def test_007_is_no_numerical_spec(self):
""" Should not classify '007' as numerical spec, because it has leading zeros. """
self.assertIs(plan_check.is_numerical_spec('007'), False)
def test_negative_one_is_no_numerical_spec(self):
""" Should not classify '-1' as numerical spec. """
self.assertIs(plan_check.is_numerical_spec('-1'), False)
def test_42_is_numerical_spec(self):
""" Should classify '42' as numerical spec. """
self.assertIs(plan_check.is_numerical_spec('42'), True)
def test_zero_is_no_numerical_spec(self):
""" Should not classify '0' as numerical spec. """
self.assertIs(plan_check.is_numerical_spec('0'), False)
def __init__(self, net_name: NetNames, dataset_name: DatasetNames,
plan_conv: list, plan_fc: list):
super(Net, self).__init__()
assert (net_name == NetNames.CONV) or (net_name == NetNames.LENET), \
f"Could not initialize net, because the given name {net_name} is invalid."
self.init_weight_count_net = dict([('conv', 0), ('fc', 0)])
self.net_name = net_name
self.dataset_name = dataset_name
self.plan_conv = plan_conv
self.plan_fc = plan_fc
# create and initialize layers with Gaussian Glorot
conv_layers = []
fc_layers = []
sample_shape = get_sample_shape(dataset_name)
filters = sample_shape[0]
pooling_count = 0
for spec in plan_conv:
if spec == 'A':
conv_layers.append(nn.AvgPool2d(2))
pooling_count += 1
elif spec == 'M':
conv_layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
pooling_count += 1
elif is_batch_norm_spec(spec):
spec_number = get_number_from_batch_norm_spec(spec)
conv_layers.append(
nn.Conv2d(filters, spec_number, kernel_size=3, padding=1))
conv_layers.append(nn.BatchNorm2d(spec_number))
conv_layers.append(get_activation(net_name))
self.init_weight_count_net['conv'] += filters * spec_number * 9
filters = spec_number
elif is_numerical_spec(spec):
spec_number = get_number_from_numerical_spec(spec)
conv_layers.append(
nn.Conv2d(filters, spec_number, kernel_size=3, padding=1))
conv_layers.append(get_activation(net_name))
self.init_weight_count_net['conv'] += filters * spec_number * 9
filters = spec_number
else:
raise AssertionError(
f"{spec} from plan_conv is an invalid spec.")
# each Pooling-layer quarters the input size (height * width)
filters = filters * round(
(sample_shape[1] * sample_shape[2]) / (4**pooling_count))
for spec in plan_fc:
assert is_numerical_spec(
spec), f"{spec} from plan_fc is not a numerical spec."
spec_number = get_number_from_numerical_spec(spec)
fc_layers.append(nn.Linear(filters, spec_number))
fc_layers.append(get_activation(net_name))
self.init_weight_count_net['fc'] += filters * spec_number
filters = spec_number
self.conv = nn.Sequential(*conv_layers)
self.fc = nn.Sequential(*fc_layers)
self.out = nn.Linear(filters, 10)
self.init_weight_count_net['fc'] += filters * 10
self.criterion = nn.CrossEntropyLoss()
self.apply(gaussian_glorot)
self.store_initial_weights()
self.apply(setup_masks)