1)
input = input.expand(input.size(0), weight.size(0), -1)
# compute distance between all embeddings and input
distance = torch.pow(input - weight.expand_as(input), 2).sum(2)
# select embeddings with minimal distance
_, indices = distance.min(1)
ctx._indices = indices
output = torch.index_select(weight, 0, indices)
return output, indices
def __feature_wct(self, cont_feat, styl_feat, cont_seg, styl_seg):
cont_c, cont_h, cont_w = cont_feat.size(0), cont_feat.size(1), cont_feat.size(2)
styl_c, styl_h, styl_w = styl_feat.size(0), styl_feat.size(1), styl_feat.size(2)
cont_feat_view = cont_feat.view(cont_c, -1).clone()
styl_feat_view = styl_feat.view(styl_c, -1).clone()
if cont_seg.size == False or styl_seg.size == False:
target_feature = self.__wct_core(cont_feat_view, styl_feat_view)
else:
target_feature = cont_feat.view(cont_c, -1).clone()
t_cont_seg = np.asarray(Image.fromarray(cont_seg, mode='RGB').resize((cont_w, cont_h), Image.NEAREST))
t_styl_seg = np.asarray(Image.fromarray(styl_seg, mode='RGB').resize((styl_w, styl_h), Image.NEAREST))
for l in self.label_set:
if self.label_indicator[l] == 0:
continue
cont_mask = np.where(t_cont_seg.reshape(t_cont_seg.shape[0] * t_cont_seg.shape[1]) == l)
styl_mask = np.where(t_styl_seg.reshape(t_styl_seg.shape[0] * t_styl_seg.shape[1]) == l)
if cont_mask[0].size <= 0 or styl_mask[0].size <= 0:
continue
cont_indi = torch.LongTensor(cont_mask[0])
styl_indi = torch.LongTensor(styl_mask[0])
if self.is_cuda:
cont_indi = cont_indi.cuda(0)
styl_indi = styl_indi.cuda(0)
cFFG = torch.index_select(cont_feat_view, 1, cont_indi)
sFFG = torch.index_select(styl_feat_view, 1, styl_indi)
tmp_target_feature = self.__wct_core(cFFG, sFFG)
target_feature.index_copy_(1, cont_indi, tmp_target_feature)
target_feature = target_feature.view_as(cont_feat)
ccsF = target_feature.float().unsqueeze(0)
return ccsF
def match_loss(image_a_pred, image_b_pred, matches_a, matches_b):
"""
Computes the match loss given by
1/num_matches * \sum_{matches} ||D(I_a, u_a, I_b, u_b)||_2^2
:param image_a_pred: Output of DCN network on image A.
:type image_a_pred: torch.Variable(torch.FloatTensor) shape [1, W * H, D]
:param image_b_pred: same as image_a_pred
:type image_b_pred:
:param matches_a: torch.Variable(torch.LongTensor) has shape [num_matches,], a (u,v) pair is mapped
to (u,v) ---> image_width * v + u, this matches the shape of one dimension of image_a_pred
:type matches_a: torch.Variable(torch.FloatTensor)
:param matches_b: same as matches_b
:return: match_loss, matches_a_descriptors, matches_b_descriptors
:rtype: torch.Variable(),
matches_a_descriptors is torch.FloatTensor with shape torch.Shape([num_matches, descriptor_dimension])
"""
num_matches = matches_a.size()[0]
matches_a_descriptors = torch.index_select(image_a_pred, 1, matches_a)
matches_b_descriptors = torch.index_select(image_b_pred, 1, matches_b)
# crazily enough, if there is only one element to index_select into
# above, then the first dimension is collapsed down, and we end up
# with shape [D,], where we want [1,D]
# this unsqueeze fixes that case
if len(matches_a) == 1:
matches_a_descriptors = matches_a_descriptors.unsqueeze(0)
matches_b_descriptors = matches_b_descriptors.unsqueeze(0)
match_loss = 1.0 / num_matches * (matches_a_descriptors - matches_b_descriptors).pow(2).sum()
return match_loss, matches_a_descriptors, matches_b_descriptors
def decode_greed(self,
word_seqs,
init_tags,
lengths,
extFeats=None,
with_snt_classifier=False,
masked_output=None):
minibatch_size = len(
lengths
) #word_seqs.size(0) if self.encoder.batch_first else word_seqs.size(1)
max_length = max(
lengths
) #word_seqs.size(1) if self.encoder.batch_first else word_seqs.size(0)
# encoder
embeds = self.get_token_embeddings(word_seqs, lengths)
if type(extFeats) != type(None):
concat_input = torch.cat((embeds, self.extFeats_linear(extFeats)),
2)
else:
concat_input = embeds
concat_input = self.dropout_layer(concat_input)
packed_word_embeds = rnn_utils.pack_padded_sequence(concat_input,
lengths,
batch_first=True)
packed_word_lstm_out, (enc_h_t, enc_c_t) = self.encoder(
packed_word_embeds) # bsize x seqlen x dim
word_lstm_out, unpacked_len = rnn_utils.pad_packed_sequence(
packed_word_lstm_out, batch_first=True)
# decoder
if self.bidirectional:
index_slices = [2 * i + 1 for i in range(self.num_layers)
] # generated from the reversed path
index_slices = torch.tensor(index_slices,
dtype=torch.long,
device=self.device)
h_t = torch.index_select(enc_h_t, 0, index_slices)
c_t = torch.index_select(enc_c_t, 0, index_slices)
else:
h_t = enc_h_t
c_t = enc_c_t
top_path = []
top_path_tag_scores = []
top_dec_h_t, top_dec_c_t = [0] * minibatch_size, [0] * minibatch_size
last_tags = init_tags # bsize x 1
for i in range(max_length):
tag_embeds = self.dropout_layer(self.tag_embeddings(last_tags))
decode_inputs = torch.cat(
(self.dropout_layer(word_lstm_out[:, i:i + 1]), tag_embeds),
2) # bsize x 1 x insize
tag_lstm_out, (dec_h_t, dec_c_t) = self.decoder(
decode_inputs,
(h_t, c_t)) # bsize x 1 x insize => bsize x 1 x hsize
for j in range(minibatch_size):
if lengths[j] == i + 1:
top_dec_h_t[j] = dec_h_t[:, j:j + 1, :]
top_dec_c_t[j] = dec_c_t[:, j:j + 1, :]
tag_lstm_out_reshape = tag_lstm_out.contiguous().view(
tag_lstm_out.size(0) * tag_lstm_out.size(1),
tag_lstm_out.size(2))
tag_space = self.hidden2tag(
self.dropout_layer(tag_lstm_out_reshape))
if masked_output is None:
tag_scores = F.log_softmax(tag_space, dim=1) # bsize x outsize
else:
tag_scores = masked_function.index_masked_log_softmax(
tag_space, masked_output, dim=1)
top_path_tag_scores.append(torch.unsqueeze(tag_scores.data, 1))
max_probs, decoder_argmax = torch.max(tag_scores, 1)
last_tags = decoder_argmax
if len(last_tags.size()) == 1:
last_tags = last_tags.unsqueeze(1)
h_t, c_t = dec_h_t, dec_c_t
top_path.append(last_tags.data)
top_path = torch.cat(top_path, 1)
top_path_tag_scores = torch.cat(top_path_tag_scores, 1)
top_dec_h_t = torch.cat(top_dec_h_t, 1)
top_dec_c_t = torch.cat(top_dec_c_t, 1)
if with_snt_classifier:
return top_path_tag_scores, top_path, ((enc_h_t, enc_c_t),
word_lstm_out, lengths)
else:
return top_path_tag_scores, top_path
def init_target_centers(net, target, records, data_dict, batch_size, beat_num,
fixed_len, num_workers, lead, thrs):
dataset = MULTI_ECG_EVAL_DATASET(target,
load_beat_with_rr,
data_dict,
test_records=records,
beat_num=beat_num,
fixed_len=fixed_len,
lead=lead)
dataloader = DataLoader(dataset,
batch_size=batch_size,
num_workers=num_workers)
features = {0: 0, 1: 0, 2: 0, 3: 0}
counters = {0: 0, 1: 0, 2: 0, 3: 0}
for idb, data_batch in enumerate(dataloader):
s_batch, _ = data_batch
s_batch = s_batch.cuda()
feat, logits = net(s_batch)
probs = F.softmax(logits, dim=1).detach().cpu().numpy()
max_probs = np.max(probs, axis=1)
indices = []
for l in range(4):
max_indices_l = np.argwhere(np.argmax(probs, axis=1) == l)
if len(max_indices_l) > 0:
max_indices_l = np.squeeze(max_indices_l, axis=1)
max_probs_l = max_probs[max_indices_l]
legal_indices_l = np.argwhere(max_probs_l >= thrs[l])
if len(legal_indices_l) > 0:
legal_indices_l = np.squeeze(legal_indices_l, axis=1)
indices_l = max_indices_l[legal_indices_l]
indices.append(indices_l)
indices = np.sort(np.concatenate(indices))
print("batch index: {}, size of avaliable pesudo label: {}".format(
idb, len(indices)))
if len(indices) > 0:
pesudo_labels = np.argmax(probs, axis=1)[indices]
feat = torch.index_select(feat,
dim=0,
index=torch.LongTensor(indices).cuda())
for l in range(4):
_index = np.argwhere(pesudo_labels == l)
if len(_index) > 0:
counters[l] += len(_index)
_index = np.squeeze(_index, axis=1)
_feat = torch.index_select(
feat, dim=0,
index=torch.LongTensor(_index).cuda()).detach().cpu()
_feat_sum = torch.sum(_feat, dim=0)
features[l] += _feat_sum
torch.cuda.empty_cache()
print('Procedure finished! Obtaining centers of target data!')
