Create resampler.py

resampler.py

@@ -1,163 +0,0 @@
-from functools import partial
-import numpy as np
-
-import torch
-from torch import nn
-from torch.nn.init import trunc_normal_
-
-def get_2d_sincos_pos_embed(embed_dim, image_size):
-    """
-    image_size: image_size or (image_height, image_width)
-    return:
-    pos_embed: [image_height, image_width, embed_dim]
-    """
-    if isinstance(image_size, int):
-        grid_h_size, grid_w_size = image_size, image_size
-    else:
-        grid_h_size, grid_w_size = image_size[0], image_size[1]
-
-    grid_h = np.arange(grid_h_size, dtype=np.float32)
-    grid_w = np.arange(grid_w_size, dtype=np.float32)
-    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
-    grid = np.stack(grid, axis=0)
-
-    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
-    return pos_embed
-
-
-def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
-    assert embed_dim % 2 == 0
-
-    # use half of dimensions to encode grid_h
-    emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[0])  # (H, W, D/2)
-    emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[1])  # (H, W, D/2)
-
-    emb = np.concatenate([emb_h, emb_w], axis=-1)  # (H, W, D)
-    return emb
-
-
-def get_1d_sincos_pos_embed_from_grid_new(embed_dim, pos):
-    """
-    embed_dim: output dimension for each position
-    pos: a list of positions to be encoded: size (H, W)
-    out: (H, W, D)
-    """
-    assert embed_dim % 2 == 0
-    omega = np.arange(embed_dim // 2, dtype=np.float32)
-    omega /= embed_dim / 2.
-    omega = 1. / 10000 ** omega  # (D/2,)
-
-    out = np.einsum('hw,d->hwd', pos, omega)  # (H, W, D/2), outer product
-
-    emb_sin = np.sin(out)  # (H, W, D/2)
-    emb_cos = np.cos(out)  # (H, W, D/2)
-
-    emb = np.concatenate([emb_sin, emb_cos], axis=-1)  # (H, W, D)
-    return emb
-
-
-class Resampler(nn.Module):
-    """
-    A 2D perceiver-resampler network with one cross attention layers by
-       given learnable queries and 2d sincos pos_emb
-    Outputs:
-        A tensor with the shape of (batch_size, num_queries, embed_dim)
-    """
-
-    def __init__(
-            self,
-            num_queries,
-            embed_dim,
-            num_heads,
-            kv_dim=None,
-            norm_layer=partial(nn.LayerNorm, eps=1e-6),
-            adaptive=False,
-            max_size=(70, 70),
-    ):
-        super().__init__()
-        self.num_queries = num_queries
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.adaptive = adaptive
-        self.max_size = max_size
-
-        self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
-        trunc_normal_(self.query, std=.02)
-
-        if kv_dim is not None and kv_dim != embed_dim:
-            self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
-        else:
-            self.kv_proj = nn.Identity()
-
-        self.attn = nn.MultiheadAttention(embed_dim, num_heads)
-        self.ln_q = norm_layer(embed_dim)
-        self.ln_kv = norm_layer(embed_dim)
-
-        self.ln_post = norm_layer(embed_dim)
-        self.proj = nn.Parameter((embed_dim ** -0.5) * torch.randn(embed_dim, embed_dim))
-
-        self._set_2d_pos_cache(self.max_size)
-        self.apply(self._init_weights)
-
-    def _set_2d_pos_cache(self, max_size, device='cpu'):
-        pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.embed_dim, max_size)).float().to(device)
-        self.register_buffer("pos_embed", pos_embed, persistent=False)
-
-    def _adjust_pos_cache(self, tgt_sizes, device):
-        max_h = torch.max(tgt_sizes[:, 0])
-        max_w = torch.max(tgt_sizes[:, 1])
-        if max_h > self.max_size[0] or max_w > self.max_size[1]:
-            self.max_size = [max(max_h, self.max_size[0]), max(max_w, self.max_size[1])]
-            self._set_2d_pos_cache(self.max_size, device)
-
-    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 forward(self, x, tgt_sizes=None):
-        assert x.shape[0] == tgt_sizes.shape[0]
-        bs = x.shape[0]
-
-        device = x.device
-        dtype = x.dtype
-
-        patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
-
-        self._adjust_pos_cache(tgt_sizes, device=device)
-
-        max_patch_len = torch.max(patch_len)
-        key_padding_mask = torch.zeros((bs, max_patch_len), dtype=torch.bool, device=device)
-
-        pos_embed = []
-        for i in range(bs):
-            tgt_h, tgt_w = tgt_sizes[i]
-            pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype))  # patches * D
-            key_padding_mask[i, patch_len[i]:] = True
-
-        pos_embed = torch.nn.utils.rnn.pad_sequence(
-            pos_embed, batch_first=True, padding_value=0.0).permute(1, 0, 2)  # BLD => L * B * D
-
-        x = self.kv_proj(x)  # B * L * D
-        x = self.ln_kv(x).permute(1, 0, 2)  # L * B * D
-
-        q = self.ln_q(self.query)  # Q * D
-
-        out = self.attn(
-            self._repeat(q, bs),  # Q * B * D
-            x + pos_embed,  # L * B * D +  L * B * D
-            x,
-            key_padding_mask=key_padding_mask)[0]
-        #  out: Q * B * D
-        x = out.permute(1, 0, 2)  # B * Q * D
-
-        x = self.ln_post(x)
-        x = x @ self.proj
-        return x
-
-    def _repeat(self, query, N: int):
-        return query.unsqueeze(1).repeat(1, N, 1)