def __init__(self, code_size, img_size, kernel_size=4, num_input_channels=3, num_feature_maps=64, batch_norm=True):
super(Image_Encoder, self).__init__()
# Get the dimension which is a power of 2
if is_power2(max(img_size)):
stable_dim = max(img_size)
else:
stable_dim = min(img_size)
if isinstance(img_size, tuple):
self.img_size = img_size
self.final_size = tuple(int(4 * x // stable_dim) for x in self.img_size)
else:
self.img_size = (img_size, img_size)
self.final_size = (4, 4)
self.code_size = code_size
self.num_feature_maps = num_feature_maps
self.cl = nn.ModuleList()
self.num_layers = int(np.log2(max(self.img_size))) - 2
stride = 2
# This ensures that we have same padding no matter if we have even or odd kernels
padding = calculate_padding(kernel_size, stride)
if batch_norm:
self.cl.append(nn.Sequential(
nn.Conv2d(num_input_channels, self.num_feature_maps, kernel_size, stride=stride, padding=padding // 2,
bias=False),
nn.BatchNorm2d(self.num_feature_maps),
nn.ReLU(True)))
else:
self.cl.append(nn.Sequential(
nn.Conv2d(num_input_channels, self.num_feature_maps, kernel_size, stride=stride, padding=padding // 2,
bias=False),
nn.ReLU(True)))
self.channels = [self.num_feature_maps]
for i in range(self.num_layers - 1):
if batch_norm:
self.cl.append(nn.Sequential(
nn.Conv2d(self.channels[-1], self.channels[-1] * 2, kernel_size, stride=stride,
padding=padding // 2,
bias=False),
nn.BatchNorm2d(self.channels[-1] * 2),
nn.ReLU(True)))
else:
self.cl.append(nn.Sequential(
nn.Conv2d(self.channels[-1], self.channels[-1] * 2, kernel_size, stride=stride,
padding=padding // 2, bias=False),
nn.ReLU(True)))
self.channels.append(2 * self.channels[-1])
self.cl.append(nn.Sequential(
nn.Conv2d(self.channels[-1], code_size, self.final_size, stride=1, padding=0, bias=False),
nn.Tanh()))
def test_imageSize(form, field):
if not isinstance(field.data, int):
raise ValueError(
"Value entered is not a integer. Please change input to an integer."
)
if not is_power2(field.data):
raise ValueError(
"Value entered is not a power of 2^n. To optimize cleaning process please change input."
)
def simpleHaarStep(list,coefficients = []):
if not utils.is_power2(len(list)):
print "list not power of 2"
return None
averages = []
ctmp = []
i = 0
while i < len(list)-1:
avg = (list[i] + list[i+1])/2
averages.append(avg)
ctmp.append(avg-list[i+1])
i+=2
coefficients.append(ctmp)
return averages,coefficients
def test_imageSize(form,field):
if not isinstance(field.data,int):
raise ValueError("Value entered is not a integer. Please change input to an integer.")
if not is_power2(field.data):
raise ValueError("Value entered is not a power of 2^n. To optimize cleaning process please change input.")
