Stack Structured Data¶
When the tensors form the tree structures, they are often needed to be stacked together, like the torch.stack()
implemented in torch.
Stack With Native PyTorch API¶
Here is the common code implement with native pytorch API.
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 | import torch B = 4 def get_item(): return { 'obs': { 'scalar': torch.randn(12), 'image': torch.randn(3, 32, 32), }, 'action': torch.randint(0, 10, size=(1,)), 'reward': torch.rand(1), 'done': False, } data = [get_item() for _ in range(B)] # execute `stack` op def stack(data, dim): elem = data[0] if isinstance(elem, torch.Tensor): return torch.stack(data, dim) elif isinstance(elem, dict): return {k: stack([item[k] for item in data], dim) for k in elem.keys()} elif isinstance(elem, bool): return torch.BoolTensor(data) else: raise TypeError("not support elem type: {}".format(type(elem))) stacked_data = stack(data, dim=0) # validate print(stacked_data) assert stacked_data['obs']['image'].shape == (B, 3, 32, 32) assert stacked_data['action'].shape == (B, 1) assert stacked_data['reward'].shape == (B, 1) assert stacked_data['done'].shape == (B,) assert stacked_data['done'].dtype == torch.bool |
The output should be like below, and the assertion statements can be all passed.
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 | {'obs': {'scalar': tensor([[-1.5359, -1.8637, 0.5147, 0.2885, 3.0232, -1.5747, -0.9136, -0.5642, -0.1107, -0.6437, -0.6876, -2.1037], [-0.4803, 0.5412, -0.9797, 1.3545, -1.6375, -0.1112, -0.6469, -0.1644, -0.1071, -0.7095, 1.3717, -0.8619], [ 0.0595, 1.1289, -1.8427, -2.3444, 1.1401, -0.3017, 0.1171, -3.0900, -0.1801, 0.7504, 0.8529, 0.3215], [-2.2841, -0.9075, 1.3475, -0.8056, 1.3093, 1.6509, -0.0150, -2.0965, 0.5121, -1.3069, -0.3245, -0.4751]]), 'image': tensor([[[[ 1.6610e+00, -6.6716e-01, -2.5674e-03, ..., -1.1119e+00, 1.0111e-01, -3.8947e-01], [-6.0846e-02, -4.2238e-01, -1.7890e+00, ..., -5.1320e-01, -3.2174e-02, 1.5891e+00], [ 6.6530e-01, 3.7910e-01, -1.1383e+00, ..., -2.8363e+00, -1.7331e+00, 8.3179e-01], ..., [-1.7364e+00, 8.8087e-02, 7.6660e-01, ..., 7.0450e-02, -1.7959e-01, 3.5613e-02], [ 4.5644e-01, -8.5190e-01, -2.1841e-01, ..., -3.5723e-01, -7.7395e-01, 1.1753e+00], [ 7.9634e-01, -9.4494e-01, 8.4530e-01, ..., -8.8762e-01, 2.0041e+00, -8.3095e-01]], [[ 3.9545e-01, 1.7853e-02, 1.2258e+00, ..., 4.6404e-01, 1.6273e+00, 