def __init__(self, squeeze_channels=False, softmax_smoothing=0.0):
super(AttentionModelColonCancer, self).__init__()
conv1 = nn.Conv2d(in_channels=3,
out_channels=8,
kernel_size=3,
padding_mode='zeros',
padding=1)
relu1 = nn.ReLU()
conv2 = nn.Conv2d(in_channels=8,
out_channels=8,
kernel_size=3,
padding_mode='zeros',
padding=1)
relu2 = nn.ReLU()
conv3 = nn.Conv2d(in_channels=8,
out_channels=1,
kernel_size=3,
padding_mode='zeros',
padding=1)
sample_softmax = SampleSoftmax(squeeze_channels, softmax_smoothing)
self.forward_pass = nn.Sequential(conv1, relu1, conv2, relu2, conv3,
sample_softmax)
def __init__(self, squeeze_channels=False, softmax_smoothing=0.0):
super(AttentionModelTrafficSigns, self).__init__()
conv1 = nn.Conv2d(in_channels=3,
out_channels=8,
kernel_size=3,
padding_mode='valid')
relu1 = nn.ReLU()
conv2 = nn.Conv2d(in_channels=8,
out_channels=16,
kernel_size=3,
padding_mode='valid')
relu2 = nn.ReLU()
conv3 = nn.Conv2d(in_channels=16,
out_channels=32,
kernel_size=3,
padding_mode='valid')
relu3 = nn.ReLU()
conv4 = nn.Conv2d(in_channels=32,
out_channels=1,
kernel_size=3,
padding_mode='valid')
pool = nn.MaxPool2d(kernel_size=8)
sample_softmax = SampleSoftmax(squeeze_channels, softmax_smoothing)
self.part1 = nn.Sequential(conv1, relu1, conv2, relu2, conv3, relu3)
self.part2 = nn.Sequential(conv4, pool, sample_softmax)
def __init__(self, squeeze_channels=False, softmax_smoothing=0.0):
super(AttentionModelMNIST, self).__init__()
self.squeeze_channels = squeeze_channels
self.softmax_smoothing = softmax_smoothing
self.conv1 = nn.Conv2d(in_channels=1,
out_channels=8,
kernel_size=3,
padding=1,
padding_mode='reflect')
self.tanh1 = nn.Tanh()
self.conv2 = nn.Conv2d(in_channels=8,
out_channels=8,
kernel_size=3,
padding=1,
padding_mode='reflect')
self.tanh2 = nn.Tanh()
self.conv3 = nn.Conv2d(in_channels=8,
out_channels=1,
kernel_size=3,
padding=1,
padding_mode='reflect')
self.sample_softmax = SampleSoftmax(squeeze_channels,
softmax_smoothing)
def _get_attention(self):
return Sequential([
Conv2D(8, input_shape=self.SHAPE_SMALL, **self._conv_args),
MaxPool2D(),
Conv2D(8, **self._conv_args),
Conv2D(1, kernel_size=3, padding="same"),
SampleSoftmax(squeeze_channels=True)
])
def attention(x):
params = dict(
activation="relu",
padding="valid",
kernel_regularizer=l2(1e-5)
)
x = Conv2D(8, kernel_size=3, **params)(x)
x = Conv2D(16, kernel_size=3, **params)(x)
x = Conv2D(32, kernel_size=3, **params)(x)
x = Conv2D(1, kernel_size=3)(x)
x = MaxPooling2D(pool_size=8)(x)
x = SampleSoftmax(squeeze_channels=True, smooth=1e-4)(x)
return x
def get_model(outputs, width, height, scale, n_patches, patch_size, reg):
# Define the shapes
shape_high = (height, width, 1)
shape_low = (int(height * scale), int(width * scale), 1)
# Make the attention and feature models
attention = Sequential([
Conv2D(8,
kernel_size=3,
activation="tanh",
padding="same",
input_shape=shape_low),
Conv2D(8, kernel_size=3, activation="tanh", padding="same"),
Conv2D(1, kernel_size=3, padding="same"),
SampleSoftmax(squeeze_channels=True, smooth=1e-5)
])
feature = Sequential([
Conv2D(32, kernel_size=7, activation="relu", input_shape=shape_high),
Conv2D(32, kernel_size=3, activation="relu"),
Conv2D(32, kernel_size=3, activation="relu"),
Conv2D(32, kernel_size=3, activation="relu"),
GlobalMaxPooling2D(),
L2Normalize()
])
# Let's build the attention sampling network
x_low = Input(shape=shape_low)
x_high = Input(shape=shape_high)
features, attention, patches = attention_sampling(
attention,
feature,
patch_size,
n_patches,
replace=False,
attention_regularizer=multinomial_entropy(reg))([x_low, x_high])
y = Dense(outputs, activation="softmax")(features)
return (Model(inputs=[x_low, x_high], outputs=[y]),
Model(inputs=[x_low, x_high], outputs=[attention, patches]))