Delete eva_vit.py

eva_vit.py

@@ -1,493 +0,0 @@
-# Based on EVA, BEIT, timm and DeiT code bases
-# https://github.com/baaivision/EVA
-# https://github.com/rwightman/pytorch-image-models/tree/master/timm
-# https://github.com/microsoft/unilm/tree/master/beit
-# https://github.com/facebookresearch/deit/
-# https://github.com/facebookresearch/dino
-# --------------------------------------------------------'
-import math
-from functools import partial
-from torch.nn import LayerNorm
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import drop_path, to_2tuple, trunc_normal_
-
-
-
-
-def _cfg(url='', **kwargs):
-    return {
-        'url': url,
-        'num_classes': 1000,
-        'input_size': (3, 224, 224),
-        'pool_size': None,
-        'crop_pct': .9,
-        'interpolation': 'bicubic',
-        'mean': (0.5, 0.5, 0.5),
-        'std': (0.5, 0.5, 0.5),
-        **kwargs
-    }
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
-    """
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)
-
-    def extra_repr(self) -> str:
-        return 'p={}'.format(self.drop_prob)
-
-
-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.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)
-        # commit this for the orignal BERT implement
-        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.,
-                 window_size=None,
-                 attn_head_dim=None):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        if attn_head_dim is not None:
-            head_dim = attn_head_dim
-        all_head_dim = head_dim * self.num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
-        if qkv_bias:
-            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
-            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
-        else:
-            self.q_bias = None
-            self.v_bias = None
-
-        if window_size:
-            self.window_size = window_size
-            self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
-            self.relative_position_bias_table = nn.Parameter(torch.zeros(self.num_relative_distance,
-                                                                         num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-            # cls to token & token 2 cls & cls to cls
-
-            # get pair-wise relative position index for each token inside the window
-            coords_h = torch.arange(window_size[0])
-            coords_w = torch.arange(window_size[1])
-            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-            coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-            relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-            relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
-            relative_coords[:, :, 1] += window_size[1] - 1
-            relative_coords[:, :, 0] *= 2 * window_size[1] - 1
-            relative_position_index = \
-                torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype)
-            relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-            relative_position_index[0, 0:] = self.num_relative_distance - 3
-            relative_position_index[0:, 0] = self.num_relative_distance - 2
-            relative_position_index[0, 0] = self.num_relative_distance - 1
-
-            self.register_buffer("relative_position_index", relative_position_index)
-        else:
-            self.window_size = None
-            self.relative_position_bias_table = None
-            self.relative_position_index = None
-
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(all_head_dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, rel_pos_bias=None):
-        B, N, C = x.shape
-        qkv_bias = None
-        if self.q_bias is not None:
-            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
-        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
-        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        if self.relative_position_bias_table is not None:
-            relative_position_bias = \
-                self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-                    self.window_size[0] * self.window_size[1] + 1,
-                    self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
-            relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-            attn = attn + relative_position_bias.unsqueeze(0)
-
-        if rel_pos_bias is not None:
-            attn = attn + rel_pos_bias
-
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
-        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.,
-                 init_values=None,
-                 act_layer=nn.GELU,
-                 norm_layer=nn.LayerNorm,
-                 window_size=None,
-                 attn_head_dim=None):
-        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,
-                              window_size=window_size,
-                              attn_head_dim=attn_head_dim)
-        # 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)
-
-        if init_values is not None and init_values > 0:
-            self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
-            self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
-        else:
-            self.gamma_1, self.gamma_2 = None, None
-
-    def forward(self, x, rel_pos_bias=None):
-        if self.gamma_1 is None:
-            x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
-            x = x + self.drop_path(self.mlp(self.norm2(x)))
-        else:
-            x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
-            x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
-        return x
-
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-
-    def forward(self, x, **kwargs):
-        B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        assert H == self.img_size[0] and W == self.img_size[1], \
-            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)
-        return x
-
-
-class RelativePositionBias(nn.Module):
-    def __init__(self, window_size, num_heads):
-        super().__init__()
-        self.window_size = window_size
-        self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
-        self.relative_position_bias_table = nn.Parameter(torch.zeros(self.num_relative_distance,
-                                                                     num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-        # cls to token & token 2 cls & cls to cls
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(window_size[0])
-        coords_w = torch.arange(window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
-        relative_position_index = \
-            torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
-        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        relative_position_index[0, 0:] = self.num_relative_distance - 3
-        relative_position_index[0:, 0] = self.num_relative_distance - 2
-        relative_position_index[0, 0] = self.num_relative_distance - 1
-
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        # trunc_normal_(self.relative_position_bias_table, std=.02)
-
-    def forward(self):
-        relative_position_bias = \
-            self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-                self.window_size[0] * self.window_size[1] + 1,
-                self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
-        return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-
-
-class VisionTransformerEvaClip(nn.Module):
-    """ Vision Transformer with support for patch or hybrid CNN input stage
-    """
-    def __init__(self,
-                 img_size=224,
