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How to use test_load method in uiautomator
Best Python code snippet using uiautomator
ram512bit_test.py
Source:ram512bit_test.py 
1import pytest2from ram512bit import RAM512Bit3def test_ram512bit():4 # MEMORY 05 test_input = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],6 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],7 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],8 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],9 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],10 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],11 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],12 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]13 test_address = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1, 0],14 [0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 1],15 [0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1],16 [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1, 0],17 [0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 1],18 [0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1]]19 test_load = [1, 1, 1, 1, 1, 1, 1, 1,20 0, 0, 0, 0, 0, 0, 0, 0]21 test_output = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],22 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],23 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],24 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],25 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],26 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],27 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],28 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]]29 ram512Bit = RAM512Bit()30 for i in range(len(test_input)):31 result = ram512Bit.compute(test_input[i], test_address[i], test_load[i])32 assert result == test_output[i], 'Error, in: {}, address: {}, load: {} should output: {} but got: {}'.format(33 test_input[i],34 test_address[i],35 test_load[i],36 test_output[i],37 result)38 # MEMORY 739 test_input = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],40 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],41 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],42 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],43 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],44 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],45 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],46 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]47 test_address = [[0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 1], [0, 0, 0, 1, 1, 1, 0, 1, 0],48 [0, 0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0, 1],49 [0, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1],50 [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 1], [0, 0, 0, 1, 1, 1, 0, 1, 0],51 [0, 0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0, 1],52 [0, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1]]53 test_load = [1, 1, 1, 1, 1, 1, 1, 1,54 0, 0, 0, 0, 0, 0, 0, 0]55 test_output = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],56 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],57 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],58 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],59 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],60 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],61 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],62 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]]63 for i in range(len(test_input)):64 result = ram512Bit.compute(test_input[i], test_address[i], test_load[i])65 assert result == test_output[i], 'Error, in: {}, address: {}, load: {} should output: {} but got: {}'.format(66 test_input[i],67 test_address[i],68 test_load[i],69 test_output[i],70 result)71 # MEMORY 1672 test_input = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],73 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],74 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],75 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],76 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],77 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],78 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],79 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]80 test_address = [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 1, 0],81 [0, 0, 1, 0, 0, 0, 0, 1, 1], [0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 1],82 [0, 0, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 0, 0, 0, 1, 1, 1],83 [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 1, 0],84 [0, 0, 1, 0, 0, 0, 0, 1, 1], [0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 1],85 [0, 0, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 0, 0, 0, 1, 1, 1]]86 test_load = [1, 1, 1, 1, 1, 1, 1, 1,87 0, 0, 0, 0, 0, 0, 0, 0]88 test_output = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],89 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],90 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],91 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],92 