# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
"""
DETR Transformer class.

Copy-paste from paddle.nn.layer.transformer with modifications:
    * positional encodings are passes in MHattention
    * extra LN at the end of encoder is removed
    * decoder returns a stack of activations from all decoding layers
"""
import copy
import numpy as np
from typing import Optional, List
import paddle.nn as nn
# import paddle.fluid.layers as L
# import paddle.fluid.dygraph as dg
from paddle.nn.layer.common import Linear, Dropout
from paddle.nn.layer.norm import LayerNorm
import paddle.nn.functional as F
from paddle.fluid.param_attr import ParamAttr
import paddle
import math

from paddle.utils.cpp_extension import load
custom_ops = load(
    name="custom_jit_ops",
    sources=["ops/ms_deform_attn.cc", "ops/ms_deform_attn_cuda.cu", "ops/ms_deform_im2col_cuda.cu"],
    extra_cuda_cflags=["-arch=sm_75"])


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

#TODO
def masked_fill(tensor, mask, value):
    cover = paddle.full_like(tensor, value)
    out = paddle.where(mask, tensor, cover)
    return out


class MSDeformAttn(nn.Layer):
    def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
        """
        Multi-Scale Deformable Attention Module
        :param d_model      hidden dimension
        :param n_levels     number of feature levels
        :param n_heads      number of attention heads
        :param n_points     number of sampling points per attention head per feature level
        """
        super().__init__()
        if d_model % n_heads != 0:
            raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))
        _d_per_head = d_model // n_heads
        # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
        if not _is_power_of_2(_d_per_head):
            print("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
                          "which is more efficient in our CUDA implementation.")

        self.im2col_step = 64

        self.d_model = d_model
        self.n_levels = n_levels
        self.n_heads = n_heads
        self.n_points = n_points

        self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
        self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
        self.value_proj = nn.Linear(d_model, d_model)
        self.output_proj = nn.Linear(d_model, d_model)

        self._reset_parameters()

    def _reset_parameters(self):
        self.sampling_offsets.weight.data = nn.initializer.Constant(0.)
        thetas = paddle.arange(self.n_heads, dtype='float32') * (2.0 * math.pi / self.n_heads)
        grid_init = paddle.stack([thetas.cos(), thetas.sin()], -1)
        grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)
        for i in range(self.n_points):
            grid_init[:, :, i, :] *= i + 1
        with paddle.no_grad():
            self.sampling_offsets.bias = nn.Parameter(grid_init.flatten(-1)) #TODO

        self.attention_weights.weight.data = nn.initializer.Constant(0.)
        self.attention_weights.bias.data = nn.initializer.Constant(0.)
        self.value_proj.bias.data = nn.initializer.Constant(0.)
        self.output_proj.bias.data = nn.initializer.Constant(0.)
        self.value_proj.weight.data = nn.initializer.XavierUniform()
        self.output_proj.weight.data = nn.initializer.XavierUniform()

    def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):
        """
        :param query                       (N, Length_{query}, C)
        :param reference_points            (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area
                                        or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes
        :param input_flatten               (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C)
        :param input_spatial_shapes        (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
        :param input_level_start_index     (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]
        :param input_padding_mask          (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements

        :return output                     (N, Length_{query}, C)
        """
        N, Len_q, _ = query.shape
        N, Len_in, _ = input_flatten.shape
        assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in

        value = self.value_proj(input_flatten)
        if input_padding_mask is not None:
            value = value.masked_fill(input_padding_mask[..., None], float(0)) #TODO

        value = value.reshape((N, Len_in, self.n_heads, self.d_model // self.n_heads))
        sampling_offsets = self.sampling_offsets(query).reshape((N, Len_q, self.n_heads, self.n_levels, self.n_points, 2))
        attention_weights = self.attention_weights(query).reshape((N, Len_q, self.n_heads, self.n_levels * self.n_points))
        attention_weights = F.softmax(attention_weights, -1).reshape((N, Len_q, self.n_heads, self.n_levels, self.n_points))

        # N, Len_q, n_heads, n_levels, n_points, 2
        if reference_points.shape[-1] == 2:
            offset_normalizer = paddle.stack([input_spatial_shapes[..., 1], input_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.n_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]))   
        output = custom_ops.custom_ms_deform_attn(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step)
        output = self.output_proj(output)
        return output

class DeformableTransformer(nn.Layer):
    def __init__(self, d_model=256, nhead=8,
                 num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=1024, dropout=0.1,
                 activation="relu", return_intermediate_dec=False,
                 num_feature_levels=4, dec_n_points=4,  enc_n_points=4,
                 two_stage=False, two_stage_num_proposals=300):
        super().__init__()

