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import streamlit as st
import torch
import torch.nn as nn
import timm
import torchvision.transforms as transforms
import pytorch_lightning as pl
from PIL import Image
import numpy as np
from torch import nn
import smp
# The accompanying inference app
PATHS = ['1.tiff', '2.tiff']
NUM_CLASSES = len(CLASSES)
IDS_TO_CLASSES_DICT = dict(zip(list(range(NUM_CLASSES)), CLASSES))
MODEL_NAME = "se_resne"
MODEL_PATH = "model.ckpt"
DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
TRANSFORM = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
BACKBONE = ""
IN_CHANNELS = ""
CLASSES = ""
# TODO: path to weights?
WEIGHTS = ""
class VesuviusModel(nn.Module):
def __init__(self, weight=None):
super().__init__()
self.cfg = cfg
self.encoder = smp.Unet(
encoder_name=BACKBONE,
encoder_weights=WEIGHTS,
in_channels=IN_CHANNELS,
classes=CLASSES,
activation=None,
)
def forward(self, image):
output = self.encoder(image)
output = output.squeeze(-1)
return output
def load_weights_into_model(model_name: str, model_path: str) -> nn.Module:
model = VesuviusModel(model_name)
state_dict = torch.load(model_path, map_location=DEVICE)["state_dict"]
model.load_state_dict(state_dict)
return model
model = load_weights_into_model(MODEL_NAME, MODEL_PATH)
model.to(DEVICE)
model.eval()
img_path = st.selectbox('Select an image to segment', PATHS)
st.write('You have selected:', img_path)
img = Image.open(img_path)
st.image(img, caption='Selected image to segment')
np_img = np.array(img)
input_batch = TRANSFORM(np_img[:, :, :3]).unsqueeze(0).to(DEVICE)
with st.spinner("Segmenting the image in progress..."):
with torch.no_grad():
# TODO: Finish...
prediction = model(input_batch).cpu()
print(prediction)
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