def load_model():
global lookupDF
global song_file_map
global column_maps
global max_list
global model
global scaler
global graph
global probDF
# Load model
model = nn.load_model('./model/working/std')
graph = tf.get_default_graph()
# Load preprocessing dependencies
with open('./data/song-file-map.json', 'r') as f:
song_file_map = json.load(f)
with open('./model/working/preprocessing/maps.json', 'r') as f:
column_maps = json.load(f)
with open('./model/working/preprocessing/max_list.json', 'r') as f:
max_list = json.load(f)
scaler = joblib.load('./model/working/preprocessing/robust.scaler')
# Load song ID lookup for frontend
lookupDF = pd.read_hdf('./frontend/data/lookup.h5', 'df')
# Model predictions for comparison
probDF = pd.read_pickle('./data/model_prob.pkl')
def main():
nn.load_model()
while True:
sound_byte = se.stream()
nn.decide(sound_byte)
#!/usr/bin/env python3
import sys
import torch
import numpy as np
from neural_net import load_model
from data import train_selector, test_selector1, test_selector2, load_images
np.random.seed(0)
torch.manual_seed(0)
net = load_model(sys.argv[1])
images = []
for selector in (train_selector, test_selector1, test_selector2):
images += load_images(selector, max_per_class=1)[0]
batch = net(torch.cat(images, dim=0))
transpose = batch.t()
norms = transpose.pow(2).sum(dim=1).clamp(min=0.001).sqrt()
norm_mat = norms.unsqueeze(0) * norms.unsqueeze(1)
redundancy = ((transpose @ batch) / norm_mat).abs().mean()
print("redudancy", redundancy)
from flask import Flask, render_template, request, jsonify
import base64
from PIL import Image
from neural_net import make_guess, load_model, Net
# Model Download: https://mega.nz/#!YU8l2ChT!VEKIfNNfL7fAfoRmKFJhU7K__XTTJw2GLOUTBkFVOX8
# Once downloaded, extract the .pth file to ml/models/ folder
MODEL_NAME = 'trained_model_49.pth' # The name of your model as found in ml/models/ folder
app = Flask(__name__)
# Create and load pre-trained neural network
net = Net(total_classes=49)
load_model(net, path='ml/models/{}'.format(MODEL_NAME))
def convert_image(image_path):
img = Image.open(image_path)
img.load()
background = Image.new("RGB", img.size, (255, 255, 255))
background.paste(img, mask=img.split()[3])
background.save(image_path, 'PNG')
fin = Image.open(image_path)
out = Image.new("RGB", img.size, (255, 255, 255))
width, height = fin.size
for x in range(width):
for y in range(height):
r, g, b = fin.getpixel((x, y))
if r == g == b: