def _get_single_item(self, index):
start_ind, end_ind, pid, label, camid = self.seqset[index]
imgseq = []
flowseq = []
for ind in range(start_ind, end_ind):
fname = self.identities[pid][camid][ind]
fpath_img = osp.join(self.root[0], fname)
imgrgb = Image.open(fpath_img).convert('RGB')
fpath_flow = osp.join(self.root[1], fname)
flowrgb = Image.open(fpath_flow).convert('RGB')
imgseq.append(imgrgb)
flowseq.append(flowrgb)
while len(imgseq) < self.seq_len:
imgseq.append(imgrgb)
flowseq.append(flowrgb)
seq = [imgseq, flowseq]
if self.transform is not None:
seq = self.transform(seq)
img_tensor = torch.stack(seq[0], 0)
if len(self.root) == 2:
flow_tensor = torch.stack(seq[1], 0)
else:
flow_tensor = None
return img_tensor, flow_tensor, pid, camid
def lk_forward_backward_batch(features, locations, window, steps):
sequence, C, H, W = list(features.size())
seq, num_pts, _ = list(locations.size())
assert seq == sequence, '{:} vs {:}'.format(features.size(), locations.size())
previous_pts = [ locations[0] ]
for iseq in range(1, sequence):
feature_old = features.narrow(0, iseq-1, 1)
feature_new = features.narrow(0, iseq , 1)
nextPts = lk_tensor_track_batch(feature_old, feature_new, previous_pts[iseq-1], window, steps, None)
previous_pts.append(nextPts)
fback_pts = [None] * (sequence-1) + [ previous_pts[-1] ]
for iseq in range(sequence-2, -1, -1):
feature_old = features.narrow(0, iseq+1, 1)
feature_new = features.narrow(0, iseq , 1)
backPts = lk_tensor_track_batch(feature_old, feature_new, fback_pts[iseq+1] , window, steps, None)
fback_pts[iseq] = backPts
back_pts = [None] * (sequence-1) + [ locations[-1] ]
for iseq in range(sequence-2, -1, -1):
feature_old = features.narrow(0, iseq+1, 1)
feature_new = features.narrow(0, iseq , 1)
backPts = lk_tensor_track_batch(feature_old, feature_new, back_pts[iseq+1] , window, steps, None)
back_pts[iseq] = backPts
return torch.stack(previous_pts), torch.stack(fback_pts), torch.stack(back_pts)
def forward(self, hidden, encoder_outputs, attn_mask):
# Create variable to store attention energies
# hidden is 16 by 512
# encoder_outputs is 16 by 72 by 512
# this just uses the top layer of the 2-layer decoder.
# okay?
hidden = hidden.squeeze(0)
batch_size = hidden.size()[0]
attn_energies = []
for i in range(batch_size):
attn_energies.append(self.score(hidden[i], encoder_outputs[i]))
attn_energies = torch.stack(attn_energies).squeeze(0)
# attn_energies is 32 by 72
if attn_mask is not None:
attn_energies = attn_mask * attn_energies
attn_energies[attn_energies == 0] = -1e10
# i want to mask the attention energies
if attn_mask is None:
attn_energies = attn_energies.view(1, -1)
attn_energies = self.softmax(attn_energies)
context_vectors = []
for i in range(batch_size):
context_vectors.append(torch.matmul(attn_energies[i], encoder_outputs[i]))
context_vectors = torch.stack(context_vectors)
return context_vectors
def process_batch_for_length(self, sequences, c_sequences):
"""
Assemble and pad data.
"""
assert len(sequences) == len(c_sequences)
lengths = Variable(self.tensor_type([len(seq) for seq in sequences]))
max_length = max(len(seq) for seq in sequences)
max_c_length = max(max(len(chars) for chars in seq)
for seq in c_sequences)
def _padded(seq, max_length):
_padded_seq = self.tensor_type(max_length).zero_()
_padded_seq[:len(seq)] = self.tensor_type(seq)
return _padded_seq
sequences = Variable(torch.stack(
[_padded(seq, max_length) for seq in sequences]))
def _padded_char(seq, max_length, max_c_length):
_padded = self.tensor_type(max_length, max_c_length).zero_()
for ind, tok in enumerate(seq):
_padded[ind, :len(tok)] = self.tensor_type(tok)
return _padded
c_sequences = Variable(torch.stack([
_padded_char(seq, max_length, max_c_length)
for seq in c_sequences]))
return (sequences, c_sequences, lengths)
def __getitem__(self, index):
if self.mode == 'test':
img_path, img_name = self.imgs[index]
img = Image.open(os.path.join(img_path, img_name + '.jpg')).convert('RGB')
if self.transform is not None:
img = self.transform(img)
return img_name, img
img_path, mask_path = self.imgs[index]
img = Image.open(img_path).convert('RGB')
if self.mode == 'train':
mask = sio.loadmat(mask_path)['GTcls']['Segmentation'][0][0]
mask = Image.fromarray(mask.astype(np.uint8))
else:
mask = Image.open(mask_path)
if self.joint_transform is not None:
img, mask = self.joint_transform(img, mask)
if self.sliding_crop is not None:
img_slices, mask_slices, slices_info = self.sliding_crop(img, mask)
if self.transform is not None:
img_slices = [self.transform(e) for e in img_slices]
if self.target_transform is not None:
mask_slices = [self.target_transform(e) for e in mask_slices]
img, mask = torch.stack(img_slices, 0), torch.stack(mask_slices, 0)
return img, mask, torch.LongTensor(slices_info)
else:
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
mask = self.target_transform(mask)
return img, mask
def _construct_previous(self, layer, direction, inputs, tree, idx):
if direction == 'up':
oidx = tree.children_idx(idx)
else:
oidx = tree.parents_idx(idx)
if oidx:
h_prev, c_prev = [], []
for i in oidx:
h_prev_i, c_prev_i = self._upward_downward(layer,
direction,
inputs,
tree, i)
h_prev.append(h_prev_i)
c_prev.append(c_prev_i)
h_prev = torch.stack(h_prev, 1)
c_prev = torch.stack(c_prev, 1)
elif inputs.is_cuda:
h_prev = torch.zeros(self.hidden_size, 1).cuda()
c_prev = torch.zeros(self.hidden_size, 1).cuda()
else:
h_prev = torch.zeros(self.hidden_size, 1)
c_prev = torch.zeros(self.hidden_size, 1)
return oidx, (h_prev, c_prev)
def singleTagLoss(pred_tag, keypoints):
"""
associative embedding loss for one image
"""
eps = 1e-6
tags = []
pull = 0
for i in keypoints:
tmp = []
for j in i:
if j[1]>0:
tmp.append(pred_tag[j[0]])
if len(tmp) == 0:
continue
tmp = torch.stack(tmp)
tags.append(torch.mean(tmp, dim=0))
pull = pull + torch.mean((tmp - tags[-1].expand_as(tmp))**2)
if len(tags) == 0:
return make_input(torch.zeros([1]).float()), make_input(torch.zeros([1]).float())
tags = torch.stack(tags)[:,0]
num = tags.size()[0]
size = (num, num, tags.size()[1])
A = tags.unsqueeze(dim=1).expand(*size)
B = A.permute(1, 0, 2)
diff = A - B
diff = torch.pow(diff, 2).sum(dim=2)[:,:,0]
push = torch.exp(-diff)
push = (torch.sum(push) - num)
return push/((num - 1) * num + eps) * 0.5, pull/(num + eps)
def predict(self, x_de, x_en):
bs = x_de.size(0)
emb_de = self.embedding_de(x_de) # bs,n_de,word_dim
emb_en = self.embedding_en(x_en) # bs,n_en,word_dim
h = Variable(torch.zeros(self.n_layers*self.directions, bs, self.hidden_dim).cuda())
c = Variable(torch.zeros(self.n_layers*self.directions, bs, self.hidden_dim).cuda())
enc_h, _ = self.encoder(emb_de, (h, c))
dec_h, _ = self.decoder(emb_en, (h, c))
# all the same. enc_h is bs,n_de,hiddensz*n_directions. h and c are both n_layers*n_directions,bs,hiddensz
if self.directions == 2:
enc_h = self.dim_reduce(enc_h) # bs,n_de,hiddensz
scores = torch.bmm(enc_h, dec_h.transpose(1,2))
# (bs,n_de,hiddensz) * (bs,hiddensz,n_en) = (bs,n_de,n_en)
y = [Variable(torch.cuda.LongTensor([sos_token]*bs))] # bs
self.attn = []
for t in range(x_en.size(1)-1): # iterate over english words, with teacher forcing
attn_dist = F.softmax(scores[:,:,t],dim=1) # bs,n_de
self.attn.append(attn_dist.data)
if self.attn_type == "hard":
_, argmax = attn_dist.max(1) # bs. for each batch, select most likely german word to pay attention to
one_hot = Variable(torch.zeros_like(attn_dist.data).scatter_(-1, argmax.data.unsqueeze(1), 1).cuda())
context = torch.bmm(one_hot.unsqueeze(1), enc_h).squeeze(1)
else:
context = torch.bmm(attn_dist.unsqueeze(1), enc_h).squeeze(1)
# the difference btwn hard and soft is just whether we use a one_hot or a distribution
# context is bs,hiddensz
pred = self.vocab_layer(torch.cat([dec_h[:,t,:], context], 1)) # bs,len(EN.vocab)
_, next_token = pred.max(1) # bs
y.append(next_token)
self.attn = torch.stack(self.attn, 0).transpose(0, 1) # bs,n_en,n_de (for visualization!)
y = torch.stack(y,0).transpose(0,1) # bs,n_en
return y,self.attn
def random_sample(batch):
imgids, sentids, imgfeats, textfeats = batch
### image as anchor
anchor_img, positive_text, negative_text = [],[],[]
for i,iid in enumerate(imgids):
for j,iid2 in enumerate(imgids):
if iid!=iid2:
anchor_img.append(imgfeats[i])
positive_text.append(textfeats[i])
negative_text.append(textfeats[j])
anchor_img, positive_text, negative_text = torch.stack(anchor_img), torch.stack(positive_text), torch.stack(negative_text)
### text as anchof
anchor_text, positive_img, negative_img = [],[],[]
for i,iid in enumerate(imgids):
for j,iid2 in enumerate(imgids):
if iid!=iid2:
anchor_text.append(textfeats[i])
positive_img.append(imgfeats[i])
negative_img.append(imgfeats[j])
anchor_text, positive_img, negative_img = torch.stack(anchor_text), torch.stack(positive_img), torch.stack(negative_img)
positive_text = positive_text.type(torch.FloatTensor)
negative_text = negative_text.type(torch.FloatTensor)
return anchor_img, positive_text, negative_text, anchor_text, positive_img, negative_img
def __getitem__(self, index):
img_path, mask_path = self.imgs[index]
img, mask = Image.open(img_path).convert('RGB'), Image.open(mask_path)
mask = np.array(mask)
mask_copy = mask.copy()
for k, v in self.id_to_trainid.items():
mask_copy[mask == k] = v
mask = Image.fromarray(mask_copy.astype(np.uint8))
if self.joint_transform is not None:
img, mask = self.joint_transform(img, mask)
if self.sliding_crop is not None:
img_slices, mask_slices, slices_info = self.sliding_crop(img, mask)
if self.transform is not None:
img_slices = [self.transform(e) for e in img_slices]
if self.target_transform is not None:
mask_slices = [self.target_transform(e) for e in mask_slices]
img, mask = torch.stack(img_slices, 0), torch.stack(mask_slices, 0)
return img, mask, torch.LongTensor(slices_info)
else:
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
mask = self.target_transform(mask)
return img, mask
def forward(self, z_seq, a_seq, term_seq):
# x: [B,2,84,84]
# T = x.size()[0]
h = torch.zeros(1,self.h_size).cuda()
z_losses = []
term_losses = []
for t in range(len(term_seq)-1):
inter = self.encode_az(a_seq[t], z_seq[t])
h = self.update_h(h, inter)
z_pred, term_pred = self.predict_output(h, inter)
z_loss = torch.mean((z_seq[t+1] - z_pred)**2)
term_loss = F.binary_cross_entropy_with_logits(input=term_pred, target=term_seq[t+1])
z_losses.append(z_loss)
term_losses.append(term_loss)
z_loss = torch.mean(torch.stack(z_losses))
term_loss = torch.mean(torch.stack(term_losses))
loss = z_loss + term_loss
return loss, z_loss, term_loss
def default_collate(batch):
"Puts each data field into a tensor with outer dimension batch size"
if torch.is_tensor(batch[0]):
out = None
if _use_shared_memory:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = batch[0].storage()._new_shared(numel)
out = batch[0].new(storage)
return torch.stack(batch, 0, out=out)
elif type(batch[0]).__module__ == 'numpy':
elem = batch[0]
if type(elem).__name__ == 'ndarray':
return torch.stack([torch.from_numpy(b) for b in batch], 0)
if elem.shape == (): # scalars
py_type = float if elem.dtype.name.startswith('float') else int
return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
elif isinstance(batch[0], int):
return torch.LongTensor(batch)
elif isinstance(batch[0], float):
return torch.DoubleTensor(batch)
elif isinstance(batch[0], string_classes):
return batch
elif isinstance(batch[0], collections.Mapping):
return {key: default_collate([d[key] for d in batch]) for key in batch[0]}
elif isinstance(batch[0], collections.Sequence):
transposed = zip(*batch)
return [default_collate(samples) for samples in transposed]
raise TypeError(("batch must contain tensors, numbers, dicts or lists; found {}"
.format(type(batch[0]))))
def predict(self, x, attn_type = "hard"):
#predict with greedy decoding
emb = self.embedding(x)
h = Variable(torch.zeros(1, x.size(0), self.hidden_dim))
c = Variable(torch.zeros(1, x.size(0), self.hidden_dim))
enc_h, _ = self.encoder(emb, (h, c))
y = [Variable(torch.zeros(x.size(0)).long())]
self.attn = []
for t in range(x.size(1)):
emb_t = self.embedding(y[-1])
dec_h, (h, c) = self.decoder(emb_t.unsqueeze(1), (h, c))
scores = torch.bmm(enc_h, dec_h.transpose(1,2)).squeeze(2)
attn_dist = F.softmax(scores, dim = 1)
self.attn.append(attn_dist.data)
if attn_type == "hard":
_, argmax = attn_dist.max(1)
one_hot = Variable(torch.zeros_like(attn_dist.data).scatter_(-1, argmax.data.unsqueeze(1), 1))
context = torch.bmm(one_hot.unsqueeze(1), enc_h).squeeze(1)
else:
context = torch.bmm(attn_dist.unsqueeze(1), enc_h).squeeze(1)
pred = self.vocab_layer(torch.cat([dec_h.squeeze(1), context], 1))
_, next_token = pred.max(1)
y.append(next_token)
self.attn = torch.stack(self.attn, 0).transpose(0, 1)
return torch.stack(y, 0).transpose(0, 1)
def forward(self, input_):
#init hidden state with xavier
vert_state = torch.zeros(input_[0].size(1), self.vert_state_dim).cuda()
edge_state = torch.zeros(input_[1].size(1), self.edge_state_dim).cuda()
'''if self.gpu_mode >= 0:
vert_state = torch.tensor(vert_state.cuda())
edge_state = torch.tensor(edge_state.cuda())'''
batch_size = input_[0].size(0)
vert_input = input_[0]
edge_input = input_[1]
#print('vert and edge input', vert_input.size(), edge_input.size())
vert_state_list = []
edge_state_list = []
#todo: can this be parallelized?
for i in range(batch_size):
torch.nn.init.xavier_uniform(vert_state)
torch.nn.init.xavier_uniform(edge_state)
vert_state = self.vert_gru(vert_input[i], vert_state)
edge_state = self.edge_gru(edge_input[i], edge_state)
#todo: check whether this way is correct, TF code uses a separate global var to keep hidden state
for i in range(self.num_steps):
edge_context = self.get_edge_context(edge_state, vert_state)
vert_context = self.get_vert_context(vert_state, edge_state)
edge_state = self.edge_gru(edge_context, edge_state)
vert_state = self.vert_gru(vert_context, vert_state)
vert_state_list.append(vert_state)
edge_state_list.append(edge_state)
return torch.stack(vert_state_list), torch.stack(edge_state_list)
def rollouts_batch(self, batch):
batch_size = batch.size()[0]
batch_rest = batch.size()[1:]
if batch_size == 1:
obs_batch_v = batch.expand(batch_size * self.n_actions, *batch_rest)
else:
obs_batch_v = batch.unsqueeze(1)
obs_batch_v = obs_batch_v.expand(batch_size, self.n_actions, *batch_rest)
obs_batch_v = obs_batch_v.contiguous().view(-1, *batch_rest)
actions = np.tile(np.arange(0, self.n_actions, dtype=np.int64), batch_size)
step_obs, step_rewards = [], []
for step_idx in range(self.rollout_steps):
actions_t = torch.tensor(actions).to(batch.device)
obs_next_v, reward_v = self.net_em(obs_batch_v, actions_t)
step_obs.append(obs_next_v.detach())
step_rewards.append(reward_v.detach())
# don't need actions for the last step
if step_idx == self.rollout_steps-1:
break
# combine the delta from EM into new observation
cur_plane_v = obs_batch_v[:, 1:2]
new_plane_v = cur_plane_v + obs_next_v
obs_batch_v = torch.cat((cur_plane_v, new_plane_v), dim=1)
# select actions
logits_v, _ = self.net_policy(obs_batch_v)
probs_v = F.softmax(logits_v, dim=1)
probs = probs_v.data.cpu().numpy()
actions = self.action_selector(probs)
step_obs_v = torch.stack(step_obs)
step_rewards_v = torch.stack(step_rewards)
flat_enc_v = self.encoder(step_obs_v, step_rewards_v)
return flat_enc_v.view(batch_size, -1)
def encode(self, article, art_lens=None):
size = (
self._init_enc_h.size(0),
len(art_lens) if art_lens else 1,
self._init_enc_h.size(1)
)
init_enc_states = (
self._init_enc_h.unsqueeze(1).expand(*size),
self._init_enc_c.unsqueeze(1).expand(*size)
)
enc_art, final_states = lstm_encoder(
article, self._enc_lstm, art_lens,
init_enc_states, self._embedding
)
if self._enc_lstm.bidirectional:
h, c = final_states
final_states = (
torch.cat(h.chunk(2, dim=0), dim=2),
torch.cat(c.chunk(2, dim=0), dim=2)
)
init_h = torch.stack([self._dec_h(s)
for s in final_states[0]], dim=0)
init_c = torch.stack([self._dec_c(s)
for s in final_states[1]], dim=0)
init_dec_states = (init_h, init_c)
attention = torch.matmul(enc_art, self._attn_wm).transpose(0, 1)
init_attn_out = self._projection(torch.cat(
[init_h[-1], sequence_mean(attention, art_lens, dim=1)], dim=1
))
return attention, (init_dec_states, init_attn_out)
def forward(self, y_pred, y_true, eps=1e-6):
return NotImplementedError
torch.nn.modules.loss._assert_no_grad(y_true)
assert y_pred.shape[1] == 2
same_left = torch.stack([y_true[:, 0], y_pred[:, 0]], dim=1)
same_left, _ = torch.max(same_left, dim=1)
same_right = torch.stack([y_true[:, 1], y_pred[:, 1]], dim=1)
same_right, _ = torch.min(same_right, dim=1)
same_len = same_right - same_left + 1 # (batch_size,)
same_len = torch.stack([same_len, torch.zeros_like(same_len)], dim=1)
same_len, _ = torch.max(same_len, dim=1)
same_len = same_len.type(torch.float)
pred_len = (y_pred[:, 1] - y_pred[:, 0] + 1).type(torch.float)
true_len = (y_true[:, 1] - y_true[:, 0] + 1).type(torch.float)
pre = same_len / (pred_len + eps)
rec = same_len / (true_len + eps)
f1 = 2 * pre * rec / (pre + rec + eps)
return -torch.mean(f1)
def setUp(self, size=(2, 5), batch=3, dtype=torch.float64, device=None,
seed=None, mu=None, cov=None, A=None, b=None):
'''Test the correctness of batch implementation of mean().
