models/pvt_v2.py

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

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

import math

from config import Config

config = Config()

class Mlp(nn.Module):
 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.dwconv = DWConv(hidden_features)
 self.act = act_layer()
 self.fc2 = nn.Linear(hidden_features, out_features)
 self.drop = nn.Dropout(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.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 forward(self, x, H, W):
 x = self.fc1(x)
 x = self.dwconv(x, H, W)
 x = self.act(x)
 x = self.drop(x)
 x = self.fc2(x)
 x = self.drop(x)
 return x


class Attention(nn.Module):
 def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
 super().__init__()
 assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

 self.dim = dim
 self.num_heads = num_heads
 head_dim = dim // num_heads
 self.scale = qk_scale or head_dim ** -0.5

 self.q = nn.Linear(dim, dim, bias=qkv_bias)
 self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
 self.attn_drop_prob = attn_drop
 self.attn_drop = nn.Dropout(attn_drop)
 self.proj = nn.Linear(dim, dim)
 self.proj_drop = nn.Dropout(proj_drop)

 self.sr_ratio = sr_ratio
 if sr_ratio > 1:
 self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
 self.norm = nn.LayerNorm(dim)

 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_()

 def forward(self, x, H, W):
 B, N, C = x.shape
 q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

 if self.sr_ratio > 1:
 x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
 x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
 x_ = self.norm(x_)
 kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
 else:
 kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
 k, v = kv[0], kv[1]

 if config.SDPA_enabled:
 x = torch.nn.functional.scaled_dot_product_attention(
 q, k, v,
 attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
 ).transpose(1, 2).reshape(B, N, C)
 else:
 attn = (q @ k.transpose(-2, -1)) * self.scale
 attn = attn.softmax(dim=-1)
 attn = self.attn_drop(attn)

 x = (attn @ v).transpose(1, 2).reshape(B, N, C)
 x = self.proj(x)
 x = self.proj_drop(x)

 return x


class Block(nn.Module):

 def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
 super().__init__()
 self.norm1 = norm_layer(dim)
 self.attn = Attention(
 dim,
 num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
 attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
 # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
 self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 self.norm2 = norm_layer(dim)
 mlp_hidden_dim = int(dim * mlp_ratio)
 self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, 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.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 forward(self, x, H, W):
 x = x + self.drop_path(self.attn(self.norm1(x), H, W))
 x = x + self.drop_path(self.mlp(self.norm2(x), H, W))

 return x


class OverlapPatchEmbed(nn.Module):
 """ Image to Patch Embedding
 """

 def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
 super().__init__()
 img_size = to_2tuple(img_size)
 patch_size = to_2tuple(patch_size)

 self.img_size = img_size
 self.patch_size = patch_size
 self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
 self.num_patches = self.H * self.W
 self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
 padding=(patch_size[0] // 2, patch_size[1] // 2))
 self.norm = nn.LayerNorm(embed_dim)

 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_()

 def forward(self, x):
 x = self.proj(x)
 _, _, H, W = x.shape
 x = x.flatten(2).transpose(1, 2)
 x = self.norm(x)

 return x, H, W


class PyramidVisionTransformerImpr(nn.Module):
 def __init__(self, img_size=224, patch_size=16, in_channels=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=nn.LayerNorm,
 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
 super().__init__()
 self.num_classes = num_classes
 self.depths = depths

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

 # transformer encoder
 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])
 for i in range(depths[0])])
 self.norm1 = norm_layer(embed_dims[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])
 for i in range(depths[1])])
 self.norm2 = norm_layer(embed_dims[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])
 for i in range(depths[2])])
 self.norm3 = norm_layer(embed_dims[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.norm4 = norm_layer(embed_dims[3])

 # classification head
 # self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 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.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 init_weights(self, pretrained=None):
 if isinstance(pretrained, str):
 logger = 1
 #load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)

 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 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]
 outs = []

 # stage 1
 x, H, W = self.patch_embed1(x)
 for i, blk in enumerate(self.block1):
 x = blk(x, H, W)
 x = self.norm1(x)
 x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
 outs.append(x)

 # stage 2
 x, H, W = self.patch_embed2(x)
 for i, blk in enumerate(self.block2):
 x = blk(x, H, W)
 x = self.norm2(x)
 x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
 outs.append(x)

 # stage 3
 x, H, W = self.patch_embed3(x)
 for i, blk in enumerate(self.block3):
 x = blk(x, H, W)
 x = self.norm3(x)
 x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
 outs.append(x)

 # stage 4
 x, H, W = self.patch_embed4(x)
 for i, blk in enumerate(self.block4):
 x = blk(x, H, W)
 x = self.norm4(x)
 x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
 outs.append(x)

 return outs

 # return x.mean(dim=1)

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

 return x


class DWConv(nn.Module):
 def __init__(self, dim=768):
 super(DWConv, self).__init__()
 self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

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

 return x


def _conv_filter(state_dict, patch_size=16):
 """ convert patch embedding weight from manual patchify + linear proj to conv"""
 out_dict = {}
 for k, v in state_dict.items():
 if 'patch_embed.proj.weight' in k:
 v = v.reshape((v.shape[0], 3, patch_size, patch_size))
 out_dict[k] = v

 return out_dict


## @register_model
class pvt_v2_b0(PyramidVisionTransformerImpr):
 def __init__(self, **kwargs):
 super(pvt_v2_b0, self).__init__(
 patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
 qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
 drop_rate=0.0, drop_path_rate=0.1)



## @register_model
class pvt_v2_b1(PyramidVisionTransformerImpr):
 def __init__(self, **kwargs):
 super(pvt_v2_b1, self).__init__(
 patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
 qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
 drop_rate=0.0, drop_path_rate=0.1)

## @register_model
class pvt_v2_b2(PyramidVisionTransformerImpr):
 def __init__(self, in_channels=3, **kwargs):
 super(pvt_v2_b2, self).__init__(
 patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
 qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
 drop_rate=0.0, drop_path_rate=0.1, in_channels=in_channels)

## @register_model
class pvt_v2_b3(PyramidVisionTransformerImpr):
 def __init__(self, **kwargs):
 super(pvt_v2_b3, self).__init__(
 patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
 qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
 drop_rate=0.0, drop_path_rate=0.1)

## @register_model
class pvt_v2_b4(PyramidVisionTransformerImpr):
 def __init__(self, **kwargs):
 super(pvt_v2_b4, self).__init__(
 patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
 qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
 drop_rate=0.0, drop_path_rate=0.1)


## @register_model
class pvt_v2_b5(PyramidVisionTransformerImpr):
 def __init__(self, **kwargs):
 super(pvt_v2_b5, self).__init__(
 patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
 qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
 drop_rate=0.0, drop_path_rate=0.1)