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adding a pretrained tf 2.0 BigGAN #15

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adding a pretrained tf 2.0 BigGAN #15

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30 changes: 30 additions & 0 deletions pytorch_pretrained_biggan/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,14 @@
import copy
import json

PRETRAINED_CONFIG_ARCHIVE_MAP = {
'biggan-deep-128': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-config.json",
'biggan-deep-256': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-config.json",
'biggan-deep-512': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-config.json",
}

CONFIG_NAME = 'config.json'

class BigGANConfig(object):
""" Configuration class to store the configuration of a `BigGAN`.
Defaults are for the 128x128 model.
Expand Down Expand Up @@ -42,6 +50,28 @@ def __init__(self,
self.eps = eps
self.n_stats = n_stats

def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
if pretrained_model_name_or_path in PRETRAINED_CONFIG_ARCHIVE_MAP:
config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
else:
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)

try:
resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
except EnvironmentError:
logger.error("Wrong config name, should be a valid path to a folder containing "
"a {} file or a config name in {}".format(
CONFIG_NAME, PRETRAINED_CONFIG_ARCHIVE_MAP.keys()))
raise

logger.info("loading config {} from cache at {}".format(pretrained_model_name_or_path, resolved_config_file))

# Load config
config = BigGANConfig.from_json_file(resolved_config_file)
logger.info("Model config {}".format(config))

return config

@classmethod
def from_dict(cls, json_object):
"""Constructs a `BigGANConfig` from a Python dictionary of parameters."""
Expand Down
20 changes: 4 additions & 16 deletions pytorch_pretrained_biggan/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,14 +32,7 @@
'biggan-deep-512': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-pytorch_model.bin",
}

PRETRAINED_CONFIG_ARCHIVE_MAP = {
'biggan-deep-128': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-config.json",
'biggan-deep-256': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-config.json",
'biggan-deep-512': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-config.json",
}

WEIGHTS_NAME = 'pytorch_model.bin'
CONFIG_NAME = 'config.json'


def snconv2d(eps=1e-12, **kwargs):
Expand Down Expand Up @@ -252,28 +245,23 @@ class BigGAN(nn.Module):

@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
config = BigGANConfig.from_pretrained(pretrained_model_name_or_path, cache_dir=None)

if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
model_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
else:
model_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)

try:
resolved_model_file = cached_path(model_file, cache_dir=cache_dir)
resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
except EnvironmentError:
logger.error("Wrong model name, should be a valid path to a folder containing "
"a {} file and a {} file or a model name in {}".format(
WEIGHTS_NAME, CONFIG_NAME, PRETRAINED_MODEL_ARCHIVE_MAP.keys()))
"a {} file or a model name in {}".format(
WEIGHTS_NAME, PRETRAINED_MODEL_ARCHIVE_MAP.keys()))
raise

logger.info("loading model {} from cache at {}".format(pretrained_model_name_or_path, resolved_model_file))

# Load config
config = BigGANConfig.from_json_file(resolved_config_file)
logger.info("Model config {}".format(config))

# Instantiate model.
model = cls(config, *inputs, **kwargs)
state_dict = torch.load(resolved_model_file, map_location='cpu' if not torch.cuda.is_available() else None)
Expand Down
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