Small typo in the example

[netrunner-exe]

Hello @felixrosberg! I decided to test the example provided in the README.md and found one typo in the import - from network.layers import AdaptiveAttention, AdaIn instead of AdaIN. I also have small suggestions for solving the points that I encountered, I have indicated in more detail in the comments in my example.

from networks.layers import AdaIN, AdaptiveAttention
from tensorflow_addons.layers import InstanceNormalization
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import cv2

# To hide "WARNING:root:The given value for groups will be overwritten."
import logging
logging.getLogger().setLevel(logging.ERROR)

# To hide very long tensorflow log like:
# Model: "model"
# __________________________________________________________________________________________________
#  Layer (type)                   Output Shape         Param #     Connected to                     
# ==================================================================================================
#  input_1 (InputLayer)           [(None, 256, 256, 3  0           []                               
#
# Can be added directly to networks/layers.py
import tensorflow as tf
tf.keras.utils.disable_interactive_logging()

# Add compile=False to hide
# "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually."

model = load_model("path/to/model.h5", compile=False, custom_objects={"AdaIN": AdaIN, "AdaptiveAttention": AdaptiveAttention, "InstanceNormalization": InstanceNormalization})
arcface = load_model("path/to/arcface.h5", compile=False)

# target and source images need to be properly cropeed and aligned
target = np.asarray(Image.open("path/to/target_face.png").resize((256, 256)))
source = np.asarray(Image.open("path/to/source_face.png").resize((112, 112)))

source_z = arcface(np.expand_dims(source / 255.0, axis=0))

face_swap = model([np.expand_dims((target - 127.5) / 127.5, axis=0), source_z]).numpy()
face_swap = (face_swap[0] + 1) / 2
face_swap = np.clip(face_swap * 255, 0, 255).astype('uint8')

cv2.imwrite("./swapped_face.png", cv2.cvtColor(face_swap, cv2.COLOR_BGR2RGB))

[felixrosberg]

Hello @netrunner-exe

Thank you very much, I will update the README with this!