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| # coding: utf-8 | |
| """ BigGAN utilities to prepare truncated noise samples and convert/save/display output images. | |
| Also comprise ImageNet utilities to prepare one hot input vectors for ImageNet classes. | |
| We use Wordnet so you can just input a name in a string and automatically get a corresponding | |
| imagenet class if it exists (or a hypo/hypernym exists in imagenet). | |
| """ | |
| from __future__ import absolute_import, division, print_function, unicode_literals | |
| import json | |
| import logging | |
| from io import BytesIO | |
| import numpy as np | |
| from scipy.stats import truncnorm | |
| logger = logging.getLogger(__name__) | |
| NUM_CLASSES = 1000 | |
| def truncated_noise_sample(batch_size=1, dim_z=128, truncation=1., seed=None): | |
| """ Create a truncated noise vector. | |
| Params: | |
| batch_size: batch size. | |
| dim_z: dimension of z | |
| truncation: truncation value to use | |
| seed: seed for the random generator | |
| Output: | |
| array of shape (batch_size, dim_z) | |
| """ | |
| state = None if seed is None else np.random.RandomState(seed) | |
| values = truncnorm.rvs(-2, 2, size=(batch_size, dim_z), random_state=state).astype(np.float32) | |
| return truncation * values | |
| def convert_to_images(obj): | |
| """ Convert an output tensor from BigGAN in a list of images. | |
| Params: | |
| obj: tensor or numpy array of shape (batch_size, channels, height, width) | |
| Output: | |
| list of Pillow Images of size (height, width) | |
| """ | |
| try: | |
| import PIL | |
| except ImportError: | |
| raise ImportError("Please install Pillow to use images: pip install Pillow") | |
| if not isinstance(obj, np.ndarray): | |
| obj = obj.detach().numpy() | |
| obj = obj.transpose((0, 2, 3, 1)) | |
| obj = np.clip(((obj + 1) / 2.0) * 256, 0, 255) | |
| img = [] | |
| for i, out in enumerate(obj): | |
| out_array = np.asarray(np.uint8(out), dtype=np.uint8) | |
| img.append(PIL.Image.fromarray(out_array)) | |
| return img | |
| def save_as_images(obj, file_name='output'): | |
| """ Convert and save an output tensor from BigGAN in a list of saved images. | |
| Params: | |
| obj: tensor or numpy array of shape (batch_size, channels, height, width) | |
| file_name: path and beggingin of filename to save. | |
| Images will be saved as `file_name_{image_number}.png` | |
| """ | |
| img = convert_to_images(obj) | |
| for i, out in enumerate(img): | |
| current_file_name = file_name + '_%d.png' % i | |
| logger.info("Saving image to {}".format(current_file_name)) | |
| out.save(current_file_name, 'png') | |
| def display_in_terminal(obj): | |
| """ Convert and display an output tensor from BigGAN in the terminal. | |
| This function use `libsixel` and will only work in a libsixel-compatible terminal. | |
| Please refer to https://github.com/saitoha/libsixel for more details. | |
| Params: | |
| obj: tensor or numpy array of shape (batch_size, channels, height, width) | |
| file_name: path and beggingin of filename to save. | |
| Images will be saved as `file_name_{image_number}.png` | |
| """ | |
| try: | |
| import PIL | |
| from libsixel import (sixel_output_new, sixel_dither_new, sixel_dither_initialize, | |
| sixel_dither_set_palette, sixel_dither_set_pixelformat, | |
| sixel_dither_get, sixel_encode, sixel_dither_unref, | |