6.7164e-01], [ 7.8439e-01, -1.7749e-02, 1.1747e+00, ..., -1.4459e+00, -2.6065e-01, -2.0704e+00], [ 2.3344e-01, -3.5753e-01, -2.0137e-01, ..., -3.0735e-01, 1.0049e+00, -7.7714e-02], ..., [-5.1728e-01, -6.1153e-01, 3.1373e+00, ..., 6.0743e-01, 8.8225e-01, -1.7986e+00], [ 1.8084e-01, -3.8146e-01, -2.8826e-01, ..., 9.1983e-01, 1.3678e+00, 1.0265e+00], [-1.7859e+00, 1.5123e+00, -5.6433e-01, ..., -7.3448e-01, 6.5039e-01, -1.4252e-01]], [[ 3.3558e-01, 7.9159e-01, -7.8051e-01, ..., -9.0632e-02, -4.1254e-01, -1.0340e+00], [ 1.4462e-01, -1.5996e-01, 1.5098e-01, ..., -6.6997e-01, -1.5398e+00, -1.1684e+00], [ 3.0423e-02, -8.4161e-02, -8.2469e-01, ..., -9.4156e-01, -6.0047e-01, 1.0833e+00], ..., [-1.2074e+00, -6.7881e-01, -8.8804e-01, ..., 1.3672e+00, -2.3897e-01, -1.4595e+00], [-1.5093e+00, 1.9467e-01, 2.6247e-01, ..., -2.4687e-01, 8.1974e-01, 8.6477e-01], [-1.0264e+00, 4.6218e-01, -1.2689e+00, ..., -1.4148e+00, -1.8802e+00, 1.1174e+00]]], [[[ 7.1722e-01, 2.7806e-01, 2.6875e-01, ..., -4.0581e-01, 7.5477e-01, -7.3399e-01], [ 1.2497e-01, -6.6935e-01, -1.1342e-01, ..., -1.9850e+00, 4.2486e-01, 4.6698e-01], [-1.7070e+00, -1.8608e+00, -2.9015e-01, ..., -3.2438e-01, 5.1105e-01, 6.0397e-01], ..., [ 2.9754e-01, -1.0941e+00, -1.7757e-01, ..., 5.7503e-01, 3.0076e-01, -2.7469e-02], [ 8.9610e-01, -4.6541e-01, 1.7398e+00, ..., 7.6940e-01, 9.9921e-01, 3.4635e-01], [-1.3408e+00, -1.1096e+00, 1.1439e+00, ..., -1.0166e+00, 1.5637e-01, 1.0217e+00]], [[ 6.9857e-01, 2.4762e-02, -1.6608e+00, ..., 2.3986e+00, 1.4482e+00, -1.1221e-01], [-1.2235e+00, 9.5569e-01, 8.0277e-01, ..., 1.2390e-01, 6.3446e-01, -4.8159e-01], [ 1.1137e-01, -4.1811e-01, 1.6600e+00, ..., 9.5241e-01, -3.9915e-01, -3.4954e-01], ..., [-6.4935e-01, 3.8598e-01, -1.9078e+00, ..., -1.0743e+00, -5.7636e-01, -9.9310e-02], [-6.7461e-01, 2.1996e+00, 6.1991e-01, ..., 1.3554e+00, 5.9770e-01, -4.0413e-01], [ 3.6842e-01, 2.8036e-01, 2.2252e-01, ..., 2.9636e-01, -4.0549e-01, 2.7558e-01]], [[ 3.5620e-01, 6.0534e-01, -3.1990e-01, ..., -2.7719e+00, -1.7094e+00, -3.4541e-02], [ 8.0527e-01, -2.9047e+00, -5.7613e-01, ..., 4.7329e-01, -3.2298e-01, 5.8782e-01], [-8.8396e-01, -5.3898e-01, -8.7339e-02, ..., 1.5157e+00, 2.5611e-01, -1.2633e+00], ..., [-1.8839e+00, -8.8443e-01, 5.7534e-01, ..., 2.1994e-01, -9.9814e-01, -6.8960e-01], [-4.5642e-01, 1.2131e+00, 7.1354e-01, ..., 2.7715e-01, -1.1295e+00, 5.8713e-01], [ 3.3697e+00, -2.3707e-01, 