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import torch |
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import torch.nn as nn |
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from .backbones.beit import ( |
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_make_pretrained_beitl16_512, |
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_make_pretrained_beitl16_384, |
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_make_pretrained_beitb16_384, |
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forward_beit, |
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) |
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from .backbones.swin_common import ( |
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forward_swin, |
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) |
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from .backbones.swin2 import ( |
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_make_pretrained_swin2l24_384, |
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_make_pretrained_swin2b24_384, |
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_make_pretrained_swin2t16_256, |
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) |
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from .backbones.swin import ( |
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_make_pretrained_swinl12_384, |
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) |
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from .backbones.levit import ( |
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_make_pretrained_levit_384, |
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forward_levit, |
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) |
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from .backbones.vit import ( |
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_make_pretrained_vitb_rn50_384, |
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_make_pretrained_vitl16_384, |
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_make_pretrained_vitb16_384, |
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forward_vit, |
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) |
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def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, |
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use_vit_only=False, use_readout="ignore", in_features=[96, 256, 512, 1024]): |
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if backbone == "beitl16_512": |
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pretrained = _make_pretrained_beitl16_512( |
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use_pretrained, hooks=hooks, use_readout=use_readout |
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) |
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scratch = _make_scratch( |
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[256, 512, 1024, 1024], features, groups=groups, expand=expand |
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) |
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elif backbone == "beitl16_384": |
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pretrained = _make_pretrained_beitl16_384( |
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use_pretrained, hooks=hooks, use_readout=use_readout |
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) |
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scratch = _make_scratch( |
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[256, 512, 1024, 1024], features, groups=groups, expand=expand |
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) |
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elif backbone == "beitb16_384": |
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pretrained = _make_pretrained_beitb16_384( |
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use_pretrained, hooks=hooks, use_readout=use_readout |
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) |
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scratch = _make_scratch( |
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[96, 192, 384, 768], features, groups=groups, expand=expand |
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) |
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elif backbone == "swin2l24_384": |
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pretrained = _make_pretrained_swin2l24_384( |
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use_pretrained, hooks=hooks |
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) |
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scratch = _make_scratch( |
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[192, 384, 768, 1536], features, groups=groups, expand=expand |
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) |
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elif backbone == "swin2b24_384": |
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pretrained = _make_pretrained_swin2b24_384( |
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use_pretrained, hooks=hooks |
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) |
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scratch = _make_scratch( |
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[128, 256, 512, 1024], features, groups=groups, expand=expand |
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) |
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elif backbone == "swin2t16_256": |
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pretrained = _make_pretrained_swin2t16_256( |
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use_pretrained, hooks=hooks |
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) |
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scratch = _make_scratch( |
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[96, 192, 384, 768], features, groups=groups, expand=expand |
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) |
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elif backbone == "swinl12_384": |
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pretrained = _make_pretrained_swinl12_384( |
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use_pretrained, hooks=hooks |
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) |
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scratch = _make_scratch( |
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[192, 384, 768, 1536], features, groups=groups, expand=expand |
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) |
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elif backbone == "next_vit_large_6m": |
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from .backbones.next_vit import _make_pretrained_next_vit_large_6m |
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pretrained = _make_pretrained_next_vit_large_6m(hooks=hooks) |
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scratch = _make_scratch( |
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in_features, features, groups=groups, expand=expand |
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) |
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elif backbone == "levit_384": |
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pretrained = _make_pretrained_levit_384( |
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use_pretrained, hooks=hooks |
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) |
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scratch = _make_scratch( |
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[384, 512, 768], features, groups=groups, expand=expand |
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) |
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elif backbone == "vitl16_384": |
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pretrained = _make_pretrained_vitl16_384( |
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use_pretrained, hooks=hooks, use_readout=use_readout |
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) |
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scratch = _make_scratch( |
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[256, 512, 1024, 1024], features, groups=groups, expand=expand |
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) |
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elif backbone == "vitb_rn50_384": |
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pretrained = _make_pretrained_vitb_rn50_384( |
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use_pretrained, |
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hooks=hooks, |
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use_vit_only=use_vit_only, |
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use_readout=use_readout, |
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) |
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scratch = _make_scratch( |
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[256, 512, 768, 768], features, groups=groups, expand=expand |
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) |
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elif backbone == "vitb16_384": |
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pretrained = _make_pretrained_vitb16_384( |
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use_pretrained, hooks=hooks, use_readout=use_readout |
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) |
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scratch = _make_scratch( |
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[96, 192, 384, 768], features, groups=groups, expand=expand |
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) |
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elif backbone == "resnext101_wsl": |
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pretrained = _make_pretrained_resnext101_wsl(use_pretrained) |
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scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) |
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elif backbone == "efficientnet_lite3": |
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pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable) |
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scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) |
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else: |
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print(f"Backbone '{backbone}' not implemented") |
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assert False |
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return pretrained, scratch |
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def _make_scratch(in_shape, out_shape, groups=1, expand=False): |
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scratch = nn.Module() |
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out_shape1 = out_shape |
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out_shape2 = out_shape |
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out_shape3 = out_shape |
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if len(in_shape) >= 4: |
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out_shape4 = out_shape |
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if expand: |
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out_shape1 = out_shape |
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out_shape2 = out_shape*2 |
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out_shape3 = out_shape*4 |
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if len(in_shape) >= 4: |
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out_shape4 = out_shape*8 |
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scratch.layer1_rn = nn.Conv2d( |
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in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups |
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) |
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scratch.layer2_rn = nn.Conv2d( |
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in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups |
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) |
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scratch.layer3_rn = nn.Conv2d( |
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in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups |
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) |
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if len(in_shape) >= 4: |
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scratch.layer4_rn = nn.Conv2d( |
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in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups |
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) |
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return scratch |
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def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): |
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efficientnet = torch.hub.load( |
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"rwightman/gen-efficientnet-pytorch", |
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"tf_efficientnet_lite3", |
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pretrained=use_pretrained, |
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exportable=exportable |
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) |
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return _make_efficientnet_backbone(efficientnet) |
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def _make_efficientnet_backbone(effnet): |
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pretrained = nn.Module() |
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pretrained.layer1 = nn.Sequential( |
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effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2] |
