def forward(self, x, batch=None):
device = x.device
if batch is None:
x = x - x.mean()
out = x / (x.std(unbiased=False) + self.eps)
else:
batch_size = int(batch.max()) + 1
batch_idx = [batch == i for i in range(batch_size)]
norm = torch.tensor([i.sum() for i in batch_idx],
dtype=x.dtype).clamp_(min=1).to(device)
norm = norm.mul_(x.size(-1)).view(-1, 1)
tmp_list = [x[i] for i in batch_idx]
mean = torch.concat([i.sum(0).unsqueeze(0) for i in tmp_list],
dim=0).sum(dim=-1, keepdim=True).to(device)
mean = mean / norm
x = x - mean.index_select(0, batch.long())
var = torch.concat([(i * i).sum(0).unsqueeze(0) for i in tmp_list],
dim=0).sum(dim=-1, keepdim=True).to(device)
var = var / norm
out = x / (var + self.eps).sqrt().index_select(0, batch.long())
if self.weight is not None and self.bias is not None:
out = out * self.weight + self.bias
return out
def vjp(self, input_, target, runs):
def vjp_(self, input_, target):
self.zero_grad()
x = input_ if self.input_ == None else self.input_
y = target if self.target == None else self.target
h = c = None
# get predictions (forward pass)
y_hat, h, c = self(x, h, c, runs=None)
self.loss = torch.mean((y_hat - y)**2)
# backprop
self.loss.backward(gradient=torch.tensor(1.0))
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
for i in range(runs):
vjp_(self, input_, target)
torch.cuda.synchronize()
end.record()
torch.cuda.synchronize()
d = {n: p.grad for n, p in self.named_parameters()}
self.grads = { 'weight_ih_l0': torch.concat([torch.transpose(g, 0, 1) for g in [d['W_i'], d['W_f'], d['W_j'], d['W_o']]])
, 'weight_hh_l0': torch.concat([torch.transpose(g, 0, 1) for g in [d['U_i'], d['U_f'], d['U_j'], d['U_o']]])
, 'bias_ih_l0' : torch.concat([d['b_ii'], d['b_if'], d['b_ij'], d['b_io']])
, 'bias_hh_l0' : torch.concat([d['b_hi'], d['b_hf'], d['b_hj'], d['b_ho']])
, 'weight' : torch.transpose(d['W_y'], 0, 1)
, 'bias' : d['b_y']
}
return start.elapsed_time(end) / runs
def learn(self):
for _ in range(self.nb_gradient_steps):
# gradient_step() function in RL5 notebook
if len(self.replay_buffer) > self.batch_size:
# NEW, samples from buffer contains goals
states, actions, rewards, new_states, dones, goals = self.replay_buffer.sample(
self.batch_size)
# NEW concatenate states and goals, because we need to put them inside our model
goal_conditioned_states = torch.concat((states, goals), dim=-1)
goal_conditioned_new_states = torch.concat((new_states, goals),
dim=-1)
q_prime = self.target_model(goal_conditioned_new_states).max(
1)[0].detach()
update = rewards + self.gamma * (1 - dones) * q_prime
q_s_a = self.model(goal_conditioned_states).gather(
1,
actions.to(torch.long).unsqueeze(1))
loss = self.criterion(q_s_a, update.unsqueeze(1))
self.model.learn(loss)
# update target network if needed
if self.time_step_id % self.update_target_freq == 0:
self.target_model.load_state_dict(self.model.state_dict())
def ae_fit_transform(x, enc_dim, batch_size=256, max_epochs=3000, patience=250, num_workers=0):
# Create train datasets and data loaders
train_data = TensorDataset(torch.tensor(x), torch.tensor(x))
train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size, num_workers=num_workers)
# Instantiate model
model = LitAutoEncoder(input_dim=x.shape[1], enc_dim=enc_dim)
# Callbacks
early_stop_callback = EarlyStopping(monitor="train_loss", patience=patience, mode="min")
# Train model
trainer = pl.Trainer(max_epochs=max_epochs, callbacks=[early_stop_callback], devices="auto", accelerator="auto",
logger=True, enable_checkpointing=False)
trainer.fit(model, train_loader)
# Transform data
model.mode = "encode"
test_enc_data = TensorDataset(torch.tensor(x))
test_enc_loader = torch.utils.data.DataLoader(test_enc_data, shuffle=False, batch_size=batch_size, num_workers=num_workers)
x_enc = trainer.predict(model, dataloaders=test_enc_loader)
x_enc = torch.concat(x_enc, dim=0).detach().cpu().numpy()
# Inverse transform
model.mode = "decode"
test_dec_data = TensorDataset(torch.tensor(x_enc))
test_dec_loader = torch.utils.data.DataLoader(test_dec_data, shuffle=False, batch_size=batch_size, num_workers=num_workers)
x_dec = trainer.predict(model, dataloaders=test_dec_loader)
x_dec = torch.concat(x_dec, dim=0).detach().cpu().numpy()
return x_enc, x_dec
