I have pre-trained model which has 227 layers. The last but one layer is the MLP. The pre-trained model is a state_dict which has key value pair. The values of all the keys are tensor objects. Now I have access the the MLP layer and extract the embeddings on the test images.How to do it?
‘mlp_head.0.weight’, tensor([0.9932, 0.9924, 1.0286, 0.9775, 1.0295, 1.0285, 1.0340, 0.9975, 1.0089,
1.0182, 1.0191, 1.0727, 1.0375, 1.0204, 1.0035, 1.0275, 1.0305, 1.0383,
0.9900, 1.0200, 1.0360, 1.0018, 1.0014, 0.9629, 0.9696, 1.0488, 0.9846,
1.0166, 1.0004, 1.0193, 1.0052, 1.0267, 1.0243, 1.0107, 0.9456, 1.0478,
0.9933, 1.0224, 1.0337, 0.8880, 1.0013, 0.9733, 1.0558, 0.9860, 1.0284,
1.0364, 1.0264, 1.0486, 1.0706, 1.0193, 0.9990, 1.0349, 1.0028, 0.9599,
1.0390, 1.0043, 1.0125, 1.0550, 1.0478, 1.0416, 0.9980, 0.9994, 0.9806,
0.9902, 1.0020, 1.0574, 0.9765, 1.0064, 0.9898, 0.9972, 1.0216, 1.0402,
0.9730, 1.0502, 1.0091, 0.9761, 1.0121, 1.0241, 1.0432, 0.9697, 1.0423,
1.0502, 1.0249, 1.0326, 1.0516, 1.0145, 1.0687, 0.9958, 1.0580, 0.9947,
0.8908, 0.9941, 1.0354, 1.0422, 1.0353, 1.0140, 1.0395, 1.0555, 1.0154,
1.0325, 1.0212, 0.9976, 1.0089, 0.9949, 1.0112, 0.8455, 1.0415, 1.0202,
1.0137, 0.9632, 1.0298, 1.0080, 1.0302, 0.9589, 1.0115, 1.0142, 1.0384,
1.0214, 1.0406, 1.0281, 1.0158, 1.0356, 1.0363, 0.9789, 1.0268, 0.9997,
0.9798, 1.0295, 1.0490, 1.0406, 1.0319, 1.0491, 0.9804, 1.0375, 1.0092,
1.0166, 0.9765, 1.0302, 1.0472, 1.0011, 0.9737, 1.0238, 0.9457, 1.0232,
1.0547, 1.0108, 1.0015, 1.0235, 1.0073, 0.9836, 1.0202, 1.0412, 0.9726,
1.0105, 1.0332, 1.0335, 1.0399, 0.9981, 0.9722, 0.9925, 1.0199, 1.0253,
0.9931, 1.0254, 1.0412, 1.0363, 0.9674, 1.0248, 0.9878, 1.0503, 1.0360,
0.9900, 1.0171, 1.0059, 1.0310, 1.0047, 1.0354, 1.0173, 1.0377, 1.0234,
1.0059, 1.0264, 1.0382, 0.9575, 1.0607, 1.0096, 1.0119, 0.9823, 0.9702,
1.0038, 1.0403, 0.9807, 1.0222, 0.9837, 0.9580, 1.0418, 1.0393, 1.0006,
1.0109, 0.9838, 1.0098, 1.0321, 1.0173, 1.0424, 0.9828, 1.0320, 1.0090,
0.7263, 1.0356, 1.0334, 1.0278, 1.0256, 1.0093, 1.0276, 1.0390, 1.0187,
1.0042, 1.0446, 1.0096, 1.0331, 1.0213, 1.0019, 1.0301, 1.0093, 1.0420,
1.0412, 0.9844, 0.9913, 1.0505, 1.0311, 0.9108, 1.0195, 1.0426, 1.0111,
