def __init__(self, num_class=1):
self.conv1_1 = rm.Conv2d(64, padding=1, filter=3)
self.bn1_1 = rm.BatchNormalize(mode='feature')
self.conv1_2 = rm.Conv2d(64, padding=1, filter=3)
self.bn1_2 = rm.BatchNormalize(mode='feature')
self.conv2_1 = rm.Conv2d(128, padding=1, filter=3)
self.bn2_1 = rm.BatchNormalize(mode='feature')
self.conv2_2 = rm.Conv2d(128, padding=1, filter=3)
self.bn2_2 = rm.BatchNormalize(mode='feature')
self.conv3_1 = rm.Conv2d(256, padding=1, filter=3)
self.bn3_1 = rm.BatchNormalize(mode='feature')
self.conv3_2 = rm.Conv2d(256, padding=1, filter=3)
self.bn3_2 = rm.BatchNormalize(mode='feature')
self.conv4_1 = rm.Conv2d(512, padding=1, filter=3)
self.bn4_1 = rm.BatchNormalize(mode='feature')
self.conv4_2 = rm.Conv2d(512, padding=1, filter=3)
self.bn4_2 = rm.BatchNormalize(mode='feature')
self.conv5_1 = rm.Conv2d(1024, padding=1, filter=3)
self.bn5_1 = rm.BatchNormalize(mode='feature')
self.conv5_2 = rm.Conv2d(1024, padding=1, filter=3)
self.bn5_2 = rm.BatchNormalize(mode='feature')
self.deconv1 = rm.Deconv2d(512, stride=2)
self.conv6_1 = rm.Conv2d(256, padding=1)
self.conv6_2 = rm.Conv2d(256, padding=1)
self.deconv2 = rm.Deconv2d(256, stride=2)
self.conv7_1 = rm.Conv2d(128, padding=1)
self.conv7_2 = rm.Conv2d(128, padding=1)
self.deconv3 = rm.Deconv2d(128, stride=2)
self.conv8_1 = rm.Conv2d(64, padding=1)
self.conv8_2 = rm.Conv2d(64, padding=1)
self.deconv4 = rm.Deconv2d(64, stride=2)
self.conv9 = rm.Conv2d(num_class, filter=1)
def __init__(self,
input_shape,
output_shape,
units=10,
depth=3,
growth_rate=12,
dropout=False,
initializer=rm.utility.initializer.Gaussian(std=0.3),
active=rm.tanh):
self.input_shape = input_shape
self.output_shape = output_shape
self.units = units
self.depth = depth
self.dropout = dropout
self.active = active
parameters = []
add_units = units
for _ in range(depth - 1):
add_units += growth_rate
parameters.append(rm.BatchNormalize())
parameters.append(rm.Dense(add_units, initializer=initializer))
self.hidden = rm.Sequential(parameters)
self.input_batch = rm.BatchNormalize()
self.input = rm.Dense(units)
self.multi_output = False
if isinstance(self.output_shape, tuple):
self.multi_output = True
parameters = []
for _ in range(output_shape[0]):
parameters.append(rm.BatchNormalize())
parameters.append(
rm.Dense(output_shape[1], initializer=initializer))
self.output = rm.Sequential(parameters)
else:
self.output = rm.Dense(output_shape)
def __init__(self, num_class):
self.base1 = rm.Sequential([
InceptionV2Stem(),
InceptionV2BlockA([64, 48, 64, 64, 96, 32]),
InceptionV2BlockA(),
InceptionV2BlockA(),
InceptionV2BlockB(),
InceptionV2BlockC([192, 128, 192, 128, 192, 192]),
InceptionV2BlockC(),
InceptionV2BlockC(),
InceptionV2BlockC()])
self.aux1 = rm.Sequential([
rm.AveragePool2d(filter=5, stride=3),
rm.Conv2d(128, filter=1),
rm.BatchNormalize(mode='feature'),
rm.Relu(),
rm.Conv2d(768, filter=1),
rm.BatchNormalize(mode='feature'),
rm.Relu(),
rm.Flatten(),
rm.Dense(num_class)])
self.base2 = rm.Sequential([
InceptionV2BlockD(),
InceptionV2BlockE(),
InceptionV2BlockE(),
rm.AveragePool2d(filter=8),
rm.Flatten()])
