644 lines
21 KiB
Python
644 lines
21 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Code was based on https://github.com/bytedance/Next-ViT/blob/main/classification/nextvit.py
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# reference: https://arxiv.org/abs/2207.05501
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from functools import partial
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import paddle
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from paddle import nn
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from paddle.nn.initializer import TruncatedNormal, Constant, Normal
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from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
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from ....utils.save_load import load_dygraph_pretrain
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MODEL_URLS = {
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"NextViT_small_224":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_small_224_pretrained.pdparams",
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"NextViT_base_224":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_224_pretrained.pdparams",
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"NextViT_large_224":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_224_pretrained.pdparams",
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"NextViT_small_384":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_small_384_pretrained.pdparams",
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"NextViT_base_384":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_384_pretrained.pdparams",
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"NextViT_large_384":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_384_pretrained.pdparams",
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}
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__all__ = list(MODEL_URLS.keys())
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NORM_EPS = 1e-5
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def rearrange(x, pattern, **axes_lengths):
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if 'b (h w) c -> b c h w' == pattern:
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b, n, c = x.shape
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h = axes_lengths.pop('h', -1)
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w = axes_lengths.pop('w', -1)
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h = h if w == -1 else n // w
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w = w if h == -1 else n // h
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return x.transpose([0, 2, 1]).reshape([b, c, h, w])
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if 'b c h w -> b (h w) c' == pattern:
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b, c, h, w = x.shape
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return x.reshape([b, c, h * w]).transpose([0, 2, 1])
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if 'b t (h d) -> b h t d' == pattern:
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b, t, h_d = x.shape
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h = axes_lengths['h']
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return x.reshape([b, t, h, h_d // h]).transpose([0, 2, 1, 3])
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if 'b h t d -> b t (h d)' == pattern:
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b, h, t, d = x.shape
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return x.transpose([0, 2, 1, 3]).reshape([b, t, h * d])
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raise NotImplementedError(
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"Rearrangement '{}' has not been implemented.".format(pattern))
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def merge_pre_bn(layer, pre_bn_1, pre_bn_2=None):
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""" Merge pre BN to reduce inference runtime.
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"""
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weight = layer.weight
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if isinstance(layer, nn.Linear):
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weight = weight.transpose([1, 0])
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bias = layer.bias
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if pre_bn_2 is None:
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scale_invstd = (pre_bn_1._variance + pre_bn_1._epsilon).pow(-0.5)
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extra_weight = scale_invstd * pre_bn_1.weight
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extra_bias = pre_bn_1.bias - pre_bn_1.weight * pre_bn_1._mean * scale_invstd
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else:
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scale_invstd_1 = (pre_bn_1._variance + pre_bn_1._epsilon).pow(-0.5)
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scale_invstd_2 = (pre_bn_2._variance + pre_bn_2._epsilon).pow(-0.5)
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extra_weight = scale_invstd_1 * pre_bn_1.weight * scale_invstd_2 * pre_bn_2.weight
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extra_bias = scale_invstd_2 * pre_bn_2.weight * (
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pre_bn_1.bias - pre_bn_1.weight * pre_bn_1._mean * scale_invstd_1 -
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pre_bn_2._mean) + pre_bn_2.bias
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if isinstance(layer, nn.Linear):
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extra_bias = weight @extra_bias
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weight = weight.multiply(
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extra_weight.reshape([1, weight.shape[1]]).expand_as(weight))
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weight = weight.transpose([1, 0])
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elif isinstance(layer, nn.Conv2D):
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assert weight.shape[2] == 1 and weight.shape[3] == 1
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weight = weight.reshape([weight.shape[0], weight.shape[1]])
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extra_bias = weight @extra_bias
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weight = weight.multiply(
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extra_weight.reshape([1, weight.shape[1]]).expand_as(weight))
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weight = weight.reshape([weight.shape[0], weight.shape[1], 1, 1])
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bias = bias.add(extra_bias)
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layer.weight.set_value(weight)
