def __init__(self, config):
super(CRNN, self).__init__()
self.batch_size = config.batch_size
self.input_size = config.input_size
self.hidden_size = config.hidden_size
self.num_classes = config.class_num
self.reshape = P.Reshape()
self.cast = P.Cast()
k = (1 / self.hidden_size)**0.5
self.rnn1 = P.DynamicRNN(forget_bias=0.0)
self.rnn1_bw = P.DynamicRNN(forget_bias=0.0)
self.rnn2 = P.DynamicRNN(forget_bias=0.0)
self.rnn2_bw = P.DynamicRNN(forget_bias=0.0)
w1 = np.random.uniform(
-k, k, (self.input_size + self.hidden_size, 4 * self.hidden_size))
self.w1 = Parameter(w1.astype(np.float32), name="w1")
w2 = np.random.uniform(
-k, k,
(2 * self.hidden_size + self.hidden_size, 4 * self.hidden_size))
self.w2 = Parameter(w2.astype(np.float32), name="w2")
w1_bw = np.random.uniform(
-k, k, (self.input_size + self.hidden_size, 4 * self.hidden_size))
self.w1_bw = Parameter(w1_bw.astype(np.float32), name="w1_bw")
w2_bw = np.random.uniform(
-k, k,
(2 * self.hidden_size + self.hidden_size, 4 * self.hidden_size))
self.w2_bw = Parameter(w2_bw.astype(np.float32), name="w2_bw")
self.b1 = Parameter(np.random.uniform(
-k, k, (4 * self.hidden_size)).astype(np.float32),
name="b1")
self.b2 = Parameter(np.random.uniform(
-k, k, (4 * self.hidden_size)).astype(np.float32),
name="b2")
self.b1_bw = Parameter(np.random.uniform(
-k, k, (4 * self.hidden_size)).astype(np.float32),
name="b1_bw")
self.b2_bw = Parameter(np.random.uniform(
-k, k, (4 * self.hidden_size)).astype(np.float32),
name="b2_bw")
self.h1 = Tensor(
np.zeros(shape=(1, self.batch_size,
self.hidden_size)).astype(np.float32))
self.h2 = Tensor(
np.zeros(shape=(1, self.batch_size,
self.hidden_size)).astype(np.float32))
self.h1_bw = Tensor(
np.zeros(shape=(1, self.batch_size,
self.hidden_size)).astype(np.float32))
self.h2_bw = Tensor(
np.zeros(shape=(1, self.batch_size,
self.hidden_size)).astype(np.float32))
self.c1 = Tensor(
np.zeros(shape=(1, self.batch_size,
self.hidden_size)).astype(np.float32))
self.c2 = Tensor(
np.zeros(shape=(1, self.batch_size,
self.hidden_size)).astype(np.float32))
self.c1_bw = Tensor(
np.zeros(shape=(1, self.batch_size,
self.hidden_size)).astype(np.float32))
self.c2_bw = Tensor(
np.zeros(shape=(1, self.batch_size,
self.hidden_size)).astype(np.float32))
self.fc_weight = np.random.random(
(self.num_classes, self.hidden_size)).astype(np.float32)
self.fc_bias = np.random.random((self.num_classes)).astype(np.float32)
self.fc = nn.Dense(in_channels=self.hidden_size,
out_channels=self.num_classes,
weight_init=Tensor(self.fc_weight),
bias_init=Tensor(self.fc_bias))
self.fc.to_float(mstype.float32)
self.expand_dims = P.ExpandDims()
self.concat = P.Concat()
self.transpose = P.Transpose()
self.squeeze = P.Squeeze(axis=0)
self.vgg = VGG()
self.reverse_seq1 = P.ReverseSequence(batch_dim=1, seq_dim=0)
self.reverse_seq2 = P.ReverseSequence(batch_dim=1, seq_dim=0)
self.reverse_seq3 = P.ReverseSequence(batch_dim=1, seq_dim=0)
self.reverse_seq4 = P.ReverseSequence(batch_dim=1, seq_dim=0)
self.seq_length = Tensor(
np.ones((self.batch_size), np.int32) * config.num_step,
mstype.int32)
self.concat1 = P.Concat(axis=2)
self.dropout = nn.Dropout(0.5)
self.rnn_dropout = nn.Dropout(0.9)
self.use_dropout = config.use_dropout
def test_check_dropout_3():
Tensor(np.ones([20, 16, 50]).astype(np.int32))
with pytest.raises(ValueError):
nn.Dropout(3, 0, 1)
def __init__(self, attention_probs_dropout_prob: float = 0.1) -> None:
super().__init__()
self.dropout = nn.Dropout(1.0 - attention_probs_dropout_prob)
def __init__(self):
super().__init__()
self.matmul1 = P.MatMul()
self.dropout = nn.Dropout()
self.matmul2 = P.MatMul()
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 = P.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 = P.BatchMatMul(transpose_b=True)
self.multiply = P.Mul()
self.transpose = P.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.batch_num = batch_size * num_attention_heads
self.matmul = P.BatchMatMul()
self.softmax = nn.Softmax()
self.dropout = nn.Dropout(1 - attention_probs_dropout_prob)
if self.has_attention_mask:
self.expand_dims = P.ExpandDims()
self.sub = P.Sub()
self.add = P.TensorAdd()
self.cast = P.Cast()
self.get_dtype = P.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 __init__(self, num_classes=10, dropout_keep_prob=0.8):
super(Logits, self).__init__()
self.avg_pool = nn.AvgPool2d(8, pad_mode='valid')
self.dropout = nn.Dropout(keep_prob=dropout_keep_prob)
self.flatten = P.Flatten()
self.fc = nn.Dense(2048, num_classes)
def __init__(self,
input_size,
hidden_size,
num_layers=1,
has_bias=True,
batch_first=False,
dropout=0,
bidirectional=False):
super(LSTM, self).__init__()
validator.check_value_type("batch_first", batch_first, [bool],
self.cls_name)
validator.check_positive_int(hidden_size, "hidden_size", self.cls_name)
validator.check_positive_int(num_layers, "num_layers", self.cls_name)
self.is_ascend = context.get_context("device_target") == "Ascend"
self.batch_first = batch_first
self.transpose = P.Transpose()
self.num_layers = num_layers
self.bidirectional = bidirectional
self.dropout = dropout
self.lstm = P.LSTM(input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
has_bias=has_bias,
bidirectional=bidirectional,
dropout=float(dropout))
weight_size = 0
gate_size = 4 * hidden_size
stdv = 1 / math.sqrt(hidden_size)
num_directions = 2 if bidirectional else 1
if self.is_ascend:
self.reverse_seq = P.ReverseSequence(batch_dim=1, seq_dim=0)
self.concat = P.Concat(axis=0)
self.concat_2dim = P.Concat(axis=2)
self.cast = P.Cast()
self.shape = P.Shape()
if dropout != 0:
self.dropout_op = nn.Dropout(float(dropout))
b0 = np.zeros(gate_size, dtype=np.float16)
self.w_list = []
self.b_list = []
self.rnns_fw = P.DynamicRNN(forget_bias=0.0)
self.rnns_bw = P.DynamicRNN(forget_bias=0.0)
for layer in range(num_layers):
