X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=picoclvr.py;h=fb791fefd76b8fcec8613a71415fd762add3990f;hb=HEAD;hp=3ecbf3aa40d3e055c8b329e94512565c3765a4cb;hpb=82ddf9ca322e6fcc8f9364a696c26d15841d13d8;p=mygpt.git diff --git a/picoclvr.py b/picoclvr.py index 3ecbf3a..fb791fe 100755 --- a/picoclvr.py +++ b/picoclvr.py @@ -7,99 +7,194 @@ import torch, torchvision -colors = [ - [ 255, 255, 255 ], [ 255, 0, 0 ], [ 0, 128, 0 ], [ 0, 0, 255 ], [ 255, 255, 0 ], - [ 0, 0, 0 ], [ 128, 0, 0 ], [ 139, 0, 0 ], [ 165, 42, 42 ], [ 178, 34, 34 ], - [ 220, 20, 60 ], [ 255, 99, 71 ], [ 255, 127, 80 ], [ 205, 92, 92 ], [ 240, 128, 128 ], - [ 233, 150, 122 ], [ 250, 128, 114 ], [ 255, 160, 122 ], [ 255, 69, 0 ], [ 255, 140, 0 ], - [ 255, 165, 0 ], [ 255, 215, 0 ], [ 184, 134, 11 ], [ 218, 165, 32 ], [ 238, 232, 170 ], - [ 189, 183, 107 ], [ 240, 230, 140 ], [ 128, 128, 0 ], [ 154, 205, 50 ], [ 85, 107, 47 ], - [ 107, 142, 35 ], [ 124, 252, 0 ], [ 127, 255, 0 ], [ 173, 255, 47 ], [ 0, 100, 0 ], - [ 34, 139, 34 ], [ 0, 255, 0 ], [ 50, 205, 50 ], [ 144, 238, 144 ], [ 152, 251, 152 ], - [ 143, 188, 143 ], [ 0, 250, 154 ], [ 0, 255, 127 ], [ 46, 139, 87 ], [ 102, 205, 170 ], - [ 60, 179, 113 ], [ 32, 178, 170 ], [ 47, 79, 79 ], [ 0, 128, 128 ], [ 0, 139, 139 ], - [ 0, 255, 255 ], [ 0, 255, 255 ], [ 224, 255, 255 ], [ 0, 206, 209 ], [ 64, 224, 208 ], - [ 72, 209, 204 ], [ 175, 238, 238 ], [ 127, 255, 212 ], [ 176, 224, 230 ], [ 95, 158, 160 ], - [ 70, 130, 180 ], [ 100, 149, 237 ], [ 0, 191, 255 ], [ 30, 144, 255 ], [ 173, 216, 230 ], - [ 135, 206, 235 ], [ 135, 206, 250 ], [ 25, 25, 112 ], [ 0, 0, 128 ], [ 0, 0, 139 ], - [ 0, 0, 205 ], [ 65, 105, 225 ], [ 138, 43, 226 ], [ 75, 0, 130 ], [ 72, 61, 139 ], - [ 106, 90, 205 ], [ 123, 104, 238 ], [ 147, 112, 219 ], [ 139, 0, 139 ], [ 148, 0, 211 ], - [ 153, 50, 204 ], [ 186, 85, 211 ], [ 128, 0, 128 ], [ 216, 191, 216 ], [ 221, 160, 221 ], - [ 238, 130, 238 ], [ 255, 0, 255 ], [ 218, 112, 214 ], [ 199, 21, 133 ], [ 219, 112, 147 ], - [ 255, 20, 147 ], [ 255, 105, 180 ], [ 255, 182, 193 ], [ 255, 192, 203 ], [ 250, 235, 215 ], - [ 245, 245, 220 ], [ 255, 228, 196 ], [ 255, 235, 205 ], [ 245, 222, 179 ], [ 255, 248, 220 ], - [ 255, 250, 205 ], [ 250, 250, 210 ], [ 255, 255, 224 ], [ 139, 69, 19 ], [ 160, 82, 45 ], - [ 210, 105, 30 ], [ 205, 133, 63 ], [ 244, 164, 96 ], [ 222, 184, 135 ], [ 210, 180, 140 ], - [ 188, 143, 143 ], [ 255, 228, 181 ], [ 255, 222, 173 ], [ 255, 218, 185 ], [ 255, 228, 225 ], - [ 255, 240, 245 ], [ 250, 240, 230 ], [ 253, 245, 230 ], [ 255, 239, 213 ], [ 255, 245, 238 ], - [ 245, 255, 250 ], [ 112, 128, 144 ], [ 119, 136, 153 ], [ 176, 196, 222 ], [ 230, 230, 250 ], - [ 255, 250, 240 ], [ 240, 248, 255 ], [ 248, 248, 255 ], [ 240, 255, 240 ], [ 255, 255, 240 ], - [ 240, 255, 255 ], [ 255, 250, 250 ], [ 192, 192, 192 ], [ 220, 220, 220 ], [ 245, 245, 245 ], -] - -color_names = [ - 'white', 'red', 'green', 'blue', 'yellow', - 'black', 'maroon', 'dark_red', 'brown', 'firebrick', - 'crimson', 'tomato', 'coral', 'indian_red', 'light_coral', - 'dark_salmon', 'salmon', 'light_salmon', 'orange_red', 'dark_orange', - 'orange', 'gold', 'dark_golden_rod', 'golden_rod', 'pale_golden_rod', - 'dark_khaki', 'khaki', 'olive', 'yellow_green', 