Python gin.unlock_config() Examples
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Example #1
Source File: ssgan_test.py From compare_gan with Apache License 2.0 | 7 votes |
def _runSingleTrainingStep(self, architecture, loss_fn, penalty_fn): parameters = { "architecture": architecture, "lambda": 1, "z_dim": 128, } with gin.unlock_config(): gin.bind_parameter("penalty.fn", penalty_fn) gin.bind_parameter("loss.fn", loss_fn) model_dir = self._get_empty_model_dir() run_config = tf.contrib.tpu.RunConfig( model_dir=model_dir, tpu_config=tf.contrib.tpu.TPUConfig(iterations_per_loop=1)) dataset = datasets.get_dataset("cifar10") gan = SSGAN( dataset=dataset, parameters=parameters, model_dir=model_dir, g_optimizer_fn=tf.train.AdamOptimizer, g_lr=0.0002, rotated_batch_size=4) estimator = gan.as_estimator(run_config, batch_size=2, use_tpu=False) estimator.train(gan.input_fn, steps=1)
Example #2
Source File: modular_gan_test.py From compare_gan with Apache License 2.0 | 6 votes |
def _runSingleTrainingStep(self, architecture, loss_fn, penalty_fn): parameters = { "architecture": architecture, "lambda": 1, "z_dim": 128, } with gin.unlock_config(): gin.bind_parameter("penalty.fn", penalty_fn) gin.bind_parameter("loss.fn", loss_fn) dataset = datasets.get_dataset("cifar10") gan = ModularGAN( dataset=dataset, parameters=parameters, model_dir=self.model_dir, conditional="biggan" in architecture) estimator = gan.as_estimator(self.run_config, batch_size=2, use_tpu=False) estimator.train(gan.input_fn, steps=1)
Example #3
Source File: modular_gan_conditional_test.py From compare_gan with Apache License 2.0 | 6 votes |
def _runSingleTrainingStep(self, architecture, loss_fn, penalty_fn, labeled_dataset): parameters = { "architecture": architecture, "lambda": 1, "z_dim": 120, } with gin.unlock_config(): gin.bind_parameter("penalty.fn", penalty_fn) gin.bind_parameter("loss.fn", loss_fn) model_dir = self._get_empty_model_dir() run_config = tf.contrib.tpu.RunConfig( model_dir=model_dir, tpu_config=tf.contrib.tpu.TPUConfig(iterations_per_loop=1)) dataset = datasets.get_dataset("cifar10") gan = ModularGAN( dataset=dataset, parameters=parameters, conditional=True, model_dir=model_dir) estimator = gan.as_estimator(run_config, batch_size=2, use_tpu=False) estimator.train(gan.input_fn, steps=1)
Example #4
Source File: modular_gan_conditional_test.py From compare_gan with Apache License 2.0 | 6 votes |
def testUnlabledDatasetRaisesError(self): parameters = { "architecture": c.RESNET_CIFAR_ARCH, "lambda": 1, "z_dim": 120, } with gin.unlock_config(): gin.bind_parameter("loss.fn", loss_lib.hinge) # Use dataset without labels. dataset = datasets.get_dataset("celeb_a") model_dir = self._get_empty_model_dir() with self.assertRaises(ValueError): gan = ModularGAN( dataset=dataset, parameters=parameters, conditional=True, model_dir=model_dir) del gan
Example #5
Source File: mtf_model.py From text-to-text-transfer-transformer with Apache License 2.0 | 5 votes |
def estimator(self, vocabulary, init_checkpoint=None, disable_tpu=False): if not self._tpu or disable_tpu: with gin.unlock_config(): gin.bind_parameter("utils.get_variable_dtype.slice_dtype", "float32") gin.bind_parameter( "utils.get_variable_dtype.activation_dtype", "float32") return utils.get_estimator( model_type=self._model_type, vocabulary=vocabulary, layout_rules=self._layout_rules, mesh_shape=mtf.Shape([]) if disable_tpu else self._mesh_shape, mesh_devices=self._mesh_devices, model_dir=self._model_dir, batch_size=self.batch_size, sequence_length=self._sequence_length, autostack=self._autostack, learning_rate_schedule=self._learning_rate_schedule, keep_checkpoint_max=self._keep_checkpoint_max, save_checkpoints_steps=self._save_checkpoints_steps, optimizer=self._optimizer, predict_fn=self._predict_fn, variable_filter=self._variable_filter, ensemble_inputs=self._ensemble_inputs, use_tpu=None if disable_tpu else self._tpu, tpu_job_name=self._tpu_job_name, iterations_per_loop=self._iterations_per_loop, cluster=self._cluster, init_checkpoint=init_checkpoint)