w_shape = input_size if layer == 0 else (num_directions *
hidden_size)
w_np = np.random.uniform(
-stdv, stdv,
(w_shape + hidden_size, gate_size)).astype(np.float16)
self.w_list.append(
Parameter(initializer(Tensor(w_np),
[w_shape + hidden_size, gate_size]),
name='weight_fw' + str(layer)))
if has_bias:
b_np = np.random.uniform(-stdv, stdv,
gate_size).astype(np.float16)
self.b_list.append(
Parameter(initializer(Tensor(b_np), [gate_size]),
name='bias_fw' + str(layer)))
else:
self.b_list.append(
Parameter(initializer(Tensor(b0), [gate_size]),
name='bias_fw' + str(layer)))
if bidirectional:
w_bw_np = np.random.uniform(
-stdv, stdv,
(w_shape + hidden_size, gate_size)).astype(np.float16)
self.w_list.append(
Parameter(
initializer(Tensor(w_bw_np),
[w_shape + hidden_size, gate_size]),
name='weight_bw' + str(layer)))
b_bw_np = np.random.uniform(
-stdv, stdv,
(4 *
hidden_size)).astype(np.float16) if has_bias else b0
self.b_list.append(
Parameter(initializer(Tensor(b_bw_np), [gate_size]),
name='bias_bw' + str(layer)))
self.w_list = ParameterTuple(self.w_list)
self.b_list = ParameterTuple(self.b_list)
else:
for layer in range(num_layers):
input_layer_size = input_size if layer == 0 else hidden_size * num_directions
increment_size = gate_size * input_layer_size
increment_size += gate_size * hidden_size
if has_bias:
increment_size += 2 * gate_size
weight_size += increment_size * num_directions
w_np = np.random.uniform(-stdv, stdv,
(weight_size, 1, 1)).astype(np.float32)
self.weight = Parameter(initializer(Tensor(w_np),
[weight_size, 1, 1]),
name='weight')
def __init__(self, num_classes=1000):
""" Constructor
Args:
num_classes: number of classes.
"""
super(Xception, self).__init__()
self.num_classes = num_classes
self.conv1 = nn.Conv2d(3,
32,
3,
2,
pad_mode='valid',
weight_init='xavier_uniform')
self.bn1 = nn.BatchNorm2d(32, momentum=0.9)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(32,
64,
3,
pad_mode='valid',
weight_init='xavier_uniform')
self.bn2 = nn.BatchNorm2d(64, momentum=0.9)
# Entry flow
self.block1 = Block(64,
128,
2,
2,
start_with_relu=False,
grow_first=True)
self.block2 = Block(128,
256,
2,
2,
start_with_relu=True,
grow_first=True)
self.block3 = Block(256,
728,
2,
2,
start_with_relu=True,
grow_first=True)
# Middle flow
self.block4 = Block(728,
728,
3,
1,
start_with_relu=True,
grow_first=True)
self.block5 = Block(728,
728,
3,
1,
start_with_relu=True,
grow_first=True)
self.block6 = Block(728,
728,
3,
1,
start_with_relu=True,
grow_first=True)
self.block7 = Block(728,
728,
3,
1,
start_with_relu=True,
grow_first=True)
self.block8 = Block(728,
728,
3,
1,
start_with_relu=True,
grow_first=True)
self.block9 = Block(728,
728,
3,
1,
start_with_relu=True,
grow_first=True)
self.block10 = Block(728,
728,
3,
1,
start_with_relu=True,
grow_first=True)
self.block11 = Block(728,
728,
3,
1,
start_with_relu=True,
grow_first=True)
# Exit flow
self.block12 = Block(728,
1024,
2,
2,
start_with_relu=True,
grow_first=False)
self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
self.bn3 = nn.BatchNorm2d(1536, momentum=0.9)
self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
self.bn4 = nn.BatchNorm2d(2048, momentum=0.9)
self.avg_pool = nn.AvgPool2d(10)
self.dropout = nn.Dropout()
self.fc = nn.Dense(2048, num_classes)
def __init__(self, dropout_prob=0.1):
super(ResidualConnection, self).__init__()
self.add = P.TensorAdd()
self.dropout = nn.Dropout(1 - dropout_prob)
self.use_dropout = dropout_prob > 0
# limitations under the License.
# ============================================================================
"""inceptionv4_train_export"""
import sys
import numpy as np
from train_utils import SaveInOut, TrainWrap
from official.cv.xception.src.Xception import Xception
import mindspore.common.dtype as mstype
from mindspore import context, Tensor, nn
from mindspore.train.serialization import export
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU", save_graphs=False)
n = Xception(num_classes=1000)
n.dropout = nn.Dropout(keep_prob=1.0)
loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
optimizer = nn.SGD(n.trainable_params(), learning_rate=0.01, momentum=0.9, dampening=0.0, weight_decay=0.0,
nesterov=True, loss_scale=1.0)
net = TrainWrap(n, loss_fn, optimizer)
batch = 2
x = Tensor(np.random.randn(batch, 3, 299, 299), mstype.float32)
label = Tensor(np.zeros([batch, 1000]).astype(np.float32))
export(net, x, label, file_name="mindir/xception_train", file_format='MINDIR')
if len(sys.argv) > 1:
SaveInOut(sys.argv[1] + "xception", x, label, n, net)
def __init__(self, config):
super(SSD300VGG16, self).__init__()
# VGG16 backbone: block1~5
self.backbone = vgg16()
# SSD blocks: block6~7
self.b6_1 = nn.Conv2d(in_channels=512,
out_channels=1024,
kernel_size=3,
padding=6,
dilation=6,
pad_mode='pad')
self.b6_2 = nn.Dropout(0.5)
self.b7_1 = nn.Conv2d(in_channels=1024,
out_channels=1024,
kernel_size=1)
self.b7_2 = nn.Dropout(0.5)
# Extra Feature Layers: block8~11
self.b8_1 = nn.Conv2d(in_channels=1024,
out_channels=256,
kernel_size=1,
padding=1,
pad_mode='pad')
self.b8_2 = nn.Conv2d(in_channels=256,
out_channels=512,
kernel_size=3,
stride=2,
pad_mode='valid')
self.b9_1 = nn.Conv2d(in_channels=512,
out_channels=128,
kernel_size=1,
padding=1,
pad_mode='pad')
self.b9_2 = nn.Conv2d(in_channels=128,
out_channels=256,
kernel_size=3,
stride=2,
pad_mode='valid')
self.b10_1 = nn.Conv2d(in_channels=256,
out_channels=128,
kernel_size=1)
self.b10_2 = nn.Conv2d(in_channels=128,
out_channels=256,
kernel_size=3,
pad_mode='valid')
self.b11_1 = nn.Conv2d(in_channels=256,
out_channels=128,
kernel_size=1)
self.b11_2 = nn.Conv2d(in_channels=128,
out_channels=256,
kernel_size=3,
pad_mode='valid')
# boxes
self.multi_box = MultiBox(config)
if not self.training:
self.activation = P.Sigmoid()