-                 patch_size=16,
-                 in_chans=3,
-                 num_classes=1000,
-                 embed_dim=768,
-                 depth=12,
-                 num_heads=12,
-                 mlp_ratio=4.,
-                 qkv_bias=False,
-                 qk_scale=None,
-                 drop_rate=0.,
-                 attn_drop_rate=0.,
-                 drop_path_rate=0.,
-                 norm_layer=nn.LayerNorm,
-                 init_values=None,
-                 use_abs_pos_emb=True,
-                 use_rel_pos_bias=False,
-                 use_shared_rel_pos_bias=False,
-                 use_mean_pooling=True,
-                 init_scale=0.001,
-                 use_checkpoint=False):
-        super().__init__()
-        self.image_size = img_size
-        self.num_classes = num_classes
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-
-        self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
-        num_patches = self.patch_embed.num_patches
-
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
-        if use_abs_pos_emb:
-            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
-        else:
-            self.pos_embed = None
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        if use_shared_rel_pos_bias:
-            self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
-        else:
-            self.rel_pos_bias = None
-        self.use_checkpoint = use_checkpoint
-
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
-        self.use_rel_pos_bias = use_rel_pos_bias
-        self.blocks = nn.ModuleList([
-            Block(dim=embed_dim,
-                  num_heads=num_heads,
-                  mlp_ratio=mlp_ratio,
-                  qkv_bias=qkv_bias,
-                  qk_scale=qk_scale,
-                  drop=drop_rate,
-                  attn_drop=attn_drop_rate,
-                  drop_path=dpr[i],
-                  norm_layer=norm_layer,
-                  init_values=init_values,
-                  window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None) for i in range(depth)
-        ])
-        #         self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
-        #         self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
-        #         self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-        if self.pos_embed is not None:
-            trunc_normal_(self.pos_embed, std=.02)
-        trunc_normal_(self.cls_token, std=.02)
-        # trunc_normal_(self.mask_token, std=.02)
-        #         if isinstance(self.head, nn.Linear):
-        #             trunc_normal_(self.head.weight, std=.02)
-        self.apply(self._init_weights)
-        self.fix_init_weight()
-        self.ln_vision =  LayerNorm(self.num_features)
-
-    def fix_init_weight(self):
-        def rescale(param, layer_id):
-            param.div_(math.sqrt(2.0 * layer_id))
-
-        for layer_id, layer in enumerate(self.blocks):
-            rescale(layer.attn.proj.weight.data, layer_id + 1)
-            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
-
-    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)
-
-    _initialize_weights = _init_weights
-    
-    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):
-        x = self.patch_embed(x)
-        batch_size, seq_len, _ = x.size()
-
-        cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-        x = torch.cat((cls_tokens, x), dim=1)
-        if self.pos_embed is not None:
-            x = x + self.pos_embed
-        x = self.pos_drop(x)
-
-        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, rel_pos_bias)
-            else:
-                x = blk(x, rel_pos_bias)
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        #         x = self.head(x)
-        return x
-
-    def get_intermediate_layers(self, x):
-        x = self.patch_embed(x)
-        batch_size, seq_len, _ = x.size()
-
-        cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-        x = torch.cat((cls_tokens, x), dim=1)
-        if self.pos_embed is not None:
-            x = x + self.pos_embed
-        x = self.pos_drop(x)
-
-        features = []
-        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
-        for blk in self.blocks:
-            x = blk(x, rel_pos_bias)
-            features.append(x)
-
-        return features
-
-    def get_num_layer(self, var_name=""):
-        if var_name in ("cls_token", "mask_token", "pos_embed"):
-            return 0
-        elif var_name.startswith("patch_embed"):
-            return 0
-        elif var_name.startswith("rel_pos_bias"):
-            return len(self.blocks) - 1
-        elif var_name.startswith("blocks"):
-            layer_id = int(var_name.split('.')[1])
-            return layer_id + 1
-        else:
-            return len(self.blocks)
-
-
-def interpolate_pos_embed(model, checkpoint_model):
-    if 'pos_embed' in checkpoint_model:
-        pos_embed_checkpoint = checkpoint_model['pos_embed'].float()
-        embedding_size = pos_embed_checkpoint.shape[-1]
-        num_patches = model.patch_embed.num_patches
-        num_extra_tokens = model.pos_embed.shape[-2] - num_patches
-        # height (== width) for the checkpoint position embedding
-        orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens)**0.5)
-        # height (== width) for the new position embedding
-        new_size = int(num_patches**0.5)
-        # class_token and dist_token are kept unchanged
-        if orig_size != new_size:
-            print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
-            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
-            # only the position tokens are interpolated
-            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
-            pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
-            pos_tokens = torch.nn.functional.interpolate(pos_tokens,
-                                                         size=(new_size, new_size),
-                                                         mode='bicubic',
-                                                         align_corners=False)
-            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
-            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
-            checkpoint_model['pos_embed'] = new_pos_embed
-
-
-def convert_weights_to_fp16(model: nn.Module):
-    """Convert applicable model parameters to fp16"""
-    def _convert_weights_to_fp16(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.half()
-            if l.bias is not None:
-                l.bias.data = l.bias.data.half()
-
-    model.apply(_convert_weights_to_fp16)
-
-
-def create_eva_vit_g(img_size=224, drop_path_rate=0.4, use_checkpoint=False, precision="fp16",         cache_dir="./",):
-    model = VisionTransformerEvaClip(
-        img_size=img_size,
-        patch_size=14,
-        use_mean_pooling=False,
-        embed_dim=1408,
-        depth=39,
-        num_heads=1408 // 88,
-        mlp_ratio=4.3637,
-        qkv_bias=True,
-        drop_path_rate=drop_path_rate,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6),
-        use_checkpoint=use_checkpoint,
-    )
-    cache_path = cache_dir
-    state_dict = torch.load(cache_path+"/eva_vit_g.pth", map_location="cpu")    
-    interpolate_pos_embed(model, state_dict)
-
-    incompatible_keys = model.load_state_dict(state_dict, strict=False)
-    print(incompatible_keys)
-
-    if precision == "fp16":
-        #         model.to("cuda")
-        convert_weights_to_fp16(model)
-    return model
-
-
-if __name__ == "__main__":
-    model = create_eva_vit_g()
-    print (model.num_features)