def process():
input_path = generate_random_filename(upload_directory, "mp3")
folder_random = str(uuid4())
output_path = '/src/output/' + folder_random
create_directory(output_path)
zip_output_path = generate_random_filename(upload_directory, "zip")
try:
url = request.json["url"]
#2stems or 4stems or 5stems
nb_stems = request.json["nb_stems"]
download(url, input_path)
separator = separators[int(nb_stems)]
waveform, rate = load_audio(input_path)
result = separator.separate(waveform)
zip = ZipFile(zip_output_path + '.zip', 'w')
for instrument, data in result.items():
save_audio(output_path, instrument, data, rate)
for (root, dirs, files) in os.walk(output_path):
with ZipFile(zip_output_path, 'w') as zip:
for file in files:
print(file)
zip.write(output_path + '/' + file, basename(file))
callback = send_file(zip_output_path, mimetype='application/zip')
return callback, 200
except:
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([input_path, output_path, zip_output_path])
except:
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
pass
clean_all([input_path, output_path])
if __name__ == '__main__':
global upload_directory
global results_img_directory
global image_colorizer
upload_directory = '/data/upload/'
create_directory(upload_directory)
results_img_directory = '/data/result_images/'
create_directory(results_img_directory)
model_directory = '/data/models/'
create_directory(model_directory)
artistic_model_url = 'https://www.dropbox.com/s/zkehq1uwahhbc2o/ColorizeArtistic_gen.pth?dl=0'
get_model_bin(artistic_model_url,
os.path.join(model_directory, 'ColorizeArtistic_gen.pth'))
image_colorizer = get_image_colorizer(artistic=True)
image_colorizer.results_dir = Path(results_img_directory)
port = 5000
except:
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([input_path, output_path])
if __name__ == '__main__':
global upload_directory
global model_directory
global args
global gan
result_directory = '/src/results/'
create_directory(result_directory)
upload_directory = '/src/UGATIT/dataset/selfie2anime/testA/'
create_directory(upload_directory)
create_directory('/src/UGATIT/dataset/selfie2anime/testB/')
create_directory('/src/UGATIT/dataset/selfie2anime/trainA/')
create_directory('/src/UGATIT/dataset/selfie2anime/trainB/')
model_directory = '/src/checkpoint/'
create_directory(model_directory)
url_prefix = 'http://pretrained-models.auth-18b62333a540498882ff446ab602528b.storage.gra5.cloud.ovh.net/image/'
model_file_rar = 'UGATIT_selfie2anime_lsgan_4resblock_6dis_1_1_10_10_1000_sn_smoothing.rar'
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([
input_path,
output_path
])
if __name__ == '__main__':
global upload_directory
global results_video_directory
global video_colorizer
upload_directory = '/data/upload/'
create_directory(upload_directory)
results_video_directory = '/data/video/result/'
create_directory(results_video_directory)
model_directory = '/data/models/'
create_directory(model_directory)
video_model_url = 'https://www.dropbox.com/s/336vn9y4qwyg9yz/ColorizeVideo_gen.pth?dl=0'
get_model_bin(video_model_url, os.path.join(model_directory, 'ColorizeVideo_gen.pth'))
video_colorizer = get_video_colorizer()
port = 5000
host = '0.0.0.0'
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([
input_path
])
shutil.rmtree(os.path.join(img_output_dir, args.img_name))
if __name__ == '__main__':
global upload_directory, weight_file
global ALLOWED_EXTENSIONS
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
upload_directory = '/src/upload/'
create_directory(upload_directory)
weight_directory = '/src/'
weight_file = 'imagenet-vgg-verydeep-19.mat'
url_prefix = 'http://pretrained-models.auth-18b62333a540498882ff446ab602528b.storage.gra.cloud.ovh.net/image/neural-style-tf/'
get_model_bin(url_prefix + weight_file , weight_directory + weight_file)
port = 5000
host = '0.0.0.0'
app.run(host=host, port=port, threaded=True)
finally:
clean_all([input_path, output_path])
if __name__ == '__main__':
