name="D3") # D1 Coarse
d_model4 = discriminator(image_shape_coarse,
label_shape_coarse,
ndf,
n_layers,
n_downsampling=1,
name="D4") # D2 Coarse
# define generator models
g_coarse_model = coarse_generator(img_shape=image_shape_coarse,
n_downsampling=2,
n_blocks=9,
n_channels=1)
g_fine_model = fine_generator(x_coarse_shape=image_shape_xglobal,
input_shape=image_shape_fine,
nff=nff,
n_blocks=3)
# define fundus2angio
gan_model = aagan(g_fine_model, g_coarse_model, d_model1, d_model2,
d_model3, d_model4, image_shape_fine, image_shape_coarse,
image_shape_xglobal, label_shape_fine,
label_shape_coarse)
# train model
train(d_model1,
d_model2,
d_model3,
d_model4,
g_coarse_model,
g_fine_model,
gan_model,
image_shape_fine = (128,128,3)
mask_shape_fine = (128,128,1)
label_shape_fine = (128,128,1)
image_shape_x_coarse = (64,64,128)
image_shape_coarse = (64,64,3)
mask_shape_coarse = (64,64,1)
label_shape_coarse = (64,64,1)
img_shape_g = (64,64,3)
ndf=64
ncf=128
nff=128
## Load models
K.clear_session()
opt = Adam()
g_local_model = fine_generator(x_coarse_shape=image_shape_x_coarse,input_shape=image_shape_fine,mask_shape=mask_shape_fine,nff=nff)
g_local_model.load_weights(args.weight_name_local)
g_local_model.compile(loss='mse', optimizer=opt)
g_global_model = coarse_generator(img_shape=image_shape_coarse,mask_shape=mask_shape_coarse,ncf=ncf)
g_global_model.load_weights(args.weight_name_global)
g_global_model.compile(loss='mse',optimizer=opt)
## Create Output Directory
out_path = args.out_dir
directories = [out_path,out_path+'/Coarse',out_path+'/Fine']
for directory in directories:
if not os.path.exists(directory):
os.makedirs(directory)
## Find file numbers,paths or names