def __init__(self,
outer_nc,
inner_nc,
in_planes=None,
dropout=False,
submodule=None,
outermost=False,
innermost=False,
alpha=0.2,
norm_mode='batch'):
super(UnetSkipConnectionBlock, self).__init__()
downnorm = nn.BatchNorm2d(inner_nc)
upnorm = nn.BatchNorm2d(outer_nc)
use_bias = False
if norm_mode == 'instance':
downnorm = nn.BatchNorm2d(inner_nc, affine=False)
upnorm = nn.BatchNorm2d(outer_nc, affine=False)
use_bias = True
if in_planes is None:
in_planes = outer_nc
downconv = nn.Conv2d(in_planes,
inner_nc,
kernel_size=4,
stride=2,
padding=1,
has_bias=use_bias,
pad_mode='pad')
downrelu = nn.LeakyReLU(alpha)
uprelu = nn.ReLU()
if outermost:
upconv = nn.Conv2dTranspose(inner_nc * 2,
outer_nc,
kernel_size=4,
stride=2,
padding=1,
pad_mode='pad')
down = [downconv]
up = [uprelu, upconv, nn.Tanh()]
model = down + [submodule] + up
elif innermost:
upconv = nn.Conv2dTranspose(inner_nc,
outer_nc,
kernel_size=4,
stride=2,
padding=1,
has_bias=use_bias,
pad_mode='pad')
down = [downrelu, downconv]
up = [uprelu, upconv, upnorm]
model = down + up
else:
upconv = nn.Conv2dTranspose(inner_nc * 2,
outer_nc,
kernel_size=4,
stride=2,
padding=1,
has_bias=use_bias,
pad_mode='pad')
down = [downrelu, downconv, downnorm]
up = [uprelu, upconv, upnorm]
model = down + [submodule] + up
if dropout:
model.append(nn.Dropout(0.5))
self.model = nn.SequentialCell(model)
self.skip_connections = not outermost
self.concat = ops.Concat(axis=1)
def __init__(self,
num_classes,
is_training=True,
stem_filters=32,
penultimate_filters=1056,
filters_multiplier=2):
super(NASNetAMobile, self).__init__()
self.is_training = is_training
self.stem_filters = stem_filters
self.penultimate_filters = penultimate_filters
self.filters_multiplier = filters_multiplier
filters = self.penultimate_filters // 24
# 24 is default value for the architecture
self.conv0 = nn.SequentialCell([
nn.Conv2d(in_channels=3,
out_channels=self.stem_filters,
kernel_size=3,
stride=2,
pad_mode='pad',
padding=0,
has_bias=False),
nn.BatchNorm2d(num_features=self.stem_filters,
eps=0.001,
momentum=0.9,
affine=True)
])
self.cell_stem_0 = CellStem0(self.stem_filters,
num_filters=filters //
(filters_multiplier**2))
self.cell_stem_1 = CellStem1(self.stem_filters,
num_filters=filters // filters_multiplier)
self.cell_0 = FirstCell(
in_channels_left=filters,
out_channels_left=filters // 2, # 1, 0.5
in_channels_right=2 * filters,
out_channels_right=filters) # 2, 1
self.cell_1 = NormalCell(
in_channels_left=2 * filters,
out_channels_left=filters, # 2, 1
in_channels_right=6 * filters,
out_channels_right=filters) # 6, 1
self.cell_2 = NormalCell(
in_channels_left=6 * filters,
out_channels_left=filters, # 6, 1
in_channels_right=6 * filters,
out_channels_right=filters) # 6, 1
self.cell_3 = NormalCell(
in_channels_left=6 * filters,
out_channels_left=filters, # 6, 1
in_channels_right=6 * filters,
out_channels_right=filters) # 6, 1
self.reduction_cell_0 = ReductionCell0(
in_channels_left=6 * filters,
out_channels_left=2 * filters, # 6, 2
in_channels_right=6 * filters,
out_channels_right=2 * filters) # 6, 2
self.cell_6 = FirstCell(
in_channels_left=6 * filters,
out_channels_left=filters, # 6, 1
in_channels_right=8 * filters,
out_channels_right=2 * filters) # 8, 2
self.cell_7 = NormalCell(
in_channels_left=8 * filters,
out_channels_left=2 * filters, # 8, 2
in_channels_right=12 * filters,
out_channels_right=2 * filters) # 12, 2
self.cell_8 = NormalCell(
in_channels_left=12 * filters,
out_channels_left=2 * filters, # 12, 2
in_channels_right=12 * filters,
out_channels_right=2 * filters) # 12, 2
self.cell_9 = NormalCell(
in_channels_left=12 * filters,
out_channels_left=2 * filters, # 12, 2
in_channels_right=12 * filters,
out_channels_right=2 * filters) # 12, 2
if is_training:
self.aux_logits = AuxLogits(in_channels=12 * filters,
out_channels=num_classes)
self.reduction_cell_1 = ReductionCell1(
in_channels_left=12 * filters,
out_channels_left=4 * filters, # 12, 4
in_channels_right=12 * filters,
out_channels_right=4 * filters) # 12, 4
self.cell_12 = FirstCell(
in_channels_left=12 * filters,
out_channels_left=2 * filters, # 12, 2
in_channels_right=16 * filters,
out_channels_right=4 * filters) # 16, 4
self.cell_13 = NormalCell(
in_channels_left=16 * filters,
out_channels_left=4 * filters, # 16, 4
in_channels_right=24 * filters,
out_channels_right=4 * filters) # 24, 4
self.cell_14 = NormalCell(
in_channels_left=24 * filters,
out_channels_left=4 * filters, # 24, 4
in_channels_right=24 * filters,
out_channels_right=4 * filters) # 24, 4
self.cell_15 = NormalCell(
in_channels_left=24 * filters,
out_channels_left=4 * filters, # 24, 4
in_channels_right=24 * filters,
out_channels_right=4 * filters) # 24, 4
self.relu = nn.ReLU()
self.dropout = nn.Dropout(keep_prob=0.5)
self.classifier = nn.Dense(in_channels=24 * filters,
out_channels=num_classes)
self.shape = P.Shape()
self.reshape = P.Reshape()
self._initialize_weights()
def __init__(self,
model_cfgs,
num_classes=1000,
multiplier=1.,
final_drop=0.,
round_nearest=8):
super(MobileNetV3, self).__init__()
self.cfgs = model_cfgs['cfg']
self.inplanes = 16
self.features = []
first_conv_in_channel = 3
first_conv_out_channel = _make_divisible(multiplier * self.inplanes)
self.features.append(
nn.Conv2d(in_channels=first_conv_in_channel,
out_channels=first_conv_out_channel,
kernel_size=3,
padding=1,
stride=2,
has_bias=False,
pad_mode='pad'))
self.features.append(nn.BatchNorm2d(first_conv_out_channel))
self.features.append(Activation('hswish'))
for layer_cfg in self.cfgs:
self.features.append(
self._make_layer(
kernel_size=layer_cfg[0],
exp_ch=_make_divisible(multiplier * layer_cfg[1]),
out_channel=_make_divisible(multiplier * layer_cfg[2]),
use_se=layer_cfg[3],
act_func=layer_cfg[4],
stride=layer_cfg[5]))
output_channel = _make_divisible(multiplier *
