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ada3f28 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
class MultiMaskWrapper(nn.Module):
def __init__(self, backbone):
super().__init__()
self.backbone = backbone
def forward(self, x, masks=None):
if masks is None:
return self.backbone(x)
if (masks is not None) and not isinstance(masks, list):
masks = [masks]
outs = []
for m in masks:
outs += [self.backbone(x, masks=m)]
return outs
class PredictorMultiMaskWrapper(nn.Module):
def __init__(self, backbone):
super().__init__()
self.backbone = backbone
def forward(self, ctxt, tgt, masks_ctxt, masks_tgt):
if type(ctxt) is not list:
ctxt = [ctxt]
if type(tgt) is not list:
tgt = [tgt]
if type(masks_ctxt) is not list:
masks_ctxt = [masks_ctxt]
if type(masks_tgt) is not list:
masks_tgt = [masks_tgt]
outs = []
for i, (zi, hi, mc, mt) in enumerate(zip(ctxt, tgt, masks_ctxt, masks_tgt)):
outs += [self.backbone(zi, hi, mc, mt, mask_index=i)]
return outs
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