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layer.bias.set_value(bias)
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def _make_divisible(v, divisor, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class ConvBNReLU(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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groups=1):
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super(ConvBNReLU, self).__init__()
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self.conv = nn.Conv2D(
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in_channels,
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out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=1,
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groups=groups,
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bias_attr=False)
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self.norm = nn.BatchNorm2D(out_channels, epsilon=NORM_EPS)
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self.act = nn.ReLU()
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def forward(self, x):
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x = self.conv(x)
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x = self.norm(x)
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x = self.act(x)
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return x
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class PatchEmbed(nn.Layer):
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def __init__(self, in_channels, out_channels, stride=1):
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super(PatchEmbed, self).__init__()
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norm_layer = partial(nn.BatchNorm2D, epsilon=NORM_EPS)
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if stride == 2:
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self.avgpool = nn.AvgPool2D((2, 2), stride=2, ceil_mode=True)
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self.conv = nn.Conv2D(
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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bias_attr=False)
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self.norm = norm_layer(out_channels)
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elif in_channels != out_channels:
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self.avgpool = nn.Identity()
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self.conv = nn.Conv2D(
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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bias_attr=False)
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self.norm = norm_layer(out_channels)
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else:
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self.avgpool = nn.Identity()
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self.conv = nn.Identity()
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self.norm = nn.Identity()
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def forward(self, x):
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return self.norm(self.conv(self.avgpool(x)))
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class MHCA(nn.Layer):
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"""
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Multi-Head Convolutional Attention
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"""
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def __init__(self, out_channels, head_dim):
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super(MHCA, self).__init__()
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norm_layer = partial(nn.BatchNorm2D, epsilon=NORM_EPS)
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self.group_conv3x3 = nn.Conv2D(
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out_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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groups=out_channels // head_dim,
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bias_attr=False)
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self.norm = norm_layer(out_channels)
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self.act = nn.ReLU()
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self.projection = nn.Conv2D(
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out_channels, out_channels, kernel_size=1, bias_attr=False)
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def forward(self, x):
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out = self.group_conv3x3(x)
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out = self.norm(out)
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out = self.act(out)
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out = self.projection(out)
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return out
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class Mlp(nn.Layer):
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def __init__(self,
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in_features,
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out_features=None,
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mlp_ratio=None,
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drop=0.,
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bias=True):
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super().__init__()
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out_features = out_features or in_features
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hidden_dim = _make_divisible(in_features * mlp_ratio, 32)
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self.conv1 = nn.Conv2D(
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in_features,
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hidden_dim,
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kernel_size=1,
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bias_attr=None if bias == True else False)
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self.act = nn.ReLU()
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self.conv2 = nn.Conv2D(
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hidden_dim,
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out_features,
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kernel_size=1,
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bias_attr=None if bias == True else False)
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self.drop = nn.Dropout(drop)
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def merge_bn(self, pre_norm):
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merge_pre_bn(self.conv1, pre_norm)
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self.is_bn_merged = True
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def forward(self, x):