print("The numbers of available pesudo labels:")
for l in range(4):
if counters[l] > 0:
print("{}: {}".format(l, counters[l]))
# features[l] = torch.cat(features[l], dim=0)
# features[l] = torch.mean(features[l], dim=0)
features[l] = features[l] / counters[l]
features[l] = features[l].cuda()
print(features[l].size())
else:
del features[l]
print('No avaliable centers')
return features, counters
def update(self):
# keep looping the whole dataset
for i in range(self.num_batches):
img, orig_img, im_name, im_dim_list = self.dataloder.getitem()
if img is None:
self.Q.put((None, None, None, None, None, None, None))
return
with torch.no_grad():
# Human Detection
img = img.cuda()
prediction = self.det_model(img, CUDA=True)
# NMS process
dets = dynamic_write_results(prediction,
opt.confidence,
opt.num_classes,
nms=True,
nms_conf=opt.nms_thesh)
if isinstance(dets, int) or dets.shape[0] == 0:
for k in range(len(orig_img)):
if self.Q.full():
time.sleep(2)
self.Q.put((orig_img[k], im_name[k], None, None, None,
None, None))
continue
dets = dets.cpu()
im_dim_list = torch.index_select(im_dim_list, 0,
dets[:, 0].long())
scaling_factor = torch.min(self.det_inp_dim / im_dim_list,
1)[0].view(-1, 1)
# coordinate transfer
dets[:, [1, 3]] -= (self.det_inp_dim - scaling_factor *
im_dim_list[:, 0].view(-1, 1)) / 2
dets[:, [2, 4]] -= (self.det_inp_dim - scaling_factor *
im_dim_list[:, 1].view(-1, 1)) / 2
dets[:, 1:5] /= scaling_factor
for j in range(dets.shape[0]):
dets[j, [1, 3]] = torch.clamp(dets[j, [1, 3]], 0.0,
im_dim_list[j, 0])
dets[j, [2, 4]] = torch.clamp(dets[j, [2, 4]], 0.0,
im_dim_list[j, 1])
boxes = dets[:, 1:5]
scores = dets[:, 5:6]
for k in range(len(orig_img)):
boxes_k = boxes[dets[:, 0] == k]
if isinstance(boxes_k, int) or boxes_k.shape[0] == 0:
if self.Q.full():
time.sleep(2)
self.Q.put((orig_img[k], im_name[k], None, None, None,
None, None))
continue
inps = torch.zeros(boxes_k.size(0), 3, opt.inputResH,
opt.inputResW)
pt1 = torch.zeros(boxes_k.size(0), 2)
pt2 = torch.zeros(boxes_k.size(0), 2)
if self.Q.full():
time.sleep(2)
self.Q.put((orig_img[k], im_name[k], boxes_k,
scores[dets[:, 0] == k], inps, pt1, pt2))
def build_loss(self,
rpn_cls_score_reshape,
rpn_bbox_pred,
rpn_data,
is_region=False):
# classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3,
1).contiguous().view(
-1, 2)
rpn_label = rpn_data[0]
# print rpn_label.size(), rpn_cls_score.size()
rpn_keep = Variable(rpn_label.data.ne(-1).nonzero().squeeze()).cuda()
rpn_cls_score = torch.index_select(rpn_cls_score, 0, rpn_keep)
rpn_label = torch.index_select(rpn_label, 0, rpn_keep)
fg_cnt = torch.sum(rpn_label.data.ne(0))
bg_cnt = rpn_label.data.numel() - fg_cnt
# ce_weights = torch.ones(rpn_cls_score.size()[1])
# ce_weights[0] = float(fg_cnt) / bg_cnt
# ce_weights = ce_weights.cuda()
_, predict = torch.max(rpn_cls_score.data, 1)
error = torch.sum(torch.abs(predict - rpn_label.data))
# try:
if predict.size()[0] < 256:
print(predict.size())
print(rpn_label.size())
print(fg_cnt)
if is_region:
self.tp_region = torch.sum(predict[:fg_cnt].eq(
rpn_label.data[:fg_cnt]))
self.tf_region = torch.sum(predict[fg_cnt:].eq(
rpn_label.data[fg_cnt:]))
self.fg_cnt_region = fg_cnt
self.bg_cnt_region = bg_cnt
if DEBUG:
print('accuracy: %2.2f%%' %
((self.tp + self.tf) / float(fg_cnt + bg_cnt) * 100))
else:
self.tp = torch.sum(predict[:fg_cnt].eq(rpn_label.data[:fg_cnt]))
self.tf = torch.sum(predict[fg_cnt:].eq(rpn_label.data[fg_cnt:]))
self.fg_cnt = fg_cnt
self.bg_cnt = bg_cnt
if DEBUG:
print('accuracy: %2.2f%%' %
((self.tp + self.tf) / float(fg_cnt + bg_cnt) * 100))
rpn_cross_entropy = F.cross_entropy(rpn_cls_score, rpn_label)
# print rpn_cross_entropy
# box loss
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[
1:]
rpn_bbox_targets = torch.mul(rpn_bbox_targets, rpn_bbox_inside_weights)
rpn_bbox_pred = torch.mul(rpn_bbox_pred, rpn_bbox_inside_weights)
# print 'Smooth L1 loss: ', F.smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, size_average=False)
# print 'fg_cnt', fg_cnt
rpn_loss_box = F.smooth_l1_loss(
rpn_bbox_pred, rpn_bbox_targets,
size_average=False) / (fg_cnt.float() + 1e-4)
# print 'rpn_loss_box', rpn_loss_box
# print rpn_loss_box
return rpn_cross_entropy, rpn_loss_box
def compute_loss(model, epoch, batch_sample, plotdir=""):
loss_function = nn.MSELoss(reduce=False)
model.zero_grad()
loss = 0
norm = 0
for num_spk in range(batch_sample.max_spk):
if batch_sample.sub_batch_lens[num_spk] > 0:
batch = batch_sample.sub_batch_lens[num_spk]
combo = batch_sample[num_spk]['combo'].cuda()
model.hidden = model.init_hidden(batch)
sources = []
for i in range(num_spk):
source = batch_sample[num_spk]['source' + str(i + 1)].cuda()
source, lens = pad_packed_sequence(source, batch_first=True)
# source: tensor of shape (batch, seq_length, feat_dim)
sources.append(source)
source_usage = [[] for _ in range(num_spk)]
for dnn_pass in range(num_spk):
mask_out = model(combo)
# mask_out: tensor of shape (batch, seq_length, feat_dim)
combos, lens = pad_packed_sequence(combo, batch_first=True)
# combos: tensor of shape (batch, seq_length, feat_dim*2)
mixes = torch.index_select(
combos, 2,
torch.LongTensor(range(model.feat_dim)).cuda())
# mixes: tensor of shape (batch, seq_length, feat_dim)
lengths = lens.float().cuda()
masked = mask_out * mixes
losses = torch.stack([
torch.sum(loss_function(masked, source).view(batch, -1),
dim=1) for source in sources
])
# losses: tensor of shape (num_spk, batch)
for source_ind in range(num_spk):
for index in source_usage[source_ind]:
losses[source_ind][index] = float("Inf")
min_losses, indices = torch.min(losses, 0)
for sample_ind in range(batch):
source_usage[indices[sample_ind]].append(sample_ind)
loss += torch.sum(min_losses) / num_spk
norm += torch.sum(lengths) * model.feat_dim
if plotdir:
os.system("mkdir -p " + plotdir)
if dnn_pass == 0:
plot.plot_spec(
combos[0].detach().cpu().numpy()[:,
0:model.feat_dim],
plotdir + '/' + str(num_spk) + '-Spk_Mix.png')
prefix = plotdir + '/' + str(num_spk) + '-Spk_Pass-' + str(
dnn_pass + 1) + '_'
plot.plot_spec(combos[0].detach().cpu().numpy(),
prefix + 'Input.png')
plot.plot_spec(
combos[0].detach().cpu().numpy()[:, model.feat_dim +
1:model.feat_dim * 2],
prefix + 'Attenmask.png')
plot.plot_spec(mask_out[0].detach().cpu().numpy(),
prefix + 'Mask_Out.png')
plot.plot_spec(masked[0].detach().cpu().numpy(),
prefix + 'Masked_Mix.png')
plot.plot_spec(
sources[indices[0]][0].detach().cpu().numpy(),
prefix + 'Chosen_Source.png')
spec_zeros = torch.zeros(mask_out.shape).cuda()
residual_comp = torch.cat((spec_zeros, mask_out), 2)
combos = F.relu_(combos - residual_comp)
combo = pack_padded_sequence(combos, lens, batch_first=True)
return loss / norm, norm
def fold(self, class_probabilities, num_classes):
"""
This function is called everytime we update the beam.
It will fold finished sentence and then take the topk over all the rest, util there is no
more finished sentences among the k options.
"""
# the follwing three tensor is to mask
stop_tensor = Variable(
torch.LongTensor(self._beam_width,
1).fill_(self._end_index)).cpu()
one_tensor = Variable(torch.ByteTensor(self._beam_width,
1).fill_(1)).cpu()
# stop_tensor = stop_tensor.cuda() if use_cuda else stop_tensor
# one_tensor = one_tensor.cuda() if use_cuda else one_tensor
# TODO: if want to change to a batched solution, probably need padding
batch_next_indices = []
batch_next_log_prob = []
for i in range(self._batch_size):
# loop through each batch
stop = False
# (batch_size, beam_width, num_classes)
cur_batch = class_probabilities[i].cpu()
cur_batch_seq = self.sequences[i]
cur_batch_log = self.seq_log_prob[i]
# keep topk unless there is no finished sentences in that batch
while not stop:
# align the log_prob in a (-1, 1) vector and then apply topk
log_prob_cur, indices = torch.topk(cur_batch.view(-1, 1),
self._beam_width,
dim=0)
log_prob_cur = log_prob_cur.view(-1, 1)
# indices is the position in the long vector
indices = indices.view(-1, 1)
nth_class_per_beam = indices % num_classes # (self._beam_width, 1)
unfinished_tensor = nth_class_per_beam - stop_tensor != 0
finished_tensor = nth_class_per_beam - stop_tensor == 0
# dimension with value 1 means we predict a stop symbol
if torch.equal(unfinished_tensor, one_tensor):
stop = True
# all the decoded sequence is not finished!
else:
assert indices.size() == unfinished_tensor.size()
# mask the indices tensor
# unfinished_index = torch.masked_select(indices, unfinished_tensor)
finished_index = torch.masked_select(
indices, finished_tensor) / num_classes
# nth_beam that is finished in this decoding step
finished_index = finished_index.cpu()
finished_seq = torch.index_select(cur_batch_seq, 0,
finished_index)
finished_log_prob = torch.index_select(
cur_batch_log, 0, finished_index)
# add the decoded seq and corresponding log_prob to the list
for seq in finished_seq:
self.decoded_seq[i].append(seq)
for log_prob in finished_log_prob:
self.decoded_log_prob[i].append(log_prob)
for index in finished_index:
# we find the finished decoding in the batch and set the log_prob
# to -sys.float_info.max so that it won't affect out next topk
nth_beam = int(index / num_classes)
cur_batch_next = cur_batch.clone()
# cur_batch = (beam_width, num_classes)
cur_batch_next[nth_beam,
self._end_index] = -sys.float_info.max
cur_batch = cur_batch_next
assert log_prob_cur.size() == indices.size() and log_prob_cur.size(
) == (self._beam_width, 1)
batch_next_indices.append(indices)
batch_next_log_prob.append(log_prob_cur)
batch_next_indices = torch.stack(batch_next_indices, 0)
batch_next_log_prob = torch.stack(batch_next_log_prob, 0)
assert len(batch_next_indices) == self._batch_size
# print("batch_next_indices: " + str(batch_next_indices.size()))
assert batch_next_indices.size(
0) == self._batch_size and batch_next_indices.size(
1) == self._beam_width
# batch_next_indices = batch_next_indices.cuda() if use_cuda else batch_next_indices
# batch_next_log_prob = batch_next_log_prob.cuda() if use_cuda else batch_next_log_prob
return batch_next_indices, batch_next_log_prob
def pooling(self, blocks, verts_pos, debug=False):
# convert vertex positions to x,y coordinates in the image, scaled to fractions of image dimension
ext_verts_pos = torch.cat(
(verts_pos,
torch.FloatTensor(
np.ones([verts_pos.shape[0], verts_pos.shape[1], 1])).cuda()),
dim=-1)
ext_verts_pos = torch.matmul(ext_verts_pos, self.matrix.permute(1, 0))
xs = ext_verts_pos[:, :, 1] / ext_verts_pos[:, :, 2] / 256.
ys = ext_verts_pos[:, :, 0] / ext_verts_pos[:, :, 2] / 256.
full_features = None
batch_size = verts_pos.shape[0]
# check camera project covers the image
if debug:
dim = 256
xs = (torch.clamp(xs * dim, 0,
dim - 1).data.cpu().numpy()).astype(np.uint8)
ys = (torch.clamp(ys * dim, 0,
dim - 1).data.cpu().numpy()).astype(np.uint8)
for ex in range(blocks.shape[0]):
img = blocks[ex].permute(1, 2, 0).data.cpu().numpy()[:, :, :3]
for x, y in zip(xs[ex], ys[ex]):
img[x, y, 0] = 1
img[x, y, 1] = 0
img[x, y, 2] = 0
from PIL import Image
Image.fromarray(
(img * 255).astype(np.uint8)).save('results/temp.png')
print('saved')
input()
for block in blocks:
# scale projected vertex points to dimension of current feature map
dim = block.shape[-1]
cur_xs = torch.clamp(xs * dim, 0, dim - 1)
cur_ys = torch.clamp(ys * dim, 0, dim - 1)
# https://en.wikipedia.org/wiki/Bilinear_interpolation
x1s, y1s, x2s, y2s = torch.floor(cur_xs), torch.floor(
cur_ys), torch.ceil(cur_xs), torch.ceil(cur_ys)
A = x2s - cur_xs
B = cur_xs - x1s
G = y2s - cur_ys
H = cur_ys - y1s
x1s = x1s.type(torch.cuda.LongTensor)
y1s = y1s.type(torch.cuda.LongTensor)
x2s = x2s.type(torch.cuda.LongTensor)
y2s = y2s.type(torch.cuda.LongTensor)
# flatten batch of feature maps to make vectorization easier
flat_block = block.permute(1, 0, 2, 3).contiguous().view(
block.shape[1], -1)
block_idx = torch.arange(
0, verts_pos.shape[0]).cuda().unsqueeze(-1).expand(
batch_size, verts_pos.shape[1])
block_idx = block_idx * dim * dim
selection = (block_idx + (x1s * dim) + y1s).view(-1)
C = torch.index_select(flat_block, 1, selection)
C = C.view(-1, batch_size, verts_pos.shape[1]).permute(1, 0, 2)
selection = (block_idx + (x1s * dim) + y2s).view(-1)
D = torch.index_select(flat_block, 1, selection)
D = D.view(-1, batch_size, verts_pos.shape[1]).permute(1, 0, 2)
selection = (block_idx + (x2s * dim) + y1s).view(-1)
E = torch.index_select(flat_block, 1, selection)
E = E.view(-1, batch_size, verts_pos.shape[1]).permute(1, 0, 2)
selection = (block_idx + (x2s * dim) + y2s).view(-1)
F = torch.index_select(flat_block, 1, selection)
F = F.view(-1, batch_size, verts_pos.shape[1]).permute(1, 0, 2)
section1 = A.unsqueeze(1) * C * G.unsqueeze(1)
section2 = H.unsqueeze(1) * D * A.unsqueeze(1)
section3 = G.unsqueeze(1) * E * B.unsqueeze(1)
section4 = B.unsqueeze(1) * F * H.unsqueeze(1)
features = (section1 + section2 + section3 + section4)
features = features.permute(0, 2, 1)
if full_features is None:
full_features = features
else:
full_features = torch.cat((full_features, features), dim=2)
return full_features
def train_vae_epoch(epoch, args, rnn, output, data_loader,
optimizer_rnn, optimizer_output,
scheduler_rnn, scheduler_output):
rnn.train()
output.train()
loss_sum = 0
for batch_idx, data in enumerate(data_loader):
rnn.zero_grad()
output.zero_grad()
x_unsorted = data['x'].float()
y_unsorted = data['y'].float()
y_len_unsorted = data['len']
y_len_max = max(y_len_unsorted)
x_unsorted = x_unsorted[:, 0:y_len_max, :]
y_unsorted = y_unsorted[:, 0:y_len_max, :]
# initialize lstm hidden state according to batch size
rnn.hidden = rnn.init_hidden(batch_size=x_unsorted.size(0))
# sort input
y_len,sort_index = torch.sort(y_len_unsorted,0,descending=True)
