update

groundingdino/__init__.py

@@ -1,15 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-from groundingdino.models.GroundingDINO.groundingdino import build_groundingdino

groundingdino/models/GroundingDINO/backbone/__init__.py

@@ -1 +0,0 @@
-from .backbone import build_backbone

groundingdino/models/GroundingDINO/backbone/backbone.py

@@ -1,221 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-"""
-Backbone modules.
-"""
-
-from typing import Dict, List
-
-import torch
-import torch.nn.functional as F
-import torchvision
-from torch import nn
-from torchvision.models._utils import IntermediateLayerGetter
-
-from groundingdino.util.misc import NestedTensor, is_main_process
-
-from .position_encoding import build_position_encoding
-from .swin_transformer import build_swin_transformer
-
-
-class FrozenBatchNorm2d(torch.nn.Module):
-    """
-    BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
-    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
-    without which any other models than torchvision.models.resnet[18,34,50,101]
-    produce nans.
-    """
-
-    def __init__(self, n):
-        super(FrozenBatchNorm2d, self).__init__()
-        self.register_buffer("weight", torch.ones(n))
-        self.register_buffer("bias", torch.zeros(n))
-        self.register_buffer("running_mean", torch.zeros(n))
-        self.register_buffer("running_var", torch.ones(n))
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        num_batches_tracked_key = prefix + "num_batches_tracked"
-        if num_batches_tracked_key in state_dict:
-            del state_dict[num_batches_tracked_key]
-
-        super(FrozenBatchNorm2d, self)._load_from_state_dict(
-            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-        )
-
-    def forward(self, x):
-        # move reshapes to the beginning
-        # to make it fuser-friendly
-        w = self.weight.reshape(1, -1, 1, 1)
-        b = self.bias.reshape(1, -1, 1, 1)
-        rv = self.running_var.reshape(1, -1, 1, 1)
-        rm = self.running_mean.reshape(1, -1, 1, 1)
-        eps = 1e-5
-        scale = w * (rv + eps).rsqrt()
-        bias = b - rm * scale
-        return x * scale + bias
-
-
-class BackboneBase(nn.Module):
-    def __init__(
-        self,
-        backbone: nn.Module,
-        train_backbone: bool,
-        num_channels: int,
-        return_interm_indices: list,
-    ):
-        super().__init__()
-        for name, parameter in backbone.named_parameters():
-            if (
-                not train_backbone
-                or "layer2" not in name
-                and "layer3" not in name
-                and "layer4" not in name
-            ):
-                parameter.requires_grad_(False)
-
-        return_layers = {}
-        for idx, layer_index in enumerate(return_interm_indices):
-            return_layers.update(
-                {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
-            )
-
-        # if len:
-        #     if use_stage1_feature:
-        #         return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
-        #     else:
-        #         return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
-        # else:
-        #     return_layers = {'layer4': "0"}
-        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
-        self.num_channels = num_channels
-
-    def forward(self, tensor_list: NestedTensor):
-        xs = self.body(tensor_list.tensors)
-        out: Dict[str, NestedTensor] = {}
-        for name, x in xs.items():
-            m = tensor_list.mask
-            assert m is not None
-            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
-            out[name] = NestedTensor(x, mask)
-        # import ipdb; ipdb.set_trace()
-        return out
-
-
-class Backbone(BackboneBase):
-    """ResNet backbone with frozen BatchNorm."""
-
-    def __init__(
-        self,
-        name: str,
-        train_backbone: bool,
-        dilation: bool,
-        return_interm_indices: list,
-        batch_norm=FrozenBatchNorm2d,
-    ):
-        if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
-            backbone = getattr(torchvision.models, name)(
-                replace_stride_with_dilation=[False, False, dilation],
-                pretrained=is_main_process(),
-                norm_layer=batch_norm,
-            )
-        else:
-            raise NotImplementedError("Why you can get here with name {}".format(name))
-        # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
-        assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
-        assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
-        num_channels_all = [256, 512, 1024, 2048]
-        num_channels = num_channels_all[4 - len(return_interm_indices) :]
-        super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
-
-
-class Joiner(nn.Sequential):
-    def __init__(self, backbone, position_embedding):
-        super().__init__(backbone, position_embedding)
-
-    def forward(self, tensor_list: NestedTensor):
-        xs = self[0](tensor_list)
-        out: List[NestedTensor] = []
-        pos = []
-        for name, x in xs.items():
-            out.append(x)
-            # position encoding
-            pos.append(self[1](x).to(x.tensors.dtype))
-
-        return out, pos
-
-
-def build_backbone(args):
-    """
-    Useful args:
-        - backbone: backbone name
-        - lr_backbone:
-        - dilation
-        - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
-        - backbone_freeze_keywords:
-        - use_checkpoint: for swin only for now
-
-    """
-    position_embedding = build_position_encoding(args)
-    train_backbone = True
-    if not train_backbone:
-        raise ValueError("Please set lr_backbone > 0")
-    return_interm_indices = args.return_interm_indices
-    assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
-    args.backbone_freeze_keywords
-    use_checkpoint = getattr(args, "use_checkpoint", False)
-
-    if args.backbone in ["resnet50", "resnet101"]:
-        backbone = Backbone(
-            args.backbone,
-            train_backbone,
-            args.dilation,
-            return_interm_indices,
-            batch_norm=FrozenBatchNorm2d,
-        )
-        bb_num_channels = backbone.num_channels
-    elif args.backbone in [
-        "swin_T_224_1k",
-        "swin_B_224_22k",
-        "swin_B_384_22k",
-        "swin_L_224_22k",
-        "swin_L_384_22k",
-    ]:
-        pretrain_img_size = int(args.backbone.split("_")[-2])
-        backbone = build_swin_transformer(
-            args.backbone,
-            pretrain_img_size=pretrain_img_size,
-            out_indices=tuple(return_interm_indices),
-            dilation=False,
-            use_checkpoint=use_checkpoint,
-        )
-
-        bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
-    else:
-        raise NotImplementedError("Unknown backbone {}".format(args.backbone))
-
-    assert len(bb_num_channels) == len(
-        return_interm_indices
-    ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
-
-    model = Joiner(backbone, position_embedding)
-    model.num_channels = bb_num_channels
-    assert isinstance(
-        bb_num_channels, List
-    ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
-    # import ipdb; ipdb.set_trace()
-    return model

groundingdino/models/GroundingDINO/backbone/position_encoding.py

@@ -1,186 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-"""
-Various positional encodings for the transformer.
-"""
-import math
-
-import torch
-from torch import nn
-
-from groundingdino.util.misc import NestedTensor
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        mask = tensor_list.mask
-        assert mask is not None
-        not_mask = ~mask
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            # if os.environ.get("SHILONG_AMP", None) == '1':
-            #     eps = 1e-4
-            # else:
-            #     eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-
-class PositionEmbeddingSineHW(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(
-        self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
-    ):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperatureH = temperatureH
-        self.temperatureW = temperatureW
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        mask = tensor_list.mask
-        assert mask is not None
-        not_mask = ~mask
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-
-        # import ipdb; ipdb.set_trace()
-
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
-        pos_x = x_embed[:, :, :, None] / dim_tx
-
-        dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
-        pos_y = y_embed[:, :, :, None] / dim_ty
-
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-
-        # import ipdb; ipdb.set_trace()
-
-        return pos
-
-
-class PositionEmbeddingLearned(nn.Module):
-    """
-    Absolute pos embedding, learned.
-    """
-
-    def __init__(self, num_pos_feats=256):
-        super().__init__()
-        self.row_embed = nn.Embedding(50, num_pos_feats)
-        self.col_embed = nn.Embedding(50, num_pos_feats)
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        nn.init.uniform_(self.row_embed.weight)
-        nn.init.uniform_(self.col_embed.weight)
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        h, w = x.shape[-2:]
-        i = torch.arange(w, device=x.device)
-        j = torch.arange(h, device=x.device)
-        x_emb = self.col_embed(i)
-        y_emb = self.row_embed(j)
-        pos = (
-            torch.cat(
-                [
-                    x_emb.unsqueeze(0).repeat(h, 1, 1),
-                    y_emb.unsqueeze(1).repeat(1, w, 1),
-                ],
-                dim=-1,
-            )
-            .permute(2, 0, 1)
-            .unsqueeze(0)
-            .repeat(x.shape[0], 1, 1, 1)
-        )
-        return pos
-
-
-def build_position_encoding(args):
-    N_steps = args.hidden_dim // 2
-    if args.position_embedding in ("v2", "sine"):
-        # TODO find a better way of exposing other arguments
-        position_embedding = PositionEmbeddingSineHW(
-            N_steps,
-            temperatureH=args.pe_temperatureH,
-            temperatureW=args.pe_temperatureW,
-            normalize=True,
-        )
-    elif args.position_embedding in ("v3", "learned"):
-        position_embedding = PositionEmbeddingLearned(N_steps)
-    else:
-        raise ValueError(f"not supported {args.position_embedding}")
-
-    return position_embedding

groundingdino/models/GroundingDINO/backbone/swin_transformer.py

@@ -1,802 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# --------------------------------------------------------
-# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
-# --------------------------------------------------------
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-from groundingdino.util.misc import NestedTensor
-
-
-class Mlp(nn.Module):
-    """Multilayer perceptron."""
-
-    def __init__(
-        self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
-    ):
-        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
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    """Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        dim,
-        window_size,
-        num_heads,
-        qkv_bias=True,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
-        )  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.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] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        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)
-
-        trunc_normal_(self.relative_position_bias_table, std=0.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """Forward function.
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        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]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)
-
-        relative_position_bias = self.relative_position_bias_table[
-            self.relative_position_index.view(-1)
-        ].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[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 mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        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 SwinTransformerBlock(nn.Module):
-    """Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        window_size=7,
-        shift_size=0,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim,
-            window_size=to_2tuple(self.window_size),
-            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 > 0.0 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.H = None
-        self.W = None
-
-    def forward(self, x, mask_matrix):
-        """Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-            mask_matrix: Attention mask for cyclic shift.
-        """
-        B, L, C = x.shape
-        H, W = self.H, self.W
-        assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # pad feature maps to multiples of window size
-        pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-            attn_mask = mask_matrix
-        else:
-            shifted_x = x
-            attn_mask = None
-
-        # partition windows
-        x_windows = window_partition(
-            shifted_x, self.window_size
-        )  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(
-            -1, self.window_size * self.window_size, C
-        )  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-
-class PatchMerging(nn.Module):
-    """Patch Merging Layer
-    Args:
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x, H, W):
-        """Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-
-        x = x.view(B, H, W, C)
-
-        # padding
-        pad_input = (H % 2 == 1) or (W % 2 == 1)
-        if pad_input:
-            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
-class BasicLayer(nn.Module):
-    """A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of feature channels
-        depth (int): Depths of this stage.
-        num_heads (int): Number of attention head.
-        window_size (int): Local window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(
-        self,
-        dim,
-        depth,
-        num_heads,
-        window_size=7,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-    ):
-        super().__init__()
-        self.window_size = window_size
-        self.shift_size = window_size // 2
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList(
-            [
-                SwinTransformerBlock(
-                    dim=dim,
-                    num_heads=num_heads,
-                    window_size=window_size,
-                    shift_size=0 if (i % 2 == 0) else window_size // 2,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                    norm_layer=norm_layer,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, H, W):
-        """Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-
-        # calculate attention mask for SW-MSA
-        Hp = int(np.ceil(H / self.window_size)) * self.window_size
-        Wp = int(np.ceil(W / self.window_size)) * self.window_size
-        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
-        h_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        w_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(
-            img_mask, self.window_size
-        )  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
-            attn_mask == 0, float(0.0)
-        )
-
-        for blk in self.blocks:
-            blk.H, blk.W = H, W
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, attn_mask)
-            else:
-                x = blk(x, attn_mask)
-        if self.downsample is not None:
-            x_down = self.downsample(x, H, W)
-            Wh, Ww = (H + 1) // 2, (W + 1) // 2
-            return x, H, W, x_down, Wh, Ww
-        else:
-            return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
-    """Image to Patch Embedding
-    Args:
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        patch_size = to_2tuple(patch_size)
-        self.patch_size = patch_size
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        """Forward function."""
-        # padding
-        _, _, H, W = x.size()
-        if W % self.patch_size[1] != 0:
-            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
-        if H % self.patch_size[0] != 0:
-            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
-        x = self.proj(x)  # B C Wh Ww
-        if self.norm is not None:
-            Wh, Ww = x.size(2), x.size(3)
-            x = x.flatten(2).transpose(1, 2)
-            x = self.norm(x)
-            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
-        return x
-
-
-class SwinTransformer(nn.Module):
-    """Swin Transformer backbone.
-        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
-          https://arxiv.org/pdf/2103.14030
-    Args:
-        pretrain_img_size (int): Input image size for training the pretrained model,
-            used in absolute postion embedding. Default 224.
-        patch_size (int | tuple(int)): Patch size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        depths (tuple[int]): Depths of each Swin Transformer stage.
-        num_heads (tuple[int]): Number of attention head of each stage.
-        window_size (int): Window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
-        drop_rate (float): Dropout rate.
-        attn_drop_rate (float): Attention dropout rate. Default: 0.
-        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
-        out_indices (Sequence[int]): Output from which stages.
-        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-            -1 means not freezing any parameters.
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.
-    """
-
-    def __init__(
-        self,
-        pretrain_img_size=224,
-        patch_size=4,
-        in_chans=3,
-        embed_dim=96,
-        depths=[2, 2, 6, 2],
-        num_heads=[3, 6, 12, 24],
-        window_size=7,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop_rate=0.0,
-        attn_drop_rate=0.0,
-        drop_path_rate=0.2,
-        norm_layer=nn.LayerNorm,
-        ape=False,
-        patch_norm=True,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=-1,
-        dilation=False,
-        use_checkpoint=False,
-    ):
-        super().__init__()
-
-        self.pretrain_img_size = pretrain_img_size
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-        self.dilation = dilation
-
-        # if use_checkpoint:
-        #     print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!")
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            patch_size=patch_size,
-            in_chans=in_chans,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-
-        # absolute position embedding
-        if self.ape:
-            pretrain_img_size = to_2tuple(pretrain_img_size)
-            patch_size = to_2tuple(patch_size)
-            patches_resolution = [
-                pretrain_img_size[0] // patch_size[0],
-                pretrain_img_size[1] // patch_size[1],
-            ]
-
-            self.absolute_pos_embed = nn.Parameter(
-                torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
-            )
-            trunc_normal_(self.absolute_pos_embed, std=0.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
-        ]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        # prepare downsample list
-        downsamplelist = [PatchMerging for i in range(self.num_layers)]
-        downsamplelist[-1] = None
-        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
-        if self.dilation:
-            downsamplelist[-2] = None
-            num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(
-                # dim=int(embed_dim * 2 ** i_layer),
-                dim=num_features[i_layer],
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
-                norm_layer=norm_layer,
-                # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                downsample=downsamplelist[i_layer],
-                use_checkpoint=use_checkpoint,
-            )
-            self.layers.append(layer)
-
-        # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
-        self.num_features = num_features
-
-        # add a norm layer for each output
-        for i_layer in out_indices:
-            layer = norm_layer(num_features[i_layer])
-            layer_name = f"norm{i_layer}"
-            self.add_module(layer_name, layer)
-
-        self._freeze_stages()
-
-    def _freeze_stages(self):
-        if self.frozen_stages >= 0:
-            self.patch_embed.eval()
-            for param in self.patch_embed.parameters():
-                param.requires_grad = False
-
-        if self.frozen_stages >= 1 and self.ape:
-            self.absolute_pos_embed.requires_grad = False
-
-        if self.frozen_stages >= 2:
-            self.pos_drop.eval()
-            for i in range(0, self.frozen_stages - 1):
-                m = self.layers[i]
-                m.eval()
-                for param in m.parameters():
-                    param.requires_grad = False
-
-    # def init_weights(self, pretrained=None):
-    #     """Initialize the weights in backbone.
-    #     Args:
-    #         pretrained (str, optional): Path to pre-trained weights.
-    #             Defaults to None.
-    #     """
-
-    #     def _init_weights(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)
-
-    #     if isinstance(pretrained, str):
-    #         self.apply(_init_weights)
-    #         logger = get_root_logger()
-    #         load_checkpoint(self, pretrained, strict=False, logger=logger)
-    #     elif pretrained is None:
-    #         self.apply(_init_weights)
-    #     else:
-    #         raise TypeError('pretrained must be a str or None')
-
-    def forward_raw(self, x):
-        """Forward function."""
-        x = self.patch_embed(x)
-
-        Wh, Ww = x.size(2), x.size(3)
-        if self.ape:
-            # interpolate the position embedding to the corresponding size
-            absolute_pos_embed = F.interpolate(
-                self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
-            )
-            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
-        else:
-            x = x.flatten(2).transpose(1, 2)
-        x = self.pos_drop(x)
-
-        outs = []
-        for i in range(self.num_layers):
-            layer = self.layers[i]
-            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-            # import ipdb; ipdb.set_trace()
-
-            if i in self.out_indices:
-                norm_layer = getattr(self, f"norm{i}")
-                x_out = norm_layer(x_out)
-
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
-                outs.append(out)
-        # in:
-        #   torch.Size([2, 3, 1024, 1024])
-        # outs:
-        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
-        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
-        return tuple(outs)
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-
-        """Forward function."""
-        x = self.patch_embed(x)
-
-        Wh, Ww = x.size(2), x.size(3)
-        if self.ape:
-            # interpolate the position embedding to the corresponding size
-            absolute_pos_embed = F.interpolate(
-                self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
-            )
-            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
-        else:
-            x = x.flatten(2).transpose(1, 2)
-        x = self.pos_drop(x)
-
-        outs = []
-        for i in range(self.num_layers):
-            layer = self.layers[i]
-            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
-            if i in self.out_indices:
-                norm_layer = getattr(self, f"norm{i}")
-                x_out = norm_layer(x_out)
-
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
-                outs.append(out)
-        # in:
-        #   torch.Size([2, 3, 1024, 1024])
-        # out:
-        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
-        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
-
-        # collect for nesttensors
-        outs_dict = {}
-        for idx, out_i in enumerate(outs):
-            m = tensor_list.mask
-            assert m is not None
-            mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]
-            outs_dict[idx] = NestedTensor(out_i, mask)
-
-        return outs_dict
-
-    def train(self, mode=True):
-        """Convert the model into training mode while keep layers freezed."""
-        super(SwinTransformer, self).train(mode)
-        self._freeze_stages()
-
-
-def build_swin_transformer(modelname, pretrain_img_size, **kw):
-    assert modelname in [
-        "swin_T_224_1k",
-        "swin_B_224_22k",
-        "swin_B_384_22k",
-        "swin_L_224_22k",
-        "swin_L_384_22k",
-    ]
-
-    model_para_dict = {
-        "swin_T_224_1k": dict(
-            embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7
-        ),
-        "swin_B_224_22k": dict(
-            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7
-        ),
-        "swin_B_384_22k": dict(
-            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12
-        ),
-        "swin_L_224_22k": dict(
-            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7
-        ),
-        "swin_L_384_22k": dict(
-            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12
-        ),
-    }
-    kw_cgf = model_para_dict[modelname]
-    kw_cgf.update(kw)
-    model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)
-    return model
-
-
-if __name__ == "__main__":
-    model = build_swin_transformer("swin_L_384_22k", 384, dilation=True)
-    x = torch.rand(2, 3, 1024, 1024)
-    y = model.forward_raw(x)
-    import ipdb
-
-    ipdb.set_trace()
-    x = torch.rand(2, 3, 384, 384)
-    y = model.forward_raw(x)

groundingdino/models/GroundingDINO/bertwarper.py

@@ -1,273 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from torch import Tensor, nn
-from torchvision.ops.boxes import nms
-from transformers import BertConfig, BertModel, BertPreTrainedModel
-from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
-
-
-class BertModelWarper(nn.Module):
-    def __init__(self, bert_model):
-        super().__init__()
-        # self.bert = bert_modelc
-
-        self.config = bert_model.config
-        self.embeddings = bert_model.embeddings
-        self.encoder = bert_model.encoder
-        self.pooler = bert_model.pooler
-
-        self.get_extended_attention_mask = bert_model.get_extended_attention_mask
-        self.invert_attention_mask = bert_model.invert_attention_mask
-        self.get_head_mask = bert_model.get_head_mask
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        """
-        output_attentions = (
-            output_attentions if output_attentions is not None else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if self.config.is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size, seq_length = input_shape
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        # past_key_values_length
-        past_key_values_length = (
-            past_key_values[0][0].shape[2] if past_key_values is not None else 0
-        )
-
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                ((batch_size, seq_length + past_key_values_length)), device=device
-            )
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
-            attention_mask, input_shape, device
-        )
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if self.config.is_decoder and encoder_hidden_states is not None:
-            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            if encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-        #     import ipdb; ipdb.set_trace()
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            inputs_embeds=inputs_embeds,
-            past_key_values_length=past_key_values_length,
-        )
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-class TextEncoderShell(nn.Module):
-    def __init__(self, text_encoder):
-        super().__init__()
-        self.text_encoder = text_encoder
-        self.config = self.text_encoder.config
-
-    def forward(self, **kw):
-        # feed into text encoder
-        return self.text_encoder(**kw)
-
-
-def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):
-    """Generate attention mask between each pair of special tokens
-    Args:
-        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
-        special_tokens_mask (list): special tokens mask.
-    Returns:
-        torch.Tensor: attention mask between each special tokens.
-    """
-    input_ids = tokenized["input_ids"]
-    bs, num_token = input_ids.shape
-    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
-    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
-    for special_token in special_tokens_list:
-        special_tokens_mask |= input_ids == special_token
-
-    # idxs: each row is a list of indices of special tokens
-    idxs = torch.nonzero(special_tokens_mask)
-
-    # generate attention mask and positional ids
-    attention_mask = (
-        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
-    )
-    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
-    previous_col = 0
-    for i in range(idxs.shape[0]):
-        row, col = idxs[i]
-        if (col == 0) or (col == num_token - 1):
-            attention_mask[row, col, col] = True
-            position_ids[row, col] = 0
-        else:
-            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
-            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
-                0, col - previous_col, device=input_ids.device
-            )
-
-        previous_col = col
-
-    # # padding mask
-    # padding_mask = tokenized['attention_mask']
-    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
-
-    return attention_mask, position_ids.to(torch.long)
-
-
-def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
-    """Generate attention mask between each pair of special tokens
-    Args:
-        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
-        special_tokens_mask (list): special tokens mask.
-    Returns:
-        torch.Tensor: attention mask between each special tokens.
-    """
-    input_ids = tokenized["input_ids"]
-    bs, num_token = input_ids.shape
-    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
-    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
-    for special_token in special_tokens_list:
-        special_tokens_mask |= input_ids == special_token
-
-    # idxs: each row is a list of indices of special tokens
-    idxs = torch.nonzero(special_tokens_mask)
-
-    # generate attention mask and positional ids
-    attention_mask = (
-        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
-    )
-    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
-    cate_to_token_mask_list = [[] for _ in range(bs)]
-    previous_col = 0
-    for i in range(idxs.shape[0]):
-        row, col = idxs[i]
-        if (col == 0) or (col == num_token - 1):
-            attention_mask[row, col, col] = True
-            position_ids[row, col] = 0
-        else:
-            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
-            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
-                0, col - previous_col, device=input_ids.device
-            )
-            c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
-            c2t_maski[previous_col + 1 : col] = True
-            cate_to_token_mask_list[row].append(c2t_maski)
-        previous_col = col
-
-    cate_to_token_mask_list = [
-        torch.stack(cate_to_token_mask_listi, dim=0)
-        for cate_to_token_mask_listi in cate_to_token_mask_list
-    ]
-
-    # # padding mask
-    # padding_mask = tokenized['attention_mask']
-    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
-
-    return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h

@@ -1,64 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#pragma once
-
-#include "ms_deform_attn_cpu.h"
-
-#ifdef WITH_CUDA
-#include "ms_deform_attn_cuda.h"
-#endif
-
-namespace groundingdino {
-
-at::Tensor
-ms_deform_attn_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step)
-{
-    if (value.type().is_cuda())
-    {
-#ifdef WITH_CUDA
-        return ms_deform_attn_cuda_forward(
-            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
-#else
-        AT_ERROR("Not compiled with GPU support");
-#endif
-    }
-    AT_ERROR("Not implemented on the CPU");
-}
-
-std::vector<at::Tensor>
-ms_deform_attn_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step)
-{
-    if (value.type().is_cuda())
-    {
-#ifdef WITH_CUDA
-        return ms_deform_attn_cuda_backward(
-            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
-#else
-        AT_ERROR("Not compiled with GPU support");
-#endif
-    }
-    AT_ERROR("Not implemented on the CPU");
-}
-
-} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp

@@ -1,43 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#include <vector>
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-
-namespace groundingdino {
-
-at::Tensor
-ms_deform_attn_cpu_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step)
-{
-    AT_ERROR("Not implement on cpu");
-}
-
-std::vector<at::Tensor>
-ms_deform_attn_cpu_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step)
-{
-    AT_ERROR("Not implement on cpu");
-}
-
-} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h

@@ -1,35 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#pragma once
-#include <torch/extension.h>
-
-namespace groundingdino {
-
-at::Tensor
-ms_deform_attn_cpu_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step);
-
-std::vector<at::Tensor>
-ms_deform_attn_cpu_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step);
-
-} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu

@@ -1,156 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#include <vector>
-#include "ms_deform_im2col_cuda.cuh"
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <cuda.h>
-#include <cuda_runtime.h>
-
-namespace groundingdino {
-
-at::Tensor ms_deform_attn_cuda_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step)
-{
-    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
-    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
-    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
-    AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
-    AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
-
-    AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
-    AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
-    AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
-    AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
-    AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
-
-    const int batch = value.size(0);
-    const int spatial_size = value.size(1);
-    const int num_heads = value.size(2);
-    const int channels = value.size(3);
-
-    const int num_levels = spatial_shapes.size(0);
-
-    const int num_query = sampling_loc.size(1);
-    const int num_point = sampling_loc.size(4);
-
-    const int im2col_step_ = std::min(batch, im2col_step);
-
-    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
-    
-    auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
-
-    const int batch_n = im2col_step_;
-    auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
-    auto per_value_size = spatial_size * num_heads * channels;
-    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
-    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
-    for (int n = 0; n < batch/im2col_step_; ++n)
-    {
-        auto columns = output_n.select(0, n);
-        AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
-            ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
-                value.data<scalar_t>() + n * im2col_step_ * per_value_size,
-                spatial_shapes.data<int64_t>(),
-                level_start_index.data<int64_t>(),
-                sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
-                attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
-                batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
-                columns.data<scalar_t>());
-
-        }));
-    }
-
-    output = output.view({batch, num_query, num_heads*channels});
-
-    return output;
-}
-
-
-std::vector<at::Tensor> ms_deform_attn_cuda_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step)
-{
-
-    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
-    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
-    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
-    AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
-    AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
-    AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
-
-    AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
-    AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
-    AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
-    AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
-    AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
-    AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
-
-    const int batch = value.size(0);
-    const int spatial_size = value.size(1);
-    const int num_heads = value.size(2);
-    const int channels = value.size(3);
-
-    const int num_levels = spatial_shapes.size(0);
-
-    const int num_query = sampling_loc.size(1);
-    const int num_point = sampling_loc.size(4);
-
-    const int im2col_step_ = std::min(batch, im2col_step);
-
-    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
-
-    auto grad_value = at::zeros_like(value);
-    auto grad_sampling_loc = at::zeros_like(sampling_loc);
-    auto grad_attn_weight = at::zeros_like(attn_weight);
-
-    const int batch_n = im2col_step_;
-    auto per_value_size = spatial_size * num_heads * channels;
-    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
-    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
-    auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
-    
-    for (int n = 0; n < batch/im2col_step_; ++n)
-    {
-        auto grad_output_g = grad_output_n.select(0, n);
-        AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
-            ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
-                                    grad_output_g.data<scalar_t>(),
-                                    value.data<scalar_t>() + n * im2col_step_ * per_value_size,
-                                    spatial_shapes.data<int64_t>(),
-                                    level_start_index.data<int64_t>(),
-                                    sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
-                                    attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
-                                    batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
-                                    grad_value.data<scalar_t>() +  n * im2col_step_ * per_value_size,
-                                    grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
-                                    grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);
-
-        }));
-    }
-
-    return {
-        grad_value, grad_sampling_loc, grad_attn_weight
-    };
-}
-
-} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h