model_cfgs["cls_ch_squeeze"])
self.features.append(
nn.Conv2d(in_channels=_make_divisible(multiplier *
self.cfgs[-1][2]),
out_channels=output_channel,
kernel_size=1,
padding=0,
stride=1,
has_bias=False,
pad_mode='pad'))
self.features.append(nn.BatchNorm2d(output_channel))
self.features.append(Activation('hswish'))
self.features.append(GlobalAvgPooling(keep_dims=True))
self.features.append(
nn.Conv2d(in_channels=output_channel,
out_channels=model_cfgs['cls_ch_expand'],
kernel_size=1,
padding=0,
stride=1,
has_bias=False,
pad_mode='pad'))
self.features.append(Activation('hswish'))
if final_drop > 0:
self.features.append((nn.Dropout(final_drop)))
# make it nn.CellList
self.features = nn.SequentialCell(self.features)
self.output = nn.Conv2d(in_channels=model_cfgs['cls_ch_expand'],
out_channels=num_classes,
kernel_size=1,
has_bias=True,
pad_mode='pad')
self.squeeze = P.Squeeze(axis=(2, 3))
self._initialize_weights()
def __init__(self,
src_dim,
tgt_dim,
attn_embed_dim,
num_attn_heads=1,
query_act=None,
key_act=None,
value_act=None,
out_act=None,
has_attention_mask=True,
attention_dropout_prob=0.0,
initializer_range=0.02,
do_return_2d_tensor=True,
compute_type=mstype.float32):
super(MultiHeadAttention, self).__init__()
if attn_embed_dim % num_attn_heads != 0:
raise ValueError(
f"The hidden size {attn_embed_dim} is not a multiple of the "
f"number of attention heads {num_attn_heads}")
self.attn_embed_dim = attn_embed_dim
self.num_attn_heads = num_attn_heads
self.size_per_head = attn_embed_dim // num_attn_heads
self.src_dim = src_dim
self.tgt_dim = tgt_dim
self.has_attention_mask = has_attention_mask
if attn_embed_dim != self.num_attn_heads * self.size_per_head:
raise ValueError(
"`attn_embed_dim` must be divided by num_attn_heads.")
self.scores_mul = Tensor([1.0 / math.sqrt(float(self.size_per_head))],
dtype=compute_type)
self.reshape = P.Reshape()
self.query_layer = nn.Dense(
src_dim,
attn_embed_dim,
activation=query_act,
has_bias=True,
weight_init=TruncatedNormal(initializer_range)).to_float(
compute_type)
self.key_layer = nn.Dense(
tgt_dim,
attn_embed_dim,
activation=key_act,
has_bias=True,
weight_init=TruncatedNormal(initializer_range)).to_float(
compute_type)
self.value_layer = nn.Dense(
tgt_dim,
attn_embed_dim,
activation=value_act,
has_bias=True,
weight_init=TruncatedNormal(initializer_range)).to_float(
compute_type)
self.out_layer = nn.Dense(
attn_embed_dim,
attn_embed_dim,
activation=out_act,
has_bias=True,
weight_init=TruncatedNormal(initializer_range)).to_float(
compute_type)
self.matmul_trans_b = P.BatchMatMul(transpose_b=True)
self.multiply = P.Mul()
self.transpose = P.Transpose()
self.multiply_data = Tensor([-10000.0], dtype=compute_type)
self.matmul = P.BatchMatMul()
self.softmax = nn.Softmax()
self.dropout = nn.Dropout(1.0 - attention_dropout_prob)
if self.has_attention_mask:
self.expand_dims = P.ExpandDims()
self.sub = P.Sub()
self.add = P.TensorAdd()
self.cast = P.Cast()
self.get_dtype = P.DType()
self.do_return_2d_tensor = do_return_2d_tensor
self.cast_compute_type = SaturateCast(dst_type=compute_type)
self.softmax_cast = P.Cast()
self.get_shape = P.Shape()
self.transpose_orders = (0, 2, 1, 3)
def __init__(self, num_classes=1000, create_aux_logits=False):
super(Inceptionv3, self).__init__()
self.create_aux_logits = create_aux_logits
# N x 3 x 299 x 299
self.Conv2d_1a_3x3 = Conv2dBlock(in_channels=3,
out_channels=32,
kernel_size=3,
stride=2,
pad_mode="valid")
# N x 32 x 149 x 149
self.Conv2d_2a_3x3 = Conv2dBlock(in_channels=32,
out_channels=32,
kernel_size=3,
pad_mode="valid")
# N x 32 x 147 x 147
self.Conv2d_2b_3x3 = Conv2dBlock(in_channels=32,
out_channels=64,
kernel_size=3,
pad_mode="same")
# N x 64 x 147 x 147
self.MaxPool_3a_3x3 = nn.MaxPool2d(kernel_size=3,
stride=2,
pad_mode="valid")
# N x 64 x 73 x 73
self.Conv2d_3b_1x1 = Conv2dBlock(in_channels=64,
out_channels=80,
kernel_size=1)
# N x 80 x 73 x 73
self.Conv2d_4a_3x3 = Conv2dBlock(in_channels=80,
out_channels=192,
kernel_size=3,
pad_mode="valid")
# N x 192 x 71 x 71
self.MaxPool_5a_3x3 = nn.MaxPool2d(kernel_size=3,
stride=2,
pad_mode="valid")
# N x 192 x 35 x 35
self.Mixed_5b = InceptionBlockA(in_channels=192, var_channels=32)
# N x 256 x 35 x 35
self.Mixed_5c = InceptionBlockA(in_channels=256, var_channels=64)
# N x 288 x 35 x 35
self.Mixed_5d = InceptionBlockA(in_channels=288, var_channels=64)
# N x 288 x 35 x 35
self.Mixed_6a = InceptionBlockB_1(in_channels=288)
# N x 768 x 17 x 17
self.Mixed_6b = InceptionBlockB_2(in_channels=768, var_channels=128)
# N x 768 x 17 x 17
self.Mixed_6c = InceptionBlockB_2(in_channels=768, var_channels=160)
# N x 768 x 17 x 17
self.Mixed_6d = InceptionBlockB_2(in_channels=768, var_channels=160)
# N x 768 x 17 x 17
self.Mixed_6e = InceptionBlockB_2(in_channels=768, var_channels=192)
# N x 768 x 17 x 17
if create_aux_logits:
self.AuxLogits = InceptionBlockAux(in_channels=768,
num_classes=num_classes)
# N x 768 x 17 x 17
self.Mixed_7a = InceptionBlockC_1(in_channels=768)
# N x 1280 x 8 x 8
self.Mixed_7b = InceptionBlockC_2(in_channels=1280)
# N x 2048 x 8 x 8
self.Mixed_7c = InceptionBlockC_2(in_channels=2048)
# N x 2048 x 8 x 8
self.mean = P.ReduceMean(keep_dims=True)
# N x 2048 x 1 x 1
self.Dropout_last = nn.Dropout(keep_prob=0.8)
# N x 2048 x 1 x 1
self.Conv2d_last = Conv2dBlock(in_channels=2048,
out_channels=num_classes,
kernel_size=1,
with_relu=False,
with_bn=False)
# N x num_classes x 1 x 1
self.fc = nn.Dense(in_channels=2048, out_channels=num_classes)
self.flatten = nn.Flatten()
def __init__(self,
num_classes,
input_nc=1,
padding=1,
pad_mode='pad',
has_bias=False,
use_dropout=False):
super(DFCNN, self).__init__()
if pad_mode == 'pad':
assert padding >= 0, "when the pad_mode is 'pad', the padding must be greater than or equal to 0!"