y_len = y_len.numpy().tolist()
x = torch.index_select(x_unsorted,0,sort_index)
y = torch.index_select(y_unsorted,0,sort_index)
x = Variable(x).to(device)
y = Variable(y).to(device)
# if using ground truth to train
h = rnn(x, pack=True, input_len=y_len)
y_pred,z_mu,z_lsgms = output(h)
y_pred = F.sigmoid(y_pred)
# clean
y_pred = pack_padded_sequence(y_pred, y_len, batch_first=True)
y_pred = pad_packed_sequence(y_pred, batch_first=True)[0]
z_mu = pack_padded_sequence(z_mu, y_len, batch_first=True)
z_mu = pad_packed_sequence(z_mu, batch_first=True)[0]
z_lsgms = pack_padded_sequence(z_lsgms, y_len, batch_first=True)
z_lsgms = pad_packed_sequence(z_lsgms, batch_first=True)[0]
# use cross entropy loss
loss_bce = binary_cross_entropy_weight(y_pred, y)
loss_kl = -0.5 * torch.sum(1 + z_lsgms - z_mu.pow(2) - z_lsgms.exp())
loss_kl /= y.size(0)*y.size(1)*sum(y_len) # normalize
loss = loss_bce + loss_kl
loss.backward()
# update deterministic and lstm
optimizer_output.step()
optimizer_rnn.step()
scheduler_output.step()
scheduler_rnn.step()
z_mu_mean = torch.mean(z_mu.data)
z_sgm_mean = torch.mean(z_lsgms.mul(0.5).exp_().data)
z_mu_min = torch.min(z_mu.data)
z_sgm_min = torch.min(z_lsgms.mul(0.5).exp_().data)
z_mu_max = torch.max(z_mu.data)
z_sgm_max = torch.max(z_lsgms.mul(0.5).exp_().data)
if epoch % args.epochs_log==0 and batch_idx==0: # only output first batch's statistics
print('Epoch: {}/{}, train bce loss: {:.6f}, train kl loss: {:.6f}, graph type: {}, num_layer: {}, hidden: {}'.format(
epoch, args.epochs,loss_bce.item(), loss_kl.item(), args.graph_type, args.num_layers, args.hidden_size_rnn))
print('z_mu_mean', z_mu_mean, 'z_mu_min', z_mu_min, 'z_mu_max', z_mu_max, 'z_sgm_mean', z_sgm_mean, 'z_sgm_min', z_sgm_min, 'z_sgm_max', z_sgm_max)
# logging
log_value('bce_loss_'+args.fname, loss_bce.item(), epoch*args.batch_ratio+batch_idx)
log_value('kl_loss_' +args.fname, loss_kl.item(), epoch*args.batch_ratio + batch_idx)
log_value('z_mu_mean_'+args.fname, z_mu_mean, epoch*args.batch_ratio + batch_idx)
log_value('z_mu_min_'+args.fname, z_mu_min, epoch*args.batch_ratio + batch_idx)
log_value('z_mu_max_'+args.fname, z_mu_max, epoch*args.batch_ratio + batch_idx)
log_value('z_sgm_mean_'+args.fname, z_sgm_mean, epoch*args.batch_ratio + batch_idx)
log_value('z_sgm_min_'+args.fname, z_sgm_min, epoch*args.batch_ratio + batch_idx)
log_value('z_sgm_max_'+args.fname, z_sgm_max, epoch*args.batch_ratio + batch_idx)
loss_sum += loss.item()
return loss_sum/(batch_idx+1)
""" Note: difference between concatenate and stack is output shape print(concatenated_tensor_0.shape) print(stacked_tensor.shape) """ # Split a Tensor splited_tensor = torch.split(x_tensor, 1) # Create tensor with Index select indices_1 = torch.tensor([0,2]) indices_2 = torch.tensor([0,1]) indices_3 = torch.tensor([0]) tensor_index_1 = torch.index_select(x_tensor, 1, indices_1) # Select element 0 and 2 for each dimension 1. tensor_index_2 = torch.index_select(x_tensor, 1, indices_2) # Select element 0 and 1 for each dimension 1. tensor_index_3 = torch.index_select(x_tensor, 0, indices_3) # Select element 0 for dimension 0. # Create mask and tensor with selected value by that mask x = torch.randn(3, 4) mask = x.ge(0.5) mask_tensor = torch.masked_select(x, mask) # Squeeze and unsqueeze """ Squeeze: Returns a tensor with all the dimensions of input of size 1 removed. For example, if input is of shape: (A×1×B×C×1×D) then the out tensor will be of shape: (A×B×C×D) When dim is given, a squeeze operation is done only in the given dimension. For example, If input is of shape: (A×1×B) , squeeze(input, 0) leaves the tensor unchanged,
def _variable(self, v):
return torch.index_select(v, 0, self.batch_indices)
def forward(self, base_feat, im_info, gt_boxes, num_boxes, file_rpn, file_proposal):
self.f = file_rpn
batch_size = base_feat.size(0)
conv1_tic = time.time()
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
conv1_toc = time.time()
conv1_time = conv1_toc - conv1_tic
cls_score_tic = time.time()
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
#reshape_tic = time.time()
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, 1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
#reshape_toc = time.time()
#reshape_time = reshape_toc - reshape_tic
cls_score_toc = time.time()
cls_score_time = cls_score_toc - cls_score_tic
bbox_pred_tic = time.time()
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
bbox_pred_toc = time.time()
bbox_pred_time = bbox_pred_toc - bbox_pred_tic
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
proposal_tic = time.time()
rois, ship_time = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key), file_proposal)
proposal_toc = time.time()
proposal_time = proposal_toc - proposal_tic - ship_time
total_rpn_time = proposal_toc - conv1_tic - ship_time
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
rpn_string = str(total_rpn_time)+' '+str(conv1_time)+' '+str(cls_score_time)+' '+str(bbox_pred_time)+' '+str(proposal_time)+'\n'
self.f.write(rpn_string)
#print('RPN TIME DISTRIBUTION: ', ' total: ', total_rpn_time, '\n conv1: ', conv1_time, ' get_cls_score: ', cls_score_time, ' get bbox_pred: ', bbox_pred_time,' make_proposal: ', proposal_time)
return rois, self.rpn_loss_cls, self.rpn_loss_box, proposal_time, ship_time
def trainModel(self,
targetmodel,
optimizer,
loss_function,
gamma=0.1,
minibatch=128):
ln = self.memory.getMemoryLen()
if ln < 5000:
return -1
self.trainCnt += 1
indexlist = self.memory.sample()
if ni.useRnn:
input = torch.FloatTensor(len(indexlist), ni.actionDuration - 1,
self.dims)
inputtarget = torch.FloatTensor(len(indexlist),
ni.actionDuration - 1, self.dims)
else:
input = torch.FloatTensor(len(indexlist), self.dims)
inputtarget = torch.FloatTensor(len(indexlist), self.dims)
target = torch.FloatTensor(len(indexlist), self.actions)
for j in range(len(indexlist)):
data = indexlist[j]
s = data[0]
a = data[1]
sn = data[2]
r = data[3]
input[j, :] = torch.squeeze(s.data)
inputtarget[j, :] = torch.squeeze(sn.data)
input = autograd.Variable(input)
inputtarget = autograd.Variable(inputtarget)
if ni.useRnn:
hidden = torch.zeros(1, len(indexlist), self.dims * 2)
hidden = autograd.Variable(hidden)
qs = self.model['qvalue'](input, hidden)
qsn = targetmodel(inputtarget, hidden)
else:
qs = self.model['qvalue'](input)
qsn = targetmodel(inputtarget)
qsdata = torch.squeeze(qs.data)
qsndata = torch.squeeze(qsn.data)
target = qsdata.clone()
for j in range(len(indexlist)):
data = indexlist[j]
a = data[1]
r = data[3]
actionset = data[4]
target[j, a] = r + gamma * max(
torch.index_select(qsndata[j, :], 0,
torch.LongTensor(actionset)))
#target[j,a] = r + gamma*max(qsndata[j,:])
target = autograd.Variable(target)
loss = loss_function(qs, target)
loss.backward()
for param in self.model['qvalue'].parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
optimizer.zero_grad()
return loss.data[0]
def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
labels=(), max_det=300, cls_exclusive=None):
"""Runs Non-Maximum Suppression (NMS) on inference results
Returns:
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
"""
bs = prediction.shape[0] # batch size
nc = prediction.shape[2] - 5 # number of classes
xc = prediction[..., 4] > conf_thres # candidates
# Checks
assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
# Settings
# min_wh = 2 # (pixels) minimum box width and height
max_wh = 7680 # (pixels) maximum box width and height
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
time_limit = 0.1 + 0.03 * bs # seconds to quit after
redundant = True # require redundant detections
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
merge = False # use merge-NMS
t = time.time()
output = [torch.zeros((0, 6), device=prediction.device)] * bs
for xi, x in enumerate(prediction): # image index, image inference
# Apply constraints
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
x = x[xc[xi]] # confidence
# Cat apriori labels if autolabelling
if labels and len(labels[xi]):
lb = labels[xi]
v = torch.zeros((len(lb), nc + 5), device=x.device)
v[:, :4] = lb[:, 1:5] # box
v[:, 4] = 1.0 # conf
v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
x = torch.cat((x, v), 0)
# If none remain process next image
if not x.shape[0]:
continue
# Compute conf
x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
box = xywh2xyxy(x[:, :4])
# Detections matrix nx6 (xyxy, conf, cls)
if multi_label:
i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
else: # best class only
conf, j = x[:, 5:].max(1, keepdim=True)
x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
# Filter by class
if classes is not None:
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
# Apply finite constraint
# if not torch.isfinite(x).all():
# x = x[torch.isfinite(x).all(1)]
# Check shape
n = x.shape[0] # number of boxes
if not n: # no boxes
continue
elif n > max_nms: # excess boxes
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence
# Batched NMS
## torch.index_select(torch.Tensor([8,9,10,11]).to(x.device), 0, x[:, 5].int())
if cls_exclusive is not None:
cls = torch.index_select(cls_exclusive.to(x.device), 0, x[:, 5].int())[:, None]
c = cls * (0 if agnostic else max_wh)
else:
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
if i.shape[0] > max_det: # limit detections
i = i[:max_det]
if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
weights = iou * scores[None] # box weights
x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
if redundant:
i = i[iou.sum(1) > 1] # require redundancy
output[xi] = x[i]
if (time.time() - t) > time_limit:
LOGGER.warning(f'WARNING: NMS time limit {time_limit:.3f}s exceeded')
break # time limit exceeded
return output
def forward(self, weight, indices, offsets):
assert not self.needs_input_grad[1], "EmbeddingBag doesn't " \
"compute the gradient w.r.t. the indices"
assert not self.needs_input_grad[2], "EmbeddingBag doesn't " \
"compute the gradient w.r.t. the offsets"
assert indices.dim() == 1
if offsets.dim() != 1:
raise ValueError("offsets has to be a 1D Tensor")
if offsets[0] != 0:
raise ValueError("offsets[0] has to be 0, i.e. the first sequence"
" in the mini-batch has to start from position 0."
"However, got {}".format(offsets[0]))
if offsets[-1] > indices.size(0):
raise ValueError(
"offsets[-1] has to be smaller than indices's length"
" ({}), but got offsets[-1] of {}".format(
indices.size(0), offsets[-1]))
self._backend = type2backend[type(weight)]
self._weight_size = weight.size()
self._offset2bag = offsets.new()
self.save_for_backward(indices)
indices = indices.contiguous().view(-1)
output = weight.new()
if self.max_norm is not None:
self._renorm(indices, weight)
if weight.is_cuda:
if self.mode == MODE_MEAN:
self.bag_size = offsets.new().resize_(offsets.size())
else:
self.bag_size = None
self._backend.LookupTableBag_updateOutput(
self._backend.library_state, indices, offsets, weight, output,
self._offset2bag, self.mode, self.bag_size)
else:
# slow CPU implementation
index_output = torch.index_select(weight, 0, indices)
# indices = [1, 2, 30, 100, 12], offsets = [0, 2, 3]
self._offset2bag.resize_(
indices.size(0)).zero_() # offset2bag = [0 0 0 0 0]
self._offset2bag.index_fill_(0, offsets,
1) # offset2bag = [1 0 1 0 1]
self._offset2bag[0] = 0 # offset2bag = [0 0 1 0 1]
self._offset2bag = self._offset2bag.cumsum(
0) # offset2bag = [0 0 1 1 2]
output.resize_(offsets.size(0), weight.size(1)).zero_()
output.index_add_(0, self._offset2bag, index_output)
if self.mode == MODE_MEAN:
if offsets.size(0) == 1:
self.bag_size = indices.size(0)
else:
self.bag_size = weight.new().resize_(offsets.size())
self.bag_size[:-1] = offsets[1:] - offsets[:-1]
self.bag_size[-1] = indices.size(0) - offsets[-1]
self.bag_size = self.bag_size[:, None].expand_as(output)
output /= self.bag_size
return output
def test(device, model,name,epoch, X_val, y_val, \
vid_lens_batch, mask_val, idxs_val):
model.eval()
y = y_val.reshape((-1, 1))
y = y.repeat(mask_val.shape[-1], axis=-1)
X=torch.from_numpy(X_val).float().to(device)
y_val=torch.from_numpy(y_val).to(device)
vid_lens_batch=torch.from_numpy(vid_lens_batch).to(device)
mask_val=torch.from_numpy(mask_val).to(device)
output,ordered_idx = model(X, vid_lens_batch)
y_val=torch.index_select(y_val,0,ordered_idx)
mask_val=torch.index_select(mask_val,0,ordered_idx)
y=torch.from_numpy(y).long().to(device)
target=torch.index_select(y,0,ordered_idx)
m=mask_val.float()
loss = temporal_ce_loss(output, target,m)
output=output.cpu().detach().numpy()
seq_len=output.shape[1]
y_val=y_val[:].contiguous()
mask_val=mask_val[:,:seq_len].contiguous()
mask_val=mask_val.cpu().numpy()
y_val=y_val.cpu().numpy()
num_classes = output.shape[-1]
ix = np.zeros((X_val.shape[0],), dtype='int')
seq_lens = np.sum(mask_val, axis=-1)
# for each example, we only consider argmax of the seq len
votes = np.zeros((num_classes,), dtype='int')
for i, eg in enumerate(output):
predictions = np.argmax(eg[:seq_lens[i]], axis=-1)
# print(predictions.shape)
for cls in range(num_classes):
count = (predictions == cls).sum(axis=-1)
votes[cls] = count
ix[i] = np.argmax(votes)
c = ix == y_val
# print(c,ix[:10],y_val[:10])
classification_rate = np.sum(c == True) / float(len(c))
print('{} Epoch: {} \tAcc: {:.6f} \tLoss: {:.6f}'.format(name,
epoch,classification_rate,loss.item() ))
preds = ix
true_labels = y_val
return classification_rate,loss.item(), preds, true_labels
def th_gather_nd(x, coords):
x = x.contiguous()
inds = coords.mv(th.LongTensor(x.stride()))
x_gather = th.index_select(th_flatten(x), 0, inds)
return x_gather
def execute_thinning_recipe(model, zeros_mask_dict, recipe, loaded_from_file=False):
"""Apply a thinning recipe to a model.