@@ -1,33 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#pragma once
-#include <torch/extension.h>
-
-namespace groundingdino {
-
-at::Tensor ms_deform_attn_cuda_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step);
-
-std::vector<at::Tensor> ms_deform_attn_cuda_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step);
-
-} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh

@@ -1,1327 +0,0 @@
-/*!
-**************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************
-* Modified from DCN (https://github.com/msracver/Deformable-ConvNets)
-* Copyright (c) 2018 Microsoft
-**************************************************************************
-*/
-
-#include <cstdio>
-#include <algorithm>
-#include <cstring>
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-
-#include <THC/THCAtomics.cuh>
-
-#define CUDA_KERNEL_LOOP(i, n)                          \
-  for (int i = blockIdx.x * blockDim.x + threadIdx.x;   \
-      i < (n);                                          \
-      i += blockDim.x * gridDim.x)
-
-const int CUDA_NUM_THREADS = 1024;
-inline int GET_BLOCKS(const int N, const int num_threads)
-{
-  return (N + num_threads - 1) / num_threads;
-}
-
-
-template <typename scalar_t>
-__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, 
-                                                   const int &height, const int &width, const int &nheads, const int &channels,
-                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c)
-{
-  const int h_low = floor(h);
-  const int w_low = floor(w);
-  const int h_high = h_low + 1;
-  const int w_high = w_low + 1;
-
-  const scalar_t lh = h - h_low;
-  const scalar_t lw = w - w_low;
-  const scalar_t hh = 1 - lh, hw = 1 - lw;
-
-  const int w_stride = nheads * channels;
-  const int h_stride = width * w_stride;
-  const int h_low_ptr_offset = h_low * h_stride;
-  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
-  const int w_low_ptr_offset = w_low * w_stride;
-  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
-  const int base_ptr = m * channels + c;
-
-  scalar_t v1 = 0;
-  if (h_low >= 0 && w_low >= 0)
-  {
-    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
-    v1 = bottom_data[ptr1];
-  }
-  scalar_t v2 = 0;
-  if (h_low >= 0 && w_high <= width - 1)
-  {
-    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
-    v2 = bottom_data[ptr2];
-  }
-  scalar_t v3 = 0;
-  if (h_high <= height - 1 && w_low >= 0)
-  {
-    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
-    v3 = bottom_data[ptr3];
-  }
-  scalar_t v4 = 0;
-  if (h_high <= height - 1 && w_high <= width - 1)
-  {
-    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
-    v4 = bottom_data[ptr4];
-  }
-
-  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
-
-  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
-  return val;
-}
-
-
-template <typename scalar_t>
-__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, 
-                                                   const int &height, const int &width, const int &nheads, const int &channels,
-                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c,
-                                                   const scalar_t &top_grad,
-                                                   const scalar_t &attn_weight,
-                                                   scalar_t* &grad_value, 
-                                                   scalar_t* grad_sampling_loc,
-                                                   scalar_t* grad_attn_weight)
-{
-  const int h_low = floor(h);
-  const int w_low = floor(w);
-  const int h_high = h_low + 1;
-  const int w_high = w_low + 1;
-
-  const scalar_t lh = h - h_low;
-  const scalar_t lw = w - w_low;
-  const scalar_t hh = 1 - lh, hw = 1 - lw;
-
-  const int w_stride = nheads * channels;
-  const int h_stride = width * w_stride;
-  const int h_low_ptr_offset = h_low * h_stride;
-  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
-  const int w_low_ptr_offset = w_low * w_stride;
-  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
-  const int base_ptr = m * channels + c;
-
-  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
-  const scalar_t top_grad_value = top_grad * attn_weight;
-  scalar_t grad_h_weight = 0, grad_w_weight = 0;
-
-  scalar_t v1 = 0;
-  if (h_low >= 0 && w_low >= 0)
-  {
-    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
-    v1 = bottom_data[ptr1];
-    grad_h_weight -= hw * v1;
-    grad_w_weight -= hh * v1;
-    atomicAdd(grad_value+ptr1, w1*top_grad_value);
-  }
-  scalar_t v2 = 0;
-  if (h_low >= 0 && w_high <= width - 1)
-  {
-    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
-    v2 = bottom_data[ptr2];
-    grad_h_weight -= lw * v2;
-    grad_w_weight += hh * v2;
-    atomicAdd(grad_value+ptr2, w2*top_grad_value);
-  }
-  scalar_t v3 = 0;
-  if (h_high <= height - 1 && w_low >= 0)
-  {
-    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
-    v3 = bottom_data[ptr3];
-    grad_h_weight += hw * v3;
-    grad_w_weight -= lh * v3;
-    atomicAdd(grad_value+ptr3, w3*top_grad_value); 
-  }
-  scalar_t v4 = 0;
-  if (h_high <= height - 1 && w_high <= width - 1)
-  {
-    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
-    v4 = bottom_data[ptr4];
-    grad_h_weight += lw * v4;
-    grad_w_weight += lh * v4;
-    atomicAdd(grad_value+ptr4, w4*top_grad_value);
-  }
-
-  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
-  *grad_attn_weight = top_grad * val;
-  *grad_sampling_loc = width * grad_w_weight * top_grad_value;
-  *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value;
-}
-
-
-template <typename scalar_t>
-__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, 
-                                                   const int &height, const int &width, const int &nheads, const int &channels,
-                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c,
-                                                   const scalar_t &top_grad,
-                                                   const scalar_t &attn_weight,
-                                                   scalar_t* &grad_value, 
-                                                   scalar_t* grad_sampling_loc,
-                                                   scalar_t* grad_attn_weight)
-{
-  const int h_low = floor(h);
-  const int w_low = floor(w);
-  const int h_high = h_low + 1;
-  const int w_high = w_low + 1;
-
-  const scalar_t lh = h - h_low;
-  const scalar_t lw = w - w_low;
-  const scalar_t hh = 1 - lh, hw = 1 - lw;
-
-  const int w_stride = nheads * channels;
-  const int h_stride = width * w_stride;
-  const int h_low_ptr_offset = h_low * h_stride;
-  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
-  const int w_low_ptr_offset = w_low * w_stride;
-  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
-  const int base_ptr = m * channels + c;
-
-  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
-  const scalar_t top_grad_value = top_grad * attn_weight;
-  scalar_t grad_h_weight = 0, grad_w_weight = 0;
-
-  scalar_t v1 = 0;
-  if (h_low >= 0 && w_low >= 0)
-  {
-    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
-    v1 = bottom_data[ptr1];
-    grad_h_weight -= hw * v1;
-    grad_w_weight -= hh * v1;
-    atomicAdd(grad_value+ptr1, w1*top_grad_value);
-  }
-  scalar_t v2 = 0;
-  if (h_low >= 0 && w_high <= width - 1)
-  {
-    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
-    v2 = bottom_data[ptr2];
-    grad_h_weight -= lw * v2;
-    grad_w_weight += hh * v2;
-    atomicAdd(grad_value+ptr2, w2*top_grad_value);
-  }
-  scalar_t v3 = 0;
-  if (h_high <= height - 1 && w_low >= 0)
-  {
-    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
-    v3 = bottom_data[ptr3];
-    grad_h_weight += hw * v3;
-    grad_w_weight -= lh * v3;
-    atomicAdd(grad_value+ptr3, w3*top_grad_value); 
-  }
-  scalar_t v4 = 0;
-  if (h_high <= height - 1 && w_high <= width - 1)
-  {
-    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
-    v4 = bottom_data[ptr4];
-    grad_h_weight += lw * v4;
-    grad_w_weight += lh * v4;
-    atomicAdd(grad_value+ptr4, w4*top_grad_value);
-  }
-
-  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
-  atomicAdd(grad_attn_weight, top_grad * val); 
-  atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value);
-  atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value);
-}
-
-
-template <typename scalar_t>
-__global__ void ms_deformable_im2col_gpu_kernel(const int n,
-                                                const scalar_t *data_value, 
-                                                const int64_t *data_spatial_shapes,
-                                                const int64_t *data_level_start_index, 
-                                                const scalar_t *data_sampling_loc,
-                                                const scalar_t *data_attn_weight,
-                                                const int batch_size, 
-                                                const int spatial_size, 
-                                                const int num_heads,
-                                                const int channels, 
-                                                const int num_levels,
-                                                const int num_query,
-                                                const int num_point,
-                                                scalar_t *data_col)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    int _temp = index;
-    const int c_col = _temp % channels;
-    _temp /= channels;
-    const int sampling_index = _temp; 
-    const int m_col = _temp % num_heads;
-    _temp /= num_heads;
-    const int q_col = _temp % num_query;
-    _temp /= num_query;
-    const int b_col = _temp;
-
-    scalar_t *data_col_ptr = data_col + index;
-    int data_weight_ptr = sampling_index * num_levels * num_point;
-    int data_loc_w_ptr = data_weight_ptr << 1;
-    const int qid_stride = num_heads * channels;
-    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
-    scalar_t col = 0;
-    
-    for (int l_col=0; l_col < num_levels; ++l_col)
-    {
-      const int level_start_id = data_level_start_index[l_col];
-      const int spatial_h_ptr = l_col << 1;
-      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
-      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
-      const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride);
-      for (int p_col=0; p_col < num_point; ++p_col)
-      {
-        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
-        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
-        const scalar_t weight = data_attn_weight[data_weight_ptr];
-
-        const scalar_t h_im = loc_h * spatial_h - 0.5;
-        const scalar_t w_im = loc_w * spatial_w - 0.5;
-
-        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
-        {
-          col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight;
-        }
-
-        data_weight_ptr += 1;
-        data_loc_w_ptr += 2;
-      }
-    }
-    *data_col_ptr = col;
-  }
-}
-
-template <typename scalar_t, unsigned int blockSize>
-__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n,
-                                                const scalar_t *grad_col,
-                                                const scalar_t *data_value,
-                                                const int64_t *data_spatial_shapes,
-                                                const int64_t *data_level_start_index, 
-                                                const scalar_t *data_sampling_loc,
-                                                const scalar_t *data_attn_weight,
-                                                const int batch_size, 
-                                                const int spatial_size, 
-                                                const int num_heads,
-                                                const int channels, 
-                                                const int num_levels,
-                                                const int num_query,
-                                                const int num_point,
-                                                scalar_t *grad_value,
-                                                scalar_t *grad_sampling_loc,
-                                                scalar_t *grad_attn_weight)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
-    __shared__ scalar_t cache_grad_attn_weight[blockSize];
-    unsigned int tid = threadIdx.x;
-    int _temp = index;
-    const int c_col = _temp % channels;
-    _temp /= channels;
-    const int sampling_index = _temp; 
-    const int m_col = _temp % num_heads;
-    _temp /= num_heads;
-    const int q_col = _temp % num_query;
-    _temp /= num_query;
-    const int b_col = _temp;
-
-    const scalar_t top_grad = grad_col[index];
-
-    int data_weight_ptr = sampling_index * num_levels * num_point;
-    int data_loc_w_ptr = data_weight_ptr << 1;
-    const int grad_sampling_ptr = data_weight_ptr;
-    grad_sampling_loc += grad_sampling_ptr << 1;
-    grad_attn_weight += grad_sampling_ptr;
-    const int grad_weight_stride = 1;
-    const int grad_loc_stride = 2;
-    const int qid_stride = num_heads * channels;
-    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
-
-    for (int l_col=0; l_col < num_levels; ++l_col)
-    {
-      const int level_start_id = data_level_start_index[l_col];
-      const int spatial_h_ptr = l_col << 1;
-      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
-      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
-      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
-      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
-      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
-
-      for (int p_col=0; p_col < num_point; ++p_col)
-      {
-        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
-        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
-        const scalar_t weight = data_attn_weight[data_weight_ptr];
-
-        const scalar_t h_im = loc_h * spatial_h - 0.5;
-        const scalar_t w_im = loc_w * spatial_w - 0.5;
-        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
-        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
-        *(cache_grad_attn_weight+threadIdx.x)=0;
-        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
-        {
-          ms_deform_attn_col2im_bilinear(
-            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
-            top_grad, weight, grad_value_ptr, 
-            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
-        }
-        
-        __syncthreads();
-        if (tid == 0)
-        {
-          scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
-          int sid=2;
-          for (unsigned int tid = 1; tid < blockSize; ++tid)
-          {
-            _grad_w += cache_grad_sampling_loc[sid];
-            _grad_h += cache_grad_sampling_loc[sid + 1];
-            _grad_a += cache_grad_attn_weight[tid];
-            sid += 2;
-          }
-          
-          
-          *grad_sampling_loc = _grad_w;
-          *(grad_sampling_loc + 1) = _grad_h;
-          *grad_attn_weight = _grad_a;
-        }
-        __syncthreads();
-
-        data_weight_ptr += 1;
-        data_loc_w_ptr += 2;
-        grad_attn_weight += grad_weight_stride;
-        grad_sampling_loc += grad_loc_stride;
-      }
-    }
-  }
-}
-
-
-template <typename scalar_t, unsigned int blockSize>
-__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n,
-                                                const scalar_t *grad_col,
-                                                const scalar_t *data_value,
-                                                const int64_t *data_spatial_shapes,
-                                                const int64_t *data_level_start_index, 
-                                                const scalar_t *data_sampling_loc,
-                                                const scalar_t *data_attn_weight,
-                                                const int batch_size, 
-                                                const int spatial_size, 
-                                                const int num_heads,
-                                                const int channels, 
-                                                const int num_levels,
-                                                const int num_query,
-                                                const int num_point,
-                                                scalar_t *grad_value,
-                                                scalar_t *grad_sampling_loc,
-                                                scalar_t *grad_attn_weight)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
-    __shared__ scalar_t cache_grad_attn_weight[blockSize];
-    unsigned int tid = threadIdx.x;
-    int _temp = index;
-    const int c_col = _temp % channels;
-    _temp /= channels;
-    const int sampling_index = _temp; 
-    const int m_col = _temp % num_heads;
-    _temp /= num_heads;
-    const int q_col = _temp % num_query;
-    _temp /= num_query;
-    const int b_col = _temp;
-
-    const scalar_t top_grad = grad_col[index];
-
-    int data_weight_ptr = sampling_index * num_levels * num_point;
-    int data_loc_w_ptr = data_weight_ptr << 1;
-    const int grad_sampling_ptr = data_weight_ptr;
-    grad_sampling_loc += grad_sampling_ptr << 1;
-    grad_attn_weight += grad_sampling_ptr;
-    const int grad_weight_stride = 1;
-    const int grad_loc_stride = 2;
-    const int qid_stride = num_heads * channels;
-    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
-
-    for (int l_col=0; l_col < num_levels; ++l_col)
-    {
-      const int level_start_id = data_level_start_index[l_col];
-      const int spatial_h_ptr = l_col << 1;
-      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
-      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
-      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
-      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
-      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
-
-      for (int p_col=0; p_col < num_point; ++p_col)
-      {
-        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
-        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
-        const scalar_t weight = data_attn_weight[data_weight_ptr];
-
-        const scalar_t h_im = loc_h * spatial_h - 0.5;
-        const scalar_t w_im = loc_w * spatial_w - 0.5;
-        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
-        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
-        *(cache_grad_attn_weight+threadIdx.x)=0;
-        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
-        {
-          ms_deform_attn_col2im_bilinear(
-            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
-            top_grad, weight, grad_value_ptr, 
-            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
-        }
-        
-        __syncthreads();
-
-        for (unsigned int s=blockSize/2; s>0; s>>=1)
-        {
-          if (tid < s) {
-            const unsigned int xid1 = tid << 1;
-            const unsigned int xid2 = (tid + s) << 1;
-            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
-            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
-            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
-          }
-          __syncthreads();
-        }
-
-        if (tid == 0)
-        { 
-          *grad_sampling_loc = cache_grad_sampling_loc[0];
-          *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
-          *grad_attn_weight = cache_grad_attn_weight[0];
-        }
-        __syncthreads();
-
-        data_weight_ptr += 1;
-        data_loc_w_ptr += 2;
-        grad_attn_weight += grad_weight_stride;
-        grad_sampling_loc += grad_loc_stride;
-      }
-    }
-  }
-}
-
-
-template <typename scalar_t>
-__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n,
-                                                const scalar_t *grad_col,
-                                                const scalar_t *data_value,
-                                                const int64_t *data_spatial_shapes,
-                                                const int64_t *data_level_start_index, 
-                                                const scalar_t *data_sampling_loc,
-                                                const scalar_t *data_attn_weight,
-                                                const int batch_size, 
-                                                const int spatial_size, 
-                                                const int num_heads,
-                                                const int channels, 
-                                                const int num_levels,
-                                                const int num_query,
-                                                const int num_point,
-                                                scalar_t *grad_value,
-                                                scalar_t *grad_sampling_loc,
-                                                scalar_t *grad_attn_weight)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    extern __shared__ int _s[];
-    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
-    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
-    unsigned int tid = threadIdx.x;
-    int _temp = index;
-    const int c_col = _temp % channels;
-    _temp /= channels;
-    const int sampling_index = _temp; 
-    const int m_col = _temp % num_heads;
-    _temp /= num_heads;
-    const int q_col = _temp % num_query;
-    _temp /= num_query;
-    const int b_col = _temp;
-
-    const scalar_t top_grad = grad_col[index];
-
-    int data_weight_ptr = sampling_index * num_levels * num_point;
-    int data_loc_w_ptr = data_weight_ptr << 1;
-    const int grad_sampling_ptr = data_weight_ptr;
-    grad_sampling_loc += grad_sampling_ptr << 1;
-    grad_attn_weight += grad_sampling_ptr;
-    const int grad_weight_stride = 1;
-    const int grad_loc_stride = 2;
-    const int qid_stride = num_heads * channels;
-    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
-
-    for (int l_col=0; l_col < num_levels; ++l_col)
-    {
-      const int level_start_id = data_level_start_index[l_col];
-      const int spatial_h_ptr = l_col << 1;
-      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
-      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
-      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
-      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
-      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
-
-      for (int p_col=0; p_col < num_point; ++p_col)
-      {
-        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
-        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
-        const scalar_t weight = data_attn_weight[data_weight_ptr];
-
-        const scalar_t h_im = loc_h * spatial_h - 0.5;
-        const scalar_t w_im = loc_w * spatial_w - 0.5;
-        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
-        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
-        *(cache_grad_attn_weight+threadIdx.x)=0;
-        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
-        {
-          ms_deform_attn_col2im_bilinear(
-            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
-            top_grad, weight, grad_value_ptr, 
-            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
-        }
-        
-        __syncthreads();
-        if (tid == 0)
-        {
-          scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
-          int sid=2;
-          for (unsigned int tid = 1; tid < blockDim.x; ++tid)
-          {
-            _grad_w += cache_grad_sampling_loc[sid];
-            _grad_h += cache_grad_sampling_loc[sid + 1];
-            _grad_a += cache_grad_attn_weight[tid];
-            sid += 2;
-          }
-          
-          
-          *grad_sampling_loc = _grad_w;
-          *(grad_sampling_loc + 1) = _grad_h;
-          *grad_attn_weight = _grad_a;
-        }
-        __syncthreads();
-
-        data_weight_ptr += 1;
-        data_loc_w_ptr += 2;
-        grad_attn_weight += grad_weight_stride;
-        grad_sampling_loc += grad_loc_stride;
-      }
-    }
-  }
-}
-
-template <typename scalar_t>
-__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n,
-                                                const scalar_t *grad_col,
-                                                const scalar_t *data_value,
-                                                const int64_t *data_spatial_shapes,
-                                                const int64_t *data_level_start_index, 
-                                                const scalar_t *data_sampling_loc,
-                                                const scalar_t *data_attn_weight,
-                                                const int batch_size, 
-                                                const int spatial_size, 
-                                                const int num_heads,
-                                                const int channels, 
-                                                const int num_levels,
-                                                const int num_query,
-                                                const int num_point,
-                                                scalar_t *grad_value,
-                                                scalar_t *grad_sampling_loc,
-                                                scalar_t *grad_attn_weight)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    extern __shared__ int _s[];
-    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
-    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
-    unsigned int tid = threadIdx.x;
-    int _temp = index;
-    const int c_col = _temp % channels;
-    _temp /= channels;
-    const int sampling_index = _temp; 
-    const int m_col = _temp % num_heads;
-    _temp /= num_heads;
-    const int q_col = _temp % num_query;
-    _temp /= num_query;
-    const int b_col = _temp;
-
-    const scalar_t top_grad = grad_col[index];
-
-    int data_weight_ptr = sampling_index * num_levels * num_point;
-    int data_loc_w_ptr = data_weight_ptr << 1;
-    const int grad_sampling_ptr = data_weight_ptr;
-    grad_sampling_loc += grad_sampling_ptr << 1;
-    grad_attn_weight += grad_sampling_ptr;
-    const int grad_weight_stride = 1;
-    const int grad_loc_stride = 2;
-    const int qid_stride = num_heads * channels;
-    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
-
-    for (int l_col=0; l_col < num_levels; ++l_col)
-    {
-      const int level_start_id = data_level_start_index[l_col];
-      const int spatial_h_ptr = l_col << 1;
-      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
-      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
-      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
-      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
-      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
-
-      for (int p_col=0; p_col < num_point; ++p_col)
-      {
-        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
-        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
-        const scalar_t weight = data_attn_weight[data_weight_ptr];
-
-        const scalar_t h_im = loc_h * spatial_h - 0.5;
-        const scalar_t w_im = loc_w * spatial_w - 0.5;
-        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
-        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
-        *(cache_grad_attn_weight+threadIdx.x)=0;
-        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
-        {
-          ms_deform_attn_col2im_bilinear(
-            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
-            top_grad, weight, grad_value_ptr, 
-            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
-        }
-        
-        __syncthreads();
-
-        for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
-        {
-          if (tid < s) {
-            const unsigned int xid1 = tid << 1;
-            const unsigned int xid2 = (tid + s) << 1;
-            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
-            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
-            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
-            if (tid + (s << 1) < spre)
-            {
-              cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
-              cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
-              cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
-            } 
-          }
-          __syncthreads();
-        }
-
-        if (tid == 0)
-        {
-          *grad_sampling_loc = cache_grad_sampling_loc[0];
-          *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
-          *grad_attn_weight = cache_grad_attn_weight[0];
-        }
-        __syncthreads();
-
-        data_weight_ptr += 1;
-        data_loc_w_ptr += 2;
-        grad_attn_weight += grad_weight_stride;
-        grad_sampling_loc += grad_loc_stride;
-      }
-    }
-  }
-}
-
-template <typename scalar_t>
-__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n,
-                                                const scalar_t *grad_col,
-                                                const scalar_t *data_value,
-                                                const int64_t *data_spatial_shapes,
-                                                const int64_t *data_level_start_index, 
-                                                const scalar_t *data_sampling_loc,
-                                                const scalar_t *data_attn_weight,
-                                                const int batch_size, 
-                                                const int spatial_size, 
-                                                const int num_heads,
-                                                const int channels, 
-                                                const int num_levels,
-                                                const int num_query,
-                                                const int num_point,
-                                                scalar_t *grad_value,
-                                                scalar_t *grad_sampling_loc,
-                                                scalar_t *grad_attn_weight)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    extern __shared__ int _s[];
-    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
-    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
-    unsigned int tid = threadIdx.x;
-    int _temp = index;
-    const int c_col = _temp % channels;
-    _temp /= channels;
-    const int sampling_index = _temp; 
-    const int m_col = _temp % num_heads;
-    _temp /= num_heads;
-    const int q_col = _temp % num_query;
-    _temp /= num_query;
-    const int b_col = _temp;
-
-    const scalar_t top_grad = grad_col[index];
-
-    int data_weight_ptr = sampling_index * num_levels * num_point;
-    int data_loc_w_ptr = data_weight_ptr << 1;
-    const int grad_sampling_ptr = data_weight_ptr;
-    grad_sampling_loc += grad_sampling_ptr << 1;
-    grad_attn_weight += grad_sampling_ptr;
-    const int grad_weight_stride = 1;
-    const int grad_loc_stride = 2;
-    const int qid_stride = num_heads * channels;
-    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
-
-    for (int l_col=0; l_col < num_levels; ++l_col)
-    {
-      const int level_start_id = data_level_start_index[l_col];
-      const int spatial_h_ptr = l_col << 1;
-      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
-      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
-      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
-      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
-      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
-
-      for (int p_col=0; p_col < num_point; ++p_col)
-      {
-        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
-        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
-        const scalar_t weight = data_attn_weight[data_weight_ptr];
-
-        const scalar_t h_im = loc_h * spatial_h - 0.5;
-        const scalar_t w_im = loc_w * spatial_w - 0.5;
-        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
-        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
-        *(cache_grad_attn_weight+threadIdx.x)=0;
-        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
-        {
-          ms_deform_attn_col2im_bilinear(
-            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
-            top_grad, weight, grad_value_ptr, 
-            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
-        }
-        
-        __syncthreads();
-
-        for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
-        {
-          if (tid < s) {
-            const unsigned int xid1 = tid << 1;
-            const unsigned int xid2 = (tid + s) << 1;
-            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
-            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
-            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
-            if (tid + (s << 1) < spre)
-            {
-              cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
-              cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
-              cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
-            }
-          }
-          __syncthreads();
-        }
-
-        if (tid == 0)
-        {
-          atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]);
-          atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]);
-          atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]);
-        }
-        __syncthreads();
-
-        data_weight_ptr += 1;
-        data_loc_w_ptr += 2;
-        grad_attn_weight += grad_weight_stride;
-        grad_sampling_loc += grad_loc_stride;
-      }
-    }
-  }
-}
-
-
-template <typename scalar_t>
-__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n,
-                                                const scalar_t *grad_col,
-                                                const scalar_t *data_value,
-                                                const int64_t *data_spatial_shapes,
-                                                const int64_t *data_level_start_index, 
-                                                const scalar_t *data_sampling_loc,
-                                                const scalar_t *data_attn_weight,
-                                                const int batch_size, 
-                                                const int spatial_size, 
-                                                const int num_heads,
-                                                const int channels, 
-                                                const int num_levels,
-                                                const int num_query,
-                                                const int num_point,
-                                                scalar_t *grad_value,
-                                                scalar_t *grad_sampling_loc,
-                                                scalar_t *grad_attn_weight)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    int _temp = index;
-    const int c_col = _temp % channels;
-    _temp /= channels;
-    const int sampling_index = _temp; 
-    const int m_col = _temp % num_heads;
-    _temp /= num_heads;
-    const int q_col = _temp % num_query;
-    _temp /= num_query;
-    const int b_col = _temp;
-
-    const scalar_t top_grad = grad_col[index];
-
-    int data_weight_ptr = sampling_index * num_levels * num_point;
-    int data_loc_w_ptr = data_weight_ptr << 1;
-    const int grad_sampling_ptr = data_weight_ptr;
-    grad_sampling_loc += grad_sampling_ptr << 1;
-    grad_attn_weight += grad_sampling_ptr;
-    const int grad_weight_stride = 1;
-    const int grad_loc_stride = 2;
-    const int qid_stride = num_heads * channels;
-    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
-
-    for (int l_col=0; l_col < num_levels; ++l_col)
-    {
-      const int level_start_id = data_level_start_index[l_col];
-      const int spatial_h_ptr = l_col << 1;
-      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
-      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
-      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
-      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
-      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
-
-      for (int p_col=0; p_col < num_point; ++p_col)
-      {
-        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
-        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
-        const scalar_t weight = data_attn_weight[data_weight_ptr];
-
-        const scalar_t h_im = loc_h * spatial_h - 0.5;
-        const scalar_t w_im = loc_w * spatial_w - 0.5;
-        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
-        {
-          ms_deform_attn_col2im_bilinear_gm(
-            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
-            top_grad, weight, grad_value_ptr, 
-            grad_sampling_loc, grad_attn_weight);
-        }
-        data_weight_ptr += 1;
-        data_loc_w_ptr += 2;
-        grad_attn_weight += grad_weight_stride;
-        grad_sampling_loc += grad_loc_stride;
-      }
-    }
-  }
-}
-
-
-template <typename scalar_t>
-void ms_deformable_im2col_cuda(cudaStream_t stream,
-                              const scalar_t* data_value,
-                              const int64_t* data_spatial_shapes, 
-                              const int64_t* data_level_start_index, 
-                              const scalar_t* data_sampling_loc,
-                              const scalar_t* data_attn_weight,
-                              const int batch_size,
-                              const int spatial_size, 
-                              const int num_heads, 
-                              const int channels, 
-                              const int num_levels, 
-                              const int num_query,
-                              const int num_point,
-                              scalar_t* data_col)
-{
-  const int num_kernels = batch_size * num_query * num_heads * channels;
-  const int num_actual_kernels = batch_size * num_query * num_heads * channels;
-  const int num_threads = CUDA_NUM_THREADS;
-  ms_deformable_im2col_gpu_kernel<scalar_t>
-      <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-          0, stream>>>(
-      num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, 
-      batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col);
-  
-  cudaError_t err = cudaGetLastError();
-  if (err != cudaSuccess)
-  {
-    printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err));
-  }
-
-}
-
-template <typename scalar_t>
-void ms_deformable_col2im_cuda(cudaStream_t stream,
-                              const scalar_t* grad_col,
-                              const scalar_t* data_value,
-                              const int64_t * data_spatial_shapes,
-                              const int64_t * data_level_start_index,
-                              const scalar_t * data_sampling_loc,
-                              const scalar_t * data_attn_weight,
-                              const int batch_size, 
-                              const int spatial_size, 
-                              const int num_heads,
-                              const int channels, 
-                              const int num_levels,
-                              const int num_query,
-                              const int num_point, 
-                              scalar_t* grad_value,
-                              scalar_t* grad_sampling_loc,
-                              scalar_t* grad_attn_weight)
-{
-  const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels;
-  const int num_kernels = batch_size * num_query * num_heads * channels;
-  const int num_actual_kernels = batch_size * num_query * num_heads * channels;
-  if (channels > 1024)
-  {
-    if ((channels & 1023) == 0)
-    {
-      ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks<scalar_t>
-          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-              num_threads*3*sizeof(scalar_t), stream>>>(
-                        num_kernels, 
-                        grad_col,
-                        data_value,
-                        data_spatial_shapes,
-                        data_level_start_index, 
-                        data_sampling_loc,
-                        data_attn_weight,
-                        batch_size, 
-                        spatial_size, 
-                        num_heads,
-                        channels, 
-                        num_levels,
-                        num_query,
-                        num_point,
-                        grad_value,
-                        grad_sampling_loc,
-                        grad_attn_weight);
-    }
-    else
-    {
-      ms_deformable_col2im_gpu_kernel_gm<scalar_t>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-    }
-  }
-  else{
-    switch(channels)
-    {
-      case 1:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 1>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      case 2:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 2>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      case 4:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 4>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      case 8:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 8>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      case 16:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 16>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      case 32:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 32>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      case 64:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 64>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      case 128:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 128>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      case 256:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 256>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      case 512:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 512>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      case 1024:
-        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 1024>
-        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-            0, stream>>>(
-                      num_kernels, 
-                      grad_col,
-                      data_value,
-                      data_spatial_shapes,
-                      data_level_start_index, 
-                      data_sampling_loc,
-                      data_attn_weight,
-                      batch_size, 
-                      spatial_size, 
-                      num_heads,
-                      channels, 
-                      num_levels,
-                      num_query,
-                      num_point,
-                      grad_value,
-                      grad_sampling_loc,
-                      grad_attn_weight);
-        break;
-      default:
-        if (channels < 64)
-        {
-          ms_deformable_col2im_gpu_kernel_shm_reduce_v1<scalar_t>
-          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-              num_threads*3*sizeof(scalar_t), stream>>>(
-                        num_kernels, 
-                        grad_col,
-                        data_value,
-                        data_spatial_shapes,
-                        data_level_start_index, 
-                        data_sampling_loc,
-                        data_attn_weight,
-                        batch_size, 
-                        spatial_size, 
-                        num_heads,
-                        channels, 
-                        num_levels,
-                        num_query,
-                        num_point,
-                        grad_value,
-                        grad_sampling_loc,
-                        grad_attn_weight);
-        }
-        else
-        {
-          ms_deformable_col2im_gpu_kernel_shm_reduce_v2<scalar_t>
-          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
-              num_threads*3*sizeof(scalar_t), stream>>>(
-                        num_kernels, 
-                        grad_col,
-                        data_value,
-                        data_spatial_shapes,
-                        data_level_start_index, 
-                        data_sampling_loc,
-                        data_attn_weight,
-                        batch_size, 
-                        spatial_size, 
-                        num_heads,
-                        channels, 
-                        num_levels,
-                        num_query,
-                        num_point,
-                        grad_value,
-                        grad_sampling_loc,
-                        grad_attn_weight);
-        }
-    }
-  }
-  cudaError_t err = cudaGetLastError();
-  if (err != cudaSuccess)
-  {
-    printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err));
-  }
-
-}

