def build(self, inputs, targets):
"""
Args:
inputs (torch.Tensor): feature matrix with shape (batch_size, feat_dim).
targets (torch.LongTensor): ground truth labels with shape (num_classes).
"""
n = inputs.shape[0]
dist = math.reduce_sum(math.pow(
inputs, flow.constant_like(inputs, 2, dtype=flow.float32)),
axis=1)
shape_tensor = flow.constant(value=0.0,
dtype=flow.float32,
shape=(n, n))
dist = flow.broadcast_like(dist, like=shape_tensor, broadcast_axes=[1])
dist = math.add(
dist, flow.transpose(dist, perm=(1, 0),
batch_axis_non_change=True))
temp1 = math.multiply(
-2,
flow.matmul(
inputs,
flow.transpose(inputs, perm=(1, 0),
batch_axis_non_change=True)))
dist = math.add(dist, temp1)
dist = math.sqrt(flow.clamp(dist, min_value=1e-12))
mask = math.equal(
flow.broadcast_like(targets, like=shape_tensor,
broadcast_axes=[1]),
flow.transpose(flow.broadcast_like(targets,
like=shape_tensor,
broadcast_axes=[1]),
perm=(1, 0),
batch_axis_non_change=True))
mask_rev = math.not_equal(
flow.broadcast_like(targets, like=shape_tensor,
broadcast_axes=[1]),
flow.transpose(flow.broadcast_like(targets,
like=shape_tensor,
broadcast_axes=[1]),
perm=(1, 0),
batch_axis_non_change=True))
dist_ap, dist_an = [], []
for i in range(n):
temp_dist = flow.slice_v2(dist, [(i, i + 1, 1)])
temp_mask = flow.slice_v2(mask, [(i, i + 1, 1)])
temp_mask_rev = flow.slice_v2(mask_rev, [(i, i + 1, 1)])
dist_ap.append(
math.reduce_max(
flow.gather_nd(temp_dist, flow.where(temp_mask))))
dist_an.append(
math.reduce_min(
flow.gather_nd(temp_dist, flow.where(temp_mask_rev))))
dist_ap = flow.concat(dist_ap, 0)
dist_an = flow.concat(dist_an, 0)
y = flow.ones_like(dist_an)
# return dist_an, dist_ap, y
return self._MarginRankingLoss(dist_an, dist_ap, y)
def test_job(
x: oft.Numpy.Placeholder(input_shape, dtype=flow.float32),
addend: oft.Numpy.Placeholder(input_shape, dtype=flow.float32),
):
v = flow.get_variable(
name="v",
shape=(1, ),
dtype=flow.float32,
initializer=flow.zeros_initializer(),
)
x = x + v
addend = addend + v
x1 = flow.identity(x)
x2 = flow.identity(x)
addend1 = flow.identity(addend)
addend2 = flow.identity(addend)
flow.watch_diff(x1, test_global_storage.Setter("x1_diff"))
flow.watch_diff(x2, test_global_storage.Setter("x2_diff"))
flow.watch_diff(addend1, test_global_storage.Setter("addend1_diff"))
flow.watch_diff(addend2, test_global_storage.Setter("addend2_diff"))
x1 = flow.cast(x1, data_type)
x2 = flow.cast(x2, data_type)
addend1 = flow.cast(addend1, data_type)
addend2 = flow.cast(addend2, data_type)
y1 = flow.layers.batch_normalization_add_relu(x1,
addend=addend1,
axis=axis,
name="BN1")
y2 = flow.math.relu(
flow.layers.batch_normalization(x2, axis=axis, name="BN2") +
addend2)
y1 = flow.cast(y1, flow.float32)
y2 = flow.cast(y2, flow.float32)
flow.watch(y1, test_global_storage.Setter("y1"))
flow.watch(y2, test_global_storage.Setter("y2"))
y1 = flow.where(flow.math.greater(y2, v), y1, v)
y2 = flow.where(flow.math.greater(y1, v), y2, v)
loss = y1 + y2
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler([],
[0.001]),
momentum=0).minimize(flow.math.reduce_sum(loss))
return loss
def do_where(condition, x, y):
with flow.scope.placement(device_type, "0:0"):
x_var = flow.get_variable(
"x",
shape=x.shape,
dtype=flow.float,
initializer=flow.constant_initializer(0),
)
x_var = flow.cast_to_current_logical_view(x_var)
x_var = x_var + x
y_var = flow.get_variable(
"y",
shape=y.shape,
dtype=flow.float,
initializer=flow.constant_initializer(0),
)
y_var = flow.cast_to_current_logical_view(y_var)
y_var = y_var + y
z = flow.where(condition, x_var, y_var)
with flow.scope.placement(device_type, "0:0"):
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler(
[], [1e-3]),
momentum=0).minimize(z)
flow.watch_diff(x_var, dz_dx_watcher)
flow.watch_diff(y_var, dz_dy_watcher)
return z
def do_where(condition, x, y):
with flow.scope.placement(device_type, "0:0"):
x_var = flow.get_variable(
"x",
shape=x.shape,
dtype=flow.float,
initializer=flow.constant_initializer(0),
)
x_var = flow.cast_to_current_logical_view(x_var)
x_var = x_var + x
y_var = flow.get_variable(
"y",
shape=y.shape,
dtype=flow.float,
initializer=flow.constant_initializer(0),
)
y_var = flow.cast_to_current_logical_view(y_var)
y_var = y_var + y
z = flow.where(condition, x_var, y_var)
with flow.scope.placement(device_type, "0:0"):
flow.losses.add_loss(z)
flow.watch_diff(x_var, dz_dx_watcher)
flow.watch_diff(y_var, dz_dy_watcher)
return z
def _test_where_scalar(test_case, device):
x = 0.5
y = 2.0
condition = flow.tensor(np.array([1]), dtype=flow.int32)
of_out = flow.where(condition, x, y)
np_out = np.array([0.5])
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05))
def _test_where_backward(test_case, device):
x = flow.tensor(
np.array([[-0.462, 0.3139], [0.3898, -0.7197], [0.0478, -0.1657]]),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
y = flow.tensor(
np.ones(shape=(3, 2)),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
condition = flow.tensor(np.array([[0, 1], [1, 0], [1, 0]]),
dtype=flow.int32,
device=flow.device(device))
of_out = flow.where(condition, x, y)
of_out = of_out.sum()
of_out.backward()
test_case.assertTrue(
np.allclose(x.grad.numpy(),
condition.numpy() == 1, 1e-05, 1e-05))
test_case.assertTrue(
np.allclose(y.grad.numpy(),
condition.numpy() == 0, 1e-05, 1e-05))
def forward(self, cosine: flow.Tensor, label):
index = flow.where(label != -1)[0]
m_hot = flow.zeros(index.size()[0],
cosine.size()[1],
device=cosine.device)
m_hot.scatter_(1, label[index, None], self.m)
cosine.acos_()
cosine[index] += m_hot
cosine.cos_().mul_(self.s)
return cosine
def forward(self, cosine, label):
index = flow.where(label != -1)[0]
m_hot = flow.zeros(index.size()[0],
cosine.size()[1],
device=cosine.device)
m_hot = flow.scatter(m_hot, 1, label[index, None], self.m)
cosine = cosine[index] - m_hot
ret = cosine * self.s
return ret
def _test_where_x_y_none(test_case, device):
condition = flow.tensor(
np.array([[[-0.462, 0.3139], [0.3898, -0.7197], [0.0478, -0.1657]]]),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
of_out = flow.where(condition)
of_nonzero = flow.nonzero(condition, as_tuple=True)
for i in range(len(of_out)):
test_case.assertTrue(
np.allclose(of_out[i].numpy(), of_nonzero[i].numpy(), 1e-05,
1e-05))
def masked_fill(
x: remote_blob_util.BlobDef,
mask: remote_blob_util.BlobDef,
value: Union[float, int],
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
r"""Fill a blob with a given value according to the given mask.
Args:
x (remote_blob_util.BlobDef): Input Blob.
mask (remote_blob_util.BlobDef): Composed with 0 and 1, the input blob 'x' will be
filled with the given value where the mask is 1.
value (Union[int, int]): The value to use for filling the input blob.
name (Optional[str], optional): The name for the operation. Defaults to None.
Attention:
x and mask must be broadcastable to each other.
mask must be int type (int8/int32/int64).
Returns:
remote_blob_util.BlobDef: The value-filled Blob
For example:
.. code-block:: python
import oneflow as flow
import numpy as np
import oneflow.typing as tp
@flow.global_function()
def masked_fill_Job(x: tp.Numpy.Placeholder((4, ), mask: tp.Numpy.Placeholder((4, ),
dtype = flow.int8))->tp.Numpy:
return flow.masked_fill(x, mask, value=5)
x = np.array([1, 2, 3, 4], dtype=np.float32)
mask = np.array([1, 0, 0, 1], dtype=np.int8)
out = masked_fill_Job(x, mask)
# output [5 2 3 5]
"""
if name is None:
name = id_util.UniqueStr("MaskedFill_")
value_like_x = flow.constant_like(like=x,
value=value,
name=name + "_ConstantLike")
return flow.where(condition=mask,
x=value_like_x,
y=x,
name=name + "_Where")
def _test_where(test_case, device):
x = flow.tensor(
np.array([[-0.462, 0.3139], [0.3898, -0.7197], [0.0478, -0.1657]]),
dtype=flow.float32,
device=flow.device(device),
)
y = flow.tensor(np.ones(shape=(3, 2)),
dtype=flow.float32,
device=flow.device(device))
condition = flow.tensor(np.array([[0, 1], [1, 0], [1, 0]]),
dtype=flow.int32,
device=flow.device(device))
of_out = flow.where(condition, x, y)
np_out = np.array([[1.0, 0.3139], [0.3898, 1.0], [0.0478, 1.0]])
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05))
def att_distill(args, student_atts, teacher_atts):
att_loss = 0.
teacher_layer_num = len(teacher_atts)
student_layer_num = len(student_atts)
assert teacher_layer_num % student_layer_num == 0
layers_per_block = int(teacher_layer_num / student_layer_num)
new_teacher_atts = [
teacher_atts[i * layers_per_block + layers_per_block - 1]
for i in range(student_layer_num)
]
for student_att, teacher_att in zip(student_atts, new_teacher_atts):
student_att = flow.where(
student_att <= flow.constant(-1e2, dtype=flow.float),
flow.zeros_like(student_att), student_att)
teacher_att = flow.where(
teacher_att <= flow.constant(-1e2, dtype=flow.float),
flow.zeros_like(teacher_att), teacher_att)
tmp_loss = mseloss(student_att, teacher_att)
att_loss += tmp_loss
return att_loss
def _attn(self, query, key, value):
attn_weights = flow.matmul(query, key.transpose(-2, -1))
if self.scale_attn_weights:
attn_weights = attn_weights / (float(value.size(-1))**0.5)
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length -
query_length:key_length, :key_length]
attn_weights = flow.where(causal_mask, attn_weights,
self.masked_bias.to(attn_weights.dtype))
attn_weights = nn.Softmax(dim=-1)(attn_weights)
attn_weights = self.attn_dropout(attn_weights)
attn_output = flow.matmul(attn_weights, value)
return attn_output, attn_weights
def _prob_in_top_k(
self, clean_values, noisy_values, noise_stddev, noisy_top_values
):
"""Helper function to NoisyTopKGating.
Computes the probability that value is in top k, given different random noise.
This gives us a way of backpropagating from a loss that balances the number
of times each expert is in the top k experts per example.
In the case of no noise, pass in None for noise_stddev, and the result will
not be differentiable.
Args:
clean_values: a `Tensor` of shape [batch, n].
noisy_values: a `Tensor` of shape [batch, n]. Equal to clean values plus
normally distributed noise with standard deviation noise_stddev.
noise_stddev: a `Tensor` of shape [batch, n], or None
noisy_top_values: a `Tensor` of shape [batch, m].
"values" Output of tf.top_k(noisy_top_values, m). m >= k+1
Returns:
a `Tensor` of shape [batch, n].
"""
batch = clean_values.size(0)
m = noisy_top_values.size(1)
top_values_flat = noisy_top_values.flatten()
threshold_positions_if_in = (
flow.arange(batch, device=noisy_values.device) * m + self.k
)
threshold_if_in = flow.unsqueeze(
flow.gather(top_values_flat, 0, threshold_positions_if_in), 1
)
is_in = flow.gt(noisy_values, threshold_if_in)
threshold_positions_if_out = threshold_positions_if_in - 1
threshold_if_out = flow.unsqueeze(
flow.gather(top_values_flat, 0, threshold_positions_if_out), 1
)
# is each value currently in the top k.
prob_if_in = cdf((clean_values - threshold_if_in) / noise_stddev)
prob_if_out = cdf((clean_values - threshold_if_out) / noise_stddev)
prob = flow.where(is_in, prob_if_in, prob_if_out)
return prob
def __call__(self, x, padding=None):
# Retrieve dynamically known shapes
batch_size = x.shape[0]
length = x.shape[1]
if padding is not None:
with flow.scope.namespace("remove_padding"):
# Flatten padding to [batch_size*length]
pad_mask = flow.reshape(padding, [-1])
nonpad_ids = flow.cast(flow.where(pad_mask < 1e-9), dtype=flow.int32)
# nonpad_ids = tf.to_int32(tf.where(pad_mask < 1e-9))
# Reshape x to [batch_size*length, hidden_size] to remove padding
x = flow.reshape(x, [-1, self.hidden_size])
x = flow.gather_nd(x, indices=nonpad_ids)
# Reshape x from 2 dimensions to 3 dimensions.
# TODO:Maybe has a batch axis error in there
x = flow.expand_dims(x, axis=0)
output = self._build_dense(x, self.filter_size, name="filter_layer")
if self.train:
# In TensorFlow the param means `keep_prob` and use `1-dropout`,
# but our dropout means drop rate so i just use dropout !
output = flow.nn.dropout(output, self.relu_dropout)
if padding is not None:
with flow.scope.namespace("re_add_padding"):
output = flow.squeeze(output, axis=[0, ])
output = flow.scatter_nd(
indices=nonpad_ids,
updates=output,
shape=[batch_size * length, self.hidden_size]
)
output = flow.reshape(output, [batch_size, length, self.hidden_size])
return output
def _where(self, x=None, y=None):
return flow.where(self, x, y)
def ctc_loss(
log_probs: oneflow_api.BlobDesc,
targets: oneflow_api.BlobDesc,
input_lengths: oneflow_api.BlobDesc,
target_lengths: oneflow_api.BlobDesc,
blank: int = 0,
reduction: str = "mean",
zero_infinity: bool = False,
name: Optional[str] = None,
) -> oneflow_api.BlobDesc:
r"""Computes the CTC(Connectionist Temporal Classification) loss.
This operator implements the CTC loss as presented in (Graves et al., 2006).
Args:
log_probs (oneflow_api.BlobDesc): A Blob of shape [input_length, batch_size, num_labels]. The logarithmized probabilities of the outputs (e.g. obtained with flow.nn.logsoftmax()).
targets (oneflow_api.BlobDesc): A Blob of shape [batch_size, max_target_length]. It represent the target sequences. Each element in the target sequence is a class index. And the target index cannot be blank (default=0).
input_lengths (oneflow_api.BlobDesc): A Blob of shape [batch_size]. It represent the lengths of the inputs. And the lengths are specified for each sequence to achieve masking under the assumption that sequences are padded to equal lengths.
target_lengths (oneflow_api.BlobDesc): A Blob of shape [batch_size]. It represent lengths of the targets. Lengths are specified for each sequence to achieve masking under the assumption that sequences are padded to equal lengths.
blank (int, optional): Blank label. Defaults to 0.
reduction (str, optional): The reduce type, it can be the one of "none", "mean", "sum". "none": no reduction will be applied, "mean": the output losses will be divided by the target lengths and then the mean over the batch is taken, "sum": the output will be summed. Defaults to "mean".
zero_infinity (bool, optional): Whether to zero infinite losses and the associated gradients. Infinite losses mainly occur when the inputs are too short to be aligned to the targets. Defaults to False.
name (Optional[str], optional): The name for the operation. Defaults to None.
Returns:
oneflow_api.BlobDesc: The result Blob.
For example:
.. code-block:: python
import oneflow as flow
import oneflow.typing as tp
import numpy as np
@flow.global_function()
def ctc_loss_job(
log_probs: tp.Numpy.Placeholder(shape=(5, 2, 3)),
targets: tp.Numpy.Placeholder(shape=(2, 3), dtype=flow.int32),
input_lengths: tp.Numpy.Placeholder(shape=(2,), dtype=flow.int32),
target_lengths: tp.Numpy.Placeholder(shape=(2,), dtype=flow.int32),
) -> tp.Numpy:
loss = flow.ctc_loss(
log_probs, targets, input_lengths, target_lengths, blank=0, reduction="none"
)
return loss
log_probs = np.array(
[
[[-1.1031, -0.7998, -1.5200], [-0.9808, -1.1363, -1.1908]],
[[-1.2258, -1.0665, -1.0153], [-1.1135, -1.2331, -0.9671]],
[[-1.3348, -0.6611, -1.5118], [-0.9823, -1.2355, -1.0941]],
[[-1.3850, -1.3273, -0.7247], [-0.8235, -1.4783, -1.0994]],
[[-0.9049, -0.8867, -1.6962], [-1.4938, -1.3630, -0.6547]],
]
).astype(np.float32)
targets = np.array([[1, 2, 2], [1, 2, 2]]).astype("int32")
input_lengths = np.array([5, 5]).astype("int32")
target_lengths = np.array([3, 3]).astype("int32")
loss = ctc_loss_job(log_probs, targets, input_lengths, target_lengths)
# loss [3.918017 2.907672]
"""
