def test_cycles_per_layer(self):
l = CaffeLoader(
None,
"./FINN/inputs/dorefanet-pruned-without-extra-messages.prototxt")
net = nn.NN(l)
dev = device.Device('XLNX:KU115.json')
perfmodel = pm.PerfModel(net, dev)
fps = perfmodel.maximise_fps()
for idx, layer in enumerate(net.layers):
in_chans = net.layers[idx].getInputSize()
out_chans = net.layers[idx].getOutputSize()
out_dim = net.layers[idx].get_out_dim()
if isinstance(in_chans, tuple):
print in_chans
in_chans = in_chans[0]
if isinstance(out_chans, tuple):
print out_chans
out_chans = out_chans[0]
if isinstance(out_dim, tuple):
print out_dim
out_dim = out_dim[0]
print perfmodel.SIMD[idx], in_chans
print perfmodel.PE[idx], out_chans
print perfmodel.MMV[idx], out_dim
self.assertLessEqual(perfmodel.SIMD[idx], in_chans)
self.assertLessEqual(perfmodel.PE[idx], out_chans)
self.assertLessEqual(perfmodel.MMV[idx], out_dim)
def test_simd_pe_mmv_constraints(self):
l = CaffeLoader(None, "./FINN/inputs/sfc.prototxt")
net = nn.NN(l)
dev = device.Device('XLNX:KU115.json')
perfmodel = pm.PerfModel(net, dev)
fps = perfmodel.maximise_fps()
for idx, layer in enumerate(net.layers):
self.assertLessEqual(perfmodel.SIMD[idx], layer.getInputSize())
self.assertLessEqual(perfmodel.PE[idx], layer.getOutputSize())
self.assertLessEqual(perfmodel.MMV[idx], layer.get_out_dim())
def test_cycles_per_op(self):
l = CaffeLoader("./FINN/inputs/sfc.caffemodel",
"./FINN/inputs/sfc.prototxt")
net = nn.NN(l)
dev = device.Device('XLNX:VU9P.json')
perfmodel = pm.PerfModel(net, dev)
ops = perfmodel.network_utilisation()
num_matrix_layers = net.count_matrix_layers()
self.assertEqual(ops['luts'],
2 * num_matrix_layers * dev.lut_cost_per_op())
def setUp(self):
nname = "lenet-hwgq-w1a2"
proto = FINN_ROOT + "/inputs/%s.prototxt" % nname
weights = FINN_ROOT + "/inputs/%s.caffemodel" % nname
l = CaffeLoader(weights, proto)
self.net = nn.NN(l)
self.streamlined_net = nn.NN(
layers=transform.makeCromulent(self.net.layers))
# use the first numImagesToTest of the test set for verification
self.numImagesToTest = 10
# expected number of successful predictions
self.ok_golden = 10
# expected number of unsuccessful predictions
self.nok_golden = 0
def setUp(self):
self.nname = "caffenet-hwgq-w1a2"
proto = config.FINN_ROOT + "/inputs/%s.prototxt" % self.nname
weights = config.FINN_ROOT + "/inputs/%s.caffemodel" % self.nname
# download weights if not already on disk
weights_url = "http://www.svcl.ucsd.edu/projects/hwgq/AlexNet_HWGQ.caffemodel"
if not os.path.exists(weights):
print("Downloading HWGQ CaffeNet weights")
urlretrieve(weights_url, weights)
l = CaffeLoader(weights, proto)
self.net = nn.NN(l)
# use the first numImagesToTest of the test set for verification
self.numImagesToTest = 10
# expected number of successful predictions
self.ok_golden = 7
# expected number of unsuccessful predictions
self.nok_golden = 3
def setUp(self):
nname = "lfc-w1a1"
proto = FINN_ROOT + "/inputs/%s.prototxt" % nname
weights = FINN_ROOT + "/inputs/%s.caffemodel" % nname
l = CaffeLoader(weights, proto)
self.net = nn.NN(l)
frequency = 300
self.dev = device.Device('XLNX:VU9P.json', frequency)
self.streamlined_net = copy.deepcopy(self.net)
print self.streamlined_net.layers
self.streamlined_net.layers = transform.makeCromulent(
self.streamlined_net.layers)
print self.streamlined_net.layers
# use the first numImagesToTest of the test set for verification
self.numImagesToTest = 1000
# expected number of successful predictions
self.ok_golden = 967
# expected number of unsuccessful predictions
self.nok_golden = 33
def demo_hwgq_import():
l = CaffeLoader(None, "inputs/sfc.prototxt")
net = FINN.core.nn.NN(l)
dev = device.Device('XLNX:KU115.json')
perf = perf_model.PerfModel(net, dev)
perf.print_folding_factors()
perf.print_hardware_cost()
for idx, val in enumerate(perf.SIMD):
perf.SIMD[idx] = 5
#perf.PE[idx] = 10
perf.print_folding_factors()
perf.print_hardware_cost()
for idx, val in enumerate(perf.SIMD):
perf.SIMD[idx] = 20
#perf.PE[idx] = 100
perf.print_folding_factors()
perf.print_hardware_cost()
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import FINN.core.perf_model as pm
import FINN.core.device as device
import FINN.core.nn as nn
from FINN.frontend.caffeloader import CaffeLoader
print "Hardware model"
frequency = 200
#dev = device.Device('XLNX:PYNQ-Z1.json', frequency)
dev = device.Device('XLNX:VU9P.json', frequency)
net = nn.NN(CaffeLoader(None, './FINN/inputs/lfc-w1a1.prototxt'))
perfmodel = pm.PerfModel(net, dev)
fps = perfmodel.maximise_fps()
print "Network Utilization"
print perfmodel.network_utilisation()['luts']/dev.luts*100
print perfmodel.network_utilisation()['brams']/dev.brams*100
def setUp(self):
l = CaffeLoader("inputs/sfc.caffemodel", "inputs/sfc.prototxt")
self.net = nn.NN(l)