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| # ------------------------------------------------------------------------ | |
| # Copyright (c) 2023-present, BAAI. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ------------------------------------------------------------------------ | |
| # pyre-unsafe | |
| """Layer utilities.""" | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| def init_cross_conv(blocks): | |
| """Initialize convolutional cross attention.""" | |
| for m in blocks.modules(): | |
| if isinstance(m, torch.nn.Conv2d): | |
| torch.nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") | |
| for blk in blocks: | |
| torch.nn.init.constant_(blk.norm3.weight, 0) | |
| def set_dropout(module, dropout): | |
| """Initialize dropout.""" | |
| for m in [m for m in module.modules() if isinstance(m, torch.nn.Dropout)]: | |
| m.p = dropout | |
| def set_drop_path(blocks, drop_path): | |
| """Initialize drop path.""" | |
| if not isinstance(blocks, torch.nn.ModuleList): | |
| blocks = getattr(blocks, "blocks", getattr(blocks, "layers", None)) | |
| for i, blk in enumerate(blocks): | |
| for m in [m for m in blk.modules() if type(m).__name__ == "DropPath"]: | |
| m.p = i * drop_path / (len(blocks) - 1) | |
| def set_sync_batch_norm(module, ddp_group): | |
| """Set data parallelism group for sync batch norm.""" | |
| for m in module.modules(): | |
| if isinstance(m, torch.nn.SyncBatchNorm): | |
| m.process_group = ddp_group | |
| def resize_pos_embed(weight, out_len): | |
| """Resize position embedding weights.""" | |
| out_h = out_w = int(out_len**0.5) | |
| h = w = int(weight.shape[0] ** 0.5) | |
| weight = weight.reshape((h, w, weight.shape[1])) | |
| out_weight = [ | |
| cv2.resize(x, (out_w, out_h), interpolation=cv2.INTER_CUBIC) | |
| for x in np.split(weight.astype("float32", copy=False), 4, axis=-1) | |
| ] | |
| out_weight = np.concatenate(out_weight, axis=-1) | |
| return out_weight.reshape((-1, weight.shape[-1])).astype(weight.dtype, copy=False) | |