This function will stack `[1 * mu, 2 * mu, ..., batch * mu]`.
Then, it will see whether the batch output is accurate or not.
Args:
size: Tuple size of matrix A.
batch: The batch size > 0.
dtype: data type.
device: In which device.
seed: Seed for the random number generator.
mu: To test a specific mean mu.
cov: To test a specific covariance matrix.
A: To test a specific A matrix.
b: To test a specific bias b.
'''
if seed is not None:
torch.manual_seed(seed)
if A is None:
A = torch.rand(size, dtype=dtype, device=device)
if b is None:
b = torch.rand(size[0], dtype=dtype, device=device)
if mu is None:
mu = torch.rand(size[1], dtype=dtype, device=device)
if cov is None:
cov = rand.definite(size[1], dtype=dtype, device=device,
positive=True, semi=False, norm=10**2)
self.A = A
self.b = b
var = torch.diag(cov)
self.batch_mean = torch.stack([(i + 1) * mu for i in range(batch)])
self.batch_cov = torch.stack([(i + 1) * cov for i in range(batch)])
self.batch_var = torch.stack([(i + 1) * var for i in range(batch)])
def plot_rec(x, netEC, netEP, netD):
x_c = x[0]
x_p = x[np.random.randint(1, opt.max_step)]
h_c = netEC(x_c)
h_p = netEP(x_p)
# print('h_c shape: ', h_c.shape)
# print('h p shape: ', h_p.shape)
rec = netD([h_c, h_p])
x_c, x_p, rec = x_c.data, x_p.data, rec.data
fname = '%s/rec/rec_test.png' % (opt.log_dir)
comparison = None
for i in range(len(x_c)):
if comparison is None:
comparison = torch.stack([x_c[i], x_p[i], rec[i]])
else:
new_comparison = torch.stack([x_c[i], x_p[i], rec[i]])
comparison = torch.cat([comparison, new_comparison])
print('comparison: ', comparison.shape)
# row_sz = 5
# nplot = 20
# for i in range(0, nplot - row_sz, row_sz):
# row = [[xc, xp, xr] for xc, xp, xr in zip(x_c[i:i + row_sz], x_p[i:i + row_sz], rec[i:i + row_sz])]
# print('row: ', row)
# to_plot.append(list(itertools.chain(*row)))
# print(len(to_plot[0]))
# utils.save_tensors_image(fname, comparison)
if not os.path.exists(os.path.dirname(fname)):
os.makedirs(os.path.dirname(fname))
save_image(comparison.cpu(), fname, nrow=3)
def collate_fn(self, batch):
'''Pad images and encode targets.
As for images are of different sizes, we need to pad them to the same size.
Args:
batch: (list) of images, cls_targets, loc_targets.
Returns:
padded images, stacked cls_targets, stacked loc_targets.
'''
imgs = [x[0] for x in batch]
boxes = [x[1] for x in batch]
labels = [x[2] for x in batch]
h = w = self.input_size
num_imgs = len(imgs)
inputs = torch.zeros(num_imgs, 3, h, w)
loc_targets = []
cls_targets = []
for i in range(num_imgs):
inputs[i] = imgs[i]
loc_target, cls_target = self.encoder.encode(boxes[i], labels[i], input_size=(w,h))
loc_targets.append(loc_target)
cls_targets.append(cls_target)
return inputs, torch.stack(loc_targets), torch.stack(cls_targets)
def adpW(self,x):
'''
calculate the pairwise_att of everypair of inputs
output_size: (x.size(0),x.size(1)/2)
'''
x = x.detach()
x = self.adp_metric_embedding1(x)
x = self.adp_metric_embedding1_bn(x)
x = F.relu(x)
x = self.adp_metric_embedding2(x)
x = self.adp_metric_embedding2_bn(x)
x = F.relu(x)
x = self.adp_metric_embedding3(x)
x = self.adp_metric_embedding3_bn(x)
x = F.relu(x)
pairwise_att = F.sigmoid(self.adp_metric_embedding4(x))
# x = self.adp_metric_embedding2_bn(x)
diag_matrix1 = []
diag_matrix2 = []
for i in range(x.size(0)):
diag_matrix1.append(torch.diag(pairwise_att[i, :x.size(1)/2]))
for i in range(x.size(0)):
diag_matrix2.append(torch.diag(pairwise_att[i, x.size(1)/2:]))
pairwise_att1 = torch.stack(diag_matrix1)
pairwise_att2 = torch.stack(diag_matrix1)
return pairwise_att1, pairwise_att2
def forward(self, inputs=None, encoder_hidden=None, encoder_outputs=None,
pg_encoder_states=None, function=F.log_softmax, teacher_forcing_ratio=0, context_embedding=None):
ret_dict = dict()
ret_dict[DecoderRNNFB.KEY_ATTN_SCORE] = list()
inputs, batch_size, max_length = self._validate_args(inputs,
encoder_hidden, encoder_outputs,
function, teacher_forcing_ratio)
decoder_hidden = self._init_state(encoder_hidden)
use_teacher_forcing = True if teacher_forcing_ratio == 1 else False
if use_teacher_forcing:
decoder_input = inputs[:, :-1]
decoder_outputs, decoder_output_states, decoder_hidden, attn = \
self.forward_step(decoder_input, pg_encoder_states,
decoder_hidden, encoder_outputs, context_embedding)
else:
decoder_outputs = []
decoder_output_states = []
sequence_symbols = []
lengths = np.array([max_length] * batch_size)
def decode(step, step_output, step_output_state=None, step_attn=None):
if step_output_state is not None:
decoder_outputs.append(step_output)
decoder_output_states.append(step_output_state)
ret_dict[DecoderRNNFB.KEY_ATTN_SCORE].append(step_attn)
symbols = step_output.topk(1)[1]
sequence_symbols.append(symbols)
eos_batches = symbols.data.eq(self.eos_id)
if eos_batches.dim() > 0:
eos_batches = eos_batches.cpu().view(-1).numpy()
update_idx = ((lengths > step) & eos_batches) != 0
lengths[update_idx] = len(sequence_symbols)
return symbols
decoder_input = inputs[:, 0].unsqueeze(1)
for di in range(max_length):
decoder_output, decoder_output_state, decoder_hidden, step_attn = \
self.forward_step(decoder_input, pg_encoder_states, decoder_hidden,
encoder_outputs, context_embedding)
# # not allow decoder to output UNK
decoder_output[:, :, 3] = -float('inf')
step_output = decoder_output.squeeze(1)
step_output_state = decoder_output_state.squeeze(1)
symbols = decode(di, step_output, step_output_state, step_attn)
decoder_input = symbols
decoder_outputs = torch.stack(decoder_outputs, dim=1)
decoder_output_states = torch.stack(decoder_output_states, dim=1)
ret_dict[DecoderRNNFB.KEY_SEQUENCE] = sequence_symbols
ret_dict[DecoderRNNFB.KEY_LENGTH] = lengths.tolist()
return decoder_outputs, decoder_output_states, ret_dict
def backward(ctx, grad_output):
input, = ctx.saved_tensors
grad_input = torch.stack((grad_output, torch.zeros_like(grad_output)), dim=len(grad_output.shape))
phase_input = angle(input)
phase_input = torch.stack((torch.cos(phase_input), torch.sin(phase_input)), dim=len(grad_output.shape))
grad_input = multiply_complex(phase_input, grad_input)
return 0.5*grad_input
def collate_fn(self, data):
x, y, lens = zip(*data)
max_len = max(lens)
x = torch.stack(x)[:, :max_len]
y = torch.stack(y)[:, :max_len]
lens = torch.tensor(lens)
return x, y, lens
def post_process_latents(latents):
z_where, z_pres = latents
z_where = [z.cpu() for z in z_where]
z_pres = [z.cpu() for z in z_pres]
z_where_t = torch.stack(z_where).transpose(0, 1)
z_pres_t = torch.stack(z_pres).transpose(0, 1)
out = []
for z_where_i, z_pres_i in zip(z_where_t, z_pres_t):
out.append([z_obj._make(torch.cat([zw.data, zp.data])) for zw, zp in zip(z_where_i, z_pres_i)])
return out
def predict_batchwise(model, dataloader):
with torch.no_grad():
X, Y = zip(*[
[x, y] for X, Y in dataloader
for x, y in zip(
model(X.cuda()).cpu(),
Y
)
])
return torch.stack(X), torch.stack(Y)
def collate_fn(self, data):
x, y, lens = zip(
*sorted(data, key=lambda x: x[-1], reverse=True)
)
max_len = lens[0]
x = torch.stack(x)[:, :max_len]
y = torch.stack(y)[:, :max_len]
lens = torch.tensor(lens)
return x, y, lens
def update(self, done):
# print(self.action_indices)
if done:
# without bootstrap
R_rev = [Variable(T.from_numpy(np.zeros(1, dtype=np.float32)))]
A_rev = [Variable(T.from_numpy(np.zeros(1, dtype=np.float32)))]
self.V_preds.append(0)
else:
# with bootstrap
R_rev = [self.V_preds[-1]]
A_rev = [Variable(T.from_numpy(np.zeros(1, dtype=np.float32)))]
self.R_preds = self.R_preds[:-1] # delete bootstrap element
self.rewards = self.rewards[:-1]
# accumulated rewards
r_rev = self.rewards[::-1]
for r in r_rev:
R_rev.append(r + GAMMA*R_rev[-1])
R = T.stack(R_rev[1:][::-1]) # (TRAIN_INTERVAL, 1)
# advantages
N = len(r_rev)
assert len(self.V_preds) == N+1
for i in range(N):
delta = r_rev[i] + GAMMA*self.V_preds[N-i] - self.V_preds[N-i-1]
A_rev.append(delta + GAMMA*LAMBDA*A_rev[-1])
A = T.stack(A_rev[1:][::-1])
# MBP loss
V_preds = T.stack(self.V_preds[:-1])
R_preds = T.stack(self.R_preds)
#assert len(R) == len(R_preds) == len(V_preds) == len(A)
R_loss = (T.sum((V_preds - R)*(V_preds - R)) + T.sum((R_preds - R)*(R_preds - R))) / 2.
self.mbp_loss = self.mbp_loss + (ALPHA_RETURN * R_loss)
self.mbp_loss = self.mbp_loss * ETA_MBP
# Policy gradient
A_ = 0
H = 0
self.actions = self.actions[1:] # delete initial action
for i in range(N):
log_pi = self.log_pies[i]
# log_pi*T.from_numpy(np.array(self.actions[i]==1).astype("float32"))
# A_ += A[i] * log_pi[self.actions[i]==1]
_t = T.sum(log_pi*T.from_numpy(np.array(self.actions[i]==1).astype("float32"))).view(1)
A_ = A_ + (A[i] * _t)
H = H - T.matmul(T.exp(log_pi), log_pi)
self.policy_loss = self.policy_loss + (A_[0] + ALPHA_ENTROPY*H ) # gradient ascend
self.policy_loss = self.policy_loss * ETA_POLICY
# update
self.mbp_loss_log.append(self.mbp_loss.data)
self.policy_loss_log.append(self.policy_loss.data)
print("(mbp loss, policy loss): ", self.mbp_loss, self.policy_loss)
return self.mbp_loss + self.policy_loss
def tagLoss(tags, keypoints):
"""
accumulate the tag loss for each image in the batch
"""
pushes, pulls = [], []
keypoints = keypoints.cpu().data.numpy()
for i in range(tags.size()[0]):
push, pull = singleTagLoss(tags[i], keypoints[i%len(keypoints)])
pushes.append(push)
pulls.append(pull)
return torch.stack(pushes), torch.stack(pulls)
def custom_collate_fn(batch):
batch = zip(*batch) # transpose
image, label, attributes, \
num_nonzero_attributes = batch
image = torch.stack(image)
label = torch.LongTensor(label)
attributes = torch.stack([torch.LongTensor(a) for a in attributes])
padding_idx = torch.LongTensor(num_nonzero_attributes)
return image, label, attributes, padding_idx
def forward(self, inputs):
x = inputs
# input shape: b,c,h,2w
batch_size, c, h, w = x.size(0), x.size(1), x.size(2), x.size(3) // 2
block_size = h // self.scale
value = self.f_value(x)
query = self.f_query(x)
key = self.f_key(x)
value = torch.stack([value[:, :, :, :w], value[:, :, :, w:]],
4) # B*N*H*W*2
query = torch.stack([query[:, :, :, :w], query[:, :, :, w:]],
4) # B*N*H*W*2
key = torch.stack([key[:, :, :, :w], key[:, :, :, w:]], 4) # B*N*H*W*2
v_list = torch.split(value, block_size, dim=2)
v_locals = torch.cat(v_list, 0)
v_list = torch.split(v_locals, block_size, dim=3)
v_locals = torch.cat(v_list)
q_list = torch.split(query, block_size, dim=2)
q_locals = torch.cat(q_list, 0)
q_list = torch.split(q_locals, block_size, dim=3)
q_locals = torch.cat(q_list)
k_list = torch.split(key, block_size, dim=2)
k_locals = torch.cat(k_list, 0)
k_list = torch.split(k_locals, block_size, dim=3)
k_locals = torch.cat(k_list)
# self-attention func
def func(value_local, query_local, key_local):
batch_size_new = value_local.size(0)
h_local, w_local = value_local.size(2), value_local.size(3)
value_local = value_local.contiguous().view(
batch_size_new, self.in_dim, -1)
query_local = query_local.contiguous().view(
batch_size_new, self.in_dim, -1)
query_local = query_local.permute(0, 2, 1)
key_local = key_local.contiguous().view(batch_size_new,
self.in_dim, -1)
sim_map = torch.bmm(query_local, key_local)
sim_map = self.softmax(sim_map)
context_local = torch.bmm(value_local, sim_map.permute(0, 2, 1))
context_local = context_local.view(batch_size_new, self.in_dim,
h_local, w_local, 2)
return context_local
context_locals = func(v_locals, q_locals, k_locals)
b, c, h, w, _ = context_locals.shape
context_list = torch.split(context_locals, b // self.scale, 0)
context = torch.cat(context_list, dim=3)
context_list = torch.split(context, b // self.scale // self.scale, 0)
context = torch.cat(context_list, dim=2)
context = torch.cat([context[:, :, :, :, 0], context[:, :, :, :, 1]],
3)
return context + x
def main():
################ load ###################
actor_path = os.path.abspath(
os.curdir) + '/PPO_Mixedinput_Navigation_Model/weight/AC_TD3_actor.pkl'
critic_path = os.path.abspath(
os.curdir
) + '/PPO_Mixedinput_Navigation_Model/weight/AC_TD3_critic.pkl'
if os.path.exists(actor_path):
actor = Actor(state_size, action_size).to(device)
actor.load_state_dict(torch.load(actor_path))
print('Actor Model loaded')
else:
actor = Actor(state_size, action_size).to(device)
if os.path.exists(critic_path):
critic = Critic(state_size, action_size).to(device)
critic.load_state_dict(torch.load(critic_path))
print('Critic Model loaded')
else:
critic = Critic(state_size, action_size).to(device)
critic_next = Critic(state_size, action_size).to(device)
critic_next.load_state_dict(critic.state_dict())
print("Waiting for GAMA...")
################### initialization ########################
reset()
episode = 2548 #540
training_stage = 80 #100#80
Decay = training_stage * 18
lr = 0.0001
sample_lr = [
0.0001, 0.00009, 0.00008, 0.00007, 0.00006, 0.00005, 0.00004, 0.00003,
0.00002, 0.00001, 0.000009, 0.000008, 0.000007, 0.000006, 0.000005,
0.000004, 0.000003, 0.000002, 0.000001
] #900 960 1020 1080 1140
if episode >= training_stage: #50 100
try:
lr = sample_lr[int(episode // training_stage)]
except (IndexError):
lr = 0.000001 * (0.9**((episode - Decay // training_stage))
) #100-1800#80-1440#65-1170#570 -- 30
optimizerA = optim.Adam(actor.parameters(), lr, betas=(0.95, 0.999))
optimizerC = optim.Adam(critic.parameters(), lr,
betas=(0.95, 0.999)) #,weight_decay=0.0001
test = "GAMA"
state, reward, done, time_pass, over, _ = GAMA_connect(test) #connect
print("done:", done, "timepass:"******"----------------------------------Net_Trained---------------------------------------"
)
print('--------------------------Iteration:', episode,
'over--------------------------------')
episode += 1
#最初の時
else:
print('Iteration:', episode, "lr:", lr)
state = np.reshape(state, (1, len(state))) #xxx
state_img = generate_img()
tensor_cv = torch.from_numpy(np.transpose(
state_img, (2, 0, 1))).double().to(device) / 255
state = torch.DoubleTensor(state).reshape(1, state_size).to(device)
for _ in range(Memory_size):
memory.states.append(state)
memory.states_img.append(tensor_cv)
state = torch.stack(memory.states).to(device).detach() ###
tensor_cv = torch.stack(memory.states_img).to(device).detach()
value, h_state_cv_c, h_state_n_c, h_state_3_c = critic(
state,
tensor_cv) #dist, # now is a tensoraction = dist.sample()
action, log_prob, entropy = actor(
state, tensor_cv) #, h_state_cv_a,h_state_n_a,h_state_3_a
print("acceleration: ", action.cpu().numpy())
send_to_GAMA([[1, float(action.cpu().numpy() * 10)]])
log_prob = log_prob.unsqueeze(0)
#entropy += entropy
state, reward, done, time_pass, over, average_speed_NPC = GAMA_connect(
test)
return None
# also use numpy-style advanced indexing
x = torch.arange(9).view(3, 3)
indices = torch.LongTensor([0, 2])
print(x[indices])
print("-" * 20)
print(x[indices, :])
print("-" * 20)
print(x[:, indices])
# We can combine tensors by concatenating them. First, concatenating on the rows
x = torch.arange(6).view(2, 3)
describe(x)
describe(torch.cat([x, x], dim=0))
describe(torch.cat([x, x], dim=1))
describe(torch.stack([x, x]))
# We can concentate along the first dimension(the columns direction)
x = torch.arange(9).view(3, 3)
print(x)
print("-" * 20)
new_x = torch.cat([x, x, x], dim=1)
print(new_x.shape)
print(new_x)
# We can also concatenate on a new 0th dimension to "stack" the tensors
x = torch.arange(9).view(3, 3)
print(x)
print("-" * 20)
new_x = torch.stack([x, x, x])
print(new_x.shape)
def dnee_ee_features(rels,
model,
config,
pred2idx,
argw2idx,
max_event_len,
rel2idx,
device=None):
x1_idx = 0
x2_idx = 0
gold2e1xs = {}
gold2e2xs = {}
x1s, x2s = [], []
arg_lens = [config['arg0_max_len'], config['arg1_max_len']]
for i_rel, rel in enumerate(rels):
s = rel['Sense'][0]
if len(rel['Arg1']['Events']) == 0:
continue
e1s = unique_event_dict(rel['Arg1']['Events'], pred2idx).values()
for e1 in e1s:
e1r = get_raw_event_repr(e1, config, pred2idx, argw2idx, device)
x1s.append(e1r)
if i_rel in gold2e1xs:
gold2e1xs[i_rel].append(x1_idx)
else:
gold2e1xs[i_rel] = [x1_idx]
x1_idx += 1
e2s = unique_event_dict(rel['Arg2']['Events'], pred2idx).values()
for e2 in e2s:
e2r = get_raw_event_repr(e2, config, pred2idx, argw2idx, device)
x2s.append(e2r)
if i_rel in gold2e2xs:
gold2e2xs[i_rel].append(x2_idx)
else:
gold2e2xs[i_rel] = [x2_idx]
x2_idx += 1
x1s = torch.stack(x1s, dim=0).squeeze()
x2s = torch.stack(x2s, dim=0).squeeze()
if device:
x1s = x1s.to(device)
x2s = x2s.to(device)
with torch.no_grad():
x1ee = model.embed_event(x1s)
x2ee = model.embed_event(x2s)
x1_out = torch.zeros((len(rels), max_event_len, x1ee.shape[1]),
dtype=torch.float32)
x2_out = torch.zeros((len(rels), max_event_len, x2ee.shape[1]),
dtype=torch.float32)
y = torch.LongTensor(len(rels))
if device:
x1_out = x1_out.to(device)
x2_out = x2_out.to(device)
y = y.to(device)
for i_rel, rel in enumerate(rels):
s = rel['Sense'][0]
y[i_rel] = rel2idx[s]
# combine scores for multiple event pairs
if i_rel in gold2e1xs:
idxs = gold2e1xs[i_rel]
fs = x1ee[idxs, :]
if fs.shape[0] > max_event_len:
fs = fs[:max_event_len, :]
x1_out[i_rel, :fs.shape[0]] = fs
if i_rel in gold2e2xs:
idxs = gold2e2xs[i_rel]
fs = x2ee[idxs, :]
if fs.shape[0] > max_event_len:
fs = fs[:max_event_len, :]
x2_out[i_rel, :fs.shape[0]] = fs
return x1_out, x2_out, y
def spherical_to_cartesian(rtp):
x = rtp[:, 0] * torch.sin(rtp[:, 1]) * torch.cos(rtp[:, 2])
y = rtp[:, 0] * torch.sin(rtp[:, 1]) * torch.sin(rtp[:, 2])
z = rtp[:, 0] * torch.cos(rtp[:, 1])
return torch.stack((x, y, z), 1)
def forward(self, xs_pad, ilens, ys_pad):
"""E2E forward.
