forked from tmbdev-tutorials/dl-2018
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathimgimg.py
183 lines (167 loc) · 6.71 KB
/
imgimg.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
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
from numpy import *
import numpy as np
from random import randint, uniform
from pylab import randn
import h5py
import torch
from torch import nn
from torch import optim
import torchvision
from scipy import ndimage as ndi
import flex
import layers
import time
import copy
import pylab
import matplotlib.pyplot as plt
from IPython import display
training_figsize = (4,4)
def evaluate(model, images, targets, bs=200):
assert images.dtype == torch.float
assert targets.dtype == torch.float
losses = []
with torch.no_grad():
for i in range(0, len(images), bs):
outputs = model.forward(images[i:i+bs].type(torch.float))
results.append(outputs)
loss = criterion(outputs, targets[i:i+bs])
losses.append(float(loss))
return mean(losses)
def train(model, images, targets, ntrain=100000, bs=20, lr=0.001, momentum=0.9, decay=0.0):
assert images.dtype == torch.float
assert targets.dtype == torch.float
with torch.no_grad():
model.forward(images[:bs].type(torch.float))
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum, weight_decay=decay)
losses = []
for i in range(ntrain//bs):
with torch.set_grad_enabled(True):
optimizer.zero_grad()
start = randint(0, len(images)-bs)
inputs = images[start:start+bs].type(torch.float)
outputs = model(inputs)
if isinstance(outputs, tuple):
loss = criterion(outputs[0], targets[start:start+bs].type(torch.float)) + \
outputs[1]
else:
loss = criterion(outputs, targets[start:start+bs].type(torch.float))
losses.append(float(loss))
loss.backward()
optimizer.step()
return losses
class AutoMLP(object):
def __init__(self, make_model, images, targets,
initial_bs=50,
initial_lrs=10**linspace(-6, 2, 20),
initial_ntrain=10000,
momentum=0.9,
ntrain=10000,
maxtrain=1e6,
selection_noise=0.05,
mintrain=1e6,
stop_no_improvement=2.0,
decay=1e-6
):
self.make_model = make_model
self.images = images
self.targets = targets
self.verbose = False
self.initial_bs = initial_bs
self.initial_lrs = initial_lrs
self.initial_ntrain = int(initial_ntrain)
self.ntrain = int(ntrain)
self.momentum = momentum
self.decay = decay
self.maxrounds = int(maxtrain) // int(ntrain)
self.selection_noise = selection_noise
self.mintrain = int(mintrain)
self.stop_no_improvement = stop_no_improvement
self.best_model = None
def initial_population(self, make_model):
population = []
for lr in self.initial_lrs:
model = make_model().cuda()
model.PARAMS = dict(bs=self.initial_bs, lr=lr, id=randint(0, 1000000000))
model.LOG = []
population.append(model)
return population
def selection(self, population, size=4, key="training_loss"):
if len(population) == 0: return []
for model in population:
model.KEY = (1.0+randn()*self.selection_noise) * model.LOG[-1][key]
population = sorted(population, key=lambda m: m.KEY)
while len(population) > size: del population[-1]
return population
def mutation(self, old_population, variants=1, variation=0.8):
population = []
for model in old_population:
population += [model]
for _ in range(variants):
cloned = copy.deepcopy(model)
cloned.PARAMS = dict(
lr = clip(cloned.PARAMS["lr"] * uniform(variation, 1.0/variation), 1e-7, 1e2),
bs = clip(int(cloned.PARAMS["bs"] * uniform(variation, 1.0/variation)), 1, 1000),
id = randint(0, 1000000000)
)
population += [cloned]
return population
def train_population(self, population, ntrain=50000, momentum=0.9, verbose=False):
infos = []
for model in population:
lr, bs = [model.PARAMS[name] for name in "lr bs".split()]
ntrained = 0 if len(model.LOG)==0 else model.LOG[-1]["ntrain"]
training_loss = train(model, self.images, self.targets, lr=lr, bs=bs,
momentum=self.momentum,
decay=self.decay,
ntrain=ntrain)
if isinstance(training_loss, list):
training_loss = mean(training_loss[-100:])
info = dict(
training_loss=training_loss,
lr=lr,
ntrain=ntrain+ntrained,
momentum=momentum,
bs=bs)
if self.verbose: print info
model.LOG += [info]
infos += [info]
return infos
def to(self, device):
for model in self.population:
model.to(device)
def cpu(self):
for model in self.population:
model.cpu()
def train(self):
self.fig = plt.figure(figsize=training_figsize)
self.fig.add_subplot(1,1,1)
self.ax = self.fig.get_axes()[0]
self.infos = []
self.population = self.initial_population(self.make_model)
initial_infos = self.train_population(self.population, ntrain=self.initial_ntrain)
# infos += initial_infos
self.population = self.selection(self.population)
for r in xrange(self.maxrounds):
old_population = [copy.deepcopy(model) for model in self.population]
self.population = self.mutation(self.population)
self.infos += self.train_population(self.population, ntrain=self.ntrain)
self.population = self.selection(self.population + old_population)
# information display
if len(self.infos)>0: self.display()
self.cpu()
self.fig.clf()
return self.best()
def display(self, key="training_loss", yscale="log", ylim=None):
self.ax.cla()
self.ax.set_yscale(yscale)
if ylim is not None: self.ax.set_ylim(ylim)
self.ax.scatter(*zip(*[(l["ntrain"], l["training_loss"]) for l in self.infos]))
display.clear_output(wait=True)
display.display(self.fig)
def best(self):
return self.population[0].cuda()
def info(self, model=None):
for i, key in enumerate("training_loss lr bs".split()):
pylab.subplot(2, 2, i+1)
pylab.plot(*zip(*[(l["ntrain"], l[key]) for l in model.LOG]))