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Zero
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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)
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