Spaces:
Runtime error
Runtime error
File size: 7,725 Bytes
4c022fe b5268ad 4c022fe ab7db7f 4c022fe ab7db7f 4c022fe ab7db7f 4c022fe ab7db7f f6e53b7 ab7db7f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
import numpy as np
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import torch
import torchvision
from pathlib import Path
def split_attention_maps_over_steps(attention_maps):
r"""Function for splitting attention maps over steps.
Args:
attention_maps (dict): Dictionary of attention maps.
sampler_order (int): Order of the sampler.
"""
# This function splits attention maps into unconditional and conditional score and over steps
attention_maps_cond = dict() # Maps corresponding to conditional score
attention_maps_uncond = dict() # Maps corresponding to unconditional score
for layer in attention_maps.keys():
for step_num in range(len(attention_maps[layer])):
if step_num not in attention_maps_cond:
attention_maps_cond[step_num] = dict()
attention_maps_uncond[step_num] = dict()
attention_maps_uncond[step_num].update(
{layer: attention_maps[layer][step_num][:1]})
attention_maps_cond[step_num].update(
{layer: attention_maps[layer][step_num][1:2]})
return attention_maps_cond, attention_maps_uncond
def plot_attention_maps(atten_map_list, obj_tokens, save_dir, seed, tokens_vis=None):
atten_names = ['presoftmax', 'postsoftmax', 'postsoftmax_erosion']
for i, (attn_map, obj_token) in enumerate(zip(atten_map_list, obj_tokens)):
n_obj = len(attn_map)
plt.figure()
plt.clf()
fig, axs = plt.subplots(
ncols=n_obj+1, gridspec_kw=dict(width_ratios=[1 for _ in range(n_obj)]+[0.1]))
fig.set_figheight(3)
fig.set_figwidth(3*n_obj+0.1)
cmap = plt.get_cmap('OrRd')
vmax = 0
vmin = 1
for tid in range(n_obj):
attention_map_cur = attn_map[tid]
vmax = max(vmax, float(attention_map_cur.max()))
vmin = min(vmin, float(attention_map_cur.min()))
for tid in range(n_obj):
sns.heatmap(
attn_map[tid][0], annot=False, cbar=False, ax=axs[tid],
cmap=cmap, vmin=vmin, vmax=vmax
)
axs[tid].set_axis_off()
if tokens_vis is not None:
if tid == n_obj-1:
axs_xlabel = 'other tokens'
else:
axs_xlabel = ''
for token_id in obj_tokens[tid]:
axs_xlabel += ' ' + tokens_vis[token_id.item() -
1][:-len('</w>')]
axs[tid].set_title(axs_xlabel)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
fig.colorbar(sm, cax=axs[-1])
canvas = fig.canvas
canvas.draw()
width, height = canvas.get_width_height()
img = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape((height, width, 3))
fig.tight_layout()
return img
def get_token_maps(attention_maps, save_dir, width, height, obj_tokens, seed=0, tokens_vis=None):
r"""Function to visualize attention maps.
Args:
save_dir (str): Path to save attention maps
batch_size (int): Batch size
sampler_order (int): Sampler order
"""
# Split attention maps over steps
attention_maps_cond, _ = split_attention_maps_over_steps(
attention_maps
)
selected_layers = [
# 'down_blocks.0.attentions.0.transformer_blocks.0.attn2',
# 'down_blocks.0.attentions.1.transformer_blocks.0.attn2',
'down_blocks.1.attentions.0.transformer_blocks.0.attn2',
# 'down_blocks.1.attentions.1.transformer_blocks.0.attn2',
'down_blocks.2.attentions.0.transformer_blocks.0.attn2',
'down_blocks.2.attentions.1.transformer_blocks.0.attn2',
'mid_block.attentions.0.transformer_blocks.0.attn2',
'up_blocks.1.attentions.0.transformer_blocks.0.attn2',
'up_blocks.1.attentions.1.transformer_blocks.0.attn2',
'up_blocks.1.attentions.2.transformer_blocks.0.attn2',
# 'up_blocks.2.attentions.0.transformer_blocks.0.attn2',
'up_blocks.2.attentions.1.transformer_blocks.0.attn2',
# 'up_blocks.2.attentions.2.transformer_blocks.0.attn2',
# 'up_blocks.3.attentions.0.transformer_blocks.0.attn2',
# 'up_blocks.3.attentions.1.transformer_blocks.0.attn2',
# 'up_blocks.3.attentions.2.transformer_blocks.0.attn2'
]
nsteps = len(attention_maps_cond)
hw_ori = width * height
attention_maps = []
for obj_token in obj_tokens:
attention_maps.append([])
for step_num in range(nsteps):
attention_maps_cur = attention_maps_cond[step_num]
for layer in attention_maps_cur.keys():
if step_num < 10 or layer not in selected_layers:
continue
attention_ind = attention_maps_cur[layer].cpu()
# Attention maps are of shape [batch_size, nkeys, 77]
# since they are averaged out while collecting from hooks to save memory.
# Now split the heads from batch dimension
bs, hw, nclip = attention_ind.shape
down_ratio = np.sqrt(hw_ori // hw)
width_cur = int(width // down_ratio)
height_cur = int(height // down_ratio)
attention_ind = attention_ind.reshape(
bs, height_cur, width_cur, nclip)
for obj_id, obj_token in enumerate(obj_tokens):
if obj_token[0] == -1:
attention_map_prev = torch.stack(
[attention_maps[i][-1] for i in range(obj_id)]).sum(0)
attention_maps[obj_id].append(
attention_map_prev.max()-attention_map_prev)
else:
obj_attention_map = attention_ind[:, :, :, obj_token].max(-1, True)[
0].permute([3, 0, 1, 2])
obj_attention_map = torchvision.transforms.functional.resize(obj_attention_map, (height, width),
interpolation=torchvision.transforms.InterpolationMode.BICUBIC, antialias=True)
attention_maps[obj_id].append(obj_attention_map)
# average attention maps over steps
attention_maps_averaged = []
for obj_id, obj_token in enumerate(obj_tokens):
if obj_id == len(obj_tokens) - 1:
attention_maps_averaged.append(
torch.cat(attention_maps[obj_id]).mean(0))
else:
attention_maps_averaged.append(
torch.cat(attention_maps[obj_id]).mean(0))
# normalize attention maps into [0, 1]
attention_maps_averaged_normalized = []
attention_maps_averaged_sum = torch.cat(attention_maps_averaged).sum(0)
for obj_id, obj_token in enumerate(obj_tokens):
attention_maps_averaged_normalized.append(
attention_maps_averaged[obj_id]/attention_maps_averaged_sum)
# softmax
attention_maps_averaged_normalized = (
torch.cat(attention_maps_averaged)/0.001).softmax(0)
attention_maps_averaged_normalized = [
attention_maps_averaged_normalized[i:i+1] for i in range(attention_maps_averaged_normalized.shape[0])]
token_maps_vis = plot_attention_maps([attention_maps_averaged, attention_maps_averaged_normalized],
obj_tokens, save_dir, seed, tokens_vis)
attention_maps_averaged_normalized = [attn_mask.unsqueeze(1).repeat(
[1, 4, 1, 1]).cuda() for attn_mask in attention_maps_averaged_normalized]
return attention_maps_averaged_normalized, token_maps_vis
|