@ptrblck
As per your suggestion, i checked the gradients of the parameters after the loss.backward() step. I get the gradients in the range of 10^-4 to 10^-7. You can find the gradients at the end of this post.
For overfitting the model, I trained the model with exactly 10 images and i dont get the perfect reconstructed output. I have attached the inputs and the corresponding outputs after overfitting(training the model with 10 images). The inputs are normalized and are shown in the 1st and 3rd row. The corresponding outputs are shown in the 2nd and the 4th row.




******************************************************************************** Example of gradients obtained*************************************************
Grad_before= None
Grad_before_shape= torch.Size([64, 192, 3, 3])
Grad_after= tensor([[[[ 1.5444e-05,  1.6913e-05,  1.5995e-05],
[ 1.6880e-05,  1.8491e-05,  1.7459e-05],
[ 1.8331e-05,  1.9917e-05,  1.8404e-05]],
     [[ 1.9927e-05,  1.7666e-05,  1.5221e-05],
      [ 1.9537e-05,  1.6954e-05,  1.4170e-05],
      [ 1.9170e-05,  1.6359e-05,  1.3342e-05]],

     [[ 2.6886e-05,  2.8304e-05,  3.0287e-05],
      [ 2.7075e-05,  2.8953e-05,  3.1588e-05],
      [ 2.6412e-05,  2.8855e-05,  3.2180e-05]],

     ...,

     [[ 3.6494e-05,  4.1480e-05,  5.3497e-05],
      [ 3.5902e-05,  3.6543e-05,  4.6513e-05],
      [ 4.2026e-05,  3.5562e-05,  4.6378e-05]],

     [[ 2.1511e-05,  5.2198e-05,  8.9409e-05],
      [ 2.1499e-05,  5.0138e-05,  8.8784e-05],
      [ 2.2240e-05,  4.8883e-05,  8.4382e-05]],

     [[ 8.5568e-07,  3.9607e-08,  9.4165e-07],
      [ 1.3130e-08, -2.6260e-07,  1.0790e-07],
      [ 6.6315e-07, -4.9992e-08,  1.2894e-07]]],


    [[[-2.7049e-05, -2.1350e-05, -1.4579e-05],
      [-2.6697e-05, -2.0302e-05, -1.4146e-05],
      [-2.4956e-05, -1.8778e-05, -1.3871e-05]],

     [[-2.1540e-05, -1.5810e-05, -1.6859e-05],
      [-1.8347e-05, -1.3086e-05, -1.5404e-05],
      [-1.6027e-05, -1.1518e-05, -1.5272e-05]],

     [[-4.0438e-05, -4.8218e-05, -5.6844e-05],
      [-4.0368e-05, -4.9173e-05, -5.8418e-05],
      [-3.7268e-05, -4.7455e-05, -5.8082e-05]],

     ...,

     [[-1.3145e-05, -8.9089e-05, -1.2546e-04],
      [-2.2529e-05, -8.7684e-05, -1.1259e-04],
      [-4.3510e-05, -1.1628e-04, -1.4264e-04]],

     [[-8.6003e-05, -1.5247e-04, -1.5601e-04],
      [-8.3001e-05, -1.3158e-04, -1.4844e-04],
      [-9.7446e-05, -1.2937e-04, -1.4405e-04]],

     [[ 2.2745e-06,  8.6252e-06,  1.6749e-06],
      [ 1.0760e-06,  9.5257e-06,  4.2126e-06],
      [-5.9326e-06,  5.3898e-06, -7.5285e-06]]],


    [[[ 5.7302e-05,  3.6736e-05, -1.1809e-05],
      [ 5.0031e-05,  2.4906e-05, -2.9053e-05],
      [ 5.9241e-05,  2.9926e-05, -2.5830e-05]],

     [[-4.2512e-04, -5.1677e-04, -4.7505e-04],
      [-4.5070e-04, -5.4368e-04, -4.9834e-04],
      [-4.5768e-04, -5.5035e-04, -4.9912e-04]],

     [[-4.5637e-04, -4.6583e-04, -3.4601e-04],
      [-4.6171e-04, -4.5984e-04, -3.3626e-04],
      [-4.8818e-04, -4.7194e-04, -3.3743e-04]],

     ...,

     [[-6.0629e-03, -5.9946e-03, -5.1200e-03],
      [-6.2693e-03, -6.2557e-03, -5.3146e-03],
      [-6.2365e-03, -6.2213e-03, -5.2145e-03]],

     [[-7.2563e-04, -2.4535e-04,  2.9517e-04],
      [-8.3638e-04, -3.7627e-04,  1.9238e-04],
      [-6.2643e-04, -2.4000e-04,  2.5850e-04]],

