def run_static(self, use_gpu=False):
x = paddle.fluid.data(name='input', shape=[10, 10], dtype='float32')
x2 = paddle.fluid.data(name='input2', shape=[2], dtype='float32')
result0 = F.normalize(x)
result1 = F.normalize(x, p=1.5)
result2 = F.normalize(x, axis=0)
result3 = F.normalize(x, name='aaa')
result4 = F.normalize(x2, axis=0)
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
static_result = exe.run(
feed={
"input": self.input_np,
"input2": self.input_np2
},
fetch_list=[result0, result1, result2, result4])
self.assertTrue(np.allclose(static_result[0], self.expected0))
self.assertTrue(np.allclose(static_result[1], self.expected1))
self.assertTrue(np.allclose(static_result[2], self.expected2))
self.assertTrue('aaa' in result3.name)
self.assertTrue(np.allclose(static_result[3], self.expected3))
self.assertRaises(ValueError, F.normalize, x2)
def forward(self, x):
x_shape = paddle.shape(x)
x = x.flatten(2)
mu = paddle.tile(self.mu, [x_shape[0], 1, 1])
with paddle.no_grad():
for i in range(self.stage_num):
x_t = paddle.transpose(x, [0, 2, 1])
z = paddle.bmm(x_t, mu)
z = F.softmax(z, axis=2)
z_ = F.normalize(z, axis=1, p=1)
mu = paddle.bmm(x, z_)
mu = F.normalize(mu, axis=1, p=2)
z_t = paddle.transpose(z, [0, 2, 1])
x = paddle.matmul(mu, z_t)
x = paddle.reshape(x, [0, self.c, x_shape[2], x_shape[3]])
if self.training:
mu = paddle.mean(mu, 0, keepdim=True)
mu = F.normalize(mu, axis=1, p=2)
mu = self.mu * (1 - self.momentum) + mu * self.momentum
if paddle.distributed.get_world_size() > 1:
mu = paddle.distributed.all_reduce(mu)
mu /= paddle.distributed.get_world_size()
self.mu = mu
return x
def forward(self, x):
b, c, h, w = x.shape
x = paddle.reshape(x, [b, c, h * w])
mu = paddle.tile(self.mu, [b, 1, 1])
with paddle.no_grad():
for i in range(self.stage_num):
x_t = paddle.transpose(x, [0, 2, 1])
z = paddle.bmm(x_t, mu)
z = F.softmax(z, axis=2)
z_ = F.normalize(z, axis=1, p=1)
mu = paddle.bmm(x, z_)
mu = F.normalize(mu, axis=1, p=2)
z_t = paddle.transpose(z, [0, 2, 1])
x = paddle.matmul(mu, z_t)
x = paddle.reshape(x, [b, c, h, w])
if self.training:
mu = paddle.mean(mu, 0, keepdim=True)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.reduce(
mu / paddle.distributed.get_world_size(), 0)
mu = F.normalize(mu, axis=1, p=2)
self.mu = self.mu * (1 - self.momentum) + mu * self.momentum
return x
def apply(layer, name, n_power_iterations, dim, eps):
for k, hook in layer._forward_pre_hooks.items():
if isinstance(hook, SpectralNorm) and hook.name == name:
raise RuntimeError("Cannot register two spectral_norm hooks on "
"the same parameter {}".format(name))
fn = SpectralNorm(name, n_power_iterations, dim, eps)
weight = layer._parameters[name]
with paddle.no_grad():
weight_mat = fn.reshape_weight_to_matrix(weight)
h, w = weight_mat.shape
# randomly initialize u and v
u = layer.create_parameter([h])
u = normal_(u, 0., 1.)
v = layer.create_parameter([w])
v = normal_(v, 0., 1.)