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],93 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],94 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],95 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]]96 for i in range(len(test_input)):97 result = ram512Bit.compute(test_input[i], test_address[i], test_load[i])98 assert result == test_output[i], 'Error, in: {}, address: {}, load: {} should output: {} but got: {}'.format(99 test_input[i],100 test_address[i],101 test_load[i],102 test_output[i],103 result)104 # MEMORY 32105 test_input = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],106 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],107 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],108 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],109 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],110 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],111 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],112 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]113 test_address = [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 0, 0, 1, 0],114 [0, 1, 0, 0, 0, 0, 0, 1, 1], [0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 1, 0, 1],115 [0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 0, 0, 0, 0, 1, 1, 1],116 [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 0, 0, 1, 0],117 [0, 1, 0, 0, 0, 0, 0, 1, 1], [0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 1, 0, 1],118 [0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 0, 0, 0, 0, 1, 1, 1]]119 test_load = [1, 1, 1, 1, 1, 1, 1, 1,120 0, 0, 0, 0, 0, 0, 0, 0]121 test_output = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],122 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],123 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],124 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],125 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],126 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],127 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],128 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]]129 for i in range(len(test_input)):130 result = ram512Bit.compute(test_input[i], test_address[i], test_load[i])131 assert result == test_output[i], 'Error, in: {}, address: {}, load: {} should output: {} but got: {}'.format(132 test_input[i],133 test_address[i],134 test_load[i],135 test_output[i],136 result)137 # MEMORY 64138 test_input = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],139 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],140 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],141 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],142 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],143 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],144 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],145 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]146 test_address = [[1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 1, 0],147 [1, 0, 0, 0, 0, 0, 0, 1, 1], [1, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 1, 0, 1],148 [1, 0, 0, 0, 0, 0, 1, 1, 0], [1, 0, 0, 0, 0, 0, 1, 1, 1],149 [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 1, 0],150 [1, 0, 0, 0, 0, 0, 0, 1, 1], [1, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 1, 0, 1],151 [1, 0, 0, 0, 0, 0, 1, 1, 0], [1, 0, 0, 0, 0, 0, 1, 1, 1]]152 test_load = [1, 1, 1, 1, 1, 1, 1, 1,153 0, 0, 0, 0, 0, 0, 0, 0]154 test_output = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],155 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],156 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],157 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],158 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],159 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],160 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],161 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]]162 for i in range(len(test_input)):163 result = ram512Bit.compute(test_input[i], test_address[i], test_load[i])164 assert result == test_output[i], 'Error, in: {}, address: {}, load: {} should output: {} but got: {}'.format(165 test_input[i],166 test_address[i],167 test_load[i],168 test_output[i],...
ram64bit_test.py
Source:ram64bit_test.py
1import pytest2from ram64bit import RAM64Bit3def test_ram64bit():4 # MEMORY 05 test_input = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],6 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],7 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],8 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],9 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],10 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],11 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],12 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]13 test_address = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1], [0, 0, 0, 1, 0, 0],14 [0, 0, 0, 1, 0, 1], [0, 0, 0, 1, 1, 0], [0, 0, 0, 1, 1, 1],15 [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1], [0, 0, 0, 1, 0, 0],16 [0, 0, 0, 1, 0, 1], [0, 0, 0, 1, 1, 0], [0, 0, 0, 1, 