Exp3_Kuroshio_forecasting/model/modules_api/evolution.py

import torch
from torch import nn
import math
import torch
import torch.nn as nn
from timm.layers import DropPath, trunc_normal_
from timm.models.convnext import ConvNeXtBlock
from timm.models.mlp_mixer import MixerBlock
from timm.models.swin_transformer import SwinTransformerBlock, window_partition, window_reverse
from timm.models.vision_transformer import Block as ViTBlock

from model.modules_api.layers import (HorBlock, ChannelAggregationFFN, MultiOrderGatedAggregation,
                     PoolFormerBlock, CBlock, SABlock, MixMlp, VANBlock)


class BasicConv2d(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size=3,
                 stride=1,
                 padding=0,
                 dilation=1,
                 upsampling=False,
                 act_norm=False,
                 act_inplace=True):
        super(BasicConv2d, self).__init__()
        self.act_norm = act_norm
        if upsampling is True:
            self.conv = nn.Sequential(*[
                nn.Conv2d(in_channels, out_channels*4, kernel_size=kernel_size,
                          stride=1, padding=padding, dilation=dilation),
                nn.PixelShuffle(2)
            ])
        else:
            self.conv = nn.Conv2d(
                in_channels, out_channels, kernel_size=kernel_size,
                stride=stride, padding=padding, dilation=dilation)

        self.norm = nn.GroupNorm(2, out_channels)
        self.act = nn.SiLU(inplace=act_inplace)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d)):
            trunc_normal_(m.weight, std=.02)
            nn.init.constant_(m.bias, 0)

    def forward(self, x):
        y = self.conv(x)
        if self.act_norm:
            y = self.act(self.norm(y))
        return y


class ConvSC(nn.Module):
    def __init__(self,
                 C_in,
                 C_out,
                 kernel_size=3,
                 downsampling=False,
                 upsampling=False,
                 act_norm=True,
                 act_inplace=True):
        super(ConvSC, self).__init__()

        stride = 2 if downsampling is True else 1
        padding = (kernel_size - stride + 1) // 2

        self.conv = BasicConv2d(C_in, C_out, kernel_size=kernel_size, stride=stride,
                                upsampling=upsampling, padding=padding,
                                act_norm=act_norm, act_inplace=act_inplace)

    def forward(self, x):
        y = self.conv(x)
        return y


class GroupConv2d(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size=3,
                 stride=1,
                 padding=0,
                 groups=1,
                 act_norm=False,
                 act_inplace=True):
        super(GroupConv2d, self).__init__()
        self.act_norm=act_norm
        if in_channels % groups != 0:
            groups=1
        self.conv = nn.Conv2d(
            in_channels, out_channels, kernel_size=kernel_size,
            stride=stride, padding=padding, groups=groups)
        self.norm = nn.GroupNorm(groups,out_channels)
        self.activate = nn.LeakyReLU(0.2, inplace=act_inplace)

    def forward(self, x):
        y = self.conv(x)
        if self.act_norm:
            y = self.activate(self.norm(y))
        return y


class gInception_ST(nn.Module):
    """A IncepU block for SimVP"""

    def __init__(self, C_in, C_hid, C_out, incep_ker = [3,5,7,11], groups = 8):        
        super(gInception_ST, self).__init__()
        self.conv1 = nn.Conv2d(C_in, C_hid, kernel_size=1, stride=1, padding=0)

        layers = []
        for ker in incep_ker:
            layers.append(GroupConv2d(
                C_hid, C_out, kernel_size=ker, stride=1,
                padding=ker//2, groups=groups, act_norm=True))
        self.layers = nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        y = 0
        for layer in self.layers:
            y += layer(x)
        return y


class AttentionModule(nn.Module):
    """Large Kernel Attention for SimVP"""

    def __init__(self, dim, kernel_size, dilation=3):
        super().__init__()
        d_k = 2 * dilation - 1
        d_p = (d_k - 1) // 2
        dd_k = kernel_size // dilation + ((kernel_size // dilation) % 2 - 1)
        dd_p = (dilation * (dd_k - 1) // 2)

