def _build_res5_block(self, cfg): # fmt: off stage_channel_factor = 2**3 # res5 is 8x res2 num_groups = cfg.MODEL.RESNETS.NUM_GROUPS width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP bottleneck_channels = num_groups * width_per_group * stage_channel_factor out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS * stage_channel_factor stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 norm = cfg.MODEL.RESNETS.NORM assert not cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE[-1], \ "Deformable conv is not yet supported in res5 head." # fmt: on blocks = make_stage( TripletAttentionBottleneckBlock, 3, first_stride=2, in_channels=out_channels // 2, bottleneck_channels=bottleneck_channels, out_channels=out_channels, num_groups=num_groups, norm=norm, stride_in_1x1=stride_in_1x1, ) return nn.Sequential(*blocks), out_channels
def build_resnet_backbone_caffe_maxpool(cfg, input_shape): """ Create a ResNet instance from config. Returns: ResNet: a :class:`ResNet` instance. """ # need registration of new blocks/stems? norm = cfg.MODEL.RESNETS.NORM stem = BasicStemCaffeeMaxPool( #modified BasicStem -> BasicStemCaffeeMaxPool in_channels=input_shape.channels, out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS, norm=norm, caffe_maxpool=cfg.MODEL.CAFFE_MAXPOOL, #added ) freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT if freeze_at >= 1: for p in stem.parameters(): p.requires_grad = False stem = FrozenBatchNorm2d.convert_frozen_batchnorm(stem) # fmt: off out_features = cfg.MODEL.RESNETS.OUT_FEATURES depth = cfg.MODEL.RESNETS.DEPTH num_groups = cfg.MODEL.RESNETS.NUM_GROUPS width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP bottleneck_channels = num_groups * width_per_group in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 res5_dilation = cfg.MODEL.RESNETS.RES5_DILATION deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE deform_modulated = cfg.MODEL.RESNETS.DEFORM_MODULATED deform_num_groups = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS # fmt: on assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation) num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3]}[depth] stages = [] # Avoid creating variables without gradients # It consumes extra memory and may cause allreduce to fail out_stage_idx = [{"res2": 2, "res3": 3, "res4": 4, "res5": 5}[f] for f in out_features] max_stage_idx = max(out_stage_idx) for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)): dilation = res5_dilation if stage_idx == 5 else 1 first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2 stage_kargs = { "num_blocks": num_blocks_per_stage[idx], "first_stride": first_stride, "in_channels": in_channels, "bottleneck_channels": bottleneck_channels, "out_channels": out_channels, "num_groups": num_groups, "norm": norm, "stride_in_1x1": stride_in_1x1, "dilation": dilation, } if deform_on_per_stage[idx]: stage_kargs["block_class"] = DeformBottleneckBlock stage_kargs["deform_modulated"] = deform_modulated stage_kargs["deform_num_groups"] = deform_num_groups else: stage_kargs["block_class"] = BottleneckBlock blocks = make_stage(**stage_kargs) in_channels = out_channels out_channels *= 2 bottleneck_channels *= 2 if freeze_at >= stage_idx: for block in blocks: block.freeze() stages.append(blocks) return ResNet(stem, stages, out_features=out_features)