groundingdino/models/GroundingDINO/csrc/cuda_version.cu

@@ -1,7 +0,0 @@
-#include <cuda_runtime_api.h>
-
-namespace groundingdino {
-int get_cudart_version() {
-  return CUDART_VERSION;
-}
-} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/vision.cpp

@@ -1,58 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-#include "MsDeformAttn/ms_deform_attn.h"
-
-namespace groundingdino {
-
-#ifdef WITH_CUDA
-extern int get_cudart_version();
-#endif
-
-std::string get_cuda_version() {
-#ifdef WITH_CUDA
-  std::ostringstream oss;
-
-  // copied from
-  // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
-  auto printCudaStyleVersion = [&](int v) {
-    oss << (v / 1000) << "." << (v / 10 % 100);
-    if (v % 10 != 0) {
-      oss << "." << (v % 10);
-    }
-  };
-  printCudaStyleVersion(get_cudart_version());
-  return oss.str();
-#else
-  return std::string("not available");
-#endif
-}
-
-// similar to
-// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
-std::string get_compiler_version() {
-  std::ostringstream ss;
-#if defined(__GNUC__)
-#ifndef __clang__
-  { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
-#endif
-#endif
-
-#if defined(__clang_major__)
-  {
-    ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
-       << __clang_patchlevel__;
-  }
-#endif
-
-#if defined(_MSC_VER)
-  { ss << "MSVC " << _MSC_FULL_VER; }
-#endif
-  return ss.str();
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
-  m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
-}
-
-} // namespace groundingdino

groundingdino/models/GroundingDINO/fuse_modules.py

@@ -1,297 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from timm.models.layers import DropPath
-
-
-class FeatureResizer(nn.Module):
-    """
-    This class takes as input a set of embeddings of dimension C1 and outputs a set of
-    embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
-    """
-
-    def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):
-        super().__init__()
-        self.do_ln = do_ln
-        # Object feature encoding
-        self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)
-        self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self, encoder_features):
-        x = self.fc(encoder_features)
-        if self.do_ln:
-            x = self.layer_norm(x)
-        output = self.dropout(x)
-        return output
-
-
-def l1norm(X, dim, eps=1e-8):
-    """L1-normalize columns of X"""
-    norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps
-    X = torch.div(X, norm)
-    return X
-
-
-def l2norm(X, dim, eps=1e-8):
-    """L2-normalize columns of X"""
-    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
-    X = torch.div(X, norm)
-    return X
-
-
-def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8):
-    """
-    query: (n_context, queryL, d)
-    context: (n_context, sourceL, d)
-    """
-    batch_size_q, queryL = query.size(0), query.size(1)
-    batch_size, sourceL = context.size(0), context.size(1)
-
-    # Get attention
-    # --> (batch, d, queryL)
-    queryT = torch.transpose(query, 1, 2)
-
-    # (batch, sourceL, d)(batch, d, queryL)
-    # --> (batch, sourceL, queryL)
-    attn = torch.bmm(context, queryT)
-    if raw_feature_norm == "softmax":
-        # --> (batch*sourceL, queryL)
-        attn = attn.view(batch_size * sourceL, queryL)
-        attn = nn.Softmax()(attn)
-        # --> (batch, sourceL, queryL)
-        attn = attn.view(batch_size, sourceL, queryL)
-    elif raw_feature_norm == "l2norm":
-        attn = l2norm(attn, 2)
-    elif raw_feature_norm == "clipped_l2norm":
-        attn = nn.LeakyReLU(0.1)(attn)
-        attn = l2norm(attn, 2)
-    else:
-        raise ValueError("unknown first norm type:", raw_feature_norm)
-    # --> (batch, queryL, sourceL)
-    attn = torch.transpose(attn, 1, 2).contiguous()
-    # --> (batch*queryL, sourceL)
-    attn = attn.view(batch_size * queryL, sourceL)
-    attn = nn.Softmax()(attn * smooth)
-    # --> (batch, queryL, sourceL)
-    attn = attn.view(batch_size, queryL, sourceL)
-    # --> (batch, sourceL, queryL)
-    attnT = torch.transpose(attn, 1, 2).contiguous()
-
-    # --> (batch, d, sourceL)
-    contextT = torch.transpose(context, 1, 2)
-    # (batch x d x sourceL)(batch x sourceL x queryL)
-    # --> (batch, d, queryL)
-    weightedContext = torch.bmm(contextT, attnT)
-    # --> (batch, queryL, d)
-    weightedContext = torch.transpose(weightedContext, 1, 2)
-
-    return weightedContext, attnT
-
-
-class BiMultiHeadAttention(nn.Module):
-    def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):
-        super(BiMultiHeadAttention, self).__init__()
-
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.head_dim = embed_dim // num_heads
-        self.v_dim = v_dim
-        self.l_dim = l_dim
-
-        assert (
-            self.head_dim * self.num_heads == self.embed_dim
-        ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
-        self.scale = self.head_dim ** (-0.5)
-        self.dropout = dropout
-
-        self.v_proj = nn.Linear(self.v_dim, self.embed_dim)
-        self.l_proj = nn.Linear(self.l_dim, self.embed_dim)
-        self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)
-        self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)
-
-        self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)
-        self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)
-
-        self.stable_softmax_2d = True
-        self.clamp_min_for_underflow = True
-        self.clamp_max_for_overflow = True
-
-        self._reset_parameters()
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def _reset_parameters(self):
-        nn.init.xavier_uniform_(self.v_proj.weight)
-        self.v_proj.bias.data.fill_(0)
-        nn.init.xavier_uniform_(self.l_proj.weight)
-        self.l_proj.bias.data.fill_(0)
-        nn.init.xavier_uniform_(self.values_v_proj.weight)
-        self.values_v_proj.bias.data.fill_(0)
-        nn.init.xavier_uniform_(self.values_l_proj.weight)
-        self.values_l_proj.bias.data.fill_(0)
-        nn.init.xavier_uniform_(self.out_v_proj.weight)
-        self.out_v_proj.bias.data.fill_(0)
-        nn.init.xavier_uniform_(self.out_l_proj.weight)
-        self.out_l_proj.bias.data.fill_(0)
-
-    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
-        """_summary_
-
-        Args:
-            v (_type_): bs, n_img, dim
-            l (_type_): bs, n_text, dim
-            attention_mask_v (_type_, optional): _description_. bs, n_img
-            attention_mask_l (_type_, optional): _description_. bs, n_text
-
-        Returns:
-            _type_: _description_
-        """
-        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-        #     import ipdb; ipdb.set_trace()
-        bsz, tgt_len, _ = v.size()
-
-        query_states = self.v_proj(v) * self.scale
-        key_states = self._shape(self.l_proj(l), -1, bsz)
-        value_v_states = self._shape(self.values_v_proj(v), -1, bsz)
-        value_l_states = self._shape(self.values_l_proj(l), -1, bsz)
-
-        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
-        key_states = key_states.view(*proj_shape)
-        value_v_states = value_v_states.view(*proj_shape)
-        value_l_states = value_l_states.view(*proj_shape)
-
-        src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))  # bs*nhead, nimg, ntxt
-
-        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
-            )
-
-        if self.stable_softmax_2d:
-            attn_weights = attn_weights - attn_weights.max()
-
-        if self.clamp_min_for_underflow:
-            attn_weights = torch.clamp(
-                attn_weights, min=-50000
-            )  # Do not increase -50000, data type half has quite limited range
-        if self.clamp_max_for_overflow:
-            attn_weights = torch.clamp(
-                attn_weights, max=50000
-            )  # Do not increase 50000, data type half has quite limited range
-
-        attn_weights_T = attn_weights.transpose(1, 2)
-        attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
-        if self.clamp_min_for_underflow:
-            attn_weights_l = torch.clamp(
-                attn_weights_l, min=-50000
-            )  # Do not increase -50000, data type half has quite limited range
-        if self.clamp_max_for_overflow:
-            attn_weights_l = torch.clamp(
-                attn_weights_l, max=50000
-            )  # Do not increase 50000, data type half has quite limited range
-
-        # mask vison for language
-        if attention_mask_v is not None:
-            attention_mask_v = (
-                attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
-            )
-            attn_weights_l.masked_fill_(attention_mask_v, float("-inf"))
-
-        attn_weights_l = attn_weights_l.softmax(dim=-1)
-
-        # mask language for vision
-        if attention_mask_l is not None:
-            attention_mask_l = (
-                attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
-            )
-            attn_weights.masked_fill_(attention_mask_l, float("-inf"))
-        attn_weights_v = attn_weights.softmax(dim=-1)
-
-        attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)
-        attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)
-
-        attn_output_v = torch.bmm(attn_probs_v, value_l_states)
-        attn_output_l = torch.bmm(attn_probs_l, value_v_states)
-
-        if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}"
-            )
-
-        if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}"
-            )
-
-        attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)
-        attn_output_v = attn_output_v.transpose(1, 2)
-        attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)
-
-        attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)
-        attn_output_l = attn_output_l.transpose(1, 2)
-        attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)
-
-        attn_output_v = self.out_v_proj(attn_output_v)
-        attn_output_l = self.out_l_proj(attn_output_l)
-
-        return attn_output_v, attn_output_l
-
-
-# Bi-Direction MHA (text->image, image->text)
-class BiAttentionBlock(nn.Module):
-    def __init__(
-        self,
-        v_dim,
-        l_dim,
-        embed_dim,
-        num_heads,
-        dropout=0.1,
-        drop_path=0.0,
-        init_values=1e-4,
-        cfg=None,
-    ):
-        """
-        Inputs:
-            embed_dim - Dimensionality of input and attention feature vectors
-            hidden_dim - Dimensionality of hidden layer in feed-forward network
-                         (usually 2-4x larger than embed_dim)
-            num_heads - Number of heads to use in the Multi-Head Attention block
-            dropout - Amount of dropout to apply in the feed-forward network
-        """
-        super(BiAttentionBlock, self).__init__()
-
-        # pre layer norm
-        self.layer_norm_v = nn.LayerNorm(v_dim)
-        self.layer_norm_l = nn.LayerNorm(l_dim)
-        self.attn = BiMultiHeadAttention(
-            v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout
-        )
-
-        # add layer scale for training stability
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
-        self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
-
-    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
-        v = self.layer_norm_v(v)
-        l = self.layer_norm_l(l)
-        delta_v, delta_l = self.attn(
-            v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l
-        )
-        # v, l = v + delta_v, l + delta_l
-        v = v + self.drop_path(self.gamma_v * delta_v)
-        l = l + self.drop_path(self.gamma_l * delta_l)
-        return v, l
-
-    # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)

groundingdino/models/GroundingDINO/groundingdino.py

@@ -1,385 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR model and criterion classes.
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Modified from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# ------------------------------------------------------------------------
-import copy
-from typing import List
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from groundingdino.util import get_tokenlizer
-from groundingdino.util.misc import (
-    NestedTensor,
-    inverse_sigmoid,
-    nested_tensor_from_tensor_list,
-)
-
-from groundingdino.models.GroundingDINO.backbone import build_backbone
-from groundingdino.models.GroundingDINO.bertwarper import (
-    BertModelWarper,
-    generate_masks_with_special_tokens_and_transfer_map,
-)
-from groundingdino.models.GroundingDINO.transformer import build_transformer
-from groundingdino.models.GroundingDINO.utils import MLP, ContrastiveEmbed
-
-
-class GroundingDINO(nn.Module):
-    """This is the Cross-Attention Detector module that performs object detection"""
-
-    def __init__(
-        self,
-        backbone,
-        transformer,
-        num_queries,
-        aux_loss=False,
-        iter_update=False,
-        query_dim=2,
-        num_feature_levels=1,
-        nheads=8,
-        # two stage
-        two_stage_type="no",  # ['no', 'standard']
-        dec_pred_bbox_embed_share=True,
-        two_stage_class_embed_share=True,
-        two_stage_bbox_embed_share=True,
-        num_patterns=0,
-        dn_number=100,
-        dn_box_noise_scale=0.4,
-        dn_label_noise_ratio=0.5,
-        dn_labelbook_size=100,
-        text_encoder_type="bert-base-uncased",
-        sub_sentence_present=True,
-        max_text_len=256,
-    ):
-        """Initializes the model.
-        Parameters:
-            backbone: torch module of the backbone to be used. See backbone.py
-            transformer: torch module of the transformer architecture. See transformer.py
-            num_queries: number of object queries, ie detection slot. This is the maximal number of objects
-                         Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
-            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
-        """
-        super().__init__()
-        self.num_queries = num_queries
-        self.transformer = transformer
-        self.hidden_dim = hidden_dim = transformer.d_model
-        self.num_feature_levels = num_feature_levels
-        self.nheads = nheads
-        self.max_text_len = 256
-        self.sub_sentence_present = sub_sentence_present
-
-        # setting query dim
-        self.query_dim = query_dim
-        assert query_dim == 4
-
-        # for dn training
-        self.num_patterns = num_patterns
-        self.dn_number = dn_number
-        self.dn_box_noise_scale = dn_box_noise_scale
-        self.dn_label_noise_ratio = dn_label_noise_ratio
-        self.dn_labelbook_size = dn_labelbook_size
-
-        # bert
-        # print("Text Encoder Type is ", text_encoder_type)
-        self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
-        self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
-        self.bert.pooler.dense.weight.requires_grad_(False)
-        self.bert.pooler.dense.bias.requires_grad_(False)
-        self.bert = BertModelWarper(bert_model=self.bert)
-
-        self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
-        nn.init.constant_(self.feat_map.bias.data, 0)
-        nn.init.xavier_uniform_(self.feat_map.weight.data)
-        # freeze
-
-        # special tokens
-        self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
-
-        # prepare input projection layers
-        if num_feature_levels > 1:
-            num_backbone_outs = len(backbone.num_channels)
-            input_proj_list = []
-            for _ in range(num_backbone_outs):
-                in_channels = backbone.num_channels[_]
-                input_proj_list.append(
-                    nn.Sequential(
-                        nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
-                        nn.GroupNorm(32, hidden_dim),
-                    )
-                )
-            for _ in range(num_feature_levels - num_backbone_outs):
-                input_proj_list.append(
-                    nn.Sequential(
-                        nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
-                        nn.GroupNorm(32, hidden_dim),
-                    )
-                )
-                in_channels = hidden_dim
-            self.input_proj = nn.ModuleList(input_proj_list)
-        else:
-            assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
-            self.input_proj = nn.ModuleList(
-                [
-                    nn.Sequential(
-                        nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
-                        nn.GroupNorm(32, hidden_dim),
-                    )
-                ]
-            )
-
-        self.backbone = backbone
-        self.aux_loss = aux_loss
-        self.box_pred_damping = box_pred_damping = None
-
-        self.iter_update = iter_update
-        assert iter_update, "Why not iter_update?"
-
-        # prepare pred layers
-        self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
-        # prepare class & box embed
-        _class_embed = ContrastiveEmbed()
-
-        _bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
-        nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
-        nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
-
-        if dec_pred_bbox_embed_share:
-            box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
-        else:
-            box_embed_layerlist = [
-                copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
-            ]
-        class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
-        self.bbox_embed = nn.ModuleList(box_embed_layerlist)
-        self.class_embed = nn.ModuleList(class_embed_layerlist)
-        self.transformer.decoder.bbox_embed = self.bbox_embed
-        self.transformer.decoder.class_embed = self.class_embed
-
-        # two stage
-        self.two_stage_type = two_stage_type
-        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
-            two_stage_type
-        )
-        if two_stage_type != "no":
-            if two_stage_bbox_embed_share:
-                assert dec_pred_bbox_embed_share
-                self.transformer.enc_out_bbox_embed = _bbox_embed
-            else:
-                self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
-
-            if two_stage_class_embed_share:
-                assert dec_pred_bbox_embed_share
-                self.transformer.enc_out_class_embed = _class_embed
-            else:
-                self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
-
-            self.refpoint_embed = None
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        # init input_proj
-        for proj in self.input_proj:
-            nn.init.xavier_uniform_(proj[0].weight, gain=1)
-            nn.init.constant_(proj[0].bias, 0)
-
-    def init_ref_points(self, use_num_queries):
-        self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
-
-    def forward(self, samples: NestedTensor, targets: List = None, **kw):
-        """The forward expects a NestedTensor, which consists of:
-           - samples.tensor: batched images, of shape [batch_size x 3 x H x W]
-           - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
-
-        It returns a dict with the following elements:
-           - "pred_logits": the classification logits (including no-object) for all queries.
-                            Shape= [batch_size x num_queries x num_classes]
-           - "pred_boxes": The normalized boxes coordinates for all queries, represented as
-                           (center_x, center_y, width, height). These values are normalized in [0, 1],
-                           relative to the size of each individual image (disregarding possible padding).
-                           See PostProcess for information on how to retrieve the unnormalized bounding box.
-           - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
-                            dictionnaries containing the two above keys for each decoder layer.
-        """
-        if targets is None:
-            captions = kw["captions"]
-        else:
-            captions = [t["caption"] for t in targets]
-        len(captions)
-
-        # encoder texts
-        tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to(
-            samples.device
-        )
-        (
-            text_self_attention_masks,
-            position_ids,
-            cate_to_token_mask_list,
-        ) = generate_masks_with_special_tokens_and_transfer_map(
-            tokenized, self.specical_tokens, self.tokenizer
-        )
-
-        if text_self_attention_masks.shape[1] > self.max_text_len:
-            text_self_attention_masks = text_self_attention_masks[
-                :, : self.max_text_len, : self.max_text_len
-            ]
-            position_ids = position_ids[:, : self.max_text_len]
-            tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
-            tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
-            tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
-
-        # extract text embeddings
-        if self.sub_sentence_present:
-            tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
-            tokenized_for_encoder["attention_mask"] = text_self_attention_masks
-            tokenized_for_encoder["position_ids"] = position_ids
-        else:
-            # import ipdb; ipdb.set_trace()
-            tokenized_for_encoder = tokenized
-
-        bert_output = self.bert(**tokenized_for_encoder)  # bs, 195, 768
-
-        encoded_text = self.feat_map(bert_output["last_hidden_state"])  # bs, 195, d_model
-        text_token_mask = tokenized.attention_mask.bool()  # bs, 195
-        # text_token_mask: True for nomask, False for mask
-        # text_self_attention_masks: True for nomask, False for mask
-
-        if encoded_text.shape[1] > self.max_text_len:
-            encoded_text = encoded_text[:, : self.max_text_len, :]
-            text_token_mask = text_token_mask[:, : self.max_text_len]
-            position_ids = position_ids[:, : self.max_text_len]
-            text_self_attention_masks = text_self_attention_masks[
-                :, : self.max_text_len, : self.max_text_len
-            ]
-
-        text_dict = {
-            "encoded_text": encoded_text,  # bs, 195, d_model
-            "text_token_mask": text_token_mask,  # bs, 195
-            "position_ids": position_ids,  # bs, 195
-            "text_self_attention_masks": text_self_attention_masks,  # bs, 195,195
-        }
-
-        # import ipdb; ipdb.set_trace()
-
-        if isinstance(samples, (list, torch.Tensor)):
-            samples = nested_tensor_from_tensor_list(samples)
-        features, poss = self.backbone(samples)
-
-        srcs = []
-        masks = []
-        for l, feat in enumerate(features):
-            src, mask = feat.decompose()
-            srcs.append(self.input_proj[l](src))
-            masks.append(mask)
-            assert mask is not None
-        if self.num_feature_levels > len(srcs):
-            _len_srcs = len(srcs)
-            for l in range(_len_srcs, self.num_feature_levels):
-                if l == _len_srcs:
-                    src = self.input_proj[l](features[-1].tensors)
-                else:
-                    src = self.input_proj[l](srcs[-1])
-                m = samples.mask
-                mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
-                pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
-                srcs.append(src)
-                masks.append(mask)
-                poss.append(pos_l)
-
-        input_query_bbox = input_query_label = attn_mask = dn_meta = None
-        hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(
-            srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict
-        )
-
-        # deformable-detr-like anchor update
-        outputs_coord_list = []
-        for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(
-            zip(reference[:-1], self.bbox_embed, hs)
-        ):
-            layer_delta_unsig = layer_bbox_embed(layer_hs)
-            layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)
-            layer_outputs_unsig = layer_outputs_unsig.sigmoid()
-            outputs_coord_list.append(layer_outputs_unsig)
-        outputs_coord_list = torch.stack(outputs_coord_list)
-
-        # output
-        outputs_class = torch.stack(
-            [
-                layer_cls_embed(layer_hs, text_dict)
-                for layer_cls_embed, layer_hs in zip(self.class_embed, hs)
-            ]
-        )
-        out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]}
-
-        # # for intermediate outputs
-        # if self.aux_loss:
-        #     out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)
-
-        # # for encoder output
-        # if hs_enc is not None:
-        #     # prepare intermediate outputs
-        #     interm_coord = ref_enc[-1]
-        #     interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict)
-        #     out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}
-        #     out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}
-
-        return out
-
-    @torch.jit.unused
-    def _set_aux_loss(self, outputs_class, outputs_coord):
-        # this is a workaround to make torchscript happy, as torchscript
-        # doesn't support dictionary with non-homogeneous values, such
-        # as a dict having both a Tensor and a list.
-        return [
-            {"pred_logits": a, "pred_boxes": b}
-            for a, b in zip(outputs_class[:-1], outputs_coord[:-1])
-        ]
-
-
-def build_groundingdino(args):
-
-    backbone = build_backbone(args)
-
-    transformer = build_transformer(args)
-
-    dn_labelbook_size = args.dn_labelbook_size
-    dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share
-    sub_sentence_present = args.sub_sentence_present
-
-    model = GroundingDINO(
-        backbone,
-        transformer,
-        num_queries=args.num_queries,
-        aux_loss=True,
-        iter_update=True,
-        query_dim=4,
-        num_feature_levels=args.num_feature_levels,
-        nheads=args.nheads,
-        dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,
-        two_stage_type=args.two_stage_type,
-        two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,
-        two_stage_class_embed_share=args.two_stage_class_embed_share,
-        num_patterns=args.num_patterns,
-        dn_number=0,
-        dn_box_noise_scale=args.dn_box_noise_scale,
-        dn_label_noise_ratio=args.dn_label_noise_ratio,
-        dn_labelbook_size=dn_labelbook_size,
-        text_encoder_type=args.text_encoder_type,
-        sub_sentence_present=sub_sentence_present,
-        max_text_len=args.max_text_len,
-    )
-
-    return model

