Python train.main() Examples
The following are 30
code examples of train.main().
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Example #1
| Source File: test_binaries.py From crosentgec with GNU General Public License v3.0 | 6 votes |
|
def train_translation_model(data_dir, arch, extra_flags=None):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
'--task', 'translation',
data_dir,
'--save-dir', data_dir,
'--arch', arch,
'--optimizer', 'nag',
'--lr', '0.05',
'--max-tokens', '500',
'--max-epoch', '1',
'--no-progress-bar',
'--distributed-world-size', '1',
'--source-lang', 'in',
'--target-lang', 'out',
] + (extra_flags or []),
)
train.main(train_args)
Example #2
| Source File: test_binaries.py From helo_word with Apache License 2.0 | 6 votes |
|
def train_translation_model(data_dir, arch, extra_flags=None):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
'--task', 'translation',
data_dir,
'--save-dir', data_dir,
'--arch', arch,
'--optimizer', 'nag',
'--lr', '0.05',
'--max-tokens', '500',
'--max-epoch', '1',
'--no-progress-bar',
'--distributed-world-size', '1',
'--source-lang', 'in',
'--target-lang', 'out',
] + (extra_flags or []),
)
train.main(train_args)
Example #3
| Source File: test_binaries.py From training_results_v0.5 with Apache License 2.0 | 6 votes |
|
def train_language_model(data_dir, arch):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
'--task', 'language_modeling',
data_dir,
'--arch', arch,
'--optimizer', 'nag',
'--lr', '1.0',
'--criterion', 'adaptive_loss',
'--adaptive-softmax-cutoff', '5,10,15',
'--decoder-layers', '[(850, 3)] * 2 + [(1024,4)]',
'--decoder-embed-dim', '280',
'--max-tokens', '500',
'--tokens-per-sample', '500',
'--save-dir', data_dir,
'--max-epoch', '1',
'--no-progress-bar',
'--distributed-world-size', '1',
],
)
train.main(train_args)
Example #4
| Source File: test_binaries.py From helo_word with Apache License 2.0 | 6 votes |
|
def train_language_model(data_dir, arch):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
'--task', 'language_modeling',
data_dir,
'--arch', arch,
'--optimizer', 'nag',
'--lr', '0.1',
'--criterion', 'adaptive_loss',
'--adaptive-softmax-cutoff', '5,10,15',
'--decoder-layers', '[(850, 3)] * 2 + [(1024,4)]',
'--decoder-embed-dim', '280',
'--max-tokens', '500',
'--tokens-per-sample', '500',
'--save-dir', data_dir,
'--max-epoch', '1',
'--no-progress-bar',
'--distributed-world-size', '1',
'--ddp-backend', 'no_c10d',
],
)
train.main(train_args)
Example #5
| Source File: test_binaries.py From training_results_v0.5 with Apache License 2.0 | 6 votes |
|
def train_translation_model(data_dir, arch, extra_flags=None):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
'--task', 'translation',
data_dir,
'--save-dir', data_dir,
'--arch', arch,
'--optimizer', 'nag',
'--lr', '0.05',
'--max-tokens', '500',
'--max-epoch', '1',
'--no-progress-bar',
'--distributed-world-size', '1',
'--source-lang', 'in',
'--target-lang', 'out',
] + (extra_flags or []),
)
train.main(train_args)
Example #6
| Source File: test_binaries.py From crosentgec with GNU General Public License v3.0 | 6 votes |
|
def train_language_model(data_dir, arch):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
'--task', 'language_modeling',
data_dir,
'--arch', arch,
'--optimizer', 'nag',
'--lr', '1.0',
'--criterion', 'adaptive_loss',
'--adaptive-softmax-cutoff', '5,10,15',
'--decoder-layers', '[(850, 3)] * 2 + [(1024,4)]',
'--decoder-embed-dim', '280',
'--max-tokens', '500',
'--tokens-per-sample', '500',
'--save-dir', data_dir,
'--max-epoch', '1',
'--no-progress-bar',
'--distributed-world-size', '1',
],
)
train.main(train_args)
Example #7
| Source File: test_binaries.py From crosentgec with GNU General Public License v3.0 | 6 votes |
|
def generate_main(data_dir):
generate_parser = options.get_generation_parser()
generate_args = options.parse_args_and_arch(
generate_parser,
[
data_dir,
'--path', os.path.join(data_dir, 'checkpoint_last.pt'),
'--beam', '3',
'--batch-size', '64',
'--max-len-b', '5',
'--gen-subset', 'valid',
'--no-progress-bar',
],
)
# evaluate model in batch mode
generate.main(generate_args)
# evaluate model interactively
generate_args.buffer_size = 0