def learn(self):
if len(self.replay_buffer) > self.batch_size:
states, actions, rewards, new_states, dones = self.replay_buffer.sample(
self.batch_size)
with torch.no_grad():
target_actions = self.target_actor.forward(new_states)
critic_value_ = self.target_critic.forward(
torch.concat((new_states, target_actions), dim=-1))
critic_value = self.critic.forward(
torch.concat((states, actions), dim=-1))
target = torch.addcmul(rewards, self.gamma, 1 - dones,
critic_value_.squeeze()).view(
self.batch_size, 1)
self.critic.optimizer.zero_grad()
critic_loss = torch.nn.functional.mse_loss(target, critic_value)
critic_loss.backward()
self.critic.optimizer.step()
self.actor.optimizer.zero_grad()
actions = self.actor.forward(states)
actor_loss = -self.critic.forward(
torch.concat((states, actions), dim=-1))
actor_loss = torch.mean(actor_loss)
actor_loss.backward()
self.actor.optimizer.step()
self.target_critic.converge_to(self.critic, tau=self.tau)
self.target_actor.converge_to(self.actor, tau=self.tau)
def get_environment(n_atoms, grid=None):
if n_atoms == 1:
neighborhood_idx = -torch.ones((1, 1), dtype=torch.float32)
offsets = torch.zeros((n_atoms, 1, 3), dtype=torch.float32)
else:
neighborhood_idx = torch.arange(
n_atoms, dtype=torch.float32).unsqueeze(0).repeat(n_atoms, 1)
neighborhood_idx = neighborhood_idx[
~torch.eye(n_atoms, dtype=torch.long).byte()].view(
n_atoms, n_atoms - 1).long()
if grid is not None:
n_grid = grid.shape[0]
neighborhood_idx = torch.concat(
[neighborhood_idx, -torch.ones((n_atoms, 1))], 1)
grid_nbh = torch.tile(
torch.arange(n_atoms, dtype=torch.float32).unsqueeze(-1),
(n_grid, 1))
neighborhood_idx = torch.concat([neighborhood_idx, grid_nbh], 0)
offsets = torch.zeros(
(neighborhood_idx.shape[0], neighborhood_idx.shape[1], 3),
dtype=torch.float32)
return neighborhood_idx, offsets
def q_train(make_obs_ph_n,
act_space_n,
q_index,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
scope="trainer",
reuse=None,
num_units=64):
with tf.variable_scope(scope, reuse=reuse):
# create distribtuions
act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n]
# set up placeholders
obs_ph_n = make_obs_ph_n
act_ph_n = [
act_pdtype_n[i].sample_placeholder([None], name="action" + str(i))
for i in range(len(act_space_n))
]
target_ph = tf.placeholder(tf.float32, [None], name="target")
q_input = tf.concat(obs_ph_n + act_ph_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[q_index], act_ph_n[q_index]], 1)
q = q_func(q_input, 1, scope="q_func", num_units=num_units)[:, 0]
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
q_loss = tf.reduce_mean(tf.square(q - target_ph))
# viscosity solution to Bellman differential equation in place of an initial condition
q_reg = tf.reduce_mean(tf.square(q))
loss = q_loss #+ 1e-3 * q_reg
optimize_expr = U.minimize_and_clip(optimizer, loss, q_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph],
outputs=loss,
updates=[optimize_expr])
q_values = U.function(obs_ph_n + act_ph_n, q)
# target network
target_q = q_func(q_input,
1,
scope="target_q_func",
num_units=num_units)[:, 0]
target_q_func_vars = U.scope_vars(
U.absolute_scope_name("target_q_func"))
update_target_q = make_update_exp(q_func_vars, target_q_func_vars)
target_q_values = U.function(obs_ph_n + act_ph_n, target_q)
return train, update_target_q, {
'q_values': q_values,
'target_q_values': target_q_values
}
def forward(self, input1, input2):
point_idx_in = input1.get_spatial_locations()[:, -1]
max_idx = torch.max(point_idx_in).max().item()
assert max_idx % self.frames
batch_size = 1 + max_idx
tmp_f = torch.concat([input1.features, input2.features])
tmp_c = input2.get_spatial_locations()
tmp_c[:, -1] = tmp_c[:, -1] + batch_size
tmp_c = torch.concat([input1.get_spatial_locations(), tmp_c])
return self.input([tmp_c, tmp_f])
def logits(self, x, i=None):
y = x
a = self.adapt_ws[i or 0]
if a is not None:
y = torch.einsum("bih,ph->bip", y, a)
t = self.table_ws[i or 0]
b = self.table_bs[i or 0]
if i == 0:
t = torch.concat([t, self.clust_w], 0)
b = torch.concat([b, self.clust_b], 0)
y = torch.einsum("bie,ne->bin", y, t) + b
return y
def create_mask(qlen, mlen, dtype=torch.float32, same_length=False):
"""Creates attention mask when single-side context allowed only."""
