first commit
This commit is contained in:
Guocheng Qian
924
dpt.py
Normal file
924
dpt.py
Normal file
@@ -0,0 +1,924 @@
|
||||
import math
|
||||
import types
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
import timm
|
||||
|
||||
class BaseModel(torch.nn.Module):
|
||||
def load(self, path):
|
||||
"""Load model from file.
|
||||
Args:
|
||||
path (str): file path
|
||||
"""
|
||||
parameters = torch.load(path, map_location=torch.device('cpu'))
|
||||
|
||||
if "optimizer" in parameters:
|
||||
parameters = parameters["model"]
|
||||
|
||||
self.load_state_dict(parameters)
|
||||
|
||||
|
||||
def unflatten_with_named_tensor(input, dim, sizes):
|
||||
"""Workaround for unflattening with named tensor."""
|
||||
# tracer acts up with unflatten. See https://github.com/pytorch/pytorch/issues/49538
|
||||
new_shape = list(input.shape)[:dim] + list(sizes) + list(input.shape)[dim+1:]
|
||||
return input.view(*new_shape)
|
||||
|
||||
class Slice(nn.Module):
|
||||
def __init__(self, start_index=1):
|
||||
super(Slice, self).__init__()
|
||||
self.start_index = start_index
|
||||
|
||||
def forward(self, x):
|
||||
return x[:, self.start_index :]
|
||||
|
||||
|
||||
class AddReadout(nn.Module):
|
||||
def __init__(self, start_index=1):
|
||||
super(AddReadout, self).__init__()
|
||||
self.start_index = start_index
|
||||
|
||||
def forward(self, x):
|
||||
if self.start_index == 2:
|
||||
readout = (x[:, 0] + x[:, 1]) / 2
|
||||
else:
|
||||
readout = x[:, 0]
|
||||
return x[:, self.start_index :] + readout.unsqueeze(1)
|
||||
|
||||
|
||||
class ProjectReadout(nn.Module):
|
||||
def __init__(self, in_features, start_index=1):
|
||||
super(ProjectReadout, self).__init__()
|
||||
self.start_index = start_index
|
||||
|
||||
self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
|
||||
|
||||
def forward(self, x):
|
||||
readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
|
||||
features = torch.cat((x[:, self.start_index :], readout), -1)
|
||||
|
||||
return self.project(features)
|
||||
|
||||
|
||||
class Transpose(nn.Module):
|
||||
def __init__(self, dim0, dim1):
|
||||
super(Transpose, self).__init__()
|
||||
self.dim0 = dim0
|
||||
self.dim1 = dim1
|
||||
|
||||
def forward(self, x):
|
||||
x = x.transpose(self.dim0, self.dim1)
|
||||
return x
|
||||
|
||||
|
||||
def forward_vit(pretrained, x):
|
||||
b, c, h, w = x.shape
|
||||
|
||||
glob = pretrained.model.forward_flex(x)
|
||||
|
||||
layer_1 = pretrained.activations["1"]
|
||||
layer_2 = pretrained.activations["2"]
|
||||
layer_3 = pretrained.activations["3"]
|
||||
layer_4 = pretrained.activations["4"]
|
||||
|
||||
layer_1 = pretrained.act_postprocess1[0:2](layer_1)
|
||||
layer_2 = pretrained.act_postprocess2[0:2](layer_2)
|
||||
layer_3 = pretrained.act_postprocess3[0:2](layer_3)
|
||||
layer_4 = pretrained.act_postprocess4[0:2](layer_4)
|
||||
|
||||
|
||||
unflattened_dim = 2
|
||||
unflattened_size = (
|
||||
int(torch.div(h, pretrained.model.patch_size[1], rounding_mode='floor')),
|
||||
int(torch.div(w, pretrained.model.patch_size[0], rounding_mode='floor')),
|
||||
)
|
||||
unflatten = nn.Sequential(nn.Unflatten(unflattened_dim, unflattened_size))
|
||||
|
||||
|
||||
if layer_1.ndim == 3:
|
||||
layer_1 = unflatten(layer_1)
|
||||
if layer_2.ndim == 3:
|
||||
layer_2 = unflatten(layer_2)
|
||||
if layer_3.ndim == 3:
|
||||
layer_3 = unflatten_with_named_tensor(layer_3, unflattened_dim, unflattened_size)
|
||||
if layer_4.ndim == 3:
|
||||
