def visualize(args, config):
if config.log:
mkfilelogger('ecGAN', config.sub('log'),
logging.DEBUG if config.get('debug') else logging.INFO)
if args.seed:
random.seed(args.seed)
Visualizer = visualizers[config.visualizer.type]
with Visualizer(config, *config.visualizer.get('args', []),
**config.visualizer.get('kwargs', {})) as vs:
for i in range(config.explanation.iterations):
vs.feed()
def main():
parser = ArgumentParser()
parser.add_argument('command', choices=commands.keys())
parser.add_argument('-f', '--config', action='append', default=[])
parser.add_argument('-c', '--chain', action='append', default=[])
parser.add_argument('-u', '--update', action='append', default=[])
parser.add_argument('--epoch_range', type=int, nargs=3)
parser.add_argument('--iter', type=int, default=1)
parser.add_argument('--seed', type=int, default=0xDEADBEEF)
parser.add_argument('--classnum', type=int, default=10)
parser.add_argument('--debug', action='store_true')
parser.add_argument('--mkdirs', action='store_true')
parser.add_argument('--crash_chain', action='store_true')
parser.add_argument('-k', '--pskip', action='append', type=int, default=[])
parser.add_argument(
'-t',
'--tag',
type=str,
default='',
help=
'-t \'(tag1|!tag2)&tag3|tag4\' := ((tag1 or not tag2) and tag3) or tag4'
)
args = parser.parse_args(sys.argv[1:])
if len(args.config):
config = Config()
for cpath in args.config:
config.update_from_file(cpath)
for ustr in args.update:
config.update(yaml.safe_load(ustr))
net_module = load_module_file(config.sub('net_file'), 'net_module')
if args.mkdirs:
mkdirs(None, lconf)
else:
config = None
if args.seed:
random.seed(args.seed)
if args.debug:
import ipdb
ipdb.set_trace()
commands[args.command](args, config)
import mxnet as mx
from mxnet import gluon
from mxnet import autograd
from mxnet import random
from mxnet import ndarray as nd
import random
import numpy as np
import matplotlib as mlp
mlp.rcParams['figure.dpi'] = 120
import matplotlib.pyplot as plt
mx.random.seed(1)
random.seed(1)
# Adam: 定义了一阶矩 + 二阶矩进行自适应学习率
def adam(params, lr, vals, sqrs, iter, batch_size, beta1=0.9, beta2=0.999):
eps_stable = 1e-8
for param, val, sqr in zip(params, vals, sqrs):
g = param.grad / batch_size
val[:] = beta1 * val + (1 - beta1) * g
sqr[:] = beta2 * sqr + (1 - beta2) * nd.square(g)
#val_next = val / (1 - nd.power(beta1, iter))
val_next = val / (1. - beta1**iter)
#sqr_next = sqr / (1. - nd.power(beta2, iter))
sqr_next = sqr / (1. - beta2**iter)
g_next = lr * val_next / (nd.sqrt(sqr_next) + eps_stable)
param[:] -= g_next
import DCGAN as dcgan
from mxnet import nd
from mxnet import random
from matplotlib import pyplot as plt
import numpy as np
#if not updating the seed by system time, you'll get the same results
import time
seed = int(time.time() * 100)
random.seed(seed)
import sys
sys.path.append('./dependencies')
import utils
ctx = utils.try_gpu()
filename = './params/dcgan.netG.save'
netG = dcgan.Generator()
netG.collect_params()
netG.load_params(filename, ctx=ctx)
z = nd.random_normal(0, 1, shape=(4, 100, 1, 1), ctx=ctx)
#print(z)
output = netG(z)
for i in range(4):
plt.subplot(1, 4, i + 1)
plt.imshow(((output[i].asnumpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(
np.uint8))
plt.axis('off')
plt.show()
def explain_cgan(args, config):
ctx = ress(make_ctx, config.device, config.device_id)
if config.log:
mkfilelogger('ecGAN', config.sub('log'),
logging.DEBUG if config.get('debug') else logging.INFO)
model = models[config.model.type](ctx=ctx,
config=config,
**config.model.kwargs)
if config.use_pattern:
model.load_pattern_params()
num = config.explanation.batch_size
K = args.classnum
net_desc = config.nets[config.model.kwargs.discriminator]
netnam = '%s<%s>' % (net_desc.name, net_desc.type)
net_epoch = net_desc.epoch
ohckw = dict(zip(['off_value', 'on_value'], config.get('ohcond', [0, 1])))
if config.use_pattern:
templ = config.pattern.output
else:
templ = config.explanation.output
if args.seed:
random.seed(args.seed)
for i in range(config.explanation.iterations):
comkw = dict(iter=i, net=netnam, net_epoch=net_epoch)
fpath = config.exsub(templ,
data_desc='result<%s>' % config.sampler.type,
ftype='h5',
**comkw)
if not config.overwrite and os.path.isfile(fpath):
getLogger('ecGAN').info('File already exits, skipping \'%s\'...',
fpath)
continue
noise, cond = samplers[config.sampler.type](num, K, ctx, ohkw=ohckw)
args = [K] + asim(noise, cond)
if config.use_pattern:
kwargs = dict(
single_out=config.pattern.get('single_out', True),
attribution=config.pattern.get('type') == 'attribution')
s_gen, gen = model.explain_pattern_top(*args, **kwargs)
s_noise, s_cond = model.explain_pattern(*args, **kwargs)
else:
kwargs = dict(single_out=config.explanation.get(
'single_out', False),
mkwargs=config.explanation.get('kwargs', {}))
s_gen, gen = model.explain_top(*args, **kwargs)
s_noise, s_cond = model.explain(*args, **kwargs)
info = {
'input/noise': noise.asnumpy(),
'input/cond': cond.asnumpy(),
'generated': gen.asnumpy(),
'prediction': model._out.argmax(axis=1).asnumpy(),
'out': model._out.asnumpy(),
'label': cond.argmax(axis=1).asnumpy(),
'relevance/noise': s_noise.squeeze().asnumpy(),
'relevance/cond': s_cond.squeeze().asnumpy(),
'relevance/generated': s_gen.asnumpy(),
}
save_data_h5(info, fpath)
def explain_clss(args, config):
ctx = ress(make_ctx, config.device, config.device_id)
if config.log:
mkfilelogger('ecGAN', config.sub('log'),
logging.DEBUG if config.get('debug') else logging.INFO)
model = models[config.model.type](ctx=ctx,
config=config,
**config.model.kwargs)
if config.use_pattern:
model.load_pattern_params()
dataset = ress(data_funcs[config.data.func],
*(config.data.args),
ctx=ctx,
**(config.data.kwargs))
data_iter = gluon.data.DataLoader(dataset,
config.explanation.batch_size,
shuffle=False,
last_batch='discard')
K = len(dataset.classes)
net_desc = config.nets[config.model.kwargs.classifier]
netnam = '%s<%s>' % (net_desc.name, net_desc.type)
net_epoch = net_desc.epoch
if config.use_pattern:
templ = config.pattern.output
else:
templ = config.explanation.output
if args.seed:
random.seed(args.seed)
for i, (data, label) in enumerate(data_iter):
if i >= config.explanation.iterations:
break
comkw = dict(iter=i, net=netnam, net_epoch=net_epoch)
fpath = config.exsub(templ,
data_desc='result<%s>' % config.data.func,
ftype='h5',
**comkw)
if not config.overwrite and os.path.isfile(fpath):
getLogger('ecGAN').info('File already exits, skipping \'%s\'...',
fpath)
continue
data = data.as_in_context(ctx)
label = label.as_in_context(ctx)
if config.use_pattern:
relevance = model.explain_pattern(
data,
cond=label.squeeze(),
single_out=config.pattern.get('single_out', True),
attribution=config.pattern.get('type') == 'attribution')
else:
relevance = model.explain(
data,
cond=label.squeeze(),
single_out=config.explanation.get('single_out', False),
mkwargs=config.explanation.get('kwargs', {}))
info = {
'input': data.asnumpy(),
'prediction': model._out.argmax(axis=1).asnumpy(),
'out': model._out.asnumpy(),
'label': label.asnumpy(),
'relevance': relevance.asnumpy(),
}
save_data_h5(info, fpath)
del model