attn_mask = torch.ones([qlen, qlen], dtype=dtype)
mask_u = torch.triu(attn_mask, 0, -1)
mask_dia = torch.triu(attn_mask, 0, 0)
attn_mask_pad = torch.zeros([qlen, mlen], dtype=dtype)
ret = torch.concat([attn_mask_pad, mask_u - mask_dia], 1)
if same_length:
mask_l = torch.triu(attn_mask, -1, 0)
ret = torch.concat([ret[:, :qlen] + mask_l - mask_dia, ret[:, qlen:]],
1)
return ret
def forward(self, x):
enc1 = self.enc1(x)
enc2 = self.enc2(enc1)
enc3 = self.enc3(enc2)
enc4 = self.enc4(enc3)
enc5 = self.enc5(enc4)
dec5 = self.dec5(enc5)
dec4 = self.dec4(torch.concat(enc4, dec5, 1))
dec3 = self.dec3(torch.concat(enc3, dec4, 1))
dec2 = self.dec2(torch.concat(enc2, dec3, 1))
dec1 = self.dec1(torch.concat(enc1, dec2, 1))
return self.final(dec1)
def forward_bboxes(self,
bboxes: torch.FloatTensor,
image_tensor=None,
width_height=None) -> torch.FloatTensor:
"""Applies a transformation on Image coordinate defined bounding boxes.
Bounding Boxes are encoded as [Left, Top, Right, Bottom]
Args:
bboxes (torch.FloatTensor) : A tensor with bboxes for a sample [N x 4] or a batch [S x N x 4]
image_tensor (torch.FloatTensor): A valid batch image tensor [S x C x H x C] or sample image tensor
[C x H x W]. In both cases it only used to normalise bbox coordinates and can be omitted if width_height
is specified.
width_height (int, int ): Values used to normalise bbox coordinates to [-1,1] and back, should be omitted if
image tensor is passed
Returns: a tensor with the bounding boxes of the transformed bounding box.