if pad_mode == 'same' or pad_mode == 'valid':
assert padding == 0, "when the pad_mode is 'same' or 'valid', the padding must be equal to 0!"
self.use_dropout = use_dropout
# structure
# seq 1
self.conv11 = nn.Conv2d(in_channels=input_nc,
out_channels=64,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn11 = nn.BatchNorm2d(64)
self.relu11 = nn.ReLU()
self.conv12 = nn.Conv2d(in_channels=64,
out_channels=64,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn12 = nn.BatchNorm2d(64)
self.relu12 = nn.ReLU()
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='valid')
# seq 2
self.conv21 = nn.Conv2d(in_channels=64,
out_channels=128,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn21 = nn.BatchNorm2d(128)
self.relu21 = nn.ReLU()
self.conv22 = nn.Conv2d(in_channels=128,
out_channels=128,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn22 = nn.BatchNorm2d(128)
self.relu22 = nn.ReLU()
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='valid')
# seq 3
self.conv31 = nn.Conv2d(in_channels=128,
out_channels=256,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn31 = nn.BatchNorm2d(256)
self.relu31 = nn.ReLU()
self.conv32 = nn.Conv2d(in_channels=256,
out_channels=256,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn32 = nn.BatchNorm2d(256)
self.relu32 = nn.ReLU()
self.conv33 = nn.Conv2d(in_channels=256,
out_channels=256,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn33 = nn.BatchNorm2d(256)
self.relu33 = nn.ReLU()
self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='valid')
# seq 4
self.conv41 = nn.Conv2d(in_channels=256,
out_channels=512,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn41 = nn.BatchNorm2d(512)
self.relu41 = nn.ReLU()
self.conv42 = nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn42 = nn.BatchNorm2d(512)
self.relu42 = nn.ReLU()
self.conv43 = nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn43 = nn.BatchNorm2d(512)
self.relu43 = nn.ReLU()
self.maxpool4 = nn.MaxPool2d(kernel_size=1, stride=1, pad_mode='valid')
# seq 5
self.conv51 = nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn51 = nn.BatchNorm2d(512)
self.relu51 = nn.ReLU()
self.conv52 = nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn52 = nn.BatchNorm2d(512)
self.relu52 = nn.ReLU()
self.conv53 = nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=padding,
has_bias=has_bias,
pad_mode=pad_mode)
self.bn53 = nn.BatchNorm2d(512)
self.relu53 = nn.ReLU()
self.maxpool5 = nn.MaxPool2d(kernel_size=1, stride=1, pad_mode='valid')
self.bn = nn.BatchNorm2d(512)
if self.use_dropout:
self.drop1 = nn.Dropout(0.8)
self.drop2 = nn.Dropout(0.8)
self.drop3 = nn.Dropout(0.8)
self.drop4 = nn.Dropout(0.8)
self.drop5 = nn.Dropout(0.8)
self.drop_fc1 = nn.Dropout(0.5)
self.drop_fc2 = nn.Dropout(0.5)
self.fc1 = nn.Dense(25 * 512, 4096, activation='relu')
self.fc2 = nn.Dense(4096, 4096, activation='relu')
self.fc3 = nn.Dense(4096, num_classes, activation='relu')
# operation
self.transpose = ops.Transpose()
self.reshape = ops.Reshape()
def __init__(self, weight, vocab_size, cell, batch_size):
super(textrcnn, self).__init__()
self.num_hiddens = 512
self.embed_size = 300
self.num_classes = 2
self.batch_size = batch_size
k = (1 / self.num_hiddens)**0.5
self.embedding = nn.Embedding(vocab_size,
self.embed_size,
embedding_table=weight)
self.embedding.embedding_table.requires_grad = False
self.cell = cell
self.cast = P.Cast()
self.h1 = Tensor(
np.zeros(shape=(self.batch_size,
self.num_hiddens)).astype(np.float16))
self.c1 = Tensor(
np.zeros(shape=(self.batch_size,
self.num_hiddens)).astype(np.float16))
if cell == "lstm":
self.lstm = P.DynamicRNN(forget_bias=0.0)
self.w1_fw = Parameter(np.random.uniform(
-k, k, (self.embed_size + self.num_hiddens,
4 * self.num_hiddens)).astype(np.float16),
name="w1_fw")
self.b1_fw = Parameter(np.random.uniform(
-k, k, (4 * self.num_hiddens)).astype(np.float16),
name="b1_fw")
self.w1_bw = Parameter(np.random.uniform(
-k, k, (self.embed_size + self.num_hiddens,
4 * self.num_hiddens)).astype(np.float16),
name="w1_bw")
self.b1_bw = Parameter(np.random.uniform(
-k, k, (4 * self.num_hiddens)).astype(np.float16),
name="b1_bw")
self.h1 = Tensor(
np.zeros(shape=(1, self.batch_size,
self.num_hiddens)).astype(np.float16))
self.c1 = Tensor(
np.zeros(shape=(1, self.batch_size,
self.num_hiddens)).astype(np.float16))
if cell == "vanilla":
self.rnnW_fw = nn.Dense(self.num_hiddens, self.num_hiddens)
self.rnnU_fw = nn.Dense(self.embed_size, self.num_hiddens)
self.rnnW_bw = nn.Dense(self.num_hiddens, self.num_hiddens)
self.rnnU_bw = nn.Dense(self.embed_size, self.num_hiddens)
if cell == "gru":
self.rnnWr_fw = nn.Dense(self.num_hiddens + self.embed_size,
self.num_hiddens)
self.rnnWz_fw = nn.Dense(self.num_hiddens + self.embed_size,
self.num_hiddens)
self.rnnWh_fw = nn.Dense(self.num_hiddens + self.embed_size,
self.num_hiddens)
self.rnnWr_bw = nn.Dense(self.num_hiddens + self.embed_size,
self.num_hiddens)
self.rnnWz_bw = nn.Dense(self.num_hiddens + self.embed_size,
self.num_hiddens)
self.rnnWh_bw = nn.Dense(self.num_hiddens + self.embed_size,
self.num_hiddens)
self.ones = Tensor(
np.ones(shape=(self.batch_size,
self.num_hiddens)).astype(np.float16))
self.rnnWr_fw.to_float(mstype.float16)
self.rnnWz_fw.to_float(mstype.float16)