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x = self.conv1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.conv2(x)
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x = self.drop(x)
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return x
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class NCB(nn.Layer):
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"""
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Next Convolution Block
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"""
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def __init__(self,
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in_channels,
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out_channels,
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stride=1,
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path_dropout=0.0,
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drop=0.0,
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head_dim=32,
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mlp_ratio=3):
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super(NCB, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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norm_layer = partial(nn.BatchNorm2D, epsilon=NORM_EPS)
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assert out_channels % head_dim == 0
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self.patch_embed = PatchEmbed(in_channels, out_channels, stride)
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self.mhca = MHCA(out_channels, head_dim)
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self.attention_path_dropout = DropPath(path_dropout)
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self.norm = norm_layer(out_channels)
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self.mlp = Mlp(out_channels, mlp_ratio=mlp_ratio, drop=drop, bias=True)
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self.mlp_path_dropout = DropPath(path_dropout)
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self.is_bn_merged = False
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def merge_bn(self):
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if not self.is_bn_merged:
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self.mlp.merge_bn(self.norm)
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self.is_bn_merged = True
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def forward(self, x):
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x = self.patch_embed(x)
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x = x + self.attention_path_dropout(self.mhca(x))
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if not self.is_bn_merged:
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out = self.norm(x)
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else:
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out = x
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x = x + self.mlp_path_dropout(self.mlp(out))
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return x
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class E_MHSA(nn.Layer):
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"""
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Efficient Multi-Head Self Attention
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"""
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def __init__(self,
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dim,
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out_dim=None,
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head_dim=32,
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qkv_bias=True,
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qk_scale=None,
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attn_drop=0,
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proj_drop=0.,
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sr_ratio=1):
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super().__init__()
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self.dim = dim
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self.out_dim = out_dim if out_dim is not None else dim
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self.num_heads = self.dim // head_dim
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self.scale = qk_scale or head_dim**-0.5
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self.q = nn.Linear(dim, self.dim, bias_attr=qkv_bias)
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self.k = nn.Linear(dim, self.dim, bias_attr=qkv_bias)
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self.v = nn.Linear(dim, self.dim, bias_attr=qkv_bias)
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self.proj = nn.Linear(self.dim, self.out_dim)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj_drop = nn.Dropout(proj_drop)
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self.sr_ratio = sr_ratio
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self.N_ratio = sr_ratio**2
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if sr_ratio > 1:
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self.sr = nn.AvgPool1D(
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kernel_size=self.N_ratio, stride=self.N_ratio)
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self.norm = nn.BatchNorm1D(dim, epsilon=NORM_EPS)
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self.is_bn_merged = False
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def merge_bn(self, pre_bn):
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merge_pre_bn(self.q, pre_bn)
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if self.sr_ratio > 1:
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merge_pre_bn(self.k, pre_bn, self.norm)
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merge_pre_bn(self.v, pre_bn, self.norm)
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else:
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merge_pre_bn(self.k, pre_bn)
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merge_pre_bn(self.v, pre_bn)
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self.is_bn_merged = True
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def forward(self, x):
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B, N, C = x.shape
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q = self.q(x)
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q = q.reshape(
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[B, N, self.num_heads, int(C // self.num_heads)]).transpose(
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[0, 2, 1, 3])
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if self.sr_ratio > 1:
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x_ = x.transpose([0, 2, 1])
