def extract_data():
"""
Construct the data files
:return: 2 List<EP>: files of content and style
"""
cp = EPath('content')
sp = EPath('style')
if cp.exists() and sp.exists():
# Check if I have to reconstruct it or not
cp_list = cp.listdir(concat=True)
sp_list = sp.listdir(concat=True)
if len(cp_list) > 0 and len(sp_list) > 0:
# Data is already there
return cp_list, sp_list
else:
# Remove everything to do it again
cp.rmdir()
sp.rmdir()
EPath('content').mkdir()
EPath('style').mkdir()
possible_parents = [
'.', '..'
] # The working folder (my computer) or the parent folder (colab)
data_found = False
for possible_parent in possible_parents:
cp_zip = EPath(possible_parent, 'content.zip')
sp_zip = EPath(possible_parent, 'style.zip')
if cp_zip.exists() and sp_zip.exists() and not data_found:
data_found = True
# the zip files are provided in the project folder
with ZipFile(cp_zip, 'r') as zip_ref:
zip_ref.extractall('./')
with ZipFile(sp_zip, 'r') as zip_ref:
zip_ref.extractall('./')
if not data_found:
# no image provided
cp = tf.keras.utils.get_file(
'YellowLabradorLooking_new.jpg',
'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg'
)
sp = tf.keras.utils.get_file(
'kandinsky5.jpg',
'https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg'
)
shutil.move(cp, 'content/YellowLabradorLooking_new.jpg')
shutil.move(sp, 'style/kandinsky5.jpg')
content_path_list = EPath('content').listdir(concat=True)
style_path_list = EPath('style').listdir(concat=True)
return content_path_list, style_path_list
def get_data():
"""
Construct the data files
:return: 2 List<EP>: files of content and style
"""
cp = EPath('content')
sp = EPath('style')
if cp.exists() and sp.exists():
cp_list = cp.listdir(concat=True)
sp_list = sp.listdir(concat=True)
if len(cp_list) > 0 and len(sp_list) > 0:
# Data is already there
return cp_list, sp_list
return extract_data()
def eval_songs_folder(path):
"""
:param path:
:return:
"""
path = EPath(path)
files = list(
filter(lambda x: x.suffix == '.mid', path.listdir(concat=True)))
all_res = {}
text = ""
res_seed = 0
res_created = 0
for file in files:
seed_length = 8 if file.rstem[20:] != "redo_song_generate_3" else None
res = eval_song_midi(file, seed_length)
all_res[file.rstem] = res
h_s = res['harmony_seed']
h_c = res['harmony_created']
if h_s is not None and not np.isnan(h_s):
res_seed += res['harmony_seed']
if h_c is not None and not np.isnan(h_c):
res_created += h_c
text += file.rstem + '\n'
for k in res:
text += f'\t{k}: {res[k]}\n'
plots.plot_res((file.parent / file.rstem +
'_harmony_measure').with_suffix('.jpg'), res)
res_seed /= len(files)
res_created /= len(files)
text = f'\t\tRes for generation\n\nMean:\n\tharmony_seed: {res_seed}\n\tharmony_created: {res_created}\n' + text
with open(path / 'res.txt', 'w') as f:
f.write(text)
with open(path / 'res.p', 'wb') as dump_file:
pickle.dump(all_res, dump_file)
def get_next_files(content_path_list, style_path_list):
"""
:param content_path_list: List of the content_path
:param style_path_list: List of the style_path
:return:
"""
for content_path in content_path_list:
for style_path in style_path_list:
result_path = EPath('results', content_path.stem, style_path.stem)
if not result_path.exists():
return content_path, style_path, result_path, var.p.image_start[
0]
else:
files = result_path.listdir(t='str')
for img_start in var.p.image_start:
if not functools.reduce(
lambda x, y: x or y.startswith(img_start), files,
False):
return content_path, style_path, result_path, img_start
return None, None, None, None
def get_next_files(content_path_list,
style_path_list,
image_start=options.image_start,
data_path=None):
"""
:param content_path_list: List of the content_path
:param style_path_list: List of the style_path
:return:
"""
for content_path in content_path_list:
for style_path in style_path_list:
result_path = EPath('results', content_path.stem, style_path.stem)
start_path_list = get_start_path_list(content_path=content_path,
style_path=style_path,
image_start=image_start,
data_path=data_path)
if not result_path.exists():
return FileCombination(content_path=content_path,
style_path=style_path,
start_path=start_path_list[0],
n=0)
else:
files = result_path.listdir(
t='str') # existing files in the result folder
for start_path in start_path_list:
for p in range(param.length):
file_combination = FileCombination(
content_path=content_path,
style_path=style_path,
start_path=start_path,
n=p)
if not functools.reduce(
lambda x, y: x or y.startswith(
file_combination.result_stem), files,
False):
return file_combination
return None
res = eval_song_midi(file, seed_length)
all_res[file.rstem] = res
h_s = res['harmony_seed']
h_c = res['harmony_created']
if h_s is not None and not np.isnan(h_s):
res_seed += res['harmony_seed']
if h_c is not None and not np.isnan(h_c):
res_created += h_c
text += file.rstem + '\n'
for k in res:
text += f'\t{k}: {res[k]}\n'
plots.plot_res((file.parent / file.rstem +
'_harmony_measure').with_suffix('.jpg'), res)
res_seed /= len(files)
res_created /= len(files)
text = f'\t\tRes for generation\n\nMean:\n\tharmony_seed: {res_seed}\n\tharmony_created: {res_created}\n' + text
with open(path / 'res.txt', 'w') as f:
f.write(text)
with open(path / 'res.p', 'wb') as dump_file:
pickle.dump(all_res, dump_file)
if __name__ == '__main__':
path = EPath('..', '..', '..', 'big_runs', 'lstm')
for folder in path.listdir(concat=True):
if not folder.is_dir():
continue
midi_folder = folder / 'generated_midis'
midi_folder /= midi_folder.listdir(concat=False)[0]
eval_songs_folder(midi_folder)