self.rnnWh_fw.to_float(mstype.float16)
self.rnnWr_bw.to_float(mstype.float16)
self.rnnWz_bw.to_float(mstype.float16)
self.rnnWh_bw.to_float(mstype.float16)
self.transpose = P.Transpose()
self.reduce_max = P.ReduceMax()
self.expand_dims = P.ExpandDims()
self.concat = P.Concat()
self.reshape = P.Reshape()
self.left_pad_tensor = Tensor(
np.zeros(
(1, self.batch_size, self.num_hiddens)).astype(np.float16))
self.right_pad_tensor = Tensor(
np.zeros(
(1, self.batch_size, self.num_hiddens)).astype(np.float16))
self.output_dense = nn.Dense(self.num_hiddens * 1, 2)
self.concat0 = P.Concat(0)
self.concat2 = P.Concat(2)
self.concat1 = P.Concat(1)
self.text_rep_dense = nn.Dense(2 * self.num_hiddens + self.embed_size,
self.num_hiddens)
self.mydense = nn.Dense(self.num_hiddens, 2)
self.drop_out = nn.Dropout(keep_prob=0.7)
self.tanh = P.Tanh()
self.sigmoid = P.Sigmoid()
self.slice = P.Slice()
self.text_rep_dense.to_float(mstype.float16)
self.mydense.to_float(mstype.float16)
self.output_dense.to_float(mstype.float16)
'desc_inputs': [[1, 512]],
'desc_bprop': [[1, 512]]}),
('LogicalNot', {
'block': P.LogicalNot(),
'desc_inputs': [convert([256], np.bool_)],
'desc_bprop': [[256]]}), # 自定义算子 input bool没转换，gongchen提单。
('Equal', {
'block': P.Equal(),
'desc_inputs': [convert([256], np.float16), convert([256], np.float16)],
'desc_bprop': [[256]]}),
('Greater', {
'block': P.Greater(),
'desc_inputs': [convert([256], np.float16), convert([256], np.float16)],
'desc_bprop': [[256]]}),
('Dropout', {
'block': nn.Dropout(),
'desc_inputs': [[1, 512, 7, 7]],
'desc_bprop': [[1, 512, 7, 7]]}), # 输入有标量插件产生了段错误。
('MatMul', {
'block': P.MatMul(),
'desc_inputs': [[64, 512], [512, 64]], # fp16不行。很有问题。
'desc_bprop': [[64, 64]]}),
('Maximum', {
'block': P.Maximum(),
'desc_inputs': [[64, 1], [64, 1]],
'desc_bprop': [[64, 1]]}),
]
test_case_lists = [test_case_reid_ops]
test_case = functools.reduce(lambda x, y: x + y, test_case_lists)
# use -k to select certain testcast
def test_check_dropout_1():
x = Tensor(np.ones([20, 16, 50]), mstype.float32)
m = nn.Dropout(0.8)
m(x)
def __init__(self, config):
super(PANGUALPHA_Model, self).__init__()
self.get_attention_mask = AttentionMask(config)
self.word_embedding = EmbeddingLookup(config).set_comm_fusion(1)
self.eod_reset = config.eod_reset
if config.load_ckpt_path:
# Loading the embedding table from the ckpt path:
embedding_path = os.path.join(config.load_ckpt_path, 'position_embedding.npy')
if os.path.exists(embedding_path):
p_table = np.load(embedding_path)
position_table_param = Tensor(p_table, mstype.float32)
else:
raise ValueError(f"{embedding_path} file not exits, please check whether position_embedding file exit.")
else:
position_table_param = TruncatedNormal(0.02)
self.position_embedding = nn.Embedding(
config.seq_length,
config.embedding_size,
embedding_table=position_table_param).set_comm_fusion(1)
self.word_embedding.embedding_table.parallel_optimizer = False
self.position_embedding.embedding_table.parallel_optimizer = False
self.position_embedding.gather.shard(((1, 1), (config.dp,)))
self.position_embedding.expand.shard(((config.dp, 1),))
self.blocks = nn.CellList()
fusion_group_num = 4
fusion_group_size = config.num_layers // fusion_group_num
fusion_group_size = max(fusion_group_size, 1)
num_layers = config.num_layers - 1
self.num_layers = num_layers
for i in range(num_layers):
per_block = Block(config, i + 1).set_comm_fusion(int(i / fusion_group_size) + 2)
per_block.recompute()
per_block.attention.dropout.dropout_gen_mask.recompute(False)
per_block.attention.prob_dropout.dropout_gen_mask.recompute(False)
per_block.output.dropout.dropout_gen_mask.recompute(False)
per_block.attention.dropout.dropout_gen_mask.add_prim_attr("_side_effect", True)
per_block.attention.prob_dropout.dropout_gen_mask.add_prim_attr("_side_effect", True)
per_block.output.dropout.dropout_gen_mask.add_prim_attr("_side_effect", True)
self.blocks.append(per_block)
if config.self_layernorm:
self.layernorm = LayerNorm((config.embedding_size,), config.dp).to_float(
mstype.float32).set_comm_fusion(
int((num_layers - 1) / fusion_group_size) + 2)
else:
self.layernorm = nn.LayerNorm((config.embedding_size,)).to_float(
mstype.float32).set_comm_fusion(
int((num_layers - 1) / fusion_group_size) + 2)
self.layernorm.layer_norm.shard(((config.dp, 1, 1), (1,), (1,)))
self.layernorm.gamma.parallel_optimizer = False
self.layernorm.beta.parallel_optimizer = False
self.use_past = config.use_past
self.past = tuple([None] * config.num_layers)
self.add = P.TensorAdd().shard(((config.dp, 1, 1), (config.dp, 1, 1)))
self.expand_dims = P.ExpandDims().shard(((config.dp, 1, 1),))
self.dtype = config.compute_dtype
self.dropout = nn.Dropout(1 - config.dropout_rate)
self.dropout.dropout_gen_mask.shard(((config.dp, 1, 1),))
self.dropout.dropout_do_mask.shard(((config.dp, 1, 1),))
if config.load_ckpt_path:
# Loading the embedding table from the ckpt path:
embedding_path = os.path.join(config.load_ckpt_path, 'top_query_embedding.npy')
if os.path.exists(embedding_path):
top_query_table = np.load(embedding_path)
top_query_table_param = Tensor(top_query_table, mstype.float32)
else:
raise ValueError(f"{embedding_path} file not exits, please check whether top_query_embedding file exist.")