"""
if len(bboxes.size()) == 2:
bboxes = bboxes.unsqueeze(dim=0)
if image_tensor is not None:
width = image_tensor.size(-1)
height = image_tensor.size(-2)
else:
width, height = width_height
normalise_bboxes = torch.tensor(
[[width * .5, height * .5, width * .5, height * .5]])
normalised_bboxes = bboxes / normalise_bboxes - 1
bboxes_left = normalised_bboxes[:, 0].unsqueeze(dim=0).unsqueeze(dim=0)
bboxes_top = normalised_bboxes[:, 1].unsqueeze(dim=0).unsqueeze(dim=0)
bboxes_right = normalised_bboxes[:,
2].unsqueeze(dim=0).unsqueeze(dim=0)
bboxes_bottom = normalised_bboxes[:,
3].unsqueeze(dim=0).unsqueeze(dim=0)
bboxes_x = torch.concat(
[bboxes_left, bboxes_right, bboxes_right, bboxes_left], dim=1)
bboxes_y = torch.concat(
[bboxes_top, bboxes_top, bboxes_bottom, bboxes_bottom], dim=1)
pointcloud = (bboxes_x, bboxes_y)
pointcloud = self.forward_pointcloud(pointcloud)
pointcloud = torch.clamp(pointcloud[0], -1,
1), torch.clamp(pointcloud[0], -1, 1)
left = ((pointcloud[0].min(dim=1) + 1) * .5 * width).view(- [1, 1])
right = ((pointcloud[0].max(dim=1) + 1) * .5 * width).view(- [1, 1])
top = ((pointcloud[1].min(dim=1) + 1) * .5 * height).view(- [1, 1])
bottom = ((pointcloud[1].max(dim=1) + 1) * .5 * height).view(- [1, 1])
result_bboxes = torch.concat([left, top, right, bottom], dim=1)
return result_bboxes
def load_torchscript_model(dummy_input):
loaded_model = torch.jit.load(TS_MODEL_PATH)
loaded_model.eval()
dummy_input_tensor = torch.concat(dummy_input) # dtype = torch.int64
all_encoder_layers, pooled_output = loaded_model(dummy_input_tensor)
print(all_encoder_layers) # dtype = tprch.float32
def simulate(self, control_idx, batch_size):
inputs = np.random.uniform(low=self.low,
high=self.high,
size=(1, self.num_nodes, 1, 1))
# Input vars are constant over the trajectory, and same for all datapoints in one batch
# Controlled var is constant over the trajectory but different for all datapoints in one batch
inputs = inputs.repeat(
(self.batch_size, self.num_nodes, self.trajectory_len, 1))
control = np.random.uniform(low=self.control_low,
high=control_high,
size=(batch_size, 1, 1, 1))
control = inputs.repeat((batch_size, 1, self.trajectory_len, 1))
inputs[:, control_idx, :, :] = control
targets = self.func(inputs)
data = torch.concat((inputs, targets), dim=-1)
outputs = torch.zeros(
(data.size(0), data.size(1), data.size(2), 1 + data.size(1)))
outputs[:, :, :, 0] = data
# Add one hot encoding
for i in range(1, outputs.size(-1)):
outputs[:, :, :, i] = 1
return outputs
def tensor_indexing_ops(self):
x = torch.randn(2, 4)
y = torch.randn(2, 4, 2)
t = torch.tensor([[0, 0], [1, 0]])
mask = x.ge(0.5)
i = [0, 1]
return (
torch.cat((x, x, x), 0),
torch.concat((x, x, x), 0),
torch.conj(x),
torch.chunk(x, 2),
torch.dsplit(y, i),
torch.column_stack((x, x)),
torch.dstack((x, x)),
torch.gather(x, 0, t),
torch.hsplit(x, i),
torch.hstack((x, x)),
torch.index_select(x, 0, torch.tensor([0, 1])),
torch.masked_select(x, mask),
torch.movedim(x, 1, 0),
torch.moveaxis(x, 1, 0),
torch.narrow(x, 0, 0, 2),
torch.nonzero(x),
torch.permute(x, (0, 1)),
torch.reshape(x, (-1, )),
)
def score(self, target_h, source_hs, source_mask):
# target_h: N, target_dim
# source_hs: seq_len, N, source_dim
# source_mask: seq_len, N
target_h = target_h.unsqueeze(0) # 1, N, target_dim
target_size = (source_hs.size(0), -1, -1) # seq_len, N, target_dim
if self.score_method == "dot":
att = torch.mul(target_h.expand(target_size),
source_hs) # seq_len, N, target_dim
att = torch.sum(att, dim=2) # seq_len, N
elif self.score_method == "general":
att = self.atten(source_hs) # seq_len, N, target_dim
att = torch.mul(target_h.expand(target_size),
att) # seq_len, N, target_dim
att = torch.sum(att, dim=2) # seq_len, N
elif self.score_method == "concat":
target_h = target_h.expand(target_size) # seq_len, N, target_dim
concat = torch.concat((source_hs, target_h),
dim=2) # seq_len, N, source_dim + target_dim
att = self.atten(concat) # seq_len, N, concat_hidden_dim
v = self.v.unsqueeze(0).unsqueeze(0) # 1, 1, concat_hidden_dim
