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import os
import random
import numpy as np
from PIL import Image
import torch
if __name__ != '__main__':
import open_clip
os.environ['CUDA_VISIBLE_DEVICES'] = ''
def seed_all(seed = 0):
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.use_deterministic_algorithms(True, warn_only=False)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def inference_text(model, model_name, batches):
y = []
tokenizer = open_clip.get_tokenizer(model_name)
with torch.no_grad():
for x in batches:
x = tokenizer(x)
y.append(model.encode_text(x))
return torch.stack(y)
def inference_image(model, preprocess_val, batches):
y = []
with torch.no_grad():
for x in batches:
x = torch.stack([preprocess_val(img) for img in x])
y.append(model.encode_image(x))
return torch.stack(y)
def random_image_batch(batch_size, size):
h, w = size
data = np.random.randint(255, size = (batch_size, h, w, 3), dtype = np.uint8)
return [ Image.fromarray(d) for d in data ]
def random_text_batch(batch_size, min_length = 75, max_length = 75):
t = open_clip.tokenizer.SimpleTokenizer()
# every token decoded as string, exclude SOT and EOT, replace EOW with space
token_words = [
x[1].replace('</w>', ' ')
for x in t.decoder.items()
if x[0] not in t.all_special_ids
]
# strings of randomly chosen tokens
return [
''.join(random.choices(
token_words,
k = random.randint(min_length, max_length)
))
for _ in range(batch_size)
]
def create_random_text_data(
path,
min_length = 75,
max_length = 75,
batches = 1,
batch_size = 1
):
text_batches = [
random_text_batch(batch_size, min_length, max_length)
for _ in range(batches)
]
print(f"{path}")
torch.save(text_batches, path)
def create_random_image_data(path, size, batches = 1, batch_size = 1):
image_batches = [
random_image_batch(batch_size, size)
for _ in range(batches)
]
print(f"{path}")
torch.save(image_batches, path)
def get_data_dirs(make_dir = True):
data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data')
input_dir = os.path.join(data_dir, 'input')
output_dir = os.path.join(data_dir, 'output')
if make_dir:
os.makedirs(input_dir, exist_ok = True)
os.makedirs(output_dir, exist_ok = True)
assert os.path.isdir(data_dir), f"data directory missing, expected at {input_dir}"
assert os.path.isdir(data_dir), f"data directory missing, expected at {output_dir}"
return input_dir, output_dir
def create_test_data_for_model(
model_name,
pretrained = None,
precision = 'fp32',
jit = False,
pretrained_hf = False,
force_quick_gelu = False,
create_missing_input_data = True,
batches = 1,
batch_size = 1,
overwrite = False
):
model_id = f'{model_name}_{pretrained or pretrained_hf}_{precision}'
input_dir, output_dir = get_data_dirs()
output_file_text = os.path.join(output_dir, f'{model_id}_random_text.pt')
output_file_image = os.path.join(output_dir, f'{model_id}_random_image.pt')
text_exists = os.path.exists(output_file_text)
image_exists = os.path.exists(output_file_image)
if not overwrite and text_exists and image_exists:
return
seed_all()
model, _, preprocess_val = open_clip.create_model_and_transforms(
model_name,
pretrained = pretrained,
precision = precision,
jit = jit,
force_quick_gelu = force_quick_gelu,
pretrained_hf = pretrained_hf
)
# text
if overwrite or not text_exists:
input_file_text = os.path.join(input_dir, 'random_text.pt')
if create_missing_input_data and not os.path.exists(input_file_text):
create_random_text_data(
input_file_text,
batches = batches,
batch_size = batch_size
)
assert os.path.isfile(input_file_text), f"missing input data, expected at {input_file_text}"
input_data_text = torch.load(input_file_text)
output_data_text = inference_text(model, model_name, input_data_text)
print(f"{output_file_text}")
torch.save(output_data_text, output_file_text)
# image
if overwrite or not image_exists:
size = model.visual.image_size
if not isinstance(size, tuple):
size = (size, size)
input_file_image = os.path.join(input_dir, f'random_image_{size[0]}_{size[1]}.pt')
if create_missing_input_data and not os.path.exists(input_file_image):
