Exemple #1
0
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()
Exemple #2
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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
Exemple #3
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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
Exemple #5
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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