cls_cvt.py

from functools import partial
from itertools import repeat
# from torch._six import container_abcs

import logging
# import os
from collections import OrderedDict

# import numpy as np
# import scipy
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
# from einops import rearrange
# from einops.layers.torch import Rearrange

# from timm.models.layers import DropPath, trunc_normal_
from drop import DropPath

# from .registry import register_model


# From PyTorch internals
def _ntuple(n):
    def parse(x):
        # if isinstance(x, container_abcs.Iterable):
        if isinstance(x, (list, tuple)):
            return x
        return tuple(repeat(x, n))

    return parse


to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple


class LayerDict(nn.LayerDict):
    def forward(self, x):
        for k in self:
            x = self[k](x)
        return x


class Rearrange(nn.Layer):
    """
    same functionality as following:
    from einops.layers.torch import Rearrange
    Rearrange('b c h w -> b (h w) c')
    """
    def forward(self, x):
        """
        x: has shape [b, c, h, w]
        """
        b, c, h, w = x.shape
        out = paddle.transpose(x, perm=[0, 2, 3, 1])
        out = paddle.reshape(out, shape=[b, h*w, c])
        return out


def rearrange(x, pattern: str = "", h: int = None, w: int = None):
    if 'b t (h d) -> b h t d' == pattern:
        _b, _t, _ = x.shape
        out = paddle.reshape(x, shape=[_b, _t, h, -1])
        out = paddle.transpose(out, perm=[0, 2, 1, 3])
    elif 'b (h w) c -> b c h w' == pattern:
        _b, _, _c = x.shape
        out = paddle.reshape(x, shape=[_b, h, w, _c])
        out = paddle.transpose(out, perm=[0, 3, 1, 2])
    elif 'b c h w -> b (h w) c' == pattern:
        _b, _c, _h, _w = x.shape
        out = paddle.transpose(x, perm=[0, 2, 3, 1])
        out = paddle.reshape(out, shape=[_b, _h*_w, _c])
    elif 'b h t d -> b t (h d)' == pattern:
        _b, _h, _t, _d = x.shape
        out = paddle.transpose(x, perm=[0, 2, 1, 3])
        out = paddle.reshape(out, shape=[_b, _t, _h*_d])
    else:
        raise NotImplementedError("Rearrangement '{}' has not been implemented !!!".format(pattern))

    return out


# class LayerNorm(nn.LayerNorm):
#     """Subclass torch's LayerNorm to handle fp16."""
#
#     def forward(self, x: paddle.Tensor):
#         orig_type = x.dtype
#         # ret = super().forward(x.type(torch.float32))
#         # return ret.type(orig_type)
#         ret = super().forward(paddle.to_tensor(x, dtype=paddle.float32))
#         return paddle.to_tensor(ret, dtype=orig_type)


# class QuickGELU(nn.Module):
#     def forward(self, x: torch.Tensor):
#         return x * torch.sigmoid(1.702 * x)
class QuickGELU(nn.Layer):
    def forward(self, x: paddle.Tensor):
        return x * F.sigmoid(1.702 * x)


# class Mlp(nn.Module):
class Mlp(nn.Layer):
    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


# class Attention(nn.Module):
class Attention(nn.Layer):
    def __init__(self,
                 dim_in,
                 dim_out,
                 num_heads,
                 qkv_bias=False,
                 attn_drop=0.,
                 proj_drop=0.,
                 method='dw_bn',
                 kernel_size=3,
                 stride_kv=1,
                 stride_q=1,
                 padding_kv=1,
                 padding_q=1,
                 with_cls_token=True,
                 **kwargs
                 ):
        super().__init__()
        self.stride_kv = stride_kv
        self.stride_q = stride_q
        self.dim = dim_out
        self.num_heads = num_heads
        # head_dim = self.qkv_dim // num_heads
        self.scale = dim_out ** -0.5
        self.with_cls_token = with_cls_token

        self.conv_proj_q = self._build_projection(
            dim_in, dim_out, kernel_size, padding_q,
            stride_q, 'linear' if method == 'avg' else method
        )
        self.conv_proj_k = self._build_projection(
            dim_in, dim_out, kernel_size, padding_kv,
            stride_kv, method
        )
        self.conv_proj_v = self._build_projection(
            dim_in, dim_out, kernel_size, padding_kv,
            stride_kv, method
        )

        self.proj_q = nn.Linear(dim_in, dim_out, bias_attr=qkv_bias)
        self.proj_k = nn.Linear(dim_in, dim_out, bias_attr=qkv_bias)
        self.proj_v = nn.Linear(dim_in, dim_out, bias_attr=qkv_bias)