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x_ = self.sr(x_)
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if not self.is_bn_merged:
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x_ = self.norm(x_)
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x_ = x_.transpose([0, 2, 1])
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k = self.k(x_)
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k = k.reshape(
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[B, k.shape[1], self.num_heads, int(C // self.num_heads)
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]).transpose([0, 2, 3, 1])
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v = self.v(x_)
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v = v.reshape(
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[B, v.shape[1], self.num_heads, int(C // self.num_heads)
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]).transpose([0, 2, 1, 3])
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else:
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k = self.k(x)
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k = k.reshape(
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[B, k.shape[1], self.num_heads, int(C // self.num_heads)
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]).transpose([0, 2, 3, 1])
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v = self.v(x)
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v = v.reshape(
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[B, v.shape[1], self.num_heads, int(C // self.num_heads)
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]).transpose([0, 2, 1, 3])
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attn = (q @k) * self.scale
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attn = nn.functional.softmax(attn, axis=-1)
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attn = self.attn_drop(attn)
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x = (attn @v).transpose([0, 2, 1, 3]).reshape([B, N, C])
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class NTB(nn.Layer):
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"""
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Next Transformer Block
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"""
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def __init__(
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self,
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in_channels,
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out_channels,
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path_dropout,
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stride=1,
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sr_ratio=1,
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mlp_ratio=2,
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head_dim=32,
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mix_block_ratio=0.75,
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attn_drop=0.0,
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drop=0.0, ):
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super(NTB, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.mix_block_ratio = mix_block_ratio
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norm_func = partial(nn.BatchNorm2D, epsilon=NORM_EPS)
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self.mhsa_out_channels = _make_divisible(
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int(out_channels * mix_block_ratio), 32)
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self.mhca_out_channels = out_channels - self.mhsa_out_channels
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self.patch_embed = PatchEmbed(in_channels, self.mhsa_out_channels,
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stride)
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self.norm1 = norm_func(self.mhsa_out_channels)
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self.e_mhsa = E_MHSA(
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self.mhsa_out_channels,
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head_dim=head_dim,
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sr_ratio=sr_ratio,
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attn_drop=attn_drop,
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proj_drop=drop)
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self.mhsa_path_dropout = DropPath(path_dropout * mix_block_ratio)
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self.projection = PatchEmbed(
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self.mhsa_out_channels, self.mhca_out_channels, stride=1)
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self.mhca = MHCA(self.mhca_out_channels, head_dim=head_dim)
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self.mhca_path_dropout = DropPath(path_dropout * (1 - mix_block_ratio))
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self.norm2 = norm_func(out_channels)
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self.mlp = Mlp(out_channels, mlp_ratio=mlp_ratio, drop=drop)
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self.mlp_path_dropout = DropPath(path_dropout)
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self.is_bn_merged = False
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def merge_bn(self):
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if not self.is_bn_merged:
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self.e_mhsa.merge_bn(self.norm1)
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self.mlp.merge_bn(self.norm2)
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self.is_bn_merged = True
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def forward(self, x):
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x = self.patch_embed(x)
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B, C, H, W = x.shape
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if not self.is_bn_merged:
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out = self.norm1(x)
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else:
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out = x
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out = rearrange(out, "b c h w -> b (h w) c") # b n c
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out = self.e_mhsa(out)
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out = self.mhsa_path_dropout(out)
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x = x + rearrange(out, "b (h w) c -> b c h w", h=H)
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out = self.projection(x)
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out = out + self.mhca_path_dropout(self.mhca(out))
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x = paddle.concat([x, out], axis=1)
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if not self.is_bn_merged:
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out = self.norm2(x)
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else:
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out = x
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x = x + self.mlp_path_dropout(self.mlp(out))
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return x
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class NextViT(nn.Layer):
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def __init__(self,
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stem_chs,
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depths,
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path_dropout,
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attn_drop=0,
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drop=0,
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class_num=1000,
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strides=[1, 2, 2, 2],
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sr_ratios=[8, 4, 2, 1],
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head_dim=32,
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mix_block_ratio=0.75):
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super(NextViT, self).__init__()
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self.stage_out_channels = [
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[96] * (depths[0]), [192] * (depths[1] - 1) + [256],
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[384, 384, 384, 384, 512] * (depths[2] // 5),
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[768] * (depths[3] - 1) + [1024]
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]
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# Next Hybrid Strategy
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self.stage_block_types = [[NCB] * depths[0],
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[NCB] * (depths[1] - 1) + [NTB],
|
|
[NCB, NCB, NCB, NCB, NTB] * (depths[2] // 5),
|
|
[NCB] * (depths[3] - 1) + [NTB]]
|
|
|
|
self.stem = nn.Sequential(
|
|
ConvBNReLU(
|
|
3, stem_chs[0], kernel_size=3, stride=2),
|
|
ConvBNReLU(
|
|
stem_chs[0], stem_chs[1], kernel_size=3, stride=1),
|
|
ConvBNReLU(
|
|
stem_chs[1], stem_chs[2], kernel_size=3, stride=1),
|
|
ConvBNReLU(
|
|
stem_chs[2], stem_chs[2], kernel_size=3, stride=2), )
|
|
input_channel = stem_chs[-1]
|
|
features = []
|
|
idx = 0
|
|
dpr = [
|
|
x.item() for x in paddle.linspace(0, path_dropout, sum(depths))
|
|
] # stochastic depth decay rule
|
|
for stage_id in range(len(depths)):
|
|
numrepeat = depths[stage_id]
|
|
output_channels = self.stage_out_channels[stage_id]
|
|
block_types = self.stage_block_types[stage_id]
|
|
for block_id in range(numrepeat):
|
|
if strides[stage_id] == 2 and block_id == 0:
|
|
stride = 2
|
|
else:
|
|
stride = 1
|
|
output_channel = output_channels[block_id]
|
|
block_type = block_types[block_id]
|
|
if block_type is NCB:
|
|
layer = NCB(input_channel,
|
|
output_channel,
|
|
stride=stride,
|
|
path_dropout=dpr[idx + block_id],
|
|
drop=drop,
|
|
head_dim=head_dim)
|
|
features.append(layer)
|
|
elif block_type is NTB:
|
|
layer = NTB(input_channel,
|
|
output_channel,
|
|
path_dropout=dpr[idx + block_id],
|
|
stride=stride,
|
|
sr_ratio=sr_ratios[stage_id],
|
|
head_dim=head_dim,
|
|
mix_block_ratio=mix_block_ratio,
|
|
attn_drop=attn_drop,
|
|
drop=drop)
|
|
features.append(layer)
|
|
input_channel = output_channel
|
|
idx += numrepeat
|
|
self.features = nn.Sequential(*features)
|
|
|
|
self.norm = nn.BatchNorm2D(output_channel, epsilon=NORM_EPS)
|
|
|
|
self.avgpool = nn.AdaptiveAvgPool2D((1, 1))
|
|
self.proj_head = nn.Sequential(nn.Linear(output_channel, class_num), )
|
|
|
|
self.stage_out_idx = [
|
|
sum(depths[:idx + 1]) - 1 for idx in range(len(depths))
|
|
]
|
|
self._initialize_weights()
|
|
|
|
def merge_bn(self):
|
|
self.eval()
|
|
for idx, layer in self.named_sublayers():
|
|
if isinstance(layer, NCB) or isinstance(layer, NTB):
|
|
layer.merge_bn()
|
|
|
|
def _initialize_weights(self):
|
|
for n, m in self.named_sublayers():
|
|
if isinstance(m, (nn.BatchNorm2D, nn.GroupNorm, nn.LayerNorm,
|
|
nn.BatchNorm1D)):
|
|
ones_(m.weight)
|
|
zeros_(m.bias)
|
|
elif isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight)
|
|
if hasattr(m, 'bias') and m.bias is not None:
|
|
zeros_(m.bias)
|
|
elif isinstance(m, nn.Conv2D):
|
|
trunc_normal_(m.weight)
|
|
if hasattr(m, 'bias') and m.bias is not None:
|
|
zeros_(m.bias)
|
|
|
|
def forward(self, x):
|
|
x = self.stem(x)
|
|
for layer in self.features:
|
|
x = layer(x)
|
|
x = self.norm(x)
|
|
x = self.avgpool(x)
|
|
x = paddle.flatten(x, 1)
|
|
x = self.proj_head(x)
|
|
return x
|
|
|
|
|
|
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
|
|
if pretrained is False:
|
|
pass
|
|
elif pretrained is True:
|
|
load_dygraph_pretrain(model, model_url, use_ssld=use_ssld)
|
|
elif isinstance(pretrained, str):
|
|
load_dygraph_pretrain(model, pretrained)
|
|
else:
|
|
raise RuntimeError(
|
|
"pretrained type is not available. Please use `string` or `boolean` type."
|
|
)
|
|
|
|
|
|
def NextViT_small_224(pretrained=False, use_ssld=False, **kwargs):
|
|
model = NextViT(
|
|
stem_chs=[64, 32, 64],
|
|
depths=[3, 4, 10, 3],
|
|
path_dropout=0.1,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["NextViT_small_224"], use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def NextViT_base_224(pretrained=False, use_ssld=False, **kwargs):
|
|
model = NextViT(
|
|
stem_chs=[64, 32, 64],
|
|
depths=[3, 4, 20, 3],
|
|
path_dropout=0.2,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["NextViT_base_224"], use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def NextViT_large_224(pretrained=False, use_ssld=False, **kwargs):
|
|
model = NextViT(
|
|
stem_chs=[64, 32, 64],
|
|
depths=[3, 4, 30, 3],
|
|
path_dropout=0.2,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["NextViT_large_224"], use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def NextViT_small_384(pretrained=False, use_ssld=False, **kwargs):
|
|
model = NextViT(
|
|
stem_chs=[64, 32, 64],
|
|
depths=[3, 4, 10, 3],
|
|
path_dropout=0.1,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["NextViT_small_384"], use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def NextViT_base_384(pretrained=False, use_ssld=False, **kwargs):
|
|
model = NextViT(
|
|
stem_chs=[64, 32, 64],
|
|
depths=[3, 4, 20, 3],
|
|
path_dropout=0.2,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["NextViT_base_384"], use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def NextViT_large_384(pretrained=False, use_ssld=False, **kwargs):
|
|
model = NextViT(
|
|
stem_chs=[64, 32, 64],
|
|
depths=[3, 4, 30, 3],
|
|
path_dropout=0.2,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["NextViT_large_384"], use_ssld=use_ssld)
|
|
return model
|