        self.conv0 = nn.Conv2d(dim, dim, d_k, padding=d_p, groups=dim)
        self.conv_spatial = nn.Conv2d(
            dim, dim, dd_k, stride=1, padding=dd_p, groups=dim, dilation=dilation)
        self.conv1 = nn.Conv2d(dim, 2*dim, 1)

    def forward(self, x):
        u = x.clone()
        attn = self.conv0(x)           # depth-wise conv
        attn = self.conv_spatial(attn) # depth-wise dilation convolution
        
        f_g = self.conv1(attn)
        split_dim = f_g.shape[1] // 2
        f_x, g_x = torch.split(f_g, split_dim, dim=1)
        return torch.sigmoid(g_x) * f_x


class SpatialAttention(nn.Module):
    """A Spatial Attention block for SimVP"""

    def __init__(self, d_model, kernel_size=21, attn_shortcut=True):
        super().__init__()

        self.proj_1 = nn.Conv2d(d_model, d_model, 1)         # 1x1 conv
        self.activation = nn.GELU()                          # GELU
        self.spatial_gating_unit = AttentionModule(d_model, kernel_size)
        self.proj_2 = nn.Conv2d(d_model, d_model, 1)         # 1x1 conv
        self.attn_shortcut = attn_shortcut

    def forward(self, x):
        if self.attn_shortcut:
            shortcut = x.clone()
        x = self.proj_1(x)
        x = self.activation(x)
        x = self.spatial_gating_unit(x)
        x = self.proj_2(x)
        if self.attn_shortcut:
            x = x + shortcut
        return x


class GASubBlock(nn.Module):
    """A GABlock (gSTA) for SimVP"""

    def __init__(self, dim, kernel_size=21, mlp_ratio=4.,
                 drop=0., drop_path=0.1, init_value=1e-2, act_layer=nn.GELU):
        super().__init__()
        self.norm1 = nn.BatchNorm2d(dim)
        self.attn = SpatialAttention(dim, kernel_size)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        self.norm2 = nn.BatchNorm2d(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = MixMlp(
            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        self.layer_scale_1 = nn.Parameter(init_value * torch.ones((dim)), requires_grad=True)
        self.layer_scale_2 = nn.Parameter(init_value * torch.ones((dim)), requires_grad=True)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'layer_scale_1', 'layer_scale_2'}

    def forward(self, x):
        x = x + self.drop_path(
            self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * self.attn(self.norm1(x)))
        x = x + self.drop_path(
            self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * self.mlp(self.norm2(x)))
        return x


class ConvMixerSubBlock(nn.Module):
    """A block of ConvMixer."""

    def __init__(self, dim, kernel_size=9, activation=nn.GELU):
        super().__init__()
        # spatial mixing
        self.conv_dw = nn.Conv2d(dim, dim, kernel_size, groups=dim, padding="same")
        self.act_1 = activation()
        self.norm_1 = nn.BatchNorm2d(dim)
        # channel mixing
        self.conv_pw = nn.Conv2d(dim, dim, kernel_size=1)
        self.act_2 = activation()
        self.norm_2 = nn.BatchNorm2d(dim)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.BatchNorm2d):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    @torch.jit.ignore
    def no_weight_decay(self):
        return dict()

    def forward(self, x):
        x = x + self.norm_1(self.act_1(self.conv_dw(x)))
        x = self.norm_2(self.act_2(self.conv_pw(x)))
        return x


class ConvNeXtSubBlock(ConvNeXtBlock):
    """A block of ConvNeXt."""

    def __init__(self, dim, mlp_ratio=4., drop=0., drop_path=0.1):
        super().__init__(dim, mlp_ratio=mlp_ratio,
                         drop_path=drop_path, ls_init_value=1e-6, conv_mlp=True)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'gamma'}

    def forward(self, x):
        x = x + self.drop_path(
            self.gamma.reshape(1, -1, 1, 1) * self.mlp(self.norm(self.conv_dw(x))))
        return x


class HorNetSubBlock(HorBlock):
    """A block of HorNet."""

