import pytest
import numpy as np
sys.path.append('..')
from batchflow import B, C, D, F, L, V, R, P, I, Dataset, Pipeline, Batch, apply_parallel, inbatch_parallel, action
#--------------------
# COMMON
#--------------------
@pytest.mark.parametrize('named_expr', [
C('option'),
C('not defined', default=10),
B('size'),
D('size'),
V('var'),
R('normal', 0, 1),
R('normal', 0, 1, size=B.size),
F(lambda batch: 0),
L(lambda: 0),
])
def test_general_get(named_expr):
pipeline = (Dataset(10).pipeline({
'option': 0
}).init_variable('var').do_nothing(named_expr).run(2, lazy=True))
failed = False
try:
_ = pipeline.next_batch()
except KeyError:
""" Test research with tf model """
import sys
sys.path.append("../../..")
from batchflow import Pipeline, B, C, V, D
from batchflow.opensets import MNIST
from batchflow.models.tf import VGG16
from batchflow.research import Research
BATCH_SIZE = 64
model_config = {
'inputs/images/shape': (28, 28, 1),
'inputs/labels/classes': D('num_classes'),
'initial_block/inputs': 'images',
'body/block/layout': 'cna',
'device': C('device') # it's technical parameter for TFModel
}
mnist = MNIST()
train_root = mnist.train.p.run(BATCH_SIZE,
shuffle=True,
n_epochs=None,
lazy=True)
test_root = mnist.test.p.run(BATCH_SIZE, shuffle=True, n_epochs=1, lazy=True)
train_template = (Pipeline().init_variable(
'loss', init_on_each_run=list).init_variable(
'accuracy', init_on_each_run=list).init_model(
'dynamic', VGG16, 'conv',