8.7955e-01, ..., 2.7682e-01, -1.1120e-01, 7.7729e-01]]], [[[-1.2380e+00, 2.2912e-01, 5.3711e-01, ..., 6.1321e-02, 2.9950e-01, -8.9248e-01], [ 1.5834e+00, 4.3304e-01, -1.2799e+00, ..., 4.7555e-01, 8.4607e-01, -1.6996e-01], [ 4.3139e-01, -1.9600e+00, 1.0313e+00, ..., 9.2116e-01, -7.9623e-01, -1.0047e+00], ..., [-1.9196e+00, -1.3142e+00, 3.0070e-01, ..., -7.7360e-01, 1.6803e-01, -5.1410e-01], [-7.7753e-01, -5.5371e-02, -3.5943e-01, ..., -5.0513e-01, -5.1643e-01, -1.5594e+00], [ 2.2311e+00, 5.3528e-01, 8.7125e-01, ..., 2.2423e-01, -8.5476e-01, -1.4118e+00]], [[ 6.4727e-01, 3.2528e-02, -6.0068e-01, ..., 8.2503e-01, -3.9128e-01, 1.0412e+00], [-5.6120e-03, -8.2240e-01, 1.0684e+00, ..., 7.7922e-01, -1.8849e+00, -9.9019e-01], [ 2.1886e-01, 4.0195e-02, -1.2756e-01, ..., -1.6997e-01, -8.5318e-01, 1.0684e+00], ..., [-1.5420e+00, 3.6812e-01, 5.6735e-01, ..., 1.4701e+00, -6.5132e-02, 7.0469e-01], [-3.5373e-01, -3.0333e-01, -1.1018e+00, ..., 1.0491e+00, -6.8210e-01, 2.9134e-01], [ 4.7230e-02, -1.1026e-01, 7.9086e-01, ..., -5.4547e-01, 1.9298e+00, -1.2211e+00]], [[-1.3403e-01, -7.8478e-01, 3.0228e-01, ..., 2.5261e-01, -1.0661e+00, 3.5013e-01], [ 1.7791e-01, 8.1826e-01, -2.2315e-01, ..., -1.3331e+00, -3.5003e-01, -4.1320e-01], [ 2.9800e-01, 4.4868e-01, -1.2710e+00, ..., 7.0277e-01, -6.2180e-01, -2.0842e+00], ..., [ 5.5338e-01, -2.1307e+00, -7.8954e-01, ..., 5.1934e-01, 1.8357e-04, 2.2463e+00], [ 7.2245e-01, 9.7585e-03, -1.3792e+00, ..., -1.3492e+00, 1.1175e+00, 9.5284e-01], [-1.0783e+00, -1.6424e+00, 2.9628e-01, ..., -3.7582e-01, 6.3133e-01, 4.2903e-01]]], [[[-1.3136e+00, 5.4848e-01, -9.0181e-02, ..., 1.7092e-01, 1.2252e+00, 1.2149e-01], [ 4.0342e-01, -4.7260e-01, -1.2950e+00, ..., -4.2847e-01, 3.4140e-01, 7.4329e-01], [-2.0541e-01, 5.0495e-01, 3.0750e-01, ..., 9.2752e-01, 1.2537e+00, 6.9711e-02], ..., [-3.9146e-01, 2.7727e-01, 2.0646e+00, ..., 1.7233e+00, 8.8880e-01, -1.0380e-01], [-9.6824e-02, -1.5755e+00, -8.1249e-01, ..., -9.0718e-01, -6.0762e-01, -5.3578e-01], [ 1.2190e+00, -5.5851e-01, 1.1164e+00, ..., -1.0015e-01, -7.5845e-02, -1.6652e+00]], [[ 1.0584e+00, -8.3761e-01, 3.9262e-01, ..., 1.8085e-01, -4.7859e-01, -1.1761e+00], [-2.3864e-01, 2.5746e-02, 2.1723e-01, ..., 7.2606e-01, 6.6296e-01, 8.9619e-01], [ 2.1951e+00, 9.7526e-01, 9.4685e-02, ..., 1.2771e+00, 8.1224e-01, -5.0146e-02], ..., [ 1.0919e-01, 4.0990e-01, 1.4036e+00, ..., -1.2443e+00, -1.3147e-01, 