def write(self, z, time, debug=False):
# update usage indicator
self.u = self.u + T.matmul(Variable(T.from_numpy(np.ones((1, Kr), dtype=np.float32))), self.W_predictor)
# update writing weights
prev_v_wr = self.v_wr
v_wr = np.zeros((N_mem, 1), dtype=np.float32)
if time < N_mem:
v_wr[time][0] = 1
else:
waste_index = int(T.argmin(self.u).data)
v_wr[waste_index][0] = 1
self.v_wr = Variable(T.from_numpy(v_wr))
# writing
# z: (1, Z_DIM)
if debug:
print(self.M)
if USE_RETROACTIVE:
# update retroactive weights
self.v_ret = GAMMA*self.v_ret + (1-GAMMA)*prev_v_wr
z_wr = T.cat([z, Variable(T.from_numpy(np.zeros((1, Z_DIM), dtype=np.float32)))], 1)
z_ret = T.cat([Variable(T.from_numpy(np.zeros((1, Z_DIM), dtype=np.float32))), z], 1)
self.M = self.M + T.matmul(self.v_wr, z_wr) + T.matmul(self.v_ret, z_ret)
else:
self.M = self.M + T.matmul(self.v_wr, z)
if debug:
return self.M
def forward(self,x):
max_sample = x.size()[1]
x = x.view(-1,self.feature_size)
assignment = th.matmul(x,self.clusters)
if self.add_batch_norm:
assignment = self.batch_norm(assignment)
assignment = F.softmax(assignment,dim=1)
assignment = assignment.view(-1, max_sample, self.cluster_size)
a_sum = th.sum(assignment,-2,keepdim=True)
a = a_sum*self.clusters2
assignment = assignment.transpose(1,2)
x = x.view(-1, max_sample, self.feature_size)
vlad = th.matmul(assignment, x)
vlad = vlad.transpose(1,2)
vlad = vlad - a
# L2 intra norm
vlad = F.normalize(vlad)
# flattening + L2 norm
vlad = vlad.view(-1, self.cluster_size*self.feature_size)
vlad = F.normalize(vlad)
return vlad
def train(ep):
model.train()
total_loss = 0
count = 0
train_idx_list = np.arange(len(X_train), dtype="int32")
np.random.shuffle(train_idx_list)
for idx in train_idx_list:
data_line = X_train[idx]
x, y = Variable(data_line[:-1]), Variable(data_line[1:])
if args.cuda:
x, y = x.cuda(), y.cuda()
optimizer.zero_grad()
output = model(x.unsqueeze(0)).squeeze(0)
loss = -torch.trace(torch.matmul(y, torch.log(output).float().t()) +
torch.matmul((1 - y), torch.log(1 - output).float().t()))
total_loss += loss.data[0]
count += output.size(0)
if args.clip > 0:
torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
loss.backward()
optimizer.step()
if idx > 0 and idx % args.log_interval == 0:
cur_loss = total_loss / count
print("Epoch {:2d} | lr {:.5f} | loss {:.5f}".format(ep, lr, cur_loss))
total_loss = 0.0
count = 0
def forward(self,x):
max_sample = x.size()[1]
x = x.view(-1,self.feature_size)
assignment = th.matmul(x,self.clusters)
if self.add_batch_norm:
assignment = self.batch_norm(assignment)
assignment = F.softmax(assignment, dim=1)
assignment = assignment.view(-1, max_sample, self.cluster_size)
assignment = assignment.transpose(1,2)
x = x.view(-1, max_sample, self.feature_size)
rvlad = th.matmul(assignment, x)
rvlad = rvlad.transpose(-1,1)
# L2 intra norm
rvlad = F.normalize(rvlad)
# flattening + L2 norm
rvlad = rvlad.view(-1, self.cluster_size*self.feature_size)
rvlad = F.normalize(rvlad)
return rvlad
def forward(self, context, state, input_):
output = (torch.matmul(context, self._v_c.unsqueeze(1))
+ torch.matmul(state, self._v_s.unsqueeze(1))
+ torch.matmul(input_, self._v_i.unsqueeze(1)))
if self._b is not None:
output = output + self._b.unsqueeze(0)
return output
def forward(self, sequence, graph): """ Apply self-attention to the sequence, ignores the graph """ sequence = sequence.squeeze(1) #get the dimension n, d = sequence.size() #project the sequence into key, value, and query sequences keySeq = f.relu(self.keyProj(sequence)) valueSeq = f.relu(self.valueProj(sequence)) querySeq = f.relu(self.queryProj(sequence)) #combine query with each key #a_ijh = softmax( (q_ih^T k_jh) / sqrt(d) ) #the result is, row i is the importance of the sequence for key i importance = f.softmax(t.matmul(querySeq, keySeq.permute(1,0)) * math.sqrt(d),0).permute(1,0) #apply the importance weights to the value sequence attention = t.matmul(valueSeq.permute(1,0), importance).permute(1,0) #sum the sequence for a complete representation final = t.sum(attention, 0) return attention.unsqueeze(1), final
def score(self, hidden, encoder_output):
if self.method == 'dot':
# hidden is 1 by 256
# encoder_output is 22 by 256
encoder_output = torch.transpose(encoder_output, 0, 1)
# encoder_output is 256 by 22
energy = torch.matmul(hidden, encoder_output)
return energy
elif self.method == 'general':
# hidden is 1 by 256
# encoder_output is 256 by 22
# encoder_output = torch.transpose(encoder_output, 0, 1)
hidden = hidden.view(1, -1)
a = self.attn(encoder_output)
a = torch.transpose(a, 0, 1)
energy = torch.matmul(hidden, a)
return energy
elif self.method == 'concat':
len_encoder_output = encoder_output.size()[1]
# hidden is 1 by 256
# encoder_output is 256 by 22
hidden = torch.transpose(hidden, 0, 1)
# hidden is 256 by 1
hidden = hidden.repeat(hidden_size, len_encoder_output)
# hidden is 256 by 22
concat = torch.cat((hidden, encoder_output), dim=0)
# concat is 512 by 22
# self.attn(concat) --> 256 by 22
energy = torch.matmul(self.v, F.tanh(self.attn(concat)))
return energy
def grad2():
W = Variable(torch.rand(2, 2), requires_grad=True)
W2 = Variable(torch.rand(2, 1), requires_grad=True)
x1 = Variable(torch.rand(1, 2), requires_grad=True)
x2 = Variable(torch.rand(1, 2), requires_grad=True)
print("w: ")
print(W)
print("x1: ")
print(x1)
print("x2: ")
print(x2)
print("--------------------")
y1 = torch.matmul(torch.matmul(x1, W), W2)
print(torch.matmul(W, W2))
# y = Variable(y, requires_grad=True)
# print("y1:")
# print(y1)
y1.backward()
# print(W.grad)
print(x1.grad)
# W.grad.data.zero_()
# x1.grad.data.zero_()
y2 = torch.matmul(torch.matmul(x2, W), W2)
y2.backward()
# print("y2: ")
# print(y2)
# print(W.grad)
print(x2.grad)
def forward(self, hidden_states, attention_mask):
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
return context_layer
def _attn(self, q, k, v):
w = torch.matmul(q, k)
if self.scale:
w = w / math.sqrt(v.size(-1))
w = w * self.b + -1e9 * (1 - self.b) # TF implem method: mask_attn_weights
w = nn.Softmax(dim=-1)(w)
w = self.attn_dropout(w)
return torch.matmul(w, v)
def test():
x = torch.ones(1, 2)
Sigma = torch.FloatTensor([[1, 0.8], [0.8, 1]])
z = torch.ones(x.size())
y = torch.matmul(x, Sigma)
y = torch.matmul(y, x.t())
print(y)
def attention_score(attention, query, v, w):
""" unnormalized attention score"""
sum_ = attention.unsqueeze(1) + torch.matmul(
query, w.unsqueeze(0)
).unsqueeze(2) # [B, Nq, Ns, D]
score = torch.matmul(
F.tanh(sum_), v.unsqueeze(0).unsqueeze(1).unsqueeze(3)
).squeeze(3) # [B, Nq, Ns]
return score
def attention(cls, query, key, value, mask=None, dropout=None):
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
p_attn = F.softmax(scores, dim=-1)
if dropout is not None:
p_attn = dropout(p_attn)
return torch.matmul(p_attn, value), p_attn
def forward(self, matrix_1: torch.Tensor, matrix_2: torch.Tensor) -> torch.Tensor:
if self._use_input_biases:
bias1 = matrix_1.new_ones(matrix_1.size()[:-1] + (1,))
bias2 = matrix_2.new_ones(matrix_2.size()[:-1] + (1,))
matrix_1 = torch.cat([matrix_1, bias1], -1)
matrix_2 = torch.cat([matrix_2, bias2], -1)
intermediate = torch.matmul(matrix_1.unsqueeze(1), self._weight_matrix.unsqueeze(0))
final = torch.matmul(intermediate, matrix_2.unsqueeze(1).transpose(2, 3))
return self._activation(final.squeeze(1) + self._bias)
def _prepare(self, attn_mem):
attn_feat = torch.matmul(attn_mem, self._attn_wm.unsqueeze(0))
hop_feat = torch.matmul(attn_mem, self._hop_wm.unsqueeze(0))
bs = attn_mem.size(0)
n_l, d = self._init_h.size()
size = (n_l, bs, d)
lstm_states = (self._init_h.unsqueeze(1).expand(*size).contiguous(),
self._init_c.unsqueeze(1).expand(*size).contiguous())
d = self._init_i.size(0)
init_i = self._init_i.unsqueeze(0).unsqueeze(1).expand(bs, 1, d)
return attn_feat, hop_feat, lstm_states, init_i
def distance_calcu(self, query, gallery):
"""
:param query:
:param gallery:
:return:
"""
query = query.expand_as(gallery).contiguous()
x = torch.cat([query, gallery, query-gallery], 1)
W1, W2 = self.adpW(x)
num = query.size(0)
dist = torch.norm((torch.matmul(W2, gallery.view(num, -1, 1))+torch.matmul(W1, query.view(num, -1, 1))) # projected gallery(combin with query)
- query.view(num, -1, 1), 2, 1) # orig query
return dist
def high_dimension_gaussain_energy(x):
u = High_mu
Sigma = High_Sigma
Sigma = torch.inverse(Sigma)
if isinstance(x, Variable):
u = Variable(u, requires_grad=True)
Sigma = Variable(Sigma, requires_grad=True)
diff = x - u
temp = 0.5 * torch.matmul(torch.matmul(diff, Sigma), diff.t())
return temp
def adpW(self,x):
