Pyramid Transformer (Version 2) has been proposed with four stages and the author using the iteration method to iterate four stages. I would like to delete this iteration method and mentioned four stages. It will helpful for me to analyze each stage easily.
Orginal Implementation Please check class PyramidVisionTransformerV2
  

      github.com
  

  
    whai362/PVT/blob/v2/classification/pvt_v2.py 2


    import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial

from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
import math


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., linear=False):
        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.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)



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My implementation divided each stage using indexing
class PyramidVisionTransformerV2(nn.Module):
    def __init__(self, *, img_size=112, patch_size=8, loss_type, GPU_ID, in_chans=3, num_classes=1000,
                 embed_dims=[64, 128, 256, 512],
                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
                 attn_drop_rate=0., drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6),
                 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], num_stages=4, linear=True):


        super().__init__()
        self.num_classes = num_classes
        self.depths = depths
        self.num_stages = num_stages

        # patch_embed
        self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_chans=in_chans, embed_dim=embed_dims[0])
        self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0], embed_dim=embed_dims[1])
        self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1], embed_dim=embed_dims[2])
        self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2],embed_dim=embed_dims[3])

        self.pos_embed1 = nn.Parameter(torch.zeros(1, self.patch_embed1.num_patches, embed_dims[0]))
        self.pos_drop1 = nn.Dropout(p=drop_rate)
        self.pos_embed2 = nn.Parameter(torch.zeros(1, self.patch_embed2.num_patches, embed_dims[1]))
        self.pos_drop2 = nn.Dropout(p=drop_rate)
        self.pos_embed3 = nn.Parameter(torch.zeros(1, self.patch_embed3.num_patches, embed_dims[2]))
        self.pos_drop3 = nn.Dropout(p=drop_rate)
        self.pos_embed4 = nn.Parameter(torch.zeros(1, self.patch_embed4.num_patches + 1, embed_dims[3]))
        self.pos_drop4 = nn.Dropout(p=drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        cur = 0
        self.block1 = nn.ModuleList([Block(
            dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[0], linear=linear)
            for i in range(depths[0])])

        cur += depths[0]
        self.block2 = nn.ModuleList([Block(
            dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[1], linear=linear)
            for i in range(depths[1])])

        cur += depths[1]
        self.block3 = nn.ModuleList([Block(
            dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[2], linear=linear)
            for i in range(depths[2])])

        cur += depths[2]
        self.block4 = nn.ModuleList([Block(
            dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[3])
            for i in range(depths[3])])
        self.norm = norm_layer(embed_dims[3])

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims[3]))

        self.mlp_head = nn.Sequential(
            nn.LayerNorm(embed_dims[3]),
        )

        self.loss_type = loss_type
        self.GPU_ID = GPU_ID
        if self.loss_type == 'None':
            print("no loss for vit_face")
        else:
            if self.loss_type == 'Softmax':
                self.loss = Softmax(in_features=embed_dims[3], out_features=num_classes, device_id=self.GPU_ID)
            elif self.loss_type == 'CosFace':
                self.loss = CosFace(in_features=embed_dims[3], out_features=num_classes, device_id=self.GPU_ID)
            elif self.loss_type == 'ArcFace':
                self.loss = ArcFace(in_features=embed_dims[3], out_features=num_classes, device_id=self.GPU_ID)
            elif self.loss_type == 'SFace':
                self.loss = SFaceLoss(in_features=embed_dims[3], out_features=num_classes, device_id=self.GPU_ID)
            elif self.loss_type == 'MagFace':
                self.loss = MagFaceHeader(in_features=embed_dims[3], out_features=num_classes)

        trunc_normal_(self.pos_embed1, std=.02)
        trunc_normal_(self.pos_embed2, std=.02)
        trunc_normal_(self.pos_embed3, std=.02)
        trunc_normal_(self.pos_embed4, std=.02)
        trunc_normal_(self.cls_token, std=.02)

        self.apply(self._init_weights)

    def reset_drop_path(self, drop_path_rate):
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
        cur = 0
        for i in range(self.depths[0]):
            self.block1[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[0]
        for i in range(self.depths[1]):
            self.block2[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[1]
        for i in range(self.depths[2]):
            self.block3[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[2]
        for i in range(self.depths[3]):
            self.block4[i].drop_path.drop_prob = dpr[cur + i]

    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_()

    def freeze_patch_emb(self):
        self.patch_embed1.requires_grad = False

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'}  # has pos_embed may be better

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        B = x.shape[0]

        # stage 1
        x, (H, W) = self.patch_embed1(x)
        x = x + self.pos_embed1
        x = self.pos_drop1(x)
        for blk in self.block1:
            x = blk(x, H, W)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()

        # stage 2
        x, (H, W) = self.patch_embed2(x)
        x = x + self.pos_embed2
        x = self.pos_drop2(x)
        for blk in self.block2:
            x = blk(x, H, W)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()

        # stage 3
        x, (H, W) = self.patch_embed3(x)
        x = x + self.pos_embed3
        x = self.pos_drop3(x)
        for blk in self.block3:
            x = blk(x, H, W)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()

        # stage 4
        x, (H, W) = self.patch_embed4(x)
        cls_tokens = self.cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x = x + self.pos_embed4
        x = self.pos_drop4(x)
        for blk in self.block4:
            x = blk(x, H, W)

        x = self.norm(x)

        return x[:, 0]

    def forward(self, x, label=None, mask=None):
        x = self.forward_features(x)
        emb = self.mlp_head(x)

        if label is not None:
            x = self.loss(emb, label)
            return x, emb
        else:
            return emb

Traceback
Traceback (most recent call last):
  File "/media/khawar/HDD_Khawar/facerectransformer-main/code/train.py", line 231, in <module>
    print_per_layer_stat=True, verbose=True)
  File "/home/khawar/anaconda3/envs/RSC/lib/python3.7/site-packages/ptflops/flops_counter.py", line 43, in get_model_complexity_info
    _ = flops_model(batch)
  File "/home/khawar/anaconda3/envs/RSC/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/media/khawar/HDD_Khawar/facerectransformer-main/code/vit_pytorch/pvt_v2.py", line 424, in forward
    x = self.forward_features(x)
  File "/media/khawar/HDD_Khawar/facerectransformer-main/code/vit_pytorch/pvt_v2.py", line 387, in forward_features
    x, (H, W) = self.patch_embed1(x)
ValueError: too many values to unpack (expected 2)