def test_2Dexperiment():
c = Config()
c.batch_size = 200
c.n_epochs = 40
c.learning_rate = 0.001
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 200
# model-specific
c.n_coupling = 8
c.prior = 'gauss'
exp = SmileyExperiment(
c,
name='gauss',
n_epochs=c.n_epochs,
seed=42,
base_dir='experiment_dir',
loggers={'visdom': ['visdom', {
"exp_name": "myenv"
}]})
exp.run()
# sampling
samples = exp.model.sample(1000).cpu().numpy()
sns.jointplot(samples[:, 0], samples[:, 1])
plt.show()
def test_MNIST_experiment():
c = Config()
c.batch_size = 64
c.n_epochs = 50
c.learning_rate = 0.001
c.weight_decay = 5e-5
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 100
c.subset_size = 10
# model-specific
c.n_coupling = 8
c.n_filters = 64
exp = MNISTExperiment(
c,
name='mnist_test',
n_epochs=c.n_epochs,
seed=42,
base_dir='experiment_dir',
loggers={'visdom': ['visdom', {
"exp_name": "myenv"
}]})
exp.run()
exp.model.eval()
exp.model.to('cpu')
with torch.no_grad():
samples = exp.model.sample(16, device='cpu')
img_grid = make_grid(samples).permute((1, 2, 0))
plt.imshow(img_grid)
plt.show()
return exp.model
def test_Resnet():
c = Config()
c.batch_size = 64
c.batch_size_test = 1000
c.n_epochs = 10
c.learning_rate = 0.01
c.momentum = 0.9
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 200
exp = MNIST_classification(config=c,
name='experiment',
n_epochs=c.n_epochs,
seed=42,
base_dir='./experiment_dir',
loggers={"visdom": "visdom"})
exp.run()
def get_config():
c = Config()
c.batch_size = 6
c.patch_size = 512
c.n_epochs = 20
c.learning_rate = 0.0002
c.do_ce_weighting = True
c.do_batchnorm = True
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 200
c.base_dir='/media/kleina/Data2/output/meddec'
c.data_dir='/media/kleina/Data2/Data/meddec/Task07_Pancreas_expert_preprocessed'
c.split_dir='/media/kleina/Data2/Data/meddec/Task07_Pancreas_preprocessed'
c.data_file = 'C:/dev/data/Endoviz2018/GIANA/polyp_detection_segmentation/image_gt_data_file_list_all_640x640.csv'
c.additional_slices=5
c.name=''
print(c)
return c
import numpy as np
import torch
from trixi.util import Config
from experiment import MNISTexperiment
from util import plot_dependency_map
import matplotlib.pyplot as plt
c = Config()
c.batch_size = 128
c.n_epochs = 10
c.learning_rate = 0.001
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 100
exp = MNISTexperiment(config=c,
name='test',
n_epochs=c.n_epochs,
seed=c.rnd_seed,
base_dir='./experiment_dir',
loggers={"visdom": ["visdom", {
"exp_name": "myenv"
}]})
# # run backpropagation for each dimension to compute what other
# # dimensions it depends on.
# exp.setup()
# d = 28
import numpy as np
import torch
from trixi.util import Config
from experiment import coloredMNISTexperiment
from util import plot_dependency_map
import matplotlib.pyplot as plt
c = Config()
c.batch_size = 128
c.n_epochs = 50
c.learning_rate = 0.001
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 100
exp = coloredMNISTexperiment(
config=c,
name='coloredMNIST',
n_epochs=c.n_epochs,
seed=c.rnd_seed,
base_dir='./experiment_dir',
loggers={"visdom": ["visdom", {
"exp_name": "myenv"
}]})
exp.run()
samples = np.transpose(exp.model.sample(9, (28, 28), device=exp.device),