# x = F.normalize(x)
x = self.adp_metric_embedding1(x)
# x = self.adp_metric_embedding1_bn(x)
x = F.prelu(x)
x = self.adp_metric_embedding2(x)
# x = self.adp_metric_embedding2_bn(x)
diag_matrix = []
for i in range(x.size(0)):
diag_matrix.append(torch.diag(x[i,:]))
x = torch.stack(diag_matrix)
W = torch.matmul(self.transform_matrix,torch.matmul(x,self.transform_matrix))
return W
def distance_calcu(self, gallery, query):
# x = torch.autograd.Variable(torch.cat([gallery,query]))
gallery = gallery.expand_as(query).contiguous()
x = torch.cat([gallery, query],1)
W = self.adpW(x)
num = query.size(0)
dist1 = torch.norm(torch.matmul(W,gallery.view(num,-1,1))
-torch.matmul(W,query.view(num,-1,1)),2,1)
x = torch.cat([query,gallery],1)
W = self.adpW(x)
dist2 = torch.norm(torch.matmul(W, gallery.view(num, -1, 1))
- torch.matmul(W, query.view(num, -1, 1)), 2, 1)
dist = 0.5*(dist1+dist2)
return dist
def attention(query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
mask: torch.Tensor = None,
dropout: Callable = None) -> Tuple[torch.Tensor, torch.Tensor]:
"""Compute 'Scaled Dot Product Attention'"""
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
p_attn = F.softmax(scores, dim=-1)
if dropout is not None:
p_attn = dropout(p_attn)
return torch.matmul(p_attn, value), p_attn
def max_singular_value(W, u=None, Ip=1):
"""
power iteration for weight parameter
"""
#xp = W.data
if u is None:
u = torch.FloatTensor(1, W.size(0)).normal_(0, 1).cuda()
_u = u
for _ in range(Ip):
#print(_u.size(), W.size())
_v = _l2normalize(torch.matmul(_u, W.data), eps=1e-12)
_u = _l2normalize(torch.matmul(_v, torch.transpose(W.data, 0, 1)), eps=1e-12)
sigma = torch.matmul(torch.matmul(_v, torch.transpose(W.data, 0, 1)), torch.transpose(_u, 0, 1))
return sigma, _v
def seqAttention(sequence, weights): """ Applies attention to the given sequence """ #compute the importance over the sequence importance = t.tanh(t.matmul(sequence, weights)) #compute the attention attention = f.softmax(importance, 0) tSeq = sequence.permute(1,0) #compute and return the representation return t.matmul(tSeq, attention)
def AdpsubM(self, x, batchsize):
batchinputs = x.view(batchsize**2,-1,1)
# x_constraint_branch = x.detach()
W = self.adpW(x)
feature_dim = x.size(1) / 2
# W_branch = self.adpW(x_constraint_branch) ###for weight constrain
# I = torch.autograd.Variable(torch.eye(feature_dim)).cuda()
# weight_constraint = torch.norm(torch.matmul(W_branch, W_branch.transpose(1,2)).sub(I), 2)
batchoutputs1 = torch.matmul(W,batchinputs[:,:feature_dim,:]).view(batchsize**2,-1)
batchoutputs2 = torch.matmul(W,batchinputs[:,feature_dim:,:]).view(batchsize**2,-1)
dist = torch.norm(batchoutputs1-batchoutputs2,2,1).view(batchsize,batchsize)
dist = dist.clamp(min=1e-6)
return dist
def CORAL_loss(source, target):
d = source.data.shape[1]
# source covariance
xm = torch.mean(source, 1, keepdim=True) - source
xc = torch.matmul(torch.transpose(xm, 0, 1), xm)
# target covariance
xmt = torch.mean(target, 1, keepdim=True) - target
xct = torch.matmul(torch.transpose(xmt, 0, 1), xmt)
# frobenius norm between source and target
loss = (xc - xct).pow(2).sum().sqrt()
loss = loss/(4*d*d)
return loss
def compute_weight(self, module):
weight = module._parameters[self.name + '_org']
u = module._buffers[self.name + '_u']
height = weight.size(0)
weight_mat = weight.view(height, -1)
for _ in range(self.n_power_iterations):
# Spectral norm of weight equals to `u^T W v`, where `u` and `v`
# are the first left and right singular vectors.
# This power iteration produces approximations of `u` and `v`.
v = normalize(torch.matmul(weight_mat.t(), u), dim=0, eps=self.eps)
u = normalize(torch.matmul(weight_mat, v), dim=0, eps=self.eps)
sigma = torch.dot(u, torch.matmul(weight_mat, v))
weight.data /= sigma
return weight, u
def relative_matmul(x, z, transpose):
"""Helper function for relative positions attention."""
batch_size = x.shape[0]
heads = x.shape[1]
length = x.shape[2]
x_t = x.permute(2, 0, 1, 3)
x_t_r = x_t.reshape(length, heads * batch_size, -1)
if transpose:
z_t = z.transpose(1, 2)
x_tz_matmul = torch.matmul(x_t_r, z_t)
else:
x_tz_matmul = torch.matmul(x_t_r, z)
x_tz_matmul_r = x_tz_matmul.reshape(length, batch_size, heads, -1)
x_tz_matmul_r_t = x_tz_matmul_r.permute(1, 2, 0, 3)
return x_tz_matmul_r_t
def forward(self, input, target):
y_true = target.int().unsqueeze(-1)
same_id = torch.eq(y_true, y_true.t()).type_as(input)
pos_mask = same_id
neg_mask = 1 - same_id
def _mask_max(input_tensor, mask, axis=None, keepdims=False):
input_tensor = input_tensor - 1e6 * (1 - mask)
_max, _idx = torch.max(input_tensor, dim=axis, keepdim=keepdims)
return _max, _idx
def _mask_min(input_tensor, mask, axis=None, keepdims=False):
input_tensor = input_tensor + 1e6 * (1 - mask)
_min, _idx = torch.min(input_tensor, dim=axis, keepdim=keepdims)
return _min, _idx
# output[i, j] = || feature[i, :] - feature[j, :] ||_2
dist_squared = torch.sum(input ** 2, dim=1, keepdim=True) + \
torch.sum(input.t() ** 2, dim=0, keepdim=True) - \
2.0 * torch.matmul(input, input.t())
dist = dist_squared.clamp(min=1e-16).sqrt()
pos_max, pos_idx = _mask_max(dist, pos_mask, axis=-1)
neg_min, neg_idx = _mask_min(dist, neg_mask, axis=-1)
# loss(x, y) = max(0, -y * (x1 - x2) + margin)
y = torch.ones(same_id.size()[0]).to(DEVICE)
return F.margin_ranking_loss(neg_min.float(),
pos_max.float(),
y,
self.margin,
self.size_average)
def __call__(self, spec_f):
spec_f, is_variable = _check_is_variable(spec_f)
n_fft = spec_f.size(2)
m_min = 0. if self.f_min == 0 else 2595 * np.log10(1. + (self.f_min / 700))
m_max = 2595 * np.log10(1. + (self.f_max / 700))
m_pts = torch.linspace(m_min, m_max, self.n_mels + 2)
f_pts = (700 * (10**(m_pts / 2595) - 1))
bins = torch.floor(((n_fft - 1) * 2) * f_pts / self.sr).long()
fb = torch.zeros(n_fft, self.n_mels)
for m in range(1, self.n_mels + 1):
f_m_minus = bins[m - 1].item()
f_m = bins[m].item()
f_m_plus = bins[m + 1].item()
if f_m_minus != f_m:
fb[f_m_minus:f_m, m - 1] = (torch.arange(f_m_minus, f_m) - f_m_minus) / (f_m - f_m_minus)
if f_m != f_m_plus:
fb[f_m:f_m_plus, m - 1] = (f_m_plus - torch.arange(f_m, f_m_plus)) / (f_m_plus - f_m)
fb = Variable(fb)
spec_m = torch.matmul(spec_f, fb) # (c, l, n_fft) dot (n_fft, n_mels) -> (c, l, n_mels)
return spec_m if is_variable else spec_m.data
def routing(self, x, b_IJ, W,batch_size,routing_iter):
x1 = x.view(batch_size, 256, 1, 6, 6)
x_tile = x1.repeat(1, 1, 10, 1, 1)
x_view = x_tile.view(batch_size, 1152, 10, 8, 1)
stride_i = W.repeat(batch_size, 1, 1, 1, 1)
stride_j = stride_i.view(batch_size, 1152, 10, 16, 8)
dot_op = torch.matmul(stride_j, x_view)
dot_op_stopped = Variable(dot_op.data.clone(), requires_grad=False)
for r_iter in range(routing_iter):
id_capsule = F.softmax(b_IJ, dim=2)
if r_iter == routing_iter - 1:
route_I = torch.mul(id_capsule, dot_op)
route_I_sum = torch.sum(route_I, dim=1, keepdim=True) + self.bias
V_J = squash(route_I_sum,self.epsilon)
if r_iter < routing_iter - 1:
dot_op_stopped_tmp = dot_op_stopped.data.numpy()
dot_op_stopped_tmp = np.reshape(dot_op_stopped_tmp, (batch_size, 1152, 10, 16, 1))
id_capsule_tmp = id_capsule.data.numpy()
route_I_tmp = id_capsule_tmp * dot_op_stopped_tmp
route_I_tmp_sum = np.sum(route_I_tmp, axis=1, keepdims=True) + self.bias.data.numpy()
V_J_tmp = squash(torch.Tensor(route_I_tmp_sum),self.epsilon)
V_J_tmp_tiled = np.tile(V_J_tmp.numpy(), (1, 1152, 1, 1, 1))
dot_op_stopped_tmp = np.reshape(dot_op_stopped_tmp, (batch_size, 1152, 10, 1, 16))
u_produce_v = np.matmul(dot_op_stopped_tmp, V_J_tmp_tiled)
b_IJ.data += torch.Tensor(u_produce_v)
return V_J
def compute_weight(self, module):
weight = getattr(module, self.name + '_org')
u = getattr(module, self.name + '_u')
height = weight.size(0)
weight_mat = weight.view(height, -1)
with torch.no_grad():
for _ in range(self.n_power_iterations):
# Spectral norm of weight equals to `u^T W v`, where `u` and `v`
# are the first left and right singular vectors.
# This power iteration produces approximations of `u` and `v`.
v = normalize(torch.matmul(weight_mat.t(), u), dim=0, eps=self.eps)
u = normalize(torch.matmul(weight_mat, v), dim=0, eps=self.eps)
sigma = torch.dot(u, torch.matmul(weight_mat, v))
weight = weight / sigma
return weight, u
def prob_v_given_h(self, h):
return (torch.matmul(h, self.weights.data, out=None).add_(
self.visible_bias.data).sigmoid_().clamp_(min=0, max=1))
def forward(self, input):
x = self.fc(input)
attn = torch.softmax(torch.matmul(x, x.transpose(1, 2)), 2)
output = torch.matmul(attn, input)
return output
def matmul(self, other):
"""Matrix product of two tensors.
