def __init__(self, test_case):
super().__init__()
self.test_case = test_case
self.conv1 = LayerChoice(
[nn.Conv2d(3, 6, 3, padding=1),
nn.Conv2d(3, 6, 5, padding=2)])
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = LayerChoice(
[nn.Conv2d(6, 16, 3, padding=1),
nn.Conv2d(6, 16, 5, padding=2)],
return_mask=True)
self.conv3 = nn.Conv2d(16, 16, 1)
self.skipconnect = InputChoice(n_candidates=1)
self.skipconnect2 = InputChoice(n_candidates=2, return_mask=True)
self.bn = nn.BatchNorm2d(16)
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(16, 10)
def __init__(self):
super(MobileNet, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3, 1, 1, bias=False)
self.bn1 = nn.BatchNorm2d(32)
self.layers = _make_mobilenet_layers(32)
self.pool = nn.AvgPool2d(2)
self.linear = nn.Linear(1024, 10)
self.skipconnect1 = InputChoice(n_candidates=2,
n_chosen=1,
key='skip1')
self.skipconnect2 = InputChoice(n_candidates=2,
n_chosen=1,
key='skip2')
self.skipconnect3 = InputChoice(n_candidates=2,
n_chosen=1,
key='skip3')
self.skipconnect4 = InputChoice(n_candidates=2,
n_chosen=1,
key='skip4')
self.skipconnect5 = InputChoice(n_candidates=2,
n_chosen=1,
key='skip5')
self.skipconnect6 = InputChoice(n_candidates=2,
n_chosen=1,
key='skip6')
def __init__(self):
super(Net, self).__init__()
self.conv1 = LayerChoice([nn.Conv2d(3, 6, 3, padding=1), nn.Conv2d(3, 6, 5, padding=2)])
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = LayerChoice([nn.Conv2d(6, 16, 3, padding=1), nn.Conv2d(6, 16, 5, padding=2)])
self.conv3 = nn.Conv2d(16, 16, 1)
self.skipconnect = InputChoice(n_candidates=1)
self.bn = nn.BatchNorm2d(16)
self.gap = nn.AdaptiveAvgPool2d(4)
self.fc1 = nn.Linear(16 * 4 * 4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def __init__(self, cell_name, prev_labels, channels):
super().__init__(cell_name)
self.input_choice = InputChoice(choose_from=prev_labels,
n_chosen=1,
return_mask=True,
key=cell_name + "_input")
self.op_choice = LayerChoice([
nn.Conv2d(channels, channels, 3, padding=1),
nn.Conv2d(channels, channels, 5, padding=2),
nn.MaxPool2d(3, stride=1, padding=1),
nn.AvgPool2d(3, stride=1, padding=1),
nn.Identity()
],
key=cell_name + "_op")
def __init__(self):
super(ToxicClassifierModel, self).__init__()
self.BiGRU = nn.GRU(300,
hidden_size=LSTM_UNITS,
bidirectional=True,
num_layers=1)
self.BiRNN = LayerChoice([
nn.RNN(input_size=2 * LSTM_UNITS,
hidden_size=LSTM_UNITS,
bidirectional=True,
num_layers=1),
nn.RNN(input_size=2 * LSTM_UNITS,
hidden_size=LSTM_UNITS,
bidirectional=True,
num_layers=2)
])
self.hidden1 = nn.Linear(DENSE_HIDDEN_UNITS, DENSE_HIDDEN_UNITS)
self.hidden2 = nn.Linear(DENSE_HIDDEN_UNITS, DENSE_HIDDEN_UNITS)
self.hidden3 = nn.Linear(DENSE_HIDDEN_UNITS, 6)
self.vectors = FastText()
self.skipconnect1 = InputChoice(n_candidates=1)
self.skipconnect2 = InputChoice(n_candidates=1)
def __init__(self, hidden_size):
super(Net, self).__init__()
# two options of conv1
self.conv1 = LayerChoice([nn.Conv2d(1, 20, 5, 1),
nn.Conv2d(1, 20, 3, 1)],
key='first_conv')
# two options of mid_conv
self.mid_conv = LayerChoice([nn.Conv2d(20, 20, 3, 1, padding=1),
nn.Conv2d(20, 20, 5, 1, padding=2)],
key='mid_conv')
self.conv2 = nn.Conv2d(20, 50, 5, 1)
self.fc1 = nn.Linear(4*4*50, hidden_size)
self.fc2 = nn.Linear(hidden_size, 10)
# skip connection over mid_conv
self.input_switch = InputChoice(n_candidates=2,
n_chosen=1,
key='skip')
def __init__(self, kernel_size):
super().__init__()
self.conv = nn.Conv2d(3, 120, kernel_size, padding=kernel_size // 2)
self.nested_mutable = InputChoice(n_candidates=10)