generate_args.max_sentences = None
orig_stdin = sys.stdin
sys.stdin = StringIO('h e l l o\n')
interactive.main(generate_args)
sys.stdin = orig_stdin
Example #8
| Source File: test_binaries.py From helo_word with Apache License 2.0 | 5 votes |
|
def generate_main(data_dir, extra_flags=None):
generate_parser = options.get_generation_parser()
generate_args = options.parse_args_and_arch(
generate_parser,
[
data_dir,
'--path', os.path.join(data_dir, 'checkpoint_last.pt'),
'--beam', '3',
'--batch-size', '64',
'--max-len-b', '5',
'--gen-subset', 'valid',
'--no-progress-bar',
'--print-alignment',
] + (extra_flags or []),
)
# evaluate model in batch mode
generate.main(generate_args)
# evaluate model interactively
generate_args.buffer_size = 0
generate_args.input = '-'
generate_args.max_sentences = None
orig_stdin = sys.stdin
sys.stdin = StringIO('h e l l o\n')
interactive.main(generate_args)
sys.stdin = orig_stdin
Example #9
| Source File: train_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def test_full_flow(self, mock_data_provider):
FLAGS.eval_dir = self.get_temp_dir()
FLAGS.batch_size = 16
FLAGS.max_number_of_steps = 2
FLAGS.noise_dims = 3
# Construct mock inputs.
mock_imgs = np.zeros([FLAGS.batch_size, 28, 28, 1], dtype=np.float32)
mock_lbls = np.concatenate(
(np.ones([FLAGS.batch_size, 1], dtype=np.int32),
np.zeros([FLAGS.batch_size, 9], dtype=np.int32)), axis=1)
mock_data_provider.provide_data.return_value = (mock_imgs, mock_lbls, None)
train.main(None)
Example #10
| Source File: train_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def test_main(self, mock_provide_data):
FLAGS.image_file_patterns = ['/tmp/A/*.jpg', '/tmp/B/*.jpg', '/tmp/C/*.jpg']
FLAGS.max_number_of_steps = 10
FLAGS.batch_size = 2
num_domains = 3
images_shape = [FLAGS.batch_size, FLAGS.patch_size, FLAGS.patch_size, 3]
img_list = [tf.zeros(images_shape)] * num_domains
lbl_list = [tf.one_hot([0] * FLAGS.batch_size, num_domains)] * num_domains
mock_provide_data.return_value = (img_list, lbl_list)
train.main(None)
Example #11
| Source File: train_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def test_build_graph(self, conditional, use_sync_replicas):
FLAGS.max_number_of_steps = 0
FLAGS.conditional = conditional
FLAGS.use_sync_replicas = use_sync_replicas
FLAGS.batch_size = 16
# Mock input pipeline.
mock_imgs = np.zeros([FLAGS.batch_size, 32, 32, 3], dtype=np.float32)
mock_lbls = np.concatenate(
(np.ones([FLAGS.batch_size, 1], dtype=np.int32),
np.zeros([FLAGS.batch_size, 9], dtype=np.int32)), axis=1)
with mock.patch.object(train, 'data_provider') as mock_data_provider:
mock_data_provider.provide_data.return_value = (
mock_imgs, mock_lbls, None, None)
train.main(None)
Example #12
| Source File: train_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def test_main(self, mock_gan_train, mock_define_train_ops, mock_cyclegan_loss,
mock_define_model, mock_data_provider, mock_gfile):
FLAGS.image_set_x_file_pattern = '/tmp/x/*.jpg'
FLAGS.image_set_y_file_pattern = '/tmp/y/*.jpg'
FLAGS.batch_size = 3
FLAGS.patch_size = 8
FLAGS.generator_lr = 0.02
FLAGS.discriminator_lr = 0.3
FLAGS.train_log_dir = '/tmp/foo'
FLAGS.master = 'master'
FLAGS.task = 0
FLAGS.cycle_consistency_loss_weight = 2.0
FLAGS.max_number_of_steps = 1
mock_data_provider.provide_custom_data.return_value = (
tf.zeros([3, 2, 2, 3], dtype=tf.float32),
tf.zeros([3, 2, 2, 3], dtype=tf.float32))
train.main(None)
mock_data_provider.provide_custom_data.assert_called_once_with(
['/tmp/x/*.jpg', '/tmp/y/*.jpg'], batch_size=3, patch_size=8)
mock_define_model.assert_called_once_with(mock.ANY, mock.ANY)
mock_cyclegan_loss.assert_called_once_with(
mock_define_model.return_value,
cycle_consistency_loss_weight=2.0,
tensor_pool_fn=mock.ANY)
mock_define_train_ops.assert_called_once_with(
mock_define_model.return_value, mock_cyclegan_loss.return_value)
mock_gan_train.assert_called_once_with(
mock_define_train_ops.return_value,
'/tmp/foo',
get_hooks_fn=mock.ANY,
hooks=mock.ANY,
master='master',
is_chief=True)
Example #13
| Source File: train_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def test_build_graph(self, gan_type):