groundingdino/models/GroundingDINO/ms_deform_attn.py

@@ -1,417 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from:
-# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py
-# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py
-# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py
-# ------------------------------------------------------------------------------------------------
-
-import math
-import warnings
-from typing import Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.init import constant_, xavier_uniform_
-
-try:
-    from groundingdino import _C
-    # from mmcv.utils import ext_loader
-    #
-    # _C = ext_loader.load_ext(
-    #     '_ext', ['ms_deform_attn_backward', 'ms_deform_attn_forward'])
-except BaseException as e:
-    print("Failed to load custom C++ ops. Running on CPU mode Only! - {}".format(e))
-
-
-# helpers
-def _is_power_of_2(n):
-    if (not isinstance(n, int)) or (n < 0):
-        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
-    return (n & (n - 1) == 0) and n != 0
-
-
-class MultiScaleDeformableAttnFunction(Function):
-    @staticmethod
-    def forward(
-        ctx,
-        value,
-        value_spatial_shapes,
-        value_level_start_index,
-        sampling_locations,
-        attention_weights,
-        im2col_step,
-    ):
-        ctx.im2col_step = im2col_step
-        output = _C.ms_deform_attn_forward(
-            value,
-            value_spatial_shapes,
-            value_level_start_index,
-            sampling_locations,
-            attention_weights,
-            ctx.im2col_step,
-        )
-        ctx.save_for_backward(
-            value,
-            value_spatial_shapes,
-            value_level_start_index,
-            sampling_locations,
-            attention_weights,
-        )
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        (
-            value,
-            value_spatial_shapes,
-            value_level_start_index,
-            sampling_locations,
-            attention_weights,
-        ) = ctx.saved_tensors
-        grad_value, grad_sampling_loc, grad_attn_weight = _C.ms_deform_attn_backward(
-            value,
-            value_spatial_shapes,
-            value_level_start_index,
-            sampling_locations,
-            attention_weights,
-            grad_output,
-            ctx.im2col_step,
-        )
-
-        return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
-
-
-def multi_scale_deformable_attn_pytorch(
-    value: torch.Tensor,
-    value_spatial_shapes: torch.Tensor,
-    sampling_locations: torch.Tensor,
-    attention_weights: torch.Tensor,
-) -> torch.Tensor:
-
-    bs, _, num_heads, embed_dims = value.shape
-    _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
-    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
-    sampling_grids = 2 * sampling_locations - 1
-    sampling_value_list = []
-    for level, (H_, W_) in enumerate(value_spatial_shapes):
-        # bs, H_*W_, num_heads, embed_dims ->
-        # bs, H_*W_, num_heads*embed_dims ->
-        # bs, num_heads*embed_dims, H_*W_ ->
-        # bs*num_heads, embed_dims, H_, W_
-        value_l_ = (
-            value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)
-        )
-        # bs, num_queries, num_heads, num_points, 2 ->
-        # bs, num_heads, num_queries, num_points, 2 ->
-        # bs*num_heads, num_queries, num_points, 2
-        sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
-        # bs*num_heads, embed_dims, num_queries, num_points
-        sampling_value_l_ = F.grid_sample(
-            value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
-        )
-        sampling_value_list.append(sampling_value_l_)
-    # (bs, num_queries, num_heads, num_levels, num_points) ->
-    # (bs, num_heads, num_queries, num_levels, num_points) ->
-    # (bs, num_heads, 1, num_queries, num_levels*num_points)
-    attention_weights = attention_weights.transpose(1, 2).reshape(
-        bs * num_heads, 1, num_queries, num_levels * num_points
-    )
-    output = (
-        (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
-        .sum(-1)
-        .view(bs, num_heads * embed_dims, num_queries)
-    )
-    return output.transpose(1, 2).contiguous()
-
-
-class MultiScaleDeformableAttention(nn.Module):
-    """Multi-Scale Deformable Attention Module used in Deformable-DETR
-
-    `Deformable DETR: Deformable Transformers for End-to-End Object Detection.
-    <https://arxiv.org/pdf/2010.04159.pdf>`_.
-
-    Args:
-        embed_dim (int): The embedding dimension of Attention. Default: 256.
-        num_heads (int): The number of attention heads. Default: 8.
-        num_levels (int): The number of feature map used in Attention. Default: 4.
-        num_points (int): The number of sampling points for each query
-            in each head. Default: 4.
-        img2col_steps (int): The step used in image_to_column. Defualt: 64.
-            dropout (float): Dropout layer used in output. Default: 0.1.
-        batch_first (bool): if ``True``, then the input and output tensor will be
-            provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)`
-    """
-
-    def __init__(
-        self,
-        embed_dim: int = 256,
-        num_heads: int = 8,
-        num_levels: int = 4,
-        num_points: int = 4,
-        img2col_step: int = 64,
-        batch_first: bool = False,
-    ):
-        super().__init__()
-        if embed_dim % num_heads != 0:
-            raise ValueError(
-                "embed_dim must be divisible by num_heads, but got {} and {}".format(
-                    embed_dim, num_heads
-                )
-            )
-        head_dim = embed_dim // num_heads
-
-        self.batch_first = batch_first
-
-        if not _is_power_of_2(head_dim):
-            warnings.warn(
-                """
-                You'd better set d_model in MSDeformAttn to make sure that
-                each dim of the attention head a power of 2, which is more efficient.
-                """
-            )
-
-        self.im2col_step = img2col_step
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.num_levels = num_levels
-        self.num_points = num_points
-        self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2)
-        self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points)
-        self.value_proj = nn.Linear(embed_dim, embed_dim)
-        self.output_proj = nn.Linear(embed_dim, embed_dim)
-
-        self.init_weights()
-
-    def _reset_parameters(self):
-        return self.init_weights()
-
-    def init_weights(self):
-        """
-        Default initialization for Parameters of Module.
-        """
-        constant_(self.sampling_offsets.weight.data, 0.0)
-        thetas = torch.arange(self.num_heads, dtype=torch.float32) * (
-            2.0 * math.pi / self.num_heads
-        )
-        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
-        grid_init = (
-            (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
-            .view(self.num_heads, 1, 1, 2)
-            .repeat(1, self.num_levels, self.num_points, 1)
-        )
-        for i in range(self.num_points):
-            grid_init[:, :, i, :] *= i + 1
-        with torch.no_grad():
-            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
-        constant_(self.attention_weights.weight.data, 0.0)
-        constant_(self.attention_weights.bias.data, 0.0)
-        xavier_uniform_(self.value_proj.weight.data)
-        constant_(self.value_proj.bias.data, 0.0)
-        xavier_uniform_(self.output_proj.weight.data)
-        constant_(self.output_proj.bias.data, 0.0)
-
-    def freeze_sampling_offsets(self):
-        print("Freeze sampling offsets")
-        self.sampling_offsets.weight.requires_grad = False
-        self.sampling_offsets.bias.requires_grad = False
-
-    def freeze_attention_weights(self):
-        print("Freeze attention weights")
-        self.attention_weights.weight.requires_grad = False
-        self.attention_weights.bias.requires_grad = False
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: Optional[torch.Tensor] = None,
-        value: Optional[torch.Tensor] = None,
-        query_pos: Optional[torch.Tensor] = None,
-        key_padding_mask: Optional[torch.Tensor] = None,
-        reference_points: Optional[torch.Tensor] = None,
-        spatial_shapes: Optional[torch.Tensor] = None,
-        level_start_index: Optional[torch.Tensor] = None,
-        **kwargs
-    ) -> torch.Tensor:
-
-        """Forward Function of MultiScaleDeformableAttention
-
-        Args:
-            query (torch.Tensor): Query embeddings with shape
-                `(num_query, bs, embed_dim)`
-            key (torch.Tensor): Key embeddings with shape
-                `(num_key, bs, embed_dim)`
-            value (torch.Tensor): Value embeddings with shape
-                `(num_key, bs, embed_dim)`
-            query_pos (torch.Tensor): The position embedding for `query`. Default: None.
-            key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`,
-                indicating which elements within `key` to be ignored in attention.
-            reference_points (torch.Tensor): The normalized reference points
-                with shape `(bs, num_query, num_levels, 2)`,
-                all elements is range in [0, 1], top-left (0, 0),
-                bottom-right (1, 1), including padding are.
-                or `(N, Length_{query}, num_levels, 4)`, add additional
-                two dimensions `(h, w)` to form reference boxes.
-            spatial_shapes (torch.Tensor): Spatial shape of features in different levels.
-                With shape `(num_levels, 2)`, last dimension represents `(h, w)`.
-            level_start_index (torch.Tensor): The start index of each level. A tensor with
-                shape `(num_levels, )` which can be represented as
-                `[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`.
-
-        Returns:
-            torch.Tensor: forward results with shape `(num_query, bs, embed_dim)`
-        """
-
-        if value is None:
-            value = query
-
-        if query_pos is not None:
-            query = query + query_pos
-
-        if not self.batch_first:
-            # change to (bs, num_query ,embed_dims)
-            query = query.permute(1, 0, 2)
-            value = value.permute(1, 0, 2)
-
-        bs, num_query, _ = query.shape
-        bs, num_value, _ = value.shape
-
-        assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
-
-        value = self.value_proj(value)
-        if key_padding_mask is not None:
-            value = value.masked_fill(key_padding_mask[..., None], float(0))
-        value = value.view(bs, num_value, self.num_heads, -1)
-        sampling_offsets = self.sampling_offsets(query).view(
-            bs, num_query, self.num_heads, self.num_levels, self.num_points, 2
-        )
-        attention_weights = self.attention_weights(query).view(
-            bs, num_query, self.num_heads, self.num_levels * self.num_points
-        )
-        attention_weights = attention_weights.softmax(-1)
-        attention_weights = attention_weights.view(
-            bs,
-            num_query,
-            self.num_heads,
-            self.num_levels,
-            self.num_points,
-        )
-
-        # bs, num_query, num_heads, num_levels, num_points, 2
-        if reference_points.shape[-1] == 2:
-            offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
-            sampling_locations = (
-                reference_points[:, :, None, :, None, :]
-                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
-            )
-        elif reference_points.shape[-1] == 4:
-            sampling_locations = (
-                reference_points[:, :, None, :, None, :2]
-                + sampling_offsets
-                / self.num_points
-                * reference_points[:, :, None, :, None, 2:]
-                * 0.5
-            )
-        else:
-            raise ValueError(
-                "Last dim of reference_points must be 2 or 4, but get {} instead.".format(
-                    reference_points.shape[-1]
-                )
-            )
-    
-        if torch.cuda.is_available() and value.is_cuda:
-            halffloat = False
-            if value.dtype == torch.float16:
-                halffloat = True
-                value = value.float()
-                sampling_locations = sampling_locations.float()
-                attention_weights = attention_weights.float()
-
-            output = MultiScaleDeformableAttnFunction.apply(
-                value,
-                spatial_shapes,
-                level_start_index,
-                sampling_locations,
-                attention_weights,
-                self.im2col_step,
-            )
-
-            if halffloat:
-                output = output.half()
-        else:
-            output = multi_scale_deformable_attn_pytorch(
-                value, spatial_shapes, sampling_locations, attention_weights
-            )
-
-        output = self.output_proj(output)
-
-        if not self.batch_first:
-            output = output.permute(1, 0, 2)
-
-        return output
-
-
-def create_dummy_class(klass, dependency, message=""):
-    """
-    When a dependency of a class is not available, create a dummy class which throws ImportError
-    when used.
-
-    Args:
-        klass (str): name of the class.
-        dependency (str): name of the dependency.
-        message: extra message to print
-    Returns:
-        class: a class object
-    """
-    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass)
-    if message:
-        err = err + " " + message
-
-    class _DummyMetaClass(type):
-        # throw error on class attribute access
-        def __getattr__(_, __):  # noqa: B902
-            raise ImportError(err)
-
-    class _Dummy(object, metaclass=_DummyMetaClass):
-        # throw error on constructor
-        def __init__(self, *args, **kwargs):
-            raise ImportError(err)
-
-    return _Dummy
-
-
-def create_dummy_func(func, dependency, message=""):
-    """
-    When a dependency of a function is not available, create a dummy function which throws
-    ImportError when used.
-
-    Args:
-        func (str): name of the function.
-        dependency (str or list[str]): name(s) of the dependency.
-        message: extra message to print
-    Returns:
-        function: a function object
-    """
-    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func)
-    if message:
-        err = err + " " + message
-
-    if isinstance(dependency, (list, tuple)):
-        dependency = ",".join(dependency)
-
-    def _dummy(*args, **kwargs):
-        raise ImportError(err)
-
-    return _dummy