1, 1]]17 test_load = [1, 1, 1, 1, 1, 1, 1, 1,18 0, 0, 0, 0, 0, 0, 0, 0]19 test_output = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],20 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],21 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],22 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],23 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],24 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],25 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],26 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]]27 ram64Bit = RAM64Bit()28 for i in range(len(test_input)):29 result = ram64Bit.compute(test_input[i], test_address[i], test_load[i])30 assert result == test_output[31 i], 'Error, in[{}], address[{}], load[{}] should output: [{}] but got: [{}]'.format(test_input[i],32 test_address[i],33 test_load[i],34 test_output[i],35 result)36 # MEMORY 137 test_input = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],38 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],39 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],40 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],41 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],42 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],43 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],44 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]45 test_address = [[0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1], [0, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 0, 0],46 [0, 0, 1, 1, 0, 1], [0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1],47 [0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1], [0, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 0, 0],48 [0, 0, 1, 1, 0, 1], [0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1]]49 test_load = [1, 1, 1, 1, 1, 1, 1, 1,50 0, 0, 0, 0, 0, 0, 0, 0]51 test_output = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],52 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],53 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],54 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],55 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],56 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],57 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],58 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]]59 for i in range(len(test_input)):60 result = ram64Bit.compute(test_input[i], test_address[i], test_load[i])61 assert result == test_output[62 i], 'Error, in[{}], address[{}], load[{}] should output: [{}] but got: [{}]'.format(test_input[i],63 test_address[i],64 test_load[i],65 test_output[i],66 result)67 # MEMORY 268 test_input = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],69 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],70 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],71 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],72 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],73 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],74 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],75 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]76 test_address = [[0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1], [0, 1, 0, 0, 1, 0], [0, 1, 0, 0, 1, 1], [0, 1, 0, 1, 0, 0],77 [0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 1, 0], [0, 1, 0, 1, 1, 1],78 [0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1], [0, 1, 0, 0, 1, 0], [0, 1, 0, 0, 1, 1], [0, 1, 0, 1, 0, 0],79 [0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 1, 0], [0, 1, 0, 1, 1, 1]]80 test_load = [1, 1, 1, 1, 1, 1, 1, 1,81 0, 0, 0, 0, 0, 0, 0, 0]82 test_output = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],83 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],84 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],85 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],86 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],87 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],88 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],89 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]]90 for i in range(len(test_input)):91 result = ram64Bit.compute(test_input[i], test_address[i], test_load[i])92 assert result == test_output[93 i], 'Error, in[{}], address[{}], load[{}] should output: [{}] but got: [{}]'.format(test_input[i],94 test_address[i],95 test_load[i],96 test_output[i],97 result)98 # MEMORY 799 test_input = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],100 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],101 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],102 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],103 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],104 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],105 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],106 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]107 test_address = [[1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 1], [1, 1, 1, 0, 1, 0], [1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 0, 0],108 [1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1],109 [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 1], [1, 1, 1, 0, 1, 0], [1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 0, 0],110 [1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1]]111 test_load = [1, 1, 1, 1, 1, 1, 1, 1,112 0, 0, 0, 0, 0, 0, 0, 0]113 test_output = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],114 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],115 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],116 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],117 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],118 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],119 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],120 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]]121 for i in range(len(test_input)):122 result = ram64Bit.compute(test_input[i], test_address[i], test_load[i])123 assert result == test_output[124 i], 'Error, in[{}], address[{}], load[{}] should output: [{}] but got: [{}]'.format(test_input[i],125 test_address[i],126 test_load[i],127 test_output[i],...
main.py
Source:main.py
1import torch2import numpy as np3import dataset4import model5import training6import time7from pathlib import Path8from types import SimpleNamespace9import dnnutil10class AllPairContrastLoss(torch.nn.Module):11 def __init__(self, m=1.0):12 # we want the margin to be somewhat small. We need the contrast loss13 # in order to avoid mode collapse, but we care most about pushing 14 # same-class representations close together15 super(AllPairContrastLoss, self).__init__()16 self.m = m17 18 def forward(self, x, labels, parts):19 pairwise_dist_sq = torch.mm(x, x.t())20 squared_norm = pairwise_dist_sq.diag()21 pairwise_dist_sq = (22 squared_norm.view(1, -1) + 23 squared_norm.view(-1, 1) - 24 2 * pairwise_dist_sq)25 pairwise_dist_sq.clamp_(min=0.0)26 same_label = labels.view(-1, 1).eq(labels)27 same_part = parts.view(-1, 1).eq(parts)28 ## only taking top half of matrix, so not duplicating every element in symmetrical matrix29 same_label_flat = same_label[torch.triu(torch.ones(x.shape[0],x.shape[0])).eq(1)] 30 same_part_flat = same_part[torch.triu(torch.ones(x.shape[0],x.shape[0])).eq(1)] 31 32 pairwise_dist_sq_flat = pairwise_dist_sq[torch.triu(torch.ones(x.shape[0],x.shape[0])).eq(1)]33 pairs_same = pairwise_dist_sq_flat[same_label_flat * same_part_flat]34 pairs_diff = pairwise_dist_sq_flat[(1-same_label_flat)*same_part_flat]35 n_same = pairs_same.size(0)36 perm = torch.randperm(pairs_diff.size(0))37 idx = perm[:n_same]38 pairs_diff_new = pairs_diff[idx]39 loss_pos = pairs_same.mean()40 loss_neg = torch.clamp(self.m - pairs_diff_new.sqrt(), min=0).pow(2).mean()41 loss = loss_pos + loss_neg42 return loss43def accuracy(prediction, label):44 acc = torch.mean(torch.eq(prediction.argmax(1), label).float()).item()45 return acc46def setup_vanilla_wings(args):47 root = '/multiview/datasets/papillon/part_crops_new'48 train_data = dataset.WingDataset(root, train=True)49 test_data = dataset.WingDataset(root, train=False)50 kwargs = {51 'batch_size': args.batch_size,52 'num_workers': min(16, args.batch_size // 2),53 }54 DataLoader = torch.utils.data.DataLoader55 train_load = DataLoader(train_data, shuffle=True, **kwargs)56 test_load = DataLoader(test_data, shuffle=False, **kwargs)57 net = dnnutil.load_model(model.BFResnet, args.model)58 loss_fn = torch.nn.CrossEntropyLoss()59 opt = torch.optim.Adam(net.parameters(), lr=args.lr, weight_decay=1e-4)60 trainer = dnnutil.ClassifierTrainer(net, opt, loss_fn, accuracy)61 state = SimpleNamespace(62 train_load=train_load,63 test_load=test_load,64 net=net,65 loss_fn=loss_fn,66 opt=opt,67 trainer=trainer)68 return state69def setup_wingnet(args):70 #root = '/multiview/datasets/papillon/part_crops_gen'71 root = '/multiview/datasets/papillon/part_crops_new'72 largs = dict(73 batch_size=args.batch_size,74 num_workers=min(24, args.batch_size // 2),75 )76 kwargs = dict()77 invariant = args.invariant78 if invariant:79 kwargs.update(num_trans=args.num_trans )##return part ids with class labels and images80 dataset_class = dataset.InvariantWingDataset81 largs.update(collate_fn=dataset.multi_collate)82 else:83 kwargs.update(parts=True)84 dataset_class = dataset.WingDataset85 train_data = dataset_class(root, True, **kwargs)86 test_data = dataset_class(root, False, **kwargs)87 n = len(train_data)88 m = len(test_data)89 tr_samp = dataset.RandomSubsetSampler(n, n // 6)90 te_samp = dataset.RandomSubsetSampler(m, m // 6)91 DataLoader = torch.utils.data.DataLoader92 train_load = DataLoader(train_data, sampler=tr_samp, **largs)93 test_load = DataLoader(test_data, sampler=te_samp, **largs)94 nargs = dict(branches=4, invar=invariant, model='resnet18')95 if args.init_pretrained:96 net = dnnutil.load_model(model.Wingnet, args.init_pretrained, **nargs)97 net.resnet = dnnutil.load_model(model.ResnetBase, args.init_pretrained)98 else:99 net = dnnutil.network.load_model(model.Wingnet, args.init_pretrained, **nargs)100 if args.freeze:101 try:102 net.freeze_base_()103 params = net.head.parameters()104 