att = torch.mul(v.expand_as(att),
att) # seq_len, N, concat_hidden_dim
att = torch.sum(att, dim=2) # seq_len, N
else:
raise RuntimeError("Unsupported score_method")
return softmax_mask(att, source_mask, dim=0) # seq_len, N
def predict_from_theta(self, theta, C):
"""
Predict labels from a distribution over topics
"""
if self.n_covariates > 0:
if self.covar_emb_dim > 0:
c_emb = self.covar_emb_layer(C)
elif self.covar_emb_dim < 0:
c_emb = C
else:
c_emb = C
Y_recon = None
if self.n_labels > 0:
if self.n_covariates > 0:
classifier_input = torch.concat([theta, c_emb], dim=1)
else:
classifier_input = theta
if self.classifier_layers == 0:
decoded_y = self.classifier_layer_0(classifier_input)
elif self.classifier_layers == 1:
cls0 = self.classifier_layer_0(classifier_input)
cls0_sp = F.softplus(cls0)
decoded_y = self.classifier_layer_1(cls0_sp)
else:
cls0 = self.classifier_layer_0(classifier_input)
cls0_sp = F.softplus(cls0)
cls1 = self.classifier_layer_1(cls0_sp)
cls1_sp = F.softplus(cls1)
decoded_y = self.classifier_layer_1(cls1_sp)
Y_recon = F.softmax(decoded_y, dim=1)
return Y_recon
def test_MultivariateNormalDistributionLoss(center, transformation):
normalizer = TorchNormalizer(center=center, transformation=transformation)
mean = torch.tensor([1.0, 1.0])
std = torch.tensor([0.2, 0.1])
cov_factor = torch.tensor([[0.0], [0.0]])
n = 1000000
loss = MultivariateNormalDistributionLoss()
target = loss.distribution_class(loc=mean,
cov_diag=std**2,
cov_factor=cov_factor).sample((n, ))
target = normalizer.inverse_preprocess(target)
target = target[:, 0]
normalized_target = normalizer.fit_transform(target).view(1, -1)
target_scale = normalizer.get_parameters().unsqueeze(0)
scale = torch.ones_like(normalized_target) * normalized_target.std()
parameters = torch.concat(
[
normalized_target[..., None], scale[..., None],
torch.zeros((1, normalized_target.size(1), loss.rank))
],
dim=-1,
)
rescaled_parameters = loss.rescale_parameters(parameters,
target_scale=target_scale,
encoder=normalizer)
samples = loss.sample(rescaled_parameters, 1)
assert torch.isclose(target.mean(), samples.mean(), atol=3.0, rtol=0.5)
if center: # if not centered, softplus distorts std too much for testing
assert torch.isclose(target.std(), samples.std(), atol=0.1, rtol=0.7)
def merge_dict(*dicts):
""" combine list of dicts, add new keys or concat on same keys
assume dict only union keys only contain only
int/float/list/np.array/torch.tensor/dict
"""
res = {}
for d in dicts:
union_d = {k:v for k, v in d.items() if k in res}
exclusion_d = {k:v for k, v in d.items() if k not in res}
res.update(exclusion_d)
for k, v in union_d.items():
if isinstance(v, (int, float)):
res[k] += v
elif isinstance(v, (list, tuple)):
res[k] += v
elif isinstance(v, np.array):
if len(v.shape) == 0 or v.shape[0] == 1:
# if scalar or global to batch, interpret as addition
res[k] += v
else:
# other wise interpret as concatenation along 1st dim
res[k] = np.concatenate([res[k], v], 0)
elif isinstance(v, torch.Tensor):
if len(v.shape) == 0 or v.shape[0] == 1:
res[k] += v
else:
res[k] = torch.concat([res[k], v], 0)
elif isinstance(v, dict):
res[k] = merge_dict(res[k], v)
return res
def message_passing(nodes,
edges,
edge_features,
message_fn,
edge_keep_prob=1.0):
"""
Pass messages between nodes and sum the incoming messages at each node.
Implements equation 1 and 2 in the paper, i.e. m_{.j}^t &= \sum_{i \in N(j)} f(h_i^{t-1}, h_j^{t-1})
:param nodes: (n_nodes, n_features) tensor of node hidden states.
:param edges: (n_edges, 2) tensor of indices (i, j) indicating an edge from nodes[i] to nodes[j].
:param edge_features: features for each edge. Set to zero if the edges don't have features.
:param message_fn: message function, will be called with input of shape (n_edges, 2*n_features + edge_features). The output shape is (n_edges, n_outputs), where you decide the size of n_outputs
:param edge_keep_prob: The probability by which edges are kept. Basically dropout for edges. Not used in the paper.
:return: (n_nodes, n_output) Sum of messages arriving at each node.