self.aux2 = rm.Dense(num_class)
def conv_block(growth_rate):
return rm.Sequential([
rm.BatchNormalize(epsilon=0.001, mode='feature'),
rm.Relu(),
rm.Conv2d(growth_rate * 4, 1, padding=0),
rm.BatchNormalize(epsilon=0.001, mode='feature'),
rm.Relu(),
rm.Conv2d(growth_rate, 3, padding=1),
])
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(planes, stride)
self.bn1 = rm.BatchNormalize(mode='feature')
self.relu = rm.Relu()
self.conv2 = conv3x3(planes)
self.bn2 = rm.BatchNormalize(mode='feature')
self.downsample = downsample
self.stride = stride
def __init__(self, channels=[64, 96, 384]):
self.conv1_reduced = rm.Conv2d(channels[0], filter=1)
self.batch_norm1_reduced = rm.BatchNormalize(mode='feature')
self.conv1_1 = rm.Conv2d(channels[1], filter=3, padding=1)
self.batch_norm1_1 = rm.BatchNormalize(mode='feature')
self.conv1_2 = rm.Conv2d(channels[1], filter=3, stride=2)
self.batch_norm1_2 = rm.BatchNormalize(mode='feature')
self.conv2 = rm.Conv2d(channels[2], filter=3, stride=2)
self.batch_norm2 = rm.BatchNormalize(mode='feature')
def __init__(self):
# k, l, m, n
# 192, 224, 256, 384
self.conv1 = rm.Conv2d(384, filter=3, stride=2)
self.batch_norm1 = rm.BatchNormalize(mode='feature')
self.conv2_red = rm.Conv2d(192, filter=1)
self.batch_norm2_red = rm.BatchNormalize(mode='feature')
self.conv2_1 = rm.Conv2d(224, filter=3, padding=1)
self.batch_norm2_1 = rm.BatchNormalize(mode='feature')
self.conv2_2 = rm.Conv2d(256, filter=3, stride=2)
self.batch_norm2_2 = rm.BatchNormalize(mode='feature')
def __init__(self, channels=[192, 320, 192, 192]):
self.conv1_reduced = rm.Conv2d(channels[0], filter=1)
self.batch_norm1_reduced = rm.BatchNormalize(mode='feature')
self.conv1 = rm.Conv2d(channels[1], filter=3, stride=2)
self.batch_norm1 = rm.BatchNormalize(mode='feature')
self.conv2_reduced = rm.Conv2d(channels[2], filter=1)
self.batch_norm2_reduced = rm.BatchNormalize(mode='feature')
self.conv2_1 = rm.Conv2d(channels[3], filter=3, padding=1)
self.batch_norm2_1 = rm.BatchNormalize(mode='feature')
self.conv2_2 = rm.Conv2d(channels[3], filter=3, stride=2)
self.batch_norm2_2 = rm.BatchNormalize(mode='feature')
def test_batch_normalize_featuremap(a):
layer = rm.BatchNormalize(mode=BATCH_NORMALIZE_FEATUREMAP, momentum=0.1)
set_cuda_active(True)
g1 = Variable(a)
for _ in range(10):
g3 = layer(g1)
g3.to_cpu()
layer.set_models(inference=True)
g4 = layer(g1)
layer.set_models(inference=False)
set_cuda_active(False)
layer._mov_mean = 0
layer._mov_std = 0
for _ in range(10):
c3 = layer(g1)
layer.set_models(inference=True)
c4 = layer(g1)
layer.set_models(inference=False)
close(g3, c3)
close(g4, c4)
close(g3.attrs._m.new_array(), c3.attrs._m)
close(g3.attrs._v.new_array(), c3.attrs._v)
close(g3.attrs._mov_m.new_array(), c3.attrs._mov_m)
close(g3.attrs._mov_v.new_array(), c3.attrs._mov_v)
def denseblock(
self,
dim_v=8,
dim_h=8,
input_channels=10,
dropout=False,
out_ch=0.5,
):
parameters = []
c = input_channels
print('-> {}'.format(c))
for _ in range(self.depth):
c += self.growth_rate
print('Batch Normalize')
parameters.append(rm.BatchNormalize())
print(' Conv2d > {}x{} {}ch'.format(dim_v, dim_h,