def build_trident_resnet_backbone(cfg, input_shape): """ Create a ResNet instance from config for TridentNet. Returns: ResNet: a :class:`ResNet` instance. """ # need registration of new blocks/stems? norm = cfg.MODEL.RESNETS.NORM stem = BasicStem( in_channels=input_shape.channels, out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS, norm=norm, ) freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT if freeze_at >= 1: for p in stem.parameters(): p.requires_grad = False stem = FrozenBatchNorm2d.convert_frozen_batchnorm(stem) # fmt: off out_features = cfg.MODEL.RESNETS.OUT_FEATURES depth = cfg.MODEL.RESNETS.DEPTH num_groups = cfg.MODEL.RESNETS.NUM_GROUPS width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP bottleneck_channels = num_groups * width_per_group in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 res5_dilation = cfg.MODEL.RESNETS.RES5_DILATION deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE deform_modulated = cfg.MODEL.RESNETS.DEFORM_MODULATED deform_num_groups = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS num_branch = cfg.MODEL.TRIDENT.NUM_BRANCH branch_dilations = cfg.MODEL.TRIDENT.BRANCH_DILATIONS trident_stage = cfg.MODEL.TRIDENT.TRIDENT_STAGE test_branch_idx = cfg.MODEL.TRIDENT.TEST_BRANCH_IDX # fmt: on assert res5_dilation in { 1, 2 }, "res5_dilation cannot be {}.".format(res5_dilation) num_blocks_per_stage = { 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3] }[depth] stages = [] res_stage_idx = {"res2": 2, "res3": 3, "res4": 4, "res5": 5} out_stage_idx = [res_stage_idx[f] for f in out_features] trident_stage_idx = res_stage_idx[trident_stage] max_stage_idx = max(out_stage_idx) for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)): dilation = res5_dilation if stage_idx == 5 else 1 first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2 stage_kargs = { "num_blocks": num_blocks_per_stage[idx], "first_stride": first_stride, "in_channels": in_channels, "bottleneck_channels": bottleneck_channels, "out_channels": out_channels, "num_groups": num_groups, "norm": norm, "stride_in_1x1": stride_in_1x1, "dilation": dilation, } if stage_idx == trident_stage_idx: assert not deform_on_per_stage[ idx], "Not support deformable conv in Trident blocks yet." stage_kargs["block_class"] = TridentBottleneckBlock stage_kargs["num_branch"] = num_branch stage_kargs["dilations"] = branch_dilations stage_kargs["test_branch_idx"] = test_branch_idx stage_kargs.pop("dilation") elif deform_on_per_stage[idx]: stage_kargs["block_class"] = DeformBottleneckBlock stage_kargs["deform_modulated"] = deform_modulated stage_kargs["deform_num_groups"] = deform_num_groups else: stage_kargs["block_class"] = BottleneckBlock blocks = (make_trident_stage(**stage_kargs) if stage_idx == trident_stage_idx else make_stage(**stage_kargs)) in_channels = out_channels out_channels *= 2 bottleneck_channels *= 2 if freeze_at >= stage_idx: for block in blocks: block.freeze() stages.append(blocks) return ResNet(stem, stages, out_features=out_features)
def build_resnet_triplet_attention_backbone(cfg, input_shape): """ Create a ResNet instance from config. Returns: ResNet: a :class:`ResNet` instance. """ # need registration of new blocks/stems? norm = cfg.MODEL.RESNETS.NORM stem = BasicStem( in_channels=input_shape.channels, out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS, norm=norm, ) # fmt: off freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT out_features = cfg.MODEL.RESNETS.OUT_FEATURES depth = cfg.MODEL.RESNETS.DEPTH num_groups = cfg.MODEL.RESNETS.NUM_GROUPS width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP bottleneck_channels = num_groups * width_per_group in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 res5_dilation = cfg.MODEL.RESNETS.RES5_DILATION deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE deform_modulated = cfg.MODEL.RESNETS.DEFORM_MODULATED deform_num_groups = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS # fmt: on assert res5_dilation in { 1, 2 }, "res5_dilation cannot be {}.".format(res5_dilation) num_blocks_per_stage = { 18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3], }[depth] if depth in [18, 34]: assert out_channels == 64, "Must set MODEL.RESNETS.RES2_OUT_CHANNELS = 64 for R18/R34" assert not any( deform_on_per_stage ), "MODEL.RESNETS.DEFORM_ON_PER_STAGE unsupported for R18/R34" assert res5_dilation == 1, "Must set MODEL.RESNETS.RES5_DILATION = 