layer_4 = unflatten_with_named_tensor(layer_4, unflattened_dim, unflattened_size)
|
||||
|
||||
layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
|
||||
layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
|
||||
layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
|
||||
layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
|
||||
|
||||
return layer_1, layer_2, layer_3, layer_4
|
||||
|
||||
|
||||
def _resize_pos_embed(self, posemb, gs_h, gs_w):
|
||||
posemb_tok, posemb_grid = (
|
||||
posemb[:, : self.start_index],
|
||||
posemb[0, self.start_index :],
|
||||
)
|
||||
|
||||
gs_old = int(math.sqrt(posemb_grid.shape[0]))
|
||||
|
||||
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
|
||||
posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
|
||||
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
|
||||
|
||||
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
|
||||
|
||||
return posemb
|
||||
|
||||
|
||||
def forward_flex(self, x):
|
||||
b, c, h, w = x.shape
|
||||
|
||||
pos_embed = self._resize_pos_embed(
|
||||
self.pos_embed, torch.div(h, self.patch_size[1], rounding_mode='floor'), torch.div(w, self.patch_size[0], rounding_mode='floor')
|
||||
)
|
||||
|
||||
B = x.shape[0]
|
||||
|
||||
if hasattr(self.patch_embed, "backbone"):
|
||||
x = self.patch_embed.backbone(x)
|
||||
if isinstance(x, (list, tuple)):
|
||||
x = x[-1] # last feature if backbone outputs list/tuple of features
|
||||
|
||||
x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
|
||||
|
||||
if getattr(self, "dist_token", None) is not None:
|
||||
cls_tokens = self.cls_token.expand(
|
||||
B, -1, -1
|
||||
) # stole cls_tokens impl from Phil Wang, thanks
|
||||
dist_token = self.dist_token.expand(B, -1, -1)
|
||||
x = torch.cat((cls_tokens, dist_token, x), dim=1)
|
||||
else:
|
||||
cls_tokens = self.cls_token.expand(
|
||||
B, -1, -1
|
||||
) # stole cls_tokens impl from Phil Wang, thanks
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
|
||||
x = x + pos_embed
|
||||
x = self.pos_drop(x)
|
||||
|
||||
for blk in self.blocks:
|
||||
x = blk(x)
|
||||
|
||||
x = self.norm(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
activations = {}
|
||||
|
||||
|
||||
def get_activation(name):
|
||||
def hook(model, input, output):
|
||||
activations[name] = output
|
||||
|
||||
return hook
|
||||
|
||||
|
||||
def get_readout_oper(vit_features, features, use_readout, start_index=1):
|
||||
if use_readout == "ignore":
|
||||
readout_oper = [Slice(start_index)] * len(features)
|
||||
elif use_readout == "add":
|
||||
readout_oper = [AddReadout(start_index)] * len(features)
|
||||
elif use_readout == "project":
|
||||
readout_oper = [
|
||||
ProjectReadout(vit_features, start_index) for out_feat in features
|
||||
]
|
||||
else:
|
||||
assert (
|
||||
False
|
||||
), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
|
||||
|
||||
return readout_oper
|
||||
|
||||
|
||||
def _make_vit_b16_backbone(
|
||||
model,
|
||||
features=[96, 192, 384, 768],
|
||||
size=[384, 384],
|
||||
hooks=[2, 5, 8, 11],
|
||||
vit_features=768,
|
||||
use_readout="ignore",
|
||||
start_index=1,
|
||||
):
|
||||
pretrained = nn.Module()
|
||||
|
||||
pretrained.model = model
|
||||
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
|
||||
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
|
||||
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
|
||||
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
|
||||
|
||||
pretrained.activations = activations