FLAGS.max_number_of_steps = 0
FLAGS.gan_type = gan_type
# Mock input pipeline.
mock_imgs = np.zeros([FLAGS.batch_size, 28, 28, 1], dtype=np.float32)
mock_lbls = np.concatenate(
(np.ones([FLAGS.batch_size, 1], dtype=np.int32),
np.zeros([FLAGS.batch_size, 9], dtype=np.int32)), axis=1)
with mock.patch.object(train, 'data_provider') as mock_data_provider:
mock_data_provider.provide_data.return_value = (
mock_imgs, mock_lbls, None)
train.main(None)
Example #14
| Source File: train_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def test_build_graph(self, weight_factor):
FLAGS.max_number_of_steps = 0
FLAGS.weight_factor = weight_factor
FLAGS.batch_size = 9
FLAGS.patch_size = 32
mock_imgs = np.zeros(
[FLAGS.batch_size, FLAGS.patch_size, FLAGS.patch_size, 3],
dtype=np.float32)
with mock.patch.object(train, 'data_provider') as mock_data_provider:
mock_data_provider.provide_data.return_value = mock_imgs
train.main(None)
Example #15
| Source File: train_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def test_build_graph(self, weight_factor):
FLAGS.max_number_of_steps = 0
FLAGS.weight_factor = weight_factor
batch_size = 3
patch_size = 16
FLAGS.batch_size = batch_size
FLAGS.patch_size = patch_size
mock_imgs = np.zeros([batch_size, patch_size, patch_size, 3],
dtype=np.float32)
with mock.patch.object(train, 'data_provider') as mock_data_provider:
mock_data_provider.provide_data.return_value = mock_imgs
train.main(None)
Example #16
| Source File: train_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def test_main(self):
FLAGS.image_file_patterns = [
os.path.join(FLAGS.test_srcdir, TESTDATA_DIR, 'black/*.jpg'),
os.path.join(FLAGS.test_srcdir, TESTDATA_DIR, 'blond/*.jpg'),
os.path.join(FLAGS.test_srcdir, TESTDATA_DIR, 'brown/*.jpg'),
]
FLAGS.max_number_of_steps = 1
FLAGS.steps_per_eval = 1
FLAGS.batch_size = 1
train.main(None, _test_generator, _test_discriminator)
Example #17
| Source File: test_binaries.py From helo_word with Apache License 2.0 | 5 votes |
|
def preprocess_translation_data(data_dir, extra_flags=None):
preprocess_parser = options.get_preprocessing_parser()
preprocess_args = preprocess_parser.parse_args(
[
'--source-lang', 'in',
'--target-lang', 'out',
'--trainpref', os.path.join(data_dir, 'train'),
'--validpref', os.path.join(data_dir, 'valid'),
'--testpref', os.path.join(data_dir, 'test'),
'--thresholdtgt', '0',
'--thresholdsrc', '0',
'--destdir', data_dir,
] + (extra_flags or []),
)
preprocess.main(preprocess_args)
Example #18
| Source File: train_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def test_main(self):
FLAGS.image_file_patterns = [
os.path.join(FLAGS.test_srcdir, TESTDATA_DIR, 'black/*.jpg'),
os.path.join(FLAGS.test_srcdir, TESTDATA_DIR, 'blond/*.jpg'),
os.path.join(FLAGS.test_srcdir, TESTDATA_DIR, 'brown/*.jpg'),
]
FLAGS.max_number_of_steps = 1
FLAGS.steps_per_eval = 1