u = F.normalize(u, axis=0, epsilon=fn.eps)
v = F.normalize(v, axis=0, epsilon=fn.eps)
# delete fn.name form parameters, otherwise you can not set attribute
del layer._parameters[fn.name]
layer.add_parameter(fn.name + "_orig", weight)
# still need to assign weight back as fn.name because all sorts of
# things may assume that it exists, e.g., when initializing weights.
# However, we can't directly assign as it could be an Parameter and
# gets added as a parameter. Instead, we register weight * 1.0 as a plain
# attribute.
setattr(layer, fn.name, weight * 1.0)
layer.register_buffer(fn.name + "_u", u)
layer.register_buffer(fn.name + "_v", v)
layer.register_forward_pre_hook(fn)
return fn
def forward(self, x):
x0 = self.linear0(x[0])
x1 = self.linear1(x[1])
bs = x1.shape[0]
if self.dropout_input > 0:
x0 = F.dropout(x0, p=self.dropout_input, training=self.training)
x1 = F.dropout(x1, p=self.dropout_input, training=self.training)
x0_chunks = paddle.split(x0, self.chunks, -1)
x1_chunks = paddle.split(x1, self.chunks, -1)
zs = []
for x0_c, x1_c, m0, m1 in zip(x0_chunks, x1_chunks, self.merge_linears0,
self.merge_linears1):
m = m0(x0_c) * m1(x1_c) # bs x split_size*rank
m = m.reshape([bs, self.rank, -1])
z = paddle.sum(m, 1)
if self.pos_norm == 'before_cat':
z = paddle.sqrt(F.relu(z)) - paddle.sqrt(F.relu(-z))
z = F.normalize(z)
zs.append(z)
z = paddle.concat(zs, 1)
if self.pos_norm == 'after_cat':
z = paddle.sqrt(F.relu(z)) - paddle.sqrt(F.relu(-z))
z = F.normalize(z)
if self.dropout_pre_lin > 0:
z = F.dropout(z, p=self.dropout_pre_lin, training=self.training)
z = self.linear_out(z)
if self.dropout_output > 0:
z = F.dropout(z, p=self.dropout_output, training=self.training)
return z
def forward(self, input, label):
# lambda = max(lambda_min,base*(1+gamma*iteration)^(-power))
self.iter += 1
self.lamb = max(
self.LambdaMin,
self.base * (1 + self.gamma * self.iter)**(-1 * self.power))
# --------------------------- cos(theta) & phi(theta) ---------------------------
self.linear.weight.Tensor = F.normalize(self.linear.weight)
x = F.normalize(input)
cos_theta = self.linear(x)
cos_theta = cos_theta.clip(min=-1, max=1)
cos_m_theta = self.mlambda[self.m](cos_theta)
theta = cos_theta.acos()
k = paddle.floor(self.m * theta / 3.14159265)
phi_theta = paddle.to_tensor(((-1.0)**k) * cos_m_theta - 2 * k)
NormOfFeature = paddle.norm(input, p=2, axis=1)
# --------------------------- convert label to one-hot ---------------------------
one_hot = F.one_hot(label, num_classes=phi_theta.shape[1])
one_hot = paddle.reshape(one_hot,
(phi_theta.shape[0], phi_theta.shape[1]))
# --------------------------- Calculate output ---------------------------
output = (one_hot * (phi_theta - cos_theta) /
(1 + self.lamb)) + cos_theta
output *= NormOfFeature.reshape((-1, 1))
return output
def compute_weight(self, module, do_power_iteration):
weight = getattr(module, self.name + '_orig')
u = getattr(module, self.name + '_u')
v = getattr(module, self.name + '_v')
weight_mat = self.reshape_weight_to_matrix(weight)
if do_power_iteration:
with paddle.no_grad():
for _ in range(self.n_power_iterations):
v.set_value(
F.normalize(
paddle.matmul(weight_mat,
u,
transpose_x=True,
transpose_y=False),
axis=0,
epsilon=self.eps,
))
u.set_value(
F.normalize(
paddle.matmul(weight_mat, v),
axis=0,
epsilon=self.eps,
))
if self.n_power_iterations > 0:
u = u.clone()
v = v.clone()
sigma = paddle.dot(u, paddle.mv(weight_mat, v))
weight = weight / sigma
return weight
def forward(self, logits, label):
logits = F.normalize(logits, p=2, axis=1, epsilon=self.eps)