|
||||
|
||||
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
|
||||
|
||||
# 32, 48, 136, 384
|
||||
pretrained.act_postprocess1 = nn.Sequential(
|
||||
readout_oper[0],
|
||||
Transpose(1, 2),
|
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
||||
nn.Conv2d(
|
||||
in_channels=vit_features,
|
||||
out_channels=features[0],
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
),
|
||||
nn.ConvTranspose2d(
|
||||
in_channels=features[0],
|
||||
out_channels=features[0],
|
||||
kernel_size=4,
|
||||
stride=4,
|
||||
padding=0,
|
||||
bias=True,
|
||||
dilation=1,
|
||||
groups=1,
|
||||
),
|
||||
)
|
||||
|
||||
pretrained.act_postprocess2 = nn.Sequential(
|
||||
readout_oper[1],
|
||||
Transpose(1, 2),
|
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
||||
nn.Conv2d(
|
||||
in_channels=vit_features,
|
||||
out_channels=features[1],
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
),
|
||||
nn.ConvTranspose2d(
|
||||
in_channels=features[1],
|
||||
out_channels=features[1],
|
||||
kernel_size=2,
|
||||
stride=2,
|
||||
padding=0,
|
||||
bias=True,
|
||||
dilation=1,
|
||||
groups=1,
|
||||
),
|
||||
)
|
||||
|
||||
pretrained.act_postprocess3 = nn.Sequential(
|
||||
readout_oper[2],
|
||||
Transpose(1, 2),
|
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
||||
nn.Conv2d(
|
||||
in_channels=vit_features,
|
||||
out_channels=features[2],
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
),
|
||||
)
|
||||
|
||||
pretrained.act_postprocess4 = nn.Sequential(
|
||||
readout_oper[3],
|
||||
Transpose(1, 2),
|
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
||||
nn.Conv2d(
|
||||
in_channels=vit_features,
|
||||
out_channels=features[3],
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
),
|
||||
nn.Conv2d(
|
||||
in_channels=features[3],
|
||||
out_channels=features[3],
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
),
|
||||
)
|
||||
|
||||
pretrained.model.start_index = start_index
|
||||
pretrained.model.patch_size = [16, 16]
|
||||
|
||||
# We inject this function into the VisionTransformer instances so that
|
||||
# we can use it with interpolated position embeddings without modifying the library source.
|
||||
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
|
||||
pretrained.model._resize_pos_embed = types.MethodType(
|
||||
_resize_pos_embed, pretrained.model
|
||||
)
|
||||
|
||||
return pretrained
|
||||
|
||||
|
||||
def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
|
||||
model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
|
||||
|
||||
hooks = [5, 11, 17, 23] if hooks == None else hooks
|
||||
return _make_vit_b16_backbone(
|
||||
model,
|
||||
features=[256, 512, 1024, 1024],
|
||||
hooks=hooks,
|
||||
vit_features=1024,
|
||||
use_readout=use_readout,
|
||||
)
|
||||
|
||||
|
||||
def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
|
||||
model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
|
||||
|
||||
hooks = [2, 5, 8, 11] if hooks == None else hooks
|
||||
return _make_vit_b16_backbone(
|
||||
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
|
||||
)
|
||||
|
||||
|
||||
def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
|
||||
model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
|
||||
|
||||
hooks = [2, 5, 8, 11] if hooks == None else hooks
|
||||
return _make_vit_b16_backbone(
|
||||
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
|
||||
)
|
||||
|
||||
|
||||
def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
