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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: 149, 2077923: 150, 2085620: 151, 2085782: 152, 2085936: 153, 2086079: 154, 2086240: 155, 2086646: 156, 2086910: 157, 2087046: 158, 2087394: 159, 2088094: 160, 2088238: 161, 2088364: 162, 2088466: 163, 2088632: 164, 2089078: 165, 2089867: 166, 2089973: 167, 2090379: 168, 2090622: 169, 2090721: 170, 2091032: 171, 2091134: 172, 2091244: 173, 2091467: 174, 2091635: 175, 2091831: 176, 2092002: 177, 2092339: 178, 2093256: 179, 2093428: 180, 2093647: 181, 2093754: 182, 2093859: 183, 2093991: 184, 2094114: 185, 2094258: 186, 2094433: 187, 2095314: 188, 2095570: 189, 2095889: 190, 2096051: 191, 2096177: 192, 2096294: 193, 2096437: 194, 2096585: 195, 2097047: 196, 2097130: 197, 2097209: 198, 2097298: 199, 2097474: 200, 2097658: 201, 2098105: 202, 2098286: 203, 2098413: 204, 2099267: 205, 2099429: 206, 2099601: 207, 2099712: 208, 2099849: 209, 2100236: 210, 2100583: 211, 2100735: 212, 2100877: 213, 2101006: 214, 2101388: 215, 2101556: 216, 2102040: 217, 2102177: 218, 2102318: 219, 2102480: 220, 2102973: 221, 2104029: 222, 2104365: 223, 2105056: 224, 2105162: 225, 2105251: 226, 2105412: 227, 2105505: 228, 2105641: 229, 2105855: 230, 2106030: 231, 2106166: 232, 2106382: 233, 2106550: 234, 2106662: 235, 2107142: 236, 2107312: 237, 2107574: 238, 2107683: 239, 2107908: 240, 2108000: 241, 2108089: 242, 2108422: 243, 2108551: 244, 2108915: 245, 2109047: 246, 2109525: 247, 2109961: 248, 2110063: 249, 2110185: 250, 2110341: 251, 2110627: 252, 2110806: 253, 2110958: 254, 2111129: 255, 2111277: 256, 2111500: 257, 2111889: 258, 2112018: 259, 2112137: 260, 2112350: 261, 2112706: 262, 2113023: 263, 2113186: 264, 2113624: 265, 2113712: 266, 2113799: 267, 2113978: 268, 2114367: 269, 2114548: 270, 2114712: 271, 2114855: 272, 2115641: 273, 2115913: 274, 2116738: 275, 2117135: 276, 2119022: 277, 2119789: 278, 2120079: 279, 2120505: 280, 2123045: 281, 2123159: 282, 2123394: 283, 2123597: 284, 2124075: 285, 2125311: 286, 2127052: 287, 2128385: 288, 2128757: 289, 2128925: 290, 2129165: 291, 2129604: 292, 2130308: 293, 2132136: 294, 2133161: 295, 2134084: 296, 2134418: 297, 2137549: 298, 2138441: 299, 2165105: 300, 2165456: 301, 2167151: 302, 2168699: 303, 2169497: 304, 2172182: 305, 2174001: 306, 2177972: 307, 2190166: 308, 2206856: 309, 2219486: 310, 2226429: 311, 2229544: 312, 2231487: 313, 2233338: 314, 2236044: 315, 2256656: 316, 2259212: 317, 2264363: 318, 2268443: 319, 2268853: 320, 2276258: 321, 2277742: 322, 2279972: 323, 2280649: 324, 2281406: 325, 2281787: 326, 2317335: 327, 2319095: 328, 2321529: 329, 2325366: 330, 2326432: 331, 2328150: 332, 2342885: 333, 2346627: 334, 2356798: 335, 2361337: 336, 2363005: 337, 2364673: 338, 2389026: 339, 2391049: 340, 2395406: 341, 2396427: 342, 2397096: 343, 2398521: 344, 2403003: 345, 2408429: 346, 2410509: 347, 2412080: 348, 2415577: 349, 2417914: 350, 2422106: 351, 2422699: 352, 2423022: 353, 2437312: 354, 2437616: 355, 2441942: 356, 2442845: 357, 2443114: 358, 2443484: 359, 2444819: 360, 2445715: 361, 2447366: 362, 2454379: 363, 2457408: 364, 2480495: 365, 2480855: 366, 2481823: 367, 2483362: 368, 2483708: 369, 2484975: 370, 2486261: 371, 2486410: 372, 2487347: 373, 2488291: 374, 2488702: 375, 2489166: 376, 2490219: 377, 2492035: 378, 2492660: 379, 2493509: 380, 2493793: 381, 2494079: 382, 2497673: 383, 2500267: 384, 2504013: 385, 2504458: 386, 2509815: 387, 2510455: 388, 2514041: 389, 2526121: 390, 2536864: 391, 2606052: 392, 2607072: 393, 2640242: 394, 2641379: 395, 2643566: 