create_random_image_data(
input_file_image,
size,
batches = batches,
batch_size = batch_size
)
assert os.path.isfile(input_file_image), f"missing input data, expected at {input_file_image}"
input_data_image = torch.load(input_file_image)
output_data_image = inference_image(model, preprocess_val, input_data_image)
print(f"{output_file_image}")
torch.save(output_data_image, output_file_image)
def create_test_data(
models,
batches = 1,
batch_size = 1,
overwrite = False
):
models = list(set(models).difference({
# not available with timm
# see https://github.com/mlfoundations/open_clip/issues/219
'timm-convnext_xlarge',
'timm-vit_medium_patch16_gap_256'
}).intersection(open_clip.list_models()))
models.sort()
print(f"generating test data for:\n{models}")
for model_name in models:
print(model_name)
create_test_data_for_model(
model_name,
batches = batches,
batch_size = batch_size,
overwrite = overwrite
)
return models
def _sytem_assert(string):
assert os.system(string) == 0
def main(args):
global open_clip
import importlib
import shutil
import subprocess
import argparse
parser = argparse.ArgumentParser(description = "Populate test data directory")
parser.add_argument(
'-a', '--all',
action = 'store_true',
help = "create test data for all models"
)
parser.add_argument(
'-m', '--model',
type = str,
default = [],
nargs = '+',
help = "model(s) to create test data for"
)
parser.add_argument(
'-f', '--model_list',
type = str,
help = "path to a text file containing a list of model names, one model per line"
)
parser.add_argument(
'-s', '--save_model_list',
type = str,
help = "path to save the list of models that data was generated for"
)
parser.add_argument(
'-g', '--git_revision',
type = str,
help = "git revision to generate test data for"
)
parser.add_argument(
'--overwrite',
action = 'store_true',
help = "overwrite existing output data"
)
parser.add_argument(
'-n', '--num_batches',
default = 1,
type = int,
help = "amount of data batches to create (default: 1)"
)
parser.add_argument(
'-b', '--batch_size',
default = 1,
type = int,
help = "test data batch size (default: 1)"
)
args = parser.parse_args(args)
model_list = []
if args.model_list is not None:
with open(args.model_list, 'r') as f:
model_list = f.read().splitlines()
if not args.all and len(args.model) < 1 and len(model_list) < 1:
print("error: at least one model name is required")
parser.print_help()
parser.exit(1)
if args.git_revision is not None:
stash_output = subprocess.check_output(['git', 'stash']).decode().splitlines()
has_stash = len(stash_output) > 0 and stash_output[0] != 'No local changes to save'
current_branch = subprocess.check_output(['git', 'branch', '--show-current'])
if len(current_branch) < 1:
# not on a branch -> detached head
current_branch = subprocess.check_output(['git', 'rev-parse', 'HEAD'])
current_branch = current_branch.splitlines()[0].decode()
try:
_sytem_assert(f'git checkout {args.git_revision}')
except AssertionError as e:
_sytem_assert(f'git checkout -f {current_branch}')
if has_stash:
os.system(f'git stash pop')
raise e
open_clip = importlib.import_module('open_clip')
models = open_clip.list_models() if args.all else args.model + model_list
try:
models = create_test_data(
models,
batches = args.num_batches,
batch_size = args.batch_size,
overwrite = args.overwrite
)
finally:
if args.git_revision is not None:
test_dir = os.path.join(os.path.dirname(__file__), 'data')
test_dir_ref = os.path.join(os.path.dirname(__file__), 'data_ref')
if os.path.exists(test_dir_ref):
shutil.rmtree(test_dir_ref, ignore_errors = True)
if os.path.exists(test_dir):
os.rename(test_dir, test_dir_ref)
_sytem_assert(f'git checkout {current_branch}')
if has_stash:
os.system(f'git stash pop')
os.rename(test_dir_ref, test_dir)
if args.save_model_list is not None:
print(f"Saving model list as {args.save_model_list}")
with open(args.save_model_list, 'w') as f:
for m in models:
print(m, file=f)
if __name__ == '__main__':
import sys
main(sys.argv[1:])
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