FLAGS.batch_size = 1
train.main(None, _test_generator, _test_discriminator)
Example #19
| Source File: train_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def test_build_graph(self, weight_factor):
FLAGS.max_number_of_steps = 0
FLAGS.weight_factor = weight_factor
batch_size = 3
patch_size = 16
FLAGS.batch_size = batch_size
FLAGS.patch_size = patch_size
mock_imgs = np.zeros([batch_size, patch_size, patch_size, 3],
dtype=np.float32)
with mock.patch.object(train, 'data_provider') as mock_data_provider:
mock_data_provider.provide_data.return_value = mock_imgs
train.main(None)
Example #20
| Source File: test_binaries.py From helo_word with Apache License 2.0 | 5 votes |
|
def preprocess_lm_data(data_dir):
preprocess_parser = options.get_preprocessing_parser()
preprocess_args = preprocess_parser.parse_args([
'--only-source',
'--trainpref', os.path.join(data_dir, 'train.out'),
'--validpref', os.path.join(data_dir, 'valid.out'),
'--testpref', os.path.join(data_dir, 'test.out'),
'--destdir', data_dir,
])
preprocess.main(preprocess_args)
Example #21
| Source File: test_binaries.py From crosentgec with GNU General Public License v3.0 | 5 votes |
|
def preprocess_translation_data(data_dir):
preprocess_parser = preprocess.get_parser()
preprocess_args = preprocess_parser.parse_args([
'--source-lang', 'in',
'--target-lang', 'out',
'--trainpref', os.path.join(data_dir, 'train'),
'--validpref', os.path.join(data_dir, 'valid'),
'--testpref', os.path.join(data_dir, 'test'),
'--thresholdtgt', '0',
'--thresholdsrc', '0',
'--destdir', data_dir,
])
preprocess.main(preprocess_args)
Example #22
| Source File: train_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def test_full_flow(self, mock_data_provider):
FLAGS.eval_dir = self.get_temp_dir()
FLAGS.batch_size = 16
FLAGS.max_number_of_steps = 2
FLAGS.noise_dims = 3
# Construct mock inputs.
mock_imgs = np.zeros([FLAGS.batch_size, 28, 28, 1], dtype=np.float32)
mock_lbls = np.concatenate(
(np.ones([FLAGS.batch_size, 1], dtype=np.int32),
np.zeros([FLAGS.batch_size, 9], dtype=np.int32)), axis=1)
mock_data_provider.provide_data.return_value = (mock_imgs, mock_lbls, None)
train.main(None)
Example #23
| Source File: train_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def test_main(self, mock_provide_data):
FLAGS.image_file_patterns = ['/tmp/A/*.jpg', '/tmp/B/*.jpg', '/tmp/C/*.jpg']
FLAGS.max_number_of_steps = 10
FLAGS.batch_size = 2
num_domains = 3
images_shape = [FLAGS.batch_size, FLAGS.patch_size, FLAGS.patch_size, 3]
img_list = [tf.zeros(images_shape)] * num_domains
lbl_list = [tf.one_hot([0] * FLAGS.batch_size, num_domains)] * num_domains
mock_provide_data.return_value = (img_list, lbl_list)
train.main(None)
Example #24
| Source File: train_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def test_build_graph(self, conditional, use_sync_replicas):