groundingdino/models/GroundingDINO/transformer.py

@@ -1,959 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR Transformer class.
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Modified from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-from typing import Optional
-
-import torch
-import torch.utils.checkpoint as checkpoint
-from torch import Tensor, nn
-
-from groundingdino.util.misc import inverse_sigmoid
-
-from .fuse_modules import BiAttentionBlock
-from .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn
-from .transformer_vanilla import TransformerEncoderLayer
-from .utils import (
-    MLP,
-    _get_activation_fn,
-    _get_clones,
-    gen_encoder_output_proposals,
-    gen_sineembed_for_position,
-    get_sine_pos_embed,
-)
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        nhead=8,
-        num_queries=300,
-        num_encoder_layers=6,
-        num_unicoder_layers=0,
-        num_decoder_layers=6,
-        dim_feedforward=2048,
-        dropout=0.0,
-        activation="relu",
-        normalize_before=False,
-        return_intermediate_dec=False,
-        query_dim=4,
-        num_patterns=0,
-        # for deformable encoder
-        num_feature_levels=1,
-        enc_n_points=4,
-        dec_n_points=4,
-        # init query
-        learnable_tgt_init=False,
-        # two stage
-        two_stage_type="no",  # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1']
-        embed_init_tgt=False,
-        # for text
-        use_text_enhancer=False,
-        use_fusion_layer=False,
-        use_checkpoint=False,
-        use_transformer_ckpt=False,
-        use_text_cross_attention=False,
-        text_dropout=0.1,
-        fusion_dropout=0.1,
-        fusion_droppath=0.0,
-    ):
-        super().__init__()
-        self.num_feature_levels = num_feature_levels
-        self.num_encoder_layers = num_encoder_layers
-        self.num_unicoder_layers = num_unicoder_layers
-        self.num_decoder_layers = num_decoder_layers
-        self.num_queries = num_queries
-        assert query_dim == 4
-
-        # choose encoder layer type
-        encoder_layer = DeformableTransformerEncoderLayer(
-            d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
-        )
-
-        if use_text_enhancer:
-            text_enhance_layer = TransformerEncoderLayer(
-                d_model=d_model,
-                nhead=nhead // 2,
-                dim_feedforward=dim_feedforward // 2,
-                dropout=text_dropout,
-            )
-        else:
-            text_enhance_layer = None
-
-        if use_fusion_layer:
-            feature_fusion_layer = BiAttentionBlock(
-                v_dim=d_model,
-                l_dim=d_model,
-                embed_dim=dim_feedforward // 2,
-                num_heads=nhead // 2,
-                dropout=fusion_dropout,
-                drop_path=fusion_droppath,
-            )
-        else:
-            feature_fusion_layer = None
-
-        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
-        assert encoder_norm is None
-        self.encoder = TransformerEncoder(
-            encoder_layer,
-            num_encoder_layers,
-            d_model=d_model,
-            num_queries=num_queries,
-            text_enhance_layer=text_enhance_layer,
-            feature_fusion_layer=feature_fusion_layer,
-            use_checkpoint=use_checkpoint,
-            use_transformer_ckpt=use_transformer_ckpt,
-        )
-
-        # choose decoder layer type
-        decoder_layer = DeformableTransformerDecoderLayer(
-            d_model,
-            dim_feedforward,
-            dropout,
-            activation,
-            num_feature_levels,
-            nhead,
-            dec_n_points,
-            use_text_cross_attention=use_text_cross_attention,
-        )
-
-        decoder_norm = nn.LayerNorm(d_model)
-        self.decoder = TransformerDecoder(
-            decoder_layer,
-            num_decoder_layers,
-            decoder_norm,
-            return_intermediate=return_intermediate_dec,
-            d_model=d_model,
-            query_dim=query_dim,
-            num_feature_levels=num_feature_levels,
-        )
-
-        self.d_model = d_model
-        self.nhead = nhead
-        self.dec_layers = num_decoder_layers
-        self.num_queries = num_queries  # useful for single stage model only
-        self.num_patterns = num_patterns
-        if not isinstance(num_patterns, int):
-            Warning("num_patterns should be int but {}".format(type(num_patterns)))
-            self.num_patterns = 0
-
-        if num_feature_levels > 1:
-            if self.num_encoder_layers > 0:
-                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
-            else:
-                self.level_embed = None
-
-        self.learnable_tgt_init = learnable_tgt_init
-        assert learnable_tgt_init, "why not learnable_tgt_init"
-        self.embed_init_tgt = embed_init_tgt
-        if (two_stage_type != "no" and embed_init_tgt) or (two_stage_type == "no"):
-            self.tgt_embed = nn.Embedding(self.num_queries, d_model)
-            nn.init.normal_(self.tgt_embed.weight.data)
-        else:
-            self.tgt_embed = None
-
-        # for two stage
-        self.two_stage_type = two_stage_type
-        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
-            two_stage_type
-        )
-        if two_stage_type == "standard":
-            # anchor selection at the output of encoder
-            self.enc_output = nn.Linear(d_model, d_model)
-            self.enc_output_norm = nn.LayerNorm(d_model)
-            self.two_stage_wh_embedding = None
-
-        if two_stage_type == "no":
-            self.init_ref_points(num_queries)  # init self.refpoint_embed
-
-        self.enc_out_class_embed = None
-        self.enc_out_bbox_embed = None
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-        for m in self.modules():
-            if isinstance(m, MSDeformAttn):
-                m._reset_parameters()
-        if self.num_feature_levels > 1 and self.level_embed is not None:
-            nn.init.normal_(self.level_embed)
-
-    def get_valid_ratio(self, mask):
-        _, H, W = mask.shape
-        valid_H = torch.sum(~mask[:, :, 0], 1)
-        valid_W = torch.sum(~mask[:, 0, :], 1)
-        valid_ratio_h = valid_H.float() / H
-        valid_ratio_w = valid_W.float() / W
-        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
-        return valid_ratio
-
-    def init_ref_points(self, use_num_queries):
-        self.refpoint_embed = nn.Embedding(use_num_queries, 4)
-
-    def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):
-        """
-        Input:
-            - srcs: List of multi features [bs, ci, hi, wi]
-            - masks: List of multi masks [bs, hi, wi]
-            - refpoint_embed: [bs, num_dn, 4]. None in infer
-            - pos_embeds: List of multi pos embeds [bs, ci, hi, wi]
-            - tgt: [bs, num_dn, d_model]. None in infer
-
-        """
-        # prepare input for encoder
-        src_flatten = []
-        mask_flatten = []
-        lvl_pos_embed_flatten = []
-        spatial_shapes = []
-        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
-            bs, c, h, w = src.shape
-            spatial_shape = (h, w)
-            spatial_shapes.append(spatial_shape)
-
-            src = src.flatten(2).transpose(1, 2)  # bs, hw, c
-            mask = mask.flatten(1)  # bs, hw
-            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c
-            if self.num_feature_levels > 1 and self.level_embed is not None:
-                lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
-            else:
-                lvl_pos_embed = pos_embed
-            lvl_pos_embed_flatten.append(lvl_pos_embed)
-            src_flatten.append(src)
-            mask_flatten.append(mask)
-        src_flatten = torch.cat(src_flatten, 1)  # bs, \sum{hxw}, c
-        mask_flatten = torch.cat(mask_flatten, 1)  # bs, \sum{hxw}
-        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \sum{hxw}, c
-        spatial_shapes = torch.as_tensor(
-            spatial_shapes, dtype=torch.long, device=src_flatten.device
-        )
-        level_start_index = torch.cat(
-            (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])
-        )
-        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
-
-        # two stage
-        enc_topk_proposals = enc_refpoint_embed = None
-
-        #########################################################
-        # Begin Encoder
-        #########################################################
-        memory, memory_text = self.encoder(
-            src_flatten,
-            pos=lvl_pos_embed_flatten,
-            level_start_index=level_start_index,
-            spatial_shapes=spatial_shapes,
-            valid_ratios=valid_ratios,
-            key_padding_mask=mask_flatten,
-            memory_text=text_dict["encoded_text"],
-            text_attention_mask=~text_dict["text_token_mask"],
-            # we ~ the mask . False means use the token; True means pad the token
-            position_ids=text_dict["position_ids"],
-            text_self_attention_masks=text_dict["text_self_attention_masks"],
-        )
-        #########################################################
-        # End Encoder
-        # - memory: bs, \sum{hw}, c
-        # - mask_flatten: bs, \sum{hw}
-        # - lvl_pos_embed_flatten: bs, \sum{hw}, c
-        # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
-        # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
-        #########################################################
-        text_dict["encoded_text"] = memory_text
-        # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-        #     if memory.isnan().any() | memory.isinf().any():
-        #         import ipdb; ipdb.set_trace()
-
-        if self.two_stage_type == "standard":
-            output_memory, output_proposals = gen_encoder_output_proposals(
-                memory, mask_flatten, spatial_shapes
-            )
-            output_memory = self.enc_output_norm(self.enc_output(output_memory))
-
-            if text_dict is not None:
-                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)
-            else:
-                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)
-
-            topk_logits = enc_outputs_class_unselected.max(-1)[0]
-            enc_outputs_coord_unselected = (
-                self.enc_out_bbox_embed(output_memory) + output_proposals
-            )  # (bs, \sum{hw}, 4) unsigmoid
-            topk = self.num_queries
-
-            topk_proposals = torch.topk(topk_logits, topk, dim=1)[1]  # bs, nq
-
-            # gather boxes
-            refpoint_embed_undetach = torch.gather(
-                enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
-            )  # unsigmoid
-            refpoint_embed_ = refpoint_embed_undetach.detach()
-            init_box_proposal = torch.gather(
-                output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
-            ).sigmoid()  # sigmoid
-
-            # gather tgt
-            tgt_undetach = torch.gather(
-                output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
-            )
-            if self.embed_init_tgt:
-                tgt_ = (
-                    self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-                )  # nq, bs, d_model
-            else:
-                tgt_ = tgt_undetach.detach()
-
-            if refpoint_embed is not None:
-                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
-                tgt = torch.cat([tgt, tgt_], dim=1)
-            else:
-                refpoint_embed, tgt = refpoint_embed_, tgt_
-
-        elif self.two_stage_type == "no":
-            tgt_ = (
-                self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-            )  # nq, bs, d_model
-            refpoint_embed_ = (
-                self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-            )  # nq, bs, 4
-
-            if refpoint_embed is not None:
-                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
-                tgt = torch.cat([tgt, tgt_], dim=1)
-            else:
-                refpoint_embed, tgt = refpoint_embed_, tgt_
-
-            if self.num_patterns > 0:
-                tgt_embed = tgt.repeat(1, self.num_patterns, 1)
-                refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1)
-                tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(
-                    self.num_queries, 1
-                )  # 1, n_q*n_pat, d_model
-                tgt = tgt_embed + tgt_pat
-
-            init_box_proposal = refpoint_embed_.sigmoid()
-
-        else:
-            raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type))
-        #########################################################
-        # End preparing tgt
-        # - tgt: bs, NQ, d_model
-        # - refpoint_embed(unsigmoid): bs, NQ, d_model
-        #########################################################
-
-        #########################################################
-        # Begin Decoder
-        #########################################################
-        hs, references = self.decoder(
-            tgt=tgt.transpose(0, 1),
-            memory=memory.transpose(0, 1),
-            memory_key_padding_mask=mask_flatten,
-            pos=lvl_pos_embed_flatten.transpose(0, 1),
-            refpoints_unsigmoid=refpoint_embed.transpose(0, 1),
-            level_start_index=level_start_index,
-            spatial_shapes=spatial_shapes,
-            valid_ratios=valid_ratios,
-            tgt_mask=attn_mask,
-            memory_text=text_dict["encoded_text"],
-            text_attention_mask=~text_dict["text_token_mask"],
-            # we ~ the mask . False means use the token; True means pad the token
-        )
-        #########################################################
-        # End Decoder
-        # hs: n_dec, bs, nq, d_model
-        # references: n_dec+1, bs, nq, query_dim
-        #########################################################
-
-        #########################################################
-        # Begin postprocess
-        #########################################################
-        if self.two_stage_type == "standard":
-            hs_enc = tgt_undetach.unsqueeze(0)
-            ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0)
-        else:
-            hs_enc = ref_enc = None
-        #########################################################
-        # End postprocess
-        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None
-        # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None
-        #########################################################
-
-        return hs, references, hs_enc, ref_enc, init_box_proposal
-        # hs: (n_dec, bs, nq, d_model)
-        # references: sigmoid coordinates. (n_dec+1, bs, bq, 4)
-        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None
-        # ref_enc: sigmoid coordinates. \
-        #           (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-        self,
-        encoder_layer,
-        num_layers,
-        d_model=256,
-        num_queries=300,
-        enc_layer_share=False,
-        text_enhance_layer=None,
-        feature_fusion_layer=None,
-        use_checkpoint=False,
-        use_transformer_ckpt=False,
-    ):
-        """_summary_
-
-        Args:
-            encoder_layer (_type_): _description_
-            num_layers (_type_): _description_
-            norm (_type_, optional): _description_. Defaults to None.
-            d_model (int, optional): _description_. Defaults to 256.
-            num_queries (int, optional): _description_. Defaults to 300.
-            enc_layer_share (bool, optional): _description_. Defaults to False.
-
-        """
-        super().__init__()
-        # prepare layers
-        self.layers = []
-        self.text_layers = []
-        self.fusion_layers = []
-        if num_layers > 0:
-            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
-
-            if text_enhance_layer is not None:
-                self.text_layers = _get_clones(
-                    text_enhance_layer, num_layers, layer_share=enc_layer_share
-                )
-            if feature_fusion_layer is not None:
-                self.fusion_layers = _get_clones(
-                    feature_fusion_layer, num_layers, layer_share=enc_layer_share
-                )
-        else:
-            self.layers = []
-            del encoder_layer
-
-            if text_enhance_layer is not None:
-                self.text_layers = []
-                del text_enhance_layer
-            if feature_fusion_layer is not None:
-                self.fusion_layers = []
-                del feature_fusion_layer
-
-        self.query_scale = None
-        self.num_queries = num_queries
-        self.num_layers = num_layers
-        self.d_model = d_model
-
-        self.use_checkpoint = use_checkpoint
-        self.use_transformer_ckpt = use_transformer_ckpt
-
-    @staticmethod
-    def get_reference_points(spatial_shapes, valid_ratios, device):
-        reference_points_list = []
-        for lvl, (H_, W_) in enumerate(spatial_shapes):
-
-            ref_y, ref_x = torch.meshgrid(
-                torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
-                torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
-            )
-            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
-            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
-            ref = torch.stack((ref_x, ref_y), -1)
-            reference_points_list.append(ref)
-        reference_points = torch.cat(reference_points_list, 1)
-        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
-        return reference_points
-
-    def forward(
-        self,
-        # for images
-        src: Tensor,
-        pos: Tensor,
-        spatial_shapes: Tensor,
-        level_start_index: Tensor,
-        valid_ratios: Tensor,
-        key_padding_mask: Tensor,
-        # for texts
-        memory_text: Tensor = None,
-        text_attention_mask: Tensor = None,
-        pos_text: Tensor = None,
-        text_self_attention_masks: Tensor = None,
-        position_ids: Tensor = None,
-    ):
-        """
-        Input:
-            - src: [bs, sum(hi*wi), 256]
-            - pos: pos embed for src. [bs, sum(hi*wi), 256]
-            - spatial_shapes: h,w of each level [num_level, 2]
-            - level_start_index: [num_level] start point of level in sum(hi*wi).
-            - valid_ratios: [bs, num_level, 2]
-            - key_padding_mask: [bs, sum(hi*wi)]
-
-            - memory_text: bs, n_text, 256
-            - text_attention_mask: bs, n_text
-                False for no padding; True for padding
-            - pos_text: bs, n_text, 256
-
-            - position_ids: bs, n_text
-        Intermedia:
-            - reference_points: [bs, sum(hi*wi), num_level, 2]
-        Outpus:
-            - output: [bs, sum(hi*wi), 256]
-        """
-
-        output = src
-
-        # preparation and reshape
-        if self.num_layers > 0:
-            reference_points = self.get_reference_points(
-                spatial_shapes, valid_ratios, device=src.device
-            )
-
-        if self.text_layers:
-            # generate pos_text
-            bs, n_text, text_dim = memory_text.shape
-            if pos_text is None and position_ids is None:
-                pos_text = (
-                    torch.arange(n_text, device=memory_text.device)
-                    .float()
-                    .unsqueeze(0)
-                    .unsqueeze(-1)
-                    .repeat(bs, 1, 1)
-                )
-                pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)
-            if position_ids is not None:
-                pos_text = get_sine_pos_embed(
-                    position_ids[..., None], num_pos_feats=256, exchange_xy=False
-                )
-
-        # main process
-        for layer_id, layer in enumerate(self.layers):
-            # if output.isnan().any() or memory_text.isnan().any():
-            #     if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-            #         import ipdb; ipdb.set_trace()
-            if self.fusion_layers:
-                if self.use_checkpoint:
-                    output, memory_text = checkpoint.checkpoint(
-                        self.fusion_layers[layer_id],
-                        output,
-                        memory_text,
-                        key_padding_mask,
-                        text_attention_mask,
-                    )
-                else:
-                    output, memory_text = self.fusion_layers[layer_id](
-                        v=output,
-                        l=memory_text,
-                        attention_mask_v=key_padding_mask,
-                        attention_mask_l=text_attention_mask,
-                    )
-
-            if self.text_layers:
-                memory_text = self.text_layers[layer_id](
-                    src=memory_text.transpose(0, 1),
-                    src_mask=~text_self_attention_masks,  # note we use ~ for mask here
-                    src_key_padding_mask=text_attention_mask,
-                    pos=(pos_text.transpose(0, 1) if pos_text is not None else None),
-                ).transpose(0, 1)
-
-            # main process
-            if self.use_transformer_ckpt:
-                output = checkpoint.checkpoint(
-                    layer,
-                    output,
-                    pos,
-                    reference_points,
-                    spatial_shapes,
-                    level_start_index,
-                    key_padding_mask,
-                )
-            else:
-                output = layer(
-                    src=output,
-                    pos=pos,
-                    reference_points=reference_points,
-                    spatial_shapes=spatial_shapes,
-                    level_start_index=level_start_index,
-                    key_padding_mask=key_padding_mask,
-                )
-
-        return output, memory_text
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(
-        self,
-        decoder_layer,
-        num_layers,
-        norm=None,
-        return_intermediate=False,
-        d_model=256,
-        query_dim=4,
-        num_feature_levels=1,
-    ):
-        super().__init__()
-        if num_layers > 0:
-            self.layers = _get_clones(decoder_layer, num_layers)
-        else:
-            self.layers = []
-        self.num_layers = num_layers
-        self.norm = norm
-        self.return_intermediate = return_intermediate
-        assert return_intermediate, "support return_intermediate only"
-        self.query_dim = query_dim
-        assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim)
-        self.num_feature_levels = num_feature_levels
-
-        self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2)
-        self.query_pos_sine_scale = None
-
-        self.query_scale = None
-        self.bbox_embed = None
-        self.class_embed = None
-
-        self.d_model = d_model
-
-        self.ref_anchor_head = None
-
-    def forward(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        refpoints_unsigmoid: Optional[Tensor] = None,  # num_queries, bs, 2
-        # for memory
-        level_start_index: Optional[Tensor] = None,  # num_levels
-        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
-        valid_ratios: Optional[Tensor] = None,
-        # for text
-        memory_text: Optional[Tensor] = None,
-        text_attention_mask: Optional[Tensor] = None,
-    ):
-        """
-        Input:
-            - tgt: nq, bs, d_model
-            - memory: hw, bs, d_model
-            - pos: hw, bs, d_model
-            - refpoints_unsigmoid: nq, bs, 2/4
-            - valid_ratios/spatial_shapes: bs, nlevel, 2
-        """
-        output = tgt
-
-        intermediate = []
-        reference_points = refpoints_unsigmoid.sigmoid()
-        ref_points = [reference_points]
-
-        for layer_id, layer in enumerate(self.layers):
-
-            if reference_points.shape[-1] == 4:
-                reference_points_input = (
-                    reference_points[:, :, None]
-                    * torch.cat([valid_ratios, valid_ratios], -1)[None, :]
-                )  # nq, bs, nlevel, 4
-            else:
-                assert reference_points.shape[-1] == 2
-                reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]
-            query_sine_embed = gen_sineembed_for_position(
-                reference_points_input[:, :, 0, :]
-            )  # nq, bs, 256*2
-
-            # conditional query
-            raw_query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, 256
-            pos_scale = self.query_scale(output) if self.query_scale is not None else 1
-            query_pos = pos_scale * raw_query_pos
-            # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-            #     if query_pos.isnan().any() | query_pos.isinf().any():
-            #         import ipdb; ipdb.set_trace()
-
-            # main process
-            output = layer(
-                tgt=output,
-                tgt_query_pos=query_pos,
-                tgt_query_sine_embed=query_sine_embed,
-                tgt_key_padding_mask=tgt_key_padding_mask,
-                tgt_reference_points=reference_points_input,
-                memory_text=memory_text,
-                text_attention_mask=text_attention_mask,
-                memory=memory,
-                memory_key_padding_mask=memory_key_padding_mask,
-                memory_level_start_index=level_start_index,
-                memory_spatial_shapes=spatial_shapes,
-                memory_pos=pos,
-                self_attn_mask=tgt_mask,
-                cross_attn_mask=memory_mask,
-            )
-            if output.isnan().any() | output.isinf().any():
-                print(f"output layer_id {layer_id} is nan")
-                try:
-                    num_nan = output.isnan().sum().item()
-                    num_inf = output.isinf().sum().item()
-                    print(f"num_nan {num_nan}, num_inf {num_inf}")
-                except Exception as e:
-                    print(e)
-                    # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-                    #     import ipdb; ipdb.set_trace()
-
-            # iter update
-            if self.bbox_embed is not None:
-                # box_holder = self.bbox_embed(output)
-                # box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points)
-                # new_reference_points = box_holder[..., :self.query_dim].sigmoid()
-
-                reference_before_sigmoid = inverse_sigmoid(reference_points)
-                delta_unsig = self.bbox_embed[layer_id](output)
-                outputs_unsig = delta_unsig + reference_before_sigmoid
-                new_reference_points = outputs_unsig.sigmoid()
-
-                reference_points = new_reference_points.detach()
-                # if layer_id != self.num_layers - 1:
-                ref_points.append(new_reference_points)
-
-            intermediate.append(self.norm(output))
-
-        return [
-            [itm_out.transpose(0, 1) for itm_out in intermediate],
-            [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points],
-        ]
-
-
-class DeformableTransformerEncoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        d_ffn=1024,
-        dropout=0.1,
-        activation="relu",
-        n_levels=4,
-        n_heads=8,
-        n_points=4,
-    ):
-        super().__init__()
-
-        # self attention
-        self.self_attn = MSDeformAttn(
-            embed_dim=d_model,
-            num_levels=n_levels,
-            num_heads=n_heads,
-            num_points=n_points,
-            batch_first=True,
-        )
-        self.dropout1 = nn.Dropout(dropout)
-        self.norm1 = nn.LayerNorm(d_model)
-
-        # ffn
-        self.linear1 = nn.Linear(d_model, d_ffn)
-        self.activation = _get_activation_fn(activation, d_model=d_ffn)
-        self.dropout2 = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(d_ffn, d_model)
-        self.dropout3 = nn.Dropout(dropout)
-        self.norm2 = nn.LayerNorm(d_model)
-
-    @staticmethod
-    def with_pos_embed(tensor, pos):
-        return tensor if pos is None else tensor + pos
-
-    def forward_ffn(self, src):
-        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
-        src = src + self.dropout3(src2)
-        src = self.norm2(src)
-        return src
-
-    def forward(
-        self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None
-    ):
-        # self attention
-        # import ipdb; ipdb.set_trace()
-        src2 = self.self_attn(
-            query=self.with_pos_embed(src, pos),
-            reference_points=reference_points,
-            value=src,
-            spatial_shapes=spatial_shapes,
-            level_start_index=level_start_index,
-            key_padding_mask=key_padding_mask,
-        )
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-
-        # ffn
-        src = self.forward_ffn(src)
-
-        return src
-
-
-class DeformableTransformerDecoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        d_ffn=1024,
-        dropout=0.1,
-        activation="relu",
-        n_levels=4,
-        n_heads=8,
-        n_points=4,
-        use_text_feat_guide=False,
-        use_text_cross_attention=False,
-    ):
-        super().__init__()
-
-        # cross attention
-        self.cross_attn = MSDeformAttn(
-            embed_dim=d_model,
-            num_levels=n_levels,
-            num_heads=n_heads,
-            num_points=n_points,
-            batch_first=True,
-        )
-        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm1 = nn.LayerNorm(d_model)
-
-        # cross attention text
-        if use_text_cross_attention:
-            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
-            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-            self.catext_norm = nn.LayerNorm(d_model)
-
-        # self attention
-        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
-        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm2 = nn.LayerNorm(d_model)
-
-        # ffn
-        self.linear1 = nn.Linear(d_model, d_ffn)
-        self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1)
-        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.linear2 = nn.Linear(d_ffn, d_model)
-        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm3 = nn.LayerNorm(d_model)
-
-        self.key_aware_proj = None
-        self.use_text_feat_guide = use_text_feat_guide
-        assert not use_text_feat_guide
-        self.use_text_cross_attention = use_text_cross_attention
-
-    def rm_self_attn_modules(self):
-        self.self_attn = None
-        self.dropout2 = None
-        self.norm2 = None
-
-    @staticmethod
-    def with_pos_embed(tensor, pos):
-        return tensor if pos is None else tensor + pos
-
-    def forward_ffn(self, tgt):
-        with torch.cuda.amp.autocast(enabled=False):
-            tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
-        tgt = tgt + self.dropout4(tgt2)
-        tgt = self.norm3(tgt)
-        return tgt
-
-    def forward(
-        self,
-        # for tgt
-        tgt: Optional[Tensor],  # nq, bs, d_model
-        tgt_query_pos: Optional[Tensor] = None,  # pos for query. MLP(Sine(pos))
-        tgt_query_sine_embed: Optional[Tensor] = None,  # pos for query. Sine(pos)
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        tgt_reference_points: Optional[Tensor] = None,  # nq, bs, 4
-        memory_text: Optional[Tensor] = None,  # bs, num_token, d_model
-        text_attention_mask: Optional[Tensor] = None,  # bs, num_token
-        # for memory
-        memory: Optional[Tensor] = None,  # hw, bs, d_model
-        memory_key_padding_mask: Optional[Tensor] = None,
-        memory_level_start_index: Optional[Tensor] = None,  # num_levels
-        memory_spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
-        memory_pos: Optional[Tensor] = None,  # pos for memory
-        # sa
-        self_attn_mask: Optional[Tensor] = None,  # mask used for self-attention
-        cross_attn_mask: Optional[Tensor] = None,  # mask used for cross-attention
-    ):
-        """
-        Input:
-            - tgt/tgt_query_pos: nq, bs, d_model
-            -
-        """
-        assert cross_attn_mask is None
-
-        # self attention
-        if self.self_attn is not None:
-            # import ipdb; ipdb.set_trace()
-            q = k = self.with_pos_embed(tgt, tgt_query_pos)
-            tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0]
-            tgt = tgt + self.dropout2(tgt2)
-            tgt = self.norm2(tgt)
-
-        if self.use_text_cross_attention:
-            tgt2 = self.ca_text(
-                self.with_pos_embed(tgt, tgt_query_pos),
-                memory_text.transpose(0, 1),
-                memory_text.transpose(0, 1),
-                key_padding_mask=text_attention_mask,
-            )[0]
-            tgt = tgt + self.catext_dropout(tgt2)
-            tgt = self.catext_norm(tgt)
-
-        tgt2 = self.cross_attn(
-            query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1),
-            reference_points=tgt_reference_points.transpose(0, 1).contiguous(),
-            value=memory.transpose(0, 1),
-            spatial_shapes=memory_spatial_shapes,
-            level_start_index=memory_level_start_index,
-            key_padding_mask=memory_key_padding_mask,
-        ).transpose(0, 1)
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
-
-        # ffn
-        tgt = self.forward_ffn(tgt)
-
-        return tgt
-
-
-def build_transformer(args):
-    return Transformer(
-        d_model=args.hidden_dim,
-        dropout=args.dropout,
-        nhead=args.nheads,
-        num_queries=args.num_queries,
-        dim_feedforward=args.dim_feedforward,
-        num_encoder_layers=args.enc_layers,
-        num_decoder_layers=args.dec_layers,
-        normalize_before=args.pre_norm,
-        return_intermediate_dec=True,
-        query_dim=args.query_dim,
-        activation=args.transformer_activation,
-        num_patterns=args.num_patterns,
-        num_feature_levels=args.num_feature_levels,
-        enc_n_points=args.enc_n_points,
-        dec_n_points=args.dec_n_points,
-        learnable_tgt_init=True,
-        # two stage
-        two_stage_type=args.two_stage_type,  # ['no', 'standard', 'early']
-        embed_init_tgt=args.embed_init_tgt,
-        use_text_enhancer=args.use_text_enhancer,
-        use_fusion_layer=args.use_fusion_layer,
-        use_checkpoint=args.use_checkpoint,
-        use_transformer_ckpt=args.use_transformer_ckpt,
-        use_text_cross_attention=args.use_text_cross_attention,
-        text_dropout=args.text_dropout,
-        fusion_dropout=args.fusion_dropout,
-        fusion_droppath=args.fusion_droppath,
-    )

groundingdino/models/GroundingDINO/transformer_vanilla.py

@@ -1,123 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-DETR Transformer class.
-
-Copy-paste from torch.nn.Transformer with modifications:
-    * positional encodings are passed in MHattention
-    * extra LN at the end of encoder is removed
-    * decoder returns a stack of activations from all decoding layers
-"""
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-from .utils import (
-    MLP,
-    _get_activation_fn,
-    _get_clones,
-    gen_encoder_output_proposals,
-    gen_sineembed_for_position,
-    sigmoid_focal_loss,
-)
-
-
-class TextTransformer(nn.Module):
-    def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
-        super().__init__()
-        self.num_layers = num_layers
-        self.d_model = d_model
-        self.nheads = nheads
-        self.dim_feedforward = dim_feedforward
-        self.norm = None
-
-        single_encoder_layer = TransformerEncoderLayer(
-            d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout
-        )
-        self.layers = _get_clones(single_encoder_layer, num_layers)
-
-    def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):
-        """
-
-        Args:
-            text_attention_mask: bs, num_token
-            memory_text: bs, num_token, d_model
-
-        Raises:
-            RuntimeError: _description_
-
-        Returns:
-            output: bs, num_token, d_model
-        """
-
-        output = memory_text.transpose(0, 1)
-
-        for layer in self.layers:
-            output = layer(output, src_key_padding_mask=text_attention_mask)
-
-        if self.norm is not None:
-            output = self.norm(output)
-
-        return output.transpose(0, 1)
-
-
-class TransformerEncoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        normalize_before=False,
-    ):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        # Implementation of Feedforward model
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2 = nn.LayerNorm(d_model)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
-        self.nhead = nhead
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        # repeat attn mask
-        if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
-            # bs, num_q, num_k
-            src_mask = src_mask.repeat(self.nhead, 1, 1)
-
-        q = k = self.with_pos_embed(src, pos)
-
-        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
-
-        # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
-        src = src + self.dropout2(src2)
-        src = self.norm2(src)
-        return src

groundingdino/models/GroundingDINO/utils.py

@@ -1,268 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-
-import copy
-import math
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-
-def _get_clones(module, N, layer_share=False):
-    # import ipdb; ipdb.set_trace()
-    if layer_share:
-        return nn.ModuleList([module for i in range(N)])
-    else:
-        return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-def get_sine_pos_embed(
-    pos_tensor: torch.Tensor,
-    num_pos_feats: int = 128,
-    temperature: int = 10000,
-    exchange_xy: bool = True,
-):
-    """generate sine position embedding from a position tensor
-    Args:
-        pos_tensor (torch.Tensor): shape: [..., n].
-        num_pos_feats (int): projected shape for each float in the tensor.
-        temperature (int): temperature in the sine/cosine function.
-        exchange_xy (bool, optional): exchange pos x and pos y. \
-            For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
-    Returns:
-        pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
-    """
-    scale = 2 * math.pi
-    dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
-
-    def sine_func(x: torch.Tensor):
-        sin_x = x * scale / dim_t
-        sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
-        return sin_x
-
-    pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
-    if exchange_xy:
-        pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
-    pos_res = torch.cat(pos_res, dim=-1)
-    return pos_res
-
-
-def gen_encoder_output_proposals(
-    memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None
-):
-    """
-    Input:
-        - memory: bs, \sum{hw}, d_model
-        - memory_padding_mask: bs, \sum{hw}
-        - spatial_shapes: nlevel, 2
-        - learnedwh: 2
-    Output:
-        - output_memory: bs, \sum{hw}, d_model
-        - output_proposals: bs, \sum{hw}, 4
-    """
-    N_, S_, C_ = memory.shape
-    proposals = []
-    _cur = 0
-    for lvl, (H_, W_) in enumerate(spatial_shapes):
-        mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1)
-        valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
-        valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
-
-        # import ipdb; ipdb.set_trace()
-
-        grid_y, grid_x = torch.meshgrid(
-            torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
-            torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device),
-        )
-        grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)  # H_, W_, 2
-
-        scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
-        grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
-
-        if learnedwh is not None:
-            # import ipdb; ipdb.set_trace()
-            wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)
-        else:
-            wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
-
-        # scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)
-        # grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
-        # wh = torch.ones_like(grid) / scale
-        proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
-        proposals.append(proposal)
-        _cur += H_ * W_
-    # import ipdb; ipdb.set_trace()
-    output_proposals = torch.cat(proposals, 1)
-    output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(
-        -1, keepdim=True
-    )
-    output_proposals = torch.log(output_proposals / (1 - output_proposals))  # unsigmoid
-    output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf"))
-    output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
-
-    output_memory = memory
-    output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
-    output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
-
-    # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
-    # output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))
-
-    return output_memory, output_proposals
-
-
-class RandomBoxPerturber:
-    def __init__(
-        self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2
-    ) -> None:
-        self.noise_scale = torch.Tensor(
-            [x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]
-        )
-
-    def __call__(self, refanchors: Tensor) -> Tensor:
-        nq, bs, query_dim = refanchors.shape
-        device = refanchors.device
-
-        noise_raw = torch.rand_like(refanchors)
-        noise_scale = self.noise_scale.to(device)[:query_dim]
-
-        new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)
-        return new_refanchors.clamp_(0, 1)
-
-
-def sigmoid_focal_loss(
-    inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False
-):
-    """
-    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
-    Args:
-        inputs: A float tensor of arbitrary shape.
-                The predictions for each example.
-        targets: A float tensor with the same shape as inputs. Stores the binary
-                 classification label for each element in inputs
-                (0 for the negative class and 1 for the positive class).
-        alpha: (optional) Weighting factor in range (0,1) to balance
-                positive vs negative examples. Default = -1 (no weighting).
-        gamma: Exponent of the modulating factor (1 - p_t) to
-               balance easy vs hard examples.
-    Returns:
-        Loss tensor
-    """
-    prob = inputs.sigmoid()
-    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
-    p_t = prob * targets + (1 - prob) * (1 - targets)
-    loss = ce_loss * ((1 - p_t) ** gamma)
-
-    if alpha >= 0:
-        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
-        loss = alpha_t * loss
-
-    if no_reduction:
-        return loss
-
-    return loss.mean(1).sum() / num_boxes
-
-
-class MLP(nn.Module):
-    """Very simple multi-layer perceptron (also called FFN)"""
-
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(
-            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
-        )
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        return x
-
-
-def _get_activation_fn(activation, d_model=256, batch_dim=0):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    if activation == "prelu":
-        return nn.PReLU()
-    if activation == "selu":
-        return F.selu
-
-    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
-
-
-def gen_sineembed_for_position(pos_tensor):
-    # n_query, bs, _ = pos_tensor.size()
-    # sineembed_tensor = torch.zeros(n_query, bs, 256)
-    scale = 2 * math.pi
-    dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128)
-    x_embed = pos_tensor[:, :, 0] * scale
-    y_embed = pos_tensor[:, :, 1] * scale
-    pos_x = x_embed[:, :, None] / dim_t
-    pos_y = y_embed[:, :, None] / dim_t
-    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
-    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
-    if pos_tensor.size(-1) == 2:
-        pos = torch.cat((pos_y, pos_x), dim=2)
-    elif pos_tensor.size(-1) == 4:
-        w_embed = pos_tensor[:, :, 2] * scale
-        pos_w = w_embed[:, :, None] / dim_t
-        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
-
-        h_embed = pos_tensor[:, :, 3] * scale
-        pos_h = h_embed[:, :, None] / dim_t
-        pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
-
-        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
-    else:
-        raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
-    return pos
-
-
-class ContrastiveEmbed(nn.Module):
-    def __init__(self, max_text_len=256):
-        """
-        Args:
-            max_text_len: max length of text.
-        """
-        super().__init__()
-        self.max_text_len = max_text_len
-
-    def forward(self, x, text_dict):
-        """_summary_
-
-        Args:
-            x (_type_): _description_
-            text_dict (_type_): _description_
-            {
-                'encoded_text': encoded_text, # bs, 195, d_model
-                'text_token_mask': text_token_mask, # bs, 195
-                        # True for used tokens. False for padding tokens
-            }
-        Returns:
-            _type_: _description_
-        """
-        assert isinstance(text_dict, dict)
-
-        y = text_dict["encoded_text"]
-        text_token_mask = text_dict["text_token_mask"]
-
-        res = x @ y.transpose(-1, -2)
-        res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
-
-        # padding to max_text_len
-        new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
-        new_res[..., : res.shape[-1]] = res
-
-        return new_res

groundingdino/models/GroundingDINO/version.py

@@ -1 +0,0 @@
-__version__ = "0.1.0"

groundingdino/util/__init__.py

@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

groundingdino/util/box_ops.py

@@ -1,140 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Utilities for bounding box manipulation and GIoU.
-"""
-import torch
-from torchvision.ops.boxes import box_area
-
-
-def box_cxcywh_to_xyxy(x):
-    x_c, y_c, w, h = x.unbind(-1)
-    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
-    return torch.stack(b, dim=-1)
-
-
-def box_xyxy_to_cxcywh(x):
-    x0, y0, x1, y1 = x.unbind(-1)
-    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
-    return torch.stack(b, dim=-1)
-
-
-# modified from torchvision to also return the union
-def box_iou(boxes1, boxes2):
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    # import ipdb; ipdb.set_trace()
-    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
-    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
-
-    wh = (rb - lt).clamp(min=0)  # [N,M,2]
-    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
-
-    union = area1[:, None] + area2 - inter
-
-    iou = inter / (union + 1e-6)
-    return iou, union
-
-
-def generalized_box_iou(boxes1, boxes2):
-    """
-    Generalized IoU from https://giou.stanford.edu/
-
-    The boxes should be in [x0, y0, x1, y1] format
-
-    Returns a [N, M] pairwise matrix, where N = len(boxes1)
-    and M = len(boxes2)
-    """
-    # degenerate boxes gives inf / nan results
-    # so do an early check
-    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
-    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
-    # except:
-    #     import ipdb; ipdb.set_trace()
-    iou, union = box_iou(boxes1, boxes2)
-
-    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
-    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
-
-    wh = (rb - lt).clamp(min=0)  # [N,M,2]
-    area = wh[:, :, 0] * wh[:, :, 1]
-
-    return iou - (area - union) / (area + 1e-6)
-
-
-# modified from torchvision to also return the union
-def box_iou_pairwise(boxes1, boxes2):
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    lt = torch.max(boxes1[:, :2], boxes2[:, :2])  # [N,2]
-    rb = torch.min(boxes1[:, 2:], boxes2[:, 2:])  # [N,2]
-
-    wh = (rb - lt).clamp(min=0)  # [N,2]
-    inter = wh[:, 0] * wh[:, 1]  # [N]
-
-    union = area1 + area2 - inter
-
-    iou = inter / union
-    return iou, union
-
-
-def generalized_box_iou_pairwise(boxes1, boxes2):
-    """
-    Generalized IoU from https://giou.stanford.edu/
-
-    Input:
-        - boxes1, boxes2: N,4
-    Output:
-        - giou: N, 4
-    """
-    # degenerate boxes gives inf / nan results
-    # so do an early check
-    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
-    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
-    assert boxes1.shape == boxes2.shape
-    iou, union = box_iou_pairwise(boxes1, boxes2)  # N, 4
-
-    lt = torch.min(boxes1[:, :2], boxes2[:, :2])
-    rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
-
-    wh = (rb - lt).clamp(min=0)  # [N,2]
-    area = wh[:, 0] * wh[:, 1]
-
-    return iou - (area - union) / area
-
-
-def masks_to_boxes(masks):
-    """Compute the bounding boxes around the provided masks
-
-    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
-
-    Returns a [N, 4] tensors, with the boxes in xyxy format
-    """
-    if masks.numel() == 0:
-        return torch.zeros((0, 4), device=masks.device)
-
-    h, w = masks.shape[-2:]
-
-    y = torch.arange(0, h, dtype=torch.float)
-    x = torch.arange(0, w, dtype=torch.float)
-    y, x = torch.meshgrid(y, x)
-
-    x_mask = masks * x.unsqueeze(0)
-    x_max = x_mask.flatten(1).max(-1)[0]
-    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
-    y_mask = masks * y.unsqueeze(0)
-    y_max = y_mask.flatten(1).max(-1)[0]
-    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
-    return torch.stack([x_min, y_min, x_max, y_max], 1)
-
-
-if __name__ == "__main__":
-    x = torch.rand(5, 4)
-    y = torch.rand(3, 4)
-    iou, union = box_iou(x, y)
-    import ipdb
-
-    ipdb.set_trace()