self.growth_rate))
parameters.append(
rm.Conv2d(self.growth_rate, filter=3, padding=(1, 1)))
if self.dropout:
print(' Dropout')
c = int(c*out_ch) if isinstance(out_ch, float) \
else out_ch
print('*Conv2d > {}x{} {}ch'.format(dim_v, dim_h, c))
parameters.append(rm.Conv2d(c, filter=1))
if self.dropout:
print(' Dropout')
print(' Average Pooling')
print('<- {}'.format(c))
return parameters, c
def __init__(self, num_classes, block, layers, cardinality):
self.inplanes = 128
self.cardinality = cardinality
super(ResNeXt, self).__init__()
self.conv1 = rm.Conv2d(64,
filter=7,
stride=2,
padding=3,
ignore_bias=True)
self.bn1 = rm.BatchNormalize(epsilon=0.00001, mode='feature')
self.relu = rm.Relu()
self.maxpool = rm.MaxPool2d(filter=3, stride=2, padding=1)
self.layer1 = self._make_layer(block,
128,
layers[0],
stride=1,
cardinality=self.cardinality)
self.layer2 = self._make_layer(block,
256,
layers[1],
stride=2,
cardinality=self.cardinality)
self.layer3 = self._make_layer(block,
512,
layers[2],
stride=2,
cardinality=self.cardinality)
self.layer4 = self._make_layer(block,
1024,
layers[3],
stride=2,
cardinality=self.cardinality)
self.flat = rm.Flatten()
self.fc = rm.Dense(num_classes)
def transition_layer(growth_rate):
return rm.Sequential([
rm.BatchNormalize(epsilon=0.001, mode='feature'),
rm.Relu(),
rm.Conv2d(growth_rate, filter=1, padding=0, stride=1),
rm.AveragePool2d(filter=2, stride=2)
])
def _gen_model(self):
N = self.batch
input_shape = self.arch['input_shape']
output_shape = self.arch['output_shape']
if 'debug' in self.arch.keys():
debug = self.arch['debug']
else:
debug = False
self.batch_input_shape = self.get_shape(N, input_shape)
self.batch_output_shape = self.get_shape(N, output_shape)
depth = self.arch['depth']
unit = self.arch['unit']
units = np.ones(depth + 1) * unit
_unit = np.prod(output_shape)
units[-1] = _unit
units = units.astype('int')
layer = [rm.Flatten()]
for _unit in units:
layer.append(rm.BatchNormalize())
layer.append(rm.Relu())
layer.append(rm.Dense(_unit))
#layer = layer[:-1] + [rm.Dropout()] + [layer[-1]]
self.fcnn = rm.Sequential(layer)
if debug:
x = np.zeros(self.batch_input_shape)
for _layer in layer:
x = _layer(x)
print(x.shape, str(_layer.__class__).split('.')[-1])
x = rm.reshape(x, self.batch_output_shape)
print(x.shape)
def __init__(
self,
latent_dim = 10,
output_shape = (28, 28),
batch_normal = False,
dropout = False,
min_channels = 16,
):
self.batch_normal = batch_normal
self.latent_dim = latent_dim
self.output_shape = output_shape
self.dropout = dropout
self.min_channels = min_channels
print('--- Generator Network ---')
parameters = []
print_params = []
dim = output_shape[0]
channels = self.min_channels
while dim%2 == 0 and dim > 2:
parameters.append(rm.Deconv2d(
channel=channels, stride=2, filter=2))
if batch_normal:
parameters.append(rm.BatchNormalize())
dim = dim // 2
print_params.append([dim, channels])
channels *= 2
if dim%2 == 1:
parameters.append(rm.Deconv2d(
channel=channels, stride=2, filter=3))
if batch_normal:
parameters.append(rm.BatchNormalize())
dim = (dim - 1) // 2
print_params.append([dim, channels])
