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 | {'obs': {'scalar': tensor([[-0.0474, -0.6267, -1.3194, 1.3442, -2.4609, -0.5388, 0.0422, -1.0698, -1.1849, -0.3751, -1.1569, 1.3876], [-0.2088, -0.7598, -1.1054, -1.1143, -0.9409, 0.1055, 1.2774, 0.7490, 0.1556, 1.1857, 0.2342, 0.9975], [ 0.7275, 0.0235, -1.3393, 0.8602, 1.8367, -0.5077, -0.8846, 1.8345, -0.3257, -1.1880, 0.0906, -0.4038], [-0.5391, -0.6842, 0.8453, -0.5388, 0.1375, -0.0336, -0.3076, -0.4212, 0.8219, 1.1042, 0.1134, -0.5790]]), 'image': tensor([[[[-0.2751, 0.2849, -0.4398, ..., -0.8361, 1.4208, -1.1693], [-1.3456, 0.8265, -1.4024, ..., 1.0499, -1.1010, -1.9177], [-0.2765, 0.4968, 0.4232, ..., 3.3506, 0.1763, -2.4639], ..., [ 1.8089, -0.0888, 0.2680, ..., 0.8729, -0.3355, -0.7094], [-0.0260, 2.3524, -0.5009, ..., 1.0931, 0.7457, -0.1578], [ 0.1746, -0.2618, -0.7880, ..., -0.7283, -0.5056, -0.6450]], [[-2.4831, 0.6116, -0.2278, ..., -0.2452, -0.6157, 1.0735], [-0.1020, -2.2370, -1.6688, ..., 0.4849, -0.5533, 0.0878], [-0.3830, 0.1553, 1.2283, ..., 0.0569, -1.4088, -0.4156], ..., [-0.7294, 0.8529, 0.7134, ..., -0.9616, -1.5958, -1.4483], [-0.3974, 0.8537, -0.0476, ..., -1.4329, 0.9812, 0.1319], [-0.6973, 1.5629, -0.2870, ..., 1.4818, -1.0325, -0.2504]], [[-0.2226, -1.3849, -2.1680, ..., -0.1532, 0.7338, -0.1767], [ 0.0933, -0.0511, 0.5087, ..., -0.1556, 0.9099, 0.2634], [-0.0989, -0.7362, 1.0113, ..., 1.1245, 1.1827, -0.4475], ..., [-0.1442, -0.5391, -2.1120, ..., -0.6900, -0.3014, -0.1759], [-1.3388, 0.2005, 0.2434, ..., 1.6742, -0.5577, 0.2453], [-0.3675, -0.5611, 0.6748, ..., 0.1551, 0.8148, 0.7405]]], [[[-0.3502, 0.8338, 0.2708, ..., -0.0123, 1.0926, 0.6610], [-0.0094, -1.0525, 0.3287, ..., 0.7370, 0.8891, 1.0431], [ 0.1888, 0.3276, 0.5379, ..., -0.7131, 1.9821, 0.7732], ..., [ 0.1138, -1.6056, 0.6366, ..., -1.2302, -0.3910, -0.0265], [-0.4184, -0.9246, 0.6108, ..., 0.2189, 0.3337, -0.2046], [-2.4134, 1.9054, -0.0702, ..., 0.3756, 0.5060, -0.1964]], [[-0.5459, 0.7165, 0.7111, ..., -0.3358, -2.8014, -0.3046], [ 0.3116, 0.9859, 0.2579, ..., -1.6148, 1.8107, -0.1606], [ 0.3406, 2.0406, 0.3973, ..., -0.4990, 0.4736, 0.5269], ..., [-0.2485, -0.4891, 2.5243, ..., 1.8118, -0.0243, 0.6212], [ 1.1387, -0.1737, 1.0413, ..., 0.8144, -0.3411, 0.1024], [-1.0099, -0.6349, 2.6201, ..., 0.0811, 0.9929, 2.0772]], [[-0.7934, -1.2366, 0.5791, ..., -0.0818, 0.4937, 0.4859], [-0.1064, 1.4198, -0.1747, ..., 0.4362, -1.2629, -0.5691], [ 0.1293, -2.0610, 1.9908, ..., 1.1718, 0.8963, -0.5334], ..., [ 0.3928, -0.0289, 2.1853, ..., 0.2185, 0.3709, 0.1734], [-0.6801, 0.0650, 0.3967, ..., 1.0298, 1.6349, -1.1922], [-0.6088, -0.7305, 