See :func:`torch.matmul`."""
return torch.matmul(self, other)
def angle_axis_to_rotation_matrix(angle_axis: torch.Tensor) -> torch.Tensor:
r"""Convert 3d vector of axis-angle rotation to 3x3 rotation matrix
Args:
angle_axis (torch.Tensor): tensor of 3d vector of axis-angle rotations.
Returns:
torch.Tensor: tensor of 3x3 rotation matrices.
Shape:
- Input: :math:`(N, 3)`
- Output: :math:`(N, 3, 3)`
Example:
>>> input = torch.rand(1, 3) # Nx3
>>> output = kornia.angle_axis_to_rotation_matrix(input) # Nx3x3
"""
if not isinstance(angle_axis, torch.Tensor):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(
type(angle_axis)))
if not angle_axis.shape[-1] == 3:
raise ValueError(
"Input size must be a (*, 3) tensor. Got {}".format(
angle_axis.shape))
def _compute_rotation_matrix(angle_axis, theta2, eps=1e-6):
# We want to be careful to only evaluate the square root if the
# norm of the angle_axis vector is greater than zero. Otherwise
# we get a division by zero.
k_one = 1.0
theta = torch.sqrt(theta2)
wxyz = angle_axis / (theta + eps)
wx, wy, wz = torch.chunk(wxyz, 3, dim=1)
cos_theta = torch.cos(theta)
sin_theta = torch.sin(theta)
r00 = cos_theta + wx * wx * (k_one - cos_theta)
r10 = wz * sin_theta + wx * wy * (k_one - cos_theta)
r20 = -wy * sin_theta + wx * wz * (k_one - cos_theta)
r01 = wx * wy * (k_one - cos_theta) - wz * sin_theta
r11 = cos_theta + wy * wy * (k_one - cos_theta)
r21 = wx * sin_theta + wy * wz * (k_one - cos_theta)
r02 = wy * sin_theta + wx * wz * (k_one - cos_theta)
r12 = -wx * sin_theta + wy * wz * (k_one - cos_theta)
r22 = cos_theta + wz * wz * (k_one - cos_theta)
rotation_matrix = torch.cat(
[r00, r01, r02, r10, r11, r12, r20, r21, r22], dim=1)
return rotation_matrix.view(-1, 3, 3)
def _compute_rotation_matrix_taylor(angle_axis):
rx, ry, rz = torch.chunk(angle_axis, 3, dim=1)
k_one = torch.ones_like(rx)
rotation_matrix = torch.cat(
[k_one, -rz, ry, rz, k_one, -rx, -ry, rx, k_one], dim=1)
return rotation_matrix.view(-1, 3, 3)
# stolen from ceres/rotation.h
_angle_axis = torch.unsqueeze(angle_axis, dim=1)
theta2 = torch.matmul(_angle_axis, _angle_axis.transpose(1, 2))
theta2 = torch.squeeze(theta2, dim=1)
# compute rotation matrices
rotation_matrix_normal = _compute_rotation_matrix(angle_axis, theta2)
rotation_matrix_taylor = _compute_rotation_matrix_taylor(angle_axis)
# create mask to handle both cases
eps = 1e-6
mask = (theta2 > eps).view(-1, 1, 1).to(theta2.device)
mask_pos = (mask).type_as(theta2)
mask_neg = (mask == False).type_as(theta2) # noqa
# create output pose matrix
batch_size = angle_axis.shape[0]
rotation_matrix = torch.eye(3).to(angle_axis.device).type_as(angle_axis)
rotation_matrix = rotation_matrix.view(1, 3, 3).repeat(batch_size, 1, 1)
# fill output matrix with masked values
rotation_matrix[..., :3, :3] = \
mask_pos * rotation_matrix_normal + mask_neg * rotation_matrix_taylor
return rotation_matrix # Nx4x4
def apply_self_attention_rules(R_ss, R_sq, cam_ss):
R_sq_addition = torch.matmul(cam_ss, R_sq)
R_ss_addition = torch.matmul(cam_ss, R_ss)
return R_ss_addition, R_sq_addition
def forward(self, x_emb_var, x_len, col_inp_var, col_name_len,
col_len, col_num, gt_where, gt_cond, reinforce):
max_x_len = max(x_len)
B = len(x_len)
if reinforce:
raise NotImplementedError('Our model doesn\'t have RL')
# Predict the number of conditions
# First use column embeddings to calculate the initial hidden unit
# Then run the LSTM and predict condition number.
e_num_col, col_num = col_name_encode(col_inp_var, col_name_len, col_len, self.cond_num_name_enc)
num_col_att_val = self.cond_num_col_att(e_num_col).squeeze()
for idx, num in enumerate(col_num):
if num < max(col_num):
num_col_att_val[idx, num:] = -100
num_col_att = self.softmax(num_col_att_val)
K_num_col = (e_num_col * num_col_att.unsqueeze(2)).sum(1)
cond_num_h1 = self.cond_num_col2hid1(K_num_col).view(B, 4, self.N_h//2).transpose(0, 1).contiguous()
cond_num_h2 = self.cond_num_col2hid2(K_num_col).view(B, 4, self.N_h//2).transpose(0, 1).contiguous()
h_num_enc, _ = run_lstm(self.cond_num_lstm, x_emb_var, x_len,
hidden=(cond_num_h1, cond_num_h2))
num_att_val = self.cond_num_att(h_num_enc).squeeze()
for idx, num in enumerate(x_len):
if num < max_x_len:
num_att_val[idx, num:] = -100
num_att = self.softmax(num_att_val)
K_cond_num = (h_num_enc * num_att.unsqueeze(2).expand_as(h_num_enc)).sum(1)
cond_num_score = self.cond_num_out(K_cond_num)
#Predict the columns of conditions
e_cond_col, _ = col_name_encode(col_inp_var, col_name_len, col_len, self.cond_col_name_enc)
h_col_enc, _ = run_lstm(self.cond_col_lstm, x_emb_var, x_len)
#h_col_enc = x_emb_var
if self.use_ca:
col_att_val = torch.bmm(e_cond_col,
self.cond_col_att(h_col_enc).transpose(1, 2))
for idx, num in enumerate(x_len):
if num < max_x_len:
col_att_val[idx, :, num:] = -100
col_att = self.softmax(col_att_val.view(
(-1, max_x_len))).view(B, -1, max_x_len)
K_cond_col = (h_col_enc.unsqueeze(1) * col_att.unsqueeze(3)).sum(2)
else:
col_att_val = self.cond_col_att(h_col_enc).squeeze()
for idx, num in enumerate(x_len):
if num < max_x_len:
col_att_val[idx, num:] = -100
col_att = self.softmax(col_att_val)
K_cond_col = (h_col_enc *
col_att_val.unsqueeze(2)).sum(1).unsqueeze(1)
cond_col_score = self.cond_col_out(self.cond_col_out_K(K_cond_col) +
self.cond_col_out_col(e_cond_col)).squeeze()
max_col_num = max(col_num)
for b, num in enumerate(col_num):
if num < max_col_num:
cond_col_score[b, num:] = -100
#Predict the operator of conditions
chosen_col_gt = []
if gt_cond is None:
cond_nums = np.argmax(cond_num_score.data.cpu().numpy(), axis=1)
col_scores = cond_col_score.data.cpu().numpy()
chosen_col_gt = [list(np.argsort(-col_scores[b])[:cond_nums[b]])
for b in range(len(cond_nums))]
else:
# print gt_cond
chosen_col_gt = [[x[0] for x in one_gt_cond] for one_gt_cond in gt_cond]
e_cond_col, _ = col_name_encode(col_inp_var, col_name_len,
col_len, self.cond_op_name_enc)
h_op_enc, _ = run_lstm(self.cond_op_lstm, x_emb_var, x_len)
col_emb = []
for b in range(B):
cur_col_emb = torch.stack([e_cond_col[b, x]
for x in chosen_col_gt[b]] + [e_cond_col[b, 0]] *
(4 - len(chosen_col_gt[b]))) # Pad the columns to maximum (4)
col_emb.append(cur_col_emb)
col_emb = torch.stack(col_emb)
if self.use_ca:
op_att_val = torch.matmul(self.cond_op_att(h_op_enc).unsqueeze(1),
col_emb.unsqueeze(3)).squeeze()
for idx, num in enumerate(x_len):
if num < max_x_len:
op_att_val[idx, :, num:] = -100
op_att = self.softmax(op_att_val.view(B*4, -1)).view(B, 4, -1)
K_cond_op = (h_op_enc.unsqueeze(1) * op_att.unsqueeze(3)).sum(2)
else:
op_att_val = self.cond_op_att(h_op_enc).squeeze()
for idx, num in enumerate(x_len):
if num < max_x_len:
op_att_val[idx, num:] = -100
op_att = self.softmax(op_att_val)
K_cond_op = (h_op_enc * op_att.unsqueeze(2)).sum(1).unsqueeze(1)
cond_op_score = self.cond_op_out(self.cond_op_out_K(K_cond_op) +
self.cond_op_out_col(col_emb)).squeeze()
#Predict the string of conditions
h_str_enc, _ = run_lstm(self.cond_str_lstm, x_emb_var, x_len)
e_cond_col, _ = col_name_encode(col_inp_var, col_name_len,
col_len, self.cond_str_name_enc)
col_emb = []
for b in range(B):
cur_col_emb = torch.stack([e_cond_col[b, x] for x in chosen_col_gt[b]] +
[e_cond_col[b, 0]] * (4 - len(chosen_col_gt[b])))
col_emb.append(cur_col_emb)
col_emb = torch.stack(col_emb)
if gt_where is not None:
gt_tok_seq, gt_tok_len = self.gen_gt_batch(gt_where)
g_str_s_flat, _ = self.cond_str_decoder(
