def get_predictor():
conf = Configs()
experiment.evaluate()
# This will download a pretrained model checkpoint and some cached files.
# It will download the archive as `saved_checkpoint.tar.gz` and extract it.
#
# If you have a locally trained model load it directly with
# run_uuid = 'RUN_UUID'
# And for latest checkpoint
# checkpoint = None
run_uuid, checkpoint = experiment.load_bundle(
lab.get_path() / 'saved_checkpoint.tar.gz',
url=
'https://github.com/lab-ml/python_autocomplete/releases/download/0.0.4/transformer_checkpoint.tar.gz'
)
conf_dict = experiment.load_configs(run_uuid)
experiment.configs(conf, conf_dict)
experiment.add_pytorch_models(get_modules(conf))
experiment.load(run_uuid, checkpoint)
experiment.start()
conf.model.eval()
return Predictor(conf.model, cache('stoi', lambda: conf.text.stoi),
cache('itos', lambda: conf.text.itos))
def load_experiment(run_uuid: str, checkpoint: Optional[int] = None):
"""
Load a saved experiment from [train model](train_model.html).
"""
# Create configurations object
conf = Configs()
# Load custom configurations used in the experiment
conf_dict = experiment.load_configs(run_uuid)
# We need to get inputs to the feed forward layer, $f(c_i)$
conf_dict['is_save_ff_input'] = True
# This experiment is just an evaluation; i.e. nothing is tracked or saved
experiment.evaluate()
# Initialize configurations
experiment.configs(conf, conf_dict, 'run')
# Set models for saving/loading
experiment.add_pytorch_models(get_modules(conf))
# Specify the experiment to load from
experiment.load(run_uuid, checkpoint)
# Start the experiment; this is when it actually loads models
experiment.start()
return conf
def load_experiment() -> Configs:
conf = Configs()
experiment.evaluate()
# This will download a pretrained model checkpoint and some cached files.
# It will download the archive as `saved_checkpoint.tar.gz` and extract it.
#
# If you have a locally trained model load it directly with
# run_uuid = 'RUN_UUID'
# And for latest checkpoint
# checkpoint = None
# run_uuid = 'a6cff3706ec411ebadd9bf753b33bae6' # bpe
# checkpoint = None
run_uuid, checkpoint = experiment.load_bundle(
lab.get_path() / 'saved_checkpoint.tar.gz',
url=
'https://github.com/lab-ml/python_autocomplete/releases/download/0.0.5/bundle.tar.gz'
)
conf_dict = experiment.load_configs(run_uuid)
conf_dict['text.is_load_data'] = False
experiment.configs(conf, conf_dict)
experiment.add_pytorch_models(get_modules(conf))
experiment.load(run_uuid, checkpoint)
experiment.start()
return conf
def main():
conf = Configs()
experiment.create(name="source_code_eval", comment='lstm model')
# Replace this with your training experiment UUID
conf_dict = experiment.load_configs('6f10a292e77211ea89d69979079dc3d6')
experiment.configs(conf, conf_dict, 'run')
experiment.add_pytorch_models(get_modules(conf))
experiment.load('6f10a292e77211ea89d69979079dc3d6')
experiment.start()
evaluator = Evaluator(conf.model, conf.text, conf.text.valid, False)
evaluator.eval()
def get_predictor():
conf = Configs()
experiment.evaluate()
# Replace this with your training experiment UUID
run_uuid = '39b03a1e454011ebbaff2b26e3148b3d'
conf_dict = experiment.load_configs(run_uuid)
experiment.configs(conf, conf_dict)
experiment.add_pytorch_models(get_modules(conf))
experiment.load(run_uuid)
experiment.start()
conf.model.eval()
return Predictor(conf.model, cache('stoi', lambda: conf.text.stoi), cache('itos', lambda: conf.text.itos))
def main():
"""Generate samples"""
# Training experiment run UUID
run_uuid = "a44333ea251411ec8007d1a1762ed686"
# Start an evaluation
experiment.evaluate()
# Create configs
configs = Configs()
# Load custom configuration of the training run
configs_dict = experiment.load_configs(run_uuid)
# Set configurations
experiment.configs(configs, configs_dict)
# Initialize
configs.init()
# Set PyTorch modules for saving and loading
experiment.add_pytorch_models({'eps_model': configs.eps_model})