else:
top_query_table_param = TruncatedNormal(0.02)
self.top_query_embedding = nn.Embedding(config.seq_length, config.embedding_size, \
embedding_table=top_query_table_param).set_comm_fusion(
int((config.num_layers - 1) / fusion_group_num) + 2)
self.top_query_embedding.embedding_table.parallel_optimizer = False
self.top_query_embedding.gather.shard(((1, 1), (config.dp,)))
self.top_query_embedding.expand.shard(((config.dp, 1),))
self.top_query_layer = QueryLayer(config)
self.top_query_layer.recompute()
self.top_query_layer.output.dropout.dropout_gen_mask.recompute(False)
self.top_query_layer.attention.dropout.dropout_gen_mask.recompute(False)
self.top_query_layer.attention.prob_dropout.dropout_gen_mask.recompute(False)
self.top_query_layer.output.dropout.dropout_gen_mask.add_prim_attr("_side_effect", True)
self.top_query_layer.attention.dropout.dropout_gen_mask.add_prim_attr("_side_effect", True)
self.top_query_layer.attention.prob_dropout.dropout_gen_mask.add_prim_attr("_side_effect", True)
self.top_query_layer.set_comm_fusion(int((config.num_layers - 1) / fusion_group_num) + 2)
def test_check_dropout_3():
x = Tensor(np.ones([20, 16, 50]), mstype.float32)
m = nn.Dropout(0.3, seed0=1, seed1=1)
m(x)
def __init__(self):
super(PReLUGradNet, self).__init__()
self.prelu_grad = G.PReLUGrad()
def construct(self, dout, x, w):
return self.prelu_grad(dout, x, w)
test_cases = [
('SoftMaxGrad', {
'block': SoftMaxGrad(VirtualNetWithLoss(P.Softmax())),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [[128, 32, 32, 64]],
}),
('DropoutGrad', {
'block': DropoutGrad(VirtualNetWithLoss(nn.Dropout())),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [[128, 32, 32, 64]],
}),
('ScalarSummary', {
'block': ScalarSummaryNet(),
'desc_inputs': [Tensor(2.2)],
}),
('L2Normalize', {
'block':
L2NormalizeNet(),
'desc_inputs': [
Tensor(np.array([[1.0, 2, 3], [4.0, 5, 6], [7.0, 8, 9]]),
mindspore.float32)
],
}),
def __init__(self,
num_classes,
feature_shape,
backbone,
channel,
depth,
scale_sizes,
atrous_rates,
decoder_output_stride,
output_stride,
fine_tune_batch_norm=False):
super(SingleDeepLabV3, self).__init__()
self.num_classes = num_classes
self.channel = channel
self.depth = depth
self.scale_sizes = []
for scale_size in np.sort(scale_sizes):
self.scale_sizes.append(scale_size)
self.net = backbone
self.aspp = ASPP(channel=self.channel,
depth=self.depth,
feature_shape=[feature_shape[2], feature_shape[3]],
scale_sizes=self.scale_sizes,
atrous_rates=atrous_rates,
output_stride=output_stride,
fine_tune_batch_norm=fine_tune_batch_norm)
atrous_rates_len = 0
if atrous_rates is not None:
atrous_rates_len = len(atrous_rates)
self.fc1 = _conv_bn_relu(depth * (2 + atrous_rates_len),
depth,
ksize=1,
stride=1,
use_batch_statistics=fine_tune_batch_norm)
self.fc2 = nn.Conv2d(depth,
num_classes,
kernel_size=1,
stride=1,
has_bias=True)
self.upsample = P.ResizeBilinear(
(int(feature_shape[2]), int(feature_shape[3])), align_corners=True)
self.samples = []
for scale_size in self.scale_sizes:
self.samples.append(SampleBlock(feature_shape, scale_size))
self.samples = nn.CellList(self.samples)
self.feature_shape = [
float(feature_shape[0]),
float(feature_shape[1]),
float(feature_shape[2]),
float(feature_shape[3])
]
self.pad = P.Pad(((0, 0), (0, 0), (1, 1), (1, 1)))
self.dropout = nn.Dropout(keep_prob=0.9)
self.shape = P.Shape()
self.decoder_output_stride = decoder_output_stride
if decoder_output_stride is not None:
self.decoder = Decoder(
low_level_channel=depth,
channel=depth,
depth=depth,
feature_shape=[feature_shape[2], feature_shape[3]],
scale_sizes=self.scale_sizes,
decoder_output_stride=decoder_output_stride,
fine_tune_batch_norm=fine_tune_batch_norm)
'desc_inputs': [[3, 2, 1, 3], Tensor(np.array([[0, 1], [0, 1], [0, 1]]).astype(np.int32))],
'desc_bprop': [[4, 1, 3]],
'skip': ['backward']}),
('DropoutGenMask', {
'block': P.DropoutGenMask(),
'desc_const': [(2, 2), Tensor(0.5, mstype.float32)],
'desc_inputs': [],
'desc_bprop': [Tensor(np.ones(1).astype(np.int8))],
'skip': ['backward']}),
('DropoutDoMask', {
'block': P.DropoutDoMask(),
'desc_const': [Tensor(0.5)],
'desc_inputs': [[64, 12, 128, 128], Tensor(np.ones(1572864).astype(np.uint8))],
'desc_bprop': [[64, 12, 128, 128]]}),
('Dropout', {
'block': nn.Dropout(0.5),
'desc_inputs': [[64, 12, 128, 128]],
'desc_bprop': [[64, 12, 128, 128]]}),
('ReduceMean0', {
'block': P.ReduceMean(),
'desc_const': [(2,)],
'desc_inputs': [[3, 2, 2]],
'desc_bprop': [[3, 2]]}),
('ReduceMean1', {
'block': P.ReduceMean(),
'desc_const': [2],
'desc_inputs': [[3, 2, 2]],
'desc_bprop': [[3, 2]]}),
('All', {
'block': P.ReduceAll(),
'desc_const': [(1,)],
def __init__(self, model_settings, model_size_info):
super(DSCNN, self).__init__()
# N C H W
label_count = model_settings['label_count']
input_frequency_size = model_settings['dct_coefficient_count']
input_time_size = model_settings['spectrogram_length']
t_dim = input_time_size
f_dim = input_frequency_size
num_layers = model_size_info[0]
conv_feat = [None] * num_layers
conv_kt = [None] * num_layers
conv_kf = [None] * num_layers
conv_st = [None] * num_layers
conv_sf = [None] * num_layers
i = 1
for layer_no in range(0, num_layers):
conv_feat[layer_no] = model_size_info[i]
i += 1
conv_kt[layer_no] = model_size_info[i]
i += 1
conv_kf[layer_no] = model_size_info[i]
i += 1
conv_st[layer_no] = model_size_info[i]