gt_tok_seq.view(B*4, -1, self.max_tok_num))
g_str_s = g_str_s_flat.contiguous().view(B, 4, -1, self.N_h)
h_ext = h_str_enc.unsqueeze(1).unsqueeze(1)
g_ext = g_str_s.unsqueeze(3)
col_ext = col_emb.unsqueeze(2).unsqueeze(2)
cond_str_score = self.cond_str_out(
self.cond_str_out_h(h_ext) + self.cond_str_out_g(g_ext) +
self.cond_str_out_col(col_ext)).squeeze()
for b, num in enumerate(x_len):
if num < max_x_len:
cond_str_score[b, :, :, num:] = -100
else:
h_ext = h_str_enc.unsqueeze(1).unsqueeze(1)
col_ext = col_emb.unsqueeze(2).unsqueeze(2)
scores = []
t = 0
init_inp = np.zeros((B*4, 1, self.max_tok_num), dtype=np.float32)
init_inp[:,0,0] = 1 #Set the <BEG> token
if self.gpu:
cur_inp = Variable(torch.from_numpy(init_inp).cuda())
else:
cur_inp = Variable(torch.from_numpy(init_inp))
cur_h = None
while t < 50:
if cur_h:
g_str_s_flat, cur_h = self.cond_str_decoder(cur_inp, cur_h)
else:
g_str_s_flat, cur_h = self.cond_str_decoder(cur_inp)
g_str_s = g_str_s_flat.view(B, 4, 1, self.N_h)
g_ext = g_str_s.unsqueeze(3)
cur_cond_str_score = self.cond_str_out(
self.cond_str_out_h(h_ext) + self.cond_str_out_g(g_ext)
+ self.cond_str_out_col(col_ext)).squeeze()
for b, num in enumerate(x_len):
if num < max_x_len:
cur_cond_str_score[b, :, num:] = -100
scores.append(cur_cond_str_score)
_, ans_tok_var = cur_cond_str_score.view(B*4, max_x_len).max(1)
ans_tok = ans_tok_var.data.cpu()
data = torch.zeros(B*4, self.max_tok_num).scatter_(
1, ans_tok.unsqueeze(1), 1)
if self.gpu: #To one-hot
cur_inp = Variable(data.cuda())
else:
cur_inp = Variable(data)
cur_inp = cur_inp.unsqueeze(1)
t += 1
cond_str_score = torch.stack(scores, 2)
for b, num in enumerate(x_len):
if num < max_x_len:
cond_str_score[b, :, :, num:] = -100 #[B, IDX, T, TOK_NUM]
cond_score = (cond_num_score,
cond_col_score, cond_op_score, cond_str_score)
return cond_score
def _get_svqb(
self,
U, # Tensor
drop, # bool
tau # float
):
# type: (Tensor, bool, float) -> Tensor
"""Return B-orthonormal U.
.. note:: When `drop` is `False` then `svqb` is based on the
Algorithm 4 from [DuerschPhD2015] that is a slight
modification of the corresponding algorithm
introduced in [StathopolousWu2002].
Arguments:
U (Tensor) : initial approximation, size is (m, n)
drop (bool) : when True, drop columns that
contribution to the `span([U])` is small.
tau (float) : positive tolerance
Returns:
U (Tensor) : B-orthonormal columns (:math:`U^T B U = I`), size
is (m, n1), where `n1 = n` if `drop` is `False,
otherwise `n1 <= n`.
"""
if torch.numel(U) == 0:
return U
UBU = _utils.qform(self.B, U)
d = UBU.diagonal(0, -2, -1)
# Detect and drop exact zero columns from U. While the test
# `abs(d) == 0` is unlikely to be True for random data, it is
# possible to construct input data to lobpcg where it will be
# True leading to a failure (notice the `d ** -0.5` operation
# in the original algorithm). To prevent the failure, we drop
# the exact zero columns here and then continue with the
# original algorithm below.
nz = torch.where(abs(d) != 0.0)
assert len(nz) == 1, nz
if len(nz[0]) < len(d):
U = U[:, nz[0]]
if torch.numel(U) == 0:
return U
UBU = _utils.qform(self.B, U)
d = UBU.diagonal(0, -2, -1)
nz = torch.where(abs(d) != 0.0)
assert len(nz[0]) == len(d)
# The original algorithm 4 from [DuerschPhD2015].
d_col = (d**-0.5).reshape(d.shape[0], 1)
DUBUD = (UBU * d_col) * _utils.transpose(d_col)
E, Z = _utils.symeig(DUBUD, eigenvectors=True)
t = tau * abs(E).max()
if drop:
keep = torch.where(E > t)
assert len(keep) == 1, keep
E = E[keep[0]]
Z = Z[:, keep[0]]
d_col = d_col[keep[0]]
else:
E[(torch.where(E < t))[0]] = t
return torch.matmul(U * _utils.transpose(d_col), Z * E**-0.5)
def __call__(self, data_view1, data_view2): H1 = data_view1.view(data_view1.size(0)*data_view1.size(1),data_view1.size(2),data_view1.size(3)) H2 = data_view2.view(data_view2.size(0)*data_view2.size(1),data_view2.size(2),data_view2.size(3)) r1 = 1e-4 r2 = 1e-4 eps = 1e-12 corr_sum = 0 o1 = o2 = H1.size(1) m = H1.size(0) n = H1.size(1) H1bar = H1 - (1.0 / m) * H1 H2bar = H2 - (1.0 / m) * H2 Hat12 = torch.zeros(m,n,n).cuda() Hat11 = torch.zeros(m,n,n).cuda() Hat22 = torch.zeros(m,n,n).cuda() #Hat12 = torch.zeros(m,n,n) #Hat11 = torch.zeros(m,n,n) #Hat22 = torch.zeros(m,n,n) for i in range(m): Hat11[i] = torch.matmul(H1bar[i],H1bar.transpose(1,2)[i]) Hat12[i] = torch.matmul(H1bar[i],H2bar.transpose(1,2)[i]) Hat22[i] = torch.matmul(H2bar[i],H2bar.transpose(1,2)[i]) SigmaHat12 = (1.0 / (m - 1)) * torch.mean(Hat12,dim=0) SigmaHat11 = (1.0 / (m - 1)) * torch.mean(Hat11,dim=0)+ r1 * torch.eye(o1, device=self.device) SigmaHat22 = (1.0 / (m - 1)) * torch.mean(Hat22,dim=0) + r2 * torch.eye(o2, device=self.device) # Calculating the root inverse of covariance matrices by using eigen decomposition [D1, V1] = torch.symeig(SigmaHat11, eigenvectors=True) [D2, V2] = torch.symeig(SigmaHat22, eigenvectors=True) # Added to increase stability posInd1 = torch.gt(D1, eps).nonzero()[:, 0] D1 = D1[posInd1] V1 = V1[:, posInd1] posInd2 = torch.gt(D2, eps).nonzero()[:, 0] D2 = D2[posInd2] V2 = V2[:, posInd2] SigmaHat11RootInv = torch.matmul( torch.matmul(V1, torch.diag(D1 ** -0.5)), V1.t()) SigmaHat22RootInv = torch.matmul( torch.matmul(V2, torch.diag(D2 ** -0.5)), V2.t()) Tval = torch.matmul(torch.matmul(SigmaHat11RootInv, SigmaHat12), SigmaHat22RootInv) if self.use_all_singular_values: # all singular values are used to calculate the correlation corr = torch.sqrt(torch.trace(torch.matmul(Tval.t(), Tval))) else: # just the top self.outdim_size singular values are used U, V = torch.symeig(torch.matmul(Tval.t(), Tval), eigenvectors=True) U = U[torch.gt(U, eps).nonzero()[:, 0]] U = U.topk(self.outdim_size)[0] corr = torch.sum(torch.sqrt(U)) return -corr
def _AXWb(A, X, W, b):
X = th.matmul(X, W)
Y = th.matmul(A, X.view(X.shape[0], -1)).view_as(X)
return Y + b
def projection_transR_pytorch(original, proj_matrix):
ent_embedding_size = original.shape[1]
rel_embedding_size = proj_matrix.shape[1] // ent_embedding_size
original = original.view(-1, ent_embedding_size, 1)
proj_matrix = proj_matrix.view(-1, rel_embedding_size, ent_embedding_size)
return torch.matmul(proj_matrix, original).view(-1, rel_embedding_size)
print()
# scalar multiplication
print('Scalar Multiplication in Numpy', 3*A)
print('Scalar Multiplication in PyTorch', 3*A_tensor)
print()
# elementwise multiplication
print('Elementwise Multiplication in Numpy', A*B)
print('Elementiwse Multiplication in PyTorch', A_tensor*B_tensor)
print()
# matrix multiplication
# this is slightly different from NumPy
print('Elementwise Multiplication in Numpy', A@B)
print('Elementiwse Multiplication in PyTorch', torch.matmul(A_tensor, B_tensor))
print()
# Elementwise comparison
print('NumPy: ', A == B)
print('PyTorch: ', A_tensor == B_tensor)
print()
# Generate a random matrix
C = np.array([[10, 9, 8], [6, 7, 5], [1, 2, 3]])
C_tensor = torch.Tensor(C)
print('C', C)
print()
# Sum along the row
def forward(self, x):
r"""
The :func:`~gpytorch.variational.VariationalStrategy.forward` method determines how to marginalize out the
inducing point function values. Specifically, forward defines how to transform a variational distribution
over the inducing point values, :math:`q(u)`, in to a variational distribution over the function values at
specified locations x, :math:`q(f|x)`, by integrating :math:`\int p(f|x, u)q(u)du`
:param torch.Tensor x: Locations x to get the variational posterior of the function values at.