1.0036e+00], [-3.5938e-01, -2.2983e-01, 3.6190e-01, ..., 7.2910e-01, -5.7873e-01, -7.2678e-01], [-2.5017e-02, -2.5089e-01, 1.7725e+00, ..., 9.0255e-01, 1.3763e-01, 5.9141e-01]], [[-1.6782e+00, 1.0001e+00, 2.7471e+00, ..., -6.1081e-02, 1.6030e+00, 1.3063e+00], [ 2.7889e+00, 3.3484e-01, 6.0145e-01, ..., -4.5082e-02, -1.0295e+00, -2.8439e-01], [ 1.0260e+00, -2.3331e-02, -4.2302e-01, ..., 1.9972e+00, -2.5823e-01, 1.6595e+00], ..., [-3.0104e-01, -9.8066e-01, 1.3314e-01, ..., 1.2524e-01, -8.2533e-01, -2.0243e-01], [-7.5341e-01, -1.9238e+00, 1.0450e+00, ..., 2.0502e-01, 1.0519e+00, -4.7291e-01], [-5.4682e-01, -1.5921e+00, 1.3838e+00, ..., 7.9409e-01, 6.9568e-01, 3.7058e-01]]]])}, 'action': tensor([[2], [0], [9], [5]]), 'reward': tensor([[0.0673], [0.3907], [0.7002], [0.3012]]), 'done': tensor([False, False, False, False])} |
We can see that the structure process function need to be fully implemented (like the function stack
). This code is actually not clear, and due to hard coding, if you need to support more data types (such as integer), you must make special modifications to the function.
Stack With TreeTensor API¶
The same workflow can be implemented with treetensor API like the code below.
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 | import torch import treetensor.torch as ttorch B = 4 def get_item(): return { 'obs': { 'scalar': torch.randn(12), 'image': torch.randn(3, 32, 32), }, 'action': torch.randint(0, 10, size=(1,)), 'reward': torch.rand(1), 'done': False, } data = [get_item() for _ in range(B)] # execute `stack` op data = [ttorch.tensor(d) for d in data] stacked_data = ttorch.stack(data, dim=0) # validate print(stacked_data) assert stacked_data.obs.image.shape == (B, 3, 32, 32) assert stacked_data.action.shape == (B, 1) assert stacked_data.reward.shape == (B, 1) assert stacked_data.done.shape == (B,) assert stacked_data.done.dtype == torch.bool |
The output should be like below, and the assertion statements can be all passed as well.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | <Tensor 0x7f67b3933b20> ├── 'action' --> tensor([[6], │ [5], │ [9], │ [1]]) ├── 'done' --> tensor([False, False, False, False]) ├── 'obs' --> <Tensor 0x7f674cb97ee0> │ ├── 'image' --> tensor([[[[ 1.5096e+00, -2.5924e+00, -1.2985e+00, ..., 4.1741e-01, │ │ -7.1140e-01, -5.9812e-01], │ │ [ 1.8629e+00, 1.3916e+00, -5.3894e-01, ..., 