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim_out, dim_out)
        self.proj_drop = nn.Dropout(proj_drop)

    def _build_projection(self,
                          dim_in,
                          dim_out,
                          kernel_size,
                          padding,
                          stride,
                          method):
        if method == 'dw_bn':
            proj = LayerDict(OrderedDict([
                ('conv', nn.Conv2D(
                    dim_in,
                    dim_in,
                    kernel_size=kernel_size,
                    padding=padding,
                    stride=stride,
                    bias_attr=False,
                    groups=dim_in
                )),
                ('bn', nn.BatchNorm2D(dim_in)),
                # ('rearrage', Rearrange('b c h w -> b (h w) c')),
                ('rearrage', Rearrange()),
            ]))
        elif method == 'avg':
            proj = LayerDict(OrderedDict([
                ('avg', nn.AvgPool2D(
                    kernel_size=kernel_size,
                    padding=padding,
                    stride=stride,
                    ceil_mode=True
                )),
                # ('rearrage', Rearrange('b c h w -> b (h w) c')),
                ('rearrage', Rearrange()),
            ]))
        elif method == 'linear':
            proj = None
        else:
            raise ValueError('Unknown method ({})'.format(method))

        return proj

    def forward_conv(self, x, h, w):
        if self.with_cls_token:
            # cls_token, x = torch.split(x, [1, h*w], 1)
            cls_token, x = paddle.split(x, num_or_sections=[1, h*w], axis=1)

        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)

        if self.conv_proj_q is not None:
            q = self.conv_proj_q(x)
        else:
            q = rearrange(x, 'b c h w -> b (h w) c')

        if self.conv_proj_k is not None:
            k = self.conv_proj_k(x)
        else:
            k = rearrange(x, 'b c h w -> b (h w) c')

        if self.conv_proj_v is not None:
            v = self.conv_proj_v(x)
        else:
            v = rearrange(x, 'b c h w -> b (h w) c')

        if self.with_cls_token:
            # q = torch.cat((cls_token, q), dim=1)
            # k = torch.cat((cls_token, k), dim=1)
            # v = torch.cat((cls_token, v), dim=1)
            q = paddle.concat([cls_token, q], axis=1)
            k = paddle.concat([cls_token, k], axis=1)
            v = paddle.concat([cls_token, v], axis=1)

        return q, k, v

    def forward(self, x, h, w):
        if (
            self.conv_proj_q is not None
            or self.conv_proj_k is not None
            or self.conv_proj_v is not None
        ):
            q, k, v = self.forward_conv(x, h, w)

        q = rearrange(self.proj_q(q), 'b t (h d) -> b h t d', h=self.num_heads)
        k = rearrange(self.proj_k(k), 'b t (h d) -> b h t d', h=self.num_heads)
        v = rearrange(self.proj_v(v), 'b t (h d) -> b h t d', h=self.num_heads)

        # attn_score = torch.einsum('bhlk,bhtk->bhlt', [q, k]) * self.scale
        attn_score = paddle.einsum('bhlk,bhtk->bhlt', q, k) * self.scale

        # attn = F.softmax(attn_score, dim=-1)
        attn = F.softmax(attn_score, axis=-1)

        attn = self.attn_drop(attn)

