def min(input, dim):
return nd.min(input, axis=dim)
def img_norm(img):
max_val = nd.max(img)
min_val = nd.min(img)
return (img - min_val) / (max_val - min_val)
netDe.load_params('checkpoints/'+opt.expname+'_'+str(opt.epochs)+'_De.params', ctx=ctx)
netD.load_params('checkpoints/'+opt.expname+'_'+str(opt.epochs)+'_D.params', ctx=ctx)
#netD2.load_params('checkpoints/'+opt.expname+'_'+str(opt.epochs)+'_D2.params', ctx=ctx)
#fakecode = nd.random.uniform(low = -1, high = 1, shape=(16, 4096,1,1), ctx=ctx)
fakecode = nd.random.uniform(low = -1, high = 1, shape=(16, 64,1,1), ctx=ctx)
out = netDe(fakecode)
import load_image
test_data = load_image.load_test_images(testclasspaths,testclasslabels,opt.batch_size, opt.img_wd, opt.img_ht, ctx, opt.noisevar)
for batch in (test_data):
real_in = batch.data[0].as_in_context(ctx)
code = netEn(real_in)
recon = netDe(code)
print(nd.max(netEn(real_in)))
print(nd.min(netEn(real_in)))
print(real_in.shape)
print(recon.shape)
fake_img1 = nd.concat(real_in[0],recon[0], recon[0], out[3],dim=1)
fake_img2 = nd.concat(out[7],out[6], out[5], out[4],dim=1)
fake_img3 = nd.concat(out[8],out[9], out[10], out[11],dim=1)
fake_img4 = nd.concat(out[15],out[14], out[13], out[12],dim=1)
fake_img = nd.concat(fake_img1,fake_img2, fake_img3,fake_img4, dim=2)
#print(np.shape(fake_img))
#visual.visualize(fake_img)
tep = code[0].asnumpy()
tep.flatten()
plt.figure(figsize=(20,10))
print(np.shape(code[0].asnumpy().flatten()))
dec = netDe(code)
dec2 = netDe(fakecode)
def inference_g(self, observed_arr):
'''
Inference with generator.
Args:
observed_arr: `mxnet.ndarray` of observed data points.
Returns:
Tuple data.
- re-parametric data.
- encoded data points.
- re-encoded data points.
'''
generated_arr, encoded_arr, re_encoded_arr = super().inference_g(observed_arr)
if autograd.is_recording():
limit = self.long_term_seq_len
seq_len = self.noise_sampler.seq_len
self.noise_sampler.seq_len = limit
long_term_observed_arr = self.noise_sampler.draw()
observed_mean_arr = nd.expand_dims(nd.mean(long_term_observed_arr, axis=1), axis=1)
sum_arr = None
for seq in range(2, long_term_observed_arr.shape[1]):
add_arr = nd.sum(long_term_observed_arr[:, :seq] - observed_mean_arr, axis=1)
if sum_arr is None:
sum_arr = nd.expand_dims(add_arr, axis=0)
else:
sum_arr = nd.concat(
sum_arr,
nd.expand_dims(add_arr, axis=0),
dim=0
)
max_arr = nd.max(sum_arr, axis=0)
min_arr = nd.min(sum_arr, axis=0)
diff_arr = long_term_observed_arr - observed_mean_arr
std_arr = nd.power(nd.mean(nd.square(diff_arr), axis=1), 1/2)
R_S_arr = (max_arr - min_arr) / std_arr
len_arr = nd.ones_like(R_S_arr, ctx=R_S_arr.context) * np.log(long_term_observed_arr.shape[1] / 2)
observed_H_arr = nd.log(R_S_arr) / len_arr
self.noise_sampler.seq_len = seq_len
g_min_arr = nd.expand_dims(generated_arr.min(axis=1), axis=1)
g_max_arr = nd.expand_dims(generated_arr.max(axis=1), axis=1)
o_min_arr = nd.expand_dims(observed_arr.min(axis=1), axis=1)
o_max_arr = nd.expand_dims(observed_arr.max(axis=1), axis=1)
_observed_arr = generated_arr
long_term_generated_arr = None
for i in range(limit):
