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import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
def VisualizeNumpyImageGrayscale(image_3d):
r"""Returns a 3D tensor as a grayscale normalized between 0 and 1 2D tensor.
"""
vmin = np.min(image_3d)
image_2d = image_3d - vmin
vmax = np.max(image_2d)
return (image_2d / vmax)
def normalize_tensor(image_3d):
r"""Returns a 3D tensor as a grayscale normalized between 0 and 1 2D tensor.
"""
vmin = torch.min(image_3d)
image_2d = image_3d - vmin
vmax = torch.max(image_2d)
return (image_2d / vmax)
def format_img(img_):
img_ = img_ # unnormalize
np_img = img_.numpy()
tp_img = np.transpose(np_img, (1, 2, 0))
return tp_img
def imshow(img, save_path=None):
img = img # unnormalize
try:
npimg = img.cpu().detach().numpy()
except:
npimg = img
tpimg = np.transpose(npimg, (1, 2, 0))
plt.imshow(tpimg)
if save_path != None:
plt.savefig(str(str(save_path) + ".png"))
#plt.show()
def imshow_img(img, imsave_path):
# works for tensors and numpy arrays
try:
npimg = VisualizeNumpyImageGrayscale(img.numpy())
except:
npimg = VisualizeNumpyImageGrayscale(img)
npimg = np.transpose(npimg, (2, 0, 1))
imshow(npimg, save_path=imsave_path)
print("Saving image as ", imsave_path)
def returnGrad(img, labels, model, compute_loss, loss_metric, augment=None, device = 'cpu'):
model.train()
model.to(device)
img = img.to(device)
img.requires_grad_(True)
labels.to(device).requires_grad_(True)
model.requires_grad_(True)
cuda = device.type != 'cpu'
scaler = amp.GradScaler(enabled=cuda)
pred = model(img)
# out, train_out = model(img, augment=augment) # inference and training outputs
loss, loss_items = compute_loss(pred, labels, metric=loss_metric)#[1][:3] # box, obj, cls
# loss = criterion(pred, torch.tensor([int(torch.max(pred[0], 0)[1])]).to(device))
# loss = torch.sum(loss).requires_grad_(True)
with torch.autograd.set_detect_anomaly(True):
scaler.scale(loss).backward(inputs=img)
# loss.backward()
# S_c = torch.max(pred[0].data, 0)[0]
Sc_dx = img.grad
model.eval()
Sc_dx = torch.tensor(Sc_dx, dtype=torch.float32)
return Sc_dx
def calculate_snr(img, attr, dB=True):
try:
img_np = img.detach().cpu().numpy()
attr_np = attr.detach().cpu().numpy()
except:
img_np = img
attr_np = attr
# Calculate the signal power
signal_power = np.mean(img_np**2)
# Calculate the noise power
noise_power = np.mean(attr_np**2)
if dB == True:
# Calculate SNR in dB
snr = 10 * np.log10(signal_power / noise_power)
else:
# Calculate SNR
snr = signal_power / noise_power
return snr |