:param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, idim)
:param torch.Tensor ilens: batch of lengths of input sequences (B)
:param torch.Tensor ys_pad:
batch of padded character id sequence tensor (B, num_spkrs, Lmax)
:return: ctc loss value
:rtype: torch.Tensor
:return: attention loss value
:rtype: torch.Tensor
:return: accuracy in attention decoder
:rtype: float
"""
# 0. Frontend
if self.frontend is not None:
hs_pad, hlens, mask = self.frontend(to_torch_tensor(xs_pad), ilens)
if isinstance(hs_pad, list):
hlens_n = [None] * self.num_spkrs
for i in range(self.num_spkrs):
hs_pad[i], hlens_n[i] = self.feature_transform(
hs_pad[i], hlens)
hlens = hlens_n
else:
hs_pad, hlens = self.feature_transform(hs_pad, hlens)
else:
hs_pad, hlens = xs_pad, ilens
# 1. Encoder
if not isinstance(
hs_pad, list
): # single-channel input xs_pad (single- or multi-speaker)
hs_pad, hlens, _ = self.enc(hs_pad, hlens)
else: # multi-channel multi-speaker input xs_pad
for i in range(self.num_spkrs):
hs_pad[i], hlens[i], _ = self.enc(hs_pad[i], hlens[i])
# 2. CTC loss
if self.mtlalpha == 0:
loss_ctc, min_perm = None, None
else:
if not isinstance(hs_pad, list): # single-speaker input xs_pad
loss_ctc = torch.mean(self.ctc(hs_pad, hlens, ys_pad))
else: # multi-speaker input xs_pad
ys_pad = ys_pad.transpose(0, 1) # (num_spkrs, B, Lmax)
loss_ctc_perm = torch.stack(
[
self.ctc(
hs_pad[i // self.num_spkrs],
hlens[i // self.num_spkrs],
ys_pad[i % self.num_spkrs],
) for i in range(self.num_spkrs**2)
],
dim=1,
) # (B, num_spkrs^2)
loss_ctc, min_perm = self.pit.pit_process(loss_ctc_perm)
logging.info("ctc loss:" + str(float(loss_ctc)))
# 3. attention loss
if self.mtlalpha == 1:
loss_att = None
acc = None
else:
if not isinstance(hs_pad, list): # single-speaker input xs_pad
loss_att, acc, _ = self.dec(hs_pad, hlens, ys_pad)
else:
for i in range(ys_pad.size(1)): # B
ys_pad[:, i] = ys_pad[min_perm[i], i]
rslt = [
self.dec(hs_pad[i], hlens[i], ys_pad[i], strm_idx=i)
for i in range(self.num_spkrs)
]
loss_att = sum([r[0] for r in rslt]) / float(len(rslt))
acc = sum([r[1] for r in rslt]) / float(len(rslt))
self.acc = acc
# 5. compute cer/wer
if (self.training or not (self.report_cer or self.report_wer)
or not isinstance(hs_pad, list)):
cer, wer = 0.0, 0.0
# oracle_cer, oracle_wer = 0.0, 0.0
else:
if self.recog_args.ctc_weight > 0.0:
lpz = [
self.ctc.log_softmax(hs_pad[i]).data
for i in range(self.num_spkrs)
]
else:
lpz = None
word_eds, char_eds, word_ref_lens, char_ref_lens = [], [], [], []
nbest_hyps = [
self.dec.recognize_beam_batch(
hs_pad[i],
torch.tensor(hlens[i]),
lpz[i],
self.recog_args,
self.char_list,
self.rnnlm,
strm_idx=i,
) for i in range(self.num_spkrs)
]
# remove <sos> and <eos>
y_hats = [[
nbest_hyp[0]["yseq"][1:-1] for nbest_hyp in nbest_hyps[i]
] for i in range(self.num_spkrs)]
for i in range(len(y_hats[0])):
hyp_words = []
hyp_chars = []
ref_words = []
ref_chars = []
for ns in range(self.num_spkrs):
y_hat = y_hats[ns][i]
y_true = ys_pad[ns][i]
seq_hat = [
self.char_list[int(idx)] for idx in y_hat
if int(idx) != -1
]
seq_true = [
self.char_list[int(idx)] for idx in y_true
if int(idx) != -1
]
seq_hat_text = "".join(seq_hat).replace(
self.recog_args.space, " ")
seq_hat_text = seq_hat_text.replace(
self.recog_args.blank, "")
seq_true_text = "".join(seq_true).replace(
self.recog_args.space, " ")
hyp_words.append(seq_hat_text.split())
ref_words.append(seq_true_text.split())
hyp_chars.append(seq_hat_text.replace(" ", ""))
ref_chars.append(seq_true_text.replace(" ", ""))
tmp_word_ed = [
editdistance.eval(hyp_words[ns // self.num_spkrs],
ref_words[ns % self.num_spkrs])
for ns in range(self.num_spkrs**2)
] # h1r1,h1r2,h2r1,h2r2
tmp_char_ed = [
editdistance.eval(hyp_chars[ns // self.num_spkrs],
ref_chars[ns % self.num_spkrs])
for ns in range(self.num_spkrs**2)
] # h1r1,h1r2,h2r1,h2r2
word_eds.append(
self.pit.min_pit_sample(torch.tensor(tmp_word_ed))[0])
word_ref_lens.append(len(sum(ref_words, [])))
char_eds.append(
self.pit.min_pit_sample(torch.tensor(tmp_char_ed))[0])
char_ref_lens.append(len("".join(ref_chars)))
wer = (0.0 if not self.report_wer else float(sum(word_eds)) /
sum(word_ref_lens))
cer = (0.0 if not self.report_cer else float(sum(char_eds)) /
sum(char_ref_lens))
alpha = self.mtlalpha
if alpha == 0:
self.loss = loss_att
loss_att_data = float(loss_att)
loss_ctc_data = None
elif alpha == 1:
self.loss = loss_ctc
loss_att_data = None
loss_ctc_data = float(loss_ctc)
else:
self.loss = alpha * loss_ctc + (1 - alpha) * loss_att
loss_att_data = float(loss_att)
loss_ctc_data = float(loss_ctc)
loss_data = float(self.loss)
if loss_data < CTC_LOSS_THRESHOLD and not math.isnan(loss_data):
self.reporter.report(loss_ctc_data, loss_att_data, acc, cer, wer,
loss_data)
else:
logging.warning("loss (=%f) is not correct", loss_data)
return self.loss
def run_mdnet(**opts):
img_list = opts['img_list']
gt = opts['gt']
# init bounding box
target_bb = np.array(opts['init_bb'])
# a bounding box per image
result = np.zeros((len(img_list), 4))
result_bb = np.zeros((len(img_list), 4))
# first image
result[0] = np.copy(target_bb)
result_bb[0] = np.copy(target_bb)
iou_result = np.zeros((len(img_list), 1))
# init model
model = MDNet(opts['model_path'])
model_g = NetG()
if opts['adaptive_align']:
align_h = model.roi_align_model.aligned_height
align_w = model.roi_align_model.aligned_width
spatial_s = model.roi_align_model.spatial_scale
model.roi_align_model = RoIAlignAdaMax(align_h, align_w, spatial_s)
if opts['use_gpu']:
model = model.cuda()
model_g = model_g.cuda()
model.set_learnable_params(opts['ft_layers'])
model_g.set_learnable_params(opts['ft_layers'])
# init image crop model
img_crop_model = ImgCropper(1.)
if opts['use_gpu']:
img_crop_model.gpu_enable()
# init criterion and optimizer
criterion = BinaryLoss()
#criterion_g = nn.MSELoss(reduction='sum')
criterion_g = nn.MSELoss(reduction='mean')
init_optimizer = set_optimizer(model, opts['lr_init'], lr_mult=opts['lr_mult'], momentum=opts['momentum'],
w_decay=opts['w_decay'])
update_optimizer = set_optimizer(model, opts['lr_update'], lr_mult=opts['lr_mult'], momentum=opts['momentum'],
w_decay=opts['w_decay'])
tic = time.time()
# Load first image
cur_image = Image.open(img_list[0]).convert('RGB')
cur_image = np.asarray(cur_image)
# Draw pos/neg samples
img_shape = cur_image.shape
pos_examples = gen_samples(SampleGenerator('gaussian', (img_shape[1], img_shape[0]), 0.1, 1.2),
target_bb, opts['n_pos_init'], opts['overlap_pos_init'])
neg_examples = gen_samples(SampleGenerator('uniform', (img_shape[1], img_shape[0]), 1, 2, 1.1),
target_bb, opts['n_neg_init'], opts['overlap_neg_init'])
neg_examples = np.random.permutation(neg_examples)
cur_bbreg_examples = gen_samples(SampleGenerator('uniform', (img_shape[1], img_shape[0]), 0.3, 1.5, 1.1),
target_bb, opts['n_bbreg'], opts['overlap_bbreg'], opts['scale_bbreg'])
# compute padded sample
padded_x1 = (neg_examples[:, 0] - neg_examples[:, 2] * (opts['padding'] - 1.) / 2.).min()
padded_y1 = (neg_examples[:, 1] - neg_examples[:, 3] * (opts['padding'] - 1.) / 2.).min()
padded_x2 = (neg_examples[:, 0] + neg_examples[:, 2] * (opts['padding'] + 1.) / 2.).max()
padded_y2 = (neg_examples[:, 1] + neg_examples[:, 3] * (opts['padding'] + 1.) / 2.).max()
padded_scene_box = np.reshape(np.asarray((padded_x1, padded_y1, padded_x2 - padded_x1, padded_y2 - padded_y1)),
(1, 4))
scene_boxes = np.reshape(np.copy(padded_scene_box), (1, 4))
if opts['jitter']:
# horizontal shift
jittered_scene_box_horizon = np.copy(padded_scene_box)
jittered_scene_box_horizon[0, 0] -= 4.
jitter_scale_horizon = 1.
# vertical shift
jittered_scene_box_vertical = np.copy(padded_scene_box)
jittered_scene_box_vertical[0, 1] -= 4.
jitter_scale_vertical = 1.
jittered_scene_box_reduce1 = np.copy(padded_scene_box)
jitter_scale_reduce1 = 1.1 ** (-1)
# vertical shift
jittered_scene_box_enlarge1 = np.copy(padded_scene_box)
jitter_scale_enlarge1 = 1.1 ** (1)
# scale reduction
jittered_scene_box_reduce2 = np.copy(padded_scene_box)
jitter_scale_reduce2 = 1.1 ** (-2)
# scale enlarge
jittered_scene_box_enlarge2 = np.copy(padded_scene_box)
jitter_scale_enlarge2 = 1.1 ** (2)
scene_boxes = np.concatenate(
[scene_boxes, jittered_scene_box_horizon, jittered_scene_box_vertical, jittered_scene_box_reduce1,
jittered_scene_box_enlarge1, jittered_scene_box_reduce2, jittered_scene_box_enlarge2], axis=0)
jitter_scale = [1., jitter_scale_horizon, jitter_scale_vertical, jitter_scale_reduce1, jitter_scale_enlarge1,
jitter_scale_reduce2, jitter_scale_enlarge2]
else:
jitter_scale = [1.]
model.eval()
for bidx in range(0, scene_boxes.shape[0]):
crop_img_size = (scene_boxes[bidx, 2:4] * ((opts['img_size'], opts['img_size']) / target_bb[2:4])).astype(
'int64') * jitter_scale[bidx]
cropped_image, cur_image_var = img_crop_model.crop_image(cur_image, np.reshape(scene_boxes[bidx], (1, 4)),
crop_img_size)
cropped_image = cropped_image - 128.
feat_map = model(cropped_image, out_layer='conv3')
rel_target_bbox = np.copy(target_bb)
rel_target_bbox[0:2] -= scene_boxes[bidx, 0:2]
batch_num = np.zeros((pos_examples.shape[0], 1))
cur_pos_rois = np.copy(pos_examples)
cur_pos_rois[:, 0:2] -= np.repeat(np.reshape(scene_boxes[bidx, 0:2], (1, 2)), cur_pos_rois.shape[0], axis=0)
scaled_obj_size = float(opts['img_size']) * jitter_scale[bidx]
cur_pos_rois = samples2maskroi(cur_pos_rois, model.receptive_field, (scaled_obj_size, scaled_obj_size),
target_bb[2:4], opts['padding'])
cur_pos_rois = np.concatenate((batch_num, cur_pos_rois), axis=1)
cur_pos_rois = Variable(torch.from_numpy(cur_pos_rois.astype('float32'))).cuda()
cur_pos_feats = model.roi_align_model(feat_map, cur_pos_rois)
cur_pos_feats = cur_pos_feats.view(cur_pos_feats.size(0), -1).data.clone()
batch_num = np.zeros((neg_examples.shape[0], 1))
cur_neg_rois = np.copy(neg_examples)
cur_neg_rois[:, 0:2] -= np.repeat(np.reshape(scene_boxes[bidx, 0:2], (1, 2)), cur_neg_rois.shape[0], axis=0)
cur_neg_rois = samples2maskroi(cur_neg_rois, model.receptive_field, (scaled_obj_size, scaled_obj_size),
target_bb[2:4], opts['padding'])
cur_neg_rois = np.concatenate((batch_num, cur_neg_rois), axis=1)
cur_neg_rois = Variable(torch.from_numpy(cur_neg_rois.astype('float32'))).cuda()
cur_neg_feats = model.roi_align_model(feat_map, cur_neg_rois)
cur_neg_feats = cur_neg_feats.view(cur_neg_feats.size(0), -1).data.clone()
# bbreg rois
batch_num = np.zeros((cur_bbreg_examples.shape[0], 1))
cur_bbreg_rois = np.copy(cur_bbreg_examples)
cur_bbreg_rois[:, 0:2] -= np.repeat(np.reshape(scene_boxes[bidx, 0:2], (1, 2)), cur_bbreg_rois.shape[0], axis=0)
scaled_obj_size = float(opts['img_size']) * jitter_scale[bidx]
cur_bbreg_rois = samples2maskroi(cur_bbreg_rois, model.receptive_field, (scaled_obj_size, scaled_obj_size),
target_bb[2:4], opts['padding'])
cur_bbreg_rois = np.concatenate((batch_num, cur_bbreg_rois), axis=1)
cur_bbreg_rois = Variable(torch.from_numpy(cur_bbreg_rois.astype('float32'))).cuda()
cur_bbreg_feats = model.roi_align_model(feat_map, cur_bbreg_rois)
cur_bbreg_feats = cur_bbreg_feats.view(cur_bbreg_feats.size(0), -1).data.clone()
feat_dim = cur_pos_feats.size(-1)
if bidx == 0:
pos_feats = cur_pos_feats
neg_feats = cur_neg_feats
# bbreg feature
bbreg_feats = cur_bbreg_feats
bbreg_examples = cur_bbreg_examples
else:
pos_feats = torch.cat((pos_feats, cur_pos_feats), dim=0)
neg_feats = torch.cat((neg_feats, cur_neg_feats), dim=0)
# bbreg feature
bbreg_feats = torch.cat((bbreg_feats, cur_bbreg_feats), dim=0)
bbreg_examples = np.concatenate((bbreg_examples, cur_bbreg_examples), axis=0)
if pos_feats.size(0) > opts['n_pos_init']:
pos_idx = np.asarray(list(range(pos_feats.size(0))))
np.random.shuffle(pos_idx)
pos_feats = pos_feats[pos_idx[0:opts['n_pos_init']], :]
if neg_feats.size(0) > opts['n_neg_init']:
neg_idx = np.asarray(list(range(neg_feats.size(0))))
np.random.shuffle(neg_idx)
neg_feats = neg_feats[neg_idx[0:opts['n_neg_init']], :]
# bbreg
if bbreg_feats.size(0) > opts['n_bbreg']:
bbreg_idx = np.asarray(list(range(bbreg_feats.size(0))))
np.random.shuffle(bbreg_idx)
bbreg_feats = bbreg_feats[bbreg_idx[0:opts['n_bbreg']], :]
bbreg_examples = bbreg_examples[bbreg_idx[0:opts['n_bbreg']], :]
# print bbreg_examples.shape
# open images and crop patch from obj
extra_obj_size = np.array((opts['img_size'], opts['img_size']))
extra_crop_img_size = extra_obj_size * (opts['padding'] + 0.6)
replicateNum = 100
for iidx in range(replicateNum):
extra_target_bbox = np.copy(target_bb)
extra_scene_box = np.copy(extra_target_bbox)
extra_scene_box_center = extra_scene_box[0:2] + extra_scene_box[2:4] / 2.
extra_scene_box_size = extra_scene_box[2:4] * (opts['padding'] + 0.6)
extra_scene_box[0:2] = extra_scene_box_center - extra_scene_box_size / 2.