    def __init__(self, dim, mlp_ratio=4., drop_path=0.1, init_value=1e-6):
        super().__init__(dim, mlp_ratio=mlp_ratio, drop_path=drop_path, init_value=init_value)
        self.apply(self._init_weights)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'gamma1', 'gamma2'}

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()


class MLPMixerSubBlock(MixerBlock):
    """A block of MLP-Mixer."""

    def __init__(self, dim, input_resolution=None, mlp_ratio=4., drop=0., drop_path=0.1):
        seq_len = input_resolution[0] * input_resolution[1]
        super().__init__(dim, seq_len=seq_len,
                         mlp_ratio=(0.5, mlp_ratio), drop_path=drop_path, drop=drop)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return dict()

    def forward(self, x):
        B, C, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = x + self.drop_path(self.mlp_tokens(self.norm1(x).transpose(1, 2)).transpose(1, 2))
        x = x + self.drop_path(self.mlp_channels(self.norm2(x)))
        return x.reshape(B, H, W, C).permute(0, 3, 1, 2)


class MogaSubBlock(nn.Module):
    """A block of MogaNet."""

    def __init__(self, embed_dims, mlp_ratio=4., drop_rate=0., drop_path_rate=0., init_value=1e-5,
                 attn_dw_dilation=[1, 2, 3], attn_channel_split=[1, 3, 4]):
        super(MogaSubBlock, self).__init__()
        self.out_channels = embed_dims
        # spatial attention
        self.norm1 = nn.BatchNorm2d(embed_dims)
        self.attn = MultiOrderGatedAggregation(
            embed_dims, attn_dw_dilation=attn_dw_dilation, attn_channel_split=attn_channel_split)
        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
        # channel MLP
        self.norm2 = nn.BatchNorm2d(embed_dims)
        mlp_hidden_dims = int(embed_dims * mlp_ratio)
        self.mlp = ChannelAggregationFFN(
            embed_dims=embed_dims, mlp_hidden_dims=mlp_hidden_dims, ffn_drop=drop_rate)
        # init layer scale
        self.layer_scale_1 = nn.Parameter(init_value * torch.ones((1, embed_dims, 1, 1)), requires_grad=True)
        self.layer_scale_2 = nn.Parameter(init_value * torch.ones((1, embed_dims, 1, 1)), requires_grad=True)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'layer_scale_1', 'layer_scale_2', 'sigma'}

    def forward(self, x):
        x = x + self.drop_path(self.layer_scale_1 * self.attn(self.norm1(x)))
        x = x + self.drop_path(self.layer_scale_2 * self.mlp(self.norm2(x)))
        return x


class PoolFormerSubBlock(PoolFormerBlock):
    """A block of PoolFormer."""

    def __init__(self, dim, mlp_ratio=4., drop=0., drop_path=0.1):
        super().__init__(dim, pool_size=3, mlp_ratio=mlp_ratio, drop_path=drop_path,
                         drop=drop, init_value=1e-5)
        self.apply(self._init_weights)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'layer_scale_1', 'layer_scale_2'}

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)


class SwinSubBlock(SwinTransformerBlock):
    """A block of Swin Transformer."""

    def __init__(self, dim, input_resolution=None, layer_i=0, mlp_ratio=4., drop=0., drop_path=0.1):
        window_size = 7 if input_resolution[0] % 7 == 0 else max(4, input_resolution[0] // 16)
        window_size = min(8, window_size)
        shift_size = 0 if (layer_i % 2 == 0) else window_size // 2
        super().__init__(dim, input_resolution, num_heads=8, window_size=window_size,
                         shift_size=shift_size, mlp_ratio=mlp_ratio,
                         drop_path=drop_path, attn_drop=drop, proj_drop=drop, qkv_bias=True)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {}

    def forward(self, x):
        B, C, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = self.norm1(x)
        x = x.view(B, H, W, C)
        x = super().forward(x)

        return x.reshape(B, H, W, C).permute(0, 3, 1, 2)



def UniformerSubBlock(embed_dims, mlp_ratio=4., drop=0., drop_path=0.,
                      init_value=1e-6, block_type='Conv'):
    """Build a block of Uniformer."""
    assert block_type in ['Conv', 'MHSA']
    if block_type == 'Conv':
        return SABlock(dim=embed_dims, num_heads=8, mlp_ratio=mlp_ratio, qkv_bias=True,
                       drop=drop, drop_path=drop_path, init_value=init_value)
    else:
        return SABlock(dim=embed_dims, num_heads=8, mlp_ratio=mlp_ratio, qkv_bias=True,
                       drop=drop, drop_path=drop_path, init_value=init_value)


class VANSubBlock(VANBlock):
    """A block of VAN."""