wn = F.normalize(self.w, p=2, axis=0, epsilon=self.eps)
cosine = paddle.matmul(logits, wn)
y = paddle.zeros((logits.shape[0], self.n_classes))
for i in range(logits.shape[0]):
y[i, label[i]] = self.margin
pred = F.log_softmax((cosine - y) * self.scale, -1)
return self.nll_loss(pred, label), pred
def forward(self, audio_sequences,
face_sequences): # audio_sequences := (B, dim, T)
face_embedding = self.face_encoder(face_sequences)
audio_embedding = self.audio_encoder(audio_sequences)
audio_embedding = audio_embedding.reshape(
[audio_embedding.shape[0], -1])
face_embedding = face_embedding.reshape([face_embedding.shape[0], -1])
audio_embedding = F.normalize(audio_embedding, p=2, axis=1)
face_embedding = F.normalize(face_embedding, p=2, axis=1)
return audio_embedding, face_embedding
def forward(self, input, label):
cosine = F.linear(F.normalize(input), F.normalize(self.weight))
sine = paddle.sqrt(
paddle.clip(1.0 - paddle.pow(cosine, 2), min=0, max=1))
phi = cosine * self.cos_m - sine * self.sin_m
if self.easy_margin:
phi = paddle.where(cosine > 0, phi, cosine)
else:
phi = paddle.where(cosine > self.th, phi, cosine - self.mm)
one_hot = paddle.nn.functional.one_hot(label, self.class_dim)
output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
output *= self.s
return output
def forward(self,
neck_feat,
inputs,
bboxes=None,
bbox_inds=None,
topk_clses=None):
reid_feat = self.reid(neck_feat)
if self.training:
if self.num_classes == 1:
loss = self.get_loss(reid_feat, inputs)
else:
loss = self.get_mc_loss(reid_feat, inputs)
return loss
else:
assert bboxes is not None and bbox_inds is not None
reid_feat = F.normalize(reid_feat)
embedding = paddle.transpose(reid_feat, [0, 2, 3, 1])
embedding = paddle.reshape(embedding, [-1, self.ch_emb])
# embedding shape: [bs * h * w, ch_emb]
if self.num_classes == 1:
pred_dets = bboxes
pred_embs = paddle.gather(embedding, bbox_inds)
else:
pred_dets, pred_embs = self.process_by_class(
bboxes, embedding, bbox_inds, topk_clses)
return pred_dets, pred_embs
def forward(self, node_feat, edge_feat):
# get size
num_tasks = node_feat.shape[0]
num_data = node_feat.shape[1]
# get eye matrix (batch_size x 2 x node_size x node_size)
diag_mask = 1.0 - paddle.expand(
paddle.eye(num_data),
[num_tasks, self.edge_dim, num_data, num_data])
# set diagonal as zero and normalize
edge_feat = F.normalize(edge_feat * diag_mask, p=1, axis=-1)
# compute attention and aggregate
aggr_feat = paddle.bmm(
paddle.concat(paddle.split(edge_feat, 2, 1),
self.edge_dim).squeeze(1), node_feat)
node_feat = paddle.transpose(
paddle.concat(
[node_feat,
paddle.concat(paddle.split(aggr_feat, 2, 1), -1)], -1),
(0, 2, 1))
# non-linear transform
node_feat = paddle.transpose(self.network(node_feat.unsqueeze(-1)),
(0, 2, 1, 3)).squeeze(-1)
return node_feat
def forward(self, input, label):
# --------------------------- cos(theta) & phi(theta) ---------------------------
self.linear.weight.Tensor = F.normalize(self.linear.weight)
x = F.normalize(input)
cosine = self.linear(x)
phi = cosine - self.m
# --------------------------- convert label to one-hot ---------------------------
label = label.astype(dtype='int64').flatten()
one_hot = F.one_hot(label, num_classes=phi.shape[1])
# -------------torch.where(out_i = {x_i if condition_i else y_i) -------------
output = (one_hot * phi) + (
(1.0 - one_hot) * cosine
) # you can use torch.where if your torch.__version__ is 0.4
output *= self.s
return output
def accumulate(self):
logger.info("Computing pairwise similairity...")
