Fingerspelling embedding + ClearML

export_model.py

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+# to run this script, you need torch 1.13.1 and torchvision 0.14.1
+
+import numpy as np
+import onnx
+import torch
+import torchvision
+import os
+
+from models.spoter_embedding_model import SPOTER_EMBEDDINGS
+
+# set parameters of the model
+model_name = 'fingerspelling_embedding_model'
+
+# load PyTorch model from .pth file
+
+device = torch.device("cpu")
+# if torch.cuda.is_available():
+#     device = torch.device("cuda")
+
+CHECKPOINT_PATH = "checkpoints/fingerspelling_checkpoint.pth"
+checkpoint = torch.load(CHECKPOINT_PATH, map_location=device)
+
+model = SPOTER_EMBEDDINGS(
+    features=checkpoint["config_args"].vector_length,
+    hidden_dim=checkpoint["config_args"].hidden_dim,
+    norm_emb=checkpoint["config_args"].normalize_embeddings,
+).to(device)
+model.load_state_dict(checkpoint["state_dict"])
+# set model to evaluation mode
+model.eval()
+
+dummy_input = torch.randn(1, 10, 54, 2)
+
+# check if models folder exists
+if not os.path.exists('out-models'):
+    os.makedirs('out-models')
+
+for model_export in ["onnx", "coreml"]:
+    if model_export == "coreml":
+        # set device for dummy input
+        dummy_input = dummy_input.to(device)
+        traced_model = torch.jit.trace(model, dummy_input)
+
+        out = traced_model(dummy_input)
+        import coremltools as ct
+
+        # Convert to Core ML
+        coreml_model = ct.convert(
+            traced_model,
+            inputs=[ct.TensorType(name="input", shape=dummy_input.shape)],
+        )
+
+        # Save Core ML model
+        coreml_model.save("out-models/" + model_name + ".mlmodel")
+    else:
+        # set device for dummy input
+        dummy_input = dummy_input.to(device)
+
+        torch.onnx.export(model,                                # model being run
+                        dummy_input,                          # model input (or a tuple for multiple inputs)
+                        'out-models/' + model_name + '.onnx',     # where to save the model (can be a file or file-like object)
+                        export_params=True,                   # store the trained parameter weights inside the model file
+                        opset_version=9,                      # the ONNX version to export the model to
+                        do_constant_folding=True,             # whether to execute constant folding for optimization
+                        input_names = ['X'],                  # the model's input names
+                        output_names = ['Y']                  # the model's output names
+                        )