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) |
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pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3]) |
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pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5]) |
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pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9]) |
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return pretrained |
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def _make_resnet_backbone(resnet): |
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pretrained = nn.Module() |
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pretrained.layer1 = nn.Sequential( |
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resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1 |
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) |
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pretrained.layer2 = resnet.layer2 |
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pretrained.layer3 = resnet.layer3 |
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pretrained.layer4 = resnet.layer4 |
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return pretrained |
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def _make_pretrained_resnext101_wsl(use_pretrained): |
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resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl") |
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return _make_resnet_backbone(resnet) |
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class Interpolate(nn.Module): |
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"""Interpolation module. |
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""" |
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def __init__(self, scale_factor, mode, align_corners=False): |
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"""Init. |
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Args: |
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scale_factor (float): scaling |
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mode (str): interpolation mode |
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""" |
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super(Interpolate, self).__init__() |
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self.interp = nn.functional.interpolate |
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self.scale_factor = scale_factor |
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self.mode = mode |
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self.align_corners = align_corners |
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def forward(self, x): |
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"""Forward pass. |
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Args: |
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x (tensor): input |
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Returns: |
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tensor: interpolated data |
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""" |
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x = self.interp( |
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x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners |
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) |
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return x |
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class ResidualConvUnit(nn.Module): |
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"""Residual convolution module. |
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""" |
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def __init__(self, features): |
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"""Init. |
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Args: |
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features (int): number of features |
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""" |
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super().__init__() |
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self.conv1 = nn.Conv2d( |
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features, features, kernel_size=3, stride=1, padding=1, bias=True |
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) |
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self.conv2 = nn.Conv2d( |
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features, features, kernel_size=3, stride=1, padding=1, bias=True |
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) |
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self.relu = nn.ReLU(inplace=True) |
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def forward(self, x): |
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"""Forward pass. |
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Args: |
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x (tensor): input |
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Returns: |
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tensor: output |
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""" |
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out = self.relu(x) |
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out = self.conv1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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return out + x |
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class FeatureFusionBlock(nn.Module): |
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"""Feature fusion block. |
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""" |
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def __init__(self, features): |
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"""Init. |
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Args: |
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features (int): number of features |
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""" |
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super(FeatureFusionBlock, self).__init__() |
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self.resConfUnit1 = ResidualConvUnit(features) |
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self.resConfUnit2 = ResidualConvUnit(features) |
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def forward(self, *xs): |
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"""Forward pass. |
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Returns: |
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tensor: output |
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""" |
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output = xs[0] |
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if len(xs) == 2: |
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output += self.resConfUnit1(xs[1]) |
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output = self.resConfUnit2(output) |
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output = nn.functional.interpolate( |
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output, scale_factor=2, mode="bilinear", align_corners=True |
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) |
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return output |
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class ResidualConvUnit_custom(nn.Module): |
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"""Residual convolution module. |
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""" |
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def __init__(self, features, activation, bn): |
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"""Init. |
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Args: |
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features (int): number of features |
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""" |
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super().__init__() |
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self.bn = bn |
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self.groups=1 |
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self.conv1 = nn.Conv2d( |
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features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups |
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) |
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self.conv2 = nn.Conv2d( |
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features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups |
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) |
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if self.bn==True: |
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self.bn1 = nn.BatchNorm2d(features) |
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self.bn2 = nn.BatchNorm2d(features) |
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self.activation = activation |
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self.skip_add = nn.quantized.FloatFunctional() |
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def forward(self, x): |
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"""Forward pass. |
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Args: |
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x (tensor): input |
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Returns: |
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tensor: output |
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""" |
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out = self.activation(x) |
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out = self.conv1(out) |
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if self.bn==True: |
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out = self.bn1(out) |
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out = self.activation(out) |
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out = self.conv2(out) |
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if self.bn==True: |
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out = self.bn2(out) |
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if self.groups > 1: |
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out = self.conv_merge(out) |
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return self.skip_add.add(out, x) |
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class FeatureFusionBlock_custom(nn.Module): |
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"""Feature fusion block. |
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""" |
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def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, size=None): |
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"""Init. |
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Args: |
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features (int): number of features |
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""" |
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super(FeatureFusionBlock_custom, self).__init__() |
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self.deconv = deconv |
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self.align_corners = align_corners |
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self.groups=1 |
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self.expand = expand |
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out_features = features |
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if self.expand==True: |
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out_features = features//2 |
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self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) |
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self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn) |
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self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn) |
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self.skip_add = nn.quantized.FloatFunctional() |
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self.size=size |
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def forward(self, *xs, size=None): |
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"""Forward pass. |
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Returns: |
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tensor: output |
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""" |
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output = xs[0] |
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if len(xs) == 2: |
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res = self.resConfUnit1(xs[1]) |
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output = self.skip_add.add(output, res) |
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output = self.resConfUnit2(output) |
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if (size is None) and (self.size is None): |
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modifier = {"scale_factor": 2} |
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elif size is None: |
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modifier = {"size": self.size} |
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else: |
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modifier = {"size": size} |
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output = nn.functional.interpolate( |
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output, **modifier, mode="bilinear", align_corners=self.align_corners |
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) |
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output = self.out_conv(output) |
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return output |
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