channels *= 2
parameters.reverse()
print_params.reverse()
print('Dense {}x{}x{} & Reshape'.format(dim, dim,channels))
self.channels = channels
self.transform = rm.Dense(channels*1*dim*dim)
for item in print_params:
print('Deconv2d to {}x{} {}ch '.format(
item[0], item[0], item[1]))
self.hidden = rm.Sequential(parameters)
self.output = rm.Conv2d(channel=1,stride=1,filter=1)
print('Conv2d to {}x{} 1ch'.format(
output_shape[0], output_shape[0]))
self.dim = dim
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = rm.Conv2d(planes, filter=1, ignore_bias=True)
self.bn1 = rm.BatchNormalize(mode='feature')
self.conv2 = rm.Conv2d(planes,
filter=3,
stride=stride,
padding=1,
ignore_bias=True)
self.bn2 = rm.BatchNormalize(mode='feature')
self.conv3 = rm.Conv2d(planes * self.expansion,
filter=1,
ignore_bias=True)
self.bn3 = rm.BatchNormalize(mode='feature')
self.relu = rm.Relu()
self.downsample = downsample
self.stride = stride
def __init__(self, channel, filter=3, prev_ch=None):
pad = int((filter - 1) / 2)
if prev_ch is not None:
self._conv = rm.Conv2d(channel=channel, filter=filter, padding=pad)
self._conv.params = {
"w":
rm.Variable(self._conv._initializer(
(channel, prev_ch, filter, filter)),
auto_update=True),
"b":
rm.Variable(np.zeros((1, channel, 1, 1), dtype=np.float32),
auto_update=False),
}
self._bn = rm.BatchNormalize(mode='feature', momentum=0.99)
else:
self._conv = rm.Conv2d(channel=channel, filter=filter, padding=pad)
self._bn = rm.BatchNormalize(mode='feature', momentum=0.99)
def __init__(self,
class_map=None,
cells=7,
bbox=2,
imsize=(224, 224),
load_pretrained_weight=False,
train_whole_network=False):
if not hasattr(cells, "__getitem__"):
cells = (cells, cells)
self._cells = cells
self._bbox = bbox
model = Darknet()
super(Yolov1, self).__init__(class_map, imsize, load_pretrained_weight,
train_whole_network, model)
self._last_dense_size = (self.num_class +
5 * bbox) * cells[0] * cells[1]
self._freezed_network = rm.Sequential(model[:-4])
self._network = rm.Sequential([
rm.Conv2d(channel=1024, filter=3, padding=1, ignore_bias=True),
rm.BatchNormalize(mode='feature'),
rm.LeakyRelu(slope=0.1),
rm.Conv2d(channel=1024,
filter=3,
padding=1,
stride=2,
ignore_bias=True),
rm.BatchNormalize(mode='feature'),
rm.LeakyRelu(slope=0.1),
rm.Conv2d(channel=1024, filter=3, padding=1, ignore_bias=True),
rm.BatchNormalize(mode='feature'),
rm.LeakyRelu(slope=0.1),
rm.Conv2d(channel=1024, filter=3, padding=1, ignore_bias=True),
rm.BatchNormalize(mode='feature'),
rm.LeakyRelu(slope=0.1),
rm.Flatten(),
rm.Dense(
4096
), # instead of locally connected layer, we are using Dense layer
rm.LeakyRelu(slope=0.1),
rm.Dropout(0.5),
rm.Dense(self._last_dense_size)
])
self._opt = rm.Sgd(0.0005, 0.9)
def __init__(self, planes, stride=1, downsample=None, cardinality=32):
super(Bottleneck, self).__init__()
self.cardinality = cardinality
self.conv1 = rm.Conv2d(planes, filter=1, ignore_bias=True)
self.bn1 = rm.BatchNormalize(epsilon=0.00001, mode='feature')
self.conv2 = rm.GroupConv2d(planes,
filter=3,
stride=stride,
padding=1,
ignore_bias=True,
groups=self.cardinality)