0.6763, ..., 0.1512, 1.6489, -1.4524]]], [[[-0.0420, 0.4647, -1.5241, ..., 0.8838, 0.1826, 1.7378], [ 0.7445, 0.7431, -2.3356, ..., -0.0118, 0.4899, -0.8337], [ 1.0866, 0.1677, -0.1634, ..., 0.4219, -0.1374, -0.0456], ..., [ 0.8754, 0.5750, 1.4385, ..., 0.7784, 0.4058, 0.4234], [ 0.6471, -0.3631, 0.6372, ..., -0.1434, -1.7048, 1.1129], [ 0.3831, 2.0619, 0.5491, ..., 1.7297, 0.2793, -0.7170]], [[ 1.5339, -1.3078, -1.3366, ..., -0.6555, 0.2967, 0.2050], [ 2.0565, -0.6604, 1.7751, ..., -0.9680, 0.0179, -0.7194], [ 1.2867, -0.6521, 0.8652, ..., -0.3076, 0.0210, -1.6127], ..., [-0.9632, -0.2090, -0.6809, ..., -0.9559, 2.3938, -0.5868], [-0.6778, 0.9912, 0.1721, ..., 0.2588, 0.3010, -0.7527], [-0.5230, 0.3787, -0.5170, ..., 0.3577, 2.4580, 0.5111]], [[-0.3902, 1.6515, -1.5147, ..., -1.2826, -0.6861, -0.3882], [-1.8575, 0.2926, -0.1161, ..., -0.8605, -0.6181, -1.0980], [-1.4343, -0.5434, 0.8706, ..., -1.0140, 0.3919, -0.4772], ..., [ 1.6469, -0.7275, -0.9569, ..., -1.1410, 0.2409, -0.6920], [-1.5096, 0.7378, -0.7718, ..., 0.8376, -1.1837, 0.5662], [ 0.1438, -1.4634, 0.0567, ..., 1.7126, -0.1545, -0.1851]]], [[[-1.9919, -2.0915, 1.6117, ..., -0.1704, -0.4007, 1.0255], [-1.9141, 0.7429, 0.3941, ..., 0.6496, -0.6669, -0.1110], [-1.2933, 0.3415, 0.6939, ..., 0.6096, 0.1182, -2.1521], ..., [-0.3522, -0.7301, 0.5915, ..., 1.0247, 0.4818, -2.8824], [-1.0129, 0.5479, -0.2106, ..., -1.2869, -0.6228, -1.7448], [ 0.6500, 0.8102, -0.1323, ..., -0.6634, 0.1760, -0.5788]], [[ 0.2172, -0.5834, -0.7828, ..., 0.1899, -1.0895, 1.2113], [ 0.2995, -2.2824, -0.8321, ..., 0.4185, 0.6189, 0.7172], [-0.2500, 0.7318, -0.7020, ..., 0.1634, -0.7869, 0.3379], ..., [-0.8895, -0.2712, -0.4805, ..., -0.9760, -0.2789, -1.1124], [ 1.8720, 0.0467, -0.1050, ..., -0.1172, -1.0756, 1.1421], [ 0.5143, -1.2392, 0.8083, ..., -1.3443, 0.7761, -1.8245]], [[ 0.3234, -0.2622, -0.8256, ..., -2.1402, -0.5509, -0.7607], [ 0.7452, 0.6947, 0.7951, ..., 0.0744, -0.4635, -0.6039], [ 0.5742, 0.3557, -0.0880, ..., 0.7482, -0.0893, 0.4751], ..., [-1.3953, 1.3579, 0.0245, ..., 0.0954, -0.1335, 0.9655], [-0.6046, -0.4598, 0.5230, ..., -0.2763, -0.6108, 1.0981], [-0.3839, -0.0712, -1.4534, ..., -0.0674, -0.7260, -0.2902]]]])}, 'action': tensor([[8], [3], [6], [6]]), 'reward': tensor([[0.1228], [0.6469], [0.5704], [0.6775]]), '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 | <Tensor 0x7f3d243ae190> ├── 'action' --> tensor([[7], │ [7], │ [2], │ [2]]) ├── 'done' --> tensor([False, False, False, False]) ├── 'obs' --> <Tensor 0x7f3d243ae2b0> │ ├── 'image' --> tensor([[[[ 0.0791, 0.8872, 1.0240, ..., -0.4558, -0.2995, 0.4920], │ │ [-0.4198, -1.7697, -0.6815, ..., 0.4069, 2.3706, 0.4846], │ │ [ 0.2284, 0.3191, 0.4235, ..., -0.6834, 0.1046, 0.4281], │ │ ..., │ │ [-0.0241, -0.2738, 0.0994, ..., -0.4086, 1.3089, 1.1076], │ │ [-1.6397, 2.2323, -1.9866, ..., -1.1885, -1.1003, 0.5095], │ │ [-0.1616, 0.4194, 0.3447, ..., -1.4410, -1.0595, -1.3054]], │ │ │ │ [[ 0.5532, 1.7987, 0.6725, ..., 0.9116, 0.1138, -0.5087], │ │ [-1.2482, -0.1449, -0.6920, ..., 0.4026, -0.3073, -1.2992], │ │ [-0.8882, 1.0068, -0.6378, ..., -1.6998, 0.0469, 1.3722], │ │ ..., │ │ [-0.1030, -1.0744, 0.0287, ..., -0.9524, -0.2263, -0.0691], │ │ [ 1.9076, -0.8239, -0.3963, ..., -1.6327, 0.1308, -0.2509], │ │ [ 1.1278, -1.4004, 1.1440, ..., -0.7515, 0.2153, -1.8422]], │ │ │ │ [[ 0.3734, 0.2164, -0.7244, ..., 1.2590, -0.0717, -1.1597], │ │ [ 0.1956, 0.6762, 0.2630, ..., 0.3518, -0.3724, 1.9386], │ │ [ 0.5327, -0.3009, -0.8345, ..., -0.4405, 1.2826, 1.3088], │ │ ..., │ │ [ 0.6457, -1.0980, -0.9002, ..., -0.8903, -0.8940, -0.6419], │ │ [ 0.9915, -1.3307, 0.4343, ..., -2.2540, -0.6882, -0.4414], │ │ [ 0.8994, -0.5798, 0.5876, ..., -0.3843, 0.1621, -0.3746]]], │ │ │ │ │ │ [[[ 0.7907, 1.2970, 0.9571, ..., -0.5582, -0.0405, -1.1310], │ │ [ 0.1079, -1.2159, -0.6550, ..., 1.2299, -1.3267, -2.4603], │ │ [ 0.3104, 0.1867, 0.1131, ..., 1.1507, 0.9352, -0.2045], │ │ ..., │ │ [-0.7419, -2.8449, -1.3869, ..., -0.6817, 0.6444, -0.7880], │ │ [-1.4280, -0.8149, -0.2399, ..., 0.4458, 1.4619, 0.1235], │ │ [-0.2581, -0.7713, -0.8334, ..., 0.4113, -0.6817, 0.4658]], │ │ │ │ [[ 0.9982, -1.3032, -0.3881, ..., 0.9156, 0.9392, 1.6742], │ │ [-1.2746, 0.4936, -0.1683, ..., 1.7532, 1.1859, 0.5688], │ │ [ 0.9160, 0.6627, -1.8103, ..., 0.4949, -0.9998, -1.3738], │ │ ..., │ │ [-0.9601, 0.0071, 1.1889, ..., 1.4923, 0.3814, -0.7239], │ │ [ 0.7037, -0.1911, -0.4539, ..., -0.1205, 1.4515, 0.7515], │ │ [ 0.9311, 0.5985, -0.0129, ..., -0.3412, 0.9672, -1.4772]], │ │ │ │ [[ 0.5502, 0.6046, 1.2751, ..., -2.0525, -0.0460, 1.8959], │ │ [ 1.1351, 0.6971, -0.5836, ..., -0.3437, -2.7175, 0.3434], │ │ [-0.7622, 1.4680, -0.0728, ..., 0.6859, -0.6063, -0.0471], │ │ ..., │ │ [-0.0408, -1.2061, 0.1526, ..., -0.1962, -1.4930, -1.3408], │ │ [-0.0765, 0.6524, -0.5823, ..., -0.4107, 0.1003, 1.1323], │ │ [-0.1797, 0.4413, -0.5075, ..., -0.8266, 1.1323, -0.8279]]], │ │ │ │ │ │ [[[ 1.2919, 0.8392, -0.4312, ..., 0.1050, -0.3016, -0.0981], │ │ [-0.6781, 0.0393, 0.2945, ..., 1.8350, -1.2779, 0.3858], │ │ [ 1.9833, -0.8696, 2.0524, ..., 0.2332, 1.6123, -2.2659], │ │ ..., │ │ [ 1.5953, -1.1926, 0.3764, ..., -1.5459, -0.6217, 0.1397], │ │ [-0.7837, -0.3437, 0.5292, ..., 0.7078, 