        self.d_model = d_model
        self.nhead = nhead
        self.two_stage = two_stage
        self.two_stage_num_proposals = two_stage_num_proposals

        encoder_layer = DeformableTransformerEncoderLayer(d_model, dim_feedforward,
                                                          dropout, activation,
                                                          num_feature_levels, nhead, enc_n_points)
        self.encoder = DeformableTransformerEncoder(encoder_layer, num_encoder_layers)

        decoder_layer = DeformableTransformerDecoderLayer(d_model, dim_feedforward,
                                                          dropout, activation,
                                                          num_feature_levels, nhead, dec_n_points)
        self.decoder = DeformableTransformerDecoder(decoder_layer, num_decoder_layers, return_intermediate_dec)

        self.level_embed = paddle.create_parameter(paddle.to_tensor(num_feature_levels, d_model)) #这里应该有问题

        if two_stage:
            self.enc_output = nn.Linear(d_model, d_model)
            self.enc_output_norm = nn.LayerNorm(d_model)
            self.pos_trans = nn.Linear(d_model * 2, d_model * 2)
            self.pos_trans_norm = nn.LayerNorm(d_model * 2)
        else:
            self.reference_points = nn.Linear(d_model, 2)

        self._reset_parameters()

    def _reset_parameters(self):
        for p in self.parameters():
            if p.dim() > 1:
                p = nn.initializer.XavierUniform()

        #TODO
        # for m in self.modules():
        #     if isinstance(m, MSDeformAttn):
        #         m._reset_parameters()

        if not self.two_stage:
            # paddle中不支持, gain=1.0可以忽略
            self.reference_points.weight.data = nn.initializer.XavierUniform() #可能有问题
            self.reference_points.bias.data = nn.initializer.Constant(0.)
        self.level_embed = nn.initializer.Normal()

    def get_proposal_pos_embed(self, proposals):
        num_pos_feats = 128
        temperature = 10000
        scale = 2 * math.pi

        dim_t = paddle.arange(num_pos_feats, dtype='float32').requires_grad_(False)
        dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
        # N, L, 4
        proposals = proposals.sigmoid() * scale
        # N, L, 4, 128
        pos = proposals[:, :, :, None] / dim_t
        # N, L, 4, 64, 2
        pos = paddle.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), axis=4).flatten(2)
        return pos


    def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes):
        N_, S_, C_ = memory.shape
        base_scale = 4.0
        proposals = []
        _cur = 0
        for lvl, (H_, W_) in enumerate(spatial_shapes):
            mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].reshape((N_, H_, W_, 1))
            valid_H = paddle.sum(~mask_flatten_[:, :, 0, 0], 1)
            valid_W = paddle.sum(~mask_flatten_[:, 0, :, 0], 1)

            grid_y, grid_x = paddle.meshgrid(paddle.linspace(0, H_ - 1, H_, 'float32'),
                                            paddle.linspace(0, W_ - 1, W_, 'float32'))
            grid = paddle.concat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)

            scale = paddle.concat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).reshape((N_, 1, 1, 2))
            grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
            wh = paddle.ones_like(grid) * 0.05 * (2.0 ** lvl)
            proposal = paddle.concat((grid, wh), -1).reshape((N_, -1, 4))
            proposals.append(proposal)
            _cur += (H_ * W_)
        output_proposals = paddle.concat(proposals, 1)
        output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True) #这里可能有问题
        output_proposals = paddle.log(output_proposals / (1 - output_proposals))
        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)) #masked_fill可能需要自定义算子, 或者转为numpy
        output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
        output_memory = self.enc_output_norm(self.enc_output(output_memory))
        return output_memory, output_proposals

    def get_valid_ratio(self, mask):
        _, H, W = mask.shape
        valid_H = paddle.sum(~mask[:, :, 0], 1)
        valid_W = paddle.sum(~mask[:, 0, :], 1)
        valid_ratio_h = valid_H.float() / H
        valid_ratio_w = valid_W.float() / W
        valid_ratio = paddle.stack([valid_ratio_w, valid_ratio_h], -1)
        return valid_ratio

    def forward(self, srcs, masks, pos_embeds, query_embed=None):
        assert self.two_stage or query_embed is not None

        # 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((0, 2, 1, 3))
            mask = mask.flatten(1)
            pos_embed = pos_embed.flatten(2).transpose((0, 2, 1, 3))
            lvl_pos_embed = pos_embed + self.level_embed[lvl].reshape((1, 1, -1))
            lvl_pos_embed_flatten.append(lvl_pos_embed)
            src_flatten.append(src)
            mask_flatten.append(mask)
        src_flatten = paddle.concat(src_flatten, 1)
        mask_flatten = paddle.concat(mask_flatten, 1)
        lvl_pos_embed_flatten = paddle.concat(lvl_pos_embed_flatten, 1)
        level_start_index = paddle.concat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) #肯定是有问题的
        valid_ratios = paddle.stacks([self.get_valid_ratio(m) for m in masks], 1)

