csm = tf.expand_dims(csm, 2)
k0 = tf.expand_dims(k0, 0) # batch
csm = tf.expand_dims(csm, 0) # batch
k0 = tf.transpose(
k0, [0, 4, 3, 1, 2]) # nb, nx, ny, nt, nc -> nb, nc, nt, nx, ny
csm = tf.transpose(csm, [0, 4, 3, 1, 2])
# k0 = k0[:,:,0:18,:,:]
# csm = csm[:,:,0:18,:,:]
mask = tf.cast(tf.abs(k0) > 0, tf.complex64)
# initialize network
net = SLR_Net(mask, niter, learnedSVT)
net.load_weights(weight_file)
# Iterate over epochs.
# forward
# with tf.GradientTape() as tape:
t0 = time.time()
recon, X_SYM = net(k0, csm)
t1 = time.time()
recon_abs = tf.abs(recon)
# loss_total = mse(LSrecon, LplusS_label)
mask = np.transpose(mask, [1, 0])
mask = np.reshape(mask, [1, 1, mask.shape[0], 1, mask.shape[1]])
mask = tf.cast(tf.constant(mask), tf.complex64)
# prepare dataset
dataset = get_dataset(mode,
dataset_name,
batch_size,
shuffle=True,
full=True)
#dataset = get_dataset('test', dataset_name, batch_size, shuffle=True, full=True)
tf.print('dataset loaded.')
# initialize network
if net_name == 'SLRNET':
net = SLR_Net(mask, niter, learnedSVT)
weight_file = 'models/stable/2020-10-23T12-09-22SLRNET_DYNAMIC_V2_MULTICOIL8/epoch-50/ckpt'
net.load_weights(weight_file)
tf.print('network initialized.')
learning_rate_org = learning_rate
learning_rate_decay = 0.95
optimizer = tf.optimizers.Adam(learning_rate_org)
# Iterate over epochs.
total_step = 0
param_num = 0
loss = 0