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import torch
import torch.nn.functional as F
from mmcv.runner import BaseModule
from .models import build_model
from .models.util.misc import NestedTensor, inverse_sigmoid
class HDetrWrapper(BaseModule):
def __init__(self,
args=None,
init_cfg=None):
super(HDetrWrapper, self).__init__(init_cfg)
model, box_postprocessor = build_model(args)
self.model = model
self.box_postprocessor = box_postprocessor
self.model.num_queries = self.model.num_queries_one2one
self.model.transformer.two_stage_num_proposals = self.model.num_queries
self.cls_index = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,
82, 84, 85, 86, 87, 88, 89, 90]
def forward(self,
img,
img_metas):
"""Forward function for training mode.
Args:
img (Tensor): of shape (N, C, H, W) encoding input images.
Typically these should be mean centered and std scaled.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
"""
input_img_h, input_img_w = img_metas[0]["batch_input_shape"]
batch_size = img.size(0)
img_masks = img.new_ones((batch_size, input_img_h, input_img_w),
dtype=torch.bool)
for img_id in range(batch_size):
img_h, img_w, _ = img_metas[img_id]["img_shape"]
img_masks[img_id, :img_h, :img_w] = False
samples = NestedTensor(tensors=img, mask=img_masks)
features, pos = self.model.backbone(samples)
srcs = []
masks = []
for l, feat in enumerate(features):
src, mask = feat.decompose()
srcs.append(self.model.input_proj[l](src))
masks.append(mask)
assert mask is not None
if self.model.num_feature_levels > len(srcs):
_len_srcs = len(srcs)
for l in range(_len_srcs, self.model.num_feature_levels):
if l == _len_srcs:
src = self.model.input_proj[l](features[-1].tensors)
else:
src = self.model.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.model.backbone[1](NestedTensor(src, mask)).to(src.dtype)
srcs.append(src)
masks.append(mask)
pos.append(pos_l)
query_embeds = None
if not self.model.two_stage or self.model.mixed_selection:
query_embeds = self.model.query_embed.weight[0: self.model.num_queries, :]
# make attn mask
""" attention mask to prevent information leakage
"""
self_attn_mask = (
torch.zeros([self.model.num_queries, self.model.num_queries, ]).bool().to(src.device)
)
self_attn_mask[self.model.num_queries_one2one:, 0: self.model.num_queries_one2one, ] = True
self_attn_mask[0: self.model.num_queries_one2one, self.model.num_queries_one2one:, ] = True
(
hs,
init_reference,
inter_references,
enc_outputs_class,
enc_outputs_coord_unact,
) = self.model.transformer(srcs, masks, pos, query_embeds, self_attn_mask)
outputs_classes_one2one = []
outputs_coords_one2one = []
outputs_classes_one2many = []
outputs_coords_one2many = []
for lvl in range(hs.shape[0]):
if lvl == 0:
reference = init_reference
else:
reference = inter_references[lvl - 1]
reference = inverse_sigmoid(reference)
outputs_class = self.model.class_embed[lvl](hs[lvl])
tmp = self.model.bbox_embed[lvl](hs[lvl])
if reference.shape[-1] == 4:
tmp += reference
else:
assert reference.shape[-1] == 2
tmp[..., :2] += reference
outputs_coord = tmp.sigmoid()
outputs_classes_one2one.append(
outputs_class[:, 0: self.model.num_queries_one2one]
)
outputs_classes_one2many.append(
outputs_class[:, self.model.num_queries_one2one:]
)
outputs_coords_one2one.append(
outputs_coord[:, 0: self.model.num_queries_one2one]
)
outputs_coords_one2many.append(outputs_coord[:, self.model.num_queries_one2one:])
outputs_classes_one2one = torch.stack(outputs_classes_one2one)
outputs_coords_one2one = torch.stack(outputs_coords_one2one)
sampled_logits = outputs_classes_one2one[-1][:, :, self.cls_index]
out = {
"pred_logits": sampled_logits,
"pred_boxes": outputs_coords_one2one[-1],
}
return out
def simple_test(self, img, img_metas, rescale=False):
# out: dict
out = self(img, img_metas)
if rescale:
ori_target_sizes = [meta_info['ori_shape'][:2] for meta_info in img_metas]
else:
ori_target_sizes = [meta_info['img_shape'][:2] for meta_info in img_metas]
ori_target_sizes = out['pred_logits'].new_tensor(ori_target_sizes, dtype=torch.int64)
# results: List[dict(scores, labels, boxes)]
results = self.box_postprocessor(out, ori_target_sizes)
return results
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