This will remove filters and channels, as well as handle batch-normalization parameter
adjustment, and thinning of weight tensors.
"""
layers = {}
for name, m in model.named_modules():
layers[name] = m
for layer_name, directives in recipe.modules.items():
for attr, val in directives.items():
if attr in ['running_mean', 'running_var']:
running = getattr(layers[layer_name], attr)
dim_to_trim = val[0]
indices_to_select = val[1]
# Check if we're trying to trim a parameter that is already "thin"
if running.size(dim_to_trim) != len(indices_to_select):
msglogger.info("[thinning] {}: setting {} to {}".format(layer_name, attr, len(indices_to_select)))
setattr(layers[layer_name], attr,
torch.index_select(running, dim=dim_to_trim, index=indices_to_select))
else:
msglogger.info("[thinning] {}: setting {} to {}".format(layer_name, attr, val))
setattr(layers[layer_name], attr, val)
assert len(recipe.parameters) > 0
for param_name, param_directives in recipe.parameters.items():
param = distiller.model_find_param(model, param_name)
for directive in param_directives:
dim = directive[0]
indices = directive[1]
if len(directive) == 4: # TODO: this code is hard to follow
selection_view = param.view(*directive[2])
# Check if we're trying to trim a parameter that is already "thin"
if param.data.size(dim) != len(indices):
param.data = torch.index_select(selection_view, dim, indices)
if param.grad is not None:
# We also need to change the dimensions of the gradient tensor.
grad_selection_view = param.grad.resize_(*directive[2])
if grad_selection_view.size(dim) != len(indices):
param.grad = torch.index_select(grad_selection_view, dim, indices)
param.data = param.view(*directive[3])
if param.grad is not None:
param.grad = param.grad.resize_(*directive[3])
else:
if param.data.size(dim) != len(indices):
param.data = torch.index_select(param.data, dim, indices)
# We also need to change the dimensions of the gradient tensor.
# If have not done a backward-pass thus far, then the gradient will
# not exist, and therefore won't need to be re-dimensioned.
if param.grad is not None and param.grad.size(dim) != len(indices):
param.grad = torch.index_select(param.grad, dim, indices)
msglogger.info("[thinning] changing param {} shape: {}".format(param_name, len(indices)))
if not loaded_from_file:
# If the masks are loaded from a checkpoint file, then we don't need to change
# their shape, because they are already correctly shaped
mask = zeros_mask_dict[param_name].mask
if mask is not None and (mask.size(dim) != len(indices)):
zeros_mask_dict[param_name].mask = torch.index_select(mask, dim, indices)
def construct_graph(
self, state: State
) -> Tuple[torch_geometric.data.Batch, bool, torch.Tensor]:
"""
Construct a batched graph object from the state
"""
partial_trees, tokens_emb, next_token_pos, batch_size = (
state.partial_trees,
state.tokens_emb,
state.n_step,
state.batch_size,
)
next_token_features = state.tokens_emb[:, next_token_pos]
device = tokens_emb.device
if next_token_pos == 0: # the first step
return (
self.construct_init_graph(batch_size, device),
True,
next_token_features,
)
node_token_pos = [
] # positions of tokens in sentences, -1 if not token
node_label_idx = [
] # labels of internal nodes, -1 if not internal node
node_label_left = []
node_label_right = []
node_batch_idx = []
edges: List[Tuple[int, int]] = []
on_rightmost_chain: List[bool] = []
num_nodes = 0
for i, tree in enumerate(partial_trees):
assert isinstance(tree, InternalParseNode)
x, edge_index, rightmost_chain_i = self.tree2graph_preorder(
tree, num_nodes, device)
edges.extend(edge_index)
on_rightmost_chain.extend(rightmost_chain_i)
for node in x:
if "label" in node: # internal node
node_token_pos.append(-1)
node_label_idx.append(self.label_idx_map[node["label"]])
node_label_left.append(node["left"])
node_label_right.append(node["right"])
else: # leaf node
node_token_pos.append(node["token_pos"])
node_label_idx.append(-1)
node_label_left.append(-1)
node_label_right.append(-1)
tree_size = len(x)
node_batch_idx.extend([i] * tree_size)
num_nodes += tree_size
node_token_pos = torch.tensor(node_token_pos,
device=device) # type: ignore
node_is_token = node_token_pos >= 0 # type: ignore
node_label_idx = node_token_pos.new_tensor(
node_label_idx) # type: ignore
node_label_left = node_token_pos.new_tensor(
node_label_left) # type: ignore
node_label_right = node_token_pos.new_tensor(
node_label_right) # type: ignore
node_is_label = node_label_idx >= 0 # type: ignore
node_batch_idx = node_token_pos.new_tensor(
node_batch_idx) # type: ignore
d_model = self.cfg.d_model
node_emb = tokens_emb.new_zeros((len(node_token_pos), d_model))
flattened_tokens_emb = tokens_emb.view(-1, d_model)
node_emb[node_is_token] = torch.index_select(
flattened_tokens_emb,
0,
node_batch_idx[node_is_token] * tokens_emb.size(1) +
node_token_pos[node_is_token],
)
label_position_emb = (
self.position_table[node_label_left[node_is_label]] +
self.position_table[node_label_right[node_is_label]])
node_emb[node_is_label] = torch.cat(
[
self.label_embedding(node_label_idx[node_is_label]),
label_position_emb
],
dim=-1,
)
all_edges_index = (torch.tensor(edges, device=device).t()
if edges != [] else torch.empty(
2, 0, dtype=torch.int64, device=device))
graph = torch_geometric.data.Batch(
batch=node_batch_idx,
x=node_emb,
edge_index=all_edges_index,
)
graph.on_rightmost_chain = torch.tensor(on_rightmost_chain,
dtype=torch.bool,
device=device)
return graph, False, next_token_features
def _generate(
self,
sample: Dict[str, Dict[str, Tensor]],
prefix_tokens: Optional[Tensor] = None,
constraints: Optional[Tensor] = None,
bos_token: Optional[int] = None,
):
incremental_states = torch.jit.annotate(
List[Dict[str, Dict[str, Optional[Tensor]]]],
[
torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {})
for i in range(self.model.models_size)
],
)
net_input = sample["net_input"]
if "src_tokens" in net_input:
src_tokens = net_input["src_tokens"]
# length of the source text being the character length except EndOfSentence and pad
src_lengths = (
(src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1)
)
elif "source" in net_input:
src_tokens = net_input["source"]
src_lengths = (
net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1)
if net_input["padding_mask"] is not None
else torch.tensor(src_tokens.size(-1)).to(src_tokens)
)
else:
raise Exception("expected src_tokens or source in net input")
# bsz: total number of sentences in beam
# Note that src_tokens may have more than 2 dimenions (i.e. audio features)
bsz, src_len = src_tokens.size()[:2]
beam_size = self.beam_size
if constraints is not None and not self.search.supports_constraints:
raise NotImplementedError(
"Target-side constraints were provided, but search method doesn't support them"
)
# Initialize constraints, when active
self.search.init_constraints(constraints, beam_size)
max_len: int = -1
if self.match_source_len:
max_len = src_lengths.max().item()
else:
max_len = min(
int(self.max_len_a * src_len + self.max_len_b),
# exclude the EOS marker
self.model.max_decoder_positions() - 1,
)
assert (
self.min_len <= max_len
), "min_len cannot be larger than max_len, please adjust these!"
# compute the encoder output for each beam
encoder_outs = self.model.forward_encoder(net_input)
# placeholder of indices for bsz * beam_size to hold tokens and accumulative scores
new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1)
new_order = new_order.to(src_tokens.device).long()
encoder_outs = self.model.reorder_encoder_out(encoder_outs[0], new_order)
# ensure encoder_outs is a List.
assert encoder_outs is not None
# initialize buffers
scores = (
torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float()
) # +1 for eos; pad is never chosen for scoring
tokens = (
torch.zeros(bsz * beam_size, max_len + 2)
.to(src_tokens)
.long()
.fill_(self.pad)
) # +2 for eos and pad
tokens[:, 0] = self.eos if bos_token is None else bos_token
attn: Optional[Tensor] = None
# A list that indicates candidates that should be ignored.
# For example, suppose we're sampling and have already finalized 2/5
# samples. Then cands_to_ignore would mark 2 positions as being ignored,
# so that we only finalize the remaining 3 samples.
cands_to_ignore = (
torch.zeros(bsz, beam_size).to(src_tokens).eq(-1)
) # forward and backward-compatible False mask
# list of completed sentences
finalized = torch.jit.annotate(
List[List[Dict[str, Tensor]]],
[torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)],
) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step
finished = [
False for i in range(bsz)
] # a boolean array indicating if the sentence at the index is finished or not
num_remaining_sent = bsz # number of sentences remaining
# number of candidate hypos per step
cand_size = 2 * beam_size # 2 x beam size in case half are EOS
# offset arrays for converting between different indexing schemes
bbsz_offsets = (torch.arange(0, bsz) * beam_size).unsqueeze(1).type_as(tokens)
cand_offsets = torch.arange(0, cand_size).type_as(tokens)
reorder_state: Optional[Tensor] = None
batch_idxs: Optional[Tensor] = None
original_batch_idxs: Optional[Tensor] = None
if "id" in sample and isinstance(sample["id"], Tensor):
original_batch_idxs = sample["id"]
else:
original_batch_idxs = torch.arange(0, bsz).type_as(tokens)
for step in range(max_len + 1): # one extra step for EOS marker
# reorder decoder internal states based on the prev choice of beams
# print(f'step: {step}')
if reorder_state is not None:
if batch_idxs is not None:
# update beam indices to take into account removed sentences
corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as(
batch_idxs
)
reorder_state.view(-1, beam_size).add_(
corr.unsqueeze(-1) * beam_size
)
original_batch_idxs = original_batch_idxs[batch_idxs]
self.model.reorder_incremental_state(incremental_states, reorder_state)
encoder_outs = self.model.reorder_encoder_out(
encoder_outs, reorder_state
)
lprobs, avg_attn_scores = self.model.forward_decoder(
tokens[:, : step + 1],
encoder_outs,
incremental_states,
self.temperature,
)
if self.lm_model is not None:
lm_out = self.lm_model(tokens[:, : step + 1])
probs = self.lm_model.get_normalized_probs(
lm_out, log_probs=True, sample=None
)
probs = probs[:, -1, :] * self.lm_weight
lprobs += probs
lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs)
lprobs[:, self.pad] = -math.inf # never select pad
lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty
# handle max length constraint
if step >= max_len:
lprobs[:, : self.eos] = -math.inf
lprobs[:, self.eos + 1 :] = -math.inf
# handle prefix tokens (possibly with different lengths)
if (
prefix_tokens is not None
and step < prefix_tokens.size(1)
and step < max_len
):
lprobs, tokens, scores = self._prefix_tokens(
step, lprobs, scores, tokens, prefix_tokens, beam_size
)
elif step < self.min_len:
# minimum length constraint (does not apply if using prefix_tokens)
lprobs[:, self.eos] = -math.inf
# Record attention scores, only support avg_attn_scores is a Tensor
if avg_attn_scores is not None:
if attn is None:
attn = torch.empty(
bsz * beam_size, avg_attn_scores.size(1), max_len + 2
).to(scores)
attn[:, :, step + 1].copy_(avg_attn_scores)
scores = scores.type_as(lprobs)
eos_bbsz_idx = torch.empty(0).to(
tokens
) # indices of hypothesis ending with eos (finished sentences)
eos_scores = torch.empty(0).to(
scores
) # scores of hypothesis ending with eos (finished sentences)
if self.should_set_src_lengths:
self.search.set_src_lengths(src_lengths)
if self.no_repeat_ngram_size > 0:
lprobs = self._no_repeat_ngram(tokens, lprobs, bsz, beam_size, step)
# Shape: (batch, cand_size)
cand_scores, cand_indices, cand_beams = self.search.step(
step,
lprobs.view(bsz, -1, self.vocab_size),
scores.view(bsz, beam_size, -1)[:, :, :step],
tokens[:, : step + 1],
original_batch_idxs,
)
# cand_bbsz_idx contains beam indices for the top candidate
# hypotheses, with a range of values: [0, bsz*beam_size),
# and dimensions: [bsz, cand_size]
cand_bbsz_idx = cand_beams.add(bbsz_offsets)
# finalize hypotheses that end in eos
# Shape of eos_mask: (batch size, beam size)
eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf)
eos_mask[:, :beam_size][cands_to_ignore] = torch.tensor(0).to(eos_mask)
# only consider eos when it's among the top beam_size indices
# Now we know what beam item(s) to finish
# Shape: 1d list of absolute-numbered
eos_bbsz_idx = torch.masked_select(
cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size]
)
finalized_sents: List[int] = []
if eos_bbsz_idx.numel() > 0:
eos_scores = torch.masked_select(
cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size]
)
finalized_sents = self.finalize_hypos(
step,
eos_bbsz_idx,
eos_scores,
tokens,
scores,
finalized,
finished,
beam_size,
attn,
src_lengths,
max_len,
)
num_remaining_sent -= len(finalized_sents)
assert num_remaining_sent >= 0
if num_remaining_sent == 0:
break
if self.search.stop_on_max_len and step >= max_len:
break
assert step < max_len
# Remove finalized sentences (ones for which {beam_size}
# finished hypotheses have been generated) from the batch.
if len(finalized_sents) > 0:
new_bsz = bsz - len(finalized_sents)
# construct batch_idxs which holds indices of batches to keep for the next pass
batch_mask = torch.ones(
bsz, dtype=torch.bool, device=cand_indices.device
)
batch_mask[finalized_sents] = False
# TODO replace `nonzero(as_tuple=False)` after TorchScript supports it
batch_idxs = torch.arange(
bsz, device=cand_indices.device
).masked_select(batch_mask)
# Choose the subset of the hypothesized constraints that will continue
self.search.prune_sentences(batch_idxs)
eos_mask = eos_mask[batch_idxs]
cand_beams = cand_beams[batch_idxs]
bbsz_offsets.resize_(new_bsz, 1)
cand_bbsz_idx = cand_beams.add(bbsz_offsets)
cand_scores = cand_scores[batch_idxs]
cand_indices = cand_indices[batch_idxs]
if prefix_tokens is not None:
prefix_tokens = prefix_tokens[batch_idxs]
src_lengths = src_lengths[batch_idxs]
cands_to_ignore = cands_to_ignore[batch_idxs]
scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1)
tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1)
if attn is not None:
attn = attn.view(bsz, -1)[batch_idxs].view(
new_bsz * beam_size, attn.size(1), -1
)
bsz = new_bsz
else:
batch_idxs = None
# Set active_mask so that values > cand_size indicate eos hypos
# and values < cand_size indicate candidate active hypos.
# After, the min values per row are the top candidate active hypos
# Rewrite the operator since the element wise or is not supported in torchscript.
eos_mask[:, :beam_size] = ~((~cands_to_ignore) & (~eos_mask[:, :beam_size]))
active_mask = torch.add(
eos_mask.type_as(cand_offsets) * cand_size,
cand_offsets[: eos_mask.size(1)],
)
# get the top beam_size active hypotheses, which are just
# the hypos with the smallest values in active_mask.
# {active_hypos} indicates which {beam_size} hypotheses
# from the list of {2 * beam_size} candidates were
# selected. Shapes: (batch size, beam size)
new_cands_to_ignore, active_hypos = torch.topk(
active_mask, k=beam_size, dim=1, largest=False
)
# update cands_to_ignore to ignore any finalized hypos.
cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size]
# Make sure there is at least one active item for each sentence in the batch.
assert (~cands_to_ignore).any(dim=1).all()
# update cands_to_ignore to ignore any finalized hypos
# {active_bbsz_idx} denotes which beam number is continued for each new hypothesis (a beam
# can be selected more than once).
active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos)
active_scores = torch.gather(cand_scores, dim=1, index=active_hypos)
active_bbsz_idx = active_bbsz_idx.view(-1)
active_scores = active_scores.view(-1)
# copy tokens and scores for active hypotheses
# Set the tokens for each beam (can select the same row more than once)
tokens[:, : step + 1] = torch.index_select(
tokens[:, : step + 1], dim=0, index=active_bbsz_idx
)
# Select the next token for each of them
tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather(
cand_indices, dim=1, index=active_hypos
)
if step > 0:
scores[:, :step] = torch.index_select(
scores[:, :step], dim=0, index=active_bbsz_idx
)
scores.view(bsz, beam_size, -1)[:, :, step] = torch.gather(
cand_scores, dim=1, index=active_hypos
)
# Update constraints based on which candidates were selected for the next beam
self.search.update_constraints(active_hypos)
# copy attention for active hypotheses
if attn is not None:
attn[:, :, : step + 2] = torch.index_select(
attn[:, :, : step + 2], dim=0, index=active_bbsz_idx
)
# reorder incremental state in decoder
reorder_state = active_bbsz_idx
# sort by score descending
for sent in range(len(finalized)):
scores = torch.tensor(
[float(elem["score"].item()) for elem in finalized[sent]]
)
_, sorted_scores_indices = torch.sort(scores, descending=True)
finalized[sent] = [finalized[sent][ssi] for ssi in sorted_scores_indices]
finalized[sent] = torch.jit.annotate(
List[Dict[str, Tensor]], finalized[sent]
)
return finalized
def recognize_beam_batch(self,
h,
hlens,
lpz,
recog_args,
char_list,
rnnlm=None,
normalize_score=True,
strm_idx=0):
logging.info('input lengths: ' + str(h.size(1)))
att_idx = min(strm_idx, len(self.att) - 1)
h = mask_by_length(h, hlens, 0.0)
# search params
batch = len(hlens)
beam = recog_args.beam_size
penalty = recog_args.penalty
ctc_weight = recog_args.ctc_weight
att_weight = 1.0 - ctc_weight
n_bb = batch * beam
n_bo = beam * self.odim
n_bbo = n_bb * self.odim
pad_b = to_device(
self,
torch.LongTensor([i * beam
for i in six.moves.range(batch)]).view(-1, 1))
pad_bo = to_device(
self,
torch.LongTensor([i * n_bo
for i in six.moves.range(batch)]).view(-1, 1))
pad_o = to_device(
self,
torch.LongTensor([i * self.odim
for i in six.moves.range(n_bb)]).view(-1, 1))
max_hlen = int(max(hlens))
if recog_args.maxlenratio == 0:
maxlen = max_hlen
else:
maxlen = max(1, int(recog_args.maxlenratio * max_hlen))
minlen = int(recog_args.minlenratio * max_hlen)
logging.info('max output length: ' + str(maxlen))
logging.info('min output length: ' + str(minlen))
# initialization
c_prev = [
to_device(self, torch.zeros(n_bb, self.dunits))
for _ in range(self.dlayers)
]
z_prev = [
to_device(self, torch.zeros(n_bb, self.dunits))
for _ in range(self.dlayers)
]
c_list = [
to_device(self, torch.zeros(n_bb, self.dunits))
for _ in range(self.dlayers)
]
z_list = [
to_device(self, torch.zeros(n_bb, self.dunits))
for _ in range(self.dlayers)
]
vscores = to_device(self, torch.zeros(batch, beam))
a_prev = None
rnnlm_prev = None
self.att[att_idx].reset() # reset pre-computation of h
yseq = [[self.sos] for _ in six.moves.range(n_bb)]
accum_odim_ids = [self.sos for _ in six.moves.range(n_bb)]
stop_search = [False for _ in six.moves.range(batch)]
nbest_hyps = [[] for _ in six.moves.range(batch)]
ended_hyps = [[] for _ in range(batch)]
exp_hlens = hlens.repeat(beam).view(beam,
batch).transpose(0,
1).contiguous()
exp_hlens = exp_hlens.view(-1).tolist()
exp_h = h.unsqueeze(1).repeat(1, beam, 1, 1).contiguous()
exp_h = exp_h.view(n_bb, h.size()[1], h.size()[2])
if lpz is not None:
device_id = torch.cuda.device_of(next(self.parameters()).data).idx
ctc_prefix_score = CTCPrefixScoreTH(lpz, 0, self.eos, beam,
exp_hlens, device_id)
ctc_states_prev = ctc_prefix_score.initial_state()
ctc_scores_prev = to_device(self, torch.zeros(batch, n_bo))
for i in six.moves.range(maxlen):
logging.debug('position ' + str(i))
vy = to_device(self, torch.LongTensor(self._get_last_yseq(yseq)))
ey = self.dropout_emb(self.embed(vy))
att_c, att_w = self.att[att_idx](exp_h, exp_hlens,
self.dropout_dec[0](z_prev[0]),
a_prev)
ey = torch.cat((ey, att_c), dim=1)
# attention decoder
z_list, c_list = self.rnn_forward(ey, z_list, c_list, z_prev,
c_prev)
local_scores = att_weight * F.log_softmax(
self.output(self.dropout_dec[-1](z_list[-1])), dim=1)
# rnnlm
if rnnlm:
rnnlm_state, local_lm_scores = rnnlm.buff_predict(
rnnlm_prev, vy, n_bb)
local_scores = local_scores + recog_args.lm_weight * local_lm_scores
local_scores = local_scores.view(batch, n_bo)
# ctc
if lpz is not None:
ctc_scores, ctc_states = ctc_prefix_score(
yseq, ctc_states_prev, accum_odim_ids)
ctc_scores = ctc_scores.view(batch, n_bo)
local_scores = local_scores + ctc_weight * (ctc_scores -
ctc_scores_prev)
local_scores = local_scores.view(batch, beam, self.odim)
if i == 0:
local_scores[:, 1:, :] = self.logzero
local_best_scores, local_best_odims = torch.topk(
local_scores.view(batch, beam, self.odim), beam, 2)
# local pruning (via xp)
local_scores = np.full((n_bbo, ), self.logzero)
_best_odims = local_best_odims.view(n_bb, beam) + pad_o
_best_odims = _best_odims.view(-1).cpu().numpy()
_best_score = local_best_scores.view(-1).cpu().detach().numpy()
local_scores[_best_odims] = _best_score
local_scores = to_device(
self,
torch.from_numpy(local_scores).float()).view(
batch, beam, self.odim)
# (or indexing)
# local_scores = to_cuda(self, torch.full((batch, beam, self.odim), self.logzero))
# _best_odims = local_best_odims
# _best_score = local_best_scores
# for si in six.moves.range(batch):
# for bj in six.moves.range(beam):
# for bk in six.moves.range(beam):
# local_scores[si, bj, _best_odims[si, bj, bk]] = _best_score[si, bj, bk]
eos_vscores = local_scores[:, :, self.eos] + vscores
vscores = vscores.view(batch, beam, 1).repeat(1, 1, self.odim)
vscores[:, :, self.eos] = self.logzero
vscores = (vscores + local_scores).view(batch, n_bo)
# global pruning
accum_best_scores, accum_best_ids = torch.topk(vscores, beam, 1)
accum_odim_ids = torch.fmod(
accum_best_ids, self.odim).view(-1).data.cpu().tolist()
accum_padded_odim_ids = (torch.fmod(accum_best_ids, n_bo) +
pad_bo).view(-1).data.cpu().tolist()
accum_padded_beam_ids = (torch.div(accum_best_ids, self.odim) +
pad_b).view(-1).data.cpu().tolist()
y_prev = yseq[:][:]
yseq = self._index_select_list(yseq, accum_padded_beam_ids)
yseq = self._append_ids(yseq, accum_odim_ids)
vscores = accum_best_scores
vidx = to_device(self, torch.LongTensor(accum_padded_beam_ids))
if isinstance(att_w, torch.Tensor):
a_prev = torch.index_select(att_w.view(n_bb, *att_w.shape[1:]),
0, vidx)
elif isinstance(att_w, list):
# handle the case of multi-head attention
a_prev = [
torch.index_select(att_w_one.view(n_bb, -1), 0, vidx)
for att_w_one in att_w
]
else:
# handle the case of location_recurrent when return is a tuple
a_prev_ = torch.index_select(att_w[0].view(n_bb, -1), 0, vidx)
h_prev_ = torch.index_select(att_w[1][0].view(n_bb, -1), 0,
vidx)
c_prev_ = torch.index_select(att_w[1][1].view(n_bb, -1), 0,
vidx)
a_prev = (a_prev_, (h_prev_, c_prev_))
z_prev = [
torch.index_select(z_list[li].view(n_bb, -1), 0, vidx)
for li in range(self.dlayers)
]
c_prev = [
torch.index_select(c_list[li].view(n_bb, -1), 0, vidx)
for li in range(self.dlayers)
]
if rnnlm:
rnnlm_prev = self._index_select_lm_state(rnnlm_state, 0, vidx)
if lpz is not None:
ctc_vidx = to_device(self,