|
||||
model = timm.create_model(
|
||||
"vit_deit_base_distilled_patch16_384", pretrained=pretrained
|
||||
)
|
||||
|
||||
hooks = [2, 5, 8, 11] if hooks == None else hooks
|
||||
return _make_vit_b16_backbone(
|
||||
model,
|
||||
features=[96, 192, 384, 768],
|
||||
hooks=hooks,
|
||||
use_readout=use_readout,
|
||||
start_index=2,
|
||||
)
|
||||
|
||||
|
||||
def _make_vit_b_rn50_backbone(
|
||||
model,
|
||||
features=[256, 512, 768, 768],
|
||||
size=[384, 384],
|
||||
hooks=[0, 1, 8, 11],
|
||||
vit_features=768,
|
||||
use_vit_only=False,
|
||||
use_readout="ignore",
|
||||
start_index=1,
|
||||
):
|
||||
pretrained = nn.Module()
|
||||
|
||||
pretrained.model = model
|
||||
|
||||
if use_vit_only == True:
|
||||
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
|
||||
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
|
||||
else:
|
||||
pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
|
||||
get_activation("1")
|
||||
)
|
||||
pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
|
||||
get_activation("2")
|
||||
)
|
||||
|
||||
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
|
||||
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
|
||||
|
||||
pretrained.activations = activations
|
||||
|
||||
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
|
||||
|
||||
if use_vit_only == True:
|
||||
pretrained.act_postprocess1 = nn.Sequential(
|
||||
readout_oper[0],
|
||||
Transpose(1, 2),
|
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
||||
nn.Conv2d(
|
||||
in_channels=vit_features,
|
||||
out_channels=features[0],
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
),
|
||||
nn.ConvTranspose2d(
|
||||
in_channels=features[0],
|
||||
out_channels=features[0],
|
||||
kernel_size=4,
|
||||
stride=4,
|
||||
padding=0,
|
||||
bias=True,
|
||||
dilation=1,
|
||||
groups=1,
|
||||
),
|
||||
)
|
||||
|
||||
pretrained.act_postprocess2 = nn.Sequential(
|
||||
readout_oper[1],
|
||||
Transpose(1, 2),
|
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
||||
nn.Conv2d(
|
||||
in_channels=vit_features,
|
||||
out_channels=features[1],
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
),
|
||||
nn.ConvTranspose2d(
|
||||
in_channels=features[1],
|
||||
out_channels=features[1],
|
||||
kernel_size=2,
|
||||
stride=2,
|
||||
padding=0,
|
||||
bias=True,
|
||||
dilation=1,
|
||||
groups=1,
|
||||
),
|
||||
)
|
||||
else:
|
||||
pretrained.act_postprocess1 = nn.Sequential(
|
||||
nn.Identity(), nn.Identity(), nn.Identity()
|
||||
)
|
||||
pretrained.act_postprocess2 = nn.Sequential(
|
||||
nn.Identity(), nn.Identity(), nn.Identity()
|
||||
)
|
||||
|
||||
pretrained.act_postprocess3 = nn.Sequential(
|
||||
readout_oper[2],
|
||||
Transpose(1, 2),
|
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
||||
nn.Conv2d(
|
||||
in_channels=vit_features,
|
||||
out_channels=features[2],
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
),
|
||||
)
|
||||
|
||||
pretrained.act_postprocess4 = nn.Sequential(
|
||||
readout_oper[3],
|
||||
Transpose(1, 2),
|
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
||||
nn.Conv2d(
|
||||
in_channels=vit_features,
|
||||
out_channels=features[3],
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
),
|
||||
nn.Conv2d(
|
||||
in_channels=features[3],
|
||||
out_channels=features[3],
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
),
|
||||
)
|
||||
|
||||
pretrained.model.start_index = start_index
|
||||
pretrained.model.patch_size = [16, 16]