0.1553, -0.2891], │ │ [-0.6039, 0.9226, -0.4776, ..., 1.6451, -1.0019, 1.7044]], │ │ │ │ [[ 0.0923, -0.3256, -2.1885, ..., -0.3502, -0.9551, -1.7428], │ │ [ 1.3288, 1.2030, 1.3103, ..., 1.7191, 0.2865, 0.2143], │ │ [-0.8596, -0.5981, -1.4051, ..., -0.0115, 1.0409, -0.8187], │ │ ..., │ │ [-0.4436, -1.0942, -0.5818, ..., 0.4918, 0.0229, 0.3424], │ │ [-1.6137, -1.7928, -0.9347, ..., -1.6439, -0.8557, 1.1684], │ │ [-0.5163, 0.1093, -0.1803, ..., 1.3647, 0.0103, -0.6510]], │ │ │ │ [[ 0.5011, -1.0931, 0.9437, ..., -0.5579, -1.9619, -0.2903], │ │ [ 0.7258, -0.1732, -1.5473, ..., -0.4777, 0.2735, -1.0263], │ │ [ 0.4865, 1.1720, -0.6594, ..., -0.4230, 1.5394, 1.4809], │ │ ..., │ │ [ 0.4357, -0.8231, 2.9393, ..., 0.5074, -1.4053, 0.3930], │ │ [ 0.8048, 1.6432, 0.1634, ..., -0.7126, 0.8041, -0.0140], │ │ [ 0.0584, 1.8815, 0.2343, ..., 0.4873, 0.6774, -0.6150]]], │ │ │ │ │ │ [[[ 1.3604, -1.6832, -0.1336, ..., 0.7399, 0.1142, -1.3361], │ │ [-1.5409, 0.6699, 0.0171, ..., -0.3744, 0.7404, -0.4100], │ │ [-0.1642, 1.1593, 0.7386, ..., -0.9751, 0.0185, -0.1900], │ │ ..., │ │ [ 0.1972, -0.5856, -0.3571, ..., 1.3776, 0.4157, -0.1640], │ │ [ 0.2938, 1.2468, -1.6340, ..., -0.7121, 0.0604, 0.3688], │ │ [ 1.0029, -2.3024, 0.3247, ..., 0.0377, -0.6577, -0.2586]], │ │ │ │ [[ 0.9143, 0.1542, -0.6300, ..., 0.3324, 0.7772, -1.6399], │ │ [ 0.1387, -1.1285, 0.8991, ..., -0.1102, -1.7244, -0.2450], │ │ [-0.1183, 0.2169, -0.1358, ..., 0.9768, -0.9488, -0.4885], │ │ ..., │ │ [-0.3008, -0.0661, -1.0024, ..., 0.8578, -0.2944, -1.7844], │ │ [ 0.3122, 1.1041, 0.9070, ..., 1.3342, -1.9862, 0.0636], │ │ [-2.1131, 1.6279, -0.6652, ..., -1.1855, 0.7817, 0.2524]], │ │ │ │ [[-0.2601, 0.4819, 0.2600, ..., -0.0747, 1.7166, 0.5367], │ │ [ 0.0428, -0.6690, -1.7239, ..., 0.9543, 2.2662, 0.0496], │ │ [-1.3442, -0.8918, -0.0961, ..., 2.7132, -0.1731, -0.2380], │ │ ..., │ │ [-2.8024, -0.6169, -0.6925, ..., -0.0106, 0.3174, -1.9551], │ │ [-0.9647, 0.4923, 2.9837, ..., -0.3070, 0.4370, -0.6259], │ │ [-0.3058, -0.3659, -0.5358, ..., -0.6162, -1.9454, -0.3422]]]]) │ └── 'scalar' --> tensor([[-1.4763, -0.0544, 0.7519, -1.0781, -0.4852, 1.7914, -1.3496, 1.0081, │ 0.5050, 0.9409, -0.2093, -0.3679], │ [ 0.5313, 0.4912, -0.3377, 1.7403, -0.6472, 0.9690, -1.1610, -0.0227, │ -1.4047, -0.3975, 0.8537, -1.2538], │ [-0.9429, 0.3886, -0.8699, 1.1482, 0.5062, -0.7958, 0.1384, 0.4363, │ 1.6568, 2.3146, -0.8395, 1.0176], │ [-1.6640, -0.9070, 0.6325, 0.6311, 0.5759, 0.4054, -0.1846, -0.8301, │ 0.0672, 2.1676, 0.1712, -0.2086]]) └── 'reward' --> tensor([[0.4076], [0.9543], [0.0243], [0.8797]]) |
This code looks much simpler and clearer.