out = (prediction[:, 1, :, :] > 0.).float()
return (out == target).float().mean().item()
def f1_score(output, target, epsilon=1e-7):
# turn output into 0-1 map
probas = (output[:, 1, :, :] > 0.).float()
TP = (probas * target).sum(dim=1)
precision = TP / (probas.sum(dim=1) + epsilon)
recall = TP / (target.sum(dim=1) + epsilon)
f1 = 2 * precision * recall / (precision + recall + epsilon)
f1 = f1.clamp(min=epsilon, max=1-epsilon)
return f1.mean().item(), (precision.mean().item(), recall.mean().item())
cc = Configs()
print("Loading stored model")
model = SegnetConvLSTM(cc.hidden_dims, decoder_out_channels=2, lstm_nlayers=len(cc.hidden_dims),
vgg_decoder_config=cc.decoder_config)
tu.load_model_checkpoint(model, '../train-results/model-fixed.torch', inference=False, map_location=device)
model.to(device)
print("Model loaded")
tu_test_dataset = TUSimpleDataset(config.ts_root, config.ts_subdirs, config.ts_flabels, shuffle=False)#, shuffle_seed=9)
# build data loader
tu_test_dataloader = DataLoader(tu_test_dataset, batch_size=cc.test_batch, shuffle=True, num_workers=2)
# using crossentropy for weighted loss
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([0.02, 1.02])).to(device)
import json
from flask import Flask, request, send_file, render_template, abort, Response, redirect
from rq import Queue
from rq.job import Job
from flask_cors import CORS
from rq_scheduler import Scheduler
from redis import Redis
from utils.config import Configs
from packages.github.github import Github
from worker import conn
from actions.actions import Actions
from mongo.db import *
configs = Configs()
actions = Actions()
github = Github()
DB()
app = Flask(__name__, static_folder="./dist/static", template_folder="./dist")
CORS(app, resources={r"/*": {"origins": "*"}})
q = Queue(connection=conn)
scheduler = Scheduler(connection=Redis())
@app.after_request
def set_response_headers(response):
response.headers["Cache-Control"] = "no-cache"