except AttributeError:105 net.module.freeze_base_()106 params = net.module.head.parameters()107 else:108 params = net.parameters()109 loss = torch.nn.CrossEntropyLoss()110 siam_loss = AllPairContrastLoss()111 opt = torch.optim.Adam(params, lr=args.lr, weight_decay=1e-4)112 113 trainer = training.WingnetInvariantTrainer(114 net, opt, loss, siam_loss, accuracy, invariant, alpha=args.alpha)115 state = SimpleNamespace(116 train_load=train_load,117 test_load=test_load,118 net=net,119 opt=opt,120 trainer=trainer)121 return state122def setup_multiwing_weighting(args, use50=False):123 root = '/multiview/datasets/papillon/part_crops_new'124 train_data = dataset.SegStatsAndWingsDataset(root, train=True)125 test_data = dataset.SegStatsAndWingsDataset(root, train=False)126 collate = dataset.seg_multiwing_collate127 kwargs = {128 'batch_size': args.batch_size,129 'num_workers': 16,130 'collate_fn': collate,131 }132 DataLoader = torch.utils.data.DataLoader133 train_load = DataLoader(train_data, shuffle=True, **kwargs)134 test_load = DataLoader(test_data, shuffle=False, **kwargs)135 136 special = args.special ## special modification, use resnet base on each wing, no invariant layers137# import pdb; pdb.set_trace()138 if args.init_pretrained:139 net = dnnutil.network.load_model(model.WingnetA, args.init_pretrained, special=special)140 if special:141 net.basenet = dnnutil.network.load_model(model.BFResnet, args.init_pretrained)142 else:143 net.basenet = dnnutil.network.load_model(model.Wingnet,144 args.init_pretrained, branches=4)145 else:146 net = dnnutil.network.load_model(model.WingnetA, args.model)147 params = net.parameters()148 opt = torch.optim.Adam(params, lr=args.lr, weight_decay=1e-4)149 loss_fn = torch.nn.CrossEntropyLoss()150 trainer = training.SegMultiwingTrainer(net, opt, loss_fn, accuracy, special=special)151 state = SimpleNamespace(152 train_load=train_load,153 test_load=test_load,154 net=net,155 loss_fn=loss_fn,156 opt=opt,157 trainer=trainer)158 return state159def get_setup(args):160 method = args.method161 if method == 'base':162 state = setup_vanilla_wings(args)163 elif method == 'invar':164 state = setup_wingnet(args )165 elif method == 'multi':166 state = setup_multiwing_weighting(args)167 else:168 raise NotImplementedError(f'Method {method} is not yet supported')169 return state170def main():171 parser = dnnutil.basic_parser(lr=1e-4, batch_size=24)172 parser.add_argument('method', choices=['base', 'invar', 'multi',],173 help='Which stage to train')174 parser.add_argument('--alpha', type=float, default=0.01,175 help='Loss tradeoff (for invar model)')176 parser.add_argument('--init-pretrained', metavar='WEIGHTS', default='',177 help='Path to the weights of a model from which the model being '178 'trained is derived')179 parser.add_argument('--num-trans', type=int, default=4,180 help='Number of transforms to perform for the transform invariant layer')181 parser.add_argument('--freeze', action='store_true',182 help='Freeze the weights of the base network')183 parser.add_argument('--invariant', action='store_true',184 help='Train Invariant network with contrast loss')185 parser.add_argument('--drop', action='store_true',186 help='Drop Learning rate by one order of magnitude after 25 epochs')187 parser.add_argument('--special', action='store_true',188 help='Use resnet, without wing branches, best performance')189 parser.add_argument("--cmatrix", action='store_true',190 help="Print out confusion matrix")191 args = parser.parse_args()192 manager = dnnutil.Manager('runs', args.rid)193 manager.set_description(args.note)194 args = manager.load_state(args, restore_lr=False)195 state = get_setup(args)196 train_load, test_load = state.train_load, state.test_load197 net = state.net198 optim = state.opt199 trainer = state.trainer200 logger = dnnutil.TextLog(manager.run_dir / 'log.txt')201 checkpointer = dnnutil.Checkpointer(manager.run_dir, save_multi=True, period=5)202 logger.text(str(args))203 with manager.run_dir.joinpath('.lr').open('w') as f:204 f.write(str(args.lr))205 206 if args.method == 'multi':207 n_classes = 150208 else:209 n_classes = 0210 for e in range(args.start, args.start + args.epochs):211 212 with manager.run_dir.joinpath('.lr').open('r') as f:213 lr = float(f.read().strip())214 if args.drop and e >= 25:215 optim.param_groups[-1]['lr'] = lr*.1216 else:217 optim.param_groups[-1]['lr'] = lr218 219 start = time.time()220 if n_classes != 0:221 train_loss, train_acc, train_class_acc = trainer.train(train_load, e, nclasses = n_classes)222 test_loss, test_acc, test_class_acc = trainer.eval(test_load, e,nclasses = n_classes)223 if args.cmatrix:224 np.savez("cmatrix",test = test_class_acc, train=train_class_acc) 225 else:226 train_loss, train_acc = trainer.train(train_load, e)227 test_loss, test_acc = trainer.eval(test_load, e)228 t = time.time() - start229 lr = optim.param_groups[-1]['lr']230 logger.log(e, t, train_loss, train_acc, test_loss, test_acc, lr)231 checkpointer.checkpoint(net, test_loss, e)232if __name__ == '__main__':...
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