|
||||
|
||||
# We inject this function into the VisionTransformer instances so that
|
||||
# we can use it with interpolated position embeddings without modifying the library source.
|
||||
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
|
||||
|
||||
# We inject this function into the VisionTransformer instances so that
|
||||
# we can use it with interpolated position embeddings without modifying the library source.
|
||||
pretrained.model._resize_pos_embed = types.MethodType(
|
||||
_resize_pos_embed, pretrained.model
|
||||
)
|
||||
|
||||
return pretrained
|
||||
|
||||
|
||||
def _make_pretrained_vitb_rn50_384(
|
||||
pretrained, use_readout="ignore", hooks=None, use_vit_only=False
|
||||
):
|
||||
model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
|
||||
|
||||
hooks = [0, 1, 8, 11] if hooks == None else hooks
|
||||
return _make_vit_b_rn50_backbone(
|
||||
model,
|
||||
features=[256, 512, 768, 768],
|
||||
size=[384, 384],
|
||||
hooks=hooks,
|
||||
use_vit_only=use_vit_only,
|
||||
use_readout=use_readout,
|
||||
)
|
||||
|
||||
def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
|
||||
if backbone == "vitl16_384":
|
||||
pretrained = _make_pretrained_vitl16_384(
|
||||
use_pretrained, hooks=hooks, use_readout=use_readout
|
||||
)
|
||||
scratch = _make_scratch(
|
||||
[256, 512, 1024, 1024], features, groups=groups, expand=expand
|
||||
) # ViT-L/16 - 85.0% Top1 (backbone)
|
||||
elif backbone == "vitb_rn50_384":
|
||||
pretrained = _make_pretrained_vitb_rn50_384(
|
||||
use_pretrained,
|
||||
hooks=hooks,
|
||||
use_vit_only=use_vit_only,
|
||||
use_readout=use_readout,
|
||||
)
|
||||
scratch = _make_scratch(
|
||||
[256, 512, 768, 768], features, groups=groups, expand=expand
|
||||
) # ViT-H/16 - 85.0% Top1 (backbone)
|
||||
elif backbone == "vitb16_384":
|
||||
pretrained = _make_pretrained_vitb16_384(
|
||||
use_pretrained, hooks=hooks, use_readout=use_readout
|
||||
)
|
||||
scratch = _make_scratch(
|
||||
[96, 192, 384, 768], features, groups=groups, expand=expand
|
||||
) # ViT-B/16 - 84.6% Top1 (backbone)
|
||||
elif backbone == "resnext101_wsl":
|
||||
pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
|
||||
scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3
|
||||
elif backbone == "efficientnet_lite3":
|
||||
pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
|
||||
scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3
|
||||
else:
|
||||
print(f"Backbone '{backbone}' not implemented")
|
||||
assert False
|
||||
|
||||
return pretrained, scratch
|
||||
|
||||
|
||||
def _make_scratch(in_shape, out_shape, groups=1, expand=False):
|
||||
scratch = nn.Module()
|
||||
|
||||
out_shape1 = out_shape
|
||||
out_shape2 = out_shape
|
||||
out_shape3 = out_shape
|
||||
out_shape4 = out_shape
|
||||
if expand==True:
|
||||
out_shape1 = out_shape
|
||||
out_shape2 = out_shape*2
|
||||
out_shape3 = out_shape*4
|
||||
out_shape4 = out_shape*8
|
||||
|
||||
scratch.layer1_rn = nn.Conv2d(
|
||||
in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
|
||||
)
|
||||
scratch.layer2_rn = nn.Conv2d(
|
||||
in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
|
||||
)
|
||||
scratch.layer3_rn = nn.Conv2d(
|
||||
in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
|
||||
)
|
||||
scratch.layer4_rn = nn.Conv2d(
|
||||
in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
|
||||
)
|
||||
|
||||
return scratch
|
||||
|
||||
|
||||
def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
|
||||
efficientnet = torch.hub.load(
|
||||
"rwightman/gen-efficientnet-pytorch",
|
||||
"tf_efficientnet_lite3",
|
||||
pretrained=use_pretrained,
|
||||
exportable=exportable
|
||||
)
|
||||
return _make_efficientnet_backbone(efficientnet)
|
||||
|
||||
|
||||
def _make_efficientnet_backbone(effnet):
|
||||
pretrained = nn.Module()
|
||||
|
||||
pretrained.layer1 = nn.Sequential(
|
||||
effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
|
||||
)
|
||||
pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
|
||||
pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
|
||||
pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
|
||||
|
||||
return pretrained
|
||||
|
||||
|
||||
def _make_resnet_backbone(resnet):
|
||||
pretrained = nn.Module()
|
||||
pretrained.layer1 = nn.Sequential(
|
||||
resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
|
||||
)
|
||||
|
||||
pretrained.layer2 = resnet.layer2
|
||||
pretrained.layer3 = resnet.layer3
|
||||
pretrained.layer4 = resnet.layer4
|
||||
|
||||
return pretrained
|
||||
|
||||
|
||||
def _make_pretrained_resnext101_wsl(use_pretrained):
|
||||
resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
|
||||
return _make_resnet_backbone(resnet)
|
||||
|
||||
|
||||
|
||||
class Interpolate(nn.Module):
|
||||
"""Interpolation module.
|
||||
"""
|
||||
|
||||
def __init__(self, scale_factor, mode, align_corners=False):
|
||||
"""Init.
|
||||
Args:
|
||||
scale_factor (float): scaling
|
||||
mode (str): interpolation mode
|
||||
"""
|
||||
super(Interpolate, self).__init__()
|
||||
|
||||
self.interp = nn.functional.interpolate
|
||||
self.scale_factor = scale_factor
|
||||
self.mode = mode
|
||||
self.align_corners = align_corners
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward pass.
