def _create_example(pathuni):
"""Bulk of dataset processing. Converts str path to serialized tf.Example."""
path, uni, binary_fp = pathuni
final = {}
# zoom out
path = svg_utils.zoom_out(path)
# make clockwise
path = svg_utils.canonicalize(path)
# render path for training
final['rendered'] = svg_utils.per_step_render(path, absolute=True)
# make path relative
path = svg_utils.make_relative(path)
# convert to vector
vector = svg_utils.path_to_vector(path, categorical=True)
# make simple vector
vector = np.array(vector)
vector = np.concatenate(
[np.take(vector, [0, 4, 5, 9], axis=-1), vector[..., -6:]], axis=-1)
# count some stats
final['seq_len'] = np.shape(vector)[0]
final['class'] = int(svg_utils.map_uni_to_alphanum(uni))
final['binary_fp'] = str(binary_fp)
# append eos
vector = svg_utils.append_eos(vector.tolist(), True, 10)
# pad path to 51 (with eos)
final['sequence'] = np.concatenate(
(vector, np.zeros(((50 - final['seq_len']), 10))), 0)
# make pure list:
final['rendered'] = np.reshape(final['rendered'][..., 0],
[64 * 64]).astype(np.float32).tolist()
final['sequence'] = np.reshape(final['sequence'],
[51 * 10]).astype(np.float32).tolist()
final['class'] = np.reshape(final['class'],
[1]).astype(np.int64).tolist()
final['seq_len'] = np.reshape(final['seq_len'],
[1]).astype(np.int64).tolist()
return generator_utils.to_example(final).SerializeToString()
def _create_example(pathuni):
"""Bulk of dataset processing. Converts str path to serialized tf.Example."""
path, uni, binary_fp = pathuni
final = {}
# print(path)
#f = open('/home1/wangyz/magenta-master/data-anypair/all_paths_' + str(uni) +'.txt','a')
#f.write(str(path) + '\n')
# f.close()
f = open('/home1/wangyz/magenta-master/data-anypair/all_paths_' +
str(uni) + '.txt', 'r')
lines = f.read().split('\n')
idx = random.randint(0, len(lines)-2)
print(uni, binary_fp)
#source_path = list(lines[idx])
# print(ast.literal_eval(lines[idx]))
source_path = ast.literal_eval(lines[idx])
# input()
# zoom out
path = svg_utils.zoom_out(path)
source_path = svg_utils.zoom_out(source_path)
# make clockwise
path = svg_utils.canonicalize(path)
source_path = svg_utils.canonicalize(source_path)
# render path for training
final['rendered'] = svg_utils.per_step_render(path, absolute=True)
final['source_rendered'] = svg_utils.per_step_render(
source_path, absolute=True)
# make path relative
path = svg_utils.make_relative(path)
source_path = svg_utils.make_relative(source_path)
# convert to vector
vector = svg_utils.path_to_vector(path, categorical=True)
source_vector = svg_utils.path_to_vector(source_path, categorical=True)
# make simple vector
vector = np.array(vector)
source_vector = np.array(source_vector)
vector = np.concatenate(
[np.take(vector, [0, 4, 5, 9], axis=-1), vector[..., -6:]], axis=-1)
source_vector = np.concatenate(
[np.take(source_vector, [0, 4, 5, 9], axis=-1), source_vector[..., -6:]], axis=-1)
# count some stats
# print(np.shape(vector))
# print(np.shape(source_vector))
# input()
final['seq_len'] = np.shape(vector)[0]
final['source_seq_len'] = np.shape(source_vector)[0]
final['class'] = int(svg_utils.map_uni_to_alphanum(uni))
final['binary_fp'] = str(binary_fp)
# append eos
vector = svg_utils.append_eos(vector.tolist(), True, 10)
source_vector = svg_utils.append_eos(source_vector.tolist(), True, 10)
# pad path to 51 (with eos)
final['sequence'] = np.concatenate(
(vector, np.zeros(((50 - final['seq_len']), 10))), 0)
final['source_sequence'] = np.concatenate(
(source_vector, np.zeros(((50 - final['source_seq_len']), 10))), 0)
# make pure list:
final['rendered'] = np.reshape(final['rendered'][..., 0],
[64*64]).astype(np.float32).tolist()
final['source_rendered'] = np.reshape(final['source_rendered'][..., 0],
[64*64]).astype(np.float32).tolist()
final['sequence'] = np.reshape(final['sequence'],
[51*10]).astype(np.float32).tolist()
final['source_sequence'] = np.reshape(final['source_sequence'],
[51*10]).astype(np.float32).tolist()
final['class'] = np.reshape(final['class'],
[1]).astype(np.int64).tolist()
final['seq_len'] = np.reshape(final['seq_len'],
[1]).astype(np.int64).tolist()
final['source_seq_len'] = np.reshape(final['source_seq_len'],
[1]).astype(np.int64).tolist()
return generator_utils.to_example(final).SerializeToString()