"""
print(f"Nodes: {nodes.size()}")
print(f"Edges: {edges.size()}")
print(f"E-features: {edge_features}")
n_nodes = nodes.size(0)
n_features = nodes.size(1)
n_edges = edges.size(0)
message_inputs = torch.gather(nodes, 1, edges).view(-1, 2 * n_features)
reshaped = torch.concat((message_inputs, edge_features))
messages = message_fn(reshaped)
messages = F.dropout(messages, 1 - edge_keep_prob)
n_output = messages.size(1)
def forward_single(self, x, idx, eval=False, upsampled_size=None):
ins_feat = x
cate_feat = x
# ins branch
# concat coord
x_range = torch.linspace(-1, 1, ins_feat.shape[-1], device=ins_feat.device)
y_range = torch.linspace(-1, 1, ins_feat.shape[-2], device=ins_feat.device)
y, x = torch.meshgrid(y_range, x_range)
y = y.expand([ins_feat.shape[0], 1, -1, -1])
x = x.expand([ins_feat.shape[0], 1, -1, -1])
coord_feat = torch.concat([x, y], 1)
ins_feat = torch.cat([ins_feat, coord_feat], 1)
for i, ins_layer in enumerate(self.ins_convs):
ins_feat = ins_layer(ins_feat)
ins_feat = F.interpolate(ins_feat, scale_factor=2., mode='bilinear')
ins_pred = self.solo_ins_list[idx](ins_feat)
# cate branch
for i, cate_layer in enumerate(self.cate_convs):
if i == self.cate_down_pos: # what's this? NOTE NOTE
seg_num_grid = self.seg_num_grids[idx]
cate_feat = F.interpolate(cate_feat, size=seg_num_grid, mode='binlinear')
cate_feat = cate_layer(cate_feat)
cate_pred = self.solo_cate(cate_feat)
if eval:
ins_pred = F.interpolate(ins_pred.sigmoid(), size=upsampled_size, mode='bilinear')
cate_pred = point_nms(cate_pred.sigmoid(), kernel=2).permute(0, 2, 3, 1) # what's this
return ins_pred, cate_pred
def latent_decoder_fn(y, n_z, is_training=True):
"""The latent decoder function, modelling p(z | y).
Args:
y: Categorical cluster variable, `Tensor` of size `[B, n_y]`.
n_z: int, number of dims of z.
is_training: Boolean, whether to build the training graph or an evaluation
graph.
Returns:
The Gaussian distribution `p(z | y)`.
"""
del is_training # Unused for now.
if len(y.shape) != 2:
raise NotImplementedError('The latent decoder function expects `y` to be '
'2D, but its shape was %s instead.' %
str(y.shape))
lin_mu = nn.Linear(y.shape[1], n_z)
lin_sigma = nn.Linear(y.shape[1], n_z)
mu = lin_mu(y)
sigma = lin_sigma(y)
logits = torch.concat([mu, sigma], axis=1)
return curl_utils.generate_gaussian(
logits=logits, sigma_nonlin='softplus', sigma_param='var')
def sample(self, x, y):
"""
:param x:
:param y:
:return: res
preds
acc
"""
embedding_out = self.embedding_layer(x)
encoder_outputs = self.encoder(embedding_out)
# begin
b = torch.ones(x.size(0), 1) * (-2)
x = torch.concat((b, x), 1)
embed_out = self.embedding_layer(x)
res = []
preds = []
for i in range(self.multi_num):
out, h = self.decoder(embed_out[:, i], h, encoder_outputs)
res = self.output_layer(out).squeeze()
res.append(res)
preds.append(nn.softmax(res, -1))
res = torch.stack(res)
preds = torch.stack(pred)
acc = self.compute_accuary(preds, y)
return res, preds, acc
def extract(data_loader, features=None, labels=None):
self.feature_net.eval()
for i, inputs in enumerate(data_loader):
if self.task_type == 'classification':
inputs, label = split_data(inputs)
elif self.task_type == 'reid':
inputs, pids, cams = split_data(inputs)
outputs = self.feature_net(inputs)
if features is None:
features = outputs
else:
features = torch.concat(features, outputs)
if labels is None:
labels = label
else:
labels = torch.concat(labels, outputs)
return features, labels
def forward(self, x1, x2):
'''
1. stack sentence vector similarity
2. for layer_size
abcnn1
convolution
stack sentence vector similarity
W-ap for next loop x1, x2
3. concatenate similarity list
size (batch_size, layer_size + 1)
4. Linear layer
size (batch_size, 1)
Parameters
----------
x1, x2 : 4-D torch Tensor
size (batch_size, 1, sentence_length, emb_dim)
Returns
-------
output : 2-D torch Tensor
size (batch_size, 1)
'''
# sim = []
# sim.append(self.distance(self.ap[0](x1), self.ap[0](x2)))
representation_x1 = self.ap[0](x1)
representation_x2 = self.ap[0](x2)
for i in range(self.layer_size):
x1, x2 = self.abcnn1[i](x1, x2)
x1 = self.conv[i](x1)
x2 = self.conv[i](x2)
# sim.append(self.distance(self.ap[i + 1](x1), self.ap[i + 1](x2)))
representation_x1 = torch.concat(
(representation_x1, self.ap[i + 1](x1)), 0)
representation_x2 = torch.concat(
(representation_x2, self.ap[i + 1](x2)), 0)
x1, x2 = self.abcnn2[i](x1, x2)
# sim_fc = torch.cat(sim, dim=1)
# output = self.fc(sim_fc)
return representation_x1, representation_x2
def fftshift(im, axis=0, name="fftshift"):
"""Perform fft shift.
