-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathgrade_p4.py
151 lines (117 loc) · 4.24 KB
/
grade_p4.py
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
import numpy as np
import easynn as nn
import easynn_golden as golden
import easynn_cpp as cpp
import grade
def random_kwargs(kwargs):
return {k: np.random.random(shape) if shape != None else np.random.random() for k, shape in kwargs.items()}
def is_same(p, n, **kwargs):
e0 = p.compile(golden.Builder())
e1 = p.compile(cpp.Builder())
nkwargs = [random_kwargs(kwargs) for i in range(n)]
return all([np.allclose(e0(**nkwargs[i]), e1(**nkwargs[i])) for i in range(n)])
def grade_Q1():
relu = nn.ReLU()
x = relu(nn.Input("x"))
return is_same(x, 1, x = (10, 11, 12, 13))
def grade_Q2():
flatten = nn.Flatten()
x = flatten(nn.Input("x"))
return is_same(x, 1, x = (10, 11, 12, 13))
def grade_Q3():
x = nn.Input2d("images", 10, 11, 3)
return is_same(x, 1, images = (50, 10, 11, 3))
def grade_Q4():
f = nn.Linear("f", 100, 10)
x = f(nn.Input("x"))
x.resolve({
"f.weight": np.random.random((10, 100)),
"f.bias": np.random.random((10,))})
return is_same(x, 1, x = (50, 100))
def grade_Q5():
pool = nn.MaxPool2d(3, 3)
x = pool(nn.Input2d("x", 12, 15, 3))
return is_same(x, 1, x = (10, 12, 15, 3))
def grade_Q6():
c = nn.Conv2d("c", 3, 16, 5)
x = c(nn.Input2d("x", 15, 20, 3))
x.resolve({
"c.weight": np.random.random((16, 3, 5, 5)),
"c.bias": np.random.random((16,))
})
return is_same(x, 1, x = (10, 15, 20, 3))
def grade_Q7():
relu = nn.ReLU()
flatten = nn.Flatten()
f1 = nn.Linear("f1", 28*28, 100)
f2 = nn.Linear("f2", 100, 10)
x = nn.Input2d("images", 28, 28, 1)
x = flatten(x)
x = f2(relu(f1(x)))
x.resolve(np.load("msimple_params.npz"))
mnist_test = np.load("mnist_test.npz")
images = mnist_test["images"][:20]
infer0 = x.compile(golden.Builder())
infer1 = x.compile(cpp.Builder())
logit0 = infer0(images = images)
logit1 = infer1(images = images)
return np.allclose(logit0, logit1)
def grade_Q8():
relu = nn.ReLU()
flatten = nn.Flatten()
f1 = nn.Linear("f1", 28*28, 100)
f2 = nn.Linear("f2", 100, 10)
x = nn.Input2d("images", 28, 28, 1)
x = flatten(x)
x = f2(relu(f1(x)))
x.resolve(np.load("msimple_params.npz"))
mnist_test = np.load("mnist_test.npz")
images = mnist_test["images"][:1000]
infer0 = x.compile(golden.Builder())
infer1 = x.compile(cpp.Builder())
label0 = infer0(images = images).argmax(axis = 1)
label1 = infer1(images = images).argmax(axis = 1)
return np.allclose(label0, label1)
def grade_Q9():
pool = nn.MaxPool2d(2, 2)
relu = nn.ReLU()
flatten = nn.Flatten()
x = nn.Input2d("images", 28, 28, 1)
c1 = nn.Conv2d("c1", 1, 8, 3) # 28->26
c2 = nn.Conv2d("c2", 8, 8, 3) # 26->24
x = pool(relu(c2(relu(c1(x))))) # 24->12
c3 = nn.Conv2d("c3", 8, 16, 3) # 12->10
c4 = nn.Conv2d("c4", 16, 16, 3) # 10->8
x = pool(relu(c4(relu(c3(x))))) # 8->4
f = nn.Linear("f", 16*4*4, 10)
x = f(flatten(x))
x.resolve(np.load("mnist_params.npz"))
mnist_test = np.load("mnist_test.npz")
images = mnist_test["images"][:20]
infer0 = x.compile(golden.Builder())
infer1 = x.compile(cpp.Builder())
logit0 = infer0(images = images)
logit1 = infer1(images = images)
return np.allclose(logit0, logit1)
def grade_Q10():
pool = nn.MaxPool2d(2, 2)
relu = nn.ReLU()
flatten = nn.Flatten()
x = nn.Input2d("images", 28, 28, 1)
c1 = nn.Conv2d("c1", 1, 8, 3) # 28->26
c2 = nn.Conv2d("c2", 8, 8, 3) # 26->24
x = pool(relu(c2(relu(c1(x))))) # 24->12
c3 = nn.Conv2d("c3", 8, 16, 3) # 12->10
c4 = nn.Conv2d("c4", 16, 16, 3) # 10->8
x = pool(relu(c4(relu(c3(x))))) # 8->4
f = nn.Linear("f", 16*4*4, 10)
x = f(flatten(x))
x.resolve(np.load("mnist_params.npz"))
mnist_test = np.load("mnist_test.npz")
images = mnist_test["images"][:1000]
infer0 = x.compile(golden.Builder())
infer1 = x.compile(cpp.Builder())
label0 = infer0(images = images).argmax(axis = 1)
label1 = infer1(images = images).argmax(axis = 1)
return np.allclose(label0, label1)
grade.grade_all("p4", 1, 11, globals())