torch.LongTensor(accum_padded_odim_ids))
ctc_scores_prev = torch.index_select(ctc_scores.view(-1), 0,
ctc_vidx)
ctc_scores_prev = ctc_scores_prev.view(-1, 1).repeat(
1, self.odim).view(batch, n_bo)
ctc_states = torch.transpose(ctc_states, 1, 3).contiguous()
ctc_states = ctc_states.view(n_bbo, 2, -1)
ctc_states_prev = torch.index_select(ctc_states, 0,
ctc_vidx).view(
n_bb, 2, -1)
ctc_states_prev = torch.transpose(ctc_states_prev, 1, 2)
# pick ended hyps
if i > minlen:
k = 0
penalty_i = (i + 1) * penalty
# thr = accum_best_scores[:, -1]
for samp_i in six.moves.range(batch):
if stop_search[samp_i]:
k = k + beam
continue
for beam_j in six.moves.range(beam):
yk = y_prev[k][:]
yk.append(self.eos)
if len(yk) < hlens[samp_i]:
_vscore = eos_vscores[samp_i][beam_j] + penalty_i
if normalize_score:
_vscore = _vscore / len(yk)
_score = _vscore.data.cpu().numpy()
ended_hyps[samp_i].append({
'yseq': yk,
'vscore': _vscore,
'score': _score
})
k = k + 1
# end detection
stop_search = [
stop_search[samp_i] for samp_i in six.moves.range(batch)
]
stop_search_summary = list(set(stop_search))
if len(stop_search_summary) == 1 and stop_search_summary[0]:
break
torch.cuda.empty_cache()
dummy_hyps = [{
'yseq': [self.sos, self.eos],
'score': np.array([-float('inf')])
}]
ended_hyps = [
ended_hyps[samp_i] if len(ended_hyps[samp_i]) != 0 else dummy_hyps
for samp_i in six.moves.range(batch)
]
nbest_hyps = [
sorted(
ended_hyps[samp_i], key=lambda x: x['score'],
reverse=True)[:min(len(ended_hyps[samp_i]), recog_args.nbest)]
for samp_i in six.moves.range(batch)
]
return nbest_hyps
def compute_edges(self, keypoints):
start = torch.index_select(keypoints, 1, self.connections[:, 0])
end = torch.index_select(keypoints, 1, self.connections[:, 1])
return start - end
def subsample(x, mask):
# sampling, extract only the values specified in the mask and the remaining values are returned as zeros
x = torch.index_select(x, 0, mask.cuda())
return x
def update(self):
# keep looping the whole video
for i in range(self.num_batches):
img = []
inp = []
orig_img = []
im_name = []
im_dim_list = []
for k in range(i * self.batchSize,
min((i + 1) * self.batchSize, self.datalen)):
(grabbed, frame) = self.stream.read()
# if the `grabbed` boolean is `False`, then we have
# reached the end of the video file
if not grabbed:
self.stop()
return
# process and add the frame to the queue
inp_dim = int(opt.inp_dim)
img_k, orig_img_k, im_dim_list_k = prep_frame(frame, inp_dim)
inp_k = im_to_torch(orig_img_k)
img.append(img_k)
inp.append(inp_k)
orig_img.append(orig_img_k)
im_dim_list.append(im_dim_list_k)
with torch.no_grad():
ht = inp[0].size(1)
wd = inp[0].size(2)
# Human Detection
img = Variable(torch.cat(img)).cuda()
im_dim_list = torch.FloatTensor(im_dim_list).repeat(1, 2)
im_dim_list = im_dim_list.cuda()
prediction = self.det_model(img, CUDA=True)
# NMS process
dets = dynamic_write_results(prediction,
opt.confidence,
opt.num_classes,
nms=True,
nms_conf=opt.nms_thesh)
if isinstance(dets, int) or dets.shape[0] == 0:
for k in range(len(inp)):
while self.Q.full():
time.sleep(0.2)
self.Q.put((inp[k], orig_img[k], None, None))
continue
im_dim_list = torch.index_select(im_dim_list, 0,
dets[:, 0].long())
scaling_factor = torch.min(self.det_inp_dim / im_dim_list,
1)[0].view(-1, 1)
# coordinate transfer
dets[:, [1, 3]] -= (self.det_inp_dim - scaling_factor *
im_dim_list[:, 0].view(-1, 1)) / 2
dets[:, [2, 4]] -= (self.det_inp_dim - scaling_factor *
im_dim_list[:, 1].view(-1, 1)) / 2
dets[:, 1:5] /= scaling_factor
for j in range(dets.shape[0]):
dets[j, [1, 3]] = torch.clamp(dets[j, [1, 3]], 0.0,
im_dim_list[j, 0])
dets[j, [2, 4]] = torch.clamp(dets[j, [2, 4]], 0.0,
im_dim_list[j, 1])
boxes = dets[:, 1:5].cpu()
scores = dets[:, 5:6].cpu()
for k in range(len(inp)):
while self.Q.full():
time.sleep(0.2)
self.Q.put((inp[k], orig_img[k], boxes[dets[:, 0] == k],
scores[dets[:, 0] == k]))
def _decode_target(
self,
encoder_input,
encoder_outs,
incremental_states,
diversity_sibling_gamma=0.0,
beam_size=None,
maxlen=None,
prefix_tokens=None,
):
src_tokens_tensor = pytorch_translate_utils.get_source_tokens_tensor(
encoder_input["src_tokens"])
beam_size = beam_size if beam_size is not None else self.beam_size
bsz = src_tokens_tensor.size(0)
reorder_indices = (torch.arange(bsz).view(-1, 1).repeat(
1, beam_size).view(-1).long())
for i, model in enumerate(self.models):
encoder_outs[i] = model.encoder.reorder_encoder_out(
encoder_out=encoder_outs[i],
new_order=reorder_indices.type_as(src_tokens_tensor),
)
maxlen = min(maxlen,
self.maxlen) if maxlen is not None else self.maxlen
# initialize buffers
scores = src_tokens_tensor.new(bsz * beam_size,
maxlen + 1).float().fill_(0)
scores_buf = scores.clone()
tokens = src_tokens_tensor.new(bsz * beam_size,
maxlen + 2).fill_(self.pad)
tokens_buf = tokens.clone()
tokens[:, 0] = self.eos
# may differ from input length
if isinstance(encoder_outs[0], (list, tuple)):
src_encoding_len = encoder_outs[0][0].size(0)
elif isinstance(encoder_outs[0], dict):
if isinstance(encoder_outs[0]["encoder_out"], tuple):
# Fairseq compatibility
src_encoding_len = encoder_outs[0]["encoder_out"][0].size(1)
else:
src_encoding_len = encoder_outs[0]["encoder_out"].size(0)
attn = scores.new(bsz * beam_size, src_encoding_len, maxlen + 2)
attn_buf = attn.clone()
# list of completed sentences
finalized = [[] for i in range(bsz)]
finished = [False for i in range(bsz)]
worst_finalized = [{
"idx": None,
"score": -math.inf
} for i in range(bsz)]
num_remaining_sent = bsz
# number of candidate hypos per step
cand_size = 2 * beam_size # 2 x beam size in case half are EOS
# offset arrays for converting between different indexing schemes
bbsz_offsets = (torch.arange(0, bsz) *
beam_size).unsqueeze(1).type_as(tokens)
cand_offsets = torch.arange(0, cand_size).type_as(tokens)
# helper function for allocating buffers on the fly
buffers = {}
# init constraints
constraints = self._build_constraints(src_tokens_tensor, beam_size)
def buffer(name, type_of=tokens): # noqa
if name not in buffers:
buffers[name] = type_of.new()
return buffers[name]
def is_finished(sent, step, unfinalized_scores=None):
"""
Check whether we've finished generation for a given sentence, by
comparing the worst score among finalized hypotheses to the best
possible score among unfinalized hypotheses.
"""
assert len(finalized[sent]) <= beam_size
if len(finalized[sent]) == beam_size:
if self.stop_early or step == maxlen or unfinalized_scores is None:
return True
# stop if the best unfinalized score is worse than the worst
# finalized one
best_unfinalized_score = unfinalized_scores[sent].max()
if self.normalize_scores:
best_unfinalized_score /= (maxlen + 1)**self.len_penalty
if worst_finalized[sent]["score"] >= best_unfinalized_score:
return True
return False
def finalize_hypos(step,
bbsz_idx,
eos_scores,
unfinalized_scores=None):
"""
Finalize the given hypotheses at this step, while keeping the total
number of finalized hypotheses per sentence <= beam_size.
Note: the input must be in the desired finalization order, so that
hypotheses that appear earlier in the input are preferred to those
that appear later.
Args:
step: current time step
bbsz_idx: A vector of indices in the range [0, bsz*beam_size),
indicating which hypotheses to finalize
eos_scores: A vector of the same size as bbsz_idx containing
scores for each hypothesis
unfinalized_scores: A vector containing scores for all
unfinalized hypotheses
"""
assert bbsz_idx.numel() == eos_scores.numel()
# clone relevant token and attention tensors
tokens_clone = tokens.index_select(0, bbsz_idx)
tokens_clone = tokens_clone[:, 1:step +
2] # skip the first index, which is EOS
tokens_clone[:, step] = self.eos
attn_clone = attn.index_select(0, bbsz_idx)[:, :, 1:step + 2]
# compute scores per token position
pos_scores = scores.index_select(0, bbsz_idx)[:, :step + 1]
pos_scores[:, step] = eos_scores
# convert from cumulative to per-position scores
pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1]
# normalize sentence-level scores
if self.normalize_scores:
eos_scores /= (step + 1)**self.len_penalty
sents_seen = set()
for i, (idx, score) in enumerate(
zip(bbsz_idx.tolist(), eos_scores.tolist())):
sent = idx // beam_size
sents_seen.add(sent)
def get_hypo():
_, alignment = attn_clone[i].max(dim=0)
return {
"tokens": tokens_clone[i],
"score": score,
"attention": attn_clone[i], # src_len x tgt_len
"alignment": alignment,
"positional_scores": pos_scores[i],
}
if len(finalized[sent]) < beam_size:
finalized[sent].append(get_hypo())
elif not self.stop_early and score > worst_finalized[sent][
"score"]:
# replace worst hypo for this sentence with new/better one
worst_idx = worst_finalized[sent]["idx"]
if worst_idx is not None:
finalized[sent][worst_idx] = get_hypo()
# find new worst finalized hypo for this sentence
idx, s = min(enumerate(finalized[sent]),
key=lambda r: r[1]["score"])
worst_finalized[sent] = {"score": s["score"], "idx": idx}
# return number of hypotheses finished this step
num_finished = 0
for sent in sents_seen:
# check termination conditions for this sentence
if not finished[sent] and is_finished(sent, step,
unfinalized_scores):
finished[sent] = True
num_finished += 1
return num_finished
reorder_state = None
for step in range(maxlen + 1): # one extra step for EOS marker
# reorder decoder internal states based on the prev choice of beams
if reorder_state is not None:
for model in self.models:
if isinstance(model.decoder, FairseqIncrementalDecoder):
model.decoder.reorder_incremental_state(
incremental_states[model], reorder_state)
# Run decoder for one step
logprobs, avg_attn, possible_translation_tokens = self._decode(
tokens[:, :step + 1], encoder_outs, incremental_states)
logprobs[:, self.pad] = -math.inf # never select pad
# apply unk reward
if possible_translation_tokens is None:
# No vocab reduction, so unk is represented by self.unk at
# position self.unk
unk_index = self.unk
logprobs[:, unk_index] += self.unk_reward
else:
