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import matplotlib
import matplotlib.cm as cm
import matplotlib.colors as mcolors
import numpy as np
import torch
import torchvision
from PIL import Image, ImageDraw, ImageFont
from einops import rearrange
from matplotlib import pyplot as plt
def get_similarity(image_encodings, label_encodings, target_shape, interpolation="bilinear", do_argmax=False):
"""
Args:
image_encodings:
label_encodings:
target_shape:
interpolation: nearest, bilinear
do_argmax:
Returns:
"""
image_encodings = image_encodings.cpu()
label_encodings = label_encodings.cpu()
image_encodings = rearrange(
image_encodings, "b (h w) d -> d b h w", h=int(np.sqrt(image_encodings.shape[-2]))
)
# assuming square inputs & targets
scale_ratio = (target_shape[-2] / image_encodings.shape[-2],
target_shape[-1] / image_encodings.shape[-1],)
temp_list = []
for i in image_encodings:
i = i.unsqueeze(1)
i = torch.nn.functional.interpolate(
i, scale_factor=scale_ratio, mode=interpolation
)
temp_list.append(i)
image_encodings = torch.cat(temp_list, dim=1)
image_encodings = rearrange(image_encodings, "b d h w -> b h w d")
similarity = image_encodings @ label_encodings.T
similarity = rearrange(similarity, "b h w d-> b d h w")
if do_argmax:
similarity = torch.argmax(similarity, dim=1, keepdim=True).to(torch.float64)
return similarity
def get_cmap(ncolors):
if ncolors > 9:
cmap = plt.cm.tab20
else:
cmap = plt.cm.tab10
cmaplist = [cmap(i) for i in range(ncolors)]
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("custom", cmaplist, ncolors)
mappable = cm.ScalarMappable(cmap=cmap)
mappable.set_array([])
mappable.set_clim(-0.5, ncolors + 0.5)
return cmap, mappable
def vis_prediction(sample_text, img_arr, similarity):
N = len(sample_text)
cmap, mappable = get_cmap(N)
fig, axs = plt.subplots(1, 2)
_ = axs[0].imshow(img_arr)
_ = axs[1].imshow(img_arr)
_ = axs[1].imshow(similarity, cmap=cmap, interpolation="nearest", vmin=0, vmax=N, alpha=0.5)
axs[0].axis("off")
axs[1].axis("off")
fig.subplots_adjust(bottom=0.2)
cbar_ax = fig.add_axes([0.0, 0.85, 1.0, 0.05])
colorbar = plt.colorbar(mappable, cax=cbar_ax, cmap=cmap, orientation="horizontal")
colorbar.set_ticks(np.linspace(0, N, N))
colorbar.set_ticklabels(sample_text)
return fig
class DummyArgs:
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
def get_transform(size=(224, 224)):
transform = torchvision.transforms.Compose([
torchvision.transforms.Resize(size),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711))
])
return transform
def ade_palette():
"""ADE20K palette that maps each class to RGB values."""
return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
[4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
[230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
[150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
[143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
[0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
[255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
[255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
[255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
[224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
[255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
[6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
[140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
[255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
[255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
[11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
[0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
[255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
[0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
[173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
[255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
[255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
[255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
[0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
[0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
[143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
[8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
[255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
[92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
[163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
[255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
[255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
[10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
[255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
[41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
[71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
[184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
[102, 255, 0], [92, 0, 255]]
def get_cmap_image(legend):
# Define the size of the legend image
width = 200
height = len(legend) * 20
# Create a new image with the desired size and background color
img = Image.new('RGB', (width, height), (255, 255, 255))
# Create a drawing context
draw = ImageDraw.Draw(img)
# Define the font to use for the legend labels
font = ImageFont.truetype('arial.ttf', 16)
# Loop through the items in legend and draw a rectangle and label for each
y = 0
for label, color in legend.items():
draw.rectangle((0, y, 20, y + 20), fill=color)
draw.text((30, y), label, font=font, fill=(0, 0, 0))
y += 20
return img
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