396, 2655020: 397, 2666196: 398, 2667093: 399, 2669723: 400, 2672831: 401, 2676566: 402, 2687172: 403, 2690373: 404, 2692877: 405, 2699494: 406, 2701002: 407, 2704792: 408, 2708093: 409, 2727426: 410, 2730930: 411, 2747177: 412, 2749479: 413, 2769748: 414, 2776631: 415, 2777292: 416, 2782093: 417, 2783161: 418, 2786058: 419, 2787622: 420, 2788148: 421, 2790996: 422, 2791124: 423, 2791270: 424, 2793495: 425, 2794156: 426, 2795169: 427, 2797295: 428, 2799071: 429, 2802426: 430, 2804414: 431, 2804610: 432, 2807133: 433, 2808304: 434, 2808440: 435, 2814533: 436, 2814860: 437, 2815834: 438, 2817516: 439, 2823428: 440, 2823750: 441, 2825657: 442, 2834397: 443, 2835271: 444, 2837789: 445, 2840245: 446, 2841315: 447, 2843684: 448, 2859443: 449, 2860847: 450, 2865351: 451, 2869837: 452, 2870880: 453, 2871525: 454, 2877765: 455, 2879718: 456, 2883205: 457, 2892201: 458, 2892767: 459, 2894605: 460, 2895154: 461, 2906734: 462, 2909870: 463, 2910353: 464, 2916936: 465, 2917067: 466, 2927161: 467, 2930766: 468, 2939185: 469, 2948072: 470, 2950826: 471, 2951358: 472, 2951585: 473, 2963159: 474, 2965783: 475, 2966193: 476, 2966687: 477, 2971356: 478, 2974003: 479, 2977058: 480, 2978881: 481, 2979186: 482, 2980441: 483, 2981792: 484, 2988304: 485, 2992211: 486, 2992529: 487, 2999410: 488, 3000134: 489, 3000247: 490, 3000684: 491, 3014705: 492, 3016953: 493, 3017168: 494, 3018349: 495, 3026506: 496, 3028079: 497, 3032252: 498, 3041632: 499, 3042490: 500, 3045698: 501, 3047690: 502, 3062245: 503, 3063599: 504, 3063689: 505, 3065424: 506, 3075370: 507, 3085013: 508, 3089624: 509, 3095699: 510, 3100240: 511, 3109150: 512, 3110669: 513, 3124043: 514, 3124170: 515, 3125729: 516, 3126707: 517, 3127747: 518, 3127925: 519, 3131574: 520, 3133878: 521, 3134739: 522, 3141823: 523, 3146219: 524, 3160309: 525, 3179701: 526, 3180011: 527, 3187595: 528, 3188531: 529, 3196217: 530, 3197337: 531, 3201208: 532, 3207743: 533, 3207941: 534, 3208938: 535, 3216828: 536, 3218198: 537, 3220513: 538, 3223299: 539, 3240683: 540, 3249569: 541, 3250847: 542, 3255030: 543, 3259280: 544, 3271574: 545, 3272010: 546, 3272562: 547, 3290653: 548, 3291819: 549, 3297495: 550, 3314780: 551, 3325584: 552, 3337140: 553, 3344393: 554, 3345487: 555, 3347037: 556, 3355925: 557, 3372029: 558, 3376595: 559, 3379051: 560, 3384352: 561, 3388043: 562, 3388183: 563, 3388549: 564, 3393912: 565, 3394916: 566, 3400231: 567, 3404251: 568, 3417042: 569, 3424325: 570, 3425413: 571, 3443371: 572, 3444034: 573, 3445777: 574, 3445924: 575, 3447447: 576, 3447721: 577, 3450230: 578, 3452741: 579, 3457902: 580, 3459775: 581, 3461385: 582, 3467068: 583, 3476684: 584, 3476991: 585, 3478589: 586, 3481172: 587, 3482405: 588, 3483316: 589, 3485407: 590, 3485794: 591, 3492542: 592, 3494278: 593, 3495258: 594, 3496892: 595, 3498962: 596, 3527444: 597, 3529860: 598, 3530642: 599, 3532672: 600, 3534580: 601, 3535780: 602, 3538406: 603, 3544143: 604, 3584254: 605, 3584829: 606, 3590841: 607, 3594734: 608, 3594945: 609, 3595614: 610, 3598930: 611, 3599486: 612, 3602883: 613, 3617480: 614, 3623198: 615, 3627232: 616, 3630383: 617, 3633091: 618, 3637318: 619, 3642806: 620, 3649909: 621, 3657121: 622, 3658185: 623, 3661043: 624, 3662601: 625, 3666591: 626, 3670208: 627, 3673027: 628, 3676483: 629, 3680355: 630, 3690938: 631, 3691459: 632, 3692522: 633, 3697007: 634, 3706229: 635, 3709823: 636, 3710193: 637, 3710637: 638, 3710721: 639, 3717622: 640, 3720891: 641, 3721384: 642, 3724870: 643, 3729826: 644, 3733131: 645, 3733281: 646, 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