# When we use vocab reduction, the token value self.unk may not
# be at the position self.unk, but somewhere else in the list
# of possible_translation_tokens. It's also possible not to
# show up in possible_translation_tokens at all, meaning we
# can't generate an unk.
unk_pos = torch.nonzero(
possible_translation_tokens == self.unk)
if unk_pos.size()[0] != 0:
# only add unk_reward if unk index appears in
# possible_translation_tokens
unk_index = unk_pos[0][0]
logprobs[:, unk_index] += self.unk_reward
# external lexicon reward
logprobs[:, self.lexicon_indices] += self.lexicon_reward
logprobs += self.word_reward
logprobs[:, self.eos] -= self.word_reward
# Record attention scores
attn[:, :, step + 1].copy_(avg_attn)
cand_scores = buffer("cand_scores", type_of=scores)
cand_indices = buffer("cand_indices")
cand_beams = buffer("cand_beams")
eos_bbsz_idx = buffer("eos_bbsz_idx")
eos_scores = buffer("eos_scores", type_of=scores)
scores = scores.type_as(logprobs)
scores_buf = scores_buf.type_as(logprobs)
if step < maxlen:
self._apply_constraint_penalty(scores) # stub call
if prefix_tokens is not None and step < prefix_tokens.size(1):
logprobs_slice = logprobs.view(bsz, -1,
logprobs.size(-1))[:, 0, :]
cand_scores = torch.gather(
logprobs_slice,
dim=1,
index=prefix_tokens[:, step].view(-1, 1)).expand(
-1, cand_size)
cand_indices = (prefix_tokens[:, step].view(-1, 1).expand(
bsz, cand_size))
cand_beams.resize_as_(cand_indices).fill_(0)
else:
possible_tokens_size = self.vocab_size
if possible_translation_tokens is not None:
possible_tokens_size = possible_translation_tokens.size(
0)
if diversity_sibling_gamma > 0:
logprobs = self.diversity_sibling_rank(
logprobs.view(bsz, -1, possible_tokens_size),
diversity_sibling_gamma,
)
cand_scores, cand_indices, cand_beams = self.search.step(
step,
logprobs.view(bsz, -1, possible_tokens_size),
scores.view(bsz, beam_size, -1)[:, :, :step],
)
# vocabulary reduction
if possible_translation_tokens is not None:
possible_translation_tokens = possible_translation_tokens.view(
1, possible_tokens_size).expand(
cand_indices.size(0), possible_tokens_size)
cand_indices = torch.gather(
possible_translation_tokens,
dim=1,
index=cand_indices,
out=cand_indices,
)
else:
# finalize all active hypotheses once we hit maxlen
# pick the hypothesis with the highest log prob of EOS right now
logprobs.add_(scores[:, step - 1].view(-1, 1))
torch.sort(
logprobs[:, self.eos],
descending=True,
out=(eos_scores, eos_bbsz_idx),
)
num_remaining_sent -= finalize_hypos(step, eos_bbsz_idx,
eos_scores)
assert num_remaining_sent == 0
break
# cand_bbsz_idx contains beam indices for the top candidate
# hypotheses, with a range of values: [0, bsz*beam_size),
# and dimensions: [bsz, cand_size]
cand_bbsz_idx = cand_beams.add_(bbsz_offsets)
# finalize hypotheses that end in eos
eos_mask = cand_indices.eq(self.eos)
if step >= self.minlen:
# only consider eos when it's among the top beam_size indices
torch.masked_select(
cand_bbsz_idx[:, :beam_size],
mask=eos_mask[:, :beam_size],
out=eos_bbsz_idx,
)
if eos_bbsz_idx.numel() > 0:
torch.masked_select(
cand_scores[:, :beam_size],
mask=eos_mask[:, :beam_size],
out=eos_scores,
)
self._apply_eos_constraints(constraints, eos_bbsz_idx,
eos_scores)
num_remaining_sent -= finalize_hypos(
step, eos_bbsz_idx, eos_scores, cand_scores)
assert num_remaining_sent >= 0
if num_remaining_sent == 0:
break
assert step < maxlen
# set active_mask so that values > cand_size indicate eos hypos
# and values < cand_size indicate candidate active hypos.
# After, the min values per row are the top candidate active hypos
active_mask = buffer("active_mask")
torch.add(
eos_mask.type_as(cand_offsets) * cand_size,
cand_offsets[:eos_mask.size(1)],
out=active_mask,
)
# get the top beam_size active hypotheses, which are just the hypos
# with the smallest values in active_mask
active_hypos, _ignore = buffer("active_hypos"), buffer("_ignore")
torch.topk(
active_mask,
k=beam_size,
dim=1,
largest=False,
out=(_ignore, active_hypos),
)
active_bbsz_idx = buffer("active_bbsz_idx")
torch.gather(cand_bbsz_idx,
dim=1,
index=active_hypos,
out=active_bbsz_idx)
active_scores = torch.gather(
cand_scores,
dim=1,
index=active_hypos,
out=scores[:, step].view(bsz, beam_size),
)
active_bbsz_idx = active_bbsz_idx.view(-1)
active_scores = active_scores.view(-1)
# copy tokens and scores for active hypotheses
torch.index_select(
tokens[:, :step + 1],
dim=0,
index=active_bbsz_idx,
out=tokens_buf[:, :step + 1],
)
torch.gather(
cand_indices,
dim=1,
index=active_hypos,
out=tokens_buf.view(bsz, beam_size, -1)[:, :, step + 1],
)
# update constraints for next step
constraints = self._reorder_constraints(constraints,
active_bbsz_idx)
self._update_constraints(constraints, tokens_buf[:, step + 1],
step)
if step > 0:
torch.index_select(
scores[:, :step],
dim=0,
index=active_bbsz_idx,
out=scores_buf[:, :step],
)
torch.gather(
cand_scores,
dim=1,
index=active_hypos,
out=scores_buf.view(bsz, beam_size, -1)[:, :, step],
)
# copy attention for active hypotheses
torch.index_select(
attn[:, :, :step + 2],
dim=0,
index=active_bbsz_idx,
out=attn_buf[:, :, :step + 2],
)
# swap buffers
tokens, tokens_buf = tokens_buf, tokens
scores, scores_buf = scores_buf, scores
attn, attn_buf = attn_buf, attn
# reorder incremental state in decoder
reorder_state = active_bbsz_idx
# sort by score descending
for sent in range(bsz):
finalized[sent] = sorted(finalized[sent],
key=lambda r: r["score"],
reverse=True)
self._finalize_constrained_results(finalized, scores.device)
return finalized
def forward(self, x):
""" Only the needed indices are kept. """
self.keep_tensor = self.keep_tensor.to("cuda" if x.is_cuda else "cpu")
y = torch.index_select(x, 1, self.keep_tensor)
return y
def compute_scores_from_embeds(av,all_embeds,query_nodes,list_test_edges,list_test_non_edges):
cos = nn.CosineSimilarity(dim=1, eps=1e-6)
#per qnode
#all_qnode_auc = []
all_qnode_ap = []
all_qnode_rr = []
#all_qnode_ndcg = []
for qnode in query_nodes :
qnode_edges = list(filter(lambda x: x[0]==qnode or x[1]==qnode, list_test_edges))
qnode_non_edges = list(filter(lambda x: x[0]==qnode or x[1]==qnode, list_test_non_edges))
if len(qnode_edges)==0 or len(qnode_non_edges)==0:
continue
a,b = zip(*qnode_edges)
self_tensors = torch.index_select(all_embeds,dim=0,index=cudavar(av,torch.tensor(a)))
nbr_tensors = torch.index_select(all_embeds,dim=0,index=cudavar(av,torch.tensor(b)))
pos_scores = cos(self_tensors,nbr_tensors)
a,b = zip(*qnode_non_edges)
self_tensors = torch.index_select(all_embeds,dim=0,index=cudavar(av,torch.tensor(a)))
nbr_tensors = torch.index_select(all_embeds,dim=0,index=cudavar(av,torch.tensor(b)))
neg_scores = cos(self_tensors,nbr_tensors)
if av.has_cuda and av.want_cuda:
all_scores = torch.cat((pos_scores,neg_scores)).cpu().numpy()
else:
all_scores = torch.cat((pos_scores,neg_scores)).numpy()
all_labels = np.hstack([np.ones(len(pos_scores)), np.zeros(len(neg_scores))])
auc_score = roc_auc_score(all_labels, all_scores)
ap_score = average_precision_score(all_labels, all_scores)
#ndcg = ndcg_score([all_labels],[all_scores])
so = np.argsort(all_scores)[::-1]
labels_rearranged = all_labels[so]
rr_score = 1/(labels_rearranged.tolist().index(1)+1)
#all_qnode_auc.append(auc_score)
all_qnode_ap.append(ap_score)
all_qnode_rr.append(rr_score)
#all_qnode_ndcg.append(ndcg)
#agglo
pos_scores = []
neg_scores = []
a,b = zip(*list_test_edges)
self_tensors = torch.index_select(all_embeds,dim=0,index=cudavar(av,torch.tensor(a)))
nbr_tensors = torch.index_select(all_embeds,dim=0,index=cudavar(av,torch.tensor(b)))
pos_scores = cos(self_tensors,nbr_tensors)
a,b = zip(*list_test_non_edges)
self_tensors = torch.index_select(all_embeds,dim=0,index=cudavar(av,torch.tensor(a)))
nbr_tensors = torch.index_select(all_embeds,dim=0,index=cudavar(av,torch.tensor(b)))
neg_scores = cos(self_tensors,nbr_tensors)
if av.has_cuda and av.want_cuda:
all_scores = torch.cat((pos_scores,neg_scores)).cpu().numpy()
else:
all_scores = torch.cat((pos_scores,neg_scores)).numpy()
all_labels = np.hstack([np.ones(len(pos_scores)), np.zeros(len(neg_scores))])
auc_score = roc_auc_score(all_labels, all_scores)
ap_score = average_precision_score(all_labels, all_scores)
#ndcg = ndcg_score([all_labels],[all_scores])
#so = np.argsort(all_scores)[::-1]
#labels_rearranged = all_labels[so]
#rr_score = 1/(labels_rearranged.tolist().index(1)+1)
return auc_score, ap_score, np.mean(all_qnode_ap), np.mean(all_qnode_rr)
def decode_beam_search(self,
word_seqs,
lengths,
beam_size,
tag2idx,
extFeats=None,
with_snt_classifier=False,
masked_output=None):
minibatch_size = len(
lengths
) #word_seqs.size(0) if self.encoder.batch_first else word_seqs.size(1)
max_length = max(
lengths
) #word_seqs.size(1) if self.encoder.batch_first else word_seqs.size(0)
# encoder
embeds = self.get_token_embeddings(word_seqs, lengths)
if type(extFeats) != type(None):
concat_input = torch.cat((embeds, self.extFeats_linear(extFeats)),
2)
else:
concat_input = embeds
concat_input = self.dropout_layer(concat_input)
packed_word_embeds = rnn_utils.pack_padded_sequence(concat_input,
lengths,
batch_first=True)
packed_word_lstm_out, (enc_h_t, enc_c_t) = self.encoder(
packed_word_embeds) # bsize x seqlen x dim
enc_word_lstm_out, unpacked_len = rnn_utils.pad_packed_sequence(
packed_word_lstm_out, batch_first=True)
# decoder
if self.bidirectional:
index_slices = [2 * i + 1 for i in range(self.num_layers)
] # generated from the reversed path
index_slices = torch.tensor(index_slices,
dtype=torch.long,
device=self.device)
h_t = torch.index_select(enc_h_t, 0, index_slices)
c_t = torch.index_select(enc_c_t, 0, index_slices)
else:
h_t = enc_h_t
c_t = enc_c_t
h_t = h_t.repeat(1, beam_size, 1)
c_t = c_t.repeat(1, beam_size, 1)
word_lstm_out = enc_word_lstm_out.repeat(beam_size, 1, 1)
beam = [
Beam(beam_size, tag2idx, device=self.device)
for k in range(minibatch_size)
]
batch_idx = list(range(minibatch_size))
remaining_sents = minibatch_size
top_dec_h_t, top_dec_c_t = [0] * minibatch_size, [0] * minibatch_size
for i in range(max_length):
last_tags = torch.stack([
b.get_current_state() for b in beam if not b.done
]).t().contiguous().view(-1,
1) # after t() -> beam_size * batch_size
last_tags = last_tags.to(self.device)
tag_embeds = self.dropout_layer(self.tag_embeddings(last_tags))
decode_inputs = torch.cat(
(self.dropout_layer(word_lstm_out[:, i:i + 1]), tag_embeds),
2) # (batch*beam) x 1 x insize
tag_lstm_out, (dec_h_t, dec_c_t) = self.decoder(
decode_inputs,
(h_t,
c_t)) # (batch*beam) x 1 x insize => (batch*beam) x 1 x hsize
tag_lstm_out_reshape = tag_lstm_out.contiguous().view(
tag_lstm_out.size(0) * tag_lstm_out.size(1),
tag_lstm_out.size(2))
tag_space = self.hidden2tag(
self.dropout_layer(tag_lstm_out_reshape))
if masked_output is None:
out = F.log_softmax(tag_space) # (batch*beam) x outsize
else:
out = masked_function.index_masked_log_softmax(tag_space,
masked_output,
dim=1)
word_lk = out.view(beam_size, remaining_sents,
-1).transpose(0, 1).contiguous()
active = []
for b in range(minibatch_size):
if beam[b].done:
continue
if lengths[b] == i + 1:
beam[b].done = True
top_dec_h_t[b] = dec_h_t[:, b:b + beam_size, :]
top_dec_c_t[b] = dec_c_t[:, b:b + beam_size, :]
idx = batch_idx[b]
beam[b].advance(word_lk.data[idx])
if not beam[b].done:
active.append(b)
for dec_state in (dec_h_t, dec_c_t):
# (layer*direction) x beam*sent x Hdim
sent_states = dec_state.view(-1, beam_size,
remaining_sents,
dec_state.size(2))[:, :, idx]
sent_states.data.copy_(
sent_states.data.index_select(
1, beam[b].get_current_origin()))
if not active:
break
active_idx = torch.tensor([batch_idx[k] for k in active],
dtype=torch.long,
device=self.device)
batch_idx = {beam: idx for idx, beam in enumerate(active)}
def update_active(t, hidden_dim):
#t_reshape = t.data.view(-1, remaining_sents, hidden_dim)
t_reshape = t.contiguous().view(-1, remaining_sents,
hidden_dim)
new_size = list(t.size())
new_size[-2] = new_size[-2] * len(
active_idx) // remaining_sents # beam*len(active_idx)
return t_reshape.index_select(1, active_idx).view(*new_size)
h_t = update_active(dec_h_t, self.hidden_dim)
c_t = update_active(dec_c_t, self.hidden_dim)
word_lstm_out = update_active(
word_lstm_out.transpose(0, 1),
self.num_directions * self.hidden_dim).transpose(0, 1)
remaining_sents = len(active)
allHyp, allScores = [], []
n_best = 1
for b in range(minibatch_size):
scores, ks = beam[b].sort_best()
allScores += [scores[:n_best]]
hyps = zip(*[beam[b].get_hyp(k) for k in ks[:n_best]])
allHyp += [hyps]
top_dec_h_t[b] = top_dec_h_t[b].data.index_select(1, ks[:n_best])
top_dec_c_t[b] = top_dec_c_t[b].data.index_select(1, ks[:n_best])
top_dec_h_t = torch.cat(top_dec_h_t, 1)
top_dec_c_t = torch.cat(top_dec_c_t, 1)
allScores = torch.cat(allScores)
if with_snt_classifier:
return allScores, allHyp, ((enc_h_t, enc_c_t), enc_word_lstm_out,
lengths)
else:
return allScores, allHyp
def run(self,
inp_transitions,
run_internal_parser=False,
use_internal_parser=False,
validate_transitions=True):
transition_loss = None
transition_acc = 0.0
num_transitions = inp_transitions.shape[1]
batch_size = inp_transitions.shape[0]
invalid_count = np.zeros(batch_size)
# Transition Loop
# ===============
attended = [[] for i in range(batch_size)]
for t_step in range(num_transitions):
transitions = inp_transitions[:, t_step]
transition_arr = list(transitions)
# A mask based on SKIP transitions.
cant_skip = np.array(transitions) != T_SKIP
must_skip = np.array(transitions) == T_SKIP
# Memories
# ========
# Keep track of key values to determine accuracy and loss.
self.memory = {}
# Prepare tracker input.
if self.debug and any(
len(buf) < 1 or len(stack)
for buf, stack in zip(self.bufs, self.stacks)):
# To elaborate on this exception, when cropping examples it is possible
# that your first 1 or 2 actions is a reduce action. It is unclear if this
# is a bug in cropping or a bug in how we think about cropping. In the meantime,
# turn on the truncate batch flag, and set the eval_seq_length
# very high.
raise IndexError(
"Warning: You are probably trying to encode examples"
"with cropped transitions. Although, this is a reasonable"
"feature, when predicting/validating transitions, you"
"probably will not get the behavior that you expect. Disable"
"this exception if you dare.")
