Caffe-HRT/python/caffe/test/test_net.py at master · bjthemost/Caffe-HRT · GitHub

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import unittest

import tempfile

import os

import numpy as np

import six

from collections import OrderedDict

import caffe

def simple_net_file(num_output):

"""Make a simple net prototxt, based on test_net.cpp, returning the name

of the (temporary) file."""

f = tempfile.NamedTemporaryFile(mode='w+', delete=False)

f.write("""name: 'testnet' force_backward: true

layer { type: 'DummyData' name: 'data' top: 'data' top: 'label'

dummy_data_param { num: 5 channels: 2 height: 3 width: 4

num: 5 channels: 1 height: 1 width: 1

data_filler { type: 'gaussian' std: 1 }

data_filler { type: 'constant' } } }

layer { type: 'Convolution' name: 'conv' bottom: 'data' top: 'conv'

convolution_param { num_output: 11 kernel_size: 2 pad: 3

weight_filler { type: 'gaussian' std: 1 }

bias_filler { type: 'constant' value: 2 } }

param { decay_mult: 1 } param { decay_mult: 0 }

}

layer { type: 'InnerProduct' name: 'ip' bottom: 'conv' top: 'ip'

inner_product_param { num_output: """ + str(num_output) + """

weight_filler { type: 'gaussian' std: 2.5 }

bias_filler { type: 'constant' value: -3 } } }

layer { type: 'SoftmaxWithLoss' name: 'loss' bottom: 'ip' bottom: 'label'

top: 'loss' }""")

f.close()

return f.name

class TestNet(unittest.TestCase):

def setUp(self):

self.num_output = 13

net_file = simple_net_file(self.num_output)

self.net = caffe.Net(net_file, caffe.TRAIN)

# fill in valid labels

self.net.blobs['label'].data[...] = \

np.random.randint(self.num_output,

size=self.net.blobs['label'].data.shape)

os.remove(net_file)

def test_memory(self):

"""Check that holding onto blob data beyond the life of a Net is OK"""

params = sum(map(list, six.itervalues(self.net.params)), [])

blobs = self.net.blobs.values()

del self.net

# now sum everything (forcing all memory to be read)

total = 0

for p in params:

total += p.data.sum() + p.diff.sum()

for bl in blobs:

total += bl.data.sum() + bl.diff.sum()

def test_layer_dict(self):

layer_dict = self.net.layer_dict

self.assertEqual(list(layer_dict.keys()), list(self.net._layer_names))

for i, name in enumerate(self.net._layer_names):

self.assertEqual(layer_dict[name].type,

self.net.layers[i].type)

def test_forward_backward(self):

self.net.forward()

self.net.backward()

def test_clear_param_diffs(self):

# Run a forward/backward step to have non-zero diffs

self.net.forward()

self.net.backward()

diff = self.net.params["conv"][0].diff

# Check that we have non-zero diffs

self.assertTrue(diff.max() > 0)

self.net.clear_param_diffs()

# Check that the diffs are now 0

self.assertTrue((diff == 0).all())

def test_inputs_outputs(self):

self.assertEqual(self.net.inputs, [])

self.assertEqual(self.net.outputs, ['loss'])

def test_top_bottom_names(self):

self.assertEqual(self.net.top_names,

OrderedDict([('data', ['data', 'label']),

('conv', ['conv']),

('ip', ['ip']),

('loss', ['loss'])]))

self.assertEqual(self.net.bottom_names,

OrderedDict([('data', []),

('conv', ['data']),

('ip', ['conv']),

('loss', ['ip', 'label'])]))

def test_save_and_read(self):

f = tempfile.NamedTemporaryFile(mode='w+', delete=False)

f.close()

self.net.save(f.name)

net_file = simple_net_file(self.num_output)

# Test legacy constructor

# should print deprecation warning

caffe.Net(net_file, f.name, caffe.TRAIN)

# Test named constructor

net2 = caffe.Net(net_file, caffe.TRAIN, weights=f.name)

os.remove(net_file)

os.remove(f.name)

for name in self.net.params:

for i in range(len(self.net.params[name])):

self.assertEqual(abs(self.net.params[name][i].data

- net2.params[name][i].data).sum(), 0)

def test_save_hdf5(self):

f = tempfile.NamedTemporaryFile(mode='w+', delete=False)

f.close()

self.net.save_hdf5(f.name)

net_file = simple_net_file(self.num_output)

net2 = caffe.Net(net_file, caffe.TRAIN)

net2.load_hdf5(f.name)

os.remove(net_file)

os.remove(f.name)

for name in self.net.params:

for i in range(len(self.net.params[name])):

self.assertEqual(abs(self.net.params[name][i].data

- net2.params[name][i].data).sum(), 0)

class TestLevels(unittest.TestCase):

