| | |
|---|
| | | |
|---|
| | | elif mode == "mt" and arch == "rnn": |
|---|
| | | # +1 means input (+1) and layers outputs (train_args.elayer) |
|---|
| | | subsample = np.ones(train_args.elayers + 1, dtype=np.int) |
|---|
| | | subsample = np.ones(train_args.elayers + 1, dtype=np.int32) |
|---|
| | | logging.warning("Subsampling is not performed for machine translation.") |
|---|
| | | logging.info("subsample: " + " ".join([str(x) for x in subsample])) |
|---|
| | | return subsample |
|---|
| | |
|---|
| | | or (mode == "mt" and arch == "rnn") |
|---|
| | | or (mode == "st" and arch == "rnn") |
|---|
| | | ): |
|---|
| | | subsample = np.ones(train_args.elayers + 1, dtype=np.int) |
|---|
| | | subsample = np.ones(train_args.elayers + 1, dtype=np.int32) |
|---|
| | | if train_args.etype.endswith("p") and not train_args.etype.startswith("vgg"): |
|---|
| | | ss = train_args.subsample.split("_") |
|---|
| | | for j in range(min(train_args.elayers + 1, len(ss))): |
|---|
| | |
|---|
| | | |
|---|
| | | elif mode == "asr" and arch == "rnn_mix": |
|---|
| | | subsample = np.ones( |
|---|
| | | train_args.elayers_sd + train_args.elayers + 1, dtype=np.int |
|---|
| | | train_args.elayers_sd + train_args.elayers + 1, dtype=np.int32 |
|---|
| | | ) |
|---|
| | | if train_args.etype.endswith("p") and not train_args.etype.startswith("vgg"): |
|---|
| | | ss = train_args.subsample.split("_") |
|---|
| | |
|---|
| | | elif mode == "asr" and arch == "rnn_mulenc": |
|---|
| | | subsample_list = [] |
|---|
| | | for idx in range(train_args.num_encs): |
|---|
| | | subsample = np.ones(train_args.elayers[idx] + 1, dtype=np.int) |
|---|
| | | subsample = np.ones(train_args.elayers[idx] + 1, dtype=np.int32) |
|---|
| | | if train_args.etype[idx].endswith("p") and not train_args.etype[ |
|---|
| | | idx |
|---|
| | | ].startswith("vgg"): |
|---|