self.memory['top_buf'] = self.wrap_items(
[buf[-1] if len(buf) > 0 else self.zeros for buf in self.bufs])
self.memory['top_stack_1'] = self.wrap_items([
stack[-1] if len(stack) > 0 else self.zeros
for stack in self.stacks
])
self.memory['top_stack_2'] = self.wrap_items([
stack[-2] if len(stack) > 1 else self.zeros
for stack in self.stacks
])
# Run if:
# A. We have a tracking component and,
# B. There is at least one transition that will not be skipped.
if hasattr(self, 'tracker') and sum(cant_skip) > 0:
# Get hidden output from the tracker. Used to predict
# transitions.
tracker_h, tracker_c = self.tracker(
self.extract_h(self.memory['top_buf']),
self.extract_h(self.memory['top_stack_1']),
self.extract_h(self.memory['top_stack_2']))
if hasattr(self, 'transition_net'):
transition_inp = [tracker_h]
if self.tracker.lateral_tracking and self.predict_use_cell:
transition_inp += [tracker_c]
transition_inp = torch.cat(transition_inp, 1)
transition_output = self.transition_net(transition_inp)
if hasattr(self, 'transition_net') and run_internal_parser:
# Predict Actions
# ===============
# TODO: Mask before predicting. This should simplify things and reduce computation.
# The downside is that in the Action Phase, need to be smarter about which stacks/bufs
# are selected.
transition_logdist, transition_preds = self.predict_actions(
transition_output)
# Distribution of transitions use to calculate transition
# loss.
self.memory["t_logprobs"] = transition_logdist
# Given transitions.
self.memory["t_given"] = transitions
# Constrain to valid actions
# ==========================
validated_preds, invalid_mask = self.validate(
transition_arr, transition_preds, self.stacks,
self.bufs)
if validate_transitions:
transition_preds = validated_preds
# Keep track of which predictions have been valid.
self.memory["t_valid_mask"] = np.logical_not(invalid_mask)
invalid_count += invalid_mask
# If the given action is skip, then must skip.
transition_preds[must_skip] = T_SKIP
# Actual transition predictions. Used to measure transition
# accuracy.
self.memory["t_preds"] = transition_preds
# Binary mask of examples that have a transition.
self.memory["t_mask"] = cant_skip
# If this FLAG is set, then use the predicted actions
# rather than the given.
if use_internal_parser:
transition_arr = transition_preds.tolist()
# Pre-Action Phase
# ================
# TODO: See if PyTorch's 'Advanced Indexing for Tensors and
# Variables' features would simplify this.
# For SHIFT
s_stacks, s_tops, s_trackings, s_idxs, r_idxs = [], [], [], [], []
# For REDUCE
r_stacks, r_lefts, r_rights, r_trackings = [], [], [], []
batch = list(
zip(
transition_arr, self.bufs, self.stacks, self.tracker.states
if hasattr(self, 'tracker') and self.tracker.h is not None
else itertools.repeat(None)))
reduced_idxs = []
for batch_idx, (transition, buf, stack,
tracking) in enumerate(batch):
if transition == T_SHIFT: # shift
#attended[batch_idx].append(buf[-1][0].unsqueeze(0))
self.t_shift(buf, stack, tracking, s_tops, s_trackings)
s_idxs.append(batch_idx)
s_stacks.append(stack)
elif transition == T_REDUCE: # reduce
self.t_reduce(buf, stack, tracking, r_lefts, r_rights,
r_trackings)
reduced_idxs.append(batch_idx)
attended[batch_idx].append(r_lefts[-1][0].unsqueeze(0))
attended[batch_idx].append(r_rights[-1][0].unsqueeze(0))
#self.attended[batch_idx].append(buf[-1])
r_stacks.append(stack)
r_idxs.append(batch_idx)
elif transition == T_SKIP: # skip
self.t_skip()
# Action Phase
# ============
self.shift_phase(s_tops, s_trackings, s_stacks)
self.reduce_phase(r_lefts, r_rights, r_trackings, r_stacks)
self.reduce_phase_hook(r_lefts, r_rights, r_trackings, r_stacks)
# Memory Phase
# ============
# APPEND ALL MEMORIES. MASK LATER.
self.memories.append(self.memory)
# Update number of reduces seen so far.
self.n_reduces += (np.array(transition_arr) == T_REDUCE)
# Update number of non-skip actions seen so far.
self.n_steps += (np.array(transition_arr) != T_SKIP)
# Loss Phase
# ==========
if hasattr(self, 'tracker') and hasattr(self, 'transition_net'):
t_preds = np.concatenate(
[m['t_preds'] for m in self.memories if 't_preds' in m])
t_given = np.concatenate(
[m['t_given'] for m in self.memories if 't_given' in m])
t_mask = np.concatenate(
[m['t_mask'] for m in self.memories if 't_mask' in m])
t_logprobs = torch.cat(
[m['t_logprobs'] for m in self.memories if 't_logprobs' in m],
0)
# We compute accuracy and loss after all transitions have complete,
# since examples can have different lengths when not using skips.
# Transition Accuracy.
n = t_mask.shape[0]
n_skips = n - t_mask.sum()
n_total = n - n_skips
n_correct = (t_preds == t_given).sum() - n_skips
transition_acc = n_correct / float(n_total)
# Transition Loss.
index = to_gpu(
Variable(torch.from_numpy(np.arange(
t_mask.shape[0])[t_mask])).long())
select_t_given = to_gpu(
Variable(torch.from_numpy(t_given[t_mask])).long())
select_t_logprobs = torch.index_select(t_logprobs, 0, index)
transition_loss = nn.NLLLoss()(select_t_logprobs, select_t_given) * \
self.transition_weight
self.n_invalid = (invalid_count > 0).sum()
self.invalid = self.n_invalid / float(batch_size)
self.loss_phase_hook()
if self.debug:
assert all(len(stack) == 3 for stack in self.stacks), \
"Stacks should be fully reduced and have 3 elements: " \
"two zeros and the sentence encoding."
assert all(len(buf) == 1 for buf in self.bufs), \
"Stacks should be fully shifted and have 1 zero."
[
attended[i].append(self.stacks[i][-1][0].unsqueeze(0))
for i in range(batch_size)
]
return [stack[-1] for stack in self.stacks
], transition_acc, transition_loss, attended
def ed_train(opt, model, train_data, optimizer, criterion, clip):
if opt.dataset_name == 'r252':
ret_INPUT_DIM = r252_src_vocab_size
ret_OUTPUT_DIM = r252_trg_vocab_size
ed_input_dim = r252_src_vocab_size
ed_output_dim = r252_trg_vocab_size
max_comment_len = r252_max_comment_len
max_code_len = r252_max_code_len
src_vocab_size = r252_src_vocab_size
trg_vocab_size = r252_trg_vocab_size
if opt.dataset_name == 'hstone':
ret_INPUT_DIM = hstone_src_vocab_size
ret_OUTPUT_DIM = hstone_trg_vocab_size
ed_input_dim = hstone_src_vocab_size
ed_output_dim = hstone_trg_vocab_size
max_comment_len = hstone_max_comment_len
max_code_len = hstone_max_code_len
src_vocab_size = hstone_src_vocab_size
trg_vocab_size = hstone_trg_vocab_size
model.train()
epoch_loss = 0
batch_num = 0
if torch.cuda.is_available():
model.cuda()
train_data = train_data.cuda()
for j in range(
0, train_data.shape[0],
batch_size): #TODO: replace batch_size with train_data.shape[0]
if j + batch_size < train_data.shape[0]:
batch_num += 1
interval = [
x for x in range(j, min(train_data.shape[0], j + batch_size))
]
interval = torch.LongTensor(interval)
if torch.cuda.is_available():
interval = interval.cuda()
batch = Variable(index_select(train_data, 0, interval))
x = batch[:, :max_comment_len]
xprime = batch[:, max_comment_len +
max_code_len:max_comment_len * 2 + max_code_len]
yprime = batch[:, max_comment_len * 2 + max_code_len:]
trg = batch[:, max_comment_len:max_comment_len + max_code_len] # y
optimizer.zero_grad()
x = torch.transpose(x, 0, 1)
xprime = torch.transpose(xprime, 0, 1)
yprime = torch.transpose(yprime, 0, 1)
trg = torch.transpose(trg, 0, 1)
output = model(x, xprime, yprime, trg)
# output shape is code_len, batch, trg_vocab_size
output = torch.reshape(output,
(batch_size * max_code_len, trg_vocab_size))
trg = torch.reshape(trg, (batch_size * max_code_len, ))
loss = criterion(output, trg)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
optimizer.step()
epoch_loss += loss.item()
print("Ed batch: {0:3d} | Loss: {1:.3f}".format(
batch_num, loss.item()))
return epoch_loss / batch_num, output
def ed_evaluate(model, valid_data, criterion):
if opt.dataset_name == 'r252':
ret_INPUT_DIM = r252_src_vocab_size
ret_OUTPUT_DIM = r252_trg_vocab_size
ed_input_dim = r252_src_vocab_size
ed_output_dim = r252_trg_vocab_size
max_comment_len = r252_max_comment_len
max_code_len = r252_max_code_len
src_vocab_size = r252_src_vocab_size
trg_vocab_size = r252_trg_vocab_size
if opt.dataset_name == 'hstone':
ret_INPUT_DIM = hstone_src_vocab_size
ret_OUTPUT_DIM = hstone_trg_vocab_size
ed_input_dim = hstone_src_vocab_size
ed_output_dim = hstone_trg_vocab_size
max_comment_len = hstone_max_comment_len
max_code_len = hstone_max_code_len
src_vocab_size = hstone_src_vocab_size
trg_vocab_size = hstone_trg_vocab_size
model.eval()
epoch_loss = 0
if torch.cuda.is_available():
model.cuda()
valid_data = valid_data.cuda()
ref_code = valid_data[:, max_comment_len:max_comment_len + max_code_len]
candidate_code = torch.zeros_like(ref_code)
with torch.no_grad():
batch_num = 0
for j in range(0, valid_data.shape[0], batch_size):
if j + batch_size < valid_data.shape[0]:
batch_num += 1
interval = [
x
for x in range(j, min(valid_data.shape[0], j + batch_size))
]
interval = torch.LongTensor(interval)
if torch.cuda.is_available():
interval = interval.cuda()
batch = Variable(index_select(valid_data, 0, interval))
x = batch[:, :max_comment_len]
xprime = batch[:, max_comment_len +
max_code_len:max_comment_len * 2 + max_code_len]
yprime = batch[:, max_comment_len * 2 + max_code_len:]
trg = batch[:, max_comment_len:max_comment_len +
max_code_len] # y
x = torch.transpose(x, 0, 1)
xprime = torch.transpose(xprime, 0, 1)
yprime = torch.transpose(yprime, 0, 1)
trg = torch.transpose(trg, 0, 1)
output = model(x, xprime, yprime, trg)
# output shape is code_len, batch, trg_vocab_size
#trg = [trg sent len, batch size]
#output = [trg sent len, batch size, output dim]
for cpj in range(batch_size):
candidate_code[j + cpj] = torch.argmax(output[cpj], dim=1)
output = torch.reshape(
output, (batch_size * max_code_len, trg_vocab_size))
trg = torch.reshape(trg, (batch_size * max_code_len, ))
loss = criterion(output, trg)
epoch_loss += loss.item()
print("Ed batch: {0:3d} | Loss: {1:.3f}".format(
batch_num, loss.item()))
return epoch_loss / batch_num, candidate_code
