def __init__(self, in_channels, growth_rate, bn_size):
super(_DenseLayer, self).__init__()
self.layer = layers.SequentialLayer([
_bn(in_channels),
layers.ReLU(),
_conv1x1(in_channels, bn_size * growth_rate),
_bn(bn_size * growth_rate),
layers.ReLU(),
_conv3x3(bn_size * growth_rate, growth_rate),
])
self.ops = Concat(axis=1)
def __init__(self, features, class_num=1000):
super(VGG, self).__init__()
self.features = features
self.flatten = layers.Flatten()
self.classifier = layers.SequentialLayer([
layers.Dense(512 * 7 * 7, 4096),
layers.ReLU(),
layers.Dropout(),
layers.Dense(4096, 4096),
layers.ReLU(),
layers.Dropout(),
layers.Dense(4096, class_num),
])
def make_layers(cfg, batch_norm=False):
Layers = []
in_channels = 3
for v in cfg:
if v == 'M':
Layers += [layers.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = _conv3x3(in_channels, v)
if batch_norm:
Layers += [conv2d, layers.BatchNorm2d(v), layers.ReLU()]
else:
Layers += [conv2d, layers.ReLU()]
in_channels = v
return layers.SequentialLayer(Layers)
def __init__(self, in_channels, out_channels):
super(_Transition, self).__init__()
self.layer = layers.SequentialLayer([
_bn(in_channels),
layers.ReLU(),
_conv1x1(in_channels, out_channels),
AvgPool2d(kernel_size=2,
stride=2,
pad_mode='same',
data_format='NCHW')
])
def test_sequential():
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
net = layers.SequentialLayer([
layers.Conv2d(1, 6, 5, pad_mode='valid', weight_init="ones"),
layers.ReLU(),
layers.MaxPool2d(kernel_size=2, stride=2)
])
model = Model(net)
model.compile()
z = model.predict(ts.ones((1, 1, 32, 32)))
print(z.asnumpy())
def __init__(self,
in_planes,
out_planes,
kernel_size=4,
stride=2,
alpha=0.2,
norm_mode='batch',
pad_mode='CONSTANT',
use_relu=True,
padding=None):
super(ConvTransposeNormReLU, self).__init__()
conv = layers.Conv2dTranspose(in_planes,
out_planes,
kernel_size,
stride=stride,
pad_mode='same')
norm = layers.BatchNorm2d(out_planes)
if norm_mode == 'instance':
# Use BatchNorm2d with batchsize=1, affine=False, training=True instead of InstanceNorm2d
norm = layers.BatchNorm2d(out_planes, affine=False)
has_bias = (norm_mode == 'instance')
if padding is None:
padding = (kernel_size - 1) // 2
if pad_mode == 'CONSTANT':
conv = layers.Conv2dTranspose(in_planes,
out_planes,
kernel_size,
stride,
pad_mode='same',
has_bias=has_bias)
layer_list = [conv, norm]
else:
paddings = ((0, 0), (0, 0), (padding, padding), (padding, padding))
pad = layers.Pad(paddings=paddings, mode=pad_mode)
conv = layers.Conv2dTranspose(in_planes,
out_planes,
kernel_size,
stride,
pad_mode='pad',
has_bias=has_bias)
layer_list = [pad, conv, norm]
if use_relu:
relu = layers.ReLU()
if alpha > 0:
relu = layers.LeakyReLU(alpha)
layer_list.append(relu)
self.features = layers.SequentialLayer(layer_list)
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 = layers.BatchNorm2d(inner_nc)
upnorm = layers.BatchNorm2d(outer_nc)
use_bias = False
if norm_mode == 'instance':
downnorm = layers.BatchNorm2d(inner_nc, affine=False)
upnorm = layers.BatchNorm2d(outer_nc, affine=False)
use_bias = True
if in_planes is None:
in_planes = outer_nc
downconv = layers.Conv2d(in_planes, inner_nc, kernel_size=4,
stride=2, padding=1, has_bias=use_bias, pad_mode='pad')
downrelu = layers.LeakyReLU(alpha)
uprelu = layers.ReLU()
if outermost:
upconv = layers.Conv2dTranspose(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1, pad_mode='pad')
down = [downconv]
up = [uprelu, upconv, layers.Tanh()]
model = down + [submodule] + up
elif innermost:
upconv = layers.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 = layers.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(layers.Dropout(0.5))
self.model = layers.SequentialLayer(model)
self.skip_connections = not outermost
self.concat = Concat(axis=1)
def __init__(self, in_channel, out_channel, stride=1):
super(ResidualBlock, self).__init__()
channel = out_channel // self.expansion
self.conv1 = _conv1x1(in_channel, channel, stride=1)
self.bn1 = _bn(channel)
self.conv2 = _conv3x3(channel, channel, stride=stride)
self.bn2 = _bn(channel)
self.conv3 = _conv1x1(channel, out_channel, stride=1)
self.bn3 = _bn_last(out_channel)
self.relu = layers.ReLU()
self.down_sample = False
self.down_sample_layer = None
if stride != 1 or in_channel != out_channel:
self.down_sample = True
if self.down_sample:
self.down_sample_layer = layers.SequentialLayer(
[_conv1x1(in_channel, out_channel, stride), _bn(out_channel)])
def __init__(self,
block,
layer_nums,
in_channels,
out_channels,
strides,
num_classes):
super(ResNet, self).__init__()
if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
raise ValueError("the length of layer_num, in_channels, out_channels list must be 4!")
self.conv1 = _conv7x7(3, 64, stride=2)
self.bn1 = _bn(64)
self.relu = layers.ReLU()
self.maxpool = layers.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
self.layer1 = self._make_layer(block,
layer_nums[0],
in_channel=in_channels[0],
out_channel=out_channels[0],
stride=strides[0])
self.layer2 = self._make_layer(block,
layer_nums[1],
in_channel=in_channels[1],
out_channel=out_channels[1],
stride=strides[1])
self.layer3 = self._make_layer(block,
layer_nums[2],
in_channel=in_channels[2],
out_channel=out_channels[2],
stride=strides[2])
self.layer4 = self._make_layer(block,
layer_nums[3],
in_channel=in_channels[3],
out_channel=out_channels[3],
stride=strides[3])
self.mean = ReduceMean(keep_dims=True)
self.flatten = layers.Flatten()
self.end_point = _fc(out_channels[3], num_classes)