assert len(self.embedding) == len(self.id_labels)
if len(self.embedding) < 1:
return None
embedding = paddle.stack(self.embedding, axis=0)
emb = F.normalize(embedding, axis=1).numpy()
pdist = np.matmul(emb, emb.T)
id_labels = np.array(self.id_labels, dtype='int32').reshape(-1, 1)
n = len(id_labels)
id_lbl = np.tile(id_labels, n).T
gt = id_lbl == id_lbl.T
up_triangle = np.where(np.triu(pdist) - np.eye(n) * pdist != 0)
pdist = pdist[up_triangle]
gt = gt[up_triangle]
# lazy import metrics here
from sklearn import metrics
far, tar, threshold = metrics.roc_curve(gt, pdist)
interp = interpolate.interp1d(far, tar)
tar_at_far = [interp(x) for x in self.far_levels]
for f, fa in enumerate(self.far_levels):
self.eval_results['TPR@FAR={:.7f}'.format(fa)] = ' {:.4f}'.format(
tar_at_far[f])
def get_emb_and_gt_outs(self, ide_outs, targets):
emb_and_gts = []
for i, p_ide in enumerate(ide_outs):
t_conf = targets['tconf{}'.format(i)]
t_ide = targets['tide{}'.format(i)]
p_ide = p_ide.transpose((0, 2, 3, 1))
p_ide_flatten = paddle.reshape(p_ide, [-1, self.embedding_dim])
mask = t_conf > 0
mask = paddle.cast(mask, dtype="int64")
emb_mask = mask.max(1).flatten()
emb_mask_inds = paddle.nonzero(emb_mask > 0).flatten()
if len(emb_mask_inds) > 0:
t_ide_flatten = paddle.reshape(t_ide.max(1), [-1, 1])
tids = paddle.gather(t_ide_flatten, emb_mask_inds)
embedding = paddle.gather(p_ide_flatten, emb_mask_inds)
embedding = self.emb_scale * F.normalize(embedding)
emb_and_gt = paddle.concat([embedding, tids], axis=1)
emb_and_gts.append(emb_and_gt)
if len(emb_and_gts) > 0:
return paddle.concat(emb_and_gts, axis=0)
else:
return paddle.zeros((1, self.embedding_dim + 1))
def forward(self, graph, feature, act=None):
"""
Args:
graph: `pgl.Graph` instance.
feature: A tensor with shape (num_nodes, input_size)
act: (default None) Activation for outputs and before normalize.