name = name if name is not None else id_util.UniqueStr("CTCLoss_")
loss, _ = (
flow.user_op_builder(name)
.Op("ctc_loss")
.Input("log_probs", [log_probs])
.Input("targets", [targets])
.Input("input_lengths", [input_lengths])
.Input("target_lengths", [target_lengths])
.Output("loss")
.Output("alpha")
.Attr("blank", int(blank))
.Attr("zero_infinity", zero_infinity)
.Build()
.InferAndTryRun()
.RemoteBlobList()
)
if zero_infinity:
cond = flow.math.equal(
loss,
flow.constant(
float("inf"),
dtype=loss.dtype,
shape=loss.shape,
name=name + "_constant",
),
name=name + "_equal",
)
loss = flow.where(
cond,
flow.zeros(dtype=loss.dtype, shape=loss.shape, name=name + "_zeros"),
loss,
name=name + "_where",
)
if reduction == "mean":
return flow.math.reduce_mean(
flow.math.xdivy(
loss,
flow.cast(
flow.math.clip_by_value(
target_lengths, min_value=1, name=name + "_clip_by_value"
),
dtype=log_probs.dtype,
name=name + "_cast",
),
name=name + "_xdivy",
),
name=name + "_reduce_mean",
)
elif reduction == "sum":
return flow.math.reduce_sum(loss, name=name + "_reduce_sum")
else:
return loss
def _TransformerModel(input_blob,
attention_mask_blob,
seq_length,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
intermediate_act_fn=_Gelu,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
do_return_all_layers=False,
replace_prob=0.0,
compress_ratio=1):
# print('| transformer num hidden layers: ', num_hidden_layers)
assert hidden_size % num_attention_heads == 0
attention_head_size = int(hidden_size / num_attention_heads)
input_width = hidden_size
prev_output_blob = flow.reshape(input_blob, (-1, input_width))
# all_layer_output_blobs = []
per_add_teacher_layers = compress_ratio
per_add_student_layers = 1
teacher_layer_idx = student_layer_idx = 0
def add_teacher_layer(base_teacher_layer_idx, sub_teacher_output_blob):
for add_teacher_layer_idx in range(per_add_teacher_layers):
sub_teacher_output_blob = addOnelayer(
layer_idx=base_teacher_layer_idx+add_teacher_layer_idx,
prev_output_blob=sub_teacher_output_blob,
attention_mask_blob=attention_mask_blob,
num_attention_heads=num_attention_heads,
attention_head_size=attention_head_size,
attention_probs_dropout_prob=attention_probs_dropout_prob,
initializer_range=initializer_range, seq_length=seq_length, hidden_size=hidden_size,
hidden_dropout_prob=hidden_dropout_prob,
intermediate_act_fn=intermediate_act_fn,
intermediate_size=intermediate_size, namescope_prefix='', is_train=False)
return sub_teacher_output_blob
def add_student_layer(base_student_layer_idx, sub_student_output_blob):
# with flow.scope.namespace("student"):
sub_student_output_blob = addOnelayer(
base_student_layer_idx, sub_student_output_blob, attention_mask_blob,
num_attention_heads, attention_head_size,
attention_probs_dropout_prob, initializer_range, seq_length, hidden_size, hidden_dropout_prob,
intermediate_act_fn, intermediate_size, namescope_prefix='student-', is_train=True)
return sub_student_output_blob
while teacher_layer_idx < num_hidden_layers:
with flow.scope.placement("cpu", "0:0"):
sample = flow.random.coin_flip(name='layer{}_replacing_prob'.format(teacher_layer_idx), probability=replace_prob)
sample = sample.with_distribute(flow.distribute.broadcast())
prev_output_blob = flow.where(
sample,
x=add_student_layer(student_layer_idx, prev_output_blob),
y=add_teacher_layer(teacher_layer_idx, prev_output_blob),
name='where_layer{}'.format(teacher_layer_idx)
)
teacher_layer_idx += per_add_teacher_layers
student_layer_idx += per_add_student_layers
# print('| current teacher_layer: ', teacher_layer_idx)
# print('| current student_layer: ', student_layer_idx)
# print('| num_hidden_layers: ', num_hidden_layers)
input_shape = (-1, seq_length, hidden_size)
final_output_blob = flow.reshape(prev_output_blob, input_shape)
return [final_output_blob]
def where_fn(input_def: oft.ListNumpy.Placeholder(input_shape,
dtype=flow.float)):