self.bn2 = rm.BatchNormalize(epsilon=0.00001, mode='feature')
self.conv3 = rm.Conv2d(planes * self.expansion,
filter=1,
ignore_bias=True)
self.bn3 = rm.BatchNormalize(epsilon=0.00001, mode='feature')
self.relu = rm.Relu()
self.downsample = downsample
self.stride = stride
def _gen_model(self):
depth = self.arch['depth']
unit = self.arch['unit']
# excluding mini-batch size
input_shape = self.arch['input_shape']
output_shape = self.arch['output_shape']
seq = []
for i in range(depth):
seq.append(rm.Dense(unit))
seq.append(rm.Relu())
if i < 1 or i == depth - 1:
seq.append(rm.BatchNormalize())
seq.append(rm.Dense(output_shape))
self._model = rm.Sequential(seq)
def __init__(self, num_class):
self.base1 = rm.Sequential([rm.Conv2d(64, filter=7, padding=3, stride=2),
rm.Relu(),
rm.MaxPool2d(filter=3, stride=2, padding=1),
rm.BatchNormalize(mode='feature'),
rm.Conv2d(64, filter=1, stride=1),
rm.Relu(),
rm.Conv2d(192, filter=3, padding=1, stride=1),
rm.Relu(),
rm.BatchNormalize(mode='feature'),
rm.MaxPool2d(filter=3, stride=2, padding=1),
InceptionV1Block(),
InceptionV1Block([128, 128, 192, 32, 96, 64]),
rm.MaxPool2d(filter=3, stride=2),
InceptionV1Block([192, 96, 208, 16, 48, 64]),
])
self.aux1 = rm.Sequential([rm.AveragePool2d(filter=5, stride=3),
rm.Flatten(),
rm.Dense(1024),
rm.Dense(num_class)])
self.base2 = rm.Sequential([InceptionV1Block([160, 112, 224, 24, 64, 64]),
InceptionV1Block([128, 128, 256, 24, 64, 64]),
InceptionV1Block([112, 144, 288, 32, 64, 64])])
self.aux2 = rm.Sequential([rm.AveragePool2d(filter=5, stride=3),
rm.Flatten(),
rm.Dense(1024),
rm.Dense(num_class)])
self.base3 = rm.Sequential([InceptionV1Block([256, 160, 320, 32, 128, 128]),
InceptionV1Block([256, 160, 320, 32, 128, 128]),
InceptionV1Block([192, 384, 320, 48, 128, 128]),
rm.AveragePool2d(filter=7, stride=1),
rm.Flatten()])
self.aux3 = rm.Dense(num_class)
def __init__(self, input_shape, output_shape,
growth_rate = 12,
depth = 3,
dropout = False,
):
self.growth_rate = growth_rate
self.depth = depth
self.dropout = dropout
self.input = rm.Dense(input_shape)
self.output = rm.Dense(output_shape)
parameters = []
for _ in range(depth):
parameters.append(rm.BatchNormalize())
parameters.append(rm.Dense(growth_rate))
self.hidden = rm.Sequential(parameters)
def __init__(self, channels=[192, 128, 192, 128, 192, 192]):
self.conv1 = rm.Conv2d(channels[0], filter=1)
self.batch_norm1 = rm.BatchNormalize(mode='feature')
self.conv2_reduced = rm.Conv2d(channels[1], filter=1)
self.batch_norm2_reduced = rm.BatchNormalize(mode='feature')
self.conv2_1 = rm.Conv2d(channels[1], filter=(3, 1), padding=(1, 0))
self.batch_norm2_1 = rm.BatchNormalize(mode='feature')
self.conv2_2 = rm.Conv2d(channels[2], filter=(1, 3), padding=(0, 1))
self.batch_norm2_2 = rm.BatchNormalize(mode='feature')
self.conv3_reduced = rm.Conv2d(channels[3], filter=1)
self.batch_norm3_reduced = rm.BatchNormalize(mode='feature')
self.conv3_1 = rm.Conv2d(channels[3], filter=(3, 1), padding=(1, 0))
self.batch_norm3_1 = rm.BatchNormalize(mode='feature')
self.conv3_2 = rm.Conv2d(channels[3], filter=(1, 3), padding=(0, 1))
self.batch_norm3_2 = rm.BatchNormalize(mode='feature')
