# Copyright 2021 Microsoft . 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.
# This code is modified by Zilliz.
import torch
from torch import nn
[docs]class PatchMerging(nn.Module):
r""" Patch Merging Layer.
Args:
input_resolution (tuple[int]): Resolution of input feature.
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
[docs] def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm, is_v2=False):
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
self.is_v2 = is_v2
if self.is_v2:
self.norm = norm_layer(2 * dim)
else:
self.norm = norm_layer(4 * dim)
[docs] def forward(self, x):
"""
x: B, H*W, C
"""
h, w = self.input_resolution
b, l, c = x.shape
assert l == h * w, 'input feature has wrong size'
assert h % 2 == 0 and w % 2 == 0, f'x size ({h}*{w}) are not even.'
x = x.view(b, h, w, c)
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
x = x.view(b, -1, 4 * c) # B H/2*W/2 4*C
if self.is_v2:
x = self.reduction(x)
x = self.norm(x)
else:
x = self.norm(x)
x = self.reduction(x)
return x
def flops(self):
h, w = self.input_resolution
flops = h * w * self.dim
flops += (h // 2) * (w // 2) * 4 * self.dim * 2 * self.dim
return flops