|
||||
Args:
|
||||
x (tensor): input
|
||||
Returns:
|
||||
tensor: interpolated data
|
||||
"""
|
||||
|
||||
x = self.interp(
|
||||
x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
|
||||
)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class ResidualConvUnit(nn.Module):
|
||||
"""Residual convolution module.
|
||||
"""
|
||||
|
||||
def __init__(self, features):
|
||||
"""Init.
|
||||
Args:
|
||||
features (int): number of features
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
self.conv1 = nn.Conv2d(
|
||||
features, features, kernel_size=3, stride=1, padding=1, bias=True
|
||||
)
|
||||
|
||||
self.conv2 = nn.Conv2d(
|
||||
features, features, kernel_size=3, stride=1, padding=1, bias=True
|
||||
)
|
||||
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward pass.
|
||||
Args:
|
||||
x (tensor): input
|
||||
Returns:
|
||||
tensor: output
|
||||
"""
|
||||
out = self.relu(x)
|
||||
out = self.conv1(out)
|
||||
out = self.relu(out)
|
||||
out = self.conv2(out)
|
||||
|
||||
return out + x
|
||||
|
||||
|
||||
class FeatureFusionBlock(nn.Module):
|
||||
"""Feature fusion block.
|
||||
"""
|
||||
|
||||
def __init__(self, features):
|
||||
"""Init.
|
||||
Args:
|
||||
features (int): number of features
|
||||
"""
|
||||
super(FeatureFusionBlock, self).__init__()
|
||||
|
||||
self.resConfUnit1 = ResidualConvUnit(features)
|
||||
self.resConfUnit2 = ResidualConvUnit(features)
|
||||
|
||||
def forward(self, *xs):
|
||||
"""Forward pass.
|
||||
Returns:
|
||||
tensor: output
|
||||
"""
|
||||
output = xs[0]
|
||||
|
||||
if len(xs) == 2:
|
||||
output += self.resConfUnit1(xs[1])
|
||||
|
||||
output = self.resConfUnit2(output)
|
||||
|
||||
output = nn.functional.interpolate(
|
||||
output, scale_factor=2, mode="bilinear", align_corners=True
|
||||
)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
|
||||
|
||||
class ResidualConvUnit_custom(nn.Module):
|
||||
"""Residual convolution module.
|
||||
"""
|
||||
|
||||
def __init__(self, features, activation, bn):
|
||||
"""Init.
|
||||
Args:
|
||||
features (int): number of features
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
self.bn = bn
|
||||
|
||||
self.groups=1
|
||||
|
||||
self.conv1 = nn.Conv2d(
|
||||
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
|
||||
)
|
||||
|
||||
self.conv2 = nn.Conv2d(
|
||||
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
|
||||
)
|
||||
|
||||
if self.bn==True:
|
||||
self.bn1 = nn.BatchNorm2d(features)
|
||||
self.bn2 = nn.BatchNorm2d(features)
|
||||
|
||||
self.activation = activation
|
||||
|
||||
self.skip_add = nn.quantized.FloatFunctional()
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward pass.
|
||||
Args:
|
||||
x (tensor): input
|
||||
Returns:
|
||||
tensor: output
|
||||
"""
|
||||
|
||||
out = self.activation(x)
|
||||
out = self.conv1(out)
|
||||
if self.bn==True:
|
||||
out = self.bn1(out)
|
||||
|
||||
out = self.activation(out)
|
||||
out = self.conv2(out)
|
||||
if self.bn==True:
|
||||
out = self.bn2(out)
|
||||
|
||||
if self.groups > 1:
|
||||
out = self.conv_merge(out)
|
||||
|
||||
return self.skip_add.add(out, x)
|
||||
|
||||
# return out + x
|
||||
|
||||
|
||||
class FeatureFusionBlock_custom(nn.Module):
|
||||
"""Feature fusion block.
|
||||
"""
|
||||
|
||||
def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
|
||||
"""Init.
