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import numpy as np
import unittest
from caffe2.proto import caffe2_pb2
from caffe2.python import (
workspace,
device_checker,
test_util,
model_helper,
brew,
)
class TestMiniAlexNet(test_util.TestCase):
def _MiniAlexNetNoDropout(self, order):
# First, AlexNet using the cnn wrapper.
model = model_helper.ModelHelper(name="alexnet")
conv1 = brew.conv(
model,
"data",
"conv1",
3,
16,
11,
("XavierFill", {}),
("ConstantFill", {}),
stride=4,
pad=0
)
relu1 = brew.relu(model, conv1, "relu1")
norm1 = brew.lrn(model, relu1, "norm1", size=5, alpha=0.0001, beta=0.75)
pool1 = brew.max_pool(model, norm1, "pool1", kernel=3, stride=2)
conv2 = brew.group_conv(
model,
pool1,
"conv2",
16,
32,
5,
("XavierFill", {}),
("ConstantFill", {"value": 0.1}),
group=2,
stride=1,
pad=2
)
relu2 = brew.relu(model, conv2, "relu2")
norm2 = brew.lrn(model, relu2, "norm2", size=5, alpha=0.0001, beta=0.75)
pool2 = brew.max_pool(model, norm2, "pool2", kernel=3, stride=2)
conv3 = brew.conv(
model,
pool2,
"conv3",
32,
64,
3,
("XavierFill", {'std': 0.01}),
("ConstantFill", {}),
pad=1
)
relu3 = brew.relu(model, conv3, "relu3")
conv4 = brew.group_conv(
model,
relu3,
"conv4",
64,
64,
3,
("XavierFill", {}),
("ConstantFill", {"value": 0.1}),
group=2,
pad=1
)
relu4 = brew.relu(model, conv4, "relu4")
conv5 = brew.group_conv(
model,
relu4,
"conv5",
64,
32,
3,
("XavierFill", {}),
("ConstantFill", {"value": 0.1}),
group=2,
pad=1
)
relu5 = brew.relu(model, conv5, "relu5")
pool5 = brew.max_pool(model, relu5, "pool5", kernel=3, stride=2)
fc6 = brew.fc(
model, pool5, "fc6", 1152, 1024, ("XavierFill", {}),
("ConstantFill", {"value": 0.1})
)
relu6 = brew.relu(model, fc6, "relu6")
fc7 = brew.fc(
model, relu6, "fc7", 1024, 1024, ("XavierFill", {}),
("ConstantFill", {"value": 0.1})
)
relu7 = brew.relu(model, fc7, "relu7")
fc8 = brew.fc(
model, relu7, "fc8", 1024, 5, ("XavierFill", {}),
("ConstantFill", {"value": 0.0})
)
pred = brew.softmax(model, fc8, "pred")
xent = model.LabelCrossEntropy([pred, "label"], "xent")
loss = model.AveragedLoss([xent], ["loss"])
model.AddGradientOperators([loss])
return model
def _testMiniAlexNet(self, order):
# First, we get all the random initialization of parameters.
model = self._MiniAlexNetNoDropout(order)
workspace.ResetWorkspace()
workspace.RunNetOnce(model.param_init_net)
inputs = dict(
[(str(name), workspace.FetchBlob(str(name))) for name in
model.params]
)
if order == "NCHW":
inputs["data"] = np.random.rand(4, 3, 227, 227).astype(np.float32)
else:
inputs["data"] = np.random.rand(4, 227, 227, 3).astype(np.float32)
inputs["label"] = np.array([1, 2, 3, 4]).astype(np.int32)
cpu_device = caffe2_pb2.DeviceOption()
cpu_device.device_type = caffe2_pb2.CPU
gpu_device = caffe2_pb2.DeviceOption()
gpu_device.device_type = workspace.GpuDeviceType
checker = device_checker.DeviceChecker(0.05, [cpu_device, gpu_device])
ret = checker.CheckNet(
model.net.Proto(),
inputs,
# The indices sometimes may be sensitive to small numerical
# differences in the input, so we ignore checking them.
ignore=['_pool1_idx', '_pool2_idx', '_pool5_idx']
)
self.assertEqual(ret, True)
@unittest.skipIf(not workspace.has_gpu_support,
"No GPU support. Skipping test.")
def testMiniAlexNetNCHW(self):
self._testMiniAlexNet("NCHW")
# No Group convolution support for NHWC right now
#@unittest.skipIf(not workspace.has_gpu_support,
# "No GPU support. Skipping test.")
#def testMiniAlexNetNHWC(self):
# self._testMiniAlexNet("NHWC")
if __name__ == '__main__':
unittest.main()
|