extra_scene_box[2:4] = extra_scene_box_size
extra_shift_offset = np.clip(2. * np.random.randn(2), -4, 4)
cur_extra_scale = 1.1 ** np.clip(np.random.randn(1), -2, 2)
extra_scene_box[0] += extra_shift_offset[0]
extra_scene_box[1] += extra_shift_offset[1]
extra_scene_box[2:4] *= cur_extra_scale[0]
scaled_obj_size = float(opts['img_size']) / cur_extra_scale[0]
cur_extra_cropped_image, _ = img_crop_model.crop_image(cur_image, np.reshape(extra_scene_box, (1, 4)),
extra_crop_img_size)
cur_extra_cropped_image = cur_extra_cropped_image.detach()
cur_extra_pos_examples = gen_samples(SampleGenerator('gaussian', (img_shape[1], img_shape[0]), 0.1, 1.2),
extra_target_bbox, opts['n_pos_init'] // replicateNum,
opts['overlap_pos_init'])
cur_extra_neg_examples = gen_samples(SampleGenerator('uniform', (img_shape[1], img_shape[0]), 0.3, 2, 1.1),
extra_target_bbox, opts['n_neg_init'] // replicateNum // 4,
opts['overlap_neg_init'])
# bbreg sample
cur_extra_bbreg_examples = gen_samples(SampleGenerator('uniform', (img_shape[1], img_shape[0]), 0.3, 1.5, 1.1),
extra_target_bbox, opts['n_bbreg'] // replicateNum // 4,
opts['overlap_bbreg'], opts['scale_bbreg'])
batch_num = iidx * np.ones((cur_extra_pos_examples.shape[0], 1))
cur_extra_pos_rois = np.copy(cur_extra_pos_examples)
cur_extra_pos_rois[:, 0:2] -= np.repeat(np.reshape(extra_scene_box[0:2], (1, 2)),
cur_extra_pos_rois.shape[0], axis=0)
cur_extra_pos_rois = samples2maskroi(cur_extra_pos_rois, model.receptive_field,
(scaled_obj_size, scaled_obj_size), extra_target_bbox[2:4],
opts['padding'])
cur_extra_pos_rois = np.concatenate((batch_num, cur_extra_pos_rois), axis=1)
batch_num = iidx * np.ones((cur_extra_neg_examples.shape[0], 1))
cur_extra_neg_rois = np.copy(cur_extra_neg_examples)
cur_extra_neg_rois[:, 0:2] -= np.repeat(np.reshape(extra_scene_box[0:2], (1, 2)), cur_extra_neg_rois.shape[0],
axis=0)
cur_extra_neg_rois = samples2maskroi(cur_extra_neg_rois, model.receptive_field,
(scaled_obj_size, scaled_obj_size), extra_target_bbox[2:4],
opts['padding'])
cur_extra_neg_rois = np.concatenate((batch_num, cur_extra_neg_rois), axis=1)
# bbreg rois
batch_num = iidx * np.ones((cur_extra_bbreg_examples.shape[0], 1))
cur_extra_bbreg_rois = np.copy(cur_extra_bbreg_examples)
cur_extra_bbreg_rois[:, 0:2] -= np.repeat(np.reshape(extra_scene_box[0:2], (1, 2)),
cur_extra_bbreg_rois.shape[0], axis=0)
cur_extra_bbreg_rois = samples2maskroi(cur_extra_bbreg_rois, model.receptive_field,
(scaled_obj_size, scaled_obj_size), extra_target_bbox[2:4],
opts['padding'])
cur_extra_bbreg_rois = np.concatenate((batch_num, cur_extra_bbreg_rois), axis=1)
if iidx == 0:
extra_cropped_image = cur_extra_cropped_image
extra_pos_rois = np.copy(cur_extra_pos_rois)
extra_neg_rois = np.copy(cur_extra_neg_rois)
# bbreg rois
extra_bbreg_rois = np.copy(cur_extra_bbreg_rois)
extra_bbreg_examples = np.copy(cur_extra_bbreg_examples)
else:
extra_cropped_image = torch.cat((extra_cropped_image, cur_extra_cropped_image), dim=0)
extra_pos_rois = np.concatenate((extra_pos_rois, np.copy(cur_extra_pos_rois)), axis=0)
extra_neg_rois = np.concatenate((extra_neg_rois, np.copy(cur_extra_neg_rois)), axis=0)
# bbreg rois
extra_bbreg_rois = np.concatenate((extra_bbreg_rois, np.copy(cur_extra_bbreg_rois)), axis=0)
extra_bbreg_examples = np.concatenate((extra_bbreg_examples, np.copy(cur_extra_bbreg_examples)), axis=0)
extra_pos_rois = Variable(torch.from_numpy(extra_pos_rois.astype('float32'))).cuda()
extra_neg_rois = Variable(torch.from_numpy(extra_neg_rois.astype('float32'))).cuda()
# bbreg rois
extra_bbreg_rois = Variable(torch.from_numpy(extra_bbreg_rois.astype('float32'))).cuda()
extra_cropped_image -= 128.
extra_feat_maps = model(extra_cropped_image, out_layer='conv3')
# Draw pos/neg samples
img_shape = cur_image.shape
extra_pos_feats = model.roi_align_model(extra_feat_maps, extra_pos_rois)
extra_pos_feats = extra_pos_feats.view(extra_pos_feats.size(0), -1).data.clone()
extra_neg_feats = model.roi_align_model(extra_feat_maps, extra_neg_rois)
extra_neg_feats = extra_neg_feats.view(extra_neg_feats.size(0), -1).data.clone()
# bbreg feat
extra_bbreg_feats = model.roi_align_model(extra_feat_maps, extra_bbreg_rois)
extra_bbreg_feats = extra_bbreg_feats.view(extra_bbreg_feats.size(0), -1).data.clone()
# concatenate extra features to original_features
pos_feats = torch.cat((pos_feats, extra_pos_feats), dim=0)
neg_feats = torch.cat((neg_feats, extra_neg_feats), dim=0)
# concatenate extra bbreg feats to original_bbreg_feats
bbreg_feats = torch.cat((bbreg_feats, extra_bbreg_feats), dim=0)
bbreg_examples = np.concatenate((bbreg_examples, extra_bbreg_examples), axis=0)
torch.cuda.empty_cache()
model.zero_grad()
# Initial training
train(model, None, criterion, init_optimizer, pos_feats, neg_feats, opts['maxiter_init'], **opts)
#del init_optimizer, neg_feats
if opts['use_gpu']:
torch.cuda.empty_cache()
g_pretrain(model, model_g, criterion_g, pos_feats, **opts)
if opts['use_gpu']:
torch.cuda.empty_cache()
# bbreg train
if bbreg_feats.size(0) > opts['n_bbreg']:
bbreg_idx = np.asarray(list(range(bbreg_feats.size(0))))
np.random.shuffle(bbreg_idx)
bbreg_feats = bbreg_feats[bbreg_idx[0:opts['n_bbreg']], :]
bbreg_examples = bbreg_examples[bbreg_idx[0:opts['n_bbreg']], :]
bbreg = BBRegressor((img_shape[1], img_shape[0]))
bbreg.train(bbreg_feats, bbreg_examples, target_bb)
if pos_feats.size(0) > opts['n_pos_update']:
pos_idx = np.asarray(list(range(pos_feats.size(0))))
np.random.shuffle(pos_idx)
pos_feats_all = [pos_feats.index_select(0, torch.from_numpy(pos_idx[0:opts['n_pos_update']]).cuda())]
if neg_feats.size(0) > opts['n_neg_update']:
neg_idx = np.asarray(list(range(neg_feats.size(0))))
np.random.shuffle(neg_idx)
neg_feats_all = [neg_feats.index_select(0, torch.from_numpy(neg_idx[0:opts['n_neg_update']]).cuda())]
spf_total = time.time() - tic
# spf_total = 0. # no first frame
# Visualize
savefig = opts['savefig_dir'] != ''
if opts['visualize'] or savefig:
dpi = 80.0
figsize = (cur_image.shape[1] / dpi, cur_image.shape[0] / dpi)
fig = plt.figure(frameon=False, figsize=figsize, dpi=dpi)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
im = ax.imshow(cur_image, aspect='normal')
if gt is not None:
gt_rect = plt.Rectangle(tuple(gt[0, :2]), gt[0, 2], gt[0, 3],
linewidth=3, edgecolor="#00ff00", zorder=1, fill=False)
ax.add_patch(gt_rect)
rect = plt.Rectangle(tuple(result_bb[0, :2]), result_bb[0, 2], result_bb[0, 3],
linewidth=3, edgecolor="#ff0000", zorder=1, fill=False)
ax.add_patch(rect)
if opts['visualize']:
plt.pause(.01)
plt.draw()
if savefig:
fig.savefig(os.path.join(opts['savefig_dir'], '0000.jpg'), dpi=dpi)
# Main loop
trans_f = opts['trans_f']
for i in range(1, len(img_list)):
tic = time.time()
# Load image
cur_image = Image.open(img_list[i]).convert('RGB')
cur_image = np.asarray(cur_image)
# Estimate target bbox
img_shape = cur_image.shape
samples = gen_samples(
SampleGenerator('gaussian', (img_shape[1], img_shape[0]), trans_f, opts['scale_f'], valid=True),
target_bb, opts['n_samples'])
padded_x1 = (samples[:, 0] - samples[:, 2] * (opts['padding'] - 1.) / 2.).min()
padded_y1 = (samples[:, 1] - samples[:, 3] * (opts['padding'] - 1.) / 2.).min()
padded_x2 = (samples[:, 0] + samples[:, 2] * (opts['padding'] + 1.) / 2.).max()
padded_y2 = (samples[:, 1] + samples[:, 3] * (opts['padding'] + 1.) / 2.).max()
padded_scene_box = np.asarray((padded_x1, padded_y1, padded_x2 - padded_x1, padded_y2 - padded_y1))
if padded_scene_box[0] > cur_image.shape[1]:
padded_scene_box[0] = cur_image.shape[1] - 1
if padded_scene_box[1] > cur_image.shape[0]:
padded_scene_box[1] = cur_image.shape[0] - 1
if padded_scene_box[0] + padded_scene_box[2] < 0:
padded_scene_box[2] = -padded_scene_box[0] + 1
if padded_scene_box[1] + padded_scene_box[3] < 0:
padded_scene_box[3] = -padded_scene_box[1] + 1
crop_img_size = (padded_scene_box[2:4] * ((opts['img_size'], opts['img_size']) / target_bb[2:4])).astype(
'int64')
cropped_image, cur_image_var = img_crop_model.crop_image(cur_image, np.reshape(padded_scene_box, (1, 4)),
crop_img_size)
cropped_image = cropped_image - 128.
model.eval()
feat_map = model(cropped_image, out_layer='conv3')
# relative target bbox with padded_scene_box
rel_target_bbox = np.copy(target_bb)
rel_target_bbox[0:2] -= padded_scene_box[0:2]
# Extract sample features and get target location
batch_num = np.zeros((samples.shape[0], 1))
sample_rois = np.copy(samples)
sample_rois[:, 0:2] -= np.repeat(np.reshape(padded_scene_box[0:2], (1, 2)), sample_rois.shape[0], axis=0)
sample_rois = samples2maskroi(sample_rois, model.receptive_field, (opts['img_size'], opts['img_size']),
target_bb[2:4], opts['padding'])
sample_rois = np.concatenate((batch_num, sample_rois), axis=1)
sample_rois = Variable(torch.from_numpy(sample_rois.astype('float32'))).cuda()
sample_feats = model.roi_align_model(feat_map, sample_rois)
sample_feats = sample_feats.view(sample_feats.size(0), -1).clone()
sample_scores = model(sample_feats, in_layer='fc4')
top_scores, top_idx = sample_scores[:, 1].topk(5)
top_idx = top_idx.data.cpu().numpy()
target_score = top_scores.data.mean()
target_bb = samples[top_idx].mean(axis=0)
success = target_score > opts['success_thr']
# Expand search area at failure
if success:
trans_f = opts['trans_f']
else:
trans_f = opts['trans_f_expand']
# bb regression
if success:
bbreg_feats = sample_feats[top_idx, :]
bbreg_samples = samples[top_idx]
bbreg_samples = bbreg.predict(bbreg_feats.data, bbreg_samples)
bbreg_bbox = bbreg_samples.mean(axis=0)
else:
bbreg_bbox = target_bb
# Save result
result[i] = target_bb
result_bb[i] = bbreg_bbox
iou_result[i] = 1.
# Data collect
if success:
# Draw pos/neg samples
pos_examples = gen_samples(
SampleGenerator('gaussian', (img_shape[1], img_shape[0]), 0.1, 1.2), target_bb,
opts['n_pos_update'],
opts['overlap_pos_update'])
neg_examples = gen_samples(
SampleGenerator('uniform', (img_shape[1], img_shape[0]), 1.5, 1.2), target_bb,
opts['n_neg_update'],
opts['overlap_neg_update'])
padded_x1 = (neg_examples[:, 0] - neg_examples[:, 2] * (opts['padding'] - 1.) / 2.).min()
padded_y1 = (neg_examples[:, 1] - neg_examples[:, 3] * (opts['padding'] - 1.) / 2.).min()
padded_x2 = (neg_examples[:, 0] + neg_examples[:, 2] * (opts['padding'] + 1.) / 2.).max()
padded_y2 = (neg_examples[:, 1] + neg_examples[:, 3] * (opts['padding'] + 1.) / 2.).max()
padded_scene_box = np.reshape(
np.asarray((padded_x1, padded_y1, padded_x2 - padded_x1, padded_y2 - padded_y1)), (1, 4))
scene_boxes = np.reshape(np.copy(padded_scene_box), (1, 4))
jitter_scale = [1.]
for bidx in range(0, scene_boxes.shape[0]):
crop_img_size = (scene_boxes[bidx, 2:4] * (
(opts['img_size'], opts['img_size']) / target_bb[2:4])).astype('int64') * jitter_scale[
bidx]
cropped_image, cur_image_var = img_crop_model.crop_image(cur_image,
np.reshape(scene_boxes[bidx], (1, 4)),
crop_img_size)
cropped_image = cropped_image - 128.
feat_map = model(cropped_image, out_layer='conv3')
rel_target_bbox = np.copy(target_bb)
rel_target_bbox[0:2] -= scene_boxes[bidx, 0:2]
batch_num = np.zeros((pos_examples.shape[0], 1))
cur_pos_rois = np.copy(pos_examples)
cur_pos_rois[:, 0:2] -= np.repeat(np.reshape(scene_boxes[bidx, 0:2], (1, 2)), cur_pos_rois.shape[0],
axis=0)
scaled_obj_size = float(opts['img_size']) * jitter_scale[bidx]
cur_pos_rois = samples2maskroi(cur_pos_rois, model.receptive_field, (scaled_obj_size, scaled_obj_size),
target_bb[2:4], opts['padding'])
cur_pos_rois = np.concatenate((batch_num, cur_pos_rois), axis=1)
cur_pos_rois = Variable(torch.from_numpy(cur_pos_rois.astype('float32'))).cuda()
cur_pos_feats = model.roi_align_model(feat_map, cur_pos_rois)
cur_pos_feats = cur_pos_feats.view(cur_pos_feats.size(0), -1).data.clone()
batch_num = np.zeros((neg_examples.shape[0], 1))
cur_neg_rois = np.copy(neg_examples)
cur_neg_rois[:, 0:2] -= np.repeat(np.reshape(scene_boxes[bidx, 0:2], (1, 2)), cur_neg_rois.shape[0],
axis=0)
cur_neg_rois = samples2maskroi(cur_neg_rois, model.receptive_field, (scaled_obj_size, scaled_obj_size),
target_bb[2:4], opts['padding'])
cur_neg_rois = np.concatenate((batch_num, cur_neg_rois), axis=1)
cur_neg_rois = Variable(torch.from_numpy(cur_neg_rois.astype('float32'))).cuda()
cur_neg_feats = model.roi_align_model(feat_map, cur_neg_rois)
cur_neg_feats = cur_neg_feats.view(cur_neg_feats.size(0), -1).data.clone()
feat_dim = cur_pos_feats.size(-1)
if bidx == 0:
pos_feats = cur_pos_feats ##index select
neg_feats = cur_neg_feats
else:
pos_feats = torch.cat((pos_feats, cur_pos_feats), dim=0)
neg_feats = torch.cat((neg_feats, cur_neg_feats), dim=0)
if pos_feats.size(0) > opts['n_pos_update']:
pos_idx = np.asarray(list(range(pos_feats.size(0))))
np.random.shuffle(pos_idx)
pos_feats = pos_feats.index_select(0, torch.from_numpy(pos_idx[0:opts['n_pos_update']]).cuda())
if neg_feats.size(0) > opts['n_neg_update']:
neg_idx = np.asarray(list(range(neg_feats.size(0))))
np.random.shuffle(neg_idx)
neg_feats = neg_feats.index_select(0, torch.from_numpy(neg_idx[0:opts['n_neg_update']]).cuda())
pos_feats_all.append(pos_feats)
neg_feats_all.append(neg_feats)
if len(pos_feats_all) > opts['n_frames_long']:
del pos_feats_all[0]
if len(neg_feats_all) > opts['n_frames_short']:
del neg_feats_all[0]
# Short term update
if not success:
nframes = min(opts['n_frames_short'], len(pos_feats_all))
pos_data = torch.stack(pos_feats_all[-nframes:], 0).view(-1, feat_dim)
neg_data = torch.stack(neg_feats_all, 0).view(-1, feat_dim)
train(model, None, criterion, update_optimizer, pos_data, neg_data, opts['maxiter_update'], **opts)
# Long term update
elif i % opts['long_interval'] == 0:
pos_data = torch.stack(pos_feats_all, 0).view(-1, feat_dim)
neg_data = torch.stack(neg_feats_all, 0).view(-1, feat_dim)
train(model, model_g, criterion, update_optimizer, pos_data, neg_data, opts['maxiter_update'], **opts)
spf = time.time() - tic
spf_total += spf
# Visualize
if opts['visualize'] or savefig:
im.set_data(cur_image)
if gt is not None:
gt_rect.set_xy(gt[i, :2])
gt_rect.set_width(gt[i, 2])
gt_rect.set_height(gt[i, 3])
rect.set_xy(result_bb[i, :2])
rect.set_width(result_bb[i, 2])
rect.set_height(result_bb[i, 3])
if opts['visualize']:
plt.pause(.01)
plt.draw()
if savefig:
fig.savefig(os.path.join(opts['savefig_dir'], '%04d.jpg' % i), dpi=dpi)
if opts['visual_log']:
if gt is None:
print("Frame %d/%d, Score %.3f, Time %.3f" % (i, len(img_list), target_score, spf))
else:
print("Frame %d/%d, Overlap %.3f, Score %.3f, Time %.3f" % \
(i, len(img_list), overlap_ratio(gt[i], result_bb[i])[0], target_score, spf))
iou_result[i] = overlap_ratio(gt[i], result_bb[i])[0]
fps = len(img_list) / spf_total
# fps = (len(img_list)-1) / spf_total #no first frame
return iou_result, result_bb, fps, result
def forward(
self, # type: ignore
question: Dict[str, torch.LongTensor],
passage: Dict[str, torch.LongTensor],
answer: Dict[str, torch.LongTensor],
dialog: Dict[str, torch.LongTensor],
previous_answer_appended: Dict[str, torch.LongTensor],
span_start: torch.IntTensor = None,
span_end: torch.IntTensor = None,
p1_answer_marker: torch.IntTensor = None,
p2_answer_marker: torch.IntTensor = None,
p3_answer_marker: torch.IntTensor = None,
yesno_list: torch.IntTensor = None,
followup_list: torch.IntTensor = None,
metadata: List[Dict[str, Any]] = None) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
question : Dict[str, torch.LongTensor]
From a ``TextField``.
passage : Dict[str, torch.LongTensor]
From a ``TextField``. The model assumes that this passage contains the answer to the
question, and predicts the beginning and ending positions of the answer within the
passage.
span_start : ``torch.IntTensor``, optional
From an ``IndexField``. This is one of the things we are trying to predict - the
beginning position of the answer with the passage. This is an `inclusive` token index.
