def construct(self, img_A, img_B, fake_A, fake_B):
weights = self.weights
ld = self.D(img_A, img_B, fake_A, fake_B)
sens_d = ops.Fill()(ops.DType()(ld), ops.Shape()(ld), self.sens)
grads_d = self.grad(self.D, weights)(img_A, img_B, fake_A, fake_B,
sens_d)
if self.reducer_flag:
# apply grad reducer on grads
grads_d = self.grad_reducer(grads_d)
return ops.depend(ld, self.optimizer(grads_d))
def construct(self, img_A, img_B):
weights = self.weights
fake_A, fake_B, lg, lga, lgb, lca, lcb, lia, lib = self.G(img_A, img_B)
sens = ops.Fill()(ops.DType()(lg), ops.Shape()(lg), self.sens)
grads_g = self.grad(self.net, weights)(img_A, img_B, sens)
if self.reducer_flag:
# apply grad reducer on grads
grads_g = self.grad_reducer(grads_g)
return fake_A, fake_B, ops.depend(
lg, self.optimizer(grads_g)), lga, lgb, lca, lcb, lia, lib
def __init__(self, begin, stride):
super(GetOffsetPosition, self).__init__()
self.begin = begin
self.stride = stride
self.meshgrid = ops.Meshgrid()
self.shape = ops.Shape()
self.reshape = ops.Reshape()
self.cat_a0 = ops.Concat(axis=0)
self.cat_a1 = ops.Concat(axis=1)
self.tile = ops.Tile()
self.dtype = ops.DType()
self.range = nn.Range(-self.begin, self.begin + 1)
self.cast = ops.Cast()
def __init__(self, alpha=2, beta=4):
super(FocalLoss, self).__init__()
self.alpha = alpha
self.beta = beta
self.pow = ops.Pow()
self.log = ops.Log()
self.select = ops.Select()
self.equal = ops.Equal()
self.less = ops.Less()
self.cast = ops.Cast()
self.fill = ops.Fill()
self.dtype = ops.DType()
self.shape = ops.Shape()
self.reduce_sum = ops.ReduceSum()
def construct(self, *inputs):
"""Defines the computation performed."""
weights = self.weights
loss = self.network(*inputs)
sens = ops.Fill()(ops.DType()(loss), ops.Shape()(loss), self.sens)
grads = self.grad(self.network, weights)(*inputs, sens)
if self.accumulation and self.accumulation_steps > 1:
accu_succ = self.hyper_map(update_accu_grads, self.accu_grads,
grads)
loss = ops.depend(loss, accu_succ)
if self.accumulation:
succ = False
else:
grads = self.grad_reducer(grads)
accu_grads = ops.depend(self.accu_grads, grads)
accu_succ = self.hyper_map(reset_accu_grads, accu_grads)
loss = ops.depend(loss, accu_succ)
succ = self.optimizer(grads)
return ops.depend(loss, succ)
def __init__(self, net_config, K=100, enable_nms_fp16=True):
super(MultiPoseDecode, self).__init__()
self.K = K
self.nms = NMS(enable_nms_fp16=enable_nms_fp16)
self.shape = ops.Shape()
self.gather_topk = GatherTopK()
self.gather_topk_channel = GatherTopKChannel()
self.gather_by_ind = GatherFeatureByInd()
self.half = ops.Split(axis=-1, output_num=2)
self.half_first = ops.Split(axis=0, output_num=2)
self.split = ops.Split(axis=-1, output_num=4)
self.flip_lr = FlipLR()
self.flip_lr_off = FlipLROff()
self.flip_tensor = FlipTensor()
self.concat = ops.Concat(axis=1)
self.concat_a2 = ops.Concat(axis=2)
self.concat_a3 = ops.Concat(axis=3)
self.trans_gather_feature = TransposeGatherFeature()
self.expand_dims = ops.ExpandDims()
self.reshape = ops.Reshape()
self.add = ops.TensorAdd()
self.dtype = ops.DType()
self.cast = ops.Cast()
self.thresh = 0.1
self.transpose = ops.Transpose()
self.perm_list = (0, 2, 1, 3)
self.tile = ops.Tile()
self.greater = ops.Greater()
self.square = ops.Square()
self.sqrt = ops.Sqrt()
self.reduce_sum = ops.ReduceSum()
self.min = ops.ArgMinWithValue(axis=3)
self.max = ops.Maximum()
self.hm_hp = net_config.hm_hp
self.dense_hp = net_config.dense_hp