return flow.where(input_def)
def forward(self, x):
return flow.where(
x * self.beta > self.threshold,
x,
1 / self.beta * flow.log(1.0 + flow.exp(self.beta * x)),
)
def insightface_train_job():
if args.use_synthetic_data:
(labels, images) = ofrecord_util.load_synthetic(args)
else:
labels, images = ofrecord_util.load_train_dataset(args)
print("train batch data: ", images.shape)
embedding = insightface(images)
def _get_initializer():
return flow.random_normal_initializer(mean=0.0, stddev=0.01)
trainable = True
if args.loss_type == "arc_loss":
s = args.margin_s
m = args.margin
fc1 = flow.math.l2_normalize(input=embedding, axis=1, epsilon=1e-10)
fc1 = flow.math.multiply(fc1, s)
fc7 = flow.get_variable(
name="fc7-weight",
shape=(args.class_num, fc1.shape[1]),
dtype=fc1.dtype,
initializer=_get_initializer(),
trainable=trainable,
model_name="weight",
)
fc7 = flow.math.l2_normalize(input=fc7, axis=1, epsilon=1e-10)
matmul = flow.matmul(a=fc1, b=fc7, transpose_b=True)
labels_expand = flow.reshape(labels, (labels.shape[0], 1))
zy = flow.gather(matmul, labels_expand, batch_dims=1)
cos_t = flow.math.multiply(zy, 1 / s)
cos_m = math.cos(m)
sin_m = math.sin(m)
mm = math.sin(math.pi - m) * m
threshold = math.cos(math.pi - m)
if args.easy_margin:
cond = flow.math.relu(cos_t)
else:
cond_v = cos_t - threshold
cond = flow.math.relu(cond_v)
body = flow.math.square(cos_t)
body = flow.math.multiply(body, -1.0)
body = flow.math.add(1, body)
sin_t = flow.math.sqrt(body)
new_zy = flow.math.multiply(cos_t, cos_m)
b = flow.math.multiply(sin_t, sin_m)
b = flow.math.multiply(b, -1.0)
new_zy = flow.math.add(new_zy, b)
new_zy = flow.math.multiply(new_zy, s)
if args.easy_margin:
zy_keep = zy
else:
zy_keep = flow.math.add(zy, -s * mm)
cond = flow.cast(cond, dtype=flow.int32)
new_zy = flow.where(cond, new_zy, zy_keep)
zy = flow.math.multiply(zy, -1.0)
diff = flow.math.add(new_zy, zy)
gt_one_hot = flow.one_hot(
labels, depth=args.class_num, dtype=flow.float
)
body = flow.math.multiply(gt_one_hot, diff)
fc7 = flow.math.add(matmul, body)
elif args.loss_type == "margin_softmax":
fc7_weight = flow.get_variable(
name="fc7-weight",
shape=(args.class_num, embedding.shape[1]),
dtype=embedding.dtype,
initializer=_get_initializer(),
trainable=trainable,
model_name="weight",
)
s = args.margin_s
fc7_weight = flow.math.l2_normalize(
input=fc7_weight, axis=1, epsilon=1e-10
)
fc1 = (
flow.math.l2_normalize(input=embedding, axis=1, epsilon=1e-10) * s
)
fc7 = flow.matmul(a=fc1, b=fc7_weight, transpose_b=True)
if args.loss_m1 != 1.0 or args.loss_m2 != 0.0 or args.loss_m3 != 0.0:
if args.loss_m1 == 1.0 and args.loss_m2 == 0.0:
s_m = s * args.loss_m3
gt_one_hot = flow.one_hot(
labels,
depth=args.class_num,
on_value=s_m,
off_value=0.0,
dtype=flow.float,
)
fc7 = fc7 - gt_one_hot
else:
labels_expand = flow.reshape(labels, (labels.shape[0], 1))
zy = flow.gather(fc7, labels_expand, batch_dims=1)
cos_t = zy * (1 / s)
t = flow.math.acos(cos_t)
if args.loss_m1 != 1.0:
t = t * args.loss_m1
if args.loss_m2 > 0.0:
t = t + args.loss_m2
body = flow.math.cos(t)
if args.loss_m3 > 0.0:
body = body - args.loss_m3
new_zy = body * s
diff = new_zy - zy
gt_one_hot = flow.one_hot(
labels,
depth=args.class_num,
on_value=1.0,
off_value=0.0,
dtype=flow.float,
)
body = gt_one_hot * diff
fc7 = fc7 + body
elif args.loss_type == "softmax":
if args.model_parallel:
labels = labels.with_distribute(flow.distribute.broadcast())
fc1_distribute = flow.distribute.broadcast()
fc7_data_distribute = flow.distribute.split(1)
fc7_model_distribute = flow.distribute.split(0)
else:
fc1_distribute = flow.distribute.split(0)
fc7_data_distribute = flow.distribute.split(0)
fc7_model_distribute = flow.distribute.broadcast()
print("loss 0")
fc7 = flow.layers.dense(
inputs=embedding.with_distribute(fc1_distribute),
units=args.class_num,
activation=None,
use_bias=False,
kernel_initializer=_get_initializer(),
