modeling_satdino.py

# Copyright (c) Facebook, Inc. and its affiliates.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Mostly copy-paste from timm library.
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
import os
import math
from functools import partial

import torch
import torch.nn as nn

from transformers import PreTrainedModel
from .utils import trunc_normal_, get_1d_sincos_pos_embed
from .configuration_satdino import SatDINOConfig

try:    
    from xformers.helpers.timm_sparse_attention import TimmSparseAttention
except:
    TimmSparseAttention = None


def drop_path(x, drop_prob: float = 0., training: bool = False):
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output


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)


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)
        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.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]

        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, attn


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, use_xformers=False):
        super().__init__()
        self.norm1 = norm_layer(dim)

        if TimmSparseAttention is not None and use_xformers:
            # print("Using xFormers attention.")
            self.attn = TimmSparseAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop,
                                            proj_drop=drop)
        else:
            # print("Using timm attention.")
            self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
                                  proj_drop=drop)

        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)

    def forward(self, x, return_attention=False):
        attn_res = self.attn(self.norm1(x))
        if not isinstance(attn_res, tuple):
            attn_res = (attn_res, None)
        y, attn = attn_res

        if return_attention:
            return attn
        x = x + self.drop_path(y)
        x = x + self.drop_path(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__()
        num_patches = (img_size // patch_size) * (img_size // patch_size)
        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):
        B, C, H, W = x.shape
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


class SatDINOModel(PreTrainedModel):
    """ Vision Transformer """
    config_class = SatDINOConfig

    def __init__(self, config):
        super().__init__(config)
        self.num_features = self.embed_dim = config.embed_dim
        self.pos_encoding_method = config.pos_encoding_method

        self.patch_embed = PatchEmbed(
            img_size=config.img_size[0],
            patch_size=config.patch_size,
            in_chans=config.in_chans,
            embed_dim=config.embed_dim
        )
        num_patches = self.patch_embed.num_patches
        self.num_patches = num_patches

        # cls token
        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.embed_dim))
        trunc_normal_(self.cls_token, std=.02)
        self.gsd_register = nn.Parameter(torch.zeros(1, 1, config.embed_dim))
        trunc_normal_(self.gsd_register, std=.02)

        # positional encoding
        if config.pos_encoding_method == "learnable":
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, config.embed_dim))
            trunc_normal_(self.pos_embed, std=.02)
        elif config.pos_encoding_method == "sin_cos":
            positions = torch.arange(num_patches + 2)
            self.pos_embed = get_1d_sincos_pos_embed(config.embed_dim, positions).unsqueeze(0).cuda()

        # define blocks
        norm_layer = partial(nn.LayerNorm, eps=config.norm_layer)
        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.depth)]  # stochastic depth decay rule
        block_kwargs = {
            "dim": config.embed_dim,
            "num_heads": config.num_heads,
            "mlp_ratio": config.mlp_ratio,
            "qkv_bias": config.qkv_bias,
            "qk_scale": config.qk_scale,
            "drop": config.drop_rate,
            "attn_drop": config.attn_drop_rate,
            "norm_layer": norm_layer,
            "use_xformers": config.use_xformers
        }
        self.blocks = nn.ModuleList([Block(drop_path=dpr[i], **block_kwargs) for i in range(config.depth)])

        self.pos_drop = nn.Dropout(p=config.drop_rate)
        self.norm = norm_layer(config.embed_dim)

        # Classifier head
        self.head = nn.Linear(config.embed_dim, config.num_classes) if config.num_classes > 0 else None

        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)

    def interpolate_pos_encoding(self, x, w, h):
        npatch = x.shape[1] - 1
        N = self.pos_embed.shape[1] - 1
        if npatch == N and w == h:
            return self.pos_embed
        class_pos_embed = self.pos_embed[:, 0]
        patch_pos_embed = self.pos_embed[:, 1:-1]
        register_pos_embed = self.pos_embed[:, -1]
        dim = x.shape[-1]
        w0 = w // self.patch_embed.patch_size
        h0 = h // self.patch_embed.patch_size
        # we add a small number to avoid floating point error in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        w0, h0 = w0 + 0.1, h0 + 0.1
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
            scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
            mode='bicubic',
        )
        assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed, register_pos_embed.unsqueeze(0)), dim=1)

    def prepare_tokens(self, x):
        B, nc, w, h = x.shape
        x = self.patch_embed(x)  # patch linear embedding

        # add the [CLS] token to the embed patch tokens
        cls_tokens = self.cls_token.expand(B, -1, -1)
        gsd_register = self.gsd_register.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x, gsd_register), dim=1)

        # add positional encoding to each token
        x = x + self.interpolate_pos_encoding(x, w, h)

        return self.pos_drop(x)

    def forward(self, x, return_all=False, return_registers=False):
        x = self.prepare_tokens(x)
        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)

        if return_all:
            return x

        if return_registers:
            return x[:, 0], x[:, -1]

        return x[:, 0]
    
    def forward_intermediate_layers(self, x, return_all=False):
        output = []
        x = self.prepare_tokens(x)
        for blk in self.blocks:
            x = blk(x)
            if return_all:
                output.append(self.norm(x[:, :-1]))
            else:
                output.append(x[:, 0])

        return output
        
    def get_last_selfattention(self, x):
        x = self.prepare_tokens(x)
        for i, blk in enumerate(self.blocks):
            if i < len(self.blocks) - 1:
                x = blk(x)
            else:
                # return attention of the last block
                return blk(x, return_attention=True)

    def get_intermediate_layers(self, x, n=1):
        x = self.prepare_tokens(x)
        # we return the output tokens from the `n` last blocks
        output = []
        for i, blk in enumerate(self.blocks):
            x = blk(x)
            if len(self.blocks) - i <= n:
                output.append(self.norm(x))
        return output