|
||||
Args:
|
||||
features (int): number of features
|
||||
"""
|
||||
super(FeatureFusionBlock_custom, self).__init__()
|
||||
|
||||
self.deconv = deconv
|
||||
self.align_corners = align_corners
|
||||
|
||||
self.groups=1
|
||||
|
||||
self.expand = expand
|
||||
out_features = features
|
||||
if self.expand==True:
|
||||
out_features = features//2
|
||||
|
||||
self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
|
||||
|
||||
self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
|
||||
self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
|
||||
|
||||
self.skip_add = nn.quantized.FloatFunctional()
|
||||
|
||||
def forward(self, *xs):
|
||||
"""Forward pass.
|
||||
Returns:
|
||||
tensor: output
|
||||
"""
|
||||
output = xs[0]
|
||||
|
||||
if len(xs) == 2:
|
||||
res = self.resConfUnit1(xs[1])
|
||||
output = self.skip_add.add(output, res)
|
||||
# output += res
|
||||
|
||||
output = self.resConfUnit2(output)
|
||||
|
||||
output = nn.functional.interpolate(
|
||||
output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
|
||||
)
|
||||
|
||||
output = self.out_conv(output)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
|
||||
def _make_fusion_block(features, use_bn):
|
||||
return FeatureFusionBlock_custom(
|
||||
features,
|
||||
nn.ReLU(False),
|
||||
deconv=False,
|
||||
bn=use_bn,
|
||||
expand=False,
|
||||
align_corners=True,
|
||||
)
|
||||
|
||||
|
||||
class DPT(BaseModel):
|
||||
def __init__(
|
||||
self,
|
||||
head,
|
||||
features=256,
|
||||
backbone="vitb_rn50_384",
|
||||
readout="project",
|
||||
channels_last=False,
|
||||
use_bn=False,
|
||||
):
|
||||
|
||||
super(DPT, self).__init__()
|
||||
|
||||
self.channels_last = channels_last
|
||||
|
||||
hooks = {
|
||||
"vitb_rn50_384": [0, 1, 8, 11],
|
||||
"vitb16_384": [2, 5, 8, 11],
|
||||
"vitl16_384": [5, 11, 17, 23],
|
||||
}
|
||||
|
||||
# Instantiate backbone and reassemble blocks
|
||||
self.pretrained, self.scratch = _make_encoder(
|
||||
backbone,
|
||||
features,
|
||||
True, # Set to true of you want to train from scratch, uses ImageNet weights
|
||||
groups=1,
|
||||
expand=False,
|
||||
exportable=False,
|
||||
hooks=hooks[backbone],
|
||||
use_readout=readout,
|
||||
)
|
||||
|
||||
self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
|
||||
self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
|
||||
self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
|
||||
self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
|
||||
|
||||
self.scratch.output_conv = head
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
if self.channels_last == True:
|
||||
x.contiguous(memory_format=torch.channels_last)
|
||||
|
||||
layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
|
||||
|
||||
layer_1_rn = self.scratch.layer1_rn(layer_1)
|
||||
layer_2_rn = self.scratch.layer2_rn(layer_2)
|
||||
layer_3_rn = self.scratch.layer3_rn(layer_3)
|
||||
layer_4_rn = self.scratch.layer4_rn(layer_4)
|
||||
|
||||
path_4 = self.scratch.refinenet4(layer_4_rn)
|
||||
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
|
||||
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
|
||||
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
|
||||
|
||||
out = self.scratch.output_conv(path_1)
|
||||
|
||||
return out
|
||||
|
||||
class DPTDepthModel(DPT):
|
||||
def __init__(self, path=None, non_negative=True, num_channels=1, **kwargs):
|
||||
features = kwargs["features"] if "features" in kwargs else 256
|
||||
|
||||
head = nn.Sequential(
|
||||
nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
|
||||
Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
|
||||
nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
|
||||
nn.ReLU(True),
|
||||
nn.Conv2d(32, num_channels, kernel_size=1, stride=1, padding=0),
|
||||
nn.ReLU(True) if non_negative else nn.Identity(),
|
||||
nn.Identity(),
|
||||
)
|
||||
|
||||
super().__init__(head, **kwargs)
|
||||
|
||||
if path is not None:
|
||||
self.load(path)
|
||||
|
||||
def forward(self, x):
|
||||
return super().forward(x).squeeze(dim=1)
|
||||
Reference in New Issue
Block a user