Example #6
Source File: mtf_model.py From text-to-text-transfer-transformer with Apache License 2.0 | 5 votes |
def eval(self, mixture_or_task_name, checkpoint_steps=None, summary_dir=None, split="validation"): """Evaluate the model on the given Mixture or Task. Args: mixture_or_task_name: str, the name of the Mixture or Task to evaluate on. Must be pre-registered in the global `TaskRegistry` or `MixtureRegistry.` checkpoint_steps: int, list of ints, or None. If an int or list of ints, evaluation will be run on the checkpoint files in `model_dir` whose global steps are closest to the global steps provided. If None, run eval continuously waiting for new checkpoints. If -1, get the latest checkpoint from the model directory. summary_dir: str, path to write TensorBoard events file summaries for eval. If None, use model_dir/eval_{split}. split: str, the mixture/task split to evaluate on. """ if checkpoint_steps == -1: checkpoint_steps = _get_latest_checkpoint_from_dir(self._model_dir) vocabulary = t5.models.mesh_transformer.get_vocabulary(mixture_or_task_name) dataset_fn = functools.partial( t5.models.mesh_transformer.mesh_eval_dataset_fn, mixture_or_task_name=mixture_or_task_name, ) with gin.unlock_config(): gin.parse_config_file(_operative_config_path(self._model_dir)) utils.eval_model(self.estimator(vocabulary), vocabulary, self._sequence_length, self.batch_size, split, self._model_dir, dataset_fn, summary_dir, checkpoint_steps)
Example #7
Source File: mtf_model.py From text-to-text-transfer-transformer with Apache License 2.0 | 5 votes |
def finetune(self, mixture_or_task_name, finetune_steps, pretrained_model_dir, pretrained_checkpoint_step=-1, split="train"): """Finetunes a model from an existing checkpoint. Args: mixture_or_task_name: str, the name of the Mixture or Task to evaluate on. Must be pre-registered in the global `TaskRegistry` or `MixtureRegistry.` finetune_steps: int, the number of additional steps to train for. pretrained_model_dir: str, directory with pretrained model checkpoints and operative config. pretrained_checkpoint_step: int, checkpoint to initialize weights from. If -1 (default), use the latest checkpoint from the pretrained model directory. split: str, the mixture/task split to finetune on. """ if pretrained_checkpoint_step == -1: checkpoint_step = _get_latest_checkpoint_from_dir(pretrained_model_dir) else: checkpoint_step = pretrained_checkpoint_step with gin.unlock_config(): gin.parse_config_file(_operative_config_path(pretrained_model_dir)) model_ckpt = "model.ckpt-" + str(checkpoint_step) self.train(mixture_or_task_name, checkpoint_step + finetune_steps, init_checkpoint=os.path.join(pretrained_model_dir, model_ckpt), split=split)
Example #8
Source File: mtf_model.py From text-to-text-transfer-transformer with Apache License 2.0 | 5 votes |
def predict(self, input_file, output_file, checkpoint_steps=-1, beam_size=1, temperature=1.0, vocabulary=None): """Predicts targets from the given inputs. Args: input_file: str, path to a text file containing newline-separated input prompts to predict from. output_file: str, path prefix of output file to write predictions to. Note the checkpoint step will be appended to the given filename. checkpoint_steps: int, list of ints, or None. If an int or list of ints, inference will be run on the checkpoint files in `model_dir` whose global steps are closest to the global steps provided. If None, run inference continuously waiting for new checkpoints. If -1, get the latest checkpoint from the model directory. beam_size: int, a number >= 1 specifying the number of beams to use for beam search. temperature: float, a value between 0 and 1 (must be 0 if beam_size > 1) 0.0 means argmax, 1.0 means sample according to predicted distribution. vocabulary: vocabularies.Vocabulary object to use for tokenization, or None to use the default SentencePieceVocabulary. """ # TODO(sharannarang) : It would be nice to have a function like # load_checkpoint that loads the model once and then call