import numpy as np
import chainer
import chainer.functions as F
import chainer.links as L
class Alex(chainer.Chain):
"""Single-GPU AlexNet without partition toward the channel axis."""
insize = 227
def __init__(self, shrink_ratio=1):
super(Alex, self).__init__()
sr = shrink_ratio
with self.init_scope():
self.conv1 = L.Convolution2D(None, 96 // sr, 11, stride=4)
self.conv2 = L.Convolution2D(None, 256 // sr, 5, pad=2)
self.conv3 = L.Convolution2D(None, 384 // sr, 3, pad=1)
self.conv4 = L.Convolution2D(None, 384 // sr, 3, pad=1)
self.conv5 = L.Convolution2D(None, 256 // sr, 3, pad=1)
self.fc6 = L.Linear(None, 4096 // sr)
self.fc7 = L.Linear(None, 4096 // sr)
self.fc8 = L.Linear(None, 1000)
def forward(self, x, t):
# def forward(self, x):
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.local_response_normalization(
F.relu(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)))
h = F.dropout(F.relu(self.fc7(h)))
h = self.fc8(h)
loss = F.softmax_cross_entropy(h, t)
#loss = h
# chainer.report({'loss': loss, 'accuracy': F.accuracy(h, t)}, self)
return loss
# from https://github.com/chainer/chainer/blob/master/examples/imagenet/alex.py
if __name__ == '__main__':
np.random.seed(314)
model = Alex(shrink_ratio=23)
# batch * channel * H * W
v = np.random.rand(2, 3, 227, 227).astype(np.float32)
w = np.random.randint(1000, size=2)
from chainer_compiler import ch2o
ch2o.generate_testcase(model, [v, w])
