K.set_learning_phase(1)
m = load_model(params["model"], compile=False)
m.load_weights(params["model"], by_name=True)
m = Model(m.input, m.get_layer("conv2d_75").output)  # Skip last layer (?, out_h, out_w, 255)
for i, layer in enumerate(m.layers):
    if i == 152:
        assert layer.name == "add_19"
    layer.trainable = (i > 152)
m = m(batch["image"])

logits = tf.layers.conv2d(inputs=m, filters=3, kernel_size=1, strides=1, padding="same")  # (?, out_h, out_w, out_c)
loss = get_loss(logits, batch["label"], batch["available"])