app.py

import os
import torch
import argparse
import numpy as np
from tqdm import tqdm
from collections import OrderedDict
import torch.nn.functional as F
from torch.utils import data
import torchvision.transforms as transform
from torch.nn.parallel.scatter_gather import gather
from additional_utils.models import LSeg_MultiEvalModule
from modules.lseg_module import LSegModule
import cv2
import math
import types
import functools
import torchvision.transforms as torch_transforms
import copy
import itertools
from PIL import Image
import matplotlib.pyplot as plt
import clip
from encoding.models.sseg import BaseNet
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import matplotlib.patches as mpatches
from matplotlib.backends.backend_agg import FigureCanvasAgg
from data import get_dataset
import torchvision.transforms as transforms

import gradio as gr

model_name = "convnext_xlarge_in22k"
device = 'cuda' if torch.cuda.is_available() else 'cpu'
def get_new_pallete(num_cls):
    n = num_cls
    pallete = [0]*(n*3)
    for j in range(0,n):
            lab = j
            pallete[j*3+0] = 0
            pallete[j*3+1] = 0
            pallete[j*3+2] = 0
            i = 0
            while (lab > 0):
                    pallete[j*3+0] |= (((lab >> 0) & 1) << (7-i))
                    pallete[j*3+1] |= (((lab >> 1) & 1) << (7-i))
                    pallete[j*3+2] |= (((lab >> 2) & 1) << (7-i))
                    i = i + 1
                    lab >>= 3
    return pallete

def get_new_mask_pallete(npimg, new_palette, out_label_flag=False, labels=None):
    """Get image color pallete for visualizing masks"""
    # put colormap
    out_img = Image.fromarray(npimg.squeeze().astype('uint8'))
    out_img.putpalette(new_palette)

    if out_label_flag:
        assert labels is not None
        u_index = np.unique(npimg)
        patches = []
        for i, index in enumerate(u_index):
            label = labels[index]
            cur_color = [new_palette[index * 3] / 255.0, new_palette[index * 3 + 1] / 255.0, new_palette[index * 3 + 2] / 255.0]
            red_patch = mpatches.Patch(color=cur_color, label=label)
            patches.append(red_patch)
    return out_img, patches

@st.cache(allow_output_mutation=True)
def load_model():
    class Options:
        def __init__(self):
            parser = argparse.ArgumentParser(description="PyTorch Segmentation")
            # model and dataset
            parser.add_argument(
                "--model", type=str, default="encnet", help="model name (default: encnet)"
            )
            parser.add_argument(
                "--backbone",
                type=str,
                default="clip_vitl16_384",
                help="backbone name (default: resnet50)",
            )
            parser.add_argument(
                "--dataset",
                type=str,
                default="ade20k",
                help="dataset name (default: pascal12)",
            )
            parser.add_argument(
                "--workers", type=int, default=16, metavar="N", help="dataloader threads"
            )
            parser.add_argument(
                "--base-size", type=int, default=520, help="base image size"
            )
            parser.add_argument(
                "--crop-size", type=int, default=480, help="crop image size"
            )
            parser.add_argument(
                "--train-split",
                type=str,
                default="train",
                help="dataset train split (default: train)",
            )
            parser.add_argument(
                "--aux", action="store_true", default=False, help="Auxilary Loss"
            )
            parser.add_argument(
                "--se-loss",
                action="store_true",
                default=False,
                help="Semantic Encoding Loss SE-loss",
            )
            parser.add_argument(
                "--se-weight", type=float, default=0.2, help="SE-loss weight (default: 0.2)"
            )
            parser.add_argument(
                "--batch-size",
                type=int,
                default=16,
                metavar="N",
                help="input batch size for \
                                training (default: auto)",
            )
            parser.add_argument(
                "--test-batch-size",
                type=int,
                default=16,
                metavar="N",
                help="input batch size for \
                                testing (default: same as batch size)",
            )
            # cuda, seed and logging
            parser.add_argument(
                "--no-cuda",
                action="store_true",
                default=False,
                help="disables CUDA training",
            )
            parser.add_argument(
                "--seed", type=int, default=1, metavar="S", help="random seed (default: 1)"
            )
            # checking point
            parser.add_argument(
                "--weights", type=str, default='', help="checkpoint to test"
            )
            # evaluation option
            parser.add_argument(
                "--eval", action="store_true", default=False, help="evaluating mIoU"
            )
            parser.add_argument(
                "--export",
                type=str,
                default=None,
                help="put the path to resuming file if needed",
            )
            parser.add_argument(
                "--acc-bn",
                action="store_true",
                default=False,
                help="Re-accumulate BN statistics",
            )
            parser.add_argument(
                "--test-val",
                action="store_true",
                default=False,
                help="generate masks on val set",
            )
            parser.add_argument(
                "--no-val",
                action="store_true",
                default=False,
                help="skip validation during training",
            )

            parser.add_argument(
                "--module",
                default='lseg',
                help="select model definition",
            )