# Load training experiment
experiment.load(run_uuid)
# Create sampler
sampler = Sampler(diffusion=configs.diffusion,
image_channels=configs.image_channels,
image_size=configs.image_size,
device=configs.device)
# Start evaluation
with experiment.start():
# No gradients
with torch.no_grad():
# Sample an image with an denoising animation
sampler.sample_animation()
if False:
# Get some images fro data
data = next(iter(configs.data_loader)).to(configs.device)
# Create an interpolation animation
sampler.interpolate_animate(data[0], data[1])
def main():
conf = Configs()
experiment.evaluate()
# Replace this with your training experiment UUID
run_uuid = '39b03a1e454011ebbaff2b26e3148b3d'
conf_dict = experiment.load_configs(run_uuid)
experiment.configs(conf, conf_dict)
experiment.add_pytorch_models(get_modules(conf))
experiment.load(run_uuid)
experiment.start()
predictor = Predictor(conf.model, cache('stoi', lambda: conf.text.stoi),
cache('itos', lambda: conf.text.itos))
conf.model.eval()
with open(str(lab.get_data_path() / 'sample.py'), 'r') as f:
sample = f.read()
evaluate(predictor, sample)
def get_saved_model(run_uuid: str, checkpoint: int):
"""
### Load [trained large model](large.html)
"""
from labml_nn.distillation.large import Configs as LargeConfigs
# In evaluation mode (no recording)
experiment.evaluate()
# Initialize configs of the large model training experiment
conf = LargeConfigs()
# Load saved configs
experiment.configs(conf, experiment.load_configs(run_uuid))
# Set models for saving/loading
experiment.add_pytorch_models({'model': conf.model})
# Set which run and checkpoint to load
experiment.load(run_uuid, checkpoint)
# Start the experiment - this will load the model, and prepare everything
experiment.start()
# Return the model
return conf.model
def evaluate():
"""
## Evaluate trained Cycle GAN
"""
# Set the run UUID from the training run
trained_run_uuid = 'f73c1164184711eb9190b74249275441'
# Create configs object
conf = Configs()
# Create experiment
experiment.create(name='cycle_gan_inference')
# Load hyper parameters set for training
conf_dict = experiment.load_configs(trained_run_uuid)
# Calculate configurations. We specify the generators `'generator_xy', 'generator_yx'`
# so that it only loads those and their dependencies.
# Configs like `device` and `img_channels` will be calculated, since these are required by
# `generator_xy` and `generator_yx`.
#
# If you want other parameters like `dataset_name` you should specify them here.
# If you specify nothing, all the configurations will be calculated, including data loaders.
# Calculation of configurations and their dependencies will happen when you call `experiment.start`
experiment.configs(conf, conf_dict)
conf.initialize()
# Register models for saving and loading.
# `get_modules` gives a dictionary of `nn.Modules` in `conf`.
# You can also specify a custom dictionary of models.
experiment.add_pytorch_models(get_modules(conf))
# Specify which run to load from.
# Loading will actually happen when you call `experiment.start`
experiment.load(trained_run_uuid)
# Start the experiment
with experiment.start():
# Image transformations
transforms_ = [
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
# Load your own data. Here we try the test set.
# I was trying with Yosemite photos, they look awesome.
# You can use `conf.dataset_name`, if you specified `dataset_name` as something you wanted to be calculated
# in the call to `experiment.configs`
dataset = ImageDataset(conf.dataset_name, transforms_, 'train')
# Get an image from dataset
x_image = dataset[10]['x']
# Display the image
plot_image(x_image)
# Evaluation mode
conf.generator_xy.eval()
conf.generator_yx.eval()
# We don't need gradients
with torch.no_grad():
# Add batch dimension and move to the device we use
data = x_image.unsqueeze(0).to(conf.device)
generated_y = conf.generator_xy(data)
# Display the generated image.
plot_image(generated_y[0].cpu())