        # x = torch.einsum('bhlt,bhtv->bhlv', [attn, v])
        x = paddle.einsum('bhlt,bhtv->bhlv', attn, v)
        x = rearrange(x, 'b h t d -> b t (h d)')

        x = self.proj(x)
        x = self.proj_drop(x)

        return x

    # # multiply-accumulate (MAC) operations
    # @staticmethod
    # def compute_macs(module, input, output):
    #     # T: num_token
    #     # S: num_token
    #     input = input[0]
    #     flops = 0
    #
    #     _, T, C = input.shape
    #     H = W = int(np.sqrt(T-1)) if module.with_cls_token else int(np.sqrt(T))
    #
    #     H_Q = H / module.stride_q
    #     W_Q = H / module.stride_q
    #     T_Q = H_Q * W_Q + 1 if module.with_cls_token else H_Q * W_Q
    #
    #     H_KV = H / module.stride_kv
    #     W_KV = W / module.stride_kv
    #     T_KV = H_KV * W_KV + 1 if module.with_cls_token else H_KV * W_KV
    #
    #     # C = module.dim
    #     # S = T
    #     # Scaled-dot-product macs
    #     # [B x T x C] x [B x C x T] --> [B x T x S]
    #     # multiplication-addition is counted as 1 because operations can be fused
    #     flops += T_Q * T_KV * module.dim
    #     # [B x T x S] x [B x S x C] --> [B x T x C]
    #     flops += T_Q * module.dim * T_KV
    #
    #     if (
    #         hasattr(module, 'conv_proj_q')
    #         and hasattr(module.conv_proj_q, 'conv')
    #     ):
    #         params = sum(
    #             [
    #                 p.numel()
    #                 for p in module.conv_proj_q.conv.parameters()
    #             ]
    #         )
    #         flops += params * H_Q * W_Q
    #
    #     if (
    #         hasattr(module, 'conv_proj_k')
    #         and hasattr(module.conv_proj_k, 'conv')
    #     ):
    #         params = sum(
    #             [
    #                 p.numel()
    #                 for p in module.conv_proj_k.conv.parameters()
    #             ]
    #         )
    #         flops += params * H_KV * W_KV
    #
    #     if (
    #         hasattr(module, 'conv_proj_v')
    #         and hasattr(module.conv_proj_v, 'conv')
    #     ):
    #         params = sum(
    #             [
    #                 p.numel()
    #                 for p in module.conv_proj_v.conv.parameters()
    #             ]
    #         )
    #         flops += params * H_KV * W_KV
    #
    #     params = sum([p.numel() for p in module.proj_q.parameters()])
    #     flops += params * T_Q
    #     params = sum([p.numel() for p in module.proj_k.parameters()])
    #     flops += params * T_KV
    #     params = sum([p.numel() for p in module.proj_v.parameters()])
    #     flops += params * T_KV
    #     params = sum([p.numel() for p in module.proj.parameters()])
    #     flops += params * T
    #
    #     module.__flops__ += flops


# class Block(nn.Module):
class Block(nn.Layer):
    def __init__(self,
                 dim_in,
                 dim_out,
                 num_heads,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 **kwargs):
        super().__init__()

        self.with_cls_token = kwargs['with_cls_token']

        self.norm1 = norm_layer(dim_in)
        self.attn = Attention(
            dim_in, dim_out, num_heads, qkv_bias, attn_drop, drop,
            **kwargs
        )

        self.drop_path = DropPath(drop_path) \
            if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim_out)

        dim_mlp_hidden = int(dim_out * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim_out,
            hidden_features=dim_mlp_hidden,
            act_layer=act_layer,
            drop=drop
        )

    def forward(self, x, h, w):
        res = x

        x = self.norm1(x)
        attn = self.attn(x, h, w)
        x = res + self.drop_path(attn)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


# class ConvEmbed(nn.Module):
class ConvEmbed(nn.Layer):
    """ Image to Conv Embedding

    """

    def __init__(self,
                 patch_size=7,
                 in_chans=3,
                 embed_dim=64,
                 stride=4,
                 padding=2,
                 norm_layer=None):
        super().__init__()
        patch_size = to_2tuple(patch_size)
        self.patch_size = patch_size