TEST_NET = """

layer {

type: "DummyData"

top: "data"

dummy_data_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }

}

layer {

type: "InnerProduct"

bottom: "data"

top: "NoLevel"

inner_product_param { num_output: 1 }

}

layer {

type: "InnerProduct"

bottom: "data"

top: "Level0Only"

include { min_level: 0 max_level: 0 }

inner_product_param { num_output: 1 }

}

layer {

type: "InnerProduct"

bottom: "data"

top: "Level1Only"

include { min_level: 1 max_level: 1 }

inner_product_param { num_output: 1 }

}

layer {

type: "InnerProduct"

bottom: "data"

top: "Level>=0"

include { min_level: 0 }

inner_product_param { num_output: 1 }

}

layer {

type: "InnerProduct"

bottom: "data"

top: "Level>=1"

include { min_level: 1 }

inner_product_param { num_output: 1 }

}

"""

def setUp(self):

self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)

self.f.write(self.TEST_NET)

self.f.close()

def tearDown(self):

os.remove(self.f.name)

def check_net(self, net, blobs):

net_blobs = [b for b in net.blobs.keys() if 'data' not in b]

self.assertEqual(net_blobs, blobs)

def test_0(self):

net = caffe.Net(self.f.name, caffe.TEST)

self.check_net(net, ['NoLevel', 'Level0Only', 'Level>=0'])

def test_1(self):

net = caffe.Net(self.f.name, caffe.TEST, level=1)

self.check_net(net, ['NoLevel', 'Level1Only', 'Level>=0', 'Level>=1'])

class TestStages(unittest.TestCase):

TEST_NET = """

layer {

type: "DummyData"

top: "data"

dummy_data_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }

}

layer {

type: "InnerProduct"

bottom: "data"

top: "A"

include { stage: "A" }

inner_product_param { num_output: 1 }

}

layer {

type: "InnerProduct"

bottom: "data"

top: "B"

include { stage: "B" }

inner_product_param { num_output: 1 }

}

layer {

type: "InnerProduct"

bottom: "data"

top: "AorB"

include { stage: "A" }

include { stage: "B" }

inner_product_param { num_output: 1 }

}

layer {

type: "InnerProduct"

bottom: "data"

top: "AandB"

include { stage: "A" stage: "B" }

inner_product_param { num_output: 1 }

}

"""

def setUp(self):

self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)

self.f.write(self.TEST_NET)

self.f.close()

def tearDown(self):

os.remove(self.f.name)

def check_net(self, net, blobs):

net_blobs = [b for b in net.blobs.keys() if 'data' not in b]

self.assertEqual(net_blobs, blobs)

def test_A(self):

net = caffe.Net(self.f.name, caffe.TEST, stages=['A'])

self.check_net(net, ['A', 'AorB'])

def test_B(self):

net = caffe.Net(self.f.name, caffe.TEST, stages=['B'])

self.check_net(net, ['B', 'AorB'])

def test_AandB(self):

net = caffe.Net(self.f.name, caffe.TEST, stages=['A', 'B'])

self.check_net(net, ['A', 'B', 'AorB', 'AandB'])

class TestAllInOne(unittest.TestCase):

TEST_NET = """

layer {

type: "DummyData"

top: "data"

top: "label"

dummy_data_param {

shape { dim: 1 dim: 1 dim: 10 dim: 10 }

shape { dim: 1 dim: 1 dim: 1 dim: 1 }

}

include { phase: TRAIN stage: "train" }

}

layer {

type: "DummyData"

top: "data"

top: "label"

dummy_data_param {

shape { dim: 1 dim: 1 dim: 10 dim: 10 }

shape { dim: 1 dim: 1 dim: 1 dim: 1 }

}

include { phase: TEST stage: "val" }

}

layer {

type: "Input"

top: "data"

input_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }

include { phase: TEST stage: "deploy" }

}

layer {

type: "InnerProduct"

bottom: "data"

top: "ip"

inner_product_param { num_output: 2 }

}

layer {

type: "SoftmaxWithLoss"

bottom: "ip"

bottom: "label"

top: "loss"

include: { phase: TRAIN stage: "train" }

include: { phase: TEST stage: "val" }

}

layer {

type: "Softmax"

bottom: "ip"

top: "pred"

include: { phase: TEST stage: "deploy" }

}

"""

def setUp(self):

self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)

self.f.write(self.TEST_NET)

self.f.close()

def tearDown(self):

os.remove(self.f.name)

def check_net(self, net, outputs):

self.assertEqual(list(net.blobs['data'].shape), [1,1,10,10])

self.assertEqual(net.outputs, outputs)

def test_train(self):

net = caffe.Net(self.f.name, caffe.TRAIN, stages=['train'])

self.check_net(net, ['loss'])

def test_val(self):

net = caffe.Net(self.f.name, caffe.TEST, stages=['val'])

self.check_net(net, ['loss'])

def test_deploy(self):

net = caffe.Net(self.f.name, caffe.TEST, stages=['deploy'])

self.check_net(net, ['pred'])

Caffe-HRT/python/caffe/test/test_net.py at master · bjthemost/Caffe-HRT · GitHub

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