        # encoder
        memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)

        # prepare input for decoder
        bs, _, c = memory.shape
        if self.two_stage:
            output_memory, output_proposals = self.gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes)

            # hack implementation for two-stage Deformable DETR
            enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory)
            enc_outputs_coord_unact = self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals

            topk = self.two_stage_num_proposals
            topk_proposals = paddle.topk(enc_outputs_class[..., 0], topk, axis=1)[1]
            topk_coords_unact = paddle.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4))
            topk_coords_unact = topk_coords_unact.detach()
            reference_points = topk_coords_unact.sigmoid()
            init_reference_out = reference_points
            pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact)))
            query_embed, tgt = paddle.split(pos_trans_out, c, axis=2)

        else:
            query_embed, tgt = paddle.split(query_embed, c, dim=1)
            query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1)
            tgt = tgt.unsqueeze(0).expand(bs, -1, -1)
            reference_points = self.reference_points(query_embed).sigmoid()
            init_reference_out = reference_points

        # decoder
        hs, inter_references = self.decoder(tgt, reference_points, memory,
                                            spatial_shapes, level_start_index, valid_ratios, query_embed,
                                            mask_flatten)

        inter_references_out = inter_references
        if self.two_stage:
            return (hs, init_reference_out, inter_references_out, enc_outputs_class, enc_outputs_coord_unact)
        return hs, init_reference_out, inter_references_out, None, None


class DeformableTransformerEncoderLayer(nn.Layer):
    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(d_model, n_levels, n_heads, n_points) #TODO
        self.dropout1 = nn.Dropout(dropout) #检查dropout是否需要1-x
        self.norm1 = nn.LayerNorm(d_model)

        # ffn
        self.linear1 = nn.Linear(d_model, d_ffn)
        self.activation = _get_activation_fn(activation)
        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, padding_mask=None):
        # self attention
        src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)
        src = src + self.dropout1(src2)
        src = self.norm1(src)

        # ffn
        src = self.forward_ffn(src)

        return src


class DeformableTransformerEncoder(nn.Layer):
    def __init__(self, encoder_layer, num_layers):
        super().__init__()
        self.layers = _get_clones(encoder_layer, num_layers)
        self.num_layers = num_layers

    @staticmethod
    def get_reference_points(spatial_shapes, valid_ratios):
        reference_points_list = []
        for lvl, (H_, W_) in enumerate(spatial_shapes):

            ref_y, ref_x = paddle.meshgrid(paddle.linspace(0.5, H_ - 0.5, H_, 'float32'),
                                          paddle.linspace(0.5, W_ - 0.5, W_, 'float32'))
            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 = paddle.stack((ref_x, ref_y), -1)
            reference_points_list.append(ref)
        reference_points = paddle.cat(reference_points_list, 1)
        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
        return reference_points

    def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
        output = src
        reference_points = self.get_reference_points(spatial_shapes, valid_ratios)
        for _, layer in enumerate(self.layers):
            output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)

        return output

class DeformableTransformerDecoderLayer(nn.Layer):
    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__()

        # cross attention
        self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
        self.dropout1 = nn.Dropout(dropout)
        self.norm1 = nn.LayerNorm(d_model)

        # self attention
        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.norm2 = nn.LayerNorm(d_model)

        # ffn
        self.linear1 = nn.Linear(d_model, d_ffn)
        self.activation = _get_activation_fn(activation)
        self.dropout3 = nn.Dropout(dropout)
        self.linear2 = nn.Linear(d_ffn, d_model)
        self.dropout4 = nn.Dropout(dropout)
        self.norm3 = nn.LayerNorm(d_model)

    @staticmethod
    def with_pos_embed(tensor, pos):
        return tensor if pos is None else tensor + pos

    def forward_ffn(self, tgt):
        tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
        tgt = tgt + self.dropout4(tgt2)
        tgt = self.norm3(tgt)
        return tgt

    def forward(self, tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index, src_padding_mask=None):
        # self attention
        q = k = self.with_pos_embed(tgt, query_pos)
        tgt2 = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), tgt.transpose(0, 1))[0].transpose(0, 1)
        tgt = tgt + self.dropout2(tgt2)
        tgt = self.norm2(tgt)

        # cross attention
        tgt2 = self.cross_attn(self.with_pos_embed(tgt, query_pos),
                               reference_points,
                               src, src_spatial_shapes, level_start_index, src_padding_mask)
        tgt = tgt + self.dropout1(tgt2)
        tgt = self.norm1(tgt)