Return:
A tensor with shape (num_nodes, output_size)
"""
def _send_func(src_feat, dst_feat, edge_feat):
return {"msg": src_feat["h"]}
def _recv_func(message):
return getattr(message, self.aggr_func)(message["msg"])
msg = graph.send(_send_func, src_feat={"h": feature})
neigh_feature = graph.recv(reduce_func=_recv_func, msg=msg)
self_feature = self.self_linear(feature)
neigh_feature = self.neigh_linear(neigh_feature)
output = self_feature + neigh_feature
if act is not None:
output = getattr(F, act)(output)
output = F.normalize(output, axis=1)
return output
def __init__(self,
base_encoder,
dim=128,
queue_size=65536,
momentum=0.999,
scale=50,
margin=0.3):
super(DCQ, self).__init__()
self.queue_size = queue_size
self.momentum = momentum
self.scale = scale
self.margin = margin
# create the encoders
# num_classes is the output fc dimension
self.encoder_q = base_encoder(num_classes=dim, name_prefix='q')
self.encoder_k = base_encoder(num_classes=dim, name_prefix='k')
for param_q, param_k in zip(
self.encoder_q.parameters(include_sublayers=True),
self.encoder_k.parameters(include_sublayers=True)):
param_k.stop_gradient = True
param_q.set_value(param_k)
self.register_buffer("weight_queue", paddle.randn([dim, queue_size]))
self.weight_queue = normalize(self.weight_queue, axis=0)
self.register_buffer("label_queue", paddle.randn([1, queue_size]))
self.register_buffer("queue_ptr", paddle.zeros([
1,
], dtype='int64'))
def emb_loss(self, p_ide, t_conf, t_ide, emb_scale, classifier):
emb_dim = p_ide.shape[1]
p_ide = p_ide.transpose((0, 2, 3, 1))
p_ide_flatten = paddle.reshape(p_ide, [-1, emb_dim])
mask = t_conf > 0
mask = paddle.cast(mask, dtype="int64")
mask.stop_gradient = True
emb_mask = mask.max(1).flatten()
emb_mask_inds = paddle.nonzero(emb_mask > 0).flatten()
emb_mask_inds.stop_gradient = True
# use max(1) to decide the id, TODO: more reseanable strategy
t_ide_flatten = t_ide.max(1).flatten()
t_ide_flatten = paddle.cast(t_ide_flatten, dtype="int64")
valid_inds = paddle.nonzero(t_ide_flatten != -1).flatten()
if emb_mask_inds.numel() == 0 or valid_inds.numel() == 0:
# loss_ide = paddle.to_tensor([0]) # will be error in gradient backward
loss_ide = self.phony * 0 # todo
else:
embedding = paddle.gather(p_ide_flatten, emb_mask_inds)
embedding = emb_scale * F.normalize(embedding)
logits = classifier(embedding)
ide_target = paddle.gather(t_ide_flatten, emb_mask_inds)
loss_ide = F.cross_entropy(logits,
ide_target,
ignore_index=-1,
reduction='mean')
loss_ide.stop_gradient = False
return loss_ide
def run_imperative(self):
x = paddle.to_tensor(self.input_np)
y = F.normalize(x)
self.assertTrue(np.allclose(y.numpy(), self.expected0))
y = F.normalize(x, p=1.5)
self.assertTrue(np.allclose(y.numpy(), self.expected1))
y = F.normalize(x, axis=0)
self.assertTrue(np.allclose(y.numpy(), self.expected2))
x = paddle.to_tensor(self.input_np2)
y = F.normalize(x, axis=0)
self.assertTrue(np.allclose(y.numpy(), self.expected3))
self.assertRaises(BaseException, F.normalize, x)
def prepare(self, label, optimizer):
# label [64, 1]
total_label = label.detach()
self.sample(total_label)
optimizer._parameter_list[0] = self.sub_weight
norm_weight = normalize(self.sub_weight)
return total_label, norm_weight
def forward(self, embedding, targets):
if isinstance(embedding, dict):
embedding = embedding['features']
# Normalize embedding features
embedding = F.normalize(embedding, axis=1)
