sess = tf.InteractiveSession()
with sess.as_default():
print('Loading model')
model = unet(inputshape=(patch_w, patch_h, n_channels),
conv_depth=conv_depth)
model.load_weights(args.weights)
print('Reformating data')
test_x = np.zeros((test_gen.__len__(), patch_w, patch_h, n_channels))
test_y = np.zeros((test_gen.__len__(), patch_w, patch_h, 1))
test_charge = np.zeros((test_gen.__len__(), patch_w, patch_h, 1))
test_energy = np.zeros((test_gen.__len__(), patch_w, patch_h, 1))
for i in range(test_gen.__len__()):
test_x[i], test_y[i] = test_gen.__getitem__(i)
test_charge[i] = test_gen.getitembykey(i, 'wire')
test_energy[i] = test_gen.getitembykey(i, 'energy')
# FIXME
# test_x = test_x[:8]
# test_y = test_y[:8]
# test_charge = test_charge[:8]
# test_energy = test_energy[:8]
print('Making predictions')
predictions = model.predict(test_x, batch_size=8, verbose=1)
del test_x, test_y
print('Made predictions')
q_flat = test_charge[..., 0].flatten()
flat = predictions.flatten()[q_flat > 0.1]
sess = tf.InteractiveSession()
with sess.as_default():
print('Loading model')
model = unet(inputshape=(patch_w, patch_h, n_channels),
conv_depth=conv_depth)
model.load_weights(args.weights)
print('Loading charge info')
if steps == 0:
test_charge = np.zeros(
(test_gen.__len__() * batch_size, patch_w, patch_h, 1))
test_energy = np.zeros(
(test_gen.__len__() * batch_size, patch_w, patch_h, 1))
for i in range(test_gen.__len__()):
wires = test_gen.getitembykey(i, 'wire')
energies = test_gen.getitembykey(i, 'energy')
for j in range(batch_size):
test_charge[(i * batch_size) + j] = wires[j]
test_energy[(i * batch_size) + j] = energies[j]
else:
test_charge = np.zeros((steps * batch_size, patch_w, patch_h, 1))
test_energy = np.zeros((steps * batch_size, patch_w, patch_h, 1))
for i in range(steps):
wires = test_gen.getitembykey(i, 'wire')
energies = test_gen.getitembykey(i, 'energy')
for j in range(batch_size):
test_charge[(i * batch_size) + j] = wires[j]
test_energy[(i * batch_size) + j] = energies[j]
print('Making predictions')
print('Loading model')
model = unet(inputshape=(patch_w, patch_h, n_channels),
conv_depth=conv_depth)
model.load_weights(args.weights)
print('Reformating data')
test_x = np.zeros((test_gen.__len__(), patch_w, patch_h, n_channels))
test_y = np.zeros((test_gen.__len__(), patch_w, patch_h, 1))
test_true = np.zeros((test_gen.__len__(), patch_w, patch_h, 1))
test_charge = np.zeros((test_gen.__len__(), patch_w, patch_h, 1))
test_energy = np.zeros((test_gen.__len__(), patch_w, patch_h, 1))
test_e = np.zeros((test_gen.__len__(), 1))
test_n = np.zeros((test_gen.__len__(), 1))
for i in range(test_gen.__len__()):
test_x[i], test_y[i] = test_gen.__getitem__(i)
test_true[i] = test_gen.getitembykey(i, 'trueEnergy')
test_charge[i] = test_gen.getitembykey(i, 'wire')
test_energy[i] = test_gen.getitembykey(i, 'energy')
test_e[i] = test_gen.getenergy(i)
test_n[i] = test_gen.getitembykey(i, 'nTrue')[0, 0, 0]
print('Making predictions')
predictions = model.predict(test_x, batch_size=8, verbose=1)
print('Made predictions')
q_flat = test_charge[..., 0].flatten()
flat = predictions.flatten()[q_flat > 0.1]
true_flat = test_y.flatten()[q_flat > 0.1]
e_flat = test_energy[..., 0].flatten()[q_flat > 0.1]
q_flat = q_flat[q_flat > 0.1]