'dark_olive_green', - 'olive_drab', 'lawn_green', 'chartreuse', 'green_yellow', 'dark_green', - 'forest_green', 'lime', 'lime_green', 'light_green', 'pale_green', - 'dark_sea_green', 'medium_spring_green', 'spring_green', 'sea_green', 'medium_aqua_marine', - 'medium_sea_green', 'light_sea_green', 'dark_slate_gray', 'teal', 'dark_cyan', - 'aqua', 'cyan', 'light_cyan', 'dark_turquoise', 'turquoise', - 'medium_turquoise', 'pale_turquoise', 'aqua_marine', 'powder_blue', 'cadet_blue', - 'steel_blue', 'corn_flower_blue', 'deep_sky_blue', 'dodger_blue', 'light_blue', - 'sky_blue', 'light_sky_blue', 'midnight_blue', 'navy', 'dark_blue', - 'medium_blue', 'royal_blue', 'blue_violet', 'indigo', 'dark_slate_blue', - 'slate_blue', 'medium_slate_blue', 'medium_purple', 'dark_magenta', 'dark_violet', - 'dark_orchid', 'medium_orchid', 'purple', 'thistle', 'plum', - 'violet', 'magenta', 'orchid', 'medium_violet_red', 'pale_violet_red', - 'deep_pink', 'hot_pink', 'light_pink', 'pink', 'antique_white', - 'beige', 'bisque', 'blanched_almond', 'wheat', 'corn_silk', - 'lemon_chiffon', 'light_golden_rod_yellow', 'light_yellow', 'saddle_brown', 'sienna', - 'chocolate', 'peru', 'sandy_brown', 'burly_wood', 'tan', - 'rosy_brown', 'moccasin', 'navajo_white', 'peach_puff', 'misty_rose', - 'lavender_blush', 'linen', 'old_lace', 'papaya_whip', 'sea_shell', - 'mint_cream', 'slate_gray', 'light_slate_gray', 'light_steel_blue', 'lavender', - 'floral_white', 'alice_blue', 'ghost_white', 'honeydew', 'ivory', - 'azure', 'snow', 'silver', 'gainsboro', 'white_smoke', -] - -color_id = dict( [ (n, k) for k, n in enumerate(color_names) ] ) -color_tokens = dict( [ (n, c) for n, c in zip(color_names, colors) ] ) +color_tokens = { + "white": [255, 255, 255], + "red": [255, 0, 0], + "green": [0, 128, 0], + "blue": [0, 0, 255], + "yellow": [255, 255, 0], + "black": [0, 0, 0], + "maroon": [128, 0, 0], + "dark_red": [139, 0, 0], + "brown": [165, 42, 42], + "firebrick": [178, 34, 34], + "crimson": [220, 20, 60], + "tomato": [255, 99, 71], + "coral": [255, 127, 80], + "indian_red": [205, 92, 92], + "light_coral": [240, 128, 128], + "dark_salmon": [233, 150, 122], + "salmon": [250, 128, 114], + "light_salmon": [255, 160, 122], + "orange_red": [255, 69, 0], + "dark_orange": [255, 140, 0], + "orange": [255, 165, 0], + "gold": [255, 215, 0], + "dark_golden_rod": [184, 134, 11], + "golden_rod": [218, 165, 32], + "pale_golden_rod": [238, 232, 170], + "dark_khaki": [189, 183, 107], + "khaki": [240, 230, 140], + "olive": [128, 128, 0], + "yellow_green": [154, 205, 50], + "dark_olive_green": [85, 107, 47], + "olive_drab": [107, 142, 35], + "lawn_green": [124, 252, 0], + "chartreuse": [127, 255, 0], + "green_yellow": [173, 255, 47], + "dark_green": [0, 100, 0], + "forest_green": [34, 139, 34], + "lime": [0, 255, 0], + "lime_green": [50, 205, 50], + "light_green": [144, 238, 144], + "pale_green": [152, 251, 152], + "dark_sea_green": [143, 188, 143], + "medium_spring_green": [0, 250, 154], + "spring_green": [0, 255, 127], + "sea_green": [46, 139, 87], + "medium_aqua_marine": [102, 205, 170], + "medium_sea_green": [60, 179, 113], + "light_sea_green": [32, 178, 170], + "dark_slate_gray": [47, 79, 79], + "teal": [0, 128, 128], + "dark_cyan": [0, 139, 139], + "aqua": [0, 255, 255], + "cyan": [0, 255, 255], + "light_cyan": [224, 255, 255], + "dark_turquoise": [0, 206, 209], + "turquoise": [64, 224, 208], + "medium_turquoise": [72, 209, 204], + "pale_turquoise": [175, 238, 238], + "aqua_marine": [127, 255, 212], + "powder_blue": [176, 224, 230], + "cadet_blue": [95, 158, 160], + "steel_blue": [70, 130, 180], + "corn_flower_blue": [100, 149, 237], + "deep_sky_blue": [0, 191, 255], + "dodger_blue": [30, 144, 255], + "light_blue": [173, 216, 230], + "sky_blue": [135, 206, 235], + "light_sky_blue": [135, 206, 250], + "midnight_blue": [25, 25, 112], + "navy": [0, 0, 128], + "dark_blue": [0, 0, 139], + "medium_blue": [0, 0, 205], + "royal_blue": [65, 105, 225], + "blue_violet": [138, 43, 226], + "indigo": [75, 0, 130], + "dark_slate_blue": [72, 61, 139], + "slate_blue": [106, 90, 205], + "medium_slate_blue": [123, 104, 238], + "medium_purple": [147, 112, 219], + "dark_magenta": [139, 0, 139], + "dark_violet": [148, 0, 211], + "dark_orchid": [153, 50, 204], + "medium_orchid": [186, 85, 211], + "purple": [128, 0, 128], + "thistle": [216, 191, 216], + "plum": [221, 160, 221], + "violet": [238, 130, 238], + "magenta": [255, 0, 255], + "orchid": [218, 112, 214], + "medium_violet_red": [199, 21, 133], + "pale_violet_red": [219, 112, 147], + "deep_pink": [255, 20, 147], + "hot_pink": [255, 105, 180], + "light_pink": [255, 182, 193], + "pink": [255, 192, 203], + "antique_white": [250, 235, 215], + "beige": [245, 245, 220], + "bisque": [255, 228, 196], + "blanched_almond": [255, 235, 205], + "wheat": [245, 222, 179], + "corn_silk": [255, 248, 220], + "lemon_chiffon": [255, 250, 205], + "light_golden_rod_yellow": [250, 250, 210], + "light_yellow": [255, 255, 224], + "saddle_brown": [139, 69, 19], + "sienna": [160, 82, 45], + "chocolate": [210, 105, 30], + "peru": [205, 133, 63], + "sandy_brown": [244, 164, 96], + "burly_wood": [222, 184, 135], + "tan": [210, 180, 140], + "rosy_brown": [188, 143, 143], + "moccasin": [255, 228, 181], + "navajo_white": [255, 222, 173], + "peach_puff": [255, 218, 185], + "misty_rose": [255, 228, 225], + "lavender_blush": [255, 240, 245], + "linen": [250, 240, 230], + "old_lace": [253, 245, 230], + "papaya_whip": [255, 239, 213], + "sea_shell": [255, 245, 238], + "mint_cream": [245, 255, 250], + "slate_gray": [112, 128, 144], + "light_slate_gray": [119, 136, 153], + "light_steel_blue": [176, 196, 222], + "lavender": [230, 230, 250], + "floral_white": [255, 250, 240], + "alice_blue": [240, 248, 255], + "ghost_white": [248, 248, 255], + "honeydew": [240, 255, 240], + "ivory": [255, 255, 240], + "azure": [240, 255, 255], + "snow": [255, 250, 250], + "silver": [192, 192, 192], + "gainsboro": [220, 220, 220], + "white_smoke": [245, 245, 245], +} + +color_id = dict([(n, k) for k, n in enumerate(color_tokens.keys())]) +color_names = dict([(k, n) for k, n in enumerate(color_tokens.keys())]) ###################################################################### -def all_properties(height, width, nb_squares, square_i, square_j, square_c): - s = [ ] - - for r, c in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]: - s += [ f'there is {c}' ] - if square_i[r] >= height - height//3: s += [ f'{c} bottom' ] - if square_i[r] < height//3: s += [ f'{c} top' ] - if square_j[r] >= width - width//3: s += [ f'{c} right' ] - if square_j[r] < width//3: s += [ f'{c} left' ] - - for t, d in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]: - if square_i[r] > square_i[t]: s += [ f'{c} below {d}' ] - if square_i[r] < square_i[t]: s += [ f'{c} above {d}' ] - if square_j[r] > square_j[t]: s += [ f'{c} right of {d}' ] - if square_j[r] < square_j[t]: s += [ f'{c} left of {d}' ] +def all_properties(height, width, nb_squares, square_i, square_j, square_c): + s = [] + + for r, c in [(k, color_names[square_c[k].item()]) for k in range(nb_squares)]: + s += [f"there is {c}"] + + if square_i[r] >= height - height // 3: + s += [f"{c} bottom"] + if square_i[r] < height // 3: + s += [f"{c} top"] + if square_j[r] >= width - width // 3: + s += [f"{c} right"] + if square_j[r] < width // 3: + s += [f"{c} left"] + + for t, d in [(k, color_names[square_c[k].item()]) for k in range(nb_squares)]: + if square_i[r] > square_i[t]: + s += [f"{c} below {d}"] + if square_i[r] < square_i[t]: + s += [f"{c} above {d}"] + if square_j[r] > square_j[t]: + s += [f"{c} right of {d}"] + if square_j[r] < square_j[t]: + s += [f"{c} left of {d}"] return s + ###################################################################### -def generate(nb, height, width, - max_nb_squares = 5, max_nb_properties = 10, - nb_colors = 5): + +def generate( + nb, + height, + width, + max_nb_squares=5, + max_nb_properties=10, + nb_colors=5, + pruning_criterion=None, +): assert nb_colors >= max_nb_squares and nb_colors <= len(color_tokens) - 1 - descr = [ ] + descr = [] for n in range(nb): @@ -107,67 +202,77 @@ def generate(nb, height, width, square_position = torch.randperm(height * width)[:nb_squares] # color 0 is white and reserved for the background square_c = torch.randperm(nb_colors)[:nb_squares] + 1 - square_i = square_position.div(width, rounding_mode = 'floor') + square_i = square_position.div(width, rounding_mode="floor") square_j = square_position % width - img = [ 0 ] * height * width - for k in range(nb_squares): img[square_position[k]] = square_c[k] + img = torch.zeros(height * width, dtype=torch.int64) + for k in range(nb_squares): + img[square_position[k]] = square_c[k] # generates all the true properties s = all_properties(height, width, nb_squares, square_i, square_j, square_c) + if pruning_criterion is not None: + s = list(filter(pruning_criterion, s)) + # pick at most max_nb_properties at random nb_properties = torch.randint(max_nb_properties, (1,)) + 1 - s = ' '.join([ s[k] for k in torch.randperm(len(s))[:nb_properties] ] ) - s += ' ' + ' '.join([ f'{color_names[n]}' for n in img ]) + s = " ".join([s[k] for k in torch.randperm(len(s))[:nb_properties]]) + s += " " + " ".join([f"{color_names[n.item()]}" for n in img]) - descr += [ s ] + descr += [s] return descr + ###################################################################### + def descr2img(descr, height, width): if type(descr) == list: - return torch.cat([ descr2img(d, height, width) for d in descr ], 0) + return torch.cat([descr2img(d, height, width) for d in descr], 0) def token2color(t): try: return color_tokens[t] except KeyError: - return [ 128, 128, 128 ] + return [128, 128, 128] - d = descr.split('', 1) - d = d[-1] if len(d) > 1 else '' - d = d.strip().split(' ')[:height * width] - d = d + [ '' ] * (height * width - len(d)) - d = [ token2color(t) for t in d ] + d = descr.split("", 1) + d = d[-1] if len(d) > 1 else "" + d = d.strip().split(" ")[: height * width] + d = d + [""] * (height * width - len(d)) + d = [token2color(t) for t in d] img = torch.tensor(d).permute(1, 0) img = img.reshape(1, 3, height, width) return img + ###################################################################### + def descr2properties(descr, height, width): if type(descr) == list: - return [ descr2properties(d, height, width) for d in descr ] + return [descr2properties(d, height, width) for d in descr] - d = descr.split('', 1) - d = d[-1] if len(d) > 1 else '' - d = d.strip().split(' ')[:height * width] + d = descr.split("", 1) + d = d[-1] if len(d) > 1 else "" + d = d.strip().split(" ")[: height * width] seen = {} - if len(d) != height * width: return [] + if len(d) != height * width: + return [] for k, x in enumerate(d): if x != color_names[0]: if x in color_tokens: - if x in seen: return [] + if x in seen: + return [] else: return [] seen[x] = (color_id[x], k // width, k % width) @@ -186,16 +291,19 @@ def descr2properties(descr, height, width): return s + ###################################################################### + def nb_properties(descr, height, width): if type(descr) == list: - return [ nb_properties(d, height, width) for d in descr ] + return [nb_properties(d, height, width) for d in descr] - d = descr.split('', 1) - if len(d) == 0: return 0 - d = d[0].strip().split('') - d = [ x.strip() for x in d ] + d = descr.split("", 1) + if len(d) == 0: + return 0 + d = d[0].strip().split("") + d = [x.strip() for x in d] requested_properties = set(d) all_properties = set(descr2properties(descr, height, width)) @@ -203,27 +311,36 @@ def nb_properties(descr, height, width): return (len(requested_properties), len(all_properties), len(missing_properties)) + ###################################################################### -if __name__ == '__main__': - descr = generate(nb = 5) +if __name__ == "__main__": + descr = generate( + nb=5, + height=12, + width=16, + pruning_criterion=lambda s: not ( + "green" in s and ("right" in s or "left" in s) + ), + ) - #print(descr2properties(descr)) - print(nb_properties(descr)) + print(descr2properties(descr, height=12, width=16)) + print(nb_properties(descr, height=12, width=16)) - with open('picoclvr_example.txt', 'w') as f: + with open("picoclvr_example.txt", "w") as f: for d in descr: - f.write(f'{d}\n\n') + f.write(f"{d}\n\n") - img = descr2img(descr) - torchvision.utils.save_image(img / 255., - 'picoclvr_example.png', nrow = 16, pad_value = 0.8) + img = descr2img(descr, height=12, width=16) + torchvision.utils.save_image( + img / 255.0, "picoclvr_example.png", nrow=16, pad_value=0.8 + ) import time start_time = time.perf_counter() - descr = generate(nb = 1000) + descr = generate(nb=1000, height=12, width=16) end_time = time.perf_counter() - print(f'{len(descr) / (end_time - start_time):.02f} samples per second') + print(f"{len(descr) / (end_time - start_time):.02f} samples per second") ######################################################################