bias_initializer=None,
trainable=trainable,
name=args.models_name,
model_distribute=fc7_model_distribute,
)
fc7 = fc7.with_distribute(fc7_data_distribute)
elif args.loss_type == "arc_loss_ms":
labels = labels.with_distribute(flow.distribute.broadcast())
fc7_model_distribute = flow.distribute.split(0)
fc7_data_distribute = flow.distribute.split(1)
fc7_weight = flow.get_variable(
name="fc7-weight",
shape=(args.class_num, embedding.shape[1]),
dtype=embedding.dtype,
initializer=_get_initializer(),
trainable=trainable,
model_name="weight",
distribute=fc7_model_distribute,
)
s = args.margin_s
fc7_weight = flow.math.l2_normalize(
input=fc7_weight, axis=1, epsilon=1e-10
)
fc1 = (
flow.math.l2_normalize(input=embedding, axis=1, epsilon=1e-10)
)
fc1 = flow.parallel_cast(fc1, distribute=flow.distribute.broadcast())
fc7 = flow.matmul(a=fc1, b=fc7_weight, transpose_b=True) #s1
fc7 = flow.arc_loss(fc7, labels, margin=args.loss_m2)*60
fc7 = fc7.with_distribute(fc7_data_distribute)
else:
raise NotImplementedError
loss = flow.nn.sparse_softmax_cross_entropy_with_logits(
labels, fc7, name="softmax_loss"
)
lr_scheduler = flow.optimizer.PiecewiseScalingScheduler(args.base_lr, [100000, 140000, 160000], [0.1, 0.01, 0.001])
flow.optimizer.SGD(lr_scheduler, momentum=0.9).minimize(loss)
return loss
def forward(self, inputs, targets):
n = inputs.shape[0]
# Compute pairwise distance, replace by the official when merged
tempname = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S.%f')
shape_tensor = flow.constant(value=0.0,
dtype=flow.float32,
shape=(n, n))
if self.distance == 'euclidean':
blob_2 = flow.get_variable(
"blob_2_" + tempname,
shape=inputs.shape,
initializer=flow.constant_initializer(2),
dtype=inputs.dtype)
dist = flow.math.pow(inputs, blob_2)
dist = flow.math.reduce_sum(dist, axis=1, keepdims=True)
dist = flow.broadcast_like(dist, shape_tensor)
tempdist = flow.transpose(dist)
dist = dist + tempdist
inputs_t = flow.transpose(inputs)
dist = addmm(dist, inputs, inputs_t, beta=1, alpha=-2)
dist = flow.clamp(dist, min_value=1e-12)
dist = flow.math.sqrt(dist)
elif self.distance == 'cosine':
#fnorm=flow.math.l2_normalize(inputs, axis=1)
fnorm = flow.math.reduce_mean(flow.math.divide(
inputs, flow.math.l2_normalize(inputs, axis=1)),
axis=1,
keepdims=True)
expand_fnorm = flow.broadcast_like(fnorm,
like=inputs,
broadcast_axes=[1])
l2norm = flow.math.divide(inputs, expand_fnorm)
l2norm_t = flow.transpose(l2norm, perm=(1, 0))
dist = flow.math.negative(flow.matmul(l2norm, l2norm_t))
# For each anchor, find the hardest positive and negative
mask = math.equal(
flow.broadcast_like(targets, like=shape_tensor,
broadcast_axes=[1]),
flow.transpose(flow.broadcast_like(targets,
like=shape_tensor,
broadcast_axes=[1]),
perm=(1, 0),
batch_axis_non_change=True))
mask_rev = math.not_equal(
flow.broadcast_like(targets, like=shape_tensor,
broadcast_axes=[1]),
flow.transpose(flow.broadcast_like(targets,
like=shape_tensor,
broadcast_axes=[1]),
perm=(1, 0),
batch_axis_non_change=True))
dist_ap, dist_an = [], []
for i in range(n):
temp_dist = flow.slice_v2(dist, [(i, i + 1, 1)])
temp_mask = flow.slice_v2(mask, [(i, i + 1, 1)])
temp_mask_rev = flow.slice_v2(mask_rev, [(i, i + 1, 1)])
temp_dist_ap = flow.expand_dims(
math.reduce_max(
flow.gather_nd(temp_dist, flow.where(temp_mask))), 0)
temp_dist_an = flow.expand_dims(
math.reduce_min(
flow.gather_nd(temp_dist, flow.where(temp_mask_rev))), 0)
dist_ap.append(temp_dist_ap)
dist_an.append(temp_dist_an)
dist_ap = flow.concat(dist_ap, 0)
dist_an = flow.concat(dist_an, 0)
y = flow.ones_like(dist_an)
return self._MarginRankingLoss(dist_an, dist_ap, y)
def loss_layer(self,
feature_map,
pred,
label,
bboxes,
stride,
prefix='loss_layer'):
'''
:param feature_map: [N, H, W, 3*(5+class_num)]
:param pred: [N, H, W, 3, 4+1+class_num]
:param label: [N, H, W, 3, 4+1+class_num]
:param bboxes: [N, V, 4]