global upload_directory
global checkpoint_dir
global deblur
global train_dir
global graph
global sess
global ALLOWED_EXTENSIONS
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
upload_directory = '/src/upload/'
create_directory(upload_directory)
checkpoint_dir = "/src/checkpoints/"
create_directory(checkpoint_dir)
url_prefix = 'http://pretrained-models.auth-18b62333a540498882ff446ab602528b.storage.gra.cloud.ovh.net/image/SRN-Deblur/'
model_zip = "srndeblur_models.zip"
get_model_bin(url_prefix + model_zip, checkpoint_dir + model_zip)
os.system("cd " + checkpoint_dir + " && unzip " + model_zip)
checkpoint_dir = os.path.join(checkpoint_dir, args.model)
except:
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([input_path, output_path])
if __name__ == '__main__':
global upload_directory
global fast_graph_def, slow_graph_def
global ALLOWED_EXTENSIONS
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
upload_directory = '/src/upload/'
create_directory(upload_directory)
mobile_net_directory = '/src/models/mobile_net/'
xception_directory = '/src/models/xception/'
create_directory(mobile_net_directory)
create_directory(xception_directory)
url_prefix = 'http://pretrained-models.auth-18b62333a540498882ff446ab602528b.storage.gra.cloud.ovh.net/image/'
todo = []
for i in [
"frozen_inference_graph.pb",
"model.ckpt-30000.data-00000-of-00001", "model.ckpt-30000.index"
]:
get_model_bin(url_prefix + "mobile-net/" + i, mobile_net_directory + i)
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([input_path])
if __name__ == '__main__':
global upload_directory, model_directory
global model, labels_map
global tfms
global ALLOWED_EXTENSIONS
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
upload_directory = '/src/upload/'
create_directory(upload_directory)
model_directory = '/src/model/'
create_directory(model_directory)
model_name = 'efficientnet-b5'
model = EfficientNet.from_pretrained(model_name)
model.eval()
model_url = "https://storage.gra.cloud.ovh.net/v1/AUTH_18b62333a540498882ff446ab602528b/pretrained-models/image/EfficientNet-PyTorch/"
labels_file = 'labels_map.txt'
get_model_bin(model_url + labels_file, model_directory + labels_file)
labels_map = json.load(open(model_directory + labels_file))
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([input_path, output_path, zip_output_path])
if __name__ == '__main__':
global separator
global model_directory
global audio_loader
global upload_directory
global separators
upload_directory = "/src/upload/"
create_directory(upload_directory)
model_directory = "/src/pretrained_models/"
create_directory(model_directory)
model_url_prefix = 'http://pretrained-models.auth-18b62333a540498882ff446ab602528b.storage.gra.cloud.ovh.net/sound/spleeter/'
separators = dict()
for model in ["2stems", "4stems", "5stems"]:
separators[int(model[0])] = Separator('spleeter:' + model)
download(model_url_prefix + model + '.tar.gz',
model_directory + model + '.tar.gz')
create_directory(model_directory + model + '/')
cmd = 'tar zxvf ' + model_directory + model + '.tar.gz' + ' -C ' + model_directory + model
finally:
clean_all([input_path, output_path])
if __name__ == '__main__':
global upload_directory
global result_directory
global model_scene_parsing, model_cityscapes, model_visual_object
global ALLOWED_EXTENSIONS
global prewarm
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
result_directory = '/src/results/'
create_directory(result_directory)
upload_directory = '/src/upload/'
create_directory(upload_directory)
prewarm = True if os.getenv('PREWARM', 'TRUE') == 'TRUE' else False
if prewarm:
model_scene_parsing = pretrained.pspnet_50_ADE_20K(
) # load the pretrained model trained on ADE20k dataset
model_cityscapes = pretrained.pspnet_101_cityscapes(
) # load the pretrained model trained on Cityscapes dataset
model_visual_object = pretrained.pspnet_101_voc12(
) # load the pretrained model trained on Pascal VOC 2012 dataset
else:
get_model_bin(