        # self.proj = nn.Conv2d(
        #     in_chans, embed_dim,
        #     kernel_size=patch_size,
        #     stride=stride,
        #     padding=padding
        # )
        self.proj = nn.Conv2D(
            in_chans, embed_dim,
            kernel_size=patch_size,
            stride=stride,
            padding=padding
        )
        self.norm = norm_layer(embed_dim) if norm_layer else None

    def forward(self, x):
        x = self.proj(x)

        B, C, H, W = x.shape
        x = rearrange(x, 'b c h w -> b (h w) c')
        if self.norm:
            x = self.norm(x)
        x = rearrange(x, 'b (h w) c -> b c h w', h=H, w=W)

        return x


# class VisionTransformer(nn.Module):
class VisionTransformer(nn.Layer):
    """ Vision Transformer with support for patch or hybrid CNN input stage
    """
    def __init__(self,
                 patch_size=16,
                 patch_stride=16,
                 patch_padding=0,
                 in_chans=3,
                 embed_dim=768,
                 depth=12,
                 num_heads=12,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 init='trunc_norm',
                 **kwargs):
        super().__init__()
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models

        self.rearrage = None

        self.patch_embed = ConvEmbed(
            # img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            stride=patch_stride,
            padding=patch_padding,
            embed_dim=embed_dim,
            norm_layer=norm_layer
        )

        with_cls_token = kwargs['with_cls_token']
        if with_cls_token:
            # self.cls_token = nn.Parameter(
            #     torch.zeros(1, 1, embed_dim)
            # )
            self.cls_token = paddle.create_parameter(shape=[1, 1, embed_dim],
                                                     dtype='float32',
                                                     default_initializer=nn.initializer.Constant(value=0.0))
        else:
            self.cls_token = None

        self.pos_drop = nn.Dropout(p=drop_rate)
        # dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        dpr = [x.item() for x in paddle.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule

        blocks = []
        for j in range(depth):
            blocks.append(
                Block(
                    dim_in=embed_dim,
                    dim_out=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[j],
                    act_layer=act_layer,
                    norm_layer=norm_layer,
                    **kwargs
                )
            )
        # self.blocks = nn.ModuleList(blocks)
        self.blocks = nn.LayerList(blocks)

        if self.cls_token is not None:
            # trunc_normal_(self.cls_token, std=.02)
            nn.initializer.TruncatedNormal(std=0.02)(self.cls_token)

        if init == 'xavier':
            self.apply(self._init_weights_xavier)
        else:
            self.apply(self._init_weights_trunc_normal)

    def _init_weights_trunc_normal(self, m):
        if isinstance(m, nn.Linear):
            logging.info('=> init weight of Linear from trunc norm')
            # trunc_normal_(m.weight, std=0.02)
            nn.initializer.TruncatedNormal(std=0.02)(m.weight)
            if m.bias is not None:
                logging.info('=> init bias of Linear to zeros')
                # nn.init.constant_(m.bias, 0)
                nn.initializer.Constant(value=0.0)(m.bias)
        # elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
        elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2D)):
            # nn.init.constant_(m.bias, 0)
            # nn.init.constant_(m.weight, 1.0)
            nn.initializer.Constant(value=0.0)(m.bias)
            nn.initializer.Constant(value=1.0)(m.weight)

    def _init_weights_xavier(self, m):
        if isinstance(m, nn.Linear):
            logging.info('=> init weight of Linear from xavier uniform')
            # nn.init.xavier_uniform_(m.weight)
            nn.initializer.XavierUniform()(m.weight)
            if m.bias is not None:
                logging.info('=> init bias of Linear to zeros')
                # nn.init.constant_(m.bias, 0)
                nn.initializer.Constant(value=0.0)(m.bias)
        # elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
        elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2D)):
            # nn.init.constant_(m.bias, 0)
            # nn.init.constant_(m.weight, 1.0)
            nn.initializer.Constant(value=0.0)(m.bias)
            nn.initializer.Constant(value=1.0)(m.weight)

    def forward(self, x):
        x = self.patch_embed(x)
        # B, C, H, W = x.size()
        B, C, H, W = x.shape

        x = rearrange(x, 'b c h w -> b (h w) c')