     [[-2.7501e-03, -2.7126e-03, -2.3582e-03],
      [-2.8471e-03, -2.8696e-03, -2.5841e-03],
      [-2.6671e-03, -2.7258e-03, -2.4540e-03]]],


    ...,


    [[[ 5.3254e-05,  6.5035e-05,  1.0622e-04],
      [ 4.4166e-05,  5.8326e-05,  1.0131e-04],
      [ 3.2802e-05,  4.8616e-05,  9.2495e-05]],

     [[ 3.4394e-03,  3.5002e-03,  3.4879e-03],
      [ 3.4689e-03,  3.5324e-03,  3.5205e-03],
      [ 3.4897e-03,  3.5536e-03,  3.5402e-03]],

     [[ 3.6269e-03,  3.5958e-03,  3.5091e-03],
      [ 3.6331e-03,  3.5956e-03,  3.5011e-03],
      [ 3.6592e-03,  3.6140e-03,  3.5089e-03]],

     ...,

     [[ 6.5372e-02,  6.5775e-02,  6.5144e-02],
      [ 6.5743e-02,  6.6213e-02,  6.5610e-02],
      [ 6.5736e-02,  6.6206e-02,  6.5699e-02]],

     [[ 6.1241e-03,  5.5962e-03,  5.1829e-03],
      [ 6.1115e-03,  5.5773e-03,  5.1191e-03],
      [ 6.0164e-03,  5.5238e-03,  5.0697e-03]],

     [[ 2.9045e-02,  2.9361e-02,  2.9242e-02],
      [ 2.9073e-02,  2.9443e-02,  2.9367e-02],
      [ 2.8875e-02,  2.9244e-02,  2.9191e-02]]],


    [[[-2.7105e-05, -3.4002e-05, -5.2283e-05],
      [-2.6984e-05, -3.5369e-05, -5.5386e-05],
      [-2.6098e-05, -3.6343e-05, -5.6643e-05]],

     [[-1.6817e-04, -1.7624e-04, -1.5094e-04],
      [-1.7449e-04, -1.8054e-04, -1.5067e-04],
      [-1.7860e-04, -1.8413e-04, -1.5044e-04]],

     [[-2.2523e-04, -2.2414e-04, -1.7151e-04],
      [-2.2442e-04, -2.2134e-04, -1.6786e-04],
      [-2.2316e-04, -2.1915e-04, -1.6463e-04]],

     ...,

     [[-1.4295e-03, -1.6756e-03, -1.4744e-03],
      [-1.4044e-03, -1.6606e-03, -1.5211e-03],
      [-1.3774e-03, -1.5886e-03, -1.4934e-03]],

     [[-2.7282e-04, -8.7181e-05, -8.0031e-06],
      [-2.9121e-04, -1.0438e-04, -2.2774e-05],
      [-2.7456e-04, -1.0704e-04, -2.9238e-05]],

     [[-6.0413e-04, -5.5349e-04, -3.9992e-04],
      [-5.8806e-04, -5.5365e-04, -4.3254e-04],
      [-5.6101e-04, -5.3138e-04, -4.4425e-04]]],


    [[[-1.1781e-04, -1.2889e-04, -1.5897e-04],
      [-1.1206e-04, -1.2501e-04, -1.5591e-04],
      [-1.0279e-04, -1.1642e-04, -1.4722e-04]],

     [[-4.2027e-03, -4.2763e-03, -4.2969e-03],
      [-4.2305e-03, -4.3049e-03, -4.3248e-03],
      [-4.2183e-03, -4.2923e-03, -4.3104e-03]],

     [[-4.4182e-03, -4.4096e-03, -4.3487e-03],
      [-4.4423e-03, -4.4278e-03, -4.3601e-03],
      [-4.4392e-03, -4.4195e-03, -4.3446e-03]],

     ...,

     [[-7.9604e-02, -8.0565e-02, -8.0215e-02],
      [-7.9806e-02, -8.0850e-02, -8.0523e-02],
      [-7.9351e-02, -8.0334e-02, -8.0084e-02]],

     [[-7.3640e-03, -6.9757e-03, -6.6576e-03],
      [-7.3242e-03, -6.9142e-03, -6.5543e-03],
      [-7.2372e-03, -6.8390e-03, -6.4863e-03]],

     [[-3.5256e-02, -3.5790e-02, -3.5839e-02],
      [-3.5032e-02, -3.5616e-02, -3.5710e-02],
      [-3.4774e-02, -3.5336e-02, -3.5424e-02]]]], device='cuda:0')