This function assumes that the axis to perform fftshift is divisible by 2.
"""
split0, split1 = torch.split(im, 2, axis=axis)
output = torch.concat((split1, split0), axis=axis)
return output
def forward(self, pos_seq, nbatch=None):
sinusoid_inp = torch.ger(pos_seq, self.inv_freq)
pos_embed = torch.concat(
[sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1)
if nbatch is None:
return pos_embed[:, None, :]
else:
return pos_embed[:, None, :].expand(-1, nbatch, -1)
def forward(self, input, sample):
feature = self.feature(input)
embedding = self.embedding(sample)
if self.reduction == 'residual':
return feature * (1 + embedding)
if self.reduction == 'concat':
return torch.concat((feature, embedding), dim=-1)
return feature * embedding
def tensor_indexing_ops(self):
x = torch.randn(2, 4)
y = torch.randn(4, 4)
t = torch.tensor([[0, 0], [1, 0]])
mask = x.ge(0.5)
i = [0, 1]
return len(
torch.cat((x, x, x), 0),
torch.concat((x, x, x), 0),
torch.conj(x),
torch.chunk(x, 2),
torch.dsplit(torch.randn(2, 2, 4), i),
torch.column_stack((x, x)),
torch.dstack((x, x)),
torch.gather(x, 0, t),
torch.hsplit(x, i),
torch.hstack((x, x)),
torch.index_select(x, 0, torch.tensor([0, 1])),
x.index(t),
torch.masked_select(x, mask),
torch.movedim(x, 1, 0),
torch.moveaxis(x, 1, 0),
torch.narrow(x, 0, 0, 2),
torch.nonzero(x),
torch.permute(x, (0, 1)),
torch.reshape(x, (-1, )),
torch.row_stack((x, x)),
torch.select(x, 0, 0),
torch.scatter(x, 0, t, x),
x.scatter(0, t, x.clone()),
torch.diagonal_scatter(y, torch.ones(4)),
torch.select_scatter(y, torch.ones(4), 0, 0),
torch.slice_scatter(x, x),
torch.scatter_add(x, 0, t, x),
x.scatter_(0, t, y),
x.scatter_add_(0, t, y),
# torch.scatter_reduce(x, 0, t, reduce="sum"),
torch.split(x, 1),
torch.squeeze(x, 0),
torch.stack([x, x]),
torch.swapaxes(x, 0, 1),
torch.swapdims(x, 0, 1),
torch.t(x),
torch.take(x, t),
torch.take_along_dim(x, torch.argmax(x)),
torch.tensor_split(x, 1),
torch.tensor_split(x, [0, 1]),
torch.tile(x, (2, 2)),
torch.transpose(x, 0, 1),
torch.unbind(x),
torch.unsqueeze(x, -1),
torch.vsplit(x, i),
torch.vstack((x, x)),
torch.where(x),
torch.where(t > 0, t, 0),
torch.where(t > 0, t, t),
)
def rescale_parameters(self, parameters: torch.Tensor,
target_scale: torch.Tensor,
encoder: BaseEstimator) -> torch.Tensor:
self._transformation = encoder.transformation
return torch.concat([
parameters,
target_scale.unsqueeze(1).expand(-1, parameters.size(1), -1)
],
dim=-1)