            # test option
            parser.add_argument(
                "--data-path", type=str, default='../datasets/', help="path to test image folder"
            )

            parser.add_argument(
                "--no-scaleinv",
                dest="scale_inv",
                default=True,
                action="store_false",
                help="turn off scaleinv layers",
            )

            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(
                "--ignore_index",
                type=int,
                default=-1,
                help="numeric value of ignore label in gt",
            )
            
            parser.add_argument(
                "--label_src",
                type=str,
                default="default",
                help="how to get the labels",
            )
            
            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",
            )

            self.parser = parser

        def parse(self):
            args = self.parser.parse_args(args=[]) 
            args.cuda = not args.no_cuda and torch.cuda.is_available()
            print(args)
            return args

    args = Options().parse()

    torch.manual_seed(args.seed)
    args.test_batch_size = 1 
    alpha=0.5
        
    args.scale_inv = False
    args.widehead = True
    args.dataset = 'ade20k'
    args.backbone = 'clip_vitl16_384'
    args.weights = 'checkpoints/demo_e200.ckpt'
    args.ignore_index = 255

    module = LSegModule.load_from_checkpoint(
        checkpoint_path=args.weights,
        data_path=args.data_path,
        dataset=args.dataset,
        backbone=args.backbone,
        aux=args.aux,
        num_features=256,
        aux_weight=0,
        se_loss=False,
        se_weight=0,
        base_lr=0,
        batch_size=1,
        max_epochs=0,
        ignore_index=args.ignore_index,
        dropout=0.0,
        scale_inv=args.scale_inv,
        augment=False,
        no_batchnorm=False,
        widehead=args.widehead,
        widehead_hr=args.widehead_hr,
        map_locatin="cpu",
        arch_option=0,
        block_depth=0,
        activation='lrelu',
    )

    input_transform = module.val_transform

    # dataloader
    loader_kwargs = (
        {"num_workers": args.workers, "pin_memory": True} if args.cuda else {}
    )

    # model
    if isinstance(module.net, BaseNet):
        model = module.net
    else:
        model = module
        
    model = model.eval()
    model = model.cpu()
    scales = (
        [0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25]
        if args.dataset == "citys"
        else [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
    )  

    model.mean = [0.5, 0.5, 0.5]
    model.std = [0.5, 0.5, 0.5]
    evaluator = LSeg_MultiEvalModule(
        model, scales=scales, flip=True
    ).cuda()
    evaluator.eval()
    
    transform = transforms.Compose(
    [
        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
        transforms.Resize([360,480]),
    ]
)

    return evaluator, transform

"""
# LSeg Demo
"""
lseg_model, lseg_transform = load_model()

# to be revised
uploaded_file = gr.inputs.Image(type='pil')
input_labels = st.text_input("Input labels", value="dog, grass, other")
gr.outputs.Label(type="confidences",num_top_classes=5)
st.write("The labels are", input_labels)

image = Image.open(uploaded_file)
pimage = lseg_transform(np.array(image)).unsqueeze(0)

labels = []
for label in input_labels.split(","):
    labels.append(label.strip())

with torch.no_grad():
    outputs = lseg_model.parallel_forward(pimage, labels)
    
    predicts = [
        torch.max(output, 1)[1].cpu().numpy()
        for output in outputs
    ]
    
image = pimage[0].permute(1,2,0)
image = image * 0.5 + 0.5
image = Image.fromarray(np.uint8(255*image)).convert("RGBA")

pred = predicts[0]
new_palette = get_new_pallete(len(labels))
mask, patches = get_new_mask_pallete(pred, new_palette, out_label_flag=True, labels=labels)
seg = mask.convert("RGBA")

fig = plt.figure()
plt.subplot(121)
plt.imshow(image)
plt.axis('off')

plt.subplot(122)
plt.imshow(seg)
plt.legend(handles=patches, loc='upper right', bbox_to_anchor=(1.3, 1), prop={'size': 5})
plt.axis('off')

plt.tight_layout()

#st.image([image,seg], width=700, caption=["Input image", "Segmentation"])
st.pyplot(fig)

title = "LSeg"

description = "Gradio demo for LSeg for semantic segmentation. To use it, simply upload your image, or click one of the examples to load them, then add any label set"

article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.03546' target='_blank'>Language-driven Semantic Segmentation</a> | <a href='hhttps://github.com/isl-org/lang-seg' target='_blank'>Github Repo</a></p>"

examples = ['test.jpeg']

gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False, examples=examples).launch(enable_queue=True)