Qualcomm AI Engine Direct - export parseq

parseq/export.py

@@ -0,0 +1,23 @@
+
+from executorch.parseq.export_utils import get_dummy_input, prepare_export_model
+
+from executorch.examples.qualcomm.utils import build_executorch_binary
+
+
+if __name__ == '__main__':
+
+    model_name = "parseq"
+    export_mode = 'executorch'
+    model = prepare_export_model(model_name, export_mode)
+    image = get_dummy_input()
+    inputs = (image,)
+
+    build_executorch_binary(
+        model,
+        inputs,
+        "SM8650",
+        "parseq_qualcomm.pte",
+        [inputs],
+        skip_node_op_set={"aten.full.default", "aten.where.self"},
+        skip_node_id_set={"aten_view_copy_default_235"},
+    )

parseq/export_utils.py

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+import torchvision
+import yaml
+
+from executorch.parseq.parseq import PARSeq, Tokenizer
+from PIL import Image
+
+
+def get_transform(img_size=(32, 128)):
+    return torchvision.transforms.Compose(
+        [
+            torchvision.transforms.Resize(
+                img_size, torchvision.transforms.InterpolationMode.BICUBIC
+            ),
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize(0.5, 0.5),
+        ]
+    )
+
+
+# Gray dummy image
+def get_dummy_input(img_h=32, img_w=128, n_channels=3):
+    transform = get_transform((img_h, img_w))
+    image = Image.new("RGB", (img_w, img_h), color=128)
+    image = transform(image)
+    image = image.view(1, *image.size())
+    return image
+
+
+def prepare_export_model(model_name, export_mode):
+    with open(f"parseq/{model_name}.yaml", "r") as f:
+        cfg = yaml.load(f, Loader=yaml.FullLoader)
+
+    print(cfg)
+    cfg.pop("name")
+    cfg.pop("_target_")
+    cfg.pop("lr")
+    cfg.pop("perm_num")
+    cfg.pop("perm_forward")
+    cfg.pop("perm_mirrored")
+    charset = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~"
+    tokenizer = Tokenizer(charset)
+    lightning_model = PARSeq(
+        tokenizer,
+        25,
+        (32, 128),
+        **cfg,
+    )
+
+    model = lightning_model
+    model.export_mode = export_mode
+    return model.eval()