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import re
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from argparse import ArgumentParser
import pytorch_lightning as pl
from .lsegmentation_module import LSegmentationModule
from .models.lseg_net import LSegNet
from encoding.models.sseg.base import up_kwargs
import os
import clip
import numpy as np
from scipy import signal
import glob
from PIL import Image
import matplotlib.pyplot as plt
import pandas as pd
class LSegModule(LSegmentationModule):
def __init__(self, data_path, dataset, batch_size, base_lr, max_epochs, **kwargs):
super(LSegModule, self).__init__(
data_path, dataset, batch_size, base_lr, max_epochs, **kwargs
)
if dataset == "citys":
self.base_size = 2048
self.crop_size = 768
else:
self.base_size = 520
self.crop_size = 480
use_pretrained = True
norm_mean= [0.5, 0.5, 0.5]
norm_std = [0.5, 0.5, 0.5]
print('** Use norm {}, {} as the mean and std **'.format(norm_mean, norm_std))
train_transform = [
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
]
val_transform = [
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
]
self.train_transform = transforms.Compose(train_transform)
self.val_transform = transforms.Compose(val_transform)
self.trainset = self.get_trainset(
dataset,
augment=kwargs["augment"],
base_size=self.base_size,
crop_size=self.crop_size,
)
self.valset = self.get_valset(
dataset,
augment=kwargs["augment"],
base_size=self.base_size,
crop_size=self.crop_size,
)
use_batchnorm = (
(not kwargs["no_batchnorm"]) if "no_batchnorm" in kwargs else True
)
# print(kwargs)
labels = self.get_labels('ade20k')
self.net = LSegNet(
labels=labels,
backbone=kwargs["backbone"],
features=kwargs["num_features"],
crop_size=self.crop_size,
arch_option=kwargs["arch_option"],
block_depth=kwargs["block_depth"],
activation=kwargs["activation"],
)
self.net.pretrained.model.patch_embed.img_size = (
self.crop_size,
self.crop_size,
)
self._up_kwargs = up_kwargs
self.mean = norm_mean
self.std = norm_std
self.criterion = self.get_criterion(**kwargs)
def get_labels(self, dataset):
labels = []
path = 'label_files/{}_objectInfo150.txt'.format(dataset)
assert os.path.exists(path), '*** Error : {} not exist !!!'.format(path)
f = open(path, 'r')
lines = f.readlines()
for line in lines:
label = line.strip().split(',')[-1].split(';')[0]
labels.append(label)
f.close()
if dataset in ['ade20k']:
labels = labels[1:]
return labels
@staticmethod
def add_model_specific_args(parent_parser):
parser = LSegmentationModule.add_model_specific_args(parent_parser)
parser = ArgumentParser(parents=[parser])
parser.add_argument(
"--backbone",
type=str,
default="clip_vitl16_384",
help="backbone network",
)
parser.add_argument(
"--num_features",
type=int,
default=256,
help="number of featurs that go from encoder to decoder",
)
parser.add_argument("--dropout", type=float, default=0.1, help="dropout rate")
parser.add_argument(
"--finetune_weights", type=str, help="load weights to finetune from"
)
parser.add_argument(
"--no-scaleinv",
default=True,
action="store_false",
help="turn off scaleinv layers",
)
parser.add_argument(
"--no-batchnorm",
default=False,
action="store_true",
help="turn off batchnorm",
)
parser.add_argument(
"--widehead", default=False, action="store_true", help="wider output head"
)
parser.add_argument(
"--widehead_hr",
default=False,
action="store_true",
help="wider output head",
)
parser.add_argument(
"--arch_option",
type=int,
default=0,
help="which kind of architecture to be used",
)
parser.add_argument(
"--block_depth",
type=int,
default=0,
help="how many blocks should be used",
)
parser.add_argument(
"--activation",
choices=['lrelu', 'tanh'],
default="lrelu",
help="use which activation to activate the block",
)
return parser
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