def run_test(folder_path, override_dict, test_path, snapshot_iter, is_large,
save_img_data):
print("Folder path: %s" % folder_path)
with open(os.path.join(folder_path, "PARAM.p"), 'rb') as f:
opt0 = pickle.load(f)
# opt = {**opt0, **override_dict}
opt = recursive_merge_dicts(opt0, override_dict)
vp = Pipeline(None,
opt,
model_dir=folder_path,
auto_save_hyperparameters=False,
use_logging=False)
print(vp.opt)
with vp.graph.as_default():
sess = vp.create_session()
vp.run_full_test_from_checkpoint(sess,
test_path=test_path,
snapshot_iter=snapshot_iter,
is_large=is_large,
save_img_data=save_img_data)
"learning_rate": learning_rate,
"max_epochs": 2000,
"weight_decay": 1e-6,
"test_steps": 5000,
"test_limit": 200,
"recon_weight": recon_weight,
}
opt["encoder_options"] = {
"keypoint_num": num_keypoints,
"patch_feature_dim": patch_feature_dim,
"ae_recon_type": opt["recon_name"],
"keypoint_concentration_loss_weight": 100.0,
"keypoint_axis_balancing_loss_weight": 200.0,
"keypoint_separation_loss_weight": keypoint_separation_loss_weight,
"keypoint_separation_bandwidth": keypoint_separation_bandwidth,
"keypoint_transform_loss_weight": kp_transform_loss,
"keypoint_decoding_heatmap_levels": decoding_levels,
"keypoint_decoding_heatmap_level_base": 0.5 ** (1 / 2),
"image_channels": 3,
}
opt["decoder_options"] = copy(opt["encoder_options"])
# -------------------------------------
model_dir = os.path.join("results/exercise_25")
vp = Pipeline(None, opt, model_dir=model_dir)
print(vp.opt)
with vp.graph.as_default():
sess = vp.create_session()
vp.run_full_train(sess, restore=True)
vp.run_full_test(sess)