:rtype: ~gpytorch.distributions.MultivariateNormal
:return: The distribution :math:`q(f|x)`
"""
variational_dist = self.variational_distribution
inducing_points = self.inducing_points
if inducing_points.dim() < x.dim():
inducing_points = inducing_points.expand(*x.shape[:-2], *inducing_points.shape[-2:])
if len(variational_dist.batch_shape) < x.dim() - 2:
variational_dist = variational_dist.expand(x.shape[:-2])
# If our points equal the inducing points, we're done
if torch.equal(x, inducing_points):
# De-whiten the prior covar
prior_covar = self.prior_distribution.lazy_covariance_matrix
if isinstance(variational_dist.lazy_covariance_matrix, RootLazyTensor):
predictive_covar = RootLazyTensor(prior_covar @ variational_dist.lazy_covariance_matrix.root.evaluate())
else:
predictive_covar = MatmulLazyTensor(prior_covar @ variational_dist.covariance_matrix, prior_covar)
# Cache some values for the KL divergence
if self.training:
self._mean_diff_inv_quad_memo, self._logdet_memo = prior_covar.inv_quad_logdet(
(variational_dist.mean - self.prior_distribution.mean), logdet=True
)
return MultivariateNormal(variational_dist.mean, predictive_covar)
# Otherwise, we have to marginalize
else:
num_induc = inducing_points.size(-2)
full_inputs = torch.cat([inducing_points, x], dim=-2)
full_output = self.model.forward(full_inputs)
full_mean, full_covar = full_output.mean, full_output.lazy_covariance_matrix
# Mean terms
test_mean = full_mean[..., num_induc:]
induc_mean = full_mean[..., :num_induc]
mean_diff = (variational_dist.mean - induc_mean).unsqueeze(-1)
# Covariance terms
induc_induc_covar = full_covar[..., :num_induc, :num_induc].add_jitter()
induc_data_covar = full_covar[..., :num_induc, num_induc:].evaluate()
data_data_covar = full_covar[..., num_induc:, num_induc:]
# If we're less than a certain size, we'll compute the Cholesky decomposition of induc_induc_covar
cholesky = False
if settings.fast_computations.log_prob.off() or (num_induc <= settings.max_cholesky_size.value()):
induc_induc_covar = CholLazyTensor(induc_induc_covar.cholesky())
cholesky = True
# Cache the CG results
# Do not use preconditioning for whitened VI, as it does not seem to improve performance.
with settings.max_preconditioner_size(0):
with torch.no_grad():
eager_rhs = torch.cat([induc_data_covar, mean_diff], -1)
solve, probe_vecs, probe_vec_norms, probe_vec_solves, tmats = CachedCGLazyTensor.precompute_terms(
induc_induc_covar,
eager_rhs.detach(),
logdet_terms=(not cholesky),
include_tmats=(not settings.skip_logdet_forward.on() and not cholesky),
)
eager_rhss = [eager_rhs.detach()]
solves = [solve.detach()]
if settings.skip_logdet_forward.on() and self.training:
eager_rhss.append(torch.cat([probe_vecs, eager_rhs], -1))
solves.append(torch.cat([probe_vec_solves, solve[..., : eager_rhs.size(-1)]], -1))
elif not self.training:
eager_rhss.append(eager_rhs[..., :-1])
solves.append(solve[..., :-1])
induc_induc_covar = CachedCGLazyTensor(
induc_induc_covar,
eager_rhss=eager_rhss,
solves=solves,
probe_vectors=probe_vecs,
probe_vector_norms=probe_vec_norms,
probe_vector_solves=probe_vec_solves,
probe_vector_tmats=tmats,
)
# Compute some terms that will be necessary for the predicitve covariance and KL divergence
if self.training:
interp_data_data_var_plus_mean_diff_inv_quad, logdet = induc_induc_covar.inv_quad_logdet(
torch.cat([induc_data_covar, mean_diff], -1), logdet=True, reduce_inv_quad=False
)
interp_data_data_var = interp_data_data_var_plus_mean_diff_inv_quad[..., :-1]
mean_diff_inv_quad = interp_data_data_var_plus_mean_diff_inv_quad[..., -1]
# Compute predictive mean
predictive_mean = torch.add(
test_mean,
induc_induc_covar.inv_matmul(mean_diff, left_tensor=induc_data_covar.transpose(-1, -2)).squeeze(-1),
)
# Compute the predictive covariance
is_root_lt = isinstance(variational_dist.lazy_covariance_matrix, RootLazyTensor)
is_repeated_root_lt = isinstance(
variational_dist.lazy_covariance_matrix, BatchRepeatLazyTensor
) and isinstance(variational_dist.lazy_covariance_matrix.base_lazy_tensor, RootLazyTensor)
if is_root_lt:
predictive_covar = RootLazyTensor(
induc_data_covar.transpose(-1, -2) @ variational_dist.lazy_covariance_matrix.root.evaluate()
)
elif is_repeated_root_lt:
predictive_covar = RootLazyTensor(
induc_data_covar.transpose(-1, -2)
@ variational_dist.lazy_covariance_matrix.root_decomposition().root.evaluate()
)
else:
predictive_covar = MatmulLazyTensor(
induc_data_covar.transpose(-1, -2), predictive_covar @ induc_data_covar
)
if self.training:
data_covariance = DiagLazyTensor((data_data_covar.diag() - interp_data_data_var).clamp(0, math.inf))
else:
neg_induc_data_data_covar = torch.matmul(
induc_data_covar.transpose(-1, -2).mul(-1), induc_induc_covar.inv_matmul(induc_data_covar)
)
data_covariance = data_data_covar + neg_induc_data_data_covar
predictive_covar = PsdSumLazyTensor(predictive_covar, data_covariance)
# Save the logdet, mean_diff_inv_quad, prior distribution for the ELBO
if self.training:
self._memoize_cache["prior_distribution_memo"] = MultivariateNormal(induc_mean, induc_induc_covar)
self._memoize_cache["logdet_memo"] = -logdet
self._memoize_cache["mean_diff_inv_quad_memo"] = mean_diff_inv_quad
return MultivariateNormal(predictive_mean, predictive_covar)
def score(self, h, t, r):
# view as matrix
R = r.view(*r.shape[:-1], self.embedding_dim, self.embedding_dim)
product = torch.matmul(h.unsqueeze(-2), R)
return torch.matmul(product, t.unsqueeze(-1)).squeeze()
def mtimes(a: torch.Tensor, b: torch.Tensor, conj_a=False, conj_b=False):
"""Complex matrix multiplication of complex tensors.
The dimensions (-3, -2) are matrix multiplied. -1 is the complex dimension."""
if is_real(a):
if a.dim() >= b.dim():
raise ValueError('Incorrect dimensions.')
return mtimes_real_complex(a, b, conj_b=conj_b)
if is_real(b):
if b.dim() >= a.dim():
raise ValueError('Incorrect dimensions.')
return mtimes_complex_real(a, b, conj_a=conj_a)
if not conj_a and not conj_b:
return complex(torch.matmul(a[..., 0], b[..., 0]) - torch.matmul(a[..., 1], b[..., 1]),
torch.matmul(a[..., 0], b[..., 1]) + torch.matmul(a[..., 1], b[..., 0]))
if conj_a and not conj_b:
return complex(torch.matmul(a[..., 0], b[..., 0]) + torch.matmul(a[..., 1], b[..., 1]),
torch.matmul(a[..., 0], b[..., 1]) - torch.matmul(a[..., 1], b[..., 0]))
if not conj_a and conj_b:
return complex(torch.matmul(a[..., 0], b[..., 0]) + torch.matmul(a[..., 1], b[..., 1]),
torch.matmul(a[..., 1], b[..., 0]) - torch.matmul(a[..., 0], b[..., 1]))
if conj_a and conj_b:
return complex(torch.matmul(a[..., 0], b[..., 0]) - torch.matmul(a[..., 1], b[..., 1]),
-torch.matmul(a[..., 0], b[..., 1]) - torch.matmul(a[..., 1], b[..., 0]))
def generate_transformer_attr(self,
input,
index=None,
method_name="transformer_attr"):
kwargs = {"alpha": 1}
output = self.model_usage.forward(input).question_answering_score
model = self.model_usage.model
# initialize relevancy matrices
text_tokens = self.model_usage.text_len
image_bboxes = self.model_usage.image_boxes_len
# text self attention matrix
self.R_t_t = torch.eye(text_tokens, text_tokens).to(model.device)
# image self attention matrix
self.R_i_i = torch.eye(image_bboxes, image_bboxes).to(model.device)
# impact of images on text
self.R_t_i = torch.zeros(text_tokens, image_bboxes).to(model.device)
# impact of text on images
self.R_i_t = torch.zeros(image_bboxes, text_tokens).to(model.device)
if index == None:
index = np.argmax(output.cpu().data.numpy(), axis=-1)
one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
one_hot[0, index] = 1
one_hot_vector = one_hot
one_hot = torch.from_numpy(one_hot).requires_grad_(True)
one_hot = torch.sum(one_hot.cuda() * output)
model.zero_grad()
one_hot.backward(retain_graph=True)
model.relprop(torch.tensor(one_hot_vector).to(output.device), **kwargs)
# language self attention
blocks = model.lxmert.encoder.layer
for blk in blocks:
grad = blk.attention.self.get_attn_gradients().detach()
cam = blk.attention.self.get_attn_cam().detach()
cam = avg_heads(cam, grad)
self.R_t_t += torch.matmul(cam, self.R_t_t)
# image self attention
blocks = model.lxmert.encoder.r_layers
for blk in blocks:
grad = blk.attention.self.get_attn_gradients().detach()
cam = blk.attention.self.get_attn_cam().detach()
cam = avg_heads(cam, grad)
self.R_i_i += torch.matmul(cam, self.R_i_i)
# cross attn layers
blocks = model.lxmert.encoder.x_layers
for i, blk in enumerate(blocks):
# in the last cross attention module, only the text cross modal
# attention has an impact on the CLS token, since it's the first
# token in the language tokens
if i == len(blocks) - 1:
break
# language self attention
grad = blk.lang_self_att.self.get_attn_gradients().detach()
cam = blk.lang_self_att.self.get_attn_cam().detach()
cam = avg_heads(cam, grad)
self.R_t_t += torch.matmul(cam, self.R_t_t)
# image self attention
grad = blk.visn_self_att.self.get_attn_gradients().detach()
cam = blk.visn_self_att.self.get_attn_cam().detach()
cam = avg_heads(cam, grad)
self.R_i_i += torch.matmul(cam, self.R_i_i)
# take care of last cross attention layer- only text
blk = model.lxmert.encoder.x_layers[-1]
# cross attn cam will be the one used for the R_t_i matrix
cam_t_i = blk.visual_attention.att.get_attn_cam().detach()
grad_t_i = blk.visual_attention.att.get_attn_gradients().detach()
cam_t_i = avg_heads(cam_t_i, grad_t_i)
# self.R_t_i = torch.matmul(self.R_t_t.t(), torch.matmul(cam_t_i, self.R_i_i))
self.R_t_i = cam_t_i
# language self attention
grad = blk.lang_self_att.self.get_attn_gradients().detach()
cam = blk.lang_self_att.self.get_attn_cam().detach()
cam = avg_heads(cam, grad)
self.R_t_t += torch.matmul(cam, self.R_t_t)
self.R_t_t[0, 0] = 0
return self.R_t_t, self.R_t_i
def routing(self, x):
"""
Routing algorithm for capsule.