:param stride:
:param anchor_per_scale:
:return:
giou_loss:
conf_loss:
prob_loss:
'''
feature_map = flow.reshape(
feature_map,
shape=(feature_map.shape[0], feature_map.shape[1],
feature_map.shape[2], self.anchor_per_scale, -1))
# shape: [N, H, W, 3, 1]
raw_conf = flow.slice(feature_map,
begin=[None, None, None, None, 4],
size=[None, None, None, None, 1])
# shape: [N, H, W, 3, class_num]
raw_prob = flow.slice(
feature_map,
begin=[None, None, None, None, 5],
size=[None, None, None, None, feature_map.shape[-1] - 5])
# [N, H, W, 3, 4]
pred_xywh = flow.slice(pred,
begin=[None, None, None, None, 0],
size=[None, None, None, None, 4])
pred_conf = flow.slice(pred,
begin=[None, None, None, None, 4],
size=[None, None, None, None, 1])
#flow.slice(label, begin=[None, None, None, None, 0], size=[None, None, None, None, 4])
label_xywh = flow.slice(label,
begin=[None, None, None, None, 0],
size=[None, None, None, None, 4])
respond_bbox = flow.slice(label,
begin=[None, None, None, None, 4],
size=[None, None, None, None, 1])
label_prob = flow.slice(
label,
begin=[None, None, None, None, 5],
size=[None, None, None, None, label.shape[-1] - 5])
# [N, H, W, 3, 1]
giou = self.bbox_giou(pred_xywh, label_xywh)
# label_w = flow.slice(label, begin=[None, None, None, None, 2], size=[None, None, None, None, 1])
# label_h = flow.slice(label, begin=[None, None, None, None, 3], size=[None, None, None, None, 1])
# bbox_loss_scale = 2.0 - 1.0 * label_w * label_h / ((stride * feature_map.shape[1]) ** 2) #???
# [N, H, W, 3, 1]
# giou_loss = respond_bbox * bbox_loss_scale * (1 - giou)
giou_loss = respond_bbox * (1 - giou)
# [N, 1, 1, 1, V, 4]
bboxes_ = flow.expand_dims(bboxes, axis=1)
bboxes_ = flow.expand_dims(bboxes_, axis=1)
bboxes_ = flow.expand_dims(bboxes_, axis=1)
# [N, H, W, 3, V]
iou = self.bbox_iou(flow.expand_dims(pred_xywh, axis=-2), bboxes_)
iou = flow.squeeze(iou, axis=[
-1,
])
# [N, H, W, 3, 1]
max_iou = flow.math.reduce_max(iou, axis=-1, keepdims=True)
# respond_bgd = (1.0 - respond_bbox) * (max_iou < self.iou_loss_thresh)
tmp = flow.math.less(
max_iou,
flow.constant_like(like=max_iou,
value=self.iou_loss_thresh,
dtype=flow.float32))
# respond_bgd = (1.0 - respond_bbox) * tmp
respond_bgd = flow.where(
tmp, 1.0 - respond_bbox,
flow.zeros_like(respond_bbox, dtype=flow.float32))
# [N, H, W, 3, 1]
# ce = flow.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=raw_conf)
# alpha_t = respond_bbox*self.focus_loss_alpha+(1.0-respond_bbox)*(1.0-self.focus_loss_alpha)
# conf_loss = alpha_t*flow.math.pow(1.0-flow.math.exp(flow.math.negative(ce)), self.focus_loss_gamma)*ce
# conf_loss = (respond_bbox+respond_bgd)*conf_loss
conf_focal = self.focal(respond_bbox, pred_conf)
conf_loss = conf_focal * (
respond_bbox * flow.nn.sigmoid_cross_entropy_with_logits(
labels=respond_bbox, logits=raw_conf) +
respond_bgd * flow.nn.sigmoid_cross_entropy_with_logits(
labels=respond_bbox, logits=raw_conf))
# [N, H, W, 3, 1]
prob_loss = respond_bbox * flow.nn.sigmoid_cross_entropy_with_logits(
labels=label_prob, logits=raw_prob)
#??
# label_w = flow.slice(label, begin=[None, None, None, None, 2], size=[None, None, None, None, 1])
# label_h = flow.slice(label, begin=[None, None, None, None, 3], size=[None, None, None, None, 1])
# bbox_loss_scale = 2.0 - 1.0 * label_w * label_h / ((stride * feature_map.shape[1]) * (stride * feature_map.shape[2])) #???
# # [N, H, W, 3, 1]
# giou_loss = respond_bbox * bbox_loss_scale * flow.smooth_l1_loss(prediction=pred_xywh, label=label_xywh)
giou_loss = flow.math.reduce_mean(
flow.math.reduce_sum(giou_loss, axis=[1, 2, 3, 4]))
conf_loss = flow.math.reduce_mean(
flow.math.reduce_sum(conf_loss, axis=[1, 2, 3, 4]))
prob_loss = flow.math.reduce_mean(
flow.math.reduce_sum(prob_loss, axis=[1, 2, 3, 4]))
return giou_loss, conf_loss, prob_loss
def do_where(condition, x, y):
return flow.where(condition, x, y)