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infer.py

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+import time
+import numpy as np
+from PIL import Image, ImageDraw
+
+import mlx.core as mx
+from mlxDeepDanBooru.mlx_deep_danbooru_model import mlxDeepDanBooruModel
+
+
+model_path = "models/model-resnet_custom_v3_mlx.npz"
+tags_path = 'models/tags-resnet_custom_v3_mlx.npy'
+
+mlx_dan = mlxDeepDanBooruModel()
+mlx_dan.load_weights(model_path)
+mx.eval(mlx_dan.parameters())
+
+
+model_tags = np.load(tags_path)
+print(f'total tags: {len(model_tags)}')
+
+def danbooru_tags(fpath):
+    tags = []
+    pic = Image.open(fpath).convert("RGB").resize((512, 512))
+    a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
+
+    x = mx.array(a)
+    y = mlx_dan(x)[0]
+
+    for n in range(10):
+        mlx_dan(x)
+    for i, p in enumerate(y):
+        if p >= 0.5:
+            #print(model_tags[i].item(), p)
+            tags.append(model_tags[i].item())
+
+    return tags
+
+image_count = 0
+def image_infer(fpath):
+    global image_count
+    tags = danbooru_tags(fpath)
+    image_count += 1
+    return tags
+
+
+t1 = time.time()
+tags_1 = image_infer("example/1.png")
+tags_2 = image_infer("example/2.png")
+
+t2 = time.time()
+
+print(tags_1)
+print(tags_2)
+# print(tags_3)
+# print(tags_4)
+# print(tags_5)
+
+print("-----------")
+print(f'infer speed(with mlx): {(t2 - t1)/image_count} seconds per image')
+
+
+
+
+
+

models/model-resnet_custom_v3_mlx.npz

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+size 643902466

models/tags-resnet_custom_v3_mlx.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8cc7848ad89cbfeef8aa935f2ce370f84c40ba61cfc928852fa7b6e034e04430
+size 1761920