groundingdino/util/get_tokenlizer.py

@@ -1,29 +0,0 @@
-from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
-import os
-
-def get_tokenlizer(text_encoder_type):
-    if not isinstance(text_encoder_type, str):
-        # print("text_encoder_type is not a str")
-        if hasattr(text_encoder_type, "text_encoder_type"):
-            text_encoder_type = text_encoder_type.text_encoder_type
-        elif text_encoder_type.get("text_encoder_type", False):
-            text_encoder_type = text_encoder_type.get("text_encoder_type")
-        elif os.path.isdir(text_encoder_type) and os.path.exists(text_encoder_type):
-            pass
-        else:
-            raise ValueError(
-                "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
-            )
-    print("final text_encoder_type: {}".format(text_encoder_type))
-
-    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
-    return tokenizer
-
-
-def get_pretrained_language_model(text_encoder_type):
-    if text_encoder_type == "bert-base-uncased" or (os.path.isdir(text_encoder_type) and os.path.exists(text_encoder_type)):
-        return BertModel.from_pretrained(text_encoder_type)
-    if text_encoder_type == "roberta-base":
-        return RobertaModel.from_pretrained(text_encoder_type)
-
-    raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))

groundingdino/util/logger.py

@@ -1,93 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import functools
-import logging
-import os
-import sys
-
-from termcolor import colored
-
-
-class _ColorfulFormatter(logging.Formatter):
-    def __init__(self, *args, **kwargs):
-        self._root_name = kwargs.pop("root_name") + "."
-        self._abbrev_name = kwargs.pop("abbrev_name", "")
-        if len(self._abbrev_name):
-            self._abbrev_name = self._abbrev_name + "."
-        super(_ColorfulFormatter, self).__init__(*args, **kwargs)
-
-    def formatMessage(self, record):
-        record.name = record.name.replace(self._root_name, self._abbrev_name)
-        log = super(_ColorfulFormatter, self).formatMessage(record)
-        if record.levelno == logging.WARNING:
-            prefix = colored("WARNING", "red", attrs=["blink"])
-        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
-            prefix = colored("ERROR", "red", attrs=["blink", "underline"])
-        else:
-            return log
-        return prefix + " " + log
-
-
-# so that calling setup_logger multiple times won't add many handlers
-@functools.lru_cache()
-def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None):
-    """
-    Initialize the detectron2 logger and set its verbosity level to "INFO".
-
-    Args:
-        output (str): a file name or a directory to save log. If None, will not save log file.
-            If ends with ".txt" or ".log", assumed to be a file name.
-            Otherwise, logs will be saved to `output/log.txt`.
-        name (str): the root module name of this logger
-
-    Returns:
-        logging.Logger: a logger
-    """
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
-    logger.propagate = False
-
-    if abbrev_name is None:
-        abbrev_name = name
-
-    plain_formatter = logging.Formatter(
-        "[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S"
-    )
-    # stdout logging: master only
-    if distributed_rank == 0:
-        ch = logging.StreamHandler(stream=sys.stdout)
-        ch.setLevel(logging.DEBUG)
-        if color:
-            formatter = _ColorfulFormatter(
-                colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s",
-                datefmt="%m/%d %H:%M:%S",
-                root_name=name,
-                abbrev_name=str(abbrev_name),
-            )
-        else:
-            formatter = plain_formatter
-        ch.setFormatter(formatter)
-        logger.addHandler(ch)
-
-    # file logging: all workers
-    if output is not None:
-        if output.endswith(".txt") or output.endswith(".log"):
-            filename = output
-        else:
-            filename = os.path.join(output, "log.txt")
-        if distributed_rank > 0:
-            filename = filename + f".rank{distributed_rank}"
-        os.makedirs(os.path.dirname(filename), exist_ok=True)
-
-        fh = logging.StreamHandler(_cached_log_stream(filename))
-        fh.setLevel(logging.DEBUG)
-        fh.setFormatter(plain_formatter)
-        logger.addHandler(fh)
-
-    return logger
-
-
-# cache the opened file object, so that different calls to `setup_logger`
-# with the same file name can safely write to the same file.
-@functools.lru_cache(maxsize=None)
-def _cached_log_stream(filename):
-    return open(filename, "a")

groundingdino/util/misc.py

@@ -1,717 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Misc functions, including distributed helpers.
-
-Mostly copy-paste from torchvision references.
-"""
-import colorsys
-import datetime
-import functools
-import io
-import json
-import os
-import pickle
-import subprocess
-import time
-from collections import OrderedDict, defaultdict, deque
-from typing import List, Optional
-
-import numpy as np
-import torch
-import torch.distributed as dist
-
-# needed due to empty tensor bug in pytorch and torchvision 0.5
-import torchvision
-from torch import Tensor
-
-__torchvision_need_compat_flag = float(torchvision.__version__.split(".")[1]) < 7
-if __torchvision_need_compat_flag:
-    from torchvision.ops import _new_empty_tensor
-    from torchvision.ops.misc import _output_size
-
-
-class SmoothedValue(object):
-    """Track a series of values and provide access to smoothed values over a
-    window or the global series average.
-    """
-
-    def __init__(self, window_size=20, fmt=None):
-        if fmt is None:
-            fmt = "{median:.4f} ({global_avg:.4f})"
-        self.deque = deque(maxlen=window_size)
-        self.total = 0.0
-        self.count = 0
-        self.fmt = fmt
-
-    def update(self, value, n=1):
-        self.deque.append(value)
-        self.count += n
-        self.total += value * n
-
-    def synchronize_between_processes(self):
-        """
-        Warning: does not synchronize the deque!
-        """
-        if not is_dist_avail_and_initialized():
-            return
-        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
-        dist.barrier()
-        dist.all_reduce(t)
-        t = t.tolist()
-        self.count = int(t[0])
-        self.total = t[1]
-
-    @property
-    def median(self):
-        d = torch.tensor(list(self.deque))
-        if d.shape[0] == 0:
-            return 0
-        return d.median().item()
-
-    @property
-    def avg(self):
-        d = torch.tensor(list(self.deque), dtype=torch.float32)
-        return d.mean().item()
-
-    @property
-    def global_avg(self):
-        if os.environ.get("SHILONG_AMP", None) == "1":
-            eps = 1e-4
-        else:
-            eps = 1e-6
-        return self.total / (self.count + eps)
-
-    @property
-    def max(self):
-        return max(self.deque)
-
-    @property
-    def value(self):
-        return self.deque[-1]
-
-    def __str__(self):
-        return self.fmt.format(
-            median=self.median,
-            avg=self.avg,
-            global_avg=self.global_avg,
-            max=self.max,
-            value=self.value,
-        )
-
-
-@functools.lru_cache()
-def _get_global_gloo_group():
-    """
-    Return a process group based on gloo backend, containing all the ranks
-    The result is cached.
-    """
-
-    if dist.get_backend() == "nccl":
-        return dist.new_group(backend="gloo")
-
-    return dist.group.WORLD
-
-
-def all_gather_cpu(data):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors)
-    Args:
-        data: any picklable object
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-
-    world_size = get_world_size()
-    if world_size == 1:
-        return [data]
-
-    cpu_group = _get_global_gloo_group()
-
-    buffer = io.BytesIO()
-    torch.save(data, buffer)
-    data_view = buffer.getbuffer()
-    device = "cuda" if cpu_group is None else "cpu"
-    tensor = torch.ByteTensor(data_view).to(device)
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
-    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
-    if cpu_group is None:
-        dist.all_gather(size_list, local_size)
-    else:
-        print("gathering on cpu")
-        dist.all_gather(size_list, local_size, group=cpu_group)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-    assert isinstance(local_size.item(), int)
-    local_size = int(local_size.item())
-
-    # receiving Tensor from all ranks
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
-        tensor = torch.cat((tensor, padding), dim=0)
-    if cpu_group is None:
-        dist.all_gather(tensor_list, tensor)
-    else:
-        dist.all_gather(tensor_list, tensor, group=cpu_group)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
-        buffer = io.BytesIO(tensor.cpu().numpy())
-        obj = torch.load(buffer)
-        data_list.append(obj)
-
-    return data_list
-
-
-def all_gather(data):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors)
-    Args:
-        data: any picklable object
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-
-    if os.getenv("CPU_REDUCE") == "1":
-        return all_gather_cpu(data)
-
-    world_size = get_world_size()
-    if world_size == 1:
-        return [data]
-
-    # serialized to a Tensor
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to("cuda")
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device="cuda")
-    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-
-    # receiving Tensor from all ranks
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
-        tensor = torch.cat((tensor, padding), dim=0)
-    dist.all_gather(tensor_list, tensor)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    return data_list
-
-
-def reduce_dict(input_dict, average=True):
-    """
-    Args:
-        input_dict (dict): all the values will be reduced
-        average (bool): whether to do average or sum
-    Reduce the values in the dictionary from all processes so that all processes
-    have the averaged results. Returns a dict with the same fields as
-    input_dict, after reduction.
-    """
-    world_size = get_world_size()
-    if world_size < 2:
-        return input_dict
-    with torch.no_grad():
-        names = []
-        values = []
-        # sort the keys so that they are consistent across processes
-        for k in sorted(input_dict.keys()):
-            names.append(k)
-            values.append(input_dict[k])
-        values = torch.stack(values, dim=0)
-        dist.all_reduce(values)
-        if average:
-            values /= world_size
-        reduced_dict = {k: v for k, v in zip(names, values)}
-    return reduced_dict
-
-
-class MetricLogger(object):
-    def __init__(self, delimiter="\t"):
-        self.meters = defaultdict(SmoothedValue)
-        self.delimiter = delimiter
-
-    def update(self, **kwargs):
-        for k, v in kwargs.items():
-            if isinstance(v, torch.Tensor):
-                v = v.item()
-            assert isinstance(v, (float, int))
-            self.meters[k].update(v)
-
-    def __getattr__(self, attr):
-        if attr in self.meters:
-            return self.meters[attr]
-        if attr in self.__dict__:
-            return self.__dict__[attr]
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
-
-    def __str__(self):
-        loss_str = []
-        for name, meter in self.meters.items():
-            # print(name, str(meter))
-            # import ipdb;ipdb.set_trace()
-            if meter.count > 0:
-                loss_str.append("{}: {}".format(name, str(meter)))
-        return self.delimiter.join(loss_str)
-
-    def synchronize_between_processes(self):
-        for meter in self.meters.values():
-            meter.synchronize_between_processes()
-
-    def add_meter(self, name, meter):
-        self.meters[name] = meter
-
-    def log_every(self, iterable, print_freq, header=None, logger=None):
-        if logger is None:
-            print_func = print
-        else:
-            print_func = logger.info
-
-        i = 0
-        if not header:
-            header = ""
-        start_time = time.time()
-        end = time.time()
-        iter_time = SmoothedValue(fmt="{avg:.4f}")
-        data_time = SmoothedValue(fmt="{avg:.4f}")
-        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
-        if torch.cuda.is_available():
-            log_msg = self.delimiter.join(
-                [
-                    header,
-                    "[{0" + space_fmt + "}/{1}]",
-                    "eta: {eta}",
-                    "{meters}",
-                    "time: {time}",
-                    "data: {data}",
-                    "max mem: {memory:.0f}",
-                ]
-            )
-        else:
-            log_msg = self.delimiter.join(
-                [
-                    header,
-                    "[{0" + space_fmt + "}/{1}]",
-                    "eta: {eta}",
-                    "{meters}",
-                    "time: {time}",
-                    "data: {data}",
-                ]
-            )
-        MB = 1024.0 * 1024.0
-        for obj in iterable:
-            data_time.update(time.time() - end)
-            yield obj
-            # import ipdb; ipdb.set_trace()
-            iter_time.update(time.time() - end)
-            if i % print_freq == 0 or i == len(iterable) - 1:
-                eta_seconds = iter_time.global_avg * (len(iterable) - i)
-                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
-                if torch.cuda.is_available():
-                    print_func(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                            memory=torch.cuda.max_memory_allocated() / MB,
-                        )
-                    )
-                else:
-                    print_func(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                        )
-                    )
-            i += 1
-            end = time.time()
-        total_time = time.time() - start_time
-        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-        print_func(
-            "{} Total time: {} ({:.4f} s / it)".format(
-                header, total_time_str, total_time / len(iterable)
-            )
-        )
-
-
-def get_sha():
-    cwd = os.path.dirname(os.path.abspath(__file__))
-
-    def _run(command):
-        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
-
-    sha = "N/A"
-    diff = "clean"
-    branch = "N/A"
-    try:
-        sha = _run(["git", "rev-parse", "HEAD"])
-        subprocess.check_output(["git", "diff"], cwd=cwd)
-        diff = _run(["git", "diff-index", "HEAD"])
-        diff = "has uncommited changes" if diff else "clean"
-        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
-    except Exception:
-        pass
-    message = f"sha: {sha}, status: {diff}, branch: {branch}"
-    return message
-
-
-def collate_fn(batch):
-    # import ipdb; ipdb.set_trace()
-    batch = list(zip(*batch))
-    batch[0] = nested_tensor_from_tensor_list(batch[0])
-    return tuple(batch)
-
-
-def _max_by_axis(the_list):
-    # type: (List[List[int]]) -> List[int]
-    maxes = the_list[0]
-    for sublist in the_list[1:]:
-        for index, item in enumerate(sublist):
-            maxes[index] = max(maxes[index], item)
-    return maxes
-
-
-class NestedTensor(object):
-    def __init__(self, tensors, mask: Optional[Tensor]):
-        self.tensors = tensors
-        self.mask = mask
-        if mask == "auto":
-            self.mask = torch.zeros_like(tensors).to(tensors.device)
-            if self.mask.dim() == 3:
-                self.mask = self.mask.sum(0).to(bool)
-            elif self.mask.dim() == 4:
-                self.mask = self.mask.sum(1).to(bool)
-            else:
-                raise ValueError(
-                    "tensors dim must be 3 or 4 but {}({})".format(
-                        self.tensors.dim(), self.tensors.shape
-                    )
-                )
-
-    def imgsize(self):
-        res = []
-        for i in range(self.tensors.shape[0]):
-            mask = self.mask[i]
-            maxH = (~mask).sum(0).max()
-            maxW = (~mask).sum(1).max()
-            res.append(torch.Tensor([maxH, maxW]))
-        return res
-
-    def to(self, device):
-        # type: (Device) -> NestedTensor # noqa
-        cast_tensor = self.tensors.to(device)
-        mask = self.mask
-        if mask is not None:
-            assert mask is not None
-            cast_mask = mask.to(device)
-        else:
-            cast_mask = None
-        return NestedTensor(cast_tensor, cast_mask)
-
-    def to_img_list_single(self, tensor, mask):
-        assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim())
-        maxH = (~mask).sum(0).max()
-        maxW = (~mask).sum(1).max()
-        img = tensor[:, :maxH, :maxW]
-        return img
-
-    def to_img_list(self):
-        """remove the padding and convert to img list
-
-        Returns:
-            [type]: [description]
-        """
-        if self.tensors.dim() == 3:
-            return self.to_img_list_single(self.tensors, self.mask)
-        else:
-            res = []
-            for i in range(self.tensors.shape[0]):
-                tensor_i = self.tensors[i]
-                mask_i = self.mask[i]
-                res.append(self.to_img_list_single(tensor_i, mask_i))
-            return res
-
-    @property
-    def device(self):
-        return self.tensors.device
-
-    def decompose(self):
-        return self.tensors, self.mask
-
-    def __repr__(self):
-        return str(self.tensors)
-
-    @property
-    def shape(self):
-        return {"tensors.shape": self.tensors.shape, "mask.shape": self.mask.shape}
-
-
-def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-    # TODO make this more general
-    if tensor_list[0].ndim == 3:
-        if torchvision._is_tracing():
-            # nested_tensor_from_tensor_list() does not export well to ONNX
-            # call _onnx_nested_tensor_from_tensor_list() instead
-            return _onnx_nested_tensor_from_tensor_list(tensor_list)
-
-        # TODO make it support different-sized images
-        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
-        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
-        batch_shape = [len(tensor_list)] + max_size
-        b, c, h, w = batch_shape
-        dtype = tensor_list[0].dtype
-        device = tensor_list[0].device
-        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
-        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
-        for img, pad_img, m in zip(tensor_list, tensor, mask):
-            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-            m[: img.shape[1], : img.shape[2]] = False
-    else:
-        raise ValueError("not supported")
-    return NestedTensor(tensor, mask)
-
-
-# _onnx_nested_tensor_from_tensor_list() is an implementation of
-# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
-@torch.jit.unused
-def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
-    max_size = []
-    for i in range(tensor_list[0].dim()):
-        max_size_i = torch.max(
-            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
-        ).to(torch.int64)
-        max_size.append(max_size_i)
-    max_size = tuple(max_size)
-
-    # work around for
-    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-    # m[: img.shape[1], :img.shape[2]] = False
-    # which is not yet supported in onnx
-    padded_imgs = []
-    padded_masks = []
-    for img in tensor_list:
-        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
-        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
-        padded_imgs.append(padded_img)
-
-        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
-        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
-        padded_masks.append(padded_mask.to(torch.bool))
-
-    tensor = torch.stack(padded_imgs)
-    mask = torch.stack(padded_masks)
-
-    return NestedTensor(tensor, mask=mask)
-
-
-def setup_for_distributed(is_master):
-    """
-    This function disables printing when not in master process
-    """
-    import builtins as __builtin__
-
-    builtin_print = __builtin__.print
-
-    def print(*args, **kwargs):
-        force = kwargs.pop("force", False)
-        if is_master or force:
-            builtin_print(*args, **kwargs)
-
-    __builtin__.print = print
-
-
-def is_dist_avail_and_initialized():
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
-
-
-def get_world_size():
-    if not is_dist_avail_and_initialized():
-        return 1
-    return dist.get_world_size()
-
-
-def get_rank():
-    if not is_dist_avail_and_initialized():
-        return 0
-    return dist.get_rank()
-
-
-def is_main_process():
-    return get_rank() == 0
-
-
-def save_on_master(*args, **kwargs):
-    if is_main_process():
-        torch.save(*args, **kwargs)
-
-
-def init_distributed_mode(args):
-    if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "":  # 'RANK' in os.environ and
-        args.rank = int(os.environ["RANK"])
-        args.world_size = int(os.environ["WORLD_SIZE"])
-        args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"])
-
-        # launch by torch.distributed.launch
-        # Single node
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ...
-        # Multi nodes
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
-        # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK'))
-        # local_world_size = int(os.environ['GPU_PER_NODE_COUNT'])
-        # args.world_size = args.world_size * local_world_size
-        # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])
-        # args.rank = args.rank * local_world_size + args.local_rank
-        print(
-            "world size: {}, rank: {}, local rank: {}".format(
-                args.world_size, args.rank, args.local_rank
-            )
-        )
-        print(json.dumps(dict(os.environ), indent=2))
-    elif "SLURM_PROCID" in os.environ:
-        args.rank = int(os.environ["SLURM_PROCID"])
-        args.gpu = args.local_rank = int(os.environ["SLURM_LOCALID"])
-        args.world_size = int(os.environ["SLURM_NPROCS"])
-
-        print(
-            "world size: {}, world rank: {}, local rank: {}, device_count: {}".format(
-                args.world_size, args.rank, args.local_rank, torch.cuda.device_count()
-            )
-        )
-    else:
-        print("Not using distributed mode")
-        args.distributed = False
-        args.world_size = 1
-        args.rank = 0
-        args.local_rank = 0
-        return
-
-    print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank))
-    args.distributed = True
-    torch.cuda.set_device(args.local_rank)
-    args.dist_backend = "nccl"
-    print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
-
-    torch.distributed.init_process_group(
-        backend=args.dist_backend,
-        world_size=args.world_size,
-        rank=args.rank,
-        init_method=args.dist_url,
-    )
-
-    print("Before torch.distributed.barrier()")
-    torch.distributed.barrier()
-    print("End torch.distributed.barrier()")
-    setup_for_distributed(args.rank == 0)
-
-
-@torch.no_grad()
-def accuracy(output, target, topk=(1,)):
-    """Computes the precision@k for the specified values of k"""
-    if target.numel() == 0:
-        return [torch.zeros([], device=output.device)]
-    maxk = max(topk)
-    batch_size = target.size(0)
-
-    _, pred = output.topk(maxk, 1, True, True)
-    pred = pred.t()
-    correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-    res = []
-    for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
-        res.append(correct_k.mul_(100.0 / batch_size))
-    return res
-
-
-@torch.no_grad()
-def accuracy_onehot(pred, gt):
-    """_summary_
-
-    Args:
-        pred (_type_): n, c
-        gt (_type_): n, c
-    """
-    tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum()
-    acc = tp / gt.shape[0] * 100
-    return acc
-
-
-def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
-    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
-    """
-    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
-    This will eventually be supported natively by PyTorch, and this
-    class can go away.
-    """
-    if __torchvision_need_compat_flag < 0.7:
-        if input.numel() > 0:
-            return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
-
-        output_shape = _output_size(2, input, size, scale_factor)
-        output_shape = list(input.shape[:-2]) + list(output_shape)
-        return _new_empty_tensor(input, output_shape)
-    else:
-        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
-
-
-class color_sys:
-    def __init__(self, num_colors) -> None:
-        self.num_colors = num_colors
-        colors = []
-        for i in np.arange(0.0, 360.0, 360.0 / num_colors):
-            hue = i / 360.0
-            lightness = (50 + np.random.rand() * 10) / 100.0
-            saturation = (90 + np.random.rand() * 10) / 100.0
-            colors.append(
-                tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])
-            )
-        self.colors = colors
-
-    def __call__(self, idx):
-        return self.colors[idx]
-
-
-def inverse_sigmoid(x, eps=1e-3):
-    x = x.clamp(min=0, max=1)
-    x1 = x.clamp(min=eps)
-    x2 = (1 - x).clamp(min=eps)
-    return torch.log(x1 / x2)
-
-
-def clean_state_dict(state_dict):
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k[:7] == "module.":
-            k = k[7:]  # remove `module.`
-        new_state_dict[k] = v
-    return new_state_dict

groundingdino/util/predict.py

@@ -1,46 +0,0 @@
-from typing import Tuple, List
-
-import torch
-
-from groundingdino.util.utils import get_phrases_from_posmap
-
-
-def preprocess_caption(caption: str) -> str:
-    result = caption.lower().strip()
-    if result.endswith("."):
-        return result
-    return result + "."
-
-def predict(
-        model,
-        image: torch.Tensor,
-        caption: str,
-        box_threshold: float,
-        text_threshold: float,
-        device: str = "cuda"
-) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
-    caption = preprocess_caption(caption=caption)
-
-    model = model.to(device)
-    image = image.to(device)
-
-    with torch.no_grad():
-        outputs = model(image[None], captions=[caption])
-
-    prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0]  # prediction_logits.shape = (nq, 256)
-    prediction_boxes = outputs["pred_boxes"].cpu()[0]  # prediction_boxes.shape = (nq, 4)
-
-    mask = prediction_logits.max(dim=1)[0] > box_threshold
-    logits = prediction_logits[mask]  # logits.shape = (n, 256)
-    boxes = prediction_boxes[mask]  # boxes.shape = (n, 4)
-
-    tokenizer = model.tokenizer
-    tokenized = tokenizer(caption)
-    print(tokenized)
-    phrases = [
-        get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')
-        for logit
-        in logits
-    ]
-
-    return boxes, logits.max(dim=1)[0], phrases