i += 1
conv_sf[layer_no] = model_size_info[i]
i += 1
seq_cell = []
in_channel = 1
for layer_no in range(0, num_layers):
if layer_no == 0:
seq_cell.append(
nn.Conv2d(in_channels=in_channel,
out_channels=conv_feat[layer_no],
kernel_size=(conv_kt[layer_no],
conv_kf[layer_no]),
stride=(conv_st[layer_no], conv_sf[layer_no]),
pad_mode="same",
padding=0,
has_bias=False))
seq_cell.append(
nn.BatchNorm2d(num_features=conv_feat[layer_no],
momentum=0.98))
in_channel = conv_feat[layer_no]
else:
seq_cell.append(
DepthWiseConv(in_planes=in_channel,
kernel_size=(conv_kt[layer_no],
conv_kf[layer_no]),
stride=(conv_st[layer_no],
conv_sf[layer_no]),
pad_mode='same',
pad=0))
seq_cell.append(
nn.BatchNorm2d(num_features=in_channel, momentum=0.98))
seq_cell.append(nn.ReLU())
seq_cell.append(
nn.Conv2d(in_channels=in_channel,
out_channels=conv_feat[layer_no],
kernel_size=(1, 1),
pad_mode="same"))
seq_cell.append(
nn.BatchNorm2d(num_features=conv_feat[layer_no],
momentum=0.98))
seq_cell.append(nn.ReLU())
in_channel = conv_feat[layer_no]
t_dim = math.ceil(t_dim / float(conv_st[layer_no]))
f_dim = math.ceil(f_dim / float(conv_sf[layer_no]))
seq_cell.append(nn.AvgPool2d(kernel_size=(t_dim, f_dim))) # to fix ?
seq_cell.append(nn.Flatten())
seq_cell.append(nn.Dropout(model_settings['dropout1']))
seq_cell.append(nn.Dense(in_channel, label_count))
self.model = nn.SequentialCell(seq_cell)
def __init__(self):
super(Net_dropout, self).__init__()
self.dropout = nn.Dropout(0.5)
def __init__(self,
device_target,
num_classes=1000,
width_mult=1.,
has_dropout=False,
inverted_residual_setting=None,
round_nearest=8):
super(MobileNetV2, self).__init__()
block = InvertedResidual
input_channel = 32
last_channel = 1280
# setting of inverted residual blocks
self.cfgs = inverted_residual_setting
if inverted_residual_setting is None:
self.cfgs = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1],
]
# building first layer
input_channel = _make_divisible(input_channel * width_mult,
round_nearest)
self.out_channels = _make_divisible(
last_channel * max(1.0, width_mult), round_nearest)
features = [ConvBNReLU(device_target, 3, input_channel, stride=2)]
# building inverted residual blocks
for t, c, n, s in self.cfgs:
output_channel = _make_divisible(c * width_mult, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
features.append(
block(device_target,
input_channel,
output_channel,
stride,
expand_ratio=t))
input_channel = output_channel
# building last several layers
features.append(
ConvBNReLU(device_target,
input_channel,
self.out_channels,
kernel_size=1))
# make it nn.CellList
self.features = nn.SequentialCell(features)
# mobilenet head
head = ([
GlobalAvgPooling(),
nn.Dense(self.out_channels, num_classes, has_bias=True)
] if not has_dropout else [
GlobalAvgPooling(),
nn.Dropout(0.2),
nn.Dense(self.out_channels, num_classes, has_bias=True)
])
self.head = nn.SequentialCell(head)
self._initialize_weights()
def __init__(self,
hidden_size: int,
hidden_dropout_prob: int = 0.1) -> None:
super().__init__()
self.layer_norm = nn.LayerNorm((hidden_size, ), epsilon=1e-12)
self.dropout = nn.Dropout(1.0 - hidden_dropout_prob)
def __init__(self,
config: TransformerConfig,
is_training: bool,
use_one_hot_embeddings: bool = False,
use_positional_embedding: bool = True):
super(Transformer, self).__init__()
self.use_positional_embedding = use_positional_embedding
config = copy.deepcopy(config)
self.is_training = is_training
if not is_training:
config.hidden_dropout_prob = 0.0
config.attention_dropout_prob = 0.0
self.input_mask_from_dataset = config.input_mask_from_dataset
self.batch_size = config.batch_size
self.max_positions = config.seq_length
self.attn_embed_dim = config.hidden_size
self.num_layers = config.num_hidden_layers
self.word_embed_dim = config.hidden_size
self.last_idx = self.num_layers - 1
self.embedding_lookup = EmbeddingLookup(
vocab_size=config.vocab_size,
embed_dim=self.word_embed_dim,
use_one_hot_embeddings=use_one_hot_embeddings)
if self.use_positional_embedding:
self.positional_embedding = PositionalEmbedding(
embedding_size=self.word_embed_dim,
max_position_embeddings=config.max_position_embeddings)
self.encoder = TransformerEncoder(
attn_embed_dim=self.attn_embed_dim,
encoder_layers=self.num_layers,
num_attn_heads=config.num_attention_heads,
intermediate_size=config.intermediate_size,
attention_dropout_prob=config.attention_dropout_prob,
initializer_range=config.initializer_range,
hidden_dropout_prob=config.hidden_dropout_prob,
hidden_act=config.hidden_act,
compute_type=config.compute_type)
self.decoder = TransformerDecoder(
attn_embed_dim=self.attn_embed_dim,
decoder_layers=self.num_layers,
num_attn_heads=config.num_attention_heads,
intermediate_size=config.intermediate_size,
attn_dropout_prob=config.attention_dropout_prob,
initializer_range=config.initializer_range,
dropout_prob=config.hidden_dropout_prob,
hidden_act=config.hidden_act,
compute_type=config.compute_type)
self.cast = P.Cast()
self.dtype = config.dtype
self.cast_compute_type = SaturateCast(dst_type=config.compute_type)
self.slice = P.StridedSlice()
self.dropout = nn.Dropout(keep_prob=1 - config.hidden_dropout_prob)
self._create_attention_mask_from_input_mask = CreateAttentionMaskFromInputMask(config)
self.scale = Tensor([math.sqrt(float(self.word_embed_dim))],
dtype=mstype.float32)
self.multiply = P.Mul()
