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import math
import types
import torch
import torch.nn as nn
import torch.nn.functional as F
from .lseg_blocks import FeatureFusionBlock, Interpolate, _make_encoder, FeatureFusionBlock_custom, forward_vit
import clip
import numpy as np
import pandas as pd
import os
class depthwise_clipseg_conv(nn.Module):
def __init__(self):
super(depthwise_clipseg_conv, self).__init__()
self.depthwise = nn.Conv2d(1, 1, kernel_size=3, padding=1)
def depthwise_clipseg(self, x, channels):
x = torch.cat([self.depthwise(x[:, i].unsqueeze(1)) for i in range(channels)], dim=1)
return x
def forward(self, x):
channels = x.shape[1]
out = self.depthwise_clipseg(x, channels)
return out
class depthwise_conv(nn.Module):
def __init__(self, kernel_size=3, stride=1, padding=1):
super(depthwise_conv, self).__init__()
self.depthwise = nn.Conv2d(1, 1, kernel_size=kernel_size, stride=stride, padding=padding)
def forward(self, x):
# support for 4D tensor with NCHW
C, H, W = x.shape[1:]
x = x.reshape(-1, 1, H, W)
x = self.depthwise(x)
x = x.view(-1, C, H, W)
return x
class depthwise_block(nn.Module):
def __init__(self, kernel_size=3, stride=1, padding=1, activation='relu'):
super(depthwise_block, self).__init__()
self.depthwise = depthwise_conv(kernel_size=3, stride=1, padding=1)
if activation == 'relu':
self.activation = nn.ReLU()
elif activation == 'lrelu':
self.activation = nn.LeakyReLU()
elif activation == 'tanh':
self.activation = nn.Tanh()
def forward(self, x, act=True):
x = self.depthwise(x)
if act:
x = self.activation(x)
return x
class bottleneck_block(nn.Module):
def __init__(self, kernel_size=3, stride=1, padding=1, activation='relu'):
super(bottleneck_block, self).__init__()
self.depthwise = depthwise_conv(kernel_size=3, stride=1, padding=1)
if activation == 'relu':
self.activation = nn.ReLU()
elif activation == 'lrelu':
self.activation = nn.LeakyReLU()
elif activation == 'tanh':
self.activation = nn.Tanh()
def forward(self, x, act=True):
sum_layer = x.max(dim=1, keepdim=True)[0]
x = self.depthwise(x)
x = x + sum_layer
if act:
x = self.activation(x)
return x
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 _make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
features,
activation=nn.ReLU(False),
deconv=False,
bn=use_bn,
expand=False,
align_corners=True,
)
class LSeg(BaseModel):
def __init__(
self,
head,
features=256,
backbone="clip_vitl16_384",
readout="project",
channels_last=False,
use_bn=False,
**kwargs,
):
super(LSeg, self).__init__()
self.channels_last = channels_last
hooks = {
"clip_vitl16_384": [5, 11, 17, 23],
"clipRN50x16_vitl16_384": [5, 11, 17, 23],
"clip_vitb32_384": [2, 5, 8, 11],
}
# Instantiate backbone and reassemble blocks
self.clip_pretrained, self.pretrained, self.scratch = _make_encoder(
backbone,
features,
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.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)).exp()
if backbone in ["clipRN50x16_vitl16_384"]:
self.out_c = 768
else:
self.out_c = 512
self.scratch.head1 = nn.Conv2d(features, self.out_c, kernel_size=1)
self.arch_option = kwargs["arch_option"]
if self.arch_option == 1:
self.scratch.head_block = bottleneck_block(activation=kwargs["activation"])
self.block_depth = kwargs['block_depth']
elif self.arch_option == 2:
self.scratch.head_block = depthwise_block(activation=kwargs["activation"])
self.block_depth = kwargs['block_depth']
self.scratch.output_conv = head
self.text = clip.tokenize(self.labels)
def forward(self, x, labelset=''):
if labelset == '':
text = self.text
else:
text = clip.tokenize(labelset)
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)
text = text.to(x.device)
self.logit_scale = self.logit_scale.to(x.device)
text_features = self.clip_pretrained.encode_text(text)
image_features = self.scratch.head1(path_1)
imshape = image_features.shape
image_features = image_features.permute(0,2,3,1).reshape(-1, self.out_c)
# normalized features
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
logits_per_image = self.logit_scale * image_features.half() @ text_features.t()
out = logits_per_image.float().view(imshape[0], imshape[2], imshape[3], -1).permute(0,3,1,2)
if self.arch_option in [1, 2]:
for _ in range(self.block_depth - 1):
out = self.scratch.head_block(out)
out = self.scratch.head_block(out, False)
out = self.scratch.output_conv(out)
return out
class LSegNet(LSeg):
"""Network for semantic segmentation."""
def __init__(self, labels, path=None, scale_factor=0.5, crop_size=480, **kwargs):
features = kwargs["features"] if "features" in kwargs else 256
kwargs["use_bn"] = True
self.crop_size = crop_size
self.scale_factor = scale_factor
self.labels = labels
head = nn.Sequential(
Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
)
super().__init__(head, **kwargs)
if path is not None:
self.load(path)
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