1.5501e+00, │ │ -5.2787e-02, 1.5425e+00], │ │ [-7.7700e-01, -9.6292e-02, -1.2389e+00, ..., -3.7741e-03, │ │ -1.0594e+00, 8.5284e-01], │ │ ..., │ │ [ 2.2239e+00, 3.5538e-01, -2.0282e-01, ..., 1.9719e-01, │ │ -2.1059e+00, 1.6486e+00], │ │ [-1.7360e+00, 5.4855e-01, 6.4995e-01, ..., 1.4169e+00, │ │ 1.9953e+00, -1.2208e+00], │ │ [-2.4211e+00, 3.2995e-01, 1.4672e+00, ..., 1.2811e+00, │ │ -6.5591e-02, -8.9733e-01]], │ │ │ │ [[ 1.0635e+00, -1.6979e+00, 1.2968e+00, ..., -1.2375e+00, │ │ 1.9904e+00, 9.4620e-01], │ │ [-4.4523e-01, 2.0002e+00, 7.0469e-02, ..., 4.8900e-01, │ │ -5.1857e-01, -2.0646e+00], │ │ [ 4.1355e-01, -1.7892e+00, -5.6645e-01, ..., 4.6617e-01, │ │ 7.5357e-01, 7.9125e-01], │ │ ..., │ │ [-3.8282e-01, -4.4059e-01, -2.5233e-02, ..., -1.1164e+00, │ │ -1.3296e+00, 8.9965e-02], │ │ [-1.2467e+00, 9.2784e-01, -8.3671e-01, ..., -4.6411e-01, │ │ 4.9811e-01, 1.3054e-01], │ │ [-7.6199e-01, -3.1625e-01, -2.5220e-01, ..., 4.2622e-01, │ │ -2.9130e-01, -5.7645e-01]], │ │ │ │ [[-8.2144e-01, 6.9815e-01, -1.1575e-01, ..., 1.0502e+00, │ │ -4.9453e-02, 4.9258e-01], │ │ [-3.4399e-01, 7.3040e-02, 2.8991e-01, ..., 9.3092e-01, │ │ 2.4635e-01, 3.2799e+00], │ │ [ 1.3362e+00, 1.0832e-01, 2.3803e+00, ..., -1.8618e-01, │ │ -1.2159e+00, 4.3580e-01], │ │ ..., │ │ [-4.3481e-01, -3.4832e-01, 1.8724e+00, ..., -8.7521e-01, │ │ 9.7985e-01, -2.6546e-01], │ │ [ 6.8399e-01, -4.6111e-01, 1.2375e+00, ..., -4.3992e-01, │ │ 7.7478e-01, -1.0183e+00], │ │ [ 1.4008e+00, 5.0574e-01, -1.0997e+00, ..., 1.5970e+00, │ │ -1.5338e-01, 3.0924e-01]]], │ │ │ │ │ │ [[[-9.8478e-01, 2.0089e+00, 6.4709e-01, ..., 2.8368e-01, │ │ -8.1748e-01, -1.7254e+00], │ │ [ 5.9830e-02, 1.7519e+00, -5.6280e-01, ..., 1.8556e+00, │ │ -4.9722e-01, -3.5804e-02], │ │ [-7.6478e-02, 8.0604e-01, -7.2962e-01, ..., 1.0894e+00, │ │ 2.1358e-01, -1.0206e+00], │ │ ..., │ │ [ 8.2956e-01, -2.9412e-01, 1.8450e+00, ..., -3.2224e-01, │ │ -2.1809e-01, 1.3046e-01], │ │ [-8.4717e-01, 6.6377e-01, 1.5835e+00, ..., -1.7277e+00, │ │ 1.1663e+00, 2.1333e-01], │ │ [ 9.6119e-01, -1.6126e+00, 1.1323e+00, ..., -5.3644e-03, │ │ -1.2464e-01, 1.0318e+00]], │ │ │ │ [[-9.8105e-01, -1.4832e+00, 1.1134e-01, ..., 1.7990e+00, │ │ -7.8146e-01, -2.8395e-01], │ │ [ 7.5591e-01, 8.6817e-01, 7.2440e-01, ..., -4.2268e-01, │ │ 1.3767e-01, -3.7290e-01], │ │ [-1.5976e-01, 7.8546e-01, 5.3195e-01, ..., 1.7872e+00, │ │ 9.8206e-01, 2.1067e+00], │ │ ..., │ │ [ 