If this is given, we will compute a loss that gets included in the output dictionary.
span_end : ``torch.IntTensor``, optional
From an ``IndexField``. This is one of the things we are trying to predict - the
ending position of the answer with the passage. This is an `inclusive` token index.
If this is given, we will compute a loss that gets included in the output dictionary.
p1_answer_marker : ``torch.IntTensor``, optional
This is one of the inputs, but only when num_context_answers > 0.
This is a tensor that has a shape [batch_size, max_qa_count, max_passage_length].
Most passage token will have assigned 'O', except the passage tokens belongs to the previous answer
in the dialog, which will be assigned labels such as <1_start>, <1_in>, <1_end>.
For more details, look into dataset_readers/util/make_reading_comprehension_instance_quac
p2_answer_marker : ``torch.IntTensor``, optional
This is one of the inputs, but only when num_context_answers > 1.
It is similar to p1_answer_marker, but marking previous previous answer in passage.
p3_answer_marker : ``torch.IntTensor``, optional
This is one of the inputs, but only when num_context_answers > 2.
It is similar to p1_answer_marker, but marking previous previous previous answer in passage.
yesno_list : ``torch.IntTensor``, optional
This is one of the outputs that we are trying to predict.
Three way classification (the yes/no/not a yes no question).
followup_list : ``torch.IntTensor``, optional
This is one of the outputs that we are trying to predict.
Three way classification (followup / maybe followup / don't followup).
metadata : ``List[Dict[str, Any]]``, optional
If present, this should contain the question ID, original passage text, and token
offsets into the passage for each instance in the batch. We use this for computing
official metrics using the official SQuAD evaluation script. The length of this list
should be the batch size, and each dictionary should have the keys ``id``,
``original_passage``, and ``token_offsets``. If you only want the best span string and
don't care about official metrics, you can omit the ``id`` key.
Returns
-------
An output dictionary consisting of the followings.
Each of the followings is a nested list because first iterates over dialog, then questions in dialog.
qid : List[List[str]]
A list of list, consisting of question ids.
followup : List[List[int]]
A list of list, consisting of continuation marker prediction index.
(y :yes, m: maybe follow up, n: don't follow up)
yesno : List[List[int]]
A list of list, consisting of affirmation marker prediction index.
(y :yes, x: not a yes/no question, n: np)
best_span_str : List[List[str]]
If sufficient metadata was provided for the instances in the batch, we also return the
string from the original passage that the model thinks is the best answer to the
question.
loss : torch.FloatTensor, optional
A scalar loss to be optimised.
"""
#question = previous_answer_appended
batch_size, max_qa_count, max_q_len, _ = question[
'token_characters'].size()
#logger.info("dialog shape token charcaters is %s %s", dialog['token_characters'].size(), dialog['elmo'].size())
#logger.info("question shape token charcaters is %s %s", question['token_characters'].size(), question['elmo'].size())
batch_size, max_dia_count, max_dia_len, _ = dialog[
'token_characters'].size()
total_qa_count = batch_size * max_qa_count
qa_mask = torch.ge(followup_list, 0).view(total_qa_count)
embedded_question = self._text_field_embedder(question,
num_wrapping_dims=1)
#logger.info("11111111111 dialog is %s", dialog['token_characters'].shape)
#logger.info("11111111111 dialog is %s", dialog['elmo'].shape)
embedded_dialog = self._text_field_embedder(dialog,
num_wrapping_dims=1)
embedded_question = embedded_question.reshape(
total_qa_count, max_q_len,
self._text_field_embedder.get_output_dim())
embedded_dialog = embedded_dialog.reshape(
total_qa_count, max_dia_len,
self._text_field_embedder.get_output_dim())
embedded_question = self._variational_dropout(embedded_question)
embedded_dialog = self._variational_dropout(embedded_dialog)
embedded_passage = self._variational_dropout(
self._text_field_embedder(passage))
passage_length = embedded_passage.size(1)
#logger.info("embedded question has shape %s", embedded_question.shape)
#logger.info("embedded dialog has shape %s", embedded_dialog.shape)
question_mask = util.get_text_field_mask(question,
num_wrapping_dims=1).float()
question_mask = question_mask.reshape(total_qa_count, max_q_len)
dialog_mask = util.get_text_field_mask(dialog,
num_wrapping_dims=1).float()
dialog_mask = dialog_mask.reshape(total_qa_count, max_dia_len)
passage_mask = util.get_text_field_mask(passage).float()
#logger.info("dialog shape token charcaters is %s %s", dialog['token_characters'].size(), dialog['elmo'].size())
#logger.info("answer shape token charcaters is %s %s", answer['token_characters'].size(), answer['elmo'].size())
#logger.info("quesion shape token charcaters is %s %s", question['token_characters'].size(), question['elmo'].size())
#logger.info("previous answer shape token charcaters is %s %s", previous_answer_appended['token_characters'].size(), previous_answer_appended['elmo'].size())
repeated_passage_mask = passage_mask.unsqueeze(1).repeat(
1, max_qa_count, 1)
repeated_passage_mask = repeated_passage_mask.view(
total_qa_count, passage_length)
if self._num_context_answers > 0:
# Encode question turn number inside the dialog into question embedding.
question_num_ind = util.get_range_vector(
max_qa_count, util.get_device_of(embedded_question))
question_num_ind = question_num_ind.unsqueeze(-1).repeat(
1, max_q_len)
question_num_ind = question_num_ind.unsqueeze(0).repeat(
batch_size, 1, 1)
question_num_ind = question_num_ind.reshape(
total_qa_count, max_q_len)
question_num_marker_emb = self._question_num_marker(
question_num_ind)
embedded_question = torch.cat(
[embedded_question, question_num_marker_emb], dim=-1)
# Append dialog number for dialog
question_num_ind = util.get_range_vector(
max_dia_count, util.get_device_of(embedded_dialog))
question_num_ind = question_num_ind.unsqueeze(-1).repeat(
1, max_dia_len)
question_num_ind = question_num_ind.unsqueeze(0).repeat(
batch_size, 1, 1)
question_num_ind = question_num_ind.reshape(
total_qa_count, max_dia_len)
question_num_marker_emb = self._question_num_marker(
question_num_ind)
embedded_dialog = torch.cat(
[embedded_dialog, question_num_marker_emb], dim=-1)
# Encode the previous answers in passage embedding.
repeated_embedded_passage = embedded_passage.unsqueeze(1).repeat(1, max_qa_count, 1, 1). \
view(total_qa_count, passage_length, self._text_field_embedder.get_output_dim())
# batch_size * max_qa_count, passage_length, word_embed_dim
p1_answer_marker = p1_answer_marker.view(total_qa_count,
passage_length)
p1_answer_marker_emb = self._prev_ans_marker(p1_answer_marker)
repeated_embedded_passage = torch.cat(
[repeated_embedded_passage, p1_answer_marker_emb], dim=-1)
if self._num_context_answers > 1:
p2_answer_marker = p2_answer_marker.view(
total_qa_count, passage_length)
p2_answer_marker_emb = self._prev_ans_marker(p2_answer_marker)
repeated_embedded_passage = torch.cat(
[repeated_embedded_passage, p2_answer_marker_emb], dim=-1)
if self._num_context_answers > 2:
p3_answer_marker = p3_answer_marker.view(
total_qa_count, passage_length)
p3_answer_marker_emb = self._prev_ans_marker(
p3_answer_marker)
repeated_embedded_passage = torch.cat(
[repeated_embedded_passage, p3_answer_marker_emb],
dim=-1)
repeated_encoded_passage = self._variational_dropout(
self._phrase_layer(repeated_embedded_passage,
repeated_passage_mask))
else:
encoded_passage = self._variational_dropout(
self._phrase_layer(embedded_passage, passage_mask))
repeated_encoded_passage = encoded_passage.unsqueeze(1).repeat(
1, max_qa_count, 1, 1)
repeated_encoded_passage = repeated_encoded_passage.view(
total_qa_count, passage_length, self._encoding_dim)
#logger.info("repeated encoded passage has shape %s", repeated_encoded_passage.shape)
#logger.info("embedded question has shape %s", embedded_question.shape)
#logger.info("question mask has shape %s", question_mask.shape)
#logger.info("embedded dialog has shape %s", embedded_dialog.shape)
#logger.info("dialog mask has shape %s", dialog_mask.shape)
encoded_question = self._variational_dropout(
self._phrase_layer(embedded_question, question_mask))
encoded_dialog = self._variational_dropout(
self._phrase_layer(embedded_dialog, dialog_mask))
#logger.info("encoded_question is %s", encoded_question.shape)
#logger.info("encoded_dialog is %s", encoded_dialog.shape)
#logger.info("encoded_passage is %s", repeated_encoded_passage.shape)
# Shape: (batch_size * max_qa_count, passage_length, question_length)
passage_question_similarity = self._matrix_attention(
repeated_encoded_passage, encoded_question)
#logger.info("passage_question_similarity is %s", passage_question_similarity.shape)
# Shape: (batch_size * max_qa_count, passage_length, question_length)
#logger.info("question_mask is %s", question_mask.shape)
passage_question_attention = util.masked_softmax(
passage_question_similarity, question_mask)
# Shape: (batch_size * max_qa_count, passage_length, encoding_dim)
passage_question_vectors = util.weighted_sum(
encoded_question, passage_question_attention)
#logger.info("passage question vectors is %s", passage_question_vectors.shape)
############################# DIALOG SIMILARITY STUFF ################################################################
dialog_question_similarity = self._matrix_attention(
encoded_question, encoded_dialog)
#logger.info("dialog question similarity is %s", dialog_question_similarity.shape)
#logger.info("dialog_mask is %s", dialog_mask.shape)
# Shape: (batch_size * max_qa_count, passage_length, question_length)
dialog_question_attention = util.masked_softmax(
dialog_question_similarity, dialog_mask)
#logger.info("dialog question attention is %s", dialog_question_attention.shape)
# Shape: (batch_size * max_qa_count, passage_length, encoding_dim)
question_dialog_vectors = util.weighted_sum(encoded_dialog,
dialog_question_attention)
#logger.info("question_dialog_vectors is %s", question_dialog_vectors.shape)
#logger.info("encoded_question 111 %s", encoded_question.shape)
#logger.info("encoded_question 2222 %s", encoded_question.shape)
#logger.info("self._encoding_dim %s", self._encoding_dim)
encoded_question = torch.cat(
[encoded_question, question_dialog_vectors], dim=-1)
encoded_question = F.relu(self.t(encoded_question))
#logger.info("encoded_question 3333333 %s", encoded_question.shape)
######################################################################################################################
# We replace masked values with something really negative here, so they don't affect the
# max below.
#if max_qa_count == 7 and batch_size == 21:
# sys.exit()
masked_similarity = util.replace_masked_values(
passage_question_similarity, question_mask.unsqueeze(1), -1e7)
question_passage_similarity = masked_similarity.max(
dim=-1)[0].squeeze(-1)
question_passage_attention = util.masked_softmax(
question_passage_similarity, repeated_passage_mask)
# Shape: (batch_size * max_qa_count, encoding_dim)
question_passage_vector = util.weighted_sum(
repeated_encoded_passage, question_passage_attention)
tiled_question_passage_vector = question_passage_vector.unsqueeze(
1).expand(total_qa_count, passage_length, self._encoding_dim)
# Shape: (batch_size * max_qa_count, passage_length, encoding_dim * 4)
final_merged_passage = torch.cat([
repeated_encoded_passage, passage_question_vectors,
repeated_encoded_passage * passage_question_vectors,
repeated_encoded_passage * tiled_question_passage_vector
],
dim=-1)
final_merged_passage = F.relu(self._merge_atten(final_merged_passage))
residual_layer = self._variational_dropout(
self._residual_encoder(final_merged_passage,
repeated_passage_mask))
self_attention_matrix = self._self_attention(residual_layer,
residual_layer)
mask = repeated_passage_mask.reshape(total_qa_count, passage_length, 1) \
* repeated_passage_mask.reshape(total_qa_count, 1, passage_length)
self_mask = torch.eye(passage_length,
passage_length,
device=self_attention_matrix.device)
self_mask = self_mask.reshape(1, passage_length, passage_length)
mask = mask * (1 - self_mask)
self_attention_probs = util.masked_softmax(self_attention_matrix, mask)
# (batch, passage_len, passage_len) * (batch, passage_len, dim) -> (batch, passage_len, dim)
self_attention_vecs = torch.matmul(self_attention_probs,
residual_layer)
self_attention_vecs = torch.cat([
self_attention_vecs, residual_layer,
residual_layer * self_attention_vecs
],
dim=-1)
residual_layer = F.relu(
self._merge_self_attention(self_attention_vecs))
final_merged_passage = final_merged_passage + residual_layer
# batch_size * maxqa_pair_len * max_passage_len * 200
final_merged_passage = self._variational_dropout(final_merged_passage)
start_rep = self._span_start_encoder(final_merged_passage,
repeated_passage_mask)
span_start_logits = self._span_start_predictor(start_rep).squeeze(-1)
end_rep = self._span_end_encoder(
torch.cat([final_merged_passage, start_rep], dim=-1),
repeated_passage_mask)
span_end_logits = self._span_end_predictor(end_rep).squeeze(-1)
span_yesno_logits = self._span_yesno_predictor(end_rep).squeeze(-1)
span_followup_logits = self._span_followup_predictor(end_rep).squeeze(
-1)
span_start_logits = util.replace_masked_values(span_start_logits,
repeated_passage_mask,
-1e7)
# batch_size * maxqa_len_pair, max_document_len
span_end_logits = util.replace_masked_values(span_end_logits,
repeated_passage_mask,
-1e7)
best_span = self._get_best_span_yesno_followup(span_start_logits,
span_end_logits,
span_yesno_logits,
span_followup_logits,
self._max_span_length)
output_dict: Dict[str, Any] = {}
# Compute the loss.
if span_start is not None:
loss = nll_loss(util.masked_log_softmax(span_start_logits,
repeated_passage_mask),
span_start.view(-1),
ignore_index=-1)
self._span_start_accuracy(span_start_logits,
span_start.view(-1),
mask=qa_mask)
loss += nll_loss(util.masked_log_softmax(span_end_logits,
repeated_passage_mask),
span_end.view(-1),
ignore_index=-1)
self._span_end_accuracy(span_end_logits,
span_end.view(-1),
mask=qa_mask)
self._span_accuracy(best_span[:, 0:2],
torch.stack([span_start, span_end],
-1).view(total_qa_count, 2),
mask=qa_mask.unsqueeze(1).expand(-1, 2).long())
# add a select for the right span to compute loss
gold_span_end_loc = []
span_end = span_end.view(
total_qa_count).squeeze().data.cpu().numpy()
for i in range(0, total_qa_count):
gold_span_end_loc.append(
max(span_end[i] * 3 + i * passage_length * 3, 0))
gold_span_end_loc.append(
max(span_end[i] * 3 + i * passage_length * 3 + 1, 0))
gold_span_end_loc.append(
max(span_end[i] * 3 + i * passage_length * 3 + 2, 0))
gold_span_end_loc = span_start.new(gold_span_end_loc)
pred_span_end_loc = []
for i in range(0, total_qa_count):
pred_span_end_loc.append(
max(best_span[i][1] * 3 + i * passage_length * 3, 0))
pred_span_end_loc.append(
max(best_span[i][1] * 3 + i * passage_length * 3 + 1, 0))
pred_span_end_loc.append(
max(best_span[i][1] * 3 + i * passage_length * 3 + 2, 0))
predicted_end = span_start.new(pred_span_end_loc)
_yesno = span_yesno_logits.view(-1).index_select(
0, gold_span_end_loc).view(-1, 3)
_followup = span_followup_logits.view(-1).index_select(
0, gold_span_end_loc).view(-1, 3)
loss += nll_loss(F.log_softmax(_yesno, dim=-1),
yesno_list.view(-1),
ignore_index=-1)
loss += nll_loss(F.log_softmax(_followup, dim=-1),
followup_list.view(-1),
ignore_index=-1)
_yesno = span_yesno_logits.view(-1).index_select(
0, predicted_end).view(-1, 3)
_followup = span_followup_logits.view(-1).index_select(
0, predicted_end).view(-1, 3)
self._span_yesno_accuracy(_yesno,
yesno_list.view(-1),
mask=qa_mask)
self._span_followup_accuracy(_followup,
followup_list.view(-1),
mask=qa_mask)
output_dict["loss"] = loss
# Compute F1 and preparing the output dictionary.
output_dict['best_span_str'] = []
output_dict['qid'] = []
output_dict['followup'] = []
output_dict['yesno'] = []
best_span_cpu = best_span.detach().cpu().numpy()
for i in range(batch_size):
passage_str = metadata[i]['original_passage']
offsets = metadata[i]['token_offsets']
f1_score = 0.0
per_dialog_best_span_list = []
per_dialog_yesno_list = []
per_dialog_followup_list = []
per_dialog_query_id_list = []
for per_dialog_query_index, (iid, answer_texts) in enumerate(
zip(metadata[i]["instance_id"],
metadata[i]["answer_texts_list"])):
predicted_span = tuple(best_span_cpu[i * max_qa_count +
per_dialog_query_index])
start_offset = offsets[predicted_span[0]][0]
end_offset = offsets[predicted_span[1]][1]
yesno_pred = predicted_span[2]
followup_pred = predicted_span[3]
per_dialog_yesno_list.append(yesno_pred)
per_dialog_followup_list.append(followup_pred)
per_dialog_query_id_list.append(iid)
best_span_string = passage_str[start_offset:end_offset]
per_dialog_best_span_list.append(best_span_string)
if answer_texts:
if len(answer_texts) > 1:
t_f1 = []