1.0421, 1.0140, 1.0066, 1.0258, 0.9818, 1.0153, 0.9956, 1.0241, 1.0067,
1.0110, 1.0109, 1.0443, 1.0040, 1.0444, 1.0420, 1.0453, 1.0007, 1.0426,
1.0081, 0.9448, 1.0177, 1.0033, 1.0049, 0.9890, 1.0073, 1.0198, 1.0485,
1.0486, 1.0514, 1.0442, 1.0128, 1.0035, 0.9939, 1.0338, 1.0136, 1.0490,
1.0483, 1.0213, 0.9902, 1.0412, 0.9877, 0.9700, 1.0446, 1.0773, 1.0425,
0.9962, 0.9884, 0.9983, 1.0213, 0.9991, 0.9959, 1.0060, 1.0242, 1.0087,
0.9715, 1.0195, 1.0285, 1.0259, 1.0072, 0.9626, 1.0100, 1.0173, 0.9628,
1.0260, 0.9918, 1.0367, 0.9541, 0.9944, 1.0355, 1.0081, 0.9988, 0.9805,
1.0187, 1.0623, 0.9889, 1.0154, 1.0200, 1.0226, 1.0471, 1.0404, 1.0180,
1.0497, 1.0200, 1.0021, 1.0176, 1.0192, 0.9869, 1.0124, 1.0370, 1.0522,
0.9919, 1.0465, 0.9398, 0.9798, 1.0182, 1.0265, 1.0638, 1.0409, 1.0433,
1.0165, 1.0251, 1.0108, 1.0179, 1.0334, 1.0235, 0.9636, 0.9982, 0.9862,
1.0315, 1.0203, 1.0328, 1.0409, 1.0232, 1.0200, 1.0183, 1.0322, 1.0324,
0.9795, 0.9581, 1.0115, 0.9940, 1.0160, 1.0612, 0.0848, 0.9953, 0.9940,
0.9840, 1.0136, 1.0366, 1.0321, 1.0162, 1.0296, 1.0038, 0.9978, 1.0298,
0.9862, 1.0045, 1.0084, 1.0136, 0.9857, 0.9729, 1.0590, 1.0305, 1.0069,
1.0403, 0.9882, 1.0400, 1.0210, 1.0071, 1.0199, 1.0361, 1.0103, 1.0321,
0.9971, 1.0242, 1.0043, 1.0083, 1.0358, 1.0249, 1.0248, 1.0253, 1.0145,
1.0308, 1.0350, 1.0026, 1.0243, 1.0622, 1.0130, 1.0450, 1.0352, 1.0044,
1.0198, 0.9947, 1.0206, 1.0624, 1.0011, 1.0171, 1.0067, 0.9833, 1.0265,
1.0177, 1.0020, 1.0103, 0.9927, 1.0338, 1.0246, 1.0028, 1.0184, 1.0532,
1.0212, 1.0513, 0.9979, 1.0056, 0.9834, 1.0311, 1.0111, 0.9987, 0.9418,
1.0497, 0.9920, 1.0564, 1.0277, 1.0527, 1.0265, 1.0009, 1.0173, 1.0130,
1.0417, 1.0247, 0.9964, 1.0203, 0.9906, 1.0352, 0.9994, 1.0124, 1.0124,
1.0030, 1.0344, 0.9895, 1.0337, 0.9933, 1.0251, 1.0052, 1.0388, 1.0371,
1.0128, 0.9976, 0.9893, 1.0142, 1.0612, 1.0193, 1.0249, 0.9843, 1.0064,
1.0315, 1.0404, 1.0377, 1.0080, 1.0284, 1.0408, 1.0599, 1.0578, 0.9919,
1.0388, 1.0239, 1.0189, 1.0336, 0.9965, 0.9937, 1.0287, 1.0425, 0.9853,
1.0257, 1.0320, 1.0446, 1.0142, 1.0003, 1.0219, 1.0120, 1.0285, 0.9889,
1.0396, 1.0238, 0.9810, 1.0061, 0.9414, 1.0253, 1.0192, 1.0393, 1.0319,
1.0012, 0.9929, 1.0029, 1.0078, 1.0248, 1.0323, 1.0434, 0.9189],
device=‘cuda:0’))