dist_mat = paddle.matmul(embedding, embedding, transpose_y=True)
N = dist_mat.shape[0]
is_pos = targets.reshape([N, 1]).expand([N, N]).equal(
paddle.t(targets.reshape([N, 1]).expand([N, N]))).astype('float')
is_neg = targets.reshape([N, 1]).expand([N, N]).not_equal(
paddle.t(targets.reshape([N, 1]).expand([N, N]))).astype('float')
# Mask scores related to itself
is_pos = is_pos - paddle.eye(N, N)
s_p = dist_mat * is_pos
s_n = dist_mat * is_neg
logit_p = -self.gamma * s_p + (-99999999.) * (1 - is_pos)
logit_n = self.gamma * (s_n + self.margin) + (-99999999.) * (1 -
is_neg)
loss = F.softplus(
paddle.logsumexp(logit_p, axis=1) +
paddle.logsumexp(logit_n, axis=1)).mean()
return {"PairwiseCosface": loss}
def get_pooled_embedding(self,
input_ids,
token_type_ids=None,
position_ids=None):
src_mask = input_ids == self.bos_id
src_mask = paddle.cast(src_mask, "float32")
# [bs, 1, 1, max_len]
src_mask = paddle.unsqueeze(src_mask, axis=[1, 2])
src_mask.stop_gradient = True
ones = paddle.ones_like(input_ids, dtype="int64")
seq_length = paddle.cumsum(ones, axis=1)
position_ids = seq_length - ones
position_ids.stop_gradient = True
embedding_output = self.ptm.embeddings(input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids)
if self.use_fp16:
embedding_output = paddle.cast(embedding_output, 'float16')
sequence_output = self.ptm.encoder(embedding_output, src_mask)
if self.use_fp16:
sequence_output = paddle.cast(sequence_output, 'float32')
cls_embedding = self.ptm.pooler(sequence_output)
if self.output_emb_size > 0:
cls_embedding = self.emb_reduce_linear(cls_embedding)
cls_embedding = self.dropout(cls_embedding)
cls_embedding = F.normalize(cls_embedding, p=2, axis=-1)
return cls_embedding
def forward(self, student, teacher):
# reshape for feature map distillation
bs = student.shape[0]
student = student.reshape([bs, -1])
teacher = teacher.reshape([bs, -1])
td = (teacher.unsqueeze(0) - teacher.unsqueeze(1))
norm_td = F.normalize(td, p=2, axis=2)
t_angle = paddle.bmm(norm_td, norm_td.transpose([0, 2, 1])).reshape(
[-1, 1])
sd = (student.unsqueeze(0) - student.unsqueeze(1))
norm_sd = F.normalize(sd, p=2, axis=2)
s_angle = paddle.bmm(norm_sd, norm_sd.transpose([0, 2, 1])).reshape(
[-1, 1])
loss = F.smooth_l1_loss(s_angle, t_angle, reduction='mean')
return loss
def forward(self, feature, label):
cos_theta = paddle.mm(F.normalize(feature, axis=1),
F.normalize(self.weight, axis=0))
sin_theta = paddle.sqrt(
paddle.clip(1.0 - paddle.pow(cos_theta, 2), min=0, max=1))
cos_theta_m = cos_theta * self.cos_m - sin_theta * self.sin_m
cos_theta_m = paddle.where(cos_theta > self.threshold, cos_theta_m,
cos_theta - self.mm)
one_hot = paddle.nn.functional.one_hot(label, self.class_dim)
output = (one_hot * cos_theta_m) + (paddle.abs(
(1.0 - one_hot)) * cos_theta)
output *= self.s
# 简单的分类方法，学习率需要设置为0.1
# cosine = self.cosine_sim(feature, self.weight)
# one_hot = paddle.nn.functional.one_hot(label, self.class_dim)
# output = self.s * (cosine - one_hot * self.m)
return output
def forward(self, feat, inputs):
reid_feat = self.reid(feat)
if self.training:
loss = self.get_loss(reid_feat, inputs)
return loss
else:
reid_feat = F.normalize(reid_feat)
return reid_feat
def forward(self, logits, targets):
logits = F.normalize(logits, p=2, axis=1, epsilon=1e-8)
wn = F.normalize(self.w, p=2, axis=0, epsilon=1e-8)
cosine = logits @ wn
