Upload 3 files

shane_models/i2c_s_i_t_t_ie_mw2_w_2.5_master_imu_encoder.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:644b1899f538a4068b4a31549876ca66d08427368f1f02af8bc735cc2b34704d
+size 3406852

shane_models/i2c_s_i_t_v_ie_mw2_w_5.0_master_imu_encoder.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9787fd787649633616d19716ed81ad442cde1bb6c2fdd93a041d9db2c0d4e2e7
+size 3406852

shane_models/run_load_imu2clip_encoder.py

@@ -0,0 +1,41 @@
+import torch
+import sys
+sys.path.append("/home/pranayr_umass_edu/imu2clip")
+from lib.imu_models import MW2StackRNNPooling
+
+if __name__ == "__main__":
+
+    # Generate random IMU-like motions as examples
+    # imu_motions: array <n_samples x 6 x 1000>
+    imu_motions = torch.rand(1, 6, 1000)
+    print(imu_motions.dtype)
+    print("Generated random IMU-like motions:", imu_motions)
+
+    # Load the IMU encoder
+    """
+        The following example .pt model is configured as
+        - i2c: IMU2CLIP
+        - s_i: source modality = IMU
+        - t_v: target modality for alignment = Video
+        - t_t: target modality for alignment = Text
+        - mw2: MW2StackRNNPooling as the encoder
+        - w_5.0: window size of 2.5 x 2 seconds
+    """
+    #path_imu_encoder = "./i2c_s_i_t_v_ie_mw2_w_5.0_master_imu_encoder.pt"
+    # path_imu_encoder = "./i2c_s_i_t_t_ie_mw2_w_2.5_master_imu_encoder.pt"
+    path_imu_encoder = "/home/pranayr_umass_edu/imu2clip/shane_models/i2c_s_i_t_v_ie_mw2_w_5.0_master_imu_encoder.pt"
+    path_imu_encoder = '/home/pranayr_umass_edu/imu2clip/saved/mw2/i2c/i2c_s_i_t_v_se_mw2_w_5.0_master-epoch=12-val_loss=3.14.ckpt'
+
+    loaded_imu_encoder = MW2StackRNNPooling(size_embeddings=512)
+    loaded_imu_encoder.load_state_dict(torch.load(path_imu_encoder))
+    print("Loaded IMU Encoder:", loaded_imu_encoder)
+    loaded_imu_encoder.eval()
+    print("Done loading the IMU Encoder")
+
+    # Inference time
+    imu2clip_embeddings = loaded_imu_encoder(imu_motions)
+    print('Raw IMU Signals (random)', imu_motions.shape)
+    print('Encoded IMU2CLIP embeddings', imu2clip_embeddings.shape)
+
+    # # check if raw and encoded are the same
+    # print('Are the raw and encoded the same?', torch.allclose(imu_motions, imu2clip_embeddings))