FLAGS.max_number_of_steps = 0
FLAGS.conditional = conditional
FLAGS.use_sync_replicas = use_sync_replicas
FLAGS.batch_size = 16
# Mock input pipeline.
mock_imgs = np.zeros([FLAGS.batch_size, 32, 32, 3], dtype=np.float32)
mock_lbls = np.concatenate(
(np.ones([FLAGS.batch_size, 1], dtype=np.int32),
np.zeros([FLAGS.batch_size, 9], dtype=np.int32)), axis=1)
with mock.patch.object(train, 'data_provider') as mock_data_provider:
mock_data_provider.provide_data.return_value = (
mock_imgs, mock_lbls, None, None)
train.main(None)
Example #25
| Source File: train_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def test_main(self, mock_gan_train, mock_define_train_ops, mock_cyclegan_loss,
mock_define_model, mock_data_provider, mock_gfile):
FLAGS.image_set_x_file_pattern = '/tmp/x/*.jpg'
FLAGS.image_set_y_file_pattern = '/tmp/y/*.jpg'
FLAGS.batch_size = 3
FLAGS.patch_size = 8
FLAGS.generator_lr = 0.02
FLAGS.discriminator_lr = 0.3
FLAGS.train_log_dir = '/tmp/foo'
FLAGS.master = 'master'
FLAGS.task = 0
FLAGS.cycle_consistency_loss_weight = 2.0
FLAGS.max_number_of_steps = 1
mock_data_provider.provide_custom_data.return_value = (
tf.zeros([3, 2, 2, 3], dtype=tf.float32),
tf.zeros([3, 2, 2, 3], dtype=tf.float32))
train.main(None)
mock_data_provider.provide_custom_data.assert_called_once_with(
['/tmp/x/*.jpg', '/tmp/y/*.jpg'], batch_size=3, patch_size=8)
mock_define_model.assert_called_once_with(mock.ANY, mock.ANY)
mock_cyclegan_loss.assert_called_once_with(
mock_define_model.return_value,
cycle_consistency_loss_weight=2.0,
tensor_pool_fn=mock.ANY)
mock_define_train_ops.assert_called_once_with(
mock_define_model.return_value, mock_cyclegan_loss.return_value)
mock_gan_train.assert_called_once_with(
mock_define_train_ops.return_value,
'/tmp/foo',
get_hooks_fn=mock.ANY,
hooks=mock.ANY,
master='master',
is_chief=True)
Example #26
| Source File: train_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def test_build_graph(self, gan_type):
FLAGS.max_number_of_steps = 0
FLAGS.gan_type = gan_type
# Mock input pipeline.
mock_imgs = np.zeros([FLAGS.batch_size, 28, 28, 1], dtype=np.float32)
mock_lbls = np.concatenate(
(np.ones([FLAGS.batch_size, 1], dtype=np.int32),
np.zeros([FLAGS.batch_size, 9], dtype=np.int32)), axis=1)
with mock.patch.object(train, 'data_provider') as mock_data_provider:
mock_data_provider.provide_data.return_value = (
mock_imgs, mock_lbls, None)
train.main(None)
Example #27
| Source File: train_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def test_build_graph(self, weight_factor):
FLAGS.max_number_of_steps = 0
FLAGS.weight_factor = weight_factor
FLAGS.batch_size = 9
FLAGS.patch_size = 32
mock_imgs = np.zeros(
[FLAGS.batch_size, FLAGS.patch_size, FLAGS.patch_size, 3],
dtype=np.float32)
with mock.patch.object(train, 'data_provider') as mock_data_provider:
mock_data_provider.provide_data.return_value = mock_imgs
train.main(None)
Example #28
| Source File: train_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def _test_build_graph_helper(self, conditional, use_sync_replicas):
FLAGS.max_number_of_steps = 0
FLAGS.conditional = conditional
FLAGS.use_sync_replicas = use_sync_replicas
FLAGS.batch_size = 16
# Mock input pipeline.
mock_imgs = np.zeros([FLAGS.batch_size, 32, 32, 3], dtype=np.float32)
mock_lbls = np.concatenate(
(np.ones([FLAGS.batch_size, 1], dtype=np.int32),
np.zeros([FLAGS.batch_size, 9], dtype=np.int32)), axis=1)
with mock.patch.object(train, 'data_provider') as mock_data_provider:
mock_data_provider.provide_data.return_value = (
mock_imgs, mock_lbls, None, None)
train.main(None)
Example #29
| Source File: test_binaries.py From crosentgec with GNU General Public License v3.0 | 5 votes |
|
def preprocess_lm_data(data_dir):
preprocess_parser = preprocess.get_parser()
preprocess_args = preprocess_parser.parse_args([
'--only-source',
'--trainpref', os.path.join(data_dir, 'train.out'),
'--validpref', os.path.join(data_dir, 'valid.out'),
'--testpref', os.path.join(data_dir, 'test.out'),
'--destdir', data_dir,
])
preprocess.main(preprocess_args)
Example #30
| Source File: train_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
|
def test_full_flow(self, mock_data_provider):
FLAGS.eval_dir = self.get_temp_dir()
FLAGS.batch_size = 16
FLAGS.max_number_of_steps = 2
FLAGS.noise_dims = 3
# Construct mock inputs.
mock_imgs = np.zeros([FLAGS.batch_size, 28, 28, 1], dtype=np.float32)
mock_lbls = np.concatenate(
(np.ones([FLAGS.batch_size, 1], dtype=np.int32),
np.zeros([FLAGS.batch_size, 9], dtype=np.int32)), axis=1)
mock_data_provider.provide_data.return_value = (mock_imgs, mock_lbls, None)
train.main(None)