sine = paddle.sqrt(1.0 - paddle.square(cosine))
phi = cosine * self.cos_m - sine * self.sin_m # cos(theta + m)
if self.easy_margin:
phi = paddle.where(cosine > 0, phi, cosine)
else:
phi = paddle.where(cosine > self.th, phi, cosine - self.mm)
target_one_hot = F.one_hot(targets, self.n_classes)
outputs = (target_one_hot * phi) + (
(1.0 - target_one_hot) * cosine) - target_one_hot * self.margin2
outputs = self.scale * outputs
pred = F.log_softmax(outputs, axis=-1)
return self.nll_loss(pred, targets), pred
def forward(self, graph, u_feat, i_feat):
h = paddle.concat([u_feat, i_feat])
embs = [h]
for i in range(self.n_layers):
h = self.ngcfs[i](graph, h)
norm_h = F.normalize(h, p=2, axis=1)
embs.append(norm_h)
embs = paddle.concat(embs, axis=1)
users, items = paddle.split(embs, [u_feat.shape[0], i_feat.shape[0]])
return users, items
def __init__(self, c, k, stage_num=3, momentum=0.1):
super(EMAU, self).__init__()
assert stage_num >= 1
self.stage_num = stage_num
self.momentum = momentum
tmp_mu = self.create_parameter(
shape=[1, c, k],
default_initializer=paddle.nn.initializer.KaimingNormal(k))
self.mu = F.normalize(paddle.to_tensor(tmp_mu), axis=1, p=2)
self.register_buffer('bases', self.mu)
def forward(self, analogy_a, analogy_b, analogy_c, true_word, all_label):
emb_a = self.embedding(analogy_a)
emb_b = self.embedding(analogy_b)
emb_c = self.embedding(analogy_c)
emb_all_label = self.embedding(all_label)
target = emb_b - emb_a + emb_c
emb_all_label_l2 = F.normalize(emb_all_label, axis=1)
dist = paddle.matmul(x=target, y=emb_all_label_l2, transpose_y=True)
values, pred_idx = paddle.topk(x=dist, k=4)
return values, pred_idx
def get_mc_loss(self, feat, inputs):
# feat.shape = [bs, ch_emb, h, w]
assert 'cls_id_map' in inputs and 'cls_tr_ids' in inputs
index = inputs['index']
mask = inputs['index_mask']
cls_id_map = inputs['cls_id_map'] # [bs, h, w]
cls_tr_ids = inputs['cls_tr_ids'] # [bs, num_classes, h, w]
feat = paddle.transpose(feat, perm=[0, 2, 3, 1])
feat_n, feat_h, feat_w, feat_c = feat.shape
feat = paddle.reshape(feat, shape=[feat_n, -1, feat_c])
index = paddle.unsqueeze(index, 2)
batch_inds = list()
for i in range(feat_n):
batch_ind = paddle.full(
shape=[1, index.shape[1], 1], fill_value=i, dtype='int64')
batch_inds.append(batch_ind)
batch_inds = paddle.concat(batch_inds, axis=0)
index = paddle.concat(x=[batch_inds, index], axis=2)
feat = paddle.gather_nd(feat, index=index)
mask = paddle.unsqueeze(mask, axis=2)
mask = paddle.expand_as(mask, feat)
mask.stop_gradient = True
feat = paddle.masked_select(feat, mask > 0)
feat = paddle.reshape(feat, shape=[-1, feat_c])
reid_losses = 0
for cls_id, id_num in self.num_identities_dict.items():
# target
cur_cls_tr_ids = paddle.reshape(
cls_tr_ids[:, cls_id, :, :], shape=[feat_n, -1]) # [bs, h*w]
cls_id_target = paddle.gather_nd(cur_cls_tr_ids, index=index)
mask = inputs['index_mask']
cls_id_target = paddle.masked_select(cls_id_target, mask > 0)
cls_id_target.stop_gradient = True
# feat
cls_id_feat = self.emb_scale_dict[str(cls_id)] * F.normalize(feat)
cls_id_pred = self.classifiers[str(cls_id)](cls_id_feat)
loss = self.reid_loss(cls_id_pred, cls_id_target)
valid = (cls_id_target != self.reid_loss.ignore_index)
valid.stop_gradient = True
count = paddle.sum((paddle.cast(valid, dtype=np.int32)))
count.stop_gradient = True
if count > 0:
loss = loss / count
reid_losses += loss
return reid_losses