# Compute F1 over N-1 human references and averages the scores.
for answer_index in range(len(answer_texts)):
idxes = list(range(len(answer_texts)))
idxes.pop(answer_index)
refs = [answer_texts[z] for z in idxes]
t_f1.append(
squad_eval.metric_max_over_ground_truths(
squad_eval.f1_score, best_span_string,
refs))
f1_score = 1.0 * sum(t_f1) / len(t_f1)
else:
f1_score = squad_eval.metric_max_over_ground_truths(
squad_eval.f1_score, best_span_string,
answer_texts)
self._official_f1(100 * f1_score)
output_dict['qid'].append(per_dialog_query_id_list)
output_dict['best_span_str'].append(per_dialog_best_span_list)
output_dict['yesno'].append(per_dialog_yesno_list)
output_dict['followup'].append(per_dialog_followup_list)
return output_dict
def forward(self, batch):
"""
:param batch: (p_index, q_index, zhengli_index, fuli_index, wfqd_index)
:return:
"""
passage = batch[0:3]
query = batch[3:6]
zhengli = batch[6:9] # (batch_size, zhengli_len)
fuli = batch[9:12]
wfqd = batch[12:15]
# mask
passage_mask = utils.get_mask(passage[0])
query_mask = utils.get_mask(query[0])
zhengli_mask = utils.get_mask(zhengli[0])
fuli_mask = utils.get_mask(fuli[0])
wfqd_mask = utils.get_mask(wfqd[0])
# embedding
passage_vec = self.embedding(passage)
query_vec = self.embedding(query)
zhengli_vec = self.embedding(zhengli)
fuli_vec = self.embedding(fuli)
wfqd_vec = self.embedding(wfqd)
# encoder: p, q
passage_vec = self.encoder(
passage_vec, passage_mask) # (p_len, batch_size. hidden_size*2)
passage_vec = self.dropout(passage_vec)
query_vec = self.encoder(query_vec, query_mask)
query_vec = self.dropout(query_vec)
# encoder: zhengli,fuli,wfqd
zhengli_vec = self.encoder(zhengli_vec, zhengli_mask)
zhengli_vec = self.dropout(zhengli_vec)
fuli_vec = self.encoder(fuli_vec, fuli_mask)
fuli_vec = self.dropout(fuli_vec)
wfqd_vec = self.encoder(wfqd_vec, wfqd_mask)
wfqd_vec = self.dropout(wfqd_vec)
# answer build
zhengli_vec = self.mean_a(zhengli_vec,
zhengli_mask) # (batch_size, hidden_size*2)
fuli_vec = self.mean_a(fuli_vec, fuli_mask)
wfqd_vec = self.mean_a(wfqd_vec, wfqd_mask)
answer = torch.stack([zhengli_vec, fuli_vec, wfqd_vec
]).transpose(0,
1) # (batch_size, 3, hidden_size)
# merge q into p, get p prep
align_ct = passage_vec
for i in range(self.num_align_hops):
qt_align_ct = self.aligner[i](align_ct, query_vec, query_mask)
bar_ct = self.aligner_sfu[i](
align_ct,
torch.cat([
qt_align_ct, align_ct * qt_align_ct, align_ct - qt_align_ct
],
dim=2))
ct_align_ct = self.self_aligner[i](bar_ct, passage_mask)
hat_ct = self.self_aligner_sfu[i](
bar_ct,
torch.cat(
[ct_align_ct, bar_ct * ct_align_ct, bar_ct - ct_align_ct],
dim=2))
align_ct = self.choose_agg[i](hat_ct, passage_mask)
p_prep = align_ct # (p_len, batch_size, hidden_size*2)
q_prep = self.mean_q(query_vec, query_mask).unsqueeze(
0) # (1, batch_size, hidden_size*2)
sj = self.vp(torch.tanh(self.wp1(p_prep) +
self.wp2(q_prep))).transpose(
0, 1) # (batch_size, p_len, 1)
mask = passage_mask.eq(0).unsqueeze(2)
sj.masked_fill_(mask, -float('inf'))
sj = f.softmax(sj, dim=1).transpose(1, 2) # (batch_size, 1, p_len)
p_prep = torch.bmm(sj, p_prep.transpose(0, 1)).squeeze(
1) # (batch_size, hidden_size*2)
# choosing
p_prep = self.bi_linear(p_prep) # (batch_size, hidden_size)
outputs = torch.bmm(answer,
p_prep.unsqueeze(2)).squeeze(2) # (batch_size, 3)
return outputs
def generate(self, mels, save_path, batched, target, overlap, mu_law,
trg_mel=None):
device = next(self.parameters()).device # use same device as parameters
mu_law = mu_law if self.mode == 'RAW' else False
self.eval()
output = []
start = time.time()
rnn1 = self.get_gru_cell(self.rnn1)
rnn2 = self.get_gru_cell(self.rnn2)
with torch.no_grad():
mels = torch.as_tensor(mels, device=device)
wave_len = (mels.size(-1) - 1) * self.hop_length
mels = self.pad_tensor(mels.transpose(1, 2), pad=self.pad, side='both')
mels = mels.transpose(1, 2)
if trg_mel is not None and hasattr(self, 'adaptnet'):
mels = self.adaptnet(mels, trg_mel)
mels, aux = self.upsample(mels)
if batched:
mels = self.fold_with_overlap(mels, target, overlap)
aux = self.fold_with_overlap(aux, target, overlap)
b_size, seq_len, _ = mels.size()
h1 = torch.zeros(b_size, self.rnn_dims, device=device)
h2 = torch.zeros(b_size, self.rnn_dims, device=device)
x = torch.zeros(b_size, 1, device=device)
d = self.aux_dims
aux_split = [aux[:, :, d * i:d * (i + 1)] for i in range(4)]
for i in range(seq_len):
m_t = mels[:, i, :]
a1_t, a2_t, a3_t, a4_t = \
(a[:, i, :] for a in aux_split)
x = torch.cat([x, m_t, a1_t], dim=1)
x = self.I(x)
h1 = rnn1(x, h1)
x = x + h1
inp = torch.cat([x, a2_t], dim=1)
h2 = rnn2(inp, h2)
x = x + h2
x = torch.cat([x, a3_t], dim=1)
x = F.relu(self.fc1(x))
x = torch.cat([x, a4_t], dim=1)
x = F.relu(self.fc2(x))
logits = self.fc3(x)
if self.mode == 'MOL':
sample = sample_from_discretized_mix_logistic(logits.unsqueeze(0).transpose(1, 2))
output.append(sample.view(-1))
# x = torch.FloatTensor([[sample]]).cuda()
x = sample.transpose(0, 1)
elif self.mode == 'RAW':
posterior = F.softmax(logits, dim=1)
distrib = torch.distributions.Categorical(posterior)
sample = 2 * distrib.sample().float() / (self.n_classes - 1.) - 1.
output.append(sample)
x = sample.unsqueeze(-1)
else:
raise RuntimeError("Unknown model mode value - ", self.mode)
#if i % 100 == 0: self.gen_display(i, seq_len, b_size, start)
output = torch.stack(output).transpose(0, 1)
output = output.cpu().numpy()
output = output.astype(np.float64)
if batched:
output = self.xfade_and_unfold(output, target, overlap)
else:
output = output[0]
if mu_law:
output = decode_mu_law(output, self.n_classes, False)
# Fade-out at the end to avoid signal cutting out suddenly
fade_out = np.linspace(1, 0, 20 * self.hop_length)
output = output[:wave_len]
output[-20 * self.hop_length:] *= fade_out
save_wav(output, save_path)
self.train()
return output
def stack(self, keys):
data = [getattr(self, k)[:self.size] for k in keys]
return map(lambda x: torch.stack(x, dim=0), data)
def normalize(self, keys):
for key in keys:
k = torch.stack(getattr(self, key))
k = (k - k.mean()) / (k.std() + 1e-10)
setattr(self, key, [i for i in k])
def get_tp_fp_fn_tn(net_output, gt, axes=None, mask=None, square=False):
"""
net_output must be (b, c, x, y(, z)))
gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x, y(, z))
if mask is provided it must have shape (b, 1, x, y(, z)))
:param net_output:
:param gt:
:param axes: can be (, ) = no summation
:param mask: mask must be 1 for valid pixels and 0 for invalid pixels
:param square: if True then fp, tp and fn will be squared before summation
:return:
"""
if axes is None:
axes = tuple(range(2, len(net_output.size())))
shp_x = net_output.shape
shp_y = gt.shape
with torch.no_grad():
if len(shp_x) != len(shp_y):
gt = gt.view((shp_y[0], 1, *shp_y[1:]))
if all([i == j for i, j in zip(net_output.shape, gt.shape)]):
# if this is the case then gt is probably already a one hot encoding
y_onehot = gt
else:
gt = gt.long()
y_onehot = torch.zeros(shp_x)
if net_output.device.type == "cuda":
y_onehot = y_onehot.cuda(net_output.device.index)
y_onehot.scatter_(1, gt, 1)
tp = net_output * y_onehot
fp = net_output * (1 - y_onehot)
fn = (1 - net_output) * y_onehot
tn = (1 - net_output) * (1 - y_onehot)
if mask is not None:
tp = torch.stack(tuple(x_i * mask[:, 0]
for x_i in torch.unbind(tp, dim=1)),
dim=1)
fp = torch.stack(tuple(x_i * mask[:, 0]
for x_i in torch.unbind(fp, dim=1)),
dim=1)
fn = torch.stack(tuple(x_i * mask[:, 0]
for x_i in torch.unbind(fn, dim=1)),
dim=1)
tn = torch.stack(tuple(x_i * mask[:, 0]
for x_i in torch.unbind(tn, dim=1)),
dim=1)
if square:
tp = tp**2
fp = fp**2
fn = fn**2
tn = tn**2
if len(axes) > 0:
tp = sum_tensor(tp, axes, keepdim=False)
fp = sum_tensor(fp, axes, keepdim=False)
fn = sum_tensor(fn, axes, keepdim=False)
tn = sum_tensor(tn, axes, keepdim=False)
return tp, fp, fn, tn
def forward(self,
support_x,
support_y,
query_x,
query_y,
train=True,
n_way=-1,
curr_shot=-1):
batch_sz, support_sz, _, h, w = support_x.size()
query_sz = query_x.size(1)
# FEATURE EXTRACTION
support_x = self.repnet(support_x.view(batch_sz * support_sz, -1, h,
w))
query_x = self.repnet(query_x.view(batch_sz * query_sz, -1, h, w))
# output [b, support_sz / query_sz, c, d, d]
support_xf = support_x.view(batch_sz, support_sz, self.c, self.d,
self.d)
query_xf = query_x.view(batch_sz, query_sz, self.c, self.d, self.d)
# SWAP
if self.swap and train:
support_xfs, query_xfs, support_ys, query_ys = \
self._generate_multiple(support_xf, query_xf, support_y, query_y, n_way)
else:
# also for test
support_xfs, query_xfs, support_ys, query_ys = \
[support_xf], [query_xf], [support_y], [query_y]
# SCORE
expand_sz = n_way if self.opts.model.sum_supp_sample else support_sz
score = torch.zeros(len(support_xfs), batch_sz, query_sz,
expand_sz).to(self.opts.ctrl.device)
for i in range(len(support_xfs)):
# expand both to [b, query_sz, support_sz/n_way, c, d, d]
if self.opts.model.sum_supp_sample:
support_xf = torch.sum(
support_xfs[i].view(batch_sz, n_way, -1, self.c, self.d,
self.d), 2).squeeze(2)
else:
support_xf = support_xfs[i]
support_xf = support_xf.unsqueeze(1).expand(
-1, query_sz, -1, -1, -1, -1)
query_xf = query_xfs[i].unsqueeze(2).expand(
-1, -1, expand_sz, -1, -1, -1)
# cat => [b, query_sz, support_sz/n_way, 2c, d, d]
comb = torch.cat([support_xf, query_xf], dim=3)
comb = comb.view(batch_sz * query_sz * expand_sz, 2 * self.c,
self.d, self.d)
comb = self.relation2(self.relation1(comb))
comb = F.avg_pool2d(comb, self.pool_size)
# [b*query_sz*support_sz/n_way, 256] => [b, query_sz, support_sz/n_way, 1]
# score: [b, query_sz, support_sz/n_way]
score[i] = self.fc(comb.view(batch_sz * query_sz * expand_sz,
-1)).view(batch_sz, query_sz,
expand_sz, 1).squeeze(3)
# LOSS OR ACCURACY
if train:
loss = torch.zeros(len(support_xfs)).to(self.opts.ctrl.device)
for i in range(len(support_xfs)):
if self.CE_loss:
# reformat score output: N, n_way (being the number of classes)
curr_score = score[i].view(batch_sz * query_sz, n_way,
-1).mean(dim=-1)
support_y_neat = support_ys[i][:, ::curr_shot] # b, n_way
target = torch.stack([
torch.nonzero(
torch.eq(support_y_neat[b], query_ys[i][b, j]))
for b, query in enumerate(query_ys[i])
for j, _, in enumerate(query)
])
target = target.view(-1) # shape: N
loss[i] = F.cross_entropy(curr_score, target)
else:
# build the label
if self.opts.model.sum_supp_sample:
support_y_neat = support_ys[i][:, ::
curr_shot] # b, n_way
support_y_expand = support_y_neat.unsqueeze(1).expand(
batch_sz, query_sz, n_way)
query_y_expand = query_ys[i].unsqueeze(2).expand(
batch_sz, query_sz, n_way)
else:
# [b, support_sz] => [b, 1, support_sz] => [b, query_sz, support_sz]
support_y_expand = support_ys[i].unsqueeze(1).expand(
batch_sz, query_sz, support_sz)
# [b, query_sz] => [b, query_sz, 1] => [b, query_sz, support_sz]
query_y_expand = query_ys[i].unsqueeze(2).expand(
batch_sz, query_sz, support_sz)
# convert byte tensor to float tensor
label = torch.eq(support_y_expand, query_y_expand).float()
loss[i] = F.mse_loss(score[i], label)
loss = (loss.sum() / len(support_xfs)).unsqueeze(0)
return loss.unsqueeze(
0) # output size: 1 x 1 (or the number of losses)
else:
# TEST
if self.opts.model.sum_supp_sample:
score = score[0].unsqueeze(-1)
else:
# score shape: b, query_sz, n_way, k_shot
score = score[0].view(batch_sz, query_sz, n_way, curr_shot)
# pred_ind shape: b, query_sz
if self.CE_loss:
pred_ind = score.mean(dim=-1).argmax(dim=-1)
else:
pred_ind = score.sum(dim=-1).argmax(dim=-1)
support_y_neat = support_ys[0][:, ::curr_shot] # b, n_way
pred = torch.stack([
support_y_neat[b, ind] for b, query in enumerate(pred_ind)
for ind in query
])
pred = pred.view(batch_sz, -1)
correct = torch.eq(pred, query_ys[0]).sum()
correct = correct.unsqueeze(0)
return pred, correct
print('completed %epoch ', inter)
inter += .10
data = []
result = []
for sm in sample:
imag, dat, res = sm
data = dat
result.append(res)
for img in imag:
preds = model.predict(img)
preds = preds.astype('float').reshape(-1)
preds = preds[0]
target = torch.stack(result)
target = target.view(-1)
final_vars = []
final_vars = torch.FloatTensor([[[abs(data - preds)]]])
x = net(*final_vars)
values, indices = x.max(1)
loss = criterion(x, Variable(target))
loss.backward()
optimizer.step()
net.zero_grad()
if (i_batch > 10):
break
# &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&TESTING &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
def forward(self, x, temp=1):
bs, N, T, ch, h, w = x.shape
x = x.flatten(0, 1)
if self.feature_dropout_prob > 0: # Note: Apply to q,k?
x_att = self.feat_dropout(x)
else:
x_att = x
# TODO: Check if normalization should also be done for the Value features. Maybe it doesn't matter.
x_att = F.normalize(x_att, p=2, dim=-3) # Note: Layernorm
# Option 1
# qs = self.to_q(x_att[:, 1:].flatten(0, 1)).flatten(-2, -1) # [bsNT, ch, hw]
# ks = self.to_k(x_att[:, :-1].flatten(0, 1)).flatten(-2, -1)
# Option 2
qs, ks = x_att[:, 1:].flatten(0, 1).flatten(-2, -1), x_att[:, :-1].flatten(0, 1).flatten(-2, -1)
# Feature linear transformation + stacked positional encoding
vs_pe = self.to_v(x.flatten(0, 1)).reshape(bs*N, T, -1, h*w)
# As = self.affinity(ks, qs) # We track backwards!
energy = torch.einsum('bcn,bcm->bnm', qs, ks).reshape(bs*N, T-1, h*w, h*w) * self.scale
As = [self.stoch_mat(energy[:, t], temp=temp, do_dropout=True) for t in range(T-1)]
# vs_list = torch.split(vs_pe, 1, dim=1)
acc_state = vs_pe[:, :-self.n_timesteps+1, self.s_sta_dim:] # Note: Make sure it keeps the temporal encoding
attn_vec = torch.stack(As, dim=1)
for t in range(self.n_timesteps-1):
if t + 2 < self.n_timesteps:
curr_state, curr_attn = vs_pe[:, t+1:-self.n_timesteps+2+t, self.s_sta_dim:], attn_vec[:, t:-self.n_timesteps+2+t]
else: curr_state, curr_attn = vs_pe[:, t+1:], attn_vec[:, t:]
# attn_vec has one less timestep, so the range is slightly different.
acc_state = torch.cat([curr_state, torch.einsum('btcm,btnm->btcn', acc_state, curr_attn)], dim=2)
# Note: IGNORE. For testing purposes: reconstruct t without (t) features
# if t + 2 < self.n_timesteps:
# acc_state = torch.cat([curr_state, torch.einsum('btcm,btnm->btcn', acc_state, curr_attn)], dim=2)
# else:
# acc_state = torch.einsum('btcm,btnm->btcn', acc_state, curr_attn)
# # Note: IGNORE. For testing purposes: Simple version of the function
# acc_state = vs_list[0]
# for t in range(T-1):
#
# if t < T-3: # Note: Test attention. If we reverse the dimensionality it works poorly. Difficult to know why
# acc_state = torch.cat([vs_list[t+1], torch.einsum('bcm,bnm->bcn', acc_state.squeeze(1), As[t]).unsqueeze(1)], dim=2)
# else:
# acc_state = torch.einsum('bcm,bnm->bcn', acc_state.squeeze(1), As[t]).unsqueeze(1)
# Option: Self-attention from SAGAN
# m_batchsize,C,width ,height = x.size()
# proj_query = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N)
# proj_key = self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H)
# energy = torch.bmm(proj_query,proj_key) # transpose check
# attention = self.softmax(energy) # BX (N) X (N)
# proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N
#
# out = torch.bmm(proj_value,attention.permute(0,2,1) )
# out = out.view(m_batchsize,C,width,height)
# out = self.gamma*out + x
return acc_state
def validation_end(self, outputs):
avg_loss = torch.stack([x["val_loss"] for x in outputs]).mean()
# tensorboard_logs = {"Validation/Loss": avg_loss}
return {"avg_val_loss": avg_loss} # "log": tensorboard_logs
def forward(self, xh, xp_list):
xp_att_list = [self.node_att(xp) for xp in xp_list]
com_att = torch.max(torch.stack(xp_att_list, dim=1), dim=1, keepdim=False)[0]
xph_message = sum([self.conv_ch(torch.cat([xh, xp*com_att], dim=1)) for xp in xp_list])
return xph_message
cut_size = 44
total_epoch = 250
path = os.path.join(opt.dataset + '_' + opt.model)
# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.RandomCrop(44),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
transform_test = transforms.Compose([
transforms.TenCrop(cut_size),
transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),
])
trainset = FER2013(split = 'Training', transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=opt.bs, shuffle=True, num_workers=1)
PublicTestset = FER2013(split = 'PublicTest', transform=transform_test)
PublicTestloader = torch.utils.data.DataLoader(PublicTestset, batch_size=opt.bs, shuffle=False, num_workers=1)
PrivateTestset = FER2013(split = 'PrivateTest', transform=transform_test)
PrivateTestloader = torch.utils.data.DataLoader(PrivateTestset, batch_size=opt.bs, shuffle=False, num_workers=1)
# Model
if opt.model == 'VGG19':
net = VGG('VGG19')
elif opt.model == 'Resnet18':
net = ResNet18()
def initProb(sData, nTrain, nVal, var0, alph, cvt):
"""
initialize the OC problem that we want to solve
:param sData: str, name of the problem
:param nTrain: int, number of samples in a batch, drawn from rho_0
:param nVal: int, number of validation samples to draw from rho_0
:param var0: float, variance of rho_0
:param alph: list, 6-value list of parameters/hyperparameters
:param cvt: func, conversion function for typing and device
:return:
prob: the problem Object
x0: nTrain -by- d tensor, training batch
x0v: nVal -by- d tensor, training batch
xInit: 1 -by- d tensor, center of rho_0
"""
if sData == 'softcorridor':
d = 4
xtarget = cvt(torch.tensor([[2, 2, -2, 2]]))
xInit = cvt(torch.tensor([[-2, -2, 2, -2]]))
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nTrain, d))
prob = Cross2D(xtarget, obstacle='softcorridor', alph_Q=alph[1], alph_W=alph[2], r=0.5)
elif sData == 'swarm':
nAgents = 32
d = nAgents*3
xtarget = cvt(torch.tensor([-2., 2., 8.,
-1., 2., 8.,
0., 2., 8.,
1., 2., 8.,
2., 2., 8.,
-2.5, 3., 8.,
-1.5, 3., 8.,
-0.5, 3., 8.,
0.5, 3., 8.,
1.5, 3., 8.,
2.5, 3., 8.,
-2., 4., 8.,
-1., 4., 8.,
0., 4., 8.,
1., 4., 8.,
2., 4., 8.]))