groundingdino/util/slconfig.py

@@ -1,427 +0,0 @@
-# ==========================================================
-# Modified from mmcv
-# ==========================================================
-import ast
-import os
-import os.path as osp
-import shutil
-import sys
-import tempfile
-from argparse import Action
-from importlib import import_module
-
-from addict import Dict
-from yapf.yapflib.yapf_api import FormatCode
-
-BASE_KEY = "_base_"
-DELETE_KEY = "_delete_"
-RESERVED_KEYS = ["filename", "text", "pretty_text", "get", "dump", "merge_from_dict"]
-
-
-def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
-    if not osp.isfile(filename):
-        raise FileNotFoundError(msg_tmpl.format(filename))
-
-
-class ConfigDict(Dict):
-    def __missing__(self, name):
-        raise KeyError(name)
-
-    def __getattr__(self, name):
-        try:
-            value = super(ConfigDict, self).__getattr__(name)
-        except KeyError:
-            ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'")
-        except Exception as e:
-            ex = e
-        else:
-            return value
-        raise ex
-
-
-class SLConfig(object):
-    """
-    config files.
-    only support .py file as config now.
-
-    ref: mmcv.utils.config
-
-    Example:
-        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
-        >>> cfg.a
-        1
-        >>> cfg.b
-        {'b1': [0, 1]}
-        >>> cfg.b.b1
-        [0, 1]
-        >>> cfg = Config.fromfile('tests/data/config/a.py')
-        >>> cfg.filename
-        "/home/kchen/projects/mmcv/tests/data/config/a.py"
-        >>> cfg.item4
-        'test'
-        >>> cfg
-        "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
-        "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
-    """
-
-    @staticmethod
-    def _validate_py_syntax(filename):
-        with open(filename) as f:
-            content = f.read()
-        try:
-            ast.parse(content)
-        except SyntaxError:
-            raise SyntaxError("There are syntax errors in config " f"file {filename}")
-
-    @staticmethod
-    def _file2dict(filename):
-        filename = osp.abspath(osp.expanduser(filename))
-        check_file_exist(filename)
-        if filename.lower().endswith(".py"):
-            with tempfile.TemporaryDirectory() as temp_config_dir:
-                temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py")
-                temp_config_name = osp.basename(temp_config_file.name)
-                if os.name == 'nt':
-                    temp_config_file.close()
-                shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))
-                temp_module_name = osp.splitext(temp_config_name)[0]
-                sys.path.insert(0, temp_config_dir)
-                SLConfig._validate_py_syntax(filename)
-                mod = import_module(temp_module_name)
-                sys.path.pop(0)
-                cfg_dict = {
-                    name: value for name, value in mod.__dict__.items() if not name.startswith("__")
-                }
-                # delete imported module
-                del sys.modules[temp_module_name]
-                # close temp file
-                temp_config_file.close()
-        elif filename.lower().endswith((".yml", ".yaml", ".json")):
-            from .slio import slload
-
-            cfg_dict = slload(filename)
-        else:
-            raise IOError("Only py/yml/yaml/json type are supported now!")
-
-        cfg_text = filename + "\n"
-        with open(filename, "r") as f:
-            cfg_text += f.read()
-
-        # parse the base file
-        if BASE_KEY in cfg_dict:
-            cfg_dir = osp.dirname(filename)
-            base_filename = cfg_dict.pop(BASE_KEY)
-            base_filename = base_filename if isinstance(base_filename, list) else [base_filename]
-
-            cfg_dict_list = list()
-            cfg_text_list = list()
-            for f in base_filename:
-                _cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f))
-                cfg_dict_list.append(_cfg_dict)
-                cfg_text_list.append(_cfg_text)
-
-            base_cfg_dict = dict()
-            for c in cfg_dict_list:
-                if len(base_cfg_dict.keys() & c.keys()) > 0:
-                    raise KeyError("Duplicate key is not allowed among bases")
-                    # TODO Allow the duplicate key while warnning user
-                base_cfg_dict.update(c)
-
-            base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict)
-            cfg_dict = base_cfg_dict
-
-            # merge cfg_text
-            cfg_text_list.append(cfg_text)
-            cfg_text = "\n".join(cfg_text_list)
-
-        return cfg_dict, cfg_text
-
-    @staticmethod
-    def _merge_a_into_b(a, b):
-        """merge dict `a` into dict `b` (non-inplace).
-            values in `a` will overwrite `b`.
-            copy first to avoid inplace modification
-
-        Args:
-            a ([type]): [description]
-            b ([type]): [description]
-
-        Returns:
-            [dict]: [description]
-        """
-        # import ipdb; ipdb.set_trace()
-        if not isinstance(a, dict):
-            return a
-
-        b = b.copy()
-        for k, v in a.items():
-            if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
-
-                if not isinstance(b[k], dict) and not isinstance(b[k], list):
-                    # if :
-                    # import ipdb; ipdb.set_trace()
-                    raise TypeError(
-                        f"{k}={v} in child config cannot inherit from base "
-                        f"because {k} is a dict in the child config but is of "
-                        f"type {type(b[k])} in base config. You may set "
-                        f"`{DELETE_KEY}=True` to ignore the base config"
-                    )
-                b[k] = SLConfig._merge_a_into_b(v, b[k])
-            elif isinstance(b, list):
-                try:
-                    _ = int(k)
-                except:
-                    raise TypeError(
-                        f"b is a list, " f"index {k} should be an int when input but {type(k)}"
-                    )
-                b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)])
-            else:
-                b[k] = v
-
-        return b
-
-    @staticmethod
-    def fromfile(filename):
-        cfg_dict, cfg_text = SLConfig._file2dict(filename)
-        return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename)
-
-    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
-        if cfg_dict is None:
-            cfg_dict = dict()
-        elif not isinstance(cfg_dict, dict):
-            raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}")
-        for key in cfg_dict:
-            if key in RESERVED_KEYS:
-                raise KeyError(f"{key} is reserved for config file")
-
-        super(SLConfig, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict))
-        super(SLConfig, self).__setattr__("_filename", filename)
-        if cfg_text:
-            text = cfg_text
-        elif filename:
-            with open(filename, "r") as f:
-                text = f.read()
-        else:
-            text = ""
-        super(SLConfig, self).__setattr__("_text", text)
-
-    @property
-    def filename(self):
-        return self._filename
-
-    @property
-    def text(self):
-        return self._text
-
-    @property
-    def pretty_text(self):
-
-        indent = 4
-
-        def _indent(s_, num_spaces):
-            s = s_.split("\n")
-            if len(s) == 1:
-                return s_
-            first = s.pop(0)
-            s = [(num_spaces * " ") + line for line in s]
-            s = "\n".join(s)
-            s = first + "\n" + s
-            return s
-
-        def _format_basic_types(k, v, use_mapping=False):
-            if isinstance(v, str):
-                v_str = f"'{v}'"
-            else:
-                v_str = str(v)
-
-            if use_mapping:
-                k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                attr_str = f"{k_str}: {v_str}"
-            else:
-                attr_str = f"{str(k)}={v_str}"
-            attr_str = _indent(attr_str, indent)
-
-            return attr_str
-
-        def _format_list(k, v, use_mapping=False):
-            # check if all items in the list are dict
-            if all(isinstance(_, dict) for _ in v):
-                v_str = "[\n"
-                v_str += "\n".join(
-                    f"dict({_indent(_format_dict(v_), indent)})," for v_ in v
-                ).rstrip(",")
-                if use_mapping:
-                    k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                    attr_str = f"{k_str}: {v_str}"
-                else:
-                    attr_str = f"{str(k)}={v_str}"
-                attr_str = _indent(attr_str, indent) + "]"
-            else:
-                attr_str = _format_basic_types(k, v, use_mapping)
-            return attr_str
-
-        def _contain_invalid_identifier(dict_str):
-            contain_invalid_identifier = False
-            for key_name in dict_str:
-                contain_invalid_identifier |= not str(key_name).isidentifier()
-            return contain_invalid_identifier
-
-        def _format_dict(input_dict, outest_level=False):
-            r = ""
-            s = []
-
-            use_mapping = _contain_invalid_identifier(input_dict)
-            if use_mapping:
-                r += "{"
-            for idx, (k, v) in enumerate(input_dict.items()):
-                is_last = idx >= len(input_dict) - 1
-                end = "" if outest_level or is_last else ","
-                if isinstance(v, dict):
-                    v_str = "\n" + _format_dict(v)
-                    if use_mapping:
-                        k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                        attr_str = f"{k_str}: dict({v_str}"
-                    else:
-                        attr_str = f"{str(k)}=dict({v_str}"
-                    attr_str = _indent(attr_str, indent) + ")" + end
-                elif isinstance(v, list):
-                    attr_str = _format_list(k, v, use_mapping) + end
-                else:
-                    attr_str = _format_basic_types(k, v, use_mapping) + end
-
-                s.append(attr_str)
-            r += "\n".join(s)
-            if use_mapping:
-                r += "}"
-            return r
-
-        cfg_dict = self._cfg_dict.to_dict()
-        text = _format_dict(cfg_dict, outest_level=True)
-        # copied from setup.cfg
-        yapf_style = dict(
-            based_on_style="pep8",
-            blank_line_before_nested_class_or_def=True,
-            split_before_expression_after_opening_paren=True,
-        )
-        text, _ = FormatCode(text, style_config=yapf_style, verify=True)
-
-        return text
-
-    def __repr__(self):
-        return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}"
-
-    def __len__(self):
-        return len(self._cfg_dict)
-
-    def __getattr__(self, name):
-        # # debug
-        # print('+'*15)
-        # print('name=%s' % name)
-        # print("addr:", id(self))
-        # # print('type(self):', type(self))
-        # print(self.__dict__)
-        # print('+'*15)
-        # if self.__dict__ == {}:
-        #     raise ValueError
-
-        return getattr(self._cfg_dict, name)
-
-    def __getitem__(self, name):
-        return self._cfg_dict.__getitem__(name)
-
-    def __setattr__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setattr__(name, value)
-
-    def __setitem__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setitem__(name, value)
-
-    def __iter__(self):
-        return iter(self._cfg_dict)
-
-    def dump(self, file=None):
-        # import ipdb; ipdb.set_trace()
-        if file is None:
-            return self.pretty_text
-        else:
-            with open(file, "w") as f:
-                f.write(self.pretty_text)
-
-    def merge_from_dict(self, options):
-        """Merge list into cfg_dict
-
-        Merge the dict parsed by MultipleKVAction into this cfg.
-
-        Examples:
-            >>> options = {'model.backbone.depth': 50,
-            ...            'model.backbone.with_cp':True}
-            >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
-            >>> cfg.merge_from_dict(options)
-            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-            >>> assert cfg_dict == dict(
-            ...     model=dict(backbone=dict(depth=50, with_cp=True)))
-
-        Args:
-            options (dict): dict of configs to merge from.
-        """
-        option_cfg_dict = {}
-        for full_key, v in options.items():
-            d = option_cfg_dict
-            key_list = full_key.split(".")
-            for subkey in key_list[:-1]:
-                d.setdefault(subkey, ConfigDict())
-                d = d[subkey]
-            subkey = key_list[-1]
-            d[subkey] = v
-
-        cfg_dict = super(SLConfig, self).__getattribute__("_cfg_dict")
-        super(SLConfig, self).__setattr__(
-            "_cfg_dict", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict)
-        )
-
-    # for multiprocess
-    def __setstate__(self, state):
-        self.__init__(state)
-
-    def copy(self):
-        return SLConfig(self._cfg_dict.copy())
-
-    def deepcopy(self):
-        return SLConfig(self._cfg_dict.deepcopy())
-
-
-class DictAction(Action):
-    """
-    argparse action to split an argument into KEY=VALUE form
-    on the first = and append to a dictionary. List options should
-    be passed as comma separated values, i.e KEY=V1,V2,V3
-    """
-
-    @staticmethod
-    def _parse_int_float_bool(val):
-        try:
-            return int(val)
-        except ValueError:
-            pass
-        try:
-            return float(val)
-        except ValueError:
-            pass
-        if val.lower() in ["true", "false"]:
-            return True if val.lower() == "true" else False
-        if val.lower() in ["none", "null"]:
-            return None
-        return val
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        options = {}
-        for kv in values:
-            key, val = kv.split("=", maxsplit=1)
-            val = [self._parse_int_float_bool(v) for v in val.split(",")]
-            if len(val) == 1:
-                val = val[0]
-            options[key] = val
-        setattr(namespace, self.dest, options)

groundingdino/util/slio.py

@@ -1,177 +0,0 @@
-# ==========================================================
-# Modified from mmcv
-# ==========================================================
-
-import json
-import pickle
-from abc import ABCMeta, abstractmethod
-from pathlib import Path
-
-import yaml
-
-try:
-    from yaml import CLoader as Loader, CDumper as Dumper
-except ImportError:
-    from yaml import Loader, Dumper
-
-
-# ===========================
-# Rigister handler
-# ===========================
-
-
-class BaseFileHandler(metaclass=ABCMeta):
-    @abstractmethod
-    def load_from_fileobj(self, file, **kwargs):
-        pass
-
-    @abstractmethod
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        pass
-
-    @abstractmethod
-    def dump_to_str(self, obj, **kwargs):
-        pass
-
-    def load_from_path(self, filepath, mode="r", **kwargs):
-        with open(filepath, mode) as f:
-            return self.load_from_fileobj(f, **kwargs)
-
-    def dump_to_path(self, obj, filepath, mode="w", **kwargs):
-        with open(filepath, mode) as f:
-            self.dump_to_fileobj(obj, f, **kwargs)
-
-
-class JsonHandler(BaseFileHandler):
-    def load_from_fileobj(self, file):
-        return json.load(file)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        json.dump(obj, file, **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        return json.dumps(obj, **kwargs)
-
-
-class PickleHandler(BaseFileHandler):
-    def load_from_fileobj(self, file, **kwargs):
-        return pickle.load(file, **kwargs)
-
-    def load_from_path(self, filepath, **kwargs):
-        return super(PickleHandler, self).load_from_path(filepath, mode="rb", **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        kwargs.setdefault("protocol", 2)
-        return pickle.dumps(obj, **kwargs)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        kwargs.setdefault("protocol", 2)
-        pickle.dump(obj, file, **kwargs)
-
-    def dump_to_path(self, obj, filepath, **kwargs):
-        super(PickleHandler, self).dump_to_path(obj, filepath, mode="wb", **kwargs)
-
-
-class YamlHandler(BaseFileHandler):
-    def load_from_fileobj(self, file, **kwargs):
-        kwargs.setdefault("Loader", Loader)
-        return yaml.load(file, **kwargs)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        kwargs.setdefault("Dumper", Dumper)
-        yaml.dump(obj, file, **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        kwargs.setdefault("Dumper", Dumper)
-        return yaml.dump(obj, **kwargs)
-
-
-file_handlers = {
-    "json": JsonHandler(),
-    "yaml": YamlHandler(),
-    "yml": YamlHandler(),
-    "pickle": PickleHandler(),
-    "pkl": PickleHandler(),
-}
-
-# ===========================
-# load and dump
-# ===========================
-
-
-def is_str(x):
-    """Whether the input is an string instance.
-
-    Note: This method is deprecated since python 2 is no longer supported.
-    """
-    return isinstance(x, str)
-
-
-def slload(file, file_format=None, **kwargs):
-    """Load data from json/yaml/pickle files.
-
-    This method provides a unified api for loading data from serialized files.
-
-    Args:
-        file (str or :obj:`Path` or file-like object): Filename or a file-like
-            object.
-        file_format (str, optional): If not specified, the file format will be
-            inferred from the file extension, otherwise use the specified one.
-            Currently supported formats include "json", "yaml/yml" and
-            "pickle/pkl".
-
-    Returns:
-        The content from the file.
-    """
-    if isinstance(file, Path):
-        file = str(file)
-    if file_format is None and is_str(file):
-        file_format = file.split(".")[-1]
-    if file_format not in file_handlers:
-        raise TypeError(f"Unsupported format: {file_format}")
-
-    handler = file_handlers[file_format]
-    if is_str(file):
-        obj = handler.load_from_path(file, **kwargs)
-    elif hasattr(file, "read"):
-        obj = handler.load_from_fileobj(file, **kwargs)
-    else:
-        raise TypeError('"file" must be a filepath str or a file-object')
-    return obj
-
-
-def sldump(obj, file=None, file_format=None, **kwargs):
-    """Dump data to json/yaml/pickle strings or files.
-
-    This method provides a unified api for dumping data as strings or to files,
-    and also supports custom arguments for each file format.
-
-    Args:
-        obj (any): The python object to be dumped.
-        file (str or :obj:`Path` or file-like object, optional): If not
-            specified, then the object is dump to a str, otherwise to a file
-            specified by the filename or file-like object.
-        file_format (str, optional): Same as :func:`load`.
-
-    Returns:
-        bool: True for success, False otherwise.
-    """
-    if isinstance(file, Path):
-        file = str(file)
-    if file_format is None:
-        if is_str(file):
-            file_format = file.split(".")[-1]
-        elif file is None:
-            raise ValueError("file_format must be specified since file is None")
-    if file_format not in file_handlers:
-        raise TypeError(f"Unsupported format: {file_format}")
-
-    handler = file_handlers[file_format]
-    if file is None:
-        return handler.dump_to_str(obj, **kwargs)
-    elif is_str(file):
-        handler.dump_to_path(obj, file, **kwargs)
-    elif hasattr(file, "write"):
-        handler.dump_to_fileobj(obj, file, **kwargs)
-    else:
-        raise TypeError('"file" must be a filename str or a file-object')

groundingdino/util/time_counter.py

@@ -1,62 +0,0 @@
-import json
-import time
-
-
-class TimeCounter:
-    def __init__(self) -> None:
-        pass
-
-    def clear(self):
-        self.timedict = {}
-        self.basetime = time.perf_counter()
-
-    def timeit(self, name):
-        nowtime = time.perf_counter() - self.basetime
-        self.timedict[name] = nowtime
-        self.basetime = time.perf_counter()
-
-
-class TimeHolder:
-    def __init__(self) -> None:
-        self.timedict = {}
-
-    def update(self, _timedict: dict):
-        for k, v in _timedict.items():
-            if k not in self.timedict:
-                self.timedict[k] = AverageMeter(name=k, val_only=True)
-            self.timedict[k].update(val=v)
-
-    def final_res(self):
-        return {k: v.avg for k, v in self.timedict.items()}
-
-    def __str__(self):
-        return json.dumps(self.final_res(), indent=2)
-
-
-class AverageMeter(object):
-    """Computes and stores the average and current value"""
-
-    def __init__(self, name, fmt=":f", val_only=False):
-        self.name = name
-        self.fmt = fmt
-        self.val_only = val_only
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-
-    def __str__(self):
-        if self.val_only:
-            fmtstr = "{name} {val" + self.fmt + "}"
-        else:
-            fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
-        return fmtstr.format(**self.__dict__)

groundingdino/util/transforms.py

@@ -1,312 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Transforms and data augmentation for both image + bbox.
-"""
-import os
-import random
-
-import PIL
-import torch
-import torchvision.transforms as T
-import torchvision.transforms.functional as F
-
-from groundingdino.util.box_ops import box_xyxy_to_cxcywh
-from groundingdino.util.misc import interpolate
-
-
-def crop(image, target, region):
-    cropped_image = F.crop(image, *region)
-
-    target = target.copy()
-    i, j, h, w = region
-
-    # should we do something wrt the original size?
-    target["size"] = torch.tensor([h, w])
-
-    fields = ["labels", "area", "iscrowd", "positive_map"]
-
-    if "boxes" in target:
-        boxes = target["boxes"]
-        max_size = torch.as_tensor([w, h], dtype=torch.float32)
-        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
-        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
-        cropped_boxes = cropped_boxes.clamp(min=0)
-        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
-        target["boxes"] = cropped_boxes.reshape(-1, 4)
-        target["area"] = area
-        fields.append("boxes")
-
-    if "masks" in target:
-        # FIXME should we update the area here if there are no boxes?
-        target["masks"] = target["masks"][:, i : i + h, j : j + w]
-        fields.append("masks")
-
-    # remove elements for which the boxes or masks that have zero area
-    if "boxes" in target or "masks" in target:
-        # favor boxes selection when defining which elements to keep
-        # this is compatible with previous implementation
-        if "boxes" in target:
-            cropped_boxes = target["boxes"].reshape(-1, 2, 2)
-            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
-        else:
-            keep = target["masks"].flatten(1).any(1)
-
-        for field in fields:
-            if field in target:
-                target[field] = target[field][keep]
-
-    if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
-        # for debug and visualization only.
-        if "strings_positive" in target:
-            target["strings_positive"] = [
-                _i for _i, _j in zip(target["strings_positive"], keep) if _j
-            ]
-
-    return cropped_image, target
-
-
-def hflip(image, target):
-    flipped_image = F.hflip(image)
-
-    w, h = image.size
-
-    target = target.copy()
-    if "boxes" in target:
-        boxes = target["boxes"]
-        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
-            [w, 0, w, 0]
-        )
-        target["boxes"] = boxes
-
-    if "masks" in target:
-        target["masks"] = target["masks"].flip(-1)
-
-    return flipped_image, target
-
-
-def resize(image, target, size, max_size=None):
-    # size can be min_size (scalar) or (w, h) tuple
-
-    def get_size_with_aspect_ratio(image_size, size, max_size=None):
-        w, h = image_size
-        if max_size is not None:
-            min_original_size = float(min((w, h)))
-            max_original_size = float(max((w, h)))
-            if max_original_size / min_original_size * size > max_size:
-                size = int(round(max_size * min_original_size / max_original_size))
-
-        if (w <= h and w == size) or (h <= w and h == size):
-            return (h, w)
-
-        if w < h:
-            ow = size
-            oh = int(size * h / w)
-        else:
-            oh = size
-            ow = int(size * w / h)
-
-        return (oh, ow)
-
-    def get_size(image_size, size, max_size=None):
-        if isinstance(size, (list, tuple)):
-            return size[::-1]
-        else:
-            return get_size_with_aspect_ratio(image_size, size, max_size)
-
-    size = get_size(image.size, size, max_size)
-    rescaled_image = F.resize(image, size)
-
-    if target is None:
-        return rescaled_image, None
-
-    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
-    ratio_width, ratio_height = ratios
-
-    target = target.copy()
-    if "boxes" in target:
-        boxes = target["boxes"]
-        scaled_boxes = boxes * torch.as_tensor(
-            [ratio_width, ratio_height, ratio_width, ratio_height]
-        )
-        target["boxes"] = scaled_boxes
-
-    if "area" in target:
-        area = target["area"]
-        scaled_area = area * (ratio_width * ratio_height)
-        target["area"] = scaled_area
-
-    h, w = size
-    target["size"] = torch.tensor([h, w])
-
-    if "masks" in target:
-        target["masks"] = (
-            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
-        )
-
-    return rescaled_image, target
-
-
-def pad(image, target, padding):
-    # assumes that we only pad on the bottom right corners
-    padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
-    if target is None:
-        return padded_image, None
-    target = target.copy()
-    # should we do something wrt the original size?
-    target["size"] = torch.tensor(padded_image.size[::-1])
-    if "masks" in target:
-        target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
-    return padded_image, target
-
-
-class ResizeDebug(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        return resize(img, target, self.size)
-
-
-class RandomCrop(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        region = T.RandomCrop.get_params(img, self.size)
-        return crop(img, target, region)
-
-
-class RandomSizeCrop(object):
-    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
-        # respect_boxes:    True to keep all boxes
-        #                   False to tolerence box filter
-        self.min_size = min_size
-        self.max_size = max_size
-        self.respect_boxes = respect_boxes
-
-    def __call__(self, img: PIL.Image.Image, target: dict):
-        init_boxes = len(target["boxes"])
-        max_patience = 10
-        for i in range(max_patience):
-            w = random.randint(self.min_size, min(img.width, self.max_size))
-            h = random.randint(self.min_size, min(img.height, self.max_size))
-            region = T.RandomCrop.get_params(img, [h, w])
-            result_img, result_target = crop(img, target, region)
-            if (
-                not self.respect_boxes
-                or len(result_target["boxes"]) == init_boxes
-                or i == max_patience - 1
-            ):
-                return result_img, result_target
-        return result_img, result_target
-
-
-class CenterCrop(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        image_width, image_height = img.size
-        crop_height, crop_width = self.size
-        crop_top = int(round((image_height - crop_height) / 2.0))
-        crop_left = int(round((image_width - crop_width) / 2.0))
-        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
-
-
-class RandomHorizontalFlip(object):
-    def __init__(self, p=0.5):
-        self.p = p
-
-    def __call__(self, img, target):
-        if random.random() < self.p:
-            return hflip(img, target)
-        return img, target
-
-
-class RandomResize(object):
-    def __init__(self, sizes, max_size=None):
-        assert isinstance(sizes, (list, tuple))
-        self.sizes = sizes
-        self.max_size = max_size
-
-    def __call__(self, img, target=None):
-        size = random.choice(self.sizes)
-        return resize(img, target, size, self.max_size)
-
-
-class RandomPad(object):
-    def __init__(self, max_pad):
-        self.max_pad = max_pad
-
-    def __call__(self, img, target):
-        pad_x = random.randint(0, self.max_pad)
-        pad_y = random.randint(0, self.max_pad)
-        return pad(img, target, (pad_x, pad_y))
-
-
-class RandomSelect(object):
-    """
-    Randomly selects between transforms1 and transforms2,
-    with probability p for transforms1 and (1 - p) for transforms2
-    """
-
-    def __init__(self, transforms1, transforms2, p=0.5):
-        self.transforms1 = transforms1
-        self.transforms2 = transforms2
-        self.p = p
-
-    def __call__(self, img, target):
-        if random.random() < self.p:
-            return self.transforms1(img, target)
-        return self.transforms2(img, target)
-
-
-class ToTensor(object):
-    def __call__(self, img, target):
-        return F.to_tensor(img), target
-
-
-class RandomErasing(object):
-    def __init__(self, *args, **kwargs):
-        self.eraser = T.RandomErasing(*args, **kwargs)
-
-    def __call__(self, img, target):
-        return self.eraser(img), target
-
-
-class Normalize(object):
-    def __init__(self, mean, std):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, image, target=None):
-        image = F.normalize(image, mean=self.mean, std=self.std)
-        if target is None:
-            return image, None
-        target = target.copy()
-        h, w = image.shape[-2:]
-        if "boxes" in target:
-            boxes = target["boxes"]
-            boxes = box_xyxy_to_cxcywh(boxes)
-            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
-            target["boxes"] = boxes
-        return image, target
-
-
-class Compose(object):
-    def __init__(self, transforms):
-        self.transforms = transforms
-
-    def __call__(self, image, target):
-        for t in self.transforms:
-            image, target = t(image, target)
-        return image, target
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + "("
-        for t in self.transforms:
-            format_string += "\n"
-            format_string += "    {0}".format(t)
-        format_string += "\n)"
-
-        return format_string