| sixel_output_unref, SIXEL_PIXELFORMAT_RGBA8888, | |
| SIXEL_PIXELFORMAT_RGB888, SIXEL_PIXELFORMAT_PAL8, | |
| SIXEL_PIXELFORMAT_G8, SIXEL_PIXELFORMAT_G1) | |
| except ImportError: | |
| raise ImportError("Display in Terminal requires Pillow, libsixel " | |
| "and a libsixel compatible terminal. " | |
| "Please read info at https://github.com/saitoha/libsixel " | |
| "and install with pip install Pillow libsixel-python") | |
| s = BytesIO() | |
| images = convert_to_images(obj) | |
| widths, heights = zip(*(i.size for i in images)) | |
| output_width = sum(widths) | |
| output_height = max(heights) | |
| output_image = PIL.Image.new('RGB', (output_width, output_height)) | |
| x_offset = 0 | |
| for im in images: | |
| output_image.paste(im, (x_offset,0)) | |
| x_offset += im.size[0] | |
| try: | |
| data = output_image.tobytes() | |
| except NotImplementedError: | |
| data = output_image.tostring() | |
| output = sixel_output_new(lambda data, s: s.write(data), s) | |
| try: | |
| if output_image.mode == 'RGBA': | |
| dither = sixel_dither_new(256) | |
| sixel_dither_initialize(dither, data, output_width, output_height, SIXEL_PIXELFORMAT_RGBA8888) | |
| elif output_image.mode == 'RGB': | |
| dither = sixel_dither_new(256) | |
| sixel_dither_initialize(dither, data, output_width, output_height, SIXEL_PIXELFORMAT_RGB888) | |
| elif output_image.mode == 'P': | |
| palette = output_image.getpalette() | |
| dither = sixel_dither_new(256) | |
| sixel_dither_set_palette(dither, palette) | |
| sixel_dither_set_pixelformat(dither, SIXEL_PIXELFORMAT_PAL8) | |
| elif output_image.mode == 'L': | |
| dither = sixel_dither_get(SIXEL_BUILTIN_G8) | |
| sixel_dither_set_pixelformat(dither, SIXEL_PIXELFORMAT_G8) | |
| elif output_image.mode == '1': | |
| dither = sixel_dither_get(SIXEL_BUILTIN_G1) | |
| sixel_dither_set_pixelformat(dither, SIXEL_PIXELFORMAT_G1) | |
| else: | |
| raise RuntimeError('unexpected output_image mode') | |
| try: | |
| sixel_encode(data, output_width, output_height, 1, dither, output) | |
| print(s.getvalue().decode('ascii')) | |
| finally: | |
| sixel_dither_unref(dither) | |
| finally: | |
| sixel_output_unref(output) | |
| def one_hot_from_int(int_or_list, batch_size=1): | |
| """ Create a one-hot vector from a class index or a list of class indices. | |
| Params: | |
| int_or_list: int, or list of int, of the imagenet classes (between 0 and 999) | |
| batch_size: batch size. | |
| If int_or_list is an int create a batch of identical classes. | |
| If int_or_list is a list, we should have `len(int_or_list) == batch_size` | |
| Output: | |
| array of shape (batch_size, 1000) | |
| """ | |
| if isinstance(int_or_list, int): | |
| int_or_list = [int_or_list] | |
| if len(int_or_list) == 1 and batch_size > 1: | |
| int_or_list = [int_or_list[0]] * batch_size | |
| assert batch_size == len(int_or_list) | |
| array = np.zeros((batch_size, NUM_CLASSES), dtype=np.float32) | |
| for i, j in enumerate(int_or_list): | |
| array[i, j] = 1.0 | |
| return array | |
| def one_hot_from_names(class_name_or_list, batch_size=1): | |
| """ Create a one-hot vector from the name of an imagenet class ('tennis ball', 'daisy', ...). | |
| We use NLTK's wordnet search to try to find the relevant synset of ImageNet and take the first one. | |
| If we can't find it direcly, we look at the hyponyms and hypernyms of the class name. | |
| Params: | |