generated_arr, _, _ = super().inference_g(_observed_arr)
g_min_arr = nd.expand_dims(generated_arr.min(axis=1), axis=1)
g_max_arr = nd.expand_dims(generated_arr.max(axis=1), axis=1)
o_min_arr = nd.expand_dims(_observed_arr.min(axis=1), axis=1)
o_max_arr = nd.expand_dims(_observed_arr.max(axis=1), axis=1)
generated_arr = (generated_arr - g_min_arr) / (g_max_arr - g_min_arr)
generated_arr = (o_max_arr - o_min_arr) * generated_arr
generated_arr = o_min_arr + generated_arr
if self.condition_sampler is not None:
self.condition_sampler.output_shape = generated_arr.shape
noise_arr = self.condition_sampler.generate()
generated_arr += noise_arr
if long_term_generated_arr is None:
long_term_generated_arr = generated_arr
else:
long_term_generated_arr = nd.concat(
long_term_generated_arr,
generated_arr,
dim=1
)
_observed_arr = generated_arr
generated_mean_arr = nd.expand_dims(nd.mean(long_term_generated_arr, axis=1), axis=1)
sum_arr = None
for seq in range(2, long_term_generated_arr.shape[1]):
add_arr = nd.sum(long_term_generated_arr[:, :seq] - generated_mean_arr, axis=1)
if sum_arr is None:
sum_arr = nd.expand_dims(add_arr, axis=0)
else:
sum_arr = nd.concat(
sum_arr,
nd.expand_dims(add_arr, axis=0),
dim=0
)
max_arr = nd.max(sum_arr, axis=0)
min_arr = nd.min(sum_arr, axis=0)
diff_arr = long_term_generated_arr - generated_mean_arr
std_arr = nd.power(nd.mean(nd.square(diff_arr), axis=1), 1/2)
R_S_arr = (max_arr - min_arr) / std_arr
len_arr = nd.ones_like(R_S_arr, ctx=R_S_arr.context) * np.log(long_term_generated_arr.shape[1] / 2)
generated_H_arr = nd.log(R_S_arr) / len_arr
multi_fractal_loss = nd.abs(generated_H_arr - observed_H_arr)
multi_fractal_loss = nd.mean(multi_fractal_loss, axis=0, exclude=True)
multi_fractal_loss = nd.expand_dims(multi_fractal_loss, axis=-1)
multi_fractal_loss = nd.expand_dims(multi_fractal_loss, axis=-1)
generated_arr = generated_arr + multi_fractal_loss
return generated_arr, encoded_arr, re_encoded_arr
# (nd.clip(valid_ds_aug, -50, 40) - min_val) / (max_val - min_val),
valid_ds_aug,
nd.array(label_valid_aug).astype('float32'))
# test_ds = ((nd.clip(test, -50, 40) - min_val) / (max_val - min_val), ids)
test_norm = []
for k in range(test.shape[0]):
imag = test[k].reshape(shape=(1, test[k].shape[0], test[k].shape[1],
test[k].shape[2]))
test_norm.append(img_norm(imag))
test_ds = (nd.concat(*test_norm, dim=0).astype('float32'), ids)
print("max/min train: %f\t%f" %
(nd.max(train_ds[0]).asscalar(), nd.min(train_ds[0]).asscalar()))
print("max/min valid: %f\t%f" %
(nd.max(valid_ds[0]).asscalar(), nd.min(valid_ds[0]).asscalar()))
print("max/min test: %f\t%f" %
(nd.max(test_ds[0]).asscalar(), nd.min(test_ds[0]).asscalar()))
print("finish gen train/valid dataset")
batch_size = 128
train_data = utils.DataLoader(train_ds, batch_size, shuffle=True)
valid_data = utils.DataLoader(valid_ds, batch_size, shuffle=False)
test_data = utils.TestDataLoader(test_ds, batch_size)
ctx = utils.try_gpu()
num_epochs = 100
def hybrid_forward(self, F, x):
x = F.min(data=x, axis=(2, 3), keepdims=True, name='GlobalMinPool2D')
return x