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utilities.py
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utilities.py
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import numpy as np
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.utils as utils
def visualize_attn_softmax(I, c, up_factor, nrow):
# image
img = I.permute((1,2,0)).cpu().numpy()
# compute the heatmap
N,C,W,H = c.size()
a = F.softmax(c.view(N,C,-1), dim=2).view(N,C,W,H)
if up_factor > 1:
a = F.interpolate(a, scale_factor=up_factor, mode='bilinear', align_corners=False)
attn = utils.make_grid(a, nrow=nrow, normalize=True, scale_each=True)
attn = attn.permute((1,2,0)).mul(255).byte().cpu().numpy()
attn = cv2.applyColorMap(attn, cv2.COLORMAP_JET)
attn = cv2.cvtColor(attn, cv2.COLOR_BGR2RGB)
attn = np.float32(attn) / 255
# add the heatmap to the image
vis = 0.6 * img + 0.4 * attn
return torch.from_numpy(vis).permute(2,0,1)
def visualize_attn_sigmoid(I, c, up_factor, nrow):
# image
img = I.permute((1,2,0)).cpu().numpy()
# compute the heatmap
a = torch.sigmoid(c)
if up_factor > 1:
a = F.interpolate(a, scale_factor=up_factor, mode='bilinear', align_corners=False)
attn = utils.make_grid(a, nrow=nrow, normalize=False)
attn = attn.permute((1,2,0)).mul(255).byte().cpu().numpy()
attn = cv2.applyColorMap(attn, cv2.COLORMAP_JET)
attn = cv2.cvtColor(attn, cv2.COLOR_BGR2RGB)
attn = np.float32(attn) / 255
# add the heatmap to the image
vis = 0.6 * img + 0.4 * attn
return torch.from_numpy(vis).permute(2,0,1)