    def __init__(self, dim, mlp_ratio=4., drop=0.,drop_path=0., init_value=1e-2, act_layer=nn.GELU):
        super().__init__(dim=dim, mlp_ratio=mlp_ratio, drop=drop, drop_path=drop_path,
                         init_value=init_value, act_layer=act_layer)
        self.apply(self._init_weights)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'layer_scale_1', 'layer_scale_2'}

    def _init_weights(self, m):
        if isinstance(m, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()


class ViTSubBlock(ViTBlock):
    """A block of Vision Transformer."""

    def __init__(self, dim, mlp_ratio=4., drop=0., drop_path=0.1):
        super().__init__(dim=dim, num_heads=8, mlp_ratio=mlp_ratio, qkv_bias=True,
                         attn_drop=drop, proj_drop=0, drop_path=drop_path, act_layer=nn.GELU, norm_layer=nn.LayerNorm)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {}

    def forward(self, x):
        B, C, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x.reshape(B, H, W, C).permute(0, 3, 1, 2)
    

class TemporalAttention(nn.Module):
    """A Temporal Attention block for Temporal Attention Unit"""

    def __init__(self, d_model, kernel_size=21, attn_shortcut=True):
        super().__init__()

        self.proj_1 = nn.Conv2d(d_model, d_model, 1)         # 1x1 conv
        self.activation = nn.GELU()                          # GELU
        self.spatial_gating_unit = TemporalAttentionModule(d_model, kernel_size)
        self.proj_2 = nn.Conv2d(d_model, d_model, 1)         # 1x1 conv
        self.attn_shortcut = attn_shortcut

    def forward(self, x):
        if self.attn_shortcut:
            shortcut = x.clone()
        x = self.proj_1(x)
        x = self.activation(x)
        x = self.spatial_gating_unit(x)
        x = self.proj_2(x)
        if self.attn_shortcut:
            x = x + shortcut
        return x
    

class TemporalAttentionModule(nn.Module):
    """Large Kernel Attention for SimVP"""

    def __init__(self, dim, kernel_size, dilation=3, reduction=16):
        super().__init__()
        d_k = 2 * dilation - 1
        d_p = (d_k - 1) // 2
        dd_k = kernel_size // dilation + ((kernel_size // dilation) % 2 - 1)
        dd_p = (dilation * (dd_k - 1) // 2)

        self.conv0 = nn.Conv2d(dim, dim, d_k, padding=d_p, groups=dim)
        self.conv_spatial = nn.Conv2d(
            dim, dim, dd_k, stride=1, padding=dd_p, groups=dim, dilation=dilation)
        self.conv1 = nn.Conv2d(dim, dim, 1)

        self.reduction = max(dim // reduction, 4)
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(dim, dim // self.reduction, bias=False), # reduction
            nn.ReLU(True),
            nn.Linear(dim // self.reduction, dim, bias=False), # expansion
            nn.Sigmoid()
        )

    def forward(self, x):
        u = x.clone()
        attn = self.conv0(x)           # depth-wise conv
        attn = self.conv_spatial(attn) # depth-wise dilation convolution
        f_x = self.conv1(attn)         # 1x1 conv
        # append a se operation
        b, c, _, _ = x.size()
        se_atten = self.avg_pool(x).view(b, c)
        se_atten = self.fc(se_atten).view(b, c, 1, 1)
        return se_atten * f_x * u


class TAUSubBlock(GASubBlock):
    """A TAUBlock (tau) for Temporal Attention Unit"""