1 for R18/R34" assert num_groups == 1, "Must set MODEL.RESNETS.NUM_GROUPS = 1 for R18/R34" stages = [] # Avoid creating variables without gradients # It consumes extra memory and may cause allreduce to fail out_stage_idx = [{ "res2": 2, "res3": 3, "res4": 4, "res5": 5 }[f] for f in out_features] max_stage_idx = max(out_stage_idx) for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)): dilation = res5_dilation if stage_idx == 5 else 1 first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2 stage_kargs = { "num_blocks": num_blocks_per_stage[idx], "first_stride": first_stride, "in_channels": in_channels, "out_channels": out_channels, "norm": norm, } # Use BasicBlock for R18 and R34. if depth in [18, 34]: stage_kargs["block_class"] = TripletAttentionBasicBlock else: stage_kargs["bottleneck_channels"] = bottleneck_channels stage_kargs["stride_in_1x1"] = stride_in_1x1 stage_kargs["dilation"] = dilation stage_kargs["num_groups"] = num_groups #if deform_on_per_stage[idx]: # stage_kargs["block_class"] = DeformBottleneckBlock # stage_kargs["deform_modulated"] = deform_modulated # stage_kargs["deform_num_groups"] = deform_num_groups #else: stage_kargs["block_class"] = TripletAttentionBottleneckBlock blocks = make_stage(**stage_kargs) in_channels = out_channels out_channels *= 2 bottleneck_channels *= 2 stages.append(blocks) return ResNet(stem, stages, out_features=out_features).freeze(freeze_at)
def build_deepent_fused_resnet_backbone(cfg, input_shape): """ Create a ResNet instance from config. Returns: ResNet: a :class:`ResNet` instance. """ # need registration of new blocks/stems? norm = cfg.MODEL.RESNETS.NORM assert input_shape.channels == 4, f'{input_shape.channels} input channels specified, should be 4' depth_shape = input_shape._replace(channels=1) input_shape = input_shape._replace(channels=3) depth_encoder = build_depth_encoder_backbone(cfg, depth_shape) stem = BasicStem( in_channels=input_shape.channels, out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS, norm=norm, ) freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT if freeze_at >= 1: for p in stem.parameters(): p.requires_grad = False stem = FrozenBatchNorm2d.convert_frozen_batchnorm(stem) # fmt: off in_features = cfg.MODEL.RESNETS.IN_FEATURES fuse_method = cfg.MODEL.RESNETS.FUSE_METHOD out_features = cfg.MODEL.RESNETS.OUT_FEATURES depth = cfg.MODEL.RESNETS.DEPTH num_groups = cfg.MODEL.RESNETS.NUM_GROUPS width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP bottleneck_channels = num_groups * width_per_group in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 res5_dilation = cfg.MODEL.RESNETS.RES5_DILATION # fmt: on assert res5_dilation in { 1, 2 }, "res5_dilation cannot be {}.".format(res5_dilation) num_blocks_per_stage = { 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3] }[depth] stages = [] # Avoid creating variables without gradients # It consumes extra memory and may cause allreduce to fail out_stage_idx = [{ "res2": 2, "res3": 3, "res4": 4, "res5": 5 }[f] for f in out_features] max_stage_idx = max(out_stage_idx) for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)): dilation = res5_dilation if stage_idx == 5 else 1 first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2 stage_kargs = { "num_blocks": num_blocks_per_stage[idx], "first_stride": first_stride, "in_channels": in_channels, "bottleneck_channels": bottleneck_channels, "out_channels": out_channels, "num_groups": num_groups, "norm": norm, "stride_in_1x1": stride_in_1x1, "dilation": dilation, "block_class": BottleneckBlock } blocks = make_stage(**stage_kargs) in_channels = out_channels out_channels *= 2 bottleneck_channels *= 2 if freeze_at >= stage_idx: for block in blocks: block.freeze() stages.append(blocks) return FusedResNet(stem, stages, depth_encoder=depth_encoder, in_features=in_features, out_features=out_features, fuse_method=fuse_method)