def imageThree():
INPUT = 'noise'
input_depth = 1
num_iter = 3001
show_every = 50
figsize = 8
reg_noise_std = 0.00
param_noise = True
if 'skip' in NET_TYPE:
depth = int(NET_TYPE[-1])
net = skip(input_depth, image_np.shape[0],
num_channels_down = [16, 32, 64, 128, 128, 128][:depth],
num_channels_up = [16, 32, 64, 128, 128, 128][:depth],
num_channels_skip = [0, 0, 0, 0, 0, 0][:depth],
filter_size_up = 3,filter_size_down = 5, filter_skip_size=1,
upsample_mode='nearest', # downsample_mode='avg',
need1x1_up=False,
need_sigmoid=True, need_bias=True, pad=pad, act_fun='LeakyReLU').type(dtype)
LR = 0.01
elif NET_TYPE == 'UNET':
net = UNet(num_input_channels=input_depth, num_output_channels=3,
feature_scale=8, more_layers=1,
concat_x=False, upsample_mode='deconv',
pad='zero', norm_layer=torch.nn.InstanceNorm2d, need_sigmoid=True, need_bias=True)
LR = 0.001
param_noise = False
elif NET_TYPE == 'ResNet':
net = ResNet(input_depth, image_np.shape[0], 8, 32, need_sigmoid=True, act_fun='LeakyReLU')
LR = 0.001
param_noise = False
else:
assert False
net = net.type(dtype)
net_input = get_noise(input_depth, INPUT, image_np.shape[1:]).type(dtype)
return net_input, net, LR, num_iter, param_noise, reg_noise_std, show_every, figsize
def imageFive():
INPUT = 'meshgrid'
input_depth = 2
LR = 0.01
num_iter = 5001
param_noise = False
show_every = 50
figsize = 5
reg_noise_std = 0.03
net = skip(input_depth, image_np.shape[0],
num_channels_down = [128] * 5,
num_channels_up = [128] * 5,
num_channels_skip = [0] * 5,
upsample_mode='nearest', filter_skip_size=1, filter_size_up=3, filter_size_down=3,
need_sigmoid=True, need_bias=True, pad=pad, act_fun='LeakyReLU').type(dtype)
net = net.type(dtype)
net_input = get_noise(input_depth, INPUT, image_np.shape[1:]).type(dtype)
return net_input, net, LR, num_iter, param_noise, reg_noise_std, show_every, figsize
INPUT = 'noise'
input_depth = 32
OPTIMIZER = 'adam'
OPT_OVER = 'net'
# imagen en color
num_iter = 1000
LR = 0.01
reg_noise_std = 0.00
# devuelve un modelo
net = skip(input_depth,
image_np.shape[0],
num_channels_down = [16, 32, 64, 128, 128],
num_channels_up = [16, 32, 64, 128, 128],
num_channels_skip = [0, 0, 0, 0, 0],
filter_size_down = 3, filter_size_up = 3, filter_skip_size=1,
upsample_mode='bilinear',
downsample_mode='avg',
need_sigmoid=True, need_bias=True, pad=pad).type(dtype)
# Perdida
mse = torch.nn.MSELoss().type(dtype)
image_var = np_to_torch(image_np).type(dtype) # tensor cuda
# tensor cuda
net_input = get_noise(input_depth, INPUT, image_np.shape[1:]).type(dtype).detach()
# Datos del modelo
print(torch_summarize(net))