:input: tensor x of shape [128, 8, 1152]
:return: vector output of capsule j
"""
batch_size = x.size(0)
#print ('routing, batch_size:', batch_size)
#print (x.shape)
x = x.transpose(1, 2) # dim 1 and dim 2 are swapped. out tensor shape: [128, 1152, 8]
# Stacking and adding a dimension to a tensor.
# stack ops output shape: [128, 1152, 10, 8]
# unsqueeze ops output shape: [128, 1152, 10, 8, 1]
x = torch.stack([x] * self.num_unit, dim=2).unsqueeze(4)
# Convert single weight to batch weight.
# [1 x 1152 x 10 x 16 x 8] to: [128, 1152, 10, 16, 8]
batch_weight = torch.cat([self.weight] * batch_size, dim=0)
# u_hat is "prediction vectors" from the capsules in the layer below.
# Transform inputs by weight matrix.
# Matrix product of 2 tensors with shape: [128, 1152, 10, 16, 8] x [128, 1152, 10, 8, 1]
# u_hat shape: [128, 1152, 10, 16, 1]
u_hat = torch.matmul(batch_weight, x)
# All the routing logits (b_ij in the paper) are initialized to zero.
# self.in_channel = primary_unit_size = 32 * 6 * 6 = 1152
# self.num_unit = num_classes = 10
# b_ij shape: [1, 1152, 10, 1]
b_ij = Variable(torch.zeros(1, self.in_channel, self.num_unit, 1))
if self.cuda_enabled:
b_ij = b_ij.cuda()
# From the paper in the "Capsules on MNIST" section,
# the sample MNIST test reconstructions of a CapsNet with 3 routing iterations.
num_iterations = self.num_routing
for iteration in range(num_iterations):
# Routing algorithm
# Calculate routing or also known as coupling coefficients (c_ij).
# c_ij shape: [1, 1152, 10, 1]
c_ij = utils.softmax(b_ij, dim=2) # Convert routing logits (b_ij) to softmax.
# c_ij shape from: [128, 1152, 10, 1] to: [128, 1152, 10, 1, 1]
c_ij = torch.cat([c_ij] * batch_size, dim=0).unsqueeze(4)
# Implement equation 2 in the paper.
# s_j is total input to a capsule, is a weigthed sum over all "prediction vectors".
# u_hat is weighted inputs, prediction ˆuj|i made by capsule i.
# c_ij * u_hat shape: [128, 1152, 10, 16, 1]
# s_j output shape: [batch_size=128, 1, 10, 16, 1]
# Sum of Primary Capsules outputs, 1152D becomes 1D.
s_j = (c_ij * u_hat).sum(dim=1, keepdim=True)
# Squash the vector output of capsule j.
# v_j shape: [batch_size, weighted sum of PrimaryCaps output,
# num_classes, output_unit_size from u_hat, 1]
# == [128, 1, 10, 16, 1]
# So, the length of the output vector of a capsule is 16, which is in dim 3.
v_j = utils.squash(s_j, dim=3)
# in_channel is 1152.
# v_j1 shape: [128, 1152, 10, 16, 1]
v_j1 = torch.cat([v_j] * self.in_channel, dim=1)
# The agreement.
# Transpose u_hat with shape [128, 1152, 10, 16, 1] to [128, 1152, 10, 1, 16],
# so we can do matrix product u_hat and v_j1.
# u_vj1 shape: [1, 1152, 10, 1]
u_vj1 = torch.matmul(u_hat.transpose(3, 4), v_j1).squeeze(4).mean(dim=0, keepdim=True)
# Update routing (b_ij) by adding the agreement to the initial logit.
b_ij = b_ij + u_vj1
return v_j.squeeze(1) # shape: [128, 10, 16, 1]
def D(dat, Theta):
c = torch.matmul(dat[:, 0:p], Theta.t())
c = torch.clamp(c, -20, 20)
out = -1.0 * (1 - dat[:, p].reshape(n, 1)) * c - torch.log(1.0 +
torch.exp(-c))
return out
def layer_A1(self):
G = defaultdict(lambda: {})
N = self.config.N
M = self.config.M
tau = self.config.tau
mtsp_instance = self.mtsp_instance
graph = mtsp_instance.graph
depot = mtsp_instance.depot
for i, v in enumerate(graph.V):
for j, u in enumerate(graph.V):
# append g_ij
vu_dist = graph[v][u] / 1000
xv_dist = graph[depot][v] / 1000
xu_dist = graph[depot][u] / 1000
a = torch.FloatTensor([vu_dist, xv_dist, xu_dist]).to(device)
G[v][u] = torch.matmul(self.W1,
F.relu(torch.matmul(self.W2, a)))
# print(G[v][u])
T = self.config.T
# random mu initialization
mu = {}
next_mu = {}
for v in mtsp_instance.remaining_cities:
mu[v] = torch.rand(M, 1).to(device)
next_mu[v] = None
dist_from_robot = mtsp_instance.distance_from_robot()
# main loop for struct2vec
for t in range(T):
for v in mtsp_instance.remaining_cities:
Z = sum([
exp(G[u][v] / tau) for u in mtsp_instance.remaining_cities
if v != u
])
p = [
exp(G[u][v] / tau) / Z
for u in mtsp_instance.remaining_cities if v != u
] # softmax of G given tau
l_not_weighted = [torch.cat(\
(graph[u][v] * F.relu(torch.matmul(self.W5, mu[v])), mu[u])) \
for u in mtsp_instance.remaining_cities if u != v]
# print(l_not_weighted[0].shape, 123213)
l = sum([p_uv * l_uv for p_uv, l_uv in zip(p, l_not_weighted)])
a = torch.matmul(self.W3_A1, l)
next_mu[v] = F.relu(torch.matmul(self.W3_A1, l) \
+ torch.matmul(self.W4_A1,
torch.FloatTensor([[dist_from_robot[v]]]).to(device)))
mu = next_mu
return mu
def rpsm(cams, heatmaps, boxes, grid_center, limb_length, pairwise_constraint,
config, **kwargs):
"""
Args:
cams : camera parameters for each view
heatmaps: 2d pose heatmaps (n, k, h, w)
boxes: on which the heatmaps are computed; n dictionaries
grid_center: 3d location of the root
limb_length: template limb length
pairwise_constrain: pre-computed pairwise terms (iteration 0 psm only)
Returns:
pose3d: 3d pose
"""
image_size = config.NETWORK.IMAGE_SIZE
heatmap_size = config.NETWORK.HEATMAP_SIZE
first_nbins = config.PICT_STRUCT.FIRST_NBINS
recur_nbins = config.PICT_STRUCT.RECUR_NBINS
recur_depth = config.PICT_STRUCT.RECUR_DEPTH
grid_size = config.PICT_STRUCT.GRID_SIZE
tolerance = config.PICT_STRUCT.LIMB_LENGTH_TOLERANCE
# Iteration 1: discretizing 3d space
# body = HumanBody()
# current_device = torch.device('cuda:{}'.format(pairwise_constraint.values()[0].get_device()))
current_device = kwargs['current_device']
body = kwargs['human_body']
grid = compute_grid(grid_size, grid_center, first_nbins)
heatmaps = torch.as_tensor(heatmaps, dtype=torch.float32).to(
current_device) # todo: do this in dataloader
extra_kwargs = kwargs
# PSM
do_bone_vectors = False
if 'do_bone_vectors' in kwargs:
if kwargs['do_bone_vectors']:
do_bone_vectors = True
bone_vectors = kwargs['bone_vectors']
if do_bone_vectors:
# merge limb length pairwise and bone orientation/vector pairwise term
orient_pairwise = kwargs['orient_pairwise']
new_pairwise_constrain = {}
for node in body.skeleton:
current = node['idx']
children = node['children']
bone_index = node['imubone']
for idx_child, child in enumerate(children):
constrain_array = pairwise_constraint[(current, child)]
if bone_index[idx_child] >= 0: # if certain bone has imu
expect_orient_vector = bone_vectors[bone_index[idx_child]]
expect_orient_vector = torch.as_tensor(
expect_orient_vector,
dtype=torch.float32).to(current_device)
norm_expect_orient_vector = expect_orient_vector / (
torch.norm(expect_orient_vector) + 1e-9)
norm_expect_orient_vector = norm_expect_orient_vector.view(
-1) # (3,)
acutal_orient_vector = orient_pairwise # (4096, 4096, 3)
cos_theta = torch.matmul(acutal_orient_vector,
-norm_expect_orient_vector)
# todo we can add cos_theta activation func here
# acutal_orient_vector refer to 2 bin direction
# norm_expect_orient_vector refer to groundtruth direction
constrain_array = torch.mul(constrain_array, cos_theta)
new_pairwise_constrain[(current, child)] = constrain_array
pairwise_constraint = new_pairwise_constrain
unary = compute_unary_term(heatmaps, [grid], boxes, cams, image_size)
pose3d_as_cube_idx = infer(unary, pairwise_constraint, body, config,
**extra_kwargs)
pose3d = get_loc_from_cube_idx([grid], pose3d_as_cube_idx)
cur_grid_size = grid_size / first_nbins
for i in range(recur_depth):
pose3d = recursive_infer(pose3d, cams, heatmaps, boxes, image_size,
heatmap_size, body, limb_length,
cur_grid_size, recur_nbins, tolerance, config,
**extra_kwargs)
cur_grid_size = cur_grid_size / recur_nbins
return pose3d
def forward(self):
A1 = self.layer_A1()
A2 = self.layer_A2()
layer_B_input = {}
for v in self.mtsp_instance.remaining_cities:
a = A1[v]
b = A2[v]
layer_B_input[v] = torch.cat((a, b))
G = defaultdict(lambda: {})
N = self.config.N
M = self.config.M
tau = self.config.tau
mtsp_instance = self.mtsp_instance
graph = mtsp_instance.graph
depot = mtsp_instance.depot
for i, v in enumerate(graph.V):
for j, u in enumerate(graph.V):
# append g_ij
vu_dist = graph[v][u] / 1000
xv_dist = graph[depot][v] / 1000
xu_dist = graph[depot][u] / 1000
a = torch.FloatTensor([vu_dist, xv_dist, xu_dist]).to(device)
G[v][u] = torch.matmul(self.W1,
F.relu(torch.matmul(self.W2, a)))
# print(G[v][u])
T = self.config.T
# random mu initialization
mu = {}
next_mu = {}
for v in mtsp_instance.remaining_cities:
mu[v] = torch.rand(M, 1)
next_mu[v] = None
# main loop for struct2vec
for t in range(T):
for v in mtsp_instance.remaining_cities:
Z = sum([
exp(G[u][v] / tau) for u in mtsp_instance.remaining_cities
if v != u
])
p = [
exp(G[u][v] / tau) / Z
for u in mtsp_instance.remaining_cities if v != u
] # softmax of G given tau
l_not_weighted = [
mu[u] for u in mtsp_instance.remaining_cities if u != v
]
l = sum([p_uv * l_uv for p_uv, l_uv in zip(p, l_not_weighted)])
next_mu[v] = F.relu(\
torch.matmul(self.W3_B, l) \
+ torch.matmul(self.W4_B, layer_B_input[v]))
# print(layer_B_input[v].shape, 123)
mu = next_mu
return F.relu(
torch.matmul(self.W7,
sum([mu[v] for v in mtsp_instance.remaining_cities])))
def forward(
self,
wid_label: torch.