self.conv3_3 = rm.Conv2d(channels[3], filter=(3, 1), padding=(1, 0))
self.batch_norm3_3 = rm.BatchNormalize(mode='feature')
self.conv3_4 = rm.Conv2d(channels[4], filter=(1, 3), padding=(0, 1))
self.batch_norm3_4 = rm.BatchNormalize(mode='feature')
self.conv4 = rm.Conv2d(channels[5], filter=1)
self.batch_norm4 = rm.BatchNormalize(mode='feature')
def __init__(self):
self.conv1 = rm.Conv2d(256, filter=1)
self.batch_norm1 = rm.BatchNormalize(mode='feature')
self.conv2 = rm.Conv2d(256, filter=1)
self.batch_norm2 = rm.BatchNormalize(mode='feature')
self.conv3_red = rm.Conv2d(384, filter=1)
self.batch_norm3_red = rm.BatchNormalize(mode='feature')
self.conv3_1 = rm.Conv2d(256, filter=(1, 3), padding=(0, 1))
self.batch_norm3_1 = rm.BatchNormalize(mode='feature')
self.conv3_2 = rm.Conv2d(256, filter=(3, 1), padding=(1, 0))
self.batch_norm3_2 = rm.BatchNormalize(mode='feature')
self.conv4_red = rm.Conv2d(384, filter=1)
self.batch_norm4_red = rm.BatchNormalize(mode='feature')
self.conv4_1 = rm.Conv2d(448, filter=(1, 3), padding=(0, 1))
self.batch_norm4_1 = rm.BatchNormalize(mode='feature')
self.conv4_2 = rm.Conv2d(512, filter=(3, 1), padding=(1, 0))
self.batch_norm4_2 = rm.BatchNormalize(mode='feature')
self.conv4_3 = rm.Conv2d(256, filter=(1, 3), padding=(0, 1))
self.batch_norm4_3 = rm.BatchNormalize(mode='feature')
self.conv4_4 = rm.Conv2d(256, filter=(3, 1), padding=(1, 0))
self.batch_norm4_4 = rm.BatchNormalize(mode='feature')
def __init__(self):
self.conv1 = rm.Conv2d(128, filter=1)
self.batch_norm1 = rm.BatchNormalize(mode='feature')
self.conv2 = rm.Conv2d(384, filter=3, padding=1)
self.batch_norm2 = rm.BatchNormalize(mode='feature')
self.conv3_1 = rm.Conv2d(192, filter=1)
self.batch_norm3_1 = rm.BatchNormalize(mode='feature')
self.conv3_2 = rm.Conv2d(224, filter=(1, 7), padding=(0, 3))
self.batch_norm3_2 = rm.BatchNormalize(mode='feature')
self.conv3_3 = rm.Conv2d(256, filter=(7, 1), padding=(3, 0))
self.batch_norm3_3 = rm.BatchNormalize(mode='feature')
self.conv4_1 = rm.Conv2d(192, filter=1)
self.batch_norm4_1 = rm.BatchNormalize(mode='feature')
self.conv4_2 = rm.Conv2d(192, filter=(1, 7), padding=(0, 3))
self.batch_norm4_2 = rm.BatchNormalize(mode='feature')
self.conv4_3 = rm.Conv2d(224, filter=(7, 1), padding=(3, 0))
self.batch_norm4_3 = rm.BatchNormalize(mode='feature')
self.conv4_4 = rm.Conv2d(224, filter=(1, 7), padding=(0, 3))
self.batch_norm4_4 = rm.BatchNormalize(mode='feature')
self.conv4_5 = rm.Conv2d(256, filter=(7, 1), padding=(3, 0))
self.batch_norm4_5 = rm.BatchNormalize(mode='feature')
def __init__(self, num_class, layer_per_block=[6, 12, 24, 16], growth_rate=32, train_whole_network=False):
self.layer_per_block = layer_per_block
self.growth_rate = growth_rate
layers = []
layers.append(rm.Conv2d(64, 7, padding=3, stride=2))
layers.append(rm.BatchNormalize(epsilon=0.001, mode='feature'))
for i in layer_per_block[:-1]:
for j in range(i):
layers.append(conv_block(growth_rate))
layers.append(transition_layer(growth_rate))
for i in range(layer_per_block[-1]):
layers.append(conv_block(growth_rate))
self.base = rm.Sequential(layers)
