first commit
This commit is contained in:
Guocheng Qian
439
midas/blocks.py
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439
midas/blocks.py
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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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) # BEiT_512-L (backbone)
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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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) # BEiT_384-L (backbone)
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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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) # BEiT_384-B (backbone)
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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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) # Swin2-L/12to24 (backbone)
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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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) # Swin2-B/12to24 (backbone)
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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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) # Swin2-T/16 (backbone)
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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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) # Swin-L/12 (backbone)
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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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) # Next-ViT-L on ImageNet-1K-6M (backbone)
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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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) # LeViT 384 (backbone)
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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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) # ViT-L/16 - 85.0% Top1 (backbone)
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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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) # ViT-H/16 - 85.0% Top1 (backbone)
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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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) # ViT-B/16 - 84.6% Top1 (backbone)
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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) # efficientnet_lite3
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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) # efficientnet_lite3
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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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# return 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 += 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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