Commit
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fae5def
1
Parent(s):
e46b601
clean up
Browse files- app.py +8 -8
- networks/layers.py +0 -0
app.py
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@@ -1,11 +1,8 @@
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import gradio
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import tensorflow as tf
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from huggingface_hub import Repository
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from utils.utils import norm_crop, estimate_norm, inverse_estimate_norm, transform_landmark_points, get_lm
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from networks.
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from networks.layers import AdaIN,
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import numpy as np
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import cv2
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from scipy.ndimage import gaussian_filter
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@@ -18,7 +15,8 @@ from options.swap_options import SwapOptions
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opt = SwapOptions().parse()
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retina_repo = Repository(local_dir="retina_model", clone_from="felixrosberg/retinaface_resnet50",
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RetinaFace = load_model("retina_model/retinaface_res50.h5",
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custom_objects={"FPN": FPN,
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"SSH": SSH,
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@@ -26,10 +24,12 @@ RetinaFace = load_model("retina_model/retinaface_res50.h5",
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"LandmarkHead": LandmarkHead,
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"ClassHead": ClassHead})
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arc_repo = Repository(local_dir="arcface_model", clone_from="felixrosberg/arcface_tf",
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ArcFace = load_model("arcface_model/arc_res50.h5")
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g_repo = Repository(local_dir="g_model", clone_from="felixrosberg/affa_f",
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G = load_model("g_model/affa_f_demo.h5", custom_objects={"AdaIN": AdaIN, "AdaptiveAttention": AdaptiveAttention})
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blend_mask_base = np.zeros(shape=(256, 256, 1))
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import gradio
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from huggingface_hub import Repository
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from utils.utils import norm_crop, estimate_norm, inverse_estimate_norm, transform_landmark_points, get_lm
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from networks.layers import AdaIN, AdaptiveAttention
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import numpy as np
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import cv2
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from scipy.ndimage import gaussian_filter
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opt = SwapOptions().parse()
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retina_repo = Repository(local_dir="retina_model", clone_from="felixrosberg/retinaface_resnet50",
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private=True, use_auth_token="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")
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RetinaFace = load_model("retina_model/retinaface_res50.h5",
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custom_objects={"FPN": FPN,
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"SSH": SSH,
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"LandmarkHead": LandmarkHead,
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"ClassHead": ClassHead})
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arc_repo = Repository(local_dir="arcface_model", clone_from="felixrosberg/arcface_tf",
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private=True, use_auth_token="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")
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ArcFace = load_model("arcface_model/arc_res50.h5")
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g_repo = Repository(local_dir="g_model", clone_from="felixrosberg/affa_f",
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private=True, use_auth_token="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")
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G = load_model("g_model/affa_f_demo.h5", custom_objects={"AdaIN": AdaIN, "AdaptiveAttention": AdaptiveAttention})
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blend_mask_base = np.zeros(shape=(256, 256, 1))
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networks/layers.py
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