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while (torch.cat(logits, 0).shape[0] if len(logits) else 0) < img_num:
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with torch.no_grad():
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images = self.generate_data()
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pool_val, logits_val = self.net(images)
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pool += [pool_val]
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logits += [F.softmax(logits_val, 1)]
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return torch.cat(pool, 0), torch.cat(logits, 0)
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def get_fid_score(self):
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pool, logits = self.accumulate_activations()
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print('Calculating FID...')
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mu, sigma = torch.mean(pool, 0), torch_cov(pool, rowvar=False)
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# fid_score = torch_calculate_frechet_distance(mu, sigma, self.mu_real, self.sigma_real)
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# return fid_score.cpu().numpy()
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fid_score = numpy_calculate_frechet_distance(mu.cpu().numpy(), sigma.cpu().numpy(),
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self.mu_real.cpu().numpy(),
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self.sigma_real.cpu().numpy())
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return fid_score
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if __name__ == '__main__':
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parser = eval_parser()
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config = vars(parser.parse_args())
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print(config)
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print('numpy calculation')
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device = torch.device('cuda:0')
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model = config['model']
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z_dim = config['z_dim']
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if model == 'dc':
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G = GoodGenerator()
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elif model == 'ResGAN':
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G = ResNet32Generator(z_dim=z_dim, num_filters=128, batchnorm=True)
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elif model == 'DCGAN':
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G = DC_generator(z_dim=z_dim)
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elif model == 'mnist':
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G = dc_G(z_dim=z_dim)
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elif model == 'dc32':
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G =dcG32(z_dim=z_dim)
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else:
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raise ValueError('No matching generator for %s' % model)
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G.to(device)
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G.eval()
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evalor = evalor(G=G, z_dim=z_dim,
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model_dir=config['model_dir'],
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device=device,
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log_path=config['logdir'],
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dim=config['dim'])
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evalor.eval_metrics(begin=config['begin'], end=config['end'], step=config['step'],
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is_flag=config['eval_is'], fid_flag=config['eval_fid'],
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dataname=config['dataset'])
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# <FILESEP>
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#!/usr/bin/env python3
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# TODO: Ad-blocking. How, you may ask? I suggest letting the user set a list of
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# "banned" track titles which usally appear when ads are playing, and replacing
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# them either with another radio stream or some pleasant elevator music (file/folder).
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# As soon as we detect the track title changing again, we can switch back to the radio stream.
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# Also, the same technique could be used to block unwanted "trending" songs!
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# Progress: Done!
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# TODO: Downloading of streams, splitting of the file in seperate music files per
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# title and storing them in an seperate folder. Command line setting to disable
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# file splitting.
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# Progress: Beta, still kinda unhappy with this.
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# TODO: Direct streaming: Let the user pass an URL and add it to the database,
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# then play it.
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# Progress: Done!
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# TODO: Clean this mess!
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# Progress: None
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import argparse
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import json
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import requests
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import signal
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import sys
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import time
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import colorama
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import vlc
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import download
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import tunein
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# Heh, ripped from the Blender build scripts
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class colors:
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GREEN = '\033[92m'
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RED = '\033[91m'
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YELLOW = '\033[93m'
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BOLD = '\033[1m'
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UNDERLINE = '\033[4m'
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ENDC = '\033[0m'
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# Load the config file, config.json
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def load_config():
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try:
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with open("config.json", "r") as f:
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