        cls_tokens = None
        if self.cls_token is not None:
            # stole cls_tokens impl from Phil Wang, thanks
            # cls_tokens = self.cls_token.expand(B, -1, -1)
            cls_tokens = paddle.expand(self.cls_token, shape=[B, -1, -1])
            # x = torch.cat((cls_tokens, x), dim=1)
            x = paddle.concat([cls_tokens, x], axis=1)

        x = self.pos_drop(x)

        for i, blk in enumerate(self.blocks):
            x = blk(x, H, W)

        if self.cls_token is not None:
            # cls_tokens, x = torch.split(x, [1, H*W], 1)
            cls_tokens, x = paddle.split(x, [1, H*W], axis=1)
        x = rearrange(x, 'b (h w) c -> b c h w', h=H, w=W)

        return x, cls_tokens


# class ConvolutionalVisionTransformer(nn.Module):
class ConvolutionalVisionTransformer(nn.Layer):
    def __init__(self,
                 in_chans=3,
                 num_classes=1000,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 init='trunc_norm',
                 spec=None):
        super().__init__()
        self.num_classes = num_classes

        self.num_stages = spec['NUM_STAGES']
        for i in range(self.num_stages):
            kwargs = {
                'patch_size': spec['PATCH_SIZE'][i],
                'patch_stride': spec['PATCH_STRIDE'][i],
                'patch_padding': spec['PATCH_PADDING'][i],
                'embed_dim': spec['DIM_EMBED'][i],
                'depth': spec['DEPTH'][i],
                'num_heads': spec['NUM_HEADS'][i],
                'mlp_ratio': spec['MLP_RATIO'][i],
                'qkv_bias': spec['QKV_BIAS'][i],
                'drop_rate': spec['DROP_RATE'][i],
                'attn_drop_rate': spec['ATTN_DROP_RATE'][i],
                'drop_path_rate': spec['DROP_PATH_RATE'][i],
                'with_cls_token': spec['CLS_TOKEN'][i],
                'method': spec['QKV_PROJ_METHOD'][i],
                'kernel_size': spec['KERNEL_QKV'][i],
                'padding_q': spec['PADDING_Q'][i],
                'padding_kv': spec['PADDING_KV'][i],
                'stride_kv': spec['STRIDE_KV'][i],
                'stride_q': spec['STRIDE_Q'][i],
            }

            stage = VisionTransformer(
                in_chans=in_chans,
                init=init,
                act_layer=act_layer,
                norm_layer=norm_layer,
                **kwargs
            )
            setattr(self, f'stage{i}', stage)

            in_chans = spec['DIM_EMBED'][i]

        dim_embed = spec['DIM_EMBED'][-1]
        self.norm = norm_layer(dim_embed)
        self.cls_token = spec['CLS_TOKEN'][-1]

        # Classifier head
        self.head = nn.Linear(dim_embed, num_classes) if num_classes > 0 else nn.Identity()
        # trunc_normal_(self.head.weight, std=0.02)
        nn.initializer.TruncatedNormal(std=0.02)(self.head.weight)

    # def init_weights(self, pretrained='', pretrained_layers=[], verbose=True):
    #     if os.path.isfile(pretrained):
    #         pretrained_dict = torch.load(pretrained, map_location='cpu')
    #         logging.info(f'=> loading pretrained model {pretrained}')
    #         model_dict = self.state_dict()
    #         pretrained_dict = {
    #             k: v for k, v in pretrained_dict.items()
    #             if k in model_dict.keys()
    #         }
    #         need_init_state_dict = {}
    #         for k, v in pretrained_dict.items():
    #             need_init = (
    #                     k.split('.')[0] in pretrained_layers
    #                     or pretrained_layers[0] is '*'
    #             )
    #             if need_init:
    #                 if verbose:
    #                     logging.info(f'=> init {k} from {pretrained}')
    #                 if 'pos_embed' in k and v.size() != model_dict[k].size():
    #                     size_pretrained = v.size()
    #                     size_new = model_dict[k].size()
    #                     logging.info(
    #                         '=> load_pretrained: resized variant: {} to {}'
    #                         .format(size_pretrained, size_new)
    #                     )
    #
    #                     ntok_new = size_new[1]
    #                     ntok_new -= 1
    #
    #                     posemb_tok, posemb_grid = v[:, :1], v[0, 1:]
    #
    #                     gs_old = int(np.sqrt(len(posemb_grid)))
    #                     gs_new = int(np.sqrt(ntok_new))
    #
    #                     logging.info(
    #                         '=> load_pretrained: grid-size from {} to {}'
    #                         .format(gs_old, gs_new)
    #                     )
    #
    #                     posemb_grid = posemb_grid.reshape(gs_old, gs_old, -1)
    #                     zoom = (gs_new / gs_old, gs_new / gs_old, 1)
    #                     posemb_grid = scipy.ndimage.zoom(
    #                         posemb_grid, zoom, order=1
    #                     )
    #                     posemb_grid = posemb_grid.reshape(1, gs_new ** 2, -1)
    #                     v = torch.tensor(
    #                         np.concatenate([posemb_tok, posemb_grid], axis=1)
    #                     )
    #
    #                 need_init_state_dict[k] = v
    #         self.load_state_dict(need_init_state_dict, strict=False)

    # @torch.jit.ignore
    def no_weight_decay(self):
        layers = set()
        for i in range(self.num_stages):
            layers.add(f'stage{i}.pos_embed')
            layers.add(f'stage{i}.cls_token')

        return layers

    def forward_features(self, x):
        cls_tokens = None
        for i in range(self.num_stages):
            x, cls_tokens = getattr(self, f'stage{i}')(x)

        if self.cls_token:
            x = self.norm(cls_tokens)
            # x = torch.squeeze(x)
            x = paddle.squeeze(x)
        else:
            x = rearrange(x, 'b c h w -> b (h w) c')
            x = self.norm(x)
            # x = torch.mean(x, dim=1)
            x = paddle.mean(x, axis=1)

        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)

        return x


# @register_model
# def get_cls_model(config, **kwargs):
def get_cls_model():
    # msvit_spec = config.MODEL.SPEC
    msvit_spec = dict(
        INIT='trunc_norm',
        NUM_STAGES=3,
        PATCH_SIZE=[7, 3, 3],
        PATCH_STRIDE=[4, 2, 2],
        PATCH_PADDING=[2, 1, 1],
        DIM_EMBED=[64, 192, 384],
        NUM_HEADS=[1, 3, 6],
        DEPTH=[1, 2, 10],
        MLP_RATIO=[4.0, 4.0, 4.0],
        ATTN_DROP_RATE=[0.0, 0.0, 0.0],
        DROP_RATE=[0.0, 0.0, 0.0],
        DROP_PATH_RATE=[0.0, 0.0, 0.1],
        QKV_BIAS=[True, True, True],
        CLS_TOKEN=[False, False, True],
        POS_EMBED=[False, False, False],
        QKV_PROJ_METHOD=['dw_bn', 'dw_bn', 'dw_bn'],
        KERNEL_QKV=[3, 3, 3],
        PADDING_KV=[1, 1, 1],
        STRIDE_KV=[2, 2, 2],
        PADDING_Q=[1, 1, 1],
        STRIDE_Q=[1, 1, 1],
    )
    msvit = ConvolutionalVisionTransformer(
        in_chans=3,
        # num_classes=config.MODEL.NUM_CLASSES,
        num_classes=1000,
        act_layer=QuickGELU,
        # norm_layer=partial(LayerNorm, eps=1e-5),
        norm_layer=partial(nn.LayerNorm, epsilon=1e-5),
        # init=getattr(msvit_spec, 'INIT', 'trunc_norm'),
        init=msvit_spec['INIT'],
        spec=msvit_spec
    )

    # if config.MODEL.INIT_WEIGHTS:
    #     msvit.init_weights(
    #         config.MODEL.PRETRAINED,
    #         config.MODEL.PRETRAINED_LAYERS,
    #         config.VERBOSE
    #     )

    return msvit


if __name__ == "__main__":
    model = get_cls_model()
    print(model)
    x = paddle.randn(shape=[2, 3, 224, 224])
    out = model(x)
    print(out.shape)
    print(out)