xtarget = torch.cat((xtarget.view(-1, 3), cvt(torch.tensor([0, -0.5, -3])) + xtarget.view(-1, 3)), dim=0).view(-1)
halfTrain = nTrain // 2
xInit = cvt(torch.tensor([1,-1,-1])) * xtarget.view(-1,3) + cvt(torch.tensor([0,0,10]))
xInit = xInit.view(1,-1)
x0 = xInit + cvt( var0 * torch.randn(halfTrain, d))
xmore = xtarget + cvt(var0 * torch.randn(halfTrain, d))
x0 = torch.cat((x0, xmore), dim=0)
# validation samples from rho_0
x0v = xInit + cvt( var0 * torch.randn(halfTrain, d))
for j in range(nAgents):
x0[ :,3*j+3:3*(j+1)] = 0. * x0[ :,3*j+3:3*(j+1)]
x0v[:,3*j+3:3*(j+1)] = 0. * x0v[:,3*j+3:3*(j+1)]
prob = SwarmTraj(xtarget, obstacle='blocks', alph_Q=alph[1], alph_W=alph[2], r= 0.2)
elif sData == 'swarm50':
nAgents = 50
d = nAgents*3
xtarget = cvt(torch.tensor([-2., 2., 6.,
-1., 2., 6.,
0., 2., 6.,
1., 2., 6.,
2., 2., 6.,
3., 2., 6.,
4., 2., 6.,
-2.5, 3., 7.,
-1.5, 3., 7.,
-0.5, 3., 7.,
0.5, 3., 7.,
1.5, 3., 7.,
2.5, 3., 7.,
3.5, 3., 7.,
-2., 4., 8.,
-1., 4., 8.,
0., 4., 8.,
1., 4., 8.,
2., 4., 8.,
3., 4., 8.,
4., 4., 8.,
-2., 3., 5.,
-1., 3., 5.,
1., 3., 5.,
2., 3., 5.]))
xtarget = torch.cat((xtarget.view(-1, 3), cvt(torch.tensor([0, -0.5, -3])) + xtarget.view(-1, 3)), dim=0).view(-1)
halfTrain = nTrain // 2
xInit = cvt(torch.tensor([1,-1,-1])) * xtarget.view(-1,3) + cvt(torch.tensor([0,0,10]))
xInit = xInit.view(1,-1)
x0 = xInit + cvt( var0 * torch.randn(halfTrain, d))
xmore = xtarget + cvt(var0 * torch.randn(halfTrain, d))
x0 = torch.cat((x0, xmore), dim=0)
# validation samples from rho_0
x0v = xInit + cvt( var0 * torch.randn(halfTrain, d))
for j in range(nAgents):
x0[ :,3*j+3:3*(j+1)] = 0. * x0[ :,3*j+3:3*(j+1)]
x0v[:,3*j+3:3*(j+1)] = 0. * x0v[:,3*j+3:3*(j+1)]
prob = SwarmTraj(xtarget, obstacle='blocks', alph_Q=alph[1], alph_W=alph[2], r= 0.1)
elif sData == 'singlequad':
d = 12
xtarget = cvt(torch.tensor([2., 2., 2., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))
init = -1.5
xInit = cvt(torch.tensor([init, init, init]))
x0 = xInit + cvt(var0 * torch.randn(nTrain, 3))
x0 = pad(x0, [0, d - 3, 0, 0], value=0)
xInit = pad(xInit.view(1,-1), [0, d - 3, 0, 0], value=0)
# validation samples from rho_0
x0v = cvt(torch.tensor([init, init, init]) + var0 * torch.randn(nVal, 3))
x0v = pad(x0v, [0, d - 3, 0, 0], value=0)
prob = Quadcopter(xtarget,obstacle=None, alph_Q = 0.0, alph_W = 0.0)
elif sData=='midcross2':
nAgents = 2
d = 2 * nAgents
xtarget = cvt(torch.tensor([2,2,-2,2]))
xInit = cvt(torch.tensor([-2,-2,2,-2])).view(1,-1)
x0 = cvt(torch.tensor([-2,-2,2,-2]) + var0 * torch.randn(nTrain, d))
x0v = cvt(torch.tensor([-2,-2,2,-2]) + var0 * torch.randn(nTrain, d))
prob = Cross2D(xtarget, obstacle=None, alph_Q=alph[1], alph_W=alph[2])
elif sData == 'midcross4':
nAgents = 4
d = 2 * nAgents
xx = torch.linspace(-2, 2, nAgents)
xtarget = cvt(torch.stack((xx.flip(dims=[0]), 2 * torch.ones(nAgents)), dim=1).reshape(1,-1))
xInit = cvt(torch.stack((xx, -2 * torch.ones(nAgents)), dim=1).reshape(1,-1))
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nVal, d))
prob = Cross2D(xtarget, obstacle=None, alph_Q=alph[1], alph_W=alph[2], r=0.4)
elif sData == 'midcross20':
nAgents = 20
d = 2 * nAgents
xx = torch.linspace(-6, 6, nAgents)
xtarget = cvt(torch.stack((xx.flip(dims=[0]), 6 * torch.ones(nAgents)), dim=1).reshape(1, -1))
xInit = cvt(torch.stack((xx, -6 * torch.ones(nAgents)), dim=1).reshape(1, -1))
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nVal, d))
prob = Cross2D(xtarget, obstacle=None, alph_Q=alph[1], alph_W=alph[2], r=0.15)
elif sData == 'midcross30':
nAgents = 30
d = 2 * nAgents
xx = torch.linspace(-6, 6, nAgents)
tmp = torch.tensor([6,4,2])
tmp = tmp.view(-1,1).repeat(nAgents//3,1).view(-1)
xtarget = cvt(torch.stack((xx.flip(dims=[0]), tmp), dim=1).reshape(1, -1))
tmp = torch.tensor([-6,-4,-2])
tmp = tmp.view(-1,1).repeat(nAgents//3,1).view(-1)
xInit = cvt(torch.stack((xx, tmp), dim=1).reshape(1, -1))
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nVal, d))
prob = Cross2D(xtarget, obstacle=None, alph_Q=alph[1], alph_W=alph[2], r=0.2)
elif sData == 'swap2':
nAgents = 2
d = 2 * nAgents
xtarget = cvt(torch.tensor([10., 0., -10., 0.]))
xInit = cvt(torch.tensor([-10., 0., 10., 0.])).reshape(1, -1)
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nVal, d))
prob = Cross2D(xtarget, obstacle='hardcorridor', alph_Q=alph[1], alph_W=alph[2], r=1.0)
elif sData == 'swap12':
nAgents = 12
d = 2 * nAgents
xtarget = cvt(torch.tensor([ 2,2, 0,0, 10,0, -10,0, 5,5, -5,-5, -4,2, -6,-1, 5,-5, -5,5, 2,-2, -2,-2 ]))
xInit = cvt(torch.tensor([0,0, 2,2, -10,0, 10,0, -5,-5, 5,5, -6,-1, -4,2, -5,5, 5,-5, -2,-2, 2,-2 ])).reshape(1,-1)
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nVal, d))
prob = Cross2D(xtarget, obstacle=None, alph_Q=alph[1], alph_W=alph[2], r=0.5)
elif sData == 'swap12_5pair':
nAgents = 10
d = 2 * nAgents
xtarget = cvt(torch.tensor([ 2,2, 0,0, 10,0, -10,0, 5,5, -5,-5, -4,2, -6,-1, 5,-5, -5,5 ]))
xInit = cvt(torch.tensor([0,0, 2,2, -10,0, 10,0, -5,-5, 5,5, -6,-1, -4,2, -5,5, 5,-5 ])).reshape(1,-1)
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nVal, d))
prob = Cross2D(xtarget, obstacle=None, alph_Q=alph[1], alph_W=alph[2], r=0.5)
elif sData == 'swap12_4pair':
nAgents = 8
d = 2 * nAgents
xtarget = cvt(torch.tensor([ 2,2, 0,0, 10,0, -10,0, 5,5, -5,-5, -4,2, -6,-1 ]))
xInit = cvt(torch.tensor([0,0, 2,2, -10,0, 10,0, -5,-5, 5,5, -6,-1, -4,2])).reshape(1,-1)
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nVal, d))
prob = Cross2D(xtarget, obstacle=None, alph_Q=alph[1], alph_W=alph[2], r=0.5)
elif sData == 'swap12_3pair':
nAgents = 6
d = 2 * nAgents
xtarget = cvt(torch.tensor([ 2,2, 0,0, 10,0, -10,0, 5,5, -5,-5 ]))
xInit = cvt(torch.tensor([0,0, 2,2, -10,0, 10,0, -5,-5, 5,5 ])).reshape(1,-1)
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nVal, d))
prob = Cross2D(xtarget, obstacle=None, alph_Q=alph[1], alph_W=alph[2], r=0.5)
elif sData == 'swap12_2pair':
nAgents = 4
d = 2 * nAgents
xtarget = cvt(torch.tensor([ 2,2, 0,0, 10,0, -10,0 ]))
xInit = cvt(torch.tensor([0,0, 2,2, -10,0, 10,0 ])).reshape(1,-1)
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nVal, d))
prob = Cross2D(xtarget, obstacle=None, alph_Q=alph[1], alph_W=alph[2], r=0.5)
elif sData == 'swap12_1pair':
nAgents = 2
d = 2 * nAgents
xtarget = cvt(torch.tensor([ 2,2, 0,0 ]))
xInit = cvt(torch.tensor([0,0, 2,2 ])).reshape(1,-1)
x0 = xInit + cvt(var0 * torch.randn(nTrain, d))
x0v = xInit + cvt(var0 * torch.randn(nVal, d))
prob = Cross2D(xtarget, obstacle=None, alph_Q=alph[1], alph_W=alph[2], r=0.5)
else:
print("incorrect value passed to --data")
exit(1)
return prob, x0, x0v, xInit
def compute_JacobianM(self, pts2d1, pts2d2, pts2d1_normed, pts2d2_normed, fixM1, fixM2, intrinsic, t, ang, lagr):
R = self.rot_from_axisangle(ang)
T = self.t2T(t)
derT0, derT1, derT2 = self.derivative_translate(intrinsic.device)
rotxd, rotyd, rotzd = self.derivative_angle(ang)
samplenum = pts2d1.shape[1]
r_bias = (torch.norm(t) - 1)
J_t0_bias = 2 * lagr * r_bias / torch.norm(t) * t[0]
J_t1_bias = 2 * lagr * r_bias / torch.norm(t) * t[1]
J_t2_bias = 2 * lagr * r_bias / torch.norm(t) * t[2]
## ============Compute DerivM2============ ##
planeparam2 = torch.inverse(fixM2 @ intrinsic).T @ T @ R @ torch.inverse(intrinsic) @ pts2d1
planeparam2_norm = torch.norm(planeparam2, dim=0, keepdim=True)
planeparam2_normed = planeparam2 / planeparam2_norm
rdist_2 = torch.sum(planeparam2_normed * pts2d2_normed, dim=0, keepdim=True)
deriv_tonorm2 = 2 * rdist_2 * pts2d2_normed
dtonx2, dtony2, dtonz2 = torch.split(deriv_tonorm2, 1, dim=0)
px2, py2, pz2 = torch.split(planeparam2, 1, dim=0)
deriv_px2 = dtonx2 / planeparam2_norm - torch.sum(px2 * planeparam2_normed * deriv_tonorm2, dim=0, keepdim=True) / (planeparam2_norm ** 2)
deriv_py2 = dtony2 / planeparam2_norm - torch.sum(py2 * planeparam2_normed * deriv_tonorm2, dim=0, keepdim=True) / (planeparam2_norm ** 2)
deriv_pz2 = dtonz2 / planeparam2_norm - torch.sum(pz2 * planeparam2_normed * deriv_tonorm2, dim=0, keepdim=True) / (planeparam2_norm ** 2)
deriv_norm2 = torch.cat([deriv_px2, deriv_py2, deriv_pz2], dim=0)
deriv_M2 = (deriv_norm2.T).unsqueeze(2) @ (pts2d1.T).unsqueeze(1) @ torch.inverse(intrinsic).T
deriv_M2 = (torch.inverse(fixM2 @ intrinsic)).unsqueeze(0).expand([samplenum, -1, -1]) @ deriv_M2
## ============Compute DerivM1============ ##
planeparam1 = (pts2d2.T @ torch.inverse(intrinsic).T @ T @ R @ torch.inverse(fixM1 @ intrinsic)).T
planeparam1_norm = torch.norm(planeparam1, dim=0, keepdim=True)
planeparam1_normed = planeparam1 / planeparam1_norm
rdist_1 = torch.sum(planeparam1_normed * pts2d1_normed, dim=0, keepdim=True)
deriv_tonorm1 = 2 * rdist_1 * pts2d1_normed
dtonx1, dtony1, dtonz1 = torch.split(deriv_tonorm1, 1, dim=0)
px1, py1, pz1 = torch.split(planeparam1, 1, dim=0)
deriv_px1 = dtonx1 / planeparam1_norm - torch.sum(px1 * planeparam1_normed * deriv_tonorm1, dim=0, keepdim=True) / (planeparam1_norm ** 2)
deriv_py1 = dtony1 / planeparam1_norm - torch.sum(py1 * planeparam1_normed * deriv_tonorm1, dim=0, keepdim=True) / (planeparam1_norm ** 2)
deriv_pz1 = dtonz1 / planeparam1_norm - torch.sum(pz1 * planeparam1_normed * deriv_tonorm1, dim=0, keepdim=True) / (planeparam1_norm ** 2)
deriv_norm1 = torch.cat([deriv_px1, deriv_py1, deriv_pz1], dim=0)
deriv_M1 = (pts2d2.T).unsqueeze(2) @ (deriv_norm1.T).unsqueeze(1)
deriv_M1 = deriv_M1 @ torch.inverse(fixM1 @ intrinsic).T
deriv_M1 = (torch.inverse(intrinsic)).unsqueeze(0).expand([samplenum, -1, -1]) @ deriv_M1
## ============== ##
J_t0 = torch.sum((deriv_M2 + deriv_M1) * (derT0 @ R), dim=[1, 2]) / samplenum + J_t0_bias / samplenum
J_t1 = torch.sum((deriv_M2 + deriv_M1) * (derT1 @ R), dim=[1, 2]) / samplenum + J_t1_bias / samplenum
J_t2 = torch.sum((deriv_M2 + deriv_M1) * (derT2 @ R), dim=[1, 2]) / samplenum + J_t2_bias / samplenum
J_ang0 = torch.sum((deriv_M2 + deriv_M1) * (T @ rotxd), dim=[1, 2]) / samplenum
J_ang1 = torch.sum((deriv_M2 + deriv_M1) * (T @ rotyd), dim=[1, 2]) / samplenum
J_ang2 = torch.sum((deriv_M2 + deriv_M1) * (T @ rotzd), dim=[1, 2]) / samplenum
JacobM = torch.stack([J_ang0, J_ang1, J_ang2, J_t0, J_t1, J_t2], dim=1)
residual = (rdist_2 ** 2 / samplenum + rdist_1 ** 2 / samplenum + lagr * r_bias ** 2 / samplenum)
return JacobM, residual.T
def fit_tau(X_t,X_tp1, tau, tau_t, vee, opt_tau, opt_vee, device, epoch):
"""
Meant to be called on a batch of states to estimate the batch gradient wrt tau and v.
"""
alpha_t = 1. / np.sqrt(epoch+1)
lam = 10
tau_xt = tau(X_t)
tau_xtp1 = tau(X_tp1)
tau_t_xt = tau_t(X_t)
tau_t_xtp1 = tau_t(X_tp1)
v = vee
grad_theta_tau_xt, grad_theta_tau_xtp1 = defaultdict(list),defaultdict(list)
for i in range(len(X_t)):
opt_tau.zero_grad()
tau_xt.backward([torch.FloatTensor([[1] if i==j else [0] for j in range(len(tau_xt))]).to(device)],retain_graph=True)
for param in tau.named_parameters():
grad_theta_tau_xt[param[0]].append(param[1].grad.clone())
opt_tau.zero_grad()
tau_xtp1.backward([torch.FloatTensor([[1] if i==j else [0] for j in range(len(tau_xtp1))]).to(device)],retain_graph=True)
for param in tau.named_parameters():
grad_theta_tau_xtp1[param[0]].append(param[1].grad.clone())
opt_tau.zero_grad()
opt_vee.zero_grad()
avg_grad_J_tau = []
avg_grad_J_v = []
for param in tau.named_parameters():
"""
grad_theta: n_batch x n_out x n_in (matrix)
n_batch x n_out (bias)
"""
grad_theta_tau_xt_MAT = torch.stack(grad_theta_tau_xt[param[0]])
grad_theta_tau_xtp1_MAT = torch.stack(grad_theta_tau_xtp1[param[0]])
"""
Defined both gradients as in Eq.17
"""
if len(grad_theta_tau_xt_MAT.shape) == 3: # Matrix
tiled_tau_xt = tau_xt.repeat(grad_theta_tau_xt_MAT.shape[1],1,grad_theta_tau_xt_MAT.shape[2]).permute(1,0,2)
tiled_tau_xtp1 = tau_xtp1.repeat(grad_theta_tau_xtp1_MAT.shape[1],1,grad_theta_tau_xtp1_MAT.shape[2]).permute(1,0,2)
tiled_tau_t_xt = tau_t_xt.repeat(grad_theta_tau_xt_MAT.shape[1],1,grad_theta_tau_xt_MAT.shape[2]).permute(1,0,2)
tiled_tau_t_xtp1 = tau_t_xtp1.repeat(grad_theta_tau_xtp1_MAT.shape[1],1,grad_theta_tau_xtp1_MAT.shape[2]).permute(1,0,2)
else: # Bias
tiled_tau_xt = tau_xt.repeat(1,grad_theta_tau_xt_MAT.shape[1])
tiled_tau_xtp1 = tau_xtp1.repeat(1,grad_theta_tau_xtp1_MAT.shape[1])
tiled_tau_t_xt = tau_t_xt.repeat(1,grad_theta_tau_xt_MAT.shape[1])
tiled_tau_t_xtp1 = tau_t_xtp1.repeat(1,grad_theta_tau_xtp1_MAT.shape[1])
grad_J_tau = (tiled_tau_xt * grad_theta_tau_xt_MAT).mean(0) - (1 - alpha_t) * (tiled_tau_t_xt * grad_theta_tau_xt_MAT).mean(0) - alpha_t * (tiled_tau_t_xtp1 * grad_theta_tau_xtp1_MAT).mean(0) + 2*lam*v*grad_theta_tau_xt_MAT.mean(0)
grad_J_v = - (2 * lam * (tau_xt.mean() - 1 - v))
param[1].grad = grad_J_tau
vee.grad = grad_J_v
avg_grad_J_tau.append( grad_J_tau.mean().item() )
avg_grad_J_v.append( grad_J_v.mean().item() )
opt_tau.step()
opt_vee.step()
tau_t.load_state_dict(tau.state_dict())
return np.mean(avg_grad_J_tau), np.mean(avg_grad_J_v)
def validation_epoch_end(self, outputs):
acc = torch.stack([x['acc'] for x in outputs]).mean()
val_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
tensorboard_logs = {'val_ce_loss': val_loss, 'val_acc': acc}
progress_bar_metrics = tensorboard_logs
return {'val_loss': val_loss, 'log': tensorboard_logs, 'progress_bar': progress_bar_metrics}
def test_epoch_end(self, outputs):
acc = torch.stack([x['acc'] for x in outputs]).mean()
test_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
tensorboard_logs = {'test_ce_loss': test_loss, 'test_acc': acc}
progress_bar_metrics = tensorboard_logs
return {'test_loss': test_loss, 'log': tensorboard_logs, 'progress_bar': progress_bar_metrics}
def forward(self, img, save_path=None, return_prob=False):
"""Run MTCNN face detection on a PIL image. This method performs both detection and
extraction of faces, returning tensors representing detected faces rather than the bounding
boxes. To access bounding boxes, see the MTCNN.detect() method below.