self.reg_offset = net_config.reg_offset
self.reg_hp_offset = net_config.reg_hp_offset
self.hm_hp_ind = 3 if self.hm_hp else 2
self.reg_ind = self.hm_hp_ind + 1 if self.reg_offset else self.hm_hp_ind
self.reg_hp_ind = self.reg_ind + 1 if self.reg_hp_offset else self.reg_ind
def __init__(self):
super(Sigmoid, self).__init__()
self.cast = ops.Cast()
self.dtype = ops.DType()
self.sigmoid = nn.Sigmoid()
self.clip_by_value = ops.clip_by_value
def __init__(self,
batch_size,
from_tensor_width,
to_tensor_width,
from_seq_length,
to_seq_length,
num_attention_heads=1,
size_per_head=512,
query_act=None,
key_act=None,
value_act=None,
has_attention_mask=False,
attention_probs_dropout_prob=0.0,
use_one_hot_embeddings=False,
initializer_range=0.02,
do_return_2d_tensor=False,
use_relative_positions=False,
compute_type=mstype.float32):
super(BertAttention, self).__init__()
self.batch_size = batch_size
self.from_seq_length = from_seq_length
self.to_seq_length = to_seq_length
self.num_attention_heads = num_attention_heads
self.size_per_head = size_per_head
self.has_attention_mask = has_attention_mask
self.use_relative_positions = use_relative_positions
self.scores_mul = Tensor([1.0 / math.sqrt(float(self.size_per_head))], dtype=compute_type)
self.reshape = ops.Reshape()
self.shape_from_2d = (-1, from_tensor_width)
self.shape_to_2d = (-1, to_tensor_width)
weight = TruncatedNormal(initializer_range)
units = num_attention_heads * size_per_head
self.query_layer = nn.Dense(from_tensor_width,
units,
activation=query_act,
weight_init=weight).to_float(compute_type)
self.key_layer = nn.Dense(to_tensor_width,
units,
activation=key_act,
weight_init=weight).to_float(compute_type)
self.value_layer = nn.Dense(to_tensor_width,
units,
activation=value_act,
weight_init=weight).to_float(compute_type)
self.shape_from = (batch_size, from_seq_length, num_attention_heads, size_per_head)
self.shape_to = (
batch_size, to_seq_length, num_attention_heads, size_per_head)
self.matmul_trans_b = ops.BatchMatMul(transpose_b=True)
self.multiply = ops.Mul()
self.transpose = ops.Transpose()
self.trans_shape = (0, 2, 1, 3)
self.trans_shape_relative = (2, 0, 1, 3)
self.trans_shape_position = (1, 2, 0, 3)
#self.multiply_data = Tensor([-10000.0,], dtype=compute_type)
self.multiply_data = Tensor([-10000.0,], dtype=mstype.float32)
self.batch_num = batch_size * num_attention_heads
self.matmul = ops.BatchMatMul()
self.softmax = nn.Softmax()
self.dropout = nn.Dropout(1 - attention_probs_dropout_prob)
if self.has_attention_mask:
self.expand_dims = ops.ExpandDims()
self.sub = ops.Sub()
self.add = ops.TensorAdd()
self.cast = ops.Cast()
self.get_dtype = ops.DType()
if do_return_2d_tensor:
self.shape_return = (batch_size * from_seq_length, num_attention_heads * size_per_head)
else:
self.shape_return = (batch_size, from_seq_length, num_attention_heads * size_per_head)
self.cast_compute_type = SaturateCast(dst_type=compute_type)
if self.use_relative_positions:
self._generate_relative_positions_embeddings = \
RelaPosEmbeddingsGenerator(length=to_seq_length,
depth=size_per_head,
max_relative_position=16,
initializer_range=initializer_range,
use_one_hot_embeddings=use_one_hot_embeddings)
def construct(self, *inputs):
weights = self.weights
loss = self.network(*inputs)
sens = ops.Fill()(ops.DType()(loss), ops.Shape()(loss), self.sens)
grads = self.grad(self.network, weights)(*inputs, sens)
return ops.depend(loss, self.hyper_map(ops.partial(_sum_op), self.grad_sum, grads))