LongTensor, # shape B string number -> int number
wiki_title_meta: List[str], # len B list
mention_surface_meta: List[str], # len B list
mention_sent_lt: torch.
FloatTensor, # shape B x seq_len x 300 (word2vec)
mention_sent_rt: torch.
FloatTensor, # shape B x seq_len x 300 (word2vec)
type_labels: torch.
LongTensor, # shap B x seq_len --> dense one hot vector from reader [no padding]
coherence_labels: List[List[int]], #
mention_sent_lt_len: List[int],
mention_sent_rt_len: List[int],
cross_wiki_candidates: List[List[int]],
cross_wiki_priors: List[List[float]]):
batch_size = wid_label.shape[0]
mask_left = self.make_mask(batch_size, mention_sent_lt.shape[1],
mention_sent_lt_len)
sent_lt_encoded = self.left_seq2vec(
mention_sent_lt,
mask=mask_left) # build mask manually for ArrayField
mask_right = self.make_mask(batch_size, mention_sent_rt.shape[1],
mention_sent_rt_len)
sent_rt_encoded = self.right_seq2vec(mention_sent_rt, mask=mask_right)
sent = torch.cat(
(sent_lt_encoded, sent_rt_encoded),
dim=1).to(self.device) # dim0 is batch no adding batch
v_local = self.ff_seq2vecs(sent) # B x 200 --> B x 100
if self.use_coherence:
v_coh_batch = torch.zeros((batch_size, 100)).to(self.device)
for i in range(batch_size):
coherence_ind = torch.LongTensor(coherence_labels[i]).to(
self.device)
v_coh1 = self.coherence_embedder(coherence_ind)
v_coh2 = torch.sum(v_coh1, dim=0).view(1, -1).to(self.device)
v_coh_batch[i, :] = v_coh2
v_coh_batch = self.coherence_embedder_relu(
v_coh_batch) # todo check
v_local = torch.cat((v_local, v_coh_batch), dim=1)
v_local = self.ff_context(v_local)
if self.use_type:
v_type = self.ff_type(type_labels)
loss = 0.0
v_e_list = []
for i in range(batch_size):
entity_id = torch.LongTensor(cross_wiki_candidates[i]).to(
self.device) # 1 x C (C > 1)
# this C, num classes changes per example pad zero/unk_wid?
target = torch.LongTensor([0]).to(self.device)
v_e = self.entity_embedder(entity_id) # C x 200
v_local_cur = v_local[i].view(-1, 1) # 200 x 1
score = torch.matmul(v_e, v_local_cur).view(1, -1) # 1 x C
temp = self.loss_context(score, target)
loss += temp
v_e_list.append(v_e)
output_dict = {
'loss': loss
} #, 'v_local': v_local, 'wid_label': wid_label }
# 'candidates': cross_wiki_candidates, 'priors': cross_wiki_priors,
# , 'v_e_list': v_e_list }
# compute accuray metrics
#with torch.no_grad():
# max_candidates = max( (len(t) for t in cross_wiki_candidates) )
# predictions_per_batch = []
# true_labels = wid_label
# for i in range(batch_size): # i is for batch
# # list of prior probabilities len > 1 list 1 x C
# cur_prior = torch.FloatTensor(cross_wiki_priors[i]).view(1, -1).to(self.device)
# cur_ve = v_e_list[i].view(-1, 200) # C x 200 tensor
# cur_vm = v_local[i].view(200, 1) # 200 x 1 tensor
# prob_text = torch.exp(torch.matmul(cur_ve, cur_vm)).view(-1, 1)
# prob_text = prob_text / torch.sum(prob_text).to(self.device) # C x 1
# temp = torch.zeros(max_candidates).to(self.device)
# prob_text = torch.squeeze(prob_text)
# cur_prior = torch.squeeze(cur_prior)
# temp[:len(cur_prior)] = cur_prior + prob_text - cur_prior * prob_text # when this value can be nan?
# predictions_per_batch.append(temp)
# predictions = torch.stack(predictions_per_batch)
# for metric in self.metrics.values():
# metric(predictions=predictions,
# gold_labels=torch.zeros(len(true_labels))) # true label is located at zero
return output_dict
def knn(x, k):
inner = -2 * torch.matmul(x.transpose(2, 1), x)
xx = torch.sum(x**2, dim=1, keepdim=True)
pairwise_distance = -xx - inner - xx.transpose(2, 1)
idx = pairwise_distance.topk(k=k, dim=-1)[1]
return idx
def prob_h_given_v(self, v):
return (torch.matmul(v, self.weights.data.t(), out=None).add_(
self.hidden_bias.data).sigmoid_().clamp_(min=0, max=1))
def gnn(self, xs, A, layer):
for i in range(layer):
hs = torch.relu(self.W_gnn[i](xs))
xs = xs + torch.matmul(A, hs)
# return torch.unsqueeze(torch.sum(xs, 0), 0)
return torch.unsqueeze(torch.mean(xs, 0), 0)
def forward(self, x, w):
return torch.matmul(x, w)
def sample_single_animal_x(self, z, x_pre, animal_idx, with_noise,
**kwargs):
"""
:param z: a scalar
:param x_pre: (1, 2)
:param animal_idx
:param with_noise:
:return: (2, )
"""
assert x_pre.shape == (1, 2), x_pre.shape
A = kwargs.get("A_a", None) if animal_idx == 0 else kwargs.get(
"A_b", None)
Sigma = kwargs.get("Sigma_a", None) if animal_idx == 0 else kwargs.get(
"Sigma_b", None)
if A is None:
#print("Not using cache. Calculating Sigma, A...")