groundingdino/util/utils.py

@@ -1,607 +0,0 @@
-import argparse
-import json
-import warnings
-from collections import OrderedDict
-from copy import deepcopy
-from typing import Any, Dict, List
-
-import numpy as np
-import torch
-from transformers import AutoTokenizer
-
-
-
-def slprint(x, name="x"):
-    if isinstance(x, (torch.Tensor, np.ndarray)):
-        print(f"{name}.shape:", x.shape)
-    elif isinstance(x, (tuple, list)):
-        print("type x:", type(x))
-        for i in range(min(10, len(x))):
-            slprint(x[i], f"{name}[{i}]")
-    elif isinstance(x, dict):
-        for k, v in x.items():
-            slprint(v, f"{name}[{k}]")
-    else:
-        print(f"{name}.type:", type(x))
-
-
-def clean_state_dict(state_dict):
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k[:7] == "module.":
-            k = k[7:]  # remove `module.`
-        new_state_dict[k] = v
-    return new_state_dict
-
-
-def renorm(
-    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-) -> torch.FloatTensor:
-    # img: tensor(3,H,W) or tensor(B,3,H,W)
-    # return: same as img
-    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
-    if img.dim() == 3:
-        assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
-            img.size(0),
-            str(img.size()),
-        )
-        img_perm = img.permute(1, 2, 0)
-        mean = torch.Tensor(mean)
-        std = torch.Tensor(std)
-        img_res = img_perm * std + mean
-        return img_res.permute(2, 0, 1)
-    else:  # img.dim() == 4
-        assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
-            img.size(1),
-            str(img.size()),
-        )
-        img_perm = img.permute(0, 2, 3, 1)
-        mean = torch.Tensor(mean)
-        std = torch.Tensor(std)
-        img_res = img_perm * std + mean
-        return img_res.permute(0, 3, 1, 2)
-
-
-class CocoClassMapper:
-    def __init__(self) -> None:
-        self.category_map_str = {
-            "1": 1,
-            "2": 2,
-            "3": 3,
-            "4": 4,
-            "5": 5,
-            "6": 6,
-            "7": 7,
-            "8": 8,
-            "9": 9,
-            "10": 10,
-            "11": 11,
-            "13": 12,
-            "14": 13,
-            "15": 14,
-            "16": 15,
-            "17": 16,
-            "18": 17,
-            "19": 18,
-            "20": 19,
-            "21": 20,
-            "22": 21,
-            "23": 22,
-            "24": 23,
-            "25": 24,
-            "27": 25,
-            "28": 26,
-            "31": 27,
-            "32": 28,
-            "33": 29,
-            "34": 30,
-            "35": 31,
-            "36": 32,
-            "37": 33,
-            "38": 34,
-            "39": 35,
-            "40": 36,
-            "41": 37,
-            "42": 38,
-            "43": 39,
-            "44": 40,
-            "46": 41,
-            "47": 42,
-            "48": 43,
-            "49": 44,
-            "50": 45,
-            "51": 46,
-            "52": 47,
-            "53": 48,
-            "54": 49,
-            "55": 50,
-            "56": 51,
-            "57": 52,
-            "58": 53,
-            "59": 54,
-            "60": 55,
-            "61": 56,
-            "62": 57,
-            "63": 58,
-            "64": 59,
-            "65": 60,
-            "67": 61,
-            "70": 62,
-            "72": 63,
-            "73": 64,
-            "74": 65,
-            "75": 66,
-            "76": 67,
-            "77": 68,
-            "78": 69,
-            "79": 70,
-            "80": 71,
-            "81": 72,
-            "82": 73,
-            "84": 74,
-            "85": 75,
-            "86": 76,
-            "87": 77,
-            "88": 78,
-            "89": 79,
-            "90": 80,
-        }
-        self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}
-        self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}
-
-    def origin2compact(self, idx):
-        return self.origin2compact_mapper[int(idx)]
-
-    def compact2origin(self, idx):
-        return self.compact2origin_mapper[int(idx)]
-
-
-def to_device(item, device):
-    if isinstance(item, torch.Tensor):
-        return item.to(device)
-    elif isinstance(item, list):
-        return [to_device(i, device) for i in item]
-    elif isinstance(item, dict):
-        return {k: to_device(v, device) for k, v in item.items()}
-    else:
-        raise NotImplementedError(
-            "Call Shilong if you use other containers! type: {}".format(type(item))
-        )
-
-
-#
-def get_gaussian_mean(x, axis, other_axis, softmax=True):
-    """
-
-    Args:
-        x (float): Input images(BxCxHxW)
-        axis (int): The index for weighted mean
-        other_axis (int): The other index
-
-    Returns: weighted index for axis, BxC
-
-    """
-    mat2line = torch.sum(x, axis=other_axis)
-    # mat2line = mat2line / mat2line.mean() * 10
-    if softmax:
-        u = torch.softmax(mat2line, axis=2)
-    else:
-        u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6)
-    size = x.shape[axis]
-    ind = torch.linspace(0, 1, size).to(x.device)
-    batch = x.shape[0]
-    channel = x.shape[1]
-    index = ind.repeat([batch, channel, 1])
-    mean_position = torch.sum(index * u, dim=2)
-    return mean_position
-
-
-def get_expected_points_from_map(hm, softmax=True):
-    """get_gaussian_map_from_points
-        B,C,H,W -> B,N,2 float(0, 1) float(0, 1)
-        softargmax function
-
-    Args:
-        hm (float): Input images(BxCxHxW)
-
-    Returns:
-        weighted index for axis, BxCx2. float between 0 and 1.
-
-    """
-    # hm = 10*hm
-    B, C, H, W = hm.shape
-    y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax)  # B,C
-    x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax)  # B,C
-    # return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2)
-    return torch.stack([x_mean, y_mean], dim=2)
-
-
-# Positional encoding (section 5.1)
-# borrow from nerf
-class Embedder:
-    def __init__(self, **kwargs):
-        self.kwargs = kwargs
-        self.create_embedding_fn()
-
-    def create_embedding_fn(self):
-        embed_fns = []
-        d = self.kwargs["input_dims"]
-        out_dim = 0
-        if self.kwargs["include_input"]:
-            embed_fns.append(lambda x: x)
-            out_dim += d
-
-        max_freq = self.kwargs["max_freq_log2"]
-        N_freqs = self.kwargs["num_freqs"]
-
-        if self.kwargs["log_sampling"]:
-            freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs)
-        else:
-            freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs)
-
-        for freq in freq_bands:
-            for p_fn in self.kwargs["periodic_fns"]:
-                embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
-                out_dim += d
-
-        self.embed_fns = embed_fns
-        self.out_dim = out_dim
-
-    def embed(self, inputs):
-        return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
-
-
-def get_embedder(multires, i=0):
-    import torch.nn as nn
-
-    if i == -1:
-        return nn.Identity(), 3
-
-    embed_kwargs = {
-        "include_input": True,
-        "input_dims": 3,
-        "max_freq_log2": multires - 1,
-        "num_freqs": multires,
-        "log_sampling": True,
-        "periodic_fns": [torch.sin, torch.cos],
-    }
-
-    embedder_obj = Embedder(**embed_kwargs)
-    embed = lambda x, eo=embedder_obj: eo.embed(x)
-    return embed, embedder_obj.out_dim
-
-
-class APOPMeter:
-    def __init__(self) -> None:
-        self.tp = 0
-        self.fp = 0
-        self.tn = 0
-        self.fn = 0
-
-    def update(self, pred, gt):
-        """
-        Input:
-            pred, gt: Tensor()
-        """
-        assert pred.shape == gt.shape
-        self.tp += torch.logical_and(pred == 1, gt == 1).sum().item()
-        self.fp += torch.logical_and(pred == 1, gt == 0).sum().item()
-        self.tn += torch.logical_and(pred == 0, gt == 0).sum().item()
-        self.tn += torch.logical_and(pred == 1, gt == 0).sum().item()
-
-    def update_cm(self, tp, fp, tn, fn):
-        self.tp += tp
-        self.fp += fp
-        self.tn += tn
-        self.tn += fn
-
-
-def inverse_sigmoid(x, eps=1e-5):
-    x = x.clamp(min=0, max=1)
-    x1 = x.clamp(min=eps)
-    x2 = (1 - x).clamp(min=eps)
-    return torch.log(x1 / x2)
-
-
-def get_raw_dict(args):
-    """
-    return the dicf contained in args.
-
-    e.g:
-        >>> with open(path, 'w') as f:
-                json.dump(get_raw_dict(args), f, indent=2)
-    """
-    if isinstance(args, argparse.Namespace):
-        return vars(args)
-    elif isinstance(args, dict):
-        return args
-    # elif isinstance(args, SLConfig):
-    #     return args._cfg_dict
-    else:
-        raise NotImplementedError("Unknown type {}".format(type(args)))
-
-
-def stat_tensors(tensor):
-    assert tensor.dim() == 1
-    tensor_sm = tensor.softmax(0)
-    entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum()
-
-    return {
-        "max": tensor.max(),
-        "min": tensor.min(),
-        "mean": tensor.mean(),
-        "var": tensor.var(),
-        "std": tensor.var() ** 0.5,
-        "entropy": entropy,
-    }
-
-
-class NiceRepr:
-    """Inherit from this class and define ``__nice__`` to "nicely" print your
-    objects.
-
-    Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
-    Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
-    If the inheriting class has a ``__len__``, method then the default
-    ``__nice__`` method will return its length.
-
-    Example:
-        >>> class Foo(NiceRepr):
-        ...    def __nice__(self):
-        ...        return 'info'
-        >>> foo = Foo()
-        >>> assert str(foo) == '<Foo(info)>'
-        >>> assert repr(foo).startswith('<Foo(info) at ')
-
-    Example:
-        >>> class Bar(NiceRepr):
-        ...    pass
-        >>> bar = Bar()
-        >>> import pytest
-        >>> with pytest.warns(None) as record:
-        >>>     assert 'object at' in str(bar)
-        >>>     assert 'object at' in repr(bar)
-
-    Example:
-        >>> class Baz(NiceRepr):
-        ...    def __len__(self):
-        ...        return 5
-        >>> baz = Baz()
-        >>> assert str(baz) == '<Baz(5)>'
-    """
-
-    def __nice__(self):
-        """str: a "nice" summary string describing this module"""
-        if hasattr(self, "__len__"):
-            # It is a common pattern for objects to use __len__ in __nice__
-            # As a convenience we define a default __nice__ for these objects
-            return str(len(self))
-        else:
-            # In all other cases force the subclass to overload __nice__
-            raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}")
-
-    def __repr__(self):
-        """str: the string of the module"""
-        try:
-            nice = self.__nice__()
-            classname = self.__class__.__name__
-            return f"<{classname}({nice}) at {hex(id(self))}>"
-        except NotImplementedError as ex:
-            warnings.warn(str(ex), category=RuntimeWarning)
-            return object.__repr__(self)
-
-    def __str__(self):
-        """str: the string of the module"""
-        try:
-            classname = self.__class__.__name__
-            nice = self.__nice__()
-            return f"<{classname}({nice})>"
-        except NotImplementedError as ex:
-            warnings.warn(str(ex), category=RuntimeWarning)
-            return object.__repr__(self)
-
-
-def ensure_rng(rng=None):
-    """Coerces input into a random number generator.
-
-    If the input is None, then a global random state is returned.
-
-    If the input is a numeric value, then that is used as a seed to construct a
-    random state. Otherwise the input is returned as-is.
-
-    Adapted from [1]_.
-
-    Args:
-        rng (int | numpy.random.RandomState | None):
-            if None, then defaults to the global rng. Otherwise this can be an
-            integer or a RandomState class
-    Returns:
-        (numpy.random.RandomState) : rng -
-            a numpy random number generator
-
-    References:
-        .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270  # noqa: E501
-    """
-
-    if rng is None:
-        rng = np.random.mtrand._rand
-    elif isinstance(rng, int):
-        rng = np.random.RandomState(rng)
-    else:
-        rng = rng
-    return rng
-
-
-def random_boxes(num=1, scale=1, rng=None):
-    """Simple version of ``kwimage.Boxes.random``
-
-    Returns:
-        Tensor: shape (n, 4) in x1, y1, x2, y2 format.
-
-    References:
-        https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
-
-    Example:
-        >>> num = 3
-        >>> scale = 512
-        >>> rng = 0
-        >>> boxes = random_boxes(num, scale, rng)
-        >>> print(boxes)
-        tensor([[280.9925, 278.9802, 308.6148, 366.1769],
-                [216.9113, 330.6978, 224.0446, 456.5878],
-                [405.3632, 196.3221, 493.3953, 270.7942]])
-    """
-    rng = ensure_rng(rng)
-
-    tlbr = rng.rand(num, 4).astype(np.float32)
-
-    tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
-    tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
-    br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
-    br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
-
-    tlbr[:, 0] = tl_x * scale
-    tlbr[:, 1] = tl_y * scale
-    tlbr[:, 2] = br_x * scale
-    tlbr[:, 3] = br_y * scale
-
-    boxes = torch.from_numpy(tlbr)
-    return boxes
-
-
-class ModelEma(torch.nn.Module):
-    def __init__(self, model, decay=0.9997, device=None):
-        super(ModelEma, self).__init__()
-        # make a copy of the model for accumulating moving average of weights
-        self.module = deepcopy(model)
-        self.module.eval()
-
-        # import ipdb; ipdb.set_trace()
-
-        self.decay = decay
-        self.device = device  # perform ema on different device from model if set
-        if self.device is not None:
-            self.module.to(device=device)
-
-    def _update(self, model, update_fn):
-        with torch.no_grad():
-            for ema_v, model_v in zip(
-                self.module.state_dict().values(), model.state_dict().values()
-            ):
-                if self.device is not None:
-                    model_v = model_v.to(device=self.device)
-                ema_v.copy_(update_fn(ema_v, model_v))
-
-    def update(self, model):
-        self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)
-
-    def set(self, model):
-        self._update(model, update_fn=lambda e, m: m)
-
-
-class BestMetricSingle:
-    def __init__(self, init_res=0.0, better="large") -> None:
-        self.init_res = init_res
-        self.best_res = init_res
-        self.best_ep = -1
-
-        self.better = better
-        assert better in ["large", "small"]
-
-    def isbetter(self, new_res, old_res):
-        if self.better == "large":
-            return new_res > old_res
-        if self.better == "small":
-            return new_res < old_res
-
-    def update(self, new_res, ep):
-        if self.isbetter(new_res, self.best_res):
-            self.best_res = new_res
-            self.best_ep = ep
-            return True
-        return False
-
-    def __str__(self) -> str:
-        return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep)
-
-    def __repr__(self) -> str:
-        return self.__str__()
-
-    def summary(self) -> dict:
-        return {
-            "best_res": self.best_res,
-            "best_ep": self.best_ep,
-        }
-
-
-class BestMetricHolder:
-    def __init__(self, init_res=0.0, better="large", use_ema=False) -> None:
-        self.best_all = BestMetricSingle(init_res, better)
-        self.use_ema = use_ema
-        if use_ema:
-            self.best_ema = BestMetricSingle(init_res, better)
-            self.best_regular = BestMetricSingle(init_res, better)
-
-    def update(self, new_res, epoch, is_ema=False):
-        """
-        return if the results is the best.
-        """
-        if not self.use_ema:
-            return self.best_all.update(new_res, epoch)
-        else:
-            if is_ema:
-                self.best_ema.update(new_res, epoch)
-                return self.best_all.update(new_res, epoch)
-            else:
-                self.best_regular.update(new_res, epoch)
-                return self.best_all.update(new_res, epoch)
-
-    def summary(self):
-        if not self.use_ema:
-            return self.best_all.summary()
-
-        res = {}
-        res.update({f"all_{k}": v for k, v in self.best_all.summary().items()})
-        res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()})
-        res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()})
-        return res
-
-    def __repr__(self) -> str:
-        return json.dumps(self.summary(), indent=2)
-
-    def __str__(self) -> str:
-        return self.__repr__()
-
-
-def targets_to(targets: List[Dict[str, Any]], device):
-    """Moves the target dicts to the given device."""
-    excluded_keys = [
-        "questionId",
-        "tokens_positive",
-        "strings_positive",
-        "tokens",
-        "dataset_name",
-        "sentence_id",
-        "original_img_id",
-        "nb_eval",
-        "task_id",
-        "original_id",
-        "token_span",
-        "caption",
-        "dataset_type",
-    ]
-    return [
-        {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets
-    ]
-
-
-def get_phrases_from_posmap(
-    posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer
-):
-    assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor"
-    if posmap.dim() == 1:
-        non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
-        token_ids = [tokenized["input_ids"][i] for i in non_zero_idx]
-        return tokenizer.decode(token_ids)
-    else:
-        raise NotImplementedError("posmap must be 1-dim")

groundingdino/util/visualizer.py

@@ -1,318 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-@File    :   visualizer.py
-@Time    :   2022/04/05 11:39:33
-@Author  :   Shilong Liu 
-@Contact :   slongliu86@gmail.com
-"""
-
-import datetime
-import os
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-from matplotlib import transforms
-from matplotlib.collections import PatchCollection
-from matplotlib.patches import Polygon
-from pycocotools import mask as maskUtils
-
-
-def renorm(
-    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-) -> torch.FloatTensor:
-    # img: tensor(3,H,W) or tensor(B,3,H,W)
-    # return: same as img
-    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
-    if img.dim() == 3:
-        assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
-            img.size(0),
-            str(img.size()),
-        )
-        img_perm = img.permute(1, 2, 0)
-        mean = torch.Tensor(mean)
-        std = torch.Tensor(std)
-        img_res = img_perm * std + mean
-        return img_res.permute(2, 0, 1)
-    else:  # img.dim() == 4
-        assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
-            img.size(1),
-            str(img.size()),
-        )
-        img_perm = img.permute(0, 2, 3, 1)
-        mean = torch.Tensor(mean)
-        std = torch.Tensor(std)
-        img_res = img_perm * std + mean
-        return img_res.permute(0, 3, 1, 2)
-
-
-class ColorMap:
-    def __init__(self, basergb=[255, 255, 0]):
-        self.basergb = np.array(basergb)
-
-    def __call__(self, attnmap):
-        # attnmap: h, w. np.uint8.
-        # return: h, w, 4. np.uint8.
-        assert attnmap.dtype == np.uint8
-        h, w = attnmap.shape
-        res = self.basergb.copy()
-        res = res[None][None].repeat(h, 0).repeat(w, 1)  # h, w, 3
-        attn1 = attnmap.copy()[..., None]  # h, w, 1
-        res = np.concatenate((res, attn1), axis=-1).astype(np.uint8)
-        return res
-
-
-def rainbow_text(x, y, ls, lc, **kw):
-    """
-    Take a list of strings ``ls`` and colors ``lc`` and place them next to each
-    other, with text ls[i] being shown in color lc[i].
-
-    This example shows how to do both vertical and horizontal text, and will
-    pass all keyword arguments to plt.text, so you can set the font size,
-    family, etc.
-    """
-    t = plt.gca().transData
-    fig = plt.gcf()
-    plt.show()
-
-    # horizontal version
-    for s, c in zip(ls, lc):
-        text = plt.text(x, y, " " + s + " ", color=c, transform=t, **kw)
-        text.draw(fig.canvas.get_renderer())
-        ex = text.get_window_extent()
-        t = transforms.offset_copy(text._transform, x=ex.width, units="dots")
-
-    # #vertical version
-    # for s,c in zip(ls,lc):
-    #     text = plt.text(x,y," "+s+" ",color=c, transform=t,
-    #             rotation=90,va='bottom',ha='center',**kw)
-    #     text.draw(fig.canvas.get_renderer())
-    #     ex = text.get_window_extent()
-    #     t = transforms.offset_copy(text._transform, y=ex.height, units='dots')
-
-
-class COCOVisualizer:
-    def __init__(self, coco=None, tokenlizer=None) -> None:
-        self.coco = coco
-
-    def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"):
-        """
-        img: tensor(3, H, W)
-        tgt: make sure they are all on cpu.
-            must have items: 'image_id', 'boxes', 'size'
-        """
-        plt.figure(dpi=dpi)
-        plt.rcParams["font.size"] = "5"
-        ax = plt.gca()
-        img = renorm(img).permute(1, 2, 0)
-        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-        #     import ipdb; ipdb.set_trace()
-        ax.imshow(img)
-
-        self.addtgt(tgt)
-
-        if tgt is None:
-            image_id = 0
-        elif "image_id" not in tgt:
-            image_id = 0
-        else:
-            image_id = tgt["image_id"]
-
-        if caption is None:
-            savename = "{}/{}-{}.png".format(
-                savedir, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
-            )
-        else:
-            savename = "{}/{}-{}-{}.png".format(
-                savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
-            )
-        print("savename: {}".format(savename))
-        os.makedirs(os.path.dirname(savename), exist_ok=True)
-        plt.savefig(savename)
-        plt.close()
-
-    def addtgt(self, tgt):
-        """ """
-        if tgt is None or not "boxes" in tgt:
-            ax = plt.gca()
-
-            if "caption" in tgt:
-                ax.set_title(tgt["caption"], wrap=True)
-
-            ax.set_axis_off()
-            return
-
-        ax = plt.gca()
-        H, W = tgt["size"]
-        numbox = tgt["boxes"].shape[0]
-
-        color = []
-        polygons = []
-        boxes = []
-        for box in tgt["boxes"].cpu():
-            unnormbbox = box * torch.Tensor([W, H, W, H])
-            unnormbbox[:2] -= unnormbbox[2:] / 2
-            [bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist()
-            boxes.append([bbox_x, bbox_y, bbox_w, bbox_h])
-            poly = [
-                [bbox_x, bbox_y],
-                [bbox_x, bbox_y + bbox_h],
-                [bbox_x + bbox_w, bbox_y + bbox_h],
-                [bbox_x + bbox_w, bbox_y],
-            ]
-            np_poly = np.array(poly).reshape((4, 2))
-            polygons.append(Polygon(np_poly))
-            c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
-            color.append(c)
-
-        p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1)
-        ax.add_collection(p)
-        p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
-        ax.add_collection(p)
-
-        if "strings_positive" in tgt and len(tgt["strings_positive"]) > 0:
-            assert (
-                len(tgt["strings_positive"]) == numbox
-            ), f"{len(tgt['strings_positive'])} = {numbox}, "
-            for idx, strlist in enumerate(tgt["strings_positive"]):
-                cate_id = int(tgt["labels"][idx])
-                _string = str(cate_id) + ":" + " ".join(strlist)
-                bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
-                # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
-                ax.text(
-                    bbox_x,
-                    bbox_y,
-                    _string,
-                    color="black",
-                    bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
-                )
-
-        if "box_label" in tgt:
-            assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, "
-            for idx, bl in enumerate(tgt["box_label"]):
-                _string = str(bl)
-                bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
-                # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
-                ax.text(
-                    bbox_x,
-                    bbox_y,
-                    _string,
-                    color="black",
-                    bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
-                )
-
-        if "caption" in tgt:
-            ax.set_title(tgt["caption"], wrap=True)
-            # plt.figure()
-            # rainbow_text(0.0,0.0,"all unicorns poop rainbows ! ! !".split(),
-            #         ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black'])
-
-        if "attn" in tgt:
-            # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-            #     import ipdb; ipdb.set_trace()
-            if isinstance(tgt["attn"], tuple):
-                tgt["attn"] = [tgt["attn"]]
-            for item in tgt["attn"]:
-                attn_map, basergb = item
-                attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3)
-                attn_map = (attn_map * 255).astype(np.uint8)
-                cm = ColorMap(basergb)
-                heatmap = cm(attn_map)
-                ax.imshow(heatmap)
-        ax.set_axis_off()
-
-    def showAnns(self, anns, draw_bbox=False):
-        """
-        Display the specified annotations.
-        :param anns (array of object): annotations to display
-        :return: None
-        """
-        if len(anns) == 0:
-            return 0
-        if "segmentation" in anns[0] or "keypoints" in anns[0]:
-            datasetType = "instances"
-        elif "caption" in anns[0]:
-            datasetType = "captions"
-        else:
-            raise Exception("datasetType not supported")
-        if datasetType == "instances":
-            ax = plt.gca()
-            ax.set_autoscale_on(False)
-            polygons = []
-            color = []
-            for ann in anns:
-                c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
-                if "segmentation" in ann:
-                    if type(ann["segmentation"]) == list:
-                        # polygon
-                        for seg in ann["segmentation"]:
-                            poly = np.array(seg).reshape((int(len(seg) / 2), 2))
-                            polygons.append(Polygon(poly))
-                            color.append(c)
-                    else:
-                        # mask
-                        t = self.imgs[ann["image_id"]]
-                        if type(ann["segmentation"]["counts"]) == list:
-                            rle = maskUtils.frPyObjects(
-                                [ann["segmentation"]], t["height"], t["width"]
-                            )
-                        else:
-                            rle = [ann["segmentation"]]
-                        m = maskUtils.decode(rle)
-                        img = np.ones((m.shape[0], m.shape[1], 3))
-                        if ann["iscrowd"] == 1:
-                            color_mask = np.array([2.0, 166.0, 101.0]) / 255
-                        if ann["iscrowd"] == 0:
-                            color_mask = np.random.random((1, 3)).tolist()[0]
-                        for i in range(3):
-                            img[:, :, i] = color_mask[i]
-                        ax.imshow(np.dstack((img, m * 0.5)))
-                if "keypoints" in ann and type(ann["keypoints"]) == list:
-                    # turn skeleton into zero-based index
-                    sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1
-                    kp = np.array(ann["keypoints"])
-                    x = kp[0::3]
-                    y = kp[1::3]
-                    v = kp[2::3]
-                    for sk in sks:
-                        if np.all(v[sk] > 0):
-                            plt.plot(x[sk], y[sk], linewidth=3, color=c)
-                    plt.plot(
-                        x[v > 0],
-                        y[v > 0],
-                        "o",
-                        markersize=8,
-                        markerfacecolor=c,
-                        markeredgecolor="k",
-                        markeredgewidth=2,
-                    )
-                    plt.plot(
-                        x[v > 1],
-                        y[v > 1],
-                        "o",
-                        markersize=8,
-                        markerfacecolor=c,
-                        markeredgecolor=c,
-                        markeredgewidth=2,
-                    )
-
-                if draw_bbox:
-                    [bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"]
-                    poly = [
-                        [bbox_x, bbox_y],
-                        [bbox_x, bbox_y + bbox_h],
-                        [bbox_x + bbox_w, bbox_y + bbox_h],
-                        [bbox_x + bbox_w, bbox_y],
-                    ]
-                    np_poly = np.array(poly).reshape((4, 2))
-                    polygons.append(Polygon(np_poly))
-                    color.append(c)
-
-            # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
-            # ax.add_collection(p)
-            p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
-            ax.add_collection(p)
-        elif datasetType == "captions":
-            for ann in anns:
-                print(ann["caption"])

groundingdino/util/vl_utils.py

@@ -1,100 +0,0 @@
-import os
-import random
-from typing import List
-
-import torch
-
-
-def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
-    """construct a map such that positive_map[i,j] = True iff box i is associated to token j
-    Input:
-        - tokenized:
-            - input_ids: Tensor[1, ntokens]
-            - attention_mask: Tensor[1, ntokens]
-        - token_span: list with length num_boxes.
-            - each item: [start_idx, end_idx]
-    """
-    positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float)
-    for j, tok_list in enumerate(token_span):
-        for (beg, end) in tok_list:
-            beg_pos = tokenized.char_to_token(beg)
-            end_pos = tokenized.char_to_token(end - 1)
-            if beg_pos is None:
-                try:
-                    beg_pos = tokenized.char_to_token(beg + 1)
-                    if beg_pos is None:
-                        beg_pos = tokenized.char_to_token(beg + 2)
-                except:
-                    beg_pos = None
-            if end_pos is None:
-                try:
-                    end_pos = tokenized.char_to_token(end - 2)
-                    if end_pos is None:
-                        end_pos = tokenized.char_to_token(end - 3)
-                except:
-                    end_pos = None
-            if beg_pos is None or end_pos is None:
-                continue
-
-            assert beg_pos is not None and end_pos is not None
-            if os.environ.get("SHILONG_DEBUG_ONLY_ONE_POS", None) == "TRUE":
-                positive_map[j, beg_pos] = 1
-                break
-            else:
-                positive_map[j, beg_pos : end_pos + 1].fill_(1)
-
-    return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)
-
-
-def build_captions_and_token_span(cat_list, force_lowercase):
-    """
-    Return:
-        captions: str
-        cat2tokenspan: dict
-            {
-                'dog': [[0, 2]],
-                ...
-            }
-    """
-
-    cat2tokenspan = {}
-    captions = ""
-    for catname in cat_list:
-        class_name = catname
-        if force_lowercase:
-            class_name = class_name.lower()
-        if "/" in class_name:
-            class_name_list: List = class_name.strip().split("/")
-            class_name_list.append(class_name)
-            class_name: str = random.choice(class_name_list)
-
-        tokens_positive_i = []
-        subnamelist = [i.strip() for i in class_name.strip().split(" ")]
-        for subname in subnamelist:
-            if len(subname) == 0:
-                continue
-            if len(captions) > 0:
-                captions = captions + " "
-            strat_idx = len(captions)
-            end_idx = strat_idx + len(subname)
-            tokens_positive_i.append([strat_idx, end_idx])
-            captions = captions + subname
-
-        if len(tokens_positive_i) > 0:
-            captions = captions + " ."
-            cat2tokenspan[class_name] = tokens_positive_i
-
-    return captions, cat2tokenspan
-
-
-def build_id2posspan_and_caption(category_dict: dict):
-    """Build id2pos_span and caption from category_dict
-
-    Args:
-        category_dict (dict): category_dict
-    """
-    cat_list = [item["name"].lower() for item in category_dict]
-    id2catname = {item["id"]: item["name"].lower() for item in category_dict}
-    caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True)
-    id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()}
-    return id2posspan, caption