| class_name_or_list: string containing the name of an imagenet object or a list of such strings (for a batch). | |
| Output: | |
| array of shape (batch_size, 1000) | |
| """ | |
| try: | |
| from nltk.corpus import wordnet as wn | |
| except ImportError: | |
| raise ImportError("You need to install nltk to use this function") | |
| if not isinstance(class_name_or_list, (list, tuple)): | |
| class_name_or_list = [class_name_or_list] | |
| else: | |
| batch_size = max(batch_size, len(class_name_or_list)) | |
| classes = [] | |
| for class_name in class_name_or_list: | |
| class_name = class_name.replace(" ", "_") | |
| original_synsets = wn.synsets(class_name) | |
| original_synsets = list(filter(lambda s: s.pos() == 'n', original_synsets)) # keep only names | |
| if not original_synsets: | |
| return None | |
| possible_synsets = list(filter(lambda s: s.offset() in IMAGENET, original_synsets)) | |
| if possible_synsets: | |
| classes.append(IMAGENET[possible_synsets[0].offset()]) | |
| else: | |
| # try hypernyms and hyponyms | |
| possible_synsets = sum([s.hypernyms() + s.hyponyms() for s in original_synsets], []) | |
| possible_synsets = list(filter(lambda s: s.offset() in IMAGENET, possible_synsets)) | |
| if possible_synsets: | |
| classes.append(IMAGENET[possible_synsets[0].offset()]) | |
| return one_hot_from_int(classes, batch_size=batch_size) | |
| IMAGENET = {1440764: 0, 1443537: 1, 1484850: 2, 1491361: 3, 1494475: 4, 1496331: 5, 1498041: 6, 1514668: 7, 1514859: 8, 1518878: 9, 1530575: 10, 1531178: 11, 1532829: 12, 1534433: 13, 1537544: 14, 1558993: 15, 1560419: 16, 1580077: 17, 1582220: 18, 1592084: 19, 1601694: 20, 1608432: 21, 1614925: 22, 1616318: 23, 1622779: 24, 1629819: 25, 1630670: 26, 1631663: 27, 1632458: 28, 1632777: 29, 1641577: 30, 1644373: 31, 1644900: 32, 1664065: 33, 1665541: 34, 1667114: 35, 1667778: 36, 1669191: 37, 1675722: 38, 1677366: 39, 1682714: 40, 1685808: 41, 1687978: 42, 1688243: 43, 1689811: 44, 1692333: 45, 1693334: 46, 1694178: 47, 1695060: 48, 1697457: 49, 1698640: 50, 1704323: 51, 1728572: 52, 1728920: 53, 1729322: 54, 1729977: 55, 1734418: 56, 1735189: 57, 1737021: 58, 1739381: 59, 1740131: 60, 1742172: 61, 1744401: 62, 1748264: 63, 1749939: 64, 1751748: 65, 1753488: 66, 1755581: 67, 1756291: 68, 1768244: 69, 1770081: 70, 1770393: 71, 1773157: 72, 1773549: 73, 1773797: 74, 1774384: 75, 1774750: 76, 1775062: 77, 1776313: 78, 1784675: 79, 1795545: 80, 1796340: 81, 1797886: 82, 1798484: 83, 1806143: 84, 1806567: 85, 1807496: 86, 1817953: 87, 1818515: 88, 1819313: 89, 1820546: 90, 1824575: 91, 1828970: 92, 1829413: 93, 1833805: 94, 1843065: 95, 1843383: 96, 1847000: 97, 1855032: 98, 1855672: 99, 1860187: 100, 1871265: 101, 1872401: 102, 1873310: 103, 1877812: 104, 1882714: 105, 1883070: 106, 1910747: 107, 1914609: 108, 1917289: 109, 1924916: 110, 1930112: 111, 1943899: 112, 1944390: 113, 1945685: 114, 1950731: 115, 1955084: 116, 1968897: 117, 1978287: 118, 1978455: 119, 1980166: 120, 1981276: 121, 1983481: 122, 1984695: 123, 1985128: 124, 1986214: 125, 1990800: 126, 2002556: 127, 2002724: 128, 2006656: 129, 2007558: 130, 2009229: 131, 2009912: 132, 2011460: 133, 2012849: 134, 2013706: 135, 2017213: 136, 2018207: 137, 2018795: 138, 2025239: 139, 2027492: 140, 2028035: 141, 2033041: 142, 2037110: 143, 2051845: 144, 2056570: 145, 2058221: 146, 2066245: 147, 2071294: 148, 2074367: 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