Sigma, A = self.get_gp_cache_condition_on_z(x_pre,
z,
animal_idx,
A_only=not with_noise,
**kwargs)
assert A.shape == (1, 2, self.n_gps * 2)
A = torch.squeeze(A, dim=0)
u = self.us[z, :, 0:2] if animal_idx == 0 else self.us[z, :, 2:4]
assert u.shape == (self.n_gps, 2), \
"the correct shape is {}, instead we got {}".format((self.n_gps, 2), u.shape)
u = torch.reshape(u, (-1, )) # (n_gps*2, )
# (2, n_gps*2) * (n_gps*2, 1) -> (2, 1)
mu = torch.matmul(A, u[..., None])
assert mu.shape == (2, 1), mu.shape
mu = torch.squeeze(mu, dim=-1) # (2, )
mu = mu + x_pre[0]
assert mu.shape == (2, )
if not with_noise:
return mu
assert Sigma.shape == (1, 2, 2)
Sigma = torch.squeeze(Sigma, dim=0)
# (2,)
sigma = torch.exp(
self.log_sigmas[z, 0:2]) if animal_idx == 0 else torch.exp(
self.log_sigmas[z, 2:4])
assert sigma.shape == (2, ), sigma.shape
sigma = torch.diag(sigma)
assert sigma.shape == (2, 2), sigma.shape
cov = Sigma + sigma
m = MultivariateNormal(mu, cov)
sample = m.sample()
assert sample.shape == (2, )
return sample
def forward(self, x):
B, C, H, W = x.size()
x_plus_pos = torch.cat([x, self.pos_emb.repeat(B, 1, 1, 1)], dim=1)
K = self.K_nn(x_plus_pos).view(B, self.n_heads, self.dim_per_head, H,
W)
Q = self.Q_nn(x_plus_pos).view(B, self.n_heads, self.dim_per_head, H,
W)
V = self.V_nn(x_plus_pos).view(B, self.n_heads, self.dim_per_head, H,
W)
QK_W = torch.matmul(Q.permute(0, 1, 3, 4, 2), K.permute(0, 1, 3, 2, 4))
# QK_W_old = torch.sum(
# Q.view(B, self.n_heads, self.dim_per_head, H, W, 1) *
# K.view(B, self.n_heads, self.dim_per_head, H, 1, W), dim=2
# )
# assert QK_W.size() == (B, self.n_heads, H, W, W)
QK_H = torch.matmul(
Q.permute(0, 1, 4, 3, 2),
K.permute(0, 1, 4, 2, 3),
).permute(0, 1, 3, 2, 4)
# QK_H_old = torch.sum(
# Q.view(B, self.n_heads, self.dim_per_head, H, 1, W) *
# K.view(B, self.n_heads, self.dim_per_head, 1, H, W), dim=2
# ).permute(0, 1, 2, 4, 3)
# print(torch.mean(torch.abs(QK_H - QK_H_old)))
# assert QK_H.size() == (B, self.n_heads, H, W, H)
# Shape: [B, heads, dims, H, W, W+H]
QK = torch.cat([QK_W, QK_H], dim=-1)
# assert QK.size() == (B, self.n_heads, H, W, W + H)
QK = F.softmax(QK / np.sqrt(W + H), dim=-1)
QK_W = QK[..., :W]
# assert QK_W.size() == (B, self.n_heads, H, W, W)
QK_H = QK[..., W:]
# assert QK_H.size() == (B, self.n_heads, H, W, H)
# V_W = V.view(B, self.n_heads, self.dim_per_head, H, 1, W)
# V_H = V.permute(0, 1, 2, 4, 3).view(B, self.n_heads, self.dim_per_head, 1, W, H)
# A_old = torch.sum(
# QK_W.view(B, self.n_heads, 1, H, W, W) * V_W, dim=-1) \
# + torch.sum(
# QK_H.view(B, self.n_heads, 1, H, W, H) * V_H, dim=-1
# )
A_W = torch.matmul(QK_W, V.permute(0, 1, 3, 4,
2)).permute(0, 1, 4, 2, 3)
QK_H = QK_H.permute(0, 1, 3, 2, 4)
V_H = V.permute(0, 1, 4, 3, 2)
A_H = torch.matmul(QK_H, V_H).permute(0, 1, 4, 3, 2)
A = (A_W + A_H).contiguous()
# assert A.size() == (B, self.n_heads, self.dim_per_head, H, W)
A = A.view(B, self.n_heads * self.dim_per_head, H, W)
return x + self.linear(A)
def generate_rollout(self, input, method_name="rollout"):
output = self.model_usage.forward(input).question_answering_score
model = self.model_usage.model
# initialize relevancy matrices
text_tokens = self.model_usage.text_len
image_bboxes = self.model_usage.image_boxes_len
# text self attention matrix
self.R_t_t = torch.eye(text_tokens, text_tokens).to(model.device)
# image self attention matrix
self.R_i_i = torch.eye(image_bboxes, image_bboxes).to(model.device)
# impact of images on text
self.R_t_i = torch.zeros(text_tokens, image_bboxes).to(model.device)
# impact of text on images
self.R_i_t = torch.zeros(image_bboxes, text_tokens).to(model.device)
cams_text = []
cams_image = []
# language self attention
blocks = model.lxmert.encoder.layer
for blk in blocks:
cam = blk.attention.self.get_attn().detach()
cam = cam.reshape(-1, cam.shape[-2], cam.shape[-1]).mean(dim=0)
cams_text.append(cam)
# image self attention
blocks = model.lxmert.encoder.r_layers
for blk in blocks:
cam = blk.attention.self.get_attn().detach()
cam = cam.reshape(-1, cam.shape[-2], cam.shape[-1]).mean(dim=0)
cams_image.append(cam)
# cross attn layers
blocks = model.lxmert.encoder.x_layers
for i, blk in enumerate(blocks):
# in the last cross attention module, only the text cross modal
# attention has an impact on the CLS token, since it's the first
# token in the language tokens
if i == len(blocks) - 1:
break
# language self attention
cam = blk.lang_self_att.self.get_attn().detach()
cam = cam.reshape(-1, cam.shape[-2], cam.shape[-1]).mean(dim=0)
cams_text.append(cam)
# image self attention
cam = blk.visn_self_att.self.get_attn().detach()
cam = cam.reshape(-1, cam.shape[-2], cam.shape[-1]).mean(dim=0)
cams_image.append(cam)
# take care of last cross attention layer- only text
blk = model.lxmert.encoder.x_layers[-1]
# cross attn cam will be the one used for the R_t_i matrix
cam_t_i = blk.visual_attention.att.get_attn().detach()
cam_t_i = cam_t_i.reshape(-1, cam_t_i.shape[-2],
cam_t_i.shape[-1]).mean(dim=0)
self.R_t_t = compute_rollout_attention(copy.deepcopy(cams_text))
self.R_i_i = compute_rollout_attention(cams_image)
self.R_t_i = torch.matmul(self.R_t_t.t(),
torch.matmul(cam_t_i, self.R_i_i))
# language self attention
cam = blk.lang_self_att.self.get_attn().detach()
cam = cam.reshape(-1, cam.shape[-2], cam.shape[-1]).mean(dim=0)
cams_text.append(cam)
self.R_t_t = compute_rollout_attention(cams_text)
# disregard the [CLS] token itself
self.R_t_t[0, 0] = 0
return self.R_t_t, self.R_t_i
def forward(self, context_idxs, ques_idxs, context_char_idxs, ques_char_idxs, context_lens, start_mapping, end_mapping, all_mapping, return_yp=False):
para_size, ques_size, char_size, bsz = context_idxs.size(1), ques_idxs.size(1), context_char_idxs.size(2), context_idxs.size(0)
context_mask = (context_idxs > 0).float()
ques_mask = (ques_idxs > 0).float()
context_ch = self.char_emb(context_char_idxs.contiguous().view(-1, char_size)).view(bsz * para_size, char_size, -1)
ques_ch = self.char_emb(ques_char_idxs.contiguous().view(-1, char_size)).view(bsz * ques_size, char_size, -1)
context_ch = self.char_cnn(context_ch.permute(0, 2, 1).contiguous()).max(dim=-1)[0].view(bsz, para_size, -1)
ques_ch = self.char_cnn(ques_ch.permute(0, 2, 1).contiguous()).max(dim=-1)[0].view(bsz, ques_size, -1)
context_word = self.word_emb(context_idxs)
ques_word = self.word_emb(ques_idxs)
# logging("Start",self.config.save)
# logging("context word "+str(context_word.shape),self.config.save)
# logging("context ch"+str(context_ch.shape),self.config.save)
# logging("question word"+str(ques_word.shape),self.config.save)
# logging("question ch"+str(context_word.shape),self.config.save)
context_output = torch.cat([context_word, context_ch], dim=2)
ques_output = torch.cat([ques_word, ques_ch], dim=2)
# logging("context output"+str(context_output.shape),self.config.save)
# logging("ques output"+str(ques_output.shape),self.config.save)
#
context_output = self.rnn(context_output, context_lens)
ques_output = self.rnn(ques_output)
# logging("context output after RNN"+str(context_output.shape),self.config.save)
# logging("ques output after RNN"+str(ques_output.shape),self.config.save)
#
output = self.qc_att(context_output, ques_output, ques_mask)
# logging("attension output"+str(output.shape),self.config.save)
output = self.linear_1(output)
# logging("attension linear output"+str(output.shape),self.config.save)
# Sentence branch
sentence_embeddings , sentence_mask = self.mean_pooling_module(all_mapping ,output ,self.config.save)
# logging("sentence embeddings "+str(sentence_embeddings.shape),self.config.save)
# sentence_rnn_out = self.rnn_sentence(sentence_embeddings)
# logging("sentence rnn output "+str(sentence_rnn_out.shape),self.config.save)
# logging("sentece mask "+str(sentence_mask.shape),self.config.save)
sentence_self_attension_out = self.self_att_sentences(sentence_embeddings,sentence_embeddings,sentence_mask)
# logging("sentence self output "+str(sentence_self_attension_out.shape),self.config.save)
# self.self_att_sentences
sentence_embeddings = self.linear_sent_att(sentence_self_attension_out)
sentence_embeddings = self.rnn_sentence(sentence_embeddings)
# end
output_t = self.rnn_2(output, context_lens)
# logging("context lens "+str(context_lens.shape),self.config.save)
# logging("output size " + str(output.shape),self.config.save)
# logging("output 2nd RNN"+str(output_t.shape),self.config.save)
output_t = self.self_att(output_t, output_t, context_mask)
# logging("context_mask "+str(context_mask.shape),self.config.save)
# logging("mask example 1 "+str(torch.sum(context_mask[0,:])),self.config.save)
# logging("mask example 2 "+str(torch.sum(context_mask[1,:])),self.config.save)
# logging("mask example 3 "+str(torch.sum(context_mask[2,:])),self.config.save)
# logging("mask example 4 "+str(torch.sum(context_mask[3,:])),self.config.save)
# logging("self attension output"+str(output_t.shape),self.config.save)
output_t = self.linear_2(output_t)
# logging("linear after self attension"+str(output_t.shape),self.config.save)
output = output + output_t
# logging("sum bi-attension and self-attension"+str(output.shape),self.config.save)
sp_output = self.rnn_sp(output, context_lens)
# logging("supporting fact RNN"+str(output.shape),self.config.save)
start_output = torch.matmul(start_mapping.permute(0, 2, 1).contiguous(), sp_output[:,:,self.hidden:])
# logging("start output sp"+str(start_output.shape),self.config.save)
end_output = torch.matmul(end_mapping.permute(0, 2, 1).contiguous(), sp_output[:,:,:self.hidden])
# logging("end output sp"+str(end_output.shape),self.config.save)
sp_output = torch.cat([start_output, end_output], dim=-1)
# logging("sp output "+str(sp_output.shape),self.config.save)
sp_output = torch.cat([sp_output, sentence_embeddings], dim=-1)
# logging("sp output sentence "+str(sp_output_sentence.shape),self.config.save)
sp_output_t = self.linear_sp(sp_output)
# logging("sp output after linear"+str(sp_output.shape),self.config.save)
sp_output_aux = Variable(sp_output_t.data.new(sp_output_t.size(0), sp_output_t.size(1), 1).zero_())
# logging("sp output aux"+str(sp_output_aux.shape),self.config.save)
predict_support = torch.cat([sp_output_aux, sp_output_t], dim=-1).contiguous()
# logging("predict_support"+str(predict_support.shape),self.config.save)
return predict_support
def complex_matmul(A_real, A_imag, B_real, B_imag):
real = torch.matmul(A_real, B_real) - torch.matmul(A_imag, B_imag)
imag = torch.matmul(A_real, B_imag) + torch.matmul(A_imag, B_real)
return (real, imag)