        # ffn
        tgt = self.forward_ffn(tgt)

        return tgt


class DeformableTransformerDecoder(nn.Layer):
    def __init__(self, decoder_layer, num_layers, return_intermediate=False):
        super().__init__()
        self.layers = _get_clones(decoder_layer, num_layers)
        self.num_layers = num_layers
        self.return_intermediate = return_intermediate
        # hack implementation for iterative bounding box refinement and two-stage Deformable DETR
        self.bbox_embed = None
        self.class_embed = None

    def forward(self, tgt, reference_points, src, src_spatial_shapes, src_level_start_index, src_valid_ratios,
                query_pos=None, src_padding_mask=None):
        output = tgt

        intermediate = []
        intermediate_reference_points = []
        for lid, layer in enumerate(self.layers):
            if reference_points.shape[-1] == 4:
                reference_points_input = reference_points[:, :, None] \
                                         * paddle.concat([src_valid_ratios, src_valid_ratios], -1)[:, None]
            else:
                assert reference_points.shape[-1] == 2
                reference_points_input = reference_points[:, :, None] * src_valid_ratios[:, None]
            output = layer(output, query_pos, reference_points_input, src, src_spatial_shapes, src_level_start_index, src_padding_mask)

            # hack implementation for iterative bounding box refinement
            if self.bbox_embed is not None:
                tmp = self.bbox_embed[lid](output)
                if reference_points.shape[-1] == 4:
                    new_reference_points = tmp + inverse_sigmoid(reference_points)
                    new_reference_points = new_reference_points.sigmoid()
                else:
                    assert reference_points.shape[-1] == 2
                    new_reference_points = tmp
                    new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points)
                    new_reference_points = new_reference_points.sigmoid()
                reference_points = new_reference_points.detach()

            if self.return_intermediate:
                intermediate.append(output)
                intermediate_reference_points.append(reference_points)

        if self.return_intermediate:
            return paddle.stack(intermediate), paddle.stack(intermediate_reference_points)

        return output, reference_points

def _get_clones(module, N):
    return nn.LayerList([copy.deepcopy(module) for i in range(N)])

def inverse_sigmoid(x, eps=1e-5):
    x = paddle.clip(x, min=0, max=1)
    x1 = paddle.clip(x, min=eps)
    x2 = paddle.clip(1-x, min=eps)
    return paddle.log(x1/x2)


def _get_activation_fn(activation):
    """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
    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")

def build_deforamble_transformer(args):
    return DeformableTransformer(
        d_model=args.hidden_dim,
        nhead=args.nheads,
        num_encoder_layers=args.enc_layers,
        num_decoder_layers=args.dec_layers,
        dim_feedforward=args.dim_feedforward,
        dropout=args.dropout,

        activation="relu",
        return_intermediate_dec=True,
        num_feature_levels=args.num_feature_levels,
        dec_n_points=args.dec_n_points,
        enc_n_points=args.enc_n_points,
        two_stage=args.two_stage,
        two_stage_num_proposals=args.num_queries
        # normalize_before=args.pre_norm,
        # return_intermediate_dec=True,
    )


'''Transformer 单元测试'''
if __name__ == '__main__':
    #字典转结构体
    class DictToStruct:
        def __init__(self, **entries):
            self.__dict__.update(entries)

    #COCO has a max_obj_id of 90, so we pass `num_classes` to be 91.
    params = {"hidden_dim": 256,
              "dec_n_points": 4,
              "dim_feedforward": 1024,
              "dropout": 0.1,
              "enc_n_points": 4,
              "nheads": 8,
              "enc_layers": 6,
              "dec_layers": 6,
              "num_feature_levels": 4,
              "two_stage": False,
              "num_queries": 300
              }
    args = DictToStruct(**params)
    trans = build_deforamble_transformer(args)

    #加载npy
    srcs, masks, pos_embeds = [], [], []
    for i in range(4):
        tmp = np.load("/f/hulei/pd_match/detr/Deformable-DETR/npy_files/src_" + str(i) + ".npy")
        tmp = paddle.tensor(tmp)
        srcs.append(tmp)

        tmp = np.load("/f/hulei/pd_match/detr/Deformable-DETR/npy_files/masks_" + str(i) + ".npy")
        tmp = paddle.tensor(tmp)
        masks.append(tmp)

        tmp = np.load("/f/hulei/pd_match/detr/Deformable-DETR/npy_files/pos_embeds_" + str(i) + ".npy")
        tmp = paddle.tensor(tmp)
        pos_embeds.append(tmp)
    tmp = np.load("/f/hulei/pd_match/detr/Deformable-DETR/npy_files/query_embed.npy")
    query_embed = paddle.tensor(tmp)

    trans(srcs, masks, pos_embeds, query_embed)