    def __init__(self, dim, kernel_size=21, mlp_ratio=4.,
                 drop=0., drop_path=0.1, init_value=1e-2, act_layer=nn.GELU):
        super().__init__(dim=dim, kernel_size=kernel_size, mlp_ratio=mlp_ratio,
                 drop=drop, drop_path=drop_path, init_value=init_value, act_layer=act_layer)
        
        self.attn = TemporalAttention(dim, kernel_size)


class Evo_Block(nn.Module):
    def __init__(self, in_channels, out_channels, input_resolution=None, model_type=None,
                 mlp_ratio=8., drop=0.0, drop_path=0.0, layer_i=0):
        super(Evo_Block, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        model_type = model_type.lower() if model_type is not None else 'gsta'

        if model_type == 'gsta':
            self.block = GASubBlock(
                in_channels, kernel_size=21, mlp_ratio=mlp_ratio,
                drop=drop, drop_path=drop_path, act_layer=nn.GELU)
        elif model_type == 'convmixer':
            self.block = ConvMixerSubBlock(in_channels, kernel_size=11, activation=nn.GELU)
        elif model_type == 'convnext':
            self.block = ConvNeXtSubBlock(
                in_channels, mlp_ratio=mlp_ratio, drop=drop, drop_path=drop_path)
        elif model_type == 'uniformer':
            block_type = 'MHSA' if in_channels == out_channels and layer_i > 0 else 'Conv'
            self.block = UniformerSubBlock(
                in_channels, mlp_ratio=mlp_ratio, drop=drop,
                drop_path=drop_path, block_type=block_type)
        elif model_type == 'vit':
            self.block = ViTSubBlock(
                in_channels, mlp_ratio=mlp_ratio, drop=drop, drop_path=drop_path)
        elif model_type == 'poolformer':
            self.block = PoolFormerSubBlock(
                in_channels, mlp_ratio=mlp_ratio, drop=drop, drop_path=drop_path)
        else:
            assert False and "Error"

        if in_channels != out_channels:
            self.reduction = nn.Conv2d(
                in_channels, out_channels, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        z = self.block(x)
        return z if self.in_channels == self.out_channels else self.reduction(z)

class Spatio_temporal_evolution(nn.Module):
    def __init__(self, channel_in, channel_hid, N2,
                 input_resolution=None, model_type=None,
                 mlp_ratio=4., drop=0.0, drop_path=0.1):
        super(Spatio_temporal_evolution, self).__init__()
        assert N2 >= 2 and mlp_ratio > 1
        self.N2 = N2
        dpr = [x.item() for x in torch.linspace(1e-2, drop_path, self.N2)]

        # down-sampling
        enc_layers = [Evo_Block(
            channel_in, channel_hid, input_resolution, model_type,
            mlp_ratio, drop, drop_path=dpr[0], layer_i=0)]

        # state-stacking
        for i in range(1, N2-1):
            enc_layers.append(Evo_Block(
                channel_hid, channel_hid, input_resolution, model_type,
                mlp_ratio, drop, drop_path=dpr[i], layer_i=i))

        # up-sampling
        enc_layers.append(Evo_Block(
            channel_hid, channel_in, input_resolution, model_type,
            mlp_ratio, drop, drop_path=drop_path, layer_i=N2-1))
        self.enc = nn.Sequential(*enc_layers)

    def forward(self, x):
        B, T, C, H, W = x.shape
        x = x.reshape(B, T * C, H, W)

        z = x
        for i in range(self.N2):
            z = self.enc[i](z)

        y = z.reshape(B, T, C, H, W)
        return y

if __name__ == '__main__':
    x = torch.randn([1, 1, 69, 180, 360])
    print("input shape:", x.shape)

    model = Spatio_temporal_evolution(channel_in=69,
                                      channel_hid=256,
                                      N2=4,
                                      input_resolution = [64, 64],
                                      model_type='poolformer',
                                      mlp_ratio=4.,
                                      drop=0.0,
                                      drop_path=0.1)
    print(model)
    output = model(x)
    print("output shape:", output.shape)