1.1195e+00, 2.1198e-02, 3.1971e-01, ..., 1.6998e-01, │ │ -4.6388e-01, -1.4809e-01], │ │ [-1.4483e+00, -1.3471e+00, -6.9803e-01, ..., 2.3547e-01, │ │ -2.3611e+00, -7.0573e-01], │ │ [ 9.2485e-01, -6.4721e-01, 3.8117e-02, ..., -2.5686e+00, │ │ 4.5731e-01, 3.4798e-01]], │ │ │ │ [[-3.5736e-01, 7.6402e-01, 2.6200e-01, ..., 5.7103e-01, │ │ 9.9594e-01, -7.4228e-01], │ │ [-8.1190e-01, -1.2320e+00, -3.3982e-01, ..., -5.5238e-01, │ │ -1.0162e+00, -9.5020e-01], │ │ [ 4.0352e-01, -7.3156e-01, 4.7960e-01, ..., -5.1536e-01, │ │ 3.9957e-01, -6.6148e-01], │ │ ..., │ │ [-3.2271e-01, -1.7166e-01, -7.7436e-01, ..., -4.5268e-01, │ │ 2.7514e-01, -1.2822e+00], │ │ [ 1.6007e+00, -9.3195e-01, -1.1446e+00, ..., -9.0853e-02, │ │ 2.7956e+00, -1.4393e+00], │ │ [ 1.0222e-01, -5.8582e-01, -1.3279e+00, ..., -1.5334e-01, │ │ 7.2398e-01, -1.4310e+00]]], │ │ │ │ │ │ [[[ 1.5704e-01, -1.7927e-01, -2.9434e-01, ..., -1.0364e+00, │ │ -3.4321e-01, -1.2803e+00], │ │ [-1.8595e+00, -1.9555e+00, 6.8676e-01, ..., -5.0676e-01, │ │ -7.8089e-01, 5.3276e-01], │ │ [ 4.8389e-01, 2.2939e+00, 1.3774e-01, ..., 7.2845e-01, │ │ 3.8985e-01, -2.2856e+00], │ │ ..., │ │ [ 1.3233e+00, 3.5061e-01, 2.2866e+00, ..., -2.6043e-01, │ │ -2.4169e+00, -1.1341e+00], │ │ [-1.9741e-01, -2.4740e+00, -8.6849e-01, ..., 6.1612e-01, │ │ 3.9695e-01, 2.8495e-01], │ │ [ 1.6165e+00, 1.3103e+00, -5.2383e-01, ..., -7.0674e-01, │ │ -5.6912e-01, 2.2296e-01]], │ │ │ │ [[-1.4132e+00, 3.3454e-01, -1.7366e+00, ..., -2.8714e-01, │ │ -1.0491e+00, 1.1373e+00], │ │ [-6.8534e-01, 4.8490e-01, 1.6887e+00, ..., 2.1171e-01, │ │ 6.4883e-01, 1.9160e+00], │ │ [-6.7286e-01, 7.7061e-01, -5.6293e-01, ..., -6.3796e-01, │ │ 1.1811e+00, -3.7864e-01], │ │ ..., │ │ [ 4.8821e-01, -7.9141e-01, 6.6298e-01, ..., -4.0639e-01, │ │ -5.6672e-01, 6.0164e-01], │ │ [ 1.6093e+00, 9.5938e-01, -2.7474e-01, ..., 4.7161e-01, │ │ 1.3705e+00, 3.5498e-01], │ │ [-3.2744e-01, -4.7183e-01, -1.4731e+00, ..., -1.2090e+00, │ │ 1.7744e+00, -1.7377e-01]], │ │ │ │ [[-7.9389e-01, -1.7566e-01, 1.1357e+00, ..., -4.1468e-01, │ │ -1.9169e+00, 2.4644e+00], │ │ [-2.8844e+00, -9.2150e-01, 4.0514e-01, ..., 4.3330e-01, │ │ -3.6057e-01, -1.0335e+00], │ │ [-6.9818e-02, -3.8488e-01, 1.1589e+00, ..., -8.3541e-01, │ │ -1.3521e+00, -6.7402e-01], │ │ ..., │ │ [ 2.0459e+00, 7.6486e-01, 1.6111e-01, ..., 8.8801e-01, │ │ 4.5756e-04, 5.9226e-01], │ │ [-3.3977e-01, 1.4775e-01, -6.0727e-02, ..., -1.4168e+00, │ │ 5.4174e-01, -5.8324e-01], │ │ [-6.2560e-01, 4.0346e-01, -3.7235e-01, ..., -1.9103e+00, │ │ 3.0439e+00, -9.0031e-01]]], │ │ │ │ │ │ [[[ 5.8669e-01, -2.1086e+00, 1.0279e+00, ..., 1.3415e-01, │ │ -3.0481e-01, 1.0725e+00], │ │ [ 1.1445e+00, -7.9624e-01, -6.9794e-01, ..., -3.9854e-01, │ │ 1.8777e+00, 1.2824e+00], │ │ [ 1.5032e+00, 2.4661e-01, -2.0454e+00, ..., -4.0469e-02, │ │ 4.1951e-01, 9.8916e-01], │ │ ..., │ │ [-8.6445e-01, -1.4327e-01, 4.5598e-01, ..., 4.3978e-01, │ │ 3.3533e-01, -3.5248e-01], │ │ [-6.1360e-01, 4.5503e-01, 6.2883e-01, ..., -5.8277e-01, │ │ 1.2092e-01, 4.2707e-01], │ │ [-4.0543e-01, 1.6866e+00, -1.2542e-01, ..., -6.2828e-01, │ │ 6.3958e-01, -1.3999e+00]], │ │ │ │ [[-4.2511e-01, -3.8454e-01, 1.0229e+00, ..., -9.9500e-01, │ │ 3.4999e-01, -8.7348e-01], │ │ [ 6.4947e-02, 2.8181e+00, 7.9655e-01, ..., -1.0276e+00, │ │ -1.4040e-01, -8.4645e-01], │ │ [-7.8912e-01, -1.4459e-01, -1.1645e-01, ..., -1.2434e+00, │ │ 5.5709e-01, -4.0519e-01], │ │ ..., │ │ [-1.5093e+00, 5.4285e-01, 1.2440e-01, ..., -2.0399e+00, │ │ 8.6725e-01, -7.4004e-01], │ │ [-1.1745e+00, -1.4943e-01, -5.6547e-01, ..., 4.3259e-01, │ │ -1.1197e+00, -1.3377e+00], │ │ [ 3.6998e-01, 1.5458e+00, -8.3772e-01, ..., 1.2480e+00, │ │ 1.1511e+00, -6.7690e-01]], │ │ │ │ [[-1.3623e+00, 7.4699e-01, -8.0144e-01, ..., 8.3665e-01, │ │ 5.7975e-01, 5.3105e-01], │ │ [ 1.0468e-01, 1.1169e-02, 8.8025e-01, ..., 2.1460e-01, │ │ 6.0716e-01, 1.7213e+00], │ │ [-1.4123e-01, 3.9434e-01, 7.4176e-01, ..., 1.2088e+00, │ │ 1.6890e+00, -9.7466e-01], │ │ ..., │ │ [-1.3751e+00, 8.6904e-02, 4.9621e-01, ..., -8.1262e-01, │ │ -6.9502e-01, 1.0489e+00], │ │ [-8.3935e-01, -1.2021e+00, -9.7100e-02, ..., -1.3282e+00, │ │ 9.0960e-01, -5.5755e-01], │ │ [-2.3514e-01, 7.6488e-01, -3.4905e-01, ..., -1.1052e+00, │ │ 1.0690e+00, -9.9615e-01]]]]) │ └── 'scalar' --> tensor([[ 1.0388, -0.5856, -0.2967, 0.3854, -0.7474, -1.2775, -0.5164, 0.2292, │ -1.3179, -0.5619, 0.7937, 1.9121], │ [ 0.9892, 0.8679, 0.9397, 0.2684, -0.7935, -1.2487, -0.8953, 0.7985, │ -0.4398, -1.3260, -0.1225, 0.8914], │ [-0.1819, -1.2881, -1.7916, 1.0303, 0.0899, -0.1217, -1.0786, 0.6543, │ 0.2724, -1.6755, 1.6962, -0.5407], │ [-1.0845, 1.0362, 1.1486, 1.7401, 0.8451, -1.6223, -0.7594, -0.5500, │ -0.2333, 1.6559, -0.4189, 0.5876]]) └── 'reward' --> tensor([[0.2597], [0.5514], [0.0990], [0.1488]]) |
This code looks much simpler and clearer.