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,
- pruning_criterion = None):
+
+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):
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))
+ s = list(filter(pruning_criterion, s))
# pick at most max_nb_properties at random
nb_properties = torch.randint(max_nb_properties, (1,)) + 1
- s = ' <sep> '.join([ s[k] for k in torch.randperm(len(s))[:nb_properties] ] )
- s += ' <img> ' + ' '.join([ f'{color_names[n]}' for n in img ])
+ s = " <sep> ".join([s[k] for k in torch.randperm(len(s))[:nb_properties]])
+ s += " <img> " + " ".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('<img>', 1)
- d = d[-1] if len(d) > 1 else ''
- d = d.strip().split(' ')[:height * width]
- d = d + [ '<unk>' ] * (height * width - len(d))
- d = [ token2color(t) for t in d ]
+ d = descr.split("<img>", 1)
+ d = d[-1] if len(d) > 1 else ""
+ d = d.strip().split(" ")[: height * width]
+ d = d + ["<unk>"] * (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('<img>', 1)
- d = d[-1] if len(d) > 1 else ''
- d = d.strip().split(' ')[:height * width]
+ d = descr.split("<img>", 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)
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('<img>', 1)
- if len(d) == 0: return 0
- d = d[0].strip().split('<sep>')
- d = [ x.strip() for x in d ]
+ d = descr.split("<img>", 1)
+ if len(d) == 0:
+ return 0
+ d = d[0].strip().split("<sep>")
+ d = [x.strip() for x in d]
requested_properties = set(d)
all_properties = set(descr2properties(descr, height, width))
return (len(requested_properties), len(all_properties), len(missing_properties))
+
######################################################################
-if __name__ == '__main__':
+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))
+ 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, height = 12, width = 16))
- print(nb_properties(descr, height = 12, width = 16))
+ 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, height = 12, width = 16)
- 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, height = 12, width = 16)
+ 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")
######################################################################
projects / mygpt.git / blobdiff