Arguments:
img {PIL.Image} -- A PIL image.
Keyword Arguments:
save_path {str} -- An optional save path for the cropped image. Note that when
self.prewhiten=True, although the returned tensor is prewhitened, the saved face
image is not, so it is a true representation of the face in the input image.
(default: {None})
return_prob {bool} -- Whether or not to return the detection probability.
(default: {False})
Returns:
Union[torch.Tensor, tuple(torch.tensor, float)] -- If detected, cropped image of a face
with dimensions 3 x image_size x image_size. Optionally, the probability that a
face was detected. If self.keep_all is True, n detected faces are returned in an
n x 3 x image_size x image_size tensor with an optional list of detection
probabilities.
Example:
>>> from facenet_pytorch import MTCNN
>>> mtcnn = MTCNN()
>>> face_tensor, prob = mtcnn(img, save_path='face.png', return_prob=True)
"""
with torch.no_grad():
boxes, probs = self.detect(img)
if boxes is None:
if return_prob:
return None, [None] if self.keep_all else None
else:
return None
if not self.keep_all:
boxes = boxes[[0]]
faces = []
for i, box in enumerate(boxes):
face_path = save_path
if save_path is not None and i > 0:
save_name, ext = os.path.splitext(save_path)
face_path = save_name + '_' + str(i + 1) + ext
face = extract_face(img, box, self.image_size, self.margin, face_path)
if self.prewhiten:
face = prewhiten(face)
faces.append(face)
if self.keep_all:
faces = torch.stack(faces)
else:
faces = faces[0]
probs = probs[0]
if return_prob:
return faces, probs
else:
return faces
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
labels = torch.tensor([example["labels"] for example in examples])
return {"pixel_values": pixel_values, "labels": labels}
def get_protein_foreground_probability(
self,
adata: Optional[AnnData] = None,
indices: Optional[Sequence[int]] = None,
transform_batch: Optional[Sequence[Union[Number, str]]] = None,
protein_list: Optional[Sequence[str]] = None,
n_samples: int = 1,
batch_size: Optional[int] = None,
return_mean: bool = True,
return_numpy: Optional[bool] = None,
):
r"""
Returns the foreground probability for proteins.
This is denoted as :math:`(1 - \pi_{nt})` in the totalVI paper.
Parameters
----------
adata
AnnData object with equivalent structure to initial AnnData. If `None`, defaults to the
AnnData object used to initialize the model.
indices
Indices of cells in adata to use. If `None`, all cells are used.
transform_batch
Batch to condition on.
If transform_batch is:
- None, then real observed batch is used
- int, then batch transform_batch is used
- List[int], then average over batches in list
protein_list
Return protein expression for a subset of genes.
This can save memory when working with large datasets and few genes are
of interest.
n_samples
Number of posterior samples to use for estimation.
batch_size
Minibatch size for data loading into model. Defaults to `scvi.settings.batch_size`.
return_mean
Whether to return the mean of the samples.
return_numpy
Return a :class:`~numpy.ndarray` instead of a :class:`~pandas.DataFrame`. DataFrame includes
gene names as columns. If either `n_samples=1` or `return_mean=True`, defaults to `False`.
Otherwise, it defaults to `True`.
Returns
-------
- **foreground_probability** - probability foreground for each protein
If `n_samples` > 1 and `return_mean` is False, then the shape is `(samples, cells, genes)`.
Otherwise, shape is `(cells, genes)`. In this case, return type is :class:`~pandas.DataFrame` unless `return_numpy` is True.
"""
adata = self._validate_anndata(adata)
post = self._make_scvi_dl(adata=adata,
indices=indices,
batch_size=batch_size)
if protein_list is None:
protein_mask = slice(None)
else:
all_proteins = adata.uns["scvi_protein_names"]
protein_mask = [
True if p in protein_list else False for p in all_proteins
]
if n_samples > 1 and return_mean is False:
if return_numpy is False:
logger.warning(
"return_numpy must be True if n_samples > 1 and return_mean is False, returning np.ndarray"
)
return_numpy = True
if indices is None:
indices = np.arange(adata.n_obs)
py_mixings = []
if not isinstance(transform_batch, IterableClass):
transform_batch = [transform_batch]
transform_batch = _get_batch_code_from_category(adata, transform_batch)
for tensors in post:
x = tensors[_CONSTANTS.X_KEY]
y = tensors[_CONSTANTS.PROTEIN_EXP_KEY]
batch_index = tensors[_CONSTANTS.BATCH_KEY]
label = tensors[_CONSTANTS.LABELS_KEY]
py_mixing = torch.zeros_like(y[..., protein_mask])
if n_samples > 1:
py_mixing = torch.stack(n_samples * [py_mixing])
for b in transform_batch:
outputs = self.model.inference(
x,
y,
batch_index=batch_index,
label=label,
n_samples=n_samples,
transform_batch=b,
)
py_mixing += torch.sigmoid(
outputs["py_"]["mixing"])[..., protein_mask]
py_mixing /= len(transform_batch)
py_mixings += [py_mixing.cpu()]
if n_samples > 1:
# concatenate along batch dimension -> result shape = (samples, cells, features)
py_mixings = torch.cat(py_mixings, dim=1)
# (cells, features, samples)
py_mixings = py_mixings.permute(1, 2, 0)
else:
py_mixings = torch.cat(py_mixings, dim=0)
if return_mean is True and n_samples > 1:
py_mixings = torch.mean(py_mixings, dim=-1)
py_mixings = py_mixings.cpu().numpy()
if return_numpy is True:
return 1 - py_mixings
else:
pro_names = self.adata.uns["scvi_protein_names"]
foreground_prob = pd.DataFrame(
1 - py_mixings,
columns=pro_names[protein_mask],
index=adata.obs_names[indices],
)
return foreground_prob
def forward(self, in_modalities):
umask = in_modalities[-1]
in_modalities = in_modalities[:-2]
batch_size = in_modalities[0].shape[0]
time_stamps = in_modalities[0].shape[1]
#Unimodal
all_h = []
for modality, dim, lstm, dropout, fc in zip(in_modalities,
self.hidden_dims,
self.lstms, self.drop_outs,
self.fcs):
self.h = torch.zeros(batch_size, dim).to(self.device)
self.c = torch.zeros(batch_size, dim).to(self.device)
h = []
for t in range(time_stamps):
#Apply the mask dirrectly on the data
input_u = modality[:, t, :] * umask[:, t].unsqueeze(dim=-1)
self.h, self.c = lstm(input_u, (self.h, self.c))
self.h = torch.tanh(self.h)
self.h = dropout(self.h)
h.append(torch.tanh(fc(self.h)))
all_h.append(h)
#Multimodal
utterance_features = [torch.stack(h, dim=-2) for h in all_h]
dialogue_utterance_feature = torch.cat(utterance_features, dim=-1)
self.h_dialogue = torch.zeros(batch_size,
self.dialogue_hidden_dim).to(self.device)
self.c_dialogue = torch.zeros(batch_size,
self.dialogue_hidden_dim).to(self.device)
all_h_dialogue = []
for t in range(time_stamps):
input_m = dialogue_utterance_feature[:, t, :] * umask[:,
t].unsqueeze(
dim=-1)
self.h_dialogue, self.c_dialogue = self.dialogue_lstm(
input_m, (self.h_dialogue, self.c_dialogue))
self.h_dialogue = self.drop_out(self.h_dialogue)
all_h_dialogue.append(torch.tanh(self.fc_out(self.h_dialogue)))
output_emo = [self.smax_fc_emo(_h) for _h in all_h_dialogue]
output_act = [self.smax_fc_act(_h) for _h in all_h_dialogue]
#Stack hidden states
output_emo = torch.stack(output_emo, dim=-2)
output_act = torch.stack(output_act, dim=-2)
log_prob_emo = F.log_softmax(output_emo,
2) # batch, seq_len, n_classes
log_prob_act = F.log_softmax(output_act,
2) # batch, seq_len, n_classes
return log_prob_emo, log_prob_act
# Add to buffer.
instruction_data_cuda = [
torch.tensor(t, dtype=torch.float, device=device)
for t in instruction_data
]
replay_buffer.append(instruction_data_cuda)
# Check for minimum replay size.
if len(replay_buffer) < REPLAY_MIN:
print('Waiting for minimum buffer size ... {}/{}'.format(
len(replay_buffer), REPLAY_MIN))
continue
# Sample training mini-batch.
sampled_evaluations = replay_buffer.sample(REPLAY_SAMPLE_SIZE)
sampled_contexts = torch.stack([t[0] for t in sampled_evaluations])
sampled_states = torch.stack([t[1] for t in sampled_evaluations])
sampled_params = torch.stack([t[2] for t in sampled_evaluations])
sampled_values = torch.stack([t[3] for t in sampled_evaluations])
# Update critic.
critic_loss = torch.distributions.Normal(*critic_model(sampled_contexts, sampled_states, sampled_params)) \
.log_prob(sampled_values).mean(dim=-1)
critic_model_optimizer.zero_grad()
gen_model_optimizer.zero_grad()
(-critic_loss).backward()
torch.nn.utils.clip_grad_norm_(critic_model.parameters(), 1.0)
critic_model_optimizer.step()
# Update params model.
(macro_actions, macro_actions_entropy) = gen_model.rsample(
def __init__(self, opts):
super(CTMNet, self).__init__()
self.opts = opts
if self.opts.fsl.ctm:
# use forward_CTM method
self.epsilon = .0001
self.L = 5
self.no_bp_P_L = False
self.deactivate_CE = self.opts.ctmnet.deactivate_CE
self.use_OT_net = self.opts.ctmnet.use_OT
self.pred_source = self.opts.ctmnet.pred_source # 'both' # 'score', 'dist', 'both'
self.use_relation_net = self.opts.ctmnet.CE_use_relation
self.dnet = self.opts.ctmnet.dnet # dnet or baseline
self.dnet_out_c = self.opts.ctmnet.dnet_out_c # define the reshaper
try:
self.dnet_supp_manner = self.opts.ctmnet.dnet_supp_manner
self.mp_mean = self.opts.ctmnet.dnet_mp_mean
self.delete_mp = self.opts.ctmnet.dnet_delete_mp
self.use_discri_loss = self.opts.ctmnet.use_discri_loss
self.discri_random_target = self.opts.ctmnet.discri_random_target
self.discri_random_weight = self.opts.ctmnet.discri_random_weight
self.discri_test_update = self.opts.ctmnet.discri_test_update
self.discri_test_update_fac = self.opts.ctmnet.discri_test_update_fac
self.discri_see_weights = self.opts.ctmnet.discri_see_weights
self.discri_zz = self.opts.ctmnet.zz
except:
self.use_discri_loss = False
try:
self.baseline_manner = self.opts.ctmnet.baseline_manner
except:
self.baseline_manner = ''
else:
self.CE_loss = opts.fsl.CE_loss
self.swap = opts.fsl.swap
if self.swap:
self.swap_num = opts.fsl.swap_num
_logger = opts.logger
_logger('Building up models ...')
# feature extractor
in_c = 1 if opts.dataset.name == 'omniglot' else 3
print("-----------------CNN ENCODER-----------------")
self.repnet = feat_extract(self.opts.model.resnet_pretrain,
opts=opts,
structure=opts.model.structure,
in_c=in_c)
input_bs = opts.fsl.n_way[0] * opts.fsl.k_shot[0]
random_input = torch.rand(input_bs, in_c, opts.data.im_size,
opts.data.im_size)
repnet_out = self.repnet(random_input)
repnet_sz = repnet_out.size()
assert repnet_sz[2] == repnet_sz[3]
_logger('\trepnet output sz: {} (assume bs=n_way*k_shot)'.format(
repnet_sz))
self.c = repnet_sz[1] # supposed to be 64
self.d = repnet_sz[2]
if self.opts.fsl.ctm:
if self.use_OT_net:
self.inplanes = 4 * self.c
self.critic_sup = nn.Sequential(
self._make_layer(Bottleneck, 128, 4, stride=1),
self._make_layer(Bottleneck, 64, 3, stride=1))
self.inplanes = 4 * self.c
self.critic_que = nn.Sequential(
self._make_layer(Bottleneck, 128, 4, stride=1),
self._make_layer(Bottleneck, 64, 3, stride=1))
_embedding = repnet_out
if self.baseline_manner == 'sample_wise_similar':
assert self.opts.model.structure == 'shallow'
input_c = _embedding.size(1)
self.additional_repnet = nn.Sequential(
nn.Conv2d(input_c, input_c, kernel_size=3, padding=1),
nn.BatchNorm2d(input_c, momentum=1, affine=True),
nn.ReLU())
# RESHAPER
if not (not self.dnet and self.baseline_manner == 'no_reshaper'):
assert np.mod(self.dnet_out_c, 4) == 0
out_size = int(self.dnet_out_c / 4)
self.inplanes = _embedding.size(1)
if self.opts.model.structure.startswith('resnet'):
self.reshaper = nn.Sequential(
self._make_layer(Bottleneck, out_size * 2, 3,
stride=1),
self._make_layer(Bottleneck, out_size, 2, stride=1))
else:
print("-----------------RESHAPER-----------------")
self.reshaper = self._make_layer(Bottleneck,
out_size,
4,
stride=1,
name="reshaper")
_out_downsample = self.reshaper(_embedding)
# CONCENTRATOR AND PROJECTOR
if self.dnet:
if self.mp_mean: ## mp = main_component
self.inplanes = _embedding.size(1)
else:
# concatenate along the channel for all samples in each class
self.inplanes = self.opts.fsl.k_shot[0] * _embedding.size(
1)
if self.opts.model.structure.startswith('resnet'):
self.main_component = nn.Sequential(
self._make_layer(Bottleneck, out_size * 2, 3,
stride=1),
self._make_layer(Bottleneck, out_size, 2, stride=1))
else:
print("-----------------CONCENTRATOR-----------------")
tmp_inplaces = self.inplanes
self.main_component1 = self._make_layer(
Bottleneck,
out_size,
4,
stride=1,
name="concentrator2",
change_inplanes=True)
self.inplanes = tmp_inplaces
self.main_component2 = self._make_layer(
Bottleneck_k5,
out_size,
4,
stride=1,
name="concentrator1",
change_inplanes=True)
# projector
if self.delete_mp: ## mp = main_component
assert self.opts.fsl.k_shot[
0] == 1 ## of k=1 one shot learning, dont need concentrator
del self.main_component
# input_c for Projector, no mp
self.inplanes = self.opts.fsl.n_way[0] * _embedding.size(1)
else:
# input_c for Projector, has mp
self.inplanes = self.opts.fsl.n_way[0] * out_size * 4
if self.opts.model.structure.startswith('resnet'):
self.projection = nn.Sequential(
self._make_layer(Bottleneck, out_size * 2, 3,
stride=1),
self._make_layer(Bottleneck, out_size, 2, stride=1))
else:
print("-----------------PROJECTOR-----------------")
self.projection = self._make_layer(Bottleneck,
out_size,
4,
stride=1,
name="projector")
# deprecated; kept for legacy
if self.use_discri_loss:
# 40 x 19 x 19 = 14440
input_c = _out_downsample.size(1) * _out_downsample.size(
2) * _out_downsample.size(2)
if self.discri_zz:
self.disc_fc = nn.ModuleList([
nn.Sequential(
nn.Linear(input_c, int(input_c / 8)),
nn.BatchNorm1d(int(input_c / 8)),
nn.ReLU(),
),
MyLinear(int(input_c / 8),
256,
True,
reset_each_iter=self.discri_random_weight)
])
else:
self.disc_fc = nn.ModuleList([
nn.Sequential(nn.Linear(input_c, int(input_c / 8)),
nn.BatchNorm1d(int(input_c / 8)),
nn.ReLU(),
nn.Linear(int(input_c / 8), 256),
nn.BatchNorm1d(256), nn.ReLU()),
# nn.Linear(int(input_c/8), self.opts.fsl.n_way[0]),
MyLinear(256,
self.opts.fsl.n_way[0],
bias=(not self.discri_random_weight),
reset_each_iter=self.discri_random_weight)
])
# RELATION METRIC
if self.use_relation_net:
# relation sub_net
if hasattr(self, 'reshaper'):
_input = _out_downsample
else:
_input = _embedding
if self.opts.model.relation_net == 'res_block':
# (256); it is "2" because combining two embedding
self.inplanes = 2 * _input.size(1)
self.relation1 = self._make_layer(Bottleneck,
32,
2,
stride=2)
self.relation2 = self._make_layer(Bottleneck,
16,
2,
stride=1)
_combine = torch.stack([_input, _input],
dim=1).view(_input.size(0), -1,
_input.size(2),
_input.size(3))
_out = self.relation2(self.relation1(_combine))
self.fc_input_c = _out.size(1) * _out.size(2) * _out.size(
3)
_half = int(self.fc_input_c / 2)
self.fc = nn.Sequential(nn.Linear(self.fc_input_c, _half),
nn.BatchNorm1d(_half),
nn.ReLU(inplace=True),
nn.Linear(_half, 1))
elif self.opts.model.relation_net == 'simple':
input_c = 2 * _input.size(1)
self.relation1 = nn.Sequential(
nn.Conv2d(input_c, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64, momentum=1, affine=True), nn.ReLU(),
nn.MaxPool2d(2))
self.relation2 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64, momentum=1, affine=True),
nn.ReLU(),
# nn.MaxPool2d(2)
)
_combine = torch.stack([_input, _input],
dim=1).view(_input.size(0), -1,
_input.size(2),
_input.size(3))
_out = self.relation2(self.relation1(_combine))
self.fc_input_c = _out.size(1) * _out.size(2) * _out.size(
3)
_half = int(self.fc_input_c / 2)
self.fc = nn.Sequential(
nn.Linear(self.fc_input_c, _half),
nn.ReLU(),
nn.Linear(_half,
1), # except no sigmoid since we use CE
)
else:
# the original relation network
self.inplanes = 2 * self.c
# the original network in the relation net
# after the relation module (three layers)
self.relation1 = self._make_layer(Bottleneck, 128, 4, stride=2)
self.relation2 = self._make_layer(Bottleneck, 64, 3, stride=2)
if self.CE_loss:
self.fc = nn.Sequential(nn.Linear(256, 64), nn.BatchNorm1d(64),
nn.ReLU(inplace=True),
nn.Linear(64, 1))
else:
self.fc = nn.Sequential(
nn.Linear(256, 64),
nn.BatchNorm1d(64),
nn.ReLU(inplace=True),
nn.Linear(64, 1),
nn.Sigmoid() # the only difference
)
combine = torch.stack([repnet_out, repnet_out],
dim=1).view(repnet_out.size(0), -1,
repnet_out.size(2),
repnet_out.size(3))
out = self.relation2(self.relation1(combine))
_logger('\tafter layer5 sz: {} (assume bs=2)\n'.format(out.size()))
self.pool_size = out.size(2)
self._initialize_weights()