self.fc = rm.Dense(num_class)
def test_batch_normalize(a):
layer = rm.Sequential([rm.BatchNormalize(momentum=0.1)])
set_cuda_active(True)
g1 = Variable(a)
g2 = layer(g1)
g3 = rm.sum(g2)
g = g3.grad()
g_g1 = g.get(g1)
g_g2 = g.get(layer.l0.params["w"])
g_g3 = g.get(layer.l0.params["b"])
layer.set_models(inference=True)
g4 = layer(g1)
layer.set_models(inference=False)
g2.to_cpu()
g3.to_cpu()
g4.to_cpu()
g_g1.to_cpu()
g_g2.to_cpu()
g_g3.to_cpu()
set_cuda_active(False)
layer.l0._mov_mean = 0
layer.l0._mov_std = 0
c2 = layer(g1)
c3 = rm.sum(c2)
c = c3.grad()
c_g1 = c.get(g1)
c_g2 = g.get(layer.l0.params["w"])
c_g3 = g.get(layer.l0.params["b"])
layer.set_models(inference=True)
c4 = layer(g1)
layer.set_models(inference=False)
close(g2, c2)
close(g3, c3)
close(g4, c4)
close(c_g1, g_g1)
close(c_g2, g_g2)
close(c_g3, g_g3)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = rm.Sequential([
rm.Conv2d(planes * block.expansion,
filter=1,
stride=stride,
ignore_bias=True),
rm.BatchNormalize(mode='feature')
])
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return rm.Sequential(layers)
def denseblock(self, dim=8, input_channels=10, dropout=False):
parameters = []
c = input_channels
print('-> {}'.format(c))
for _ in range(self.depth):
c += self.growth_rate
print('Batch Normalize')
parameters.append(rm.BatchNormalize())
print(' Conv2d > {}x{} {}ch'.format(dim, dim, self.growth_rate))
parameters.append(
rm.Conv2d(self.growth_rate, filter=3, padding=(1, 1)))
if self.dropout:
print('Dropout')
c = int(c * self.compression)
print('*Conv2d > {}x{} {}ch'.format(dim, dim, c))
parameters.append(rm.Conv2d(c, filter=1))
print(' Average Pooling')
print('<- {}'.format(c))
return parameters, c
def __init__(
self,
enc_base, dec,
batch_size,
latent_dim = 2,
mode = 'simple',
label_dim = 0,
prior = 'normal',
prior_dist = None,
hidden = 1000,
full_rate=0.1, # 全体の形を重視するかラベル毎を重視するか
fm_rate=1., # full_rateと同じ目的
):
self.latent_dim = latent_dim
self.mode = mode
self.label_dim = label_dim
self.batch_size = batch_size
self.prior = prior
self.prior_dist = prior_dist
self.full_rate = full_rate
self.fm_rate = fm_rate
if self.mode=='clustering' or self.mode=='reduction':
self.enc = Enc(enc_base, (latent_dim, label_dim),
output_act=(None, rm.softmax))
else:
self.enc = Enc(enc_base, latent_dim)
self.dec = dec
self.dis = rm.Sequential([
rm.Dense(hidden), rm.LeakyRelu(),
rm.Dense(hidden), rm.LeakyRelu(),
rm.Dense(1), rm.Sigmoid()
])
if self.mode=='clustering' or self.mode=='reduction':
self.cds = rm.Sequential([
# xxx rm.BatchNormalize(),
# Disの最初にBNは配置してはだめ
rm.Dense(hidden), rm.LeakyRelu(),
rm.BatchNormalize(),
rm.Dense(hidden), rm.LeakyRelu(),
#rm.BatchNormalize(),
rm.Dense(1), rm.Sigmoid()
])
def __init__(
self, input_shape,
output_shape,
growth_rate=12,
depth=3,
dropout=False):
self.depth = depth
self.dropout = dropout
if depth != 1:
under_growth_rate = input_shape + growth_rate
self.under_model = mymodel(
under_growth_rate,
output_shape,
growth_rate = growth_rate,
depth = depth - 1)
else:
self.output = rm.Dense(output_shape)
self.batch = rm.BatchNormalize()
self.conv = rm.Dense(growth_rate)