# ------------------------------------------------------------------------------------------------ # 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 # ------------------------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import print_function from __future__ import division import warnings import math, os import torch from torch import nn import torch.nn.functional as F from torch.nn.init import xavier_uniform_, constant_ try: from ..functions import MSDeformAttnFunction except: warnings.warn("Failed to import MSDeformAttnFunction.") 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 MSDeformAttn(nn.Module): def __init__( self, d_model=256, n_levels=4, n_heads=8, n_points=4, use_4D_normalizer=False ): """ 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): warnings.warn( "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.use_4D_normalizer = use_4D_normalizer self._reset_parameters() def _reset_parameters(self): constant_(self.sampling_offsets.weight.data, 0.0) thetas = torch.arange(self.n_heads, dtype=torch.float32) * ( 2.0 * math.pi / self.n_heads ) grid_init = torch.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 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) @torch.cuda.amp.autocast(enabled=False) 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)) value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads) sampling_offsets = self.sampling_offsets(query).view( N, Len_q, self.n_heads, self.n_levels, self.n_points, 2 ) attention_weights = self.attention_weights(query).view( N, Len_q, self.n_heads, self.n_levels * self.n_points ) attention_weights = F.softmax(attention_weights, -1).view( N, Len_q, self.n_heads, self.n_levels, self.n_points ) # N, Len_q, n_heads, n_levels, n_points, 2 # if os.environ.get('IPDB_DEBUG_SHILONG', False) == 'INFO': # import ipdb; ipdb.set_trace() if reference_points.shape[-1] == 2: offset_normalizer = torch.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: if self.use_4D_normalizer: offset_normalizer = torch.stack( [input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1 ) sampling_locations = ( reference_points[:, :, None, :, None, :2] + sampling_offsets / offset_normalizer[None, None, None, :, None, :] * reference_points[:, :, None, :, None, 2:] * 0.5 ) else: 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] ) ) # if os.environ.get('IPDB_DEBUG_SHILONG', False) == 'INFO': # import ipdb; ipdb.set_trace() # for amp if value.dtype == torch.float16: # for mixed precision output = MSDeformAttnFunction.apply( value.to(torch.float32), input_spatial_shapes, input_level_start_index, sampling_locations.to(torch.float32), attention_weights, self.im2col_step, ) output = output.to(torch.float16) output = self.output_proj(output) return output output = MSDeformAttnFunction.apply( value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step, ) output = self.output_proj(output) return output