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60212c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | 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))
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