loss, ap = train_encoder_decoder(**trainer_args)
print("Train loss: {0:.5f} AP: {1:.4f}".format(loss, ap))
val_args = {
"encoder": encoder,
"decoder": decoder,
"device": DEVICE,
"test_loader": val_loader,
"loss_function": loss_function,
"debug_mode": DEBUG_MODE,
"coordinate_trajectory_inputs": False,
"compute_ap": False,
}
loss, ap = test_encoder_decoder(**val_args)
print("Validation loss: {0:.5f} AP: {1:.4f}".format(
loss, ap))
if loss <= best_loss:
best_loss = loss
best_encoder = copy.deepcopy(encoder)
best_decoder = copy.deepcopy(decoder)
encoder_save_name = "encoder_fold_" + str(
fold) + ".weights"
decoder_save_name = "decoder_fold_" + str(
fold) + ".weights"
if not MODEL_SAVE_PATH is None:
torch.save(
best_encoder.state_dict(),
os.path.join(MODEL_SAVE_PATH,
decoder = RecurrentDecoder(**decoder_args).to(DEVICE)
encoder.load_state_dict(
torch.load(
os.path.join(MODEL_LOAD_PATH,
"encoder_fold_" + str(fold) + ".weights")))
decoder.load_state_dict(
torch.load(
os.path.join(MODEL_LOAD_PATH,
"decoder_fold_" + str(fold) + ".weights")))
loss_function = nn.BCELoss()
# ########## EVALUATE ########## #
test_args = {
"encoder": encoder,
"decoder": decoder,
"device": DEVICE,
"test_loader": test_loader,
"loss_function": loss_function,
"debug_mode": False,
"fold_num": fold,
"predictions_save_path": None,
"variable_batch_size": True,
}
loss, ap = test_encoder_decoder(**test_args)
print("Test loss: {0:.5f} AP: {1:.4f}".format(loss, ap))
all_aps.append(ap)
print("MEAN AP:", np.round(np.mean(all_aps), 3) * 100)
loss, _ = train_encoder_decoder(**trainer_args)
print("Train loss: {0:.5f}".format(loss))
val_args = {
"encoder": encoder,
"decoder": decoder,
"device": DEVICE,
"test_loader": val_loader,
"loss_function": loss_function,
"debug_mode": DEBUG_MODE,
"coordinate_trajectory_inputs": False,
"compute_ap": False,
}
loss, _ = test_encoder_decoder(**val_args)
print("Validation loss: {0:.5f}".format(loss))
if loss < best_loss:
best_loss = loss
best_encoder = copy.deepcopy(encoder)
best_decoder = copy.deepcopy(decoder)
encoder_save_name = "encoder_fold_" + str(fold) + ".weights"
decoder_save_name = "decoder_fold_" + str(fold) + ".weights"
if not MODEL_SAVE_PATH is None:
if multi_view_tensors:
multi_view = "multi_view"
else:
multi_view = "single_view"