decode_from_file # multiple times without having to restore the checkpoint weights again. # This would be particularly useful in colab demo. if checkpoint_steps == -1: checkpoint_steps = _get_latest_checkpoint_from_dir(self._model_dir) with gin.unlock_config(): gin.parse_config_file(_operative_config_path(self._model_dir)) gin.bind_parameter("Bitransformer.decode.beam_size", beam_size) gin.bind_parameter("Bitransformer.decode.temperature", temperature) if vocabulary is None: vocabulary = t5.data.get_default_vocabulary() utils.infer_model( self.estimator(vocabulary), vocabulary, self._sequence_length, self.batch_size, self._model_type, self._model_dir, checkpoint_steps, input_file, output_file)
Example #9
Source File: mtf_model.py From text-to-text-transfer-transformer with Apache License 2.0 | 5 votes |
def score(self, inputs, targets, scores_file=None, checkpoint_steps=-1, vocabulary=None): """Computes log-likelihood of target per example in targets. Args: inputs: optional - a string (filename), or a list of strings (inputs) targets: a string (filename), or a list of strings (targets) scores_file: str, path to write example scores to, one per line. checkpoint_steps: int, list of ints, or None. If an int or list of ints, inference will be run on the checkpoint files in `model_dir` whose global steps are closest to the global steps provided. If None, run inference continuously waiting for new checkpoints. If -1, get the latest checkpoint from the model directory. vocabulary: vocabularies.Vocabulary object to use for tokenization, or None to use the default SentencePieceVocabulary. """ if checkpoint_steps == -1: checkpoint_steps = _get_latest_checkpoint_from_dir(self._model_dir) with gin.unlock_config(): gin.parse_config_file(_operative_config_path(self._model_dir)) # The following config setting ensures we do scoring instead of inference. gin.bind_parameter("tpu_estimator_model_fn.score_in_predict_mode", True) if vocabulary is None: vocabulary = t5.data.get_default_vocabulary() utils.score_from_strings(self.estimator(vocabulary), vocabulary, self._model_type, self.batch_size, self._sequence_length, self._model_dir, checkpoint_steps, inputs, targets, scores_file)
Example #10
Source File: s3gan_test.py From compare_gan with Apache License 2.0 | 5 votes |
def testSingleTrainingStepArchitectures( self, use_predictor, project_y=True, self_supervision="none"): parameters = { "architecture": c.RESNET_BIGGAN_ARCH, "lambda": 1, "z_dim": 120, } with gin.unlock_config(): gin.bind_parameter("ModularGAN.conditional", True) gin.bind_parameter("loss.fn", loss_lib.hinge) gin.bind_parameter("S3GAN.use_predictor", use_predictor) gin.bind_parameter("S3GAN.project_y", project_y) gin.bind_parameter("S3GAN.self_supervision", self_supervision) # Fake ImageNet dataset by overriding the properties. dataset = datasets.get_dataset("imagenet_128") model_dir = self._get_empty_model_dir() run_config = tf.contrib.tpu.RunConfig( model_dir=model_dir, tpu_config=tf.contrib.tpu.TPUConfig(iterations_per_loop=1)) gan = S3GAN( dataset=dataset, parameters=parameters, model_dir=model_dir, g_optimizer_fn=tf.train.AdamOptimizer, g_lr=0.0002, rotated_batch_fraction=2) estimator = gan.as_estimator(run_config, batch_size=8, use_tpu=False) estimator.train(gan.input_fn, steps=1)
Example #11
Source File: train.py From meta-dataset with Apache License 2.0 | 5 votes |
def parse_cmdline_gin_configurations(): """Parse Gin configurations from all command-line sources.""" with gin.unlock_config(): gin.parse_config_files_and_bindings( FLAGS.gin_config, FLAGS.gin_bindings, finalize_config=True)
Example #12
Source File: train.py From meta-dataset with Apache License 2.0 | 5 votes |
def load_operative_gin_configurations(operative_config_dir): """Load operative Gin configurations from the given directory.""" gin_log_file = operative_config_path(operative_config_dir) with gin.unlock_config(): gin.parse_config_file(gin_log_file) gin.finalize() logging.info('Operative Gin configurations loaded from %s.', gin_log_file)