def test_update_diff(self):
"""
Test update diff for blobs
"""
import scikits.cuda.cublas as cublas
import pycuda.gpuarray as gpuarray
import copy
import caffe_facade
from caffe_facade import pycuda_util
import numpy as np
blobs = list()
for (blob_name, blob) in self.net.params.items():
blobs.append(blob[0])
blobs.append(blob[1])
mult = 0.0001
blobs_update_cpu = [np.random.rand(*blob.diff.shape).astype(np.float32) * mult for blob in blobs]
initial_params_diff = copy.deepcopy(self.net.params_diff)
with pycuda_util.caffe_cuda_context():
h = caffe_facade.cublas_handle()
blobs_gpu = [blob.diff_as_pycuda_gpuarray() for blob in blobs]
blobs_update_gpu = [gpuarray.to_gpu(blob_update_cpu) for blob_update_cpu in blobs_update_cpu]
for (blob_gpu, blob_update_gpu) in zip(blobs_gpu, blobs_update_gpu):
cublas.cublasSaxpy(h, blob_gpu.size, 1.0, blob_update_gpu.gpudata, 1, blob_gpu.gpudata, 1)
for (blob_gpu, initial_param_diff, blob_update_cpu) in zip(blobs_gpu, initial_params_diff, blobs_update_cpu):
assert np.allclose(blob_gpu.get(), initial_param_diff.reshape(blob_gpu.shape) + blob_update_cpu)
params_diff = self.net.params_diff
for (blob_gpu, param_diff) in zip(blobs_gpu, params_diff):
assert np.allclose(blob_gpu.get(), param_diff.reshape(blob_gpu.shape))
def test_blob_diff_to_gpuarray(self):
"""
Test diff_as_pycuda_gpuarray works for all blobs
"""
from caffe_facade import pycuda_util
with pycuda_util.caffe_cuda_context():
for (blob_name, blob) in self.net.params.items():
blob[0].diff_as_pycuda_gpuarray()
blob[1].diff_as_pycuda_gpuarray()
def test_axpy(self):
"""
Test axpy function from scikits.cuda.cublas
"""
import caffe_facade
import scikits.cuda.cublas as cublas
import numpy as np
import pycuda.gpuarray as gpuarray
from caffe_facade import pycuda_util
caffe_facade.set_mode_gpu()
caffe_facade.set_device(0)
x = np.random.randn(5, 4, 3, 2).astype(np.float32)
y = np.random.randn(5, 4, 3, 2).astype(np.float32)
with pycuda_util.caffe_cuda_context():
h = caffe_facade.cublas_handle()
x_gpu = gpuarray.to_gpu(x)
y_gpu = gpuarray.to_gpu(y)
cublas.cublasSaxpy(h, x.size, 1.0, x_gpu.gpudata, 1, y_gpu.gpudata,
1)
y = x + y
assert np.allclose(y_gpu.get(), y)
def test_average(self):
"""
Test average function on GPU
"""
import caffe_facade
import scikits.cuda.cublas as cublas
import numpy as np
import pycuda.gpuarray as gpuarray
from caffe_facade import pycuda_util
shape = (64, 32, 5, 5)
num_elements = np.prod(shape)
num_samples = 10
data_cpu = np.zeros(shape, np.float32)
data_cpu_received = [
np.random.rand(*shape).astype(np.float32)
for i in range(num_samples)
]
with pycuda_util.caffe_cuda_context():
#GPU average
data_gpu = gpuarray.to_gpu(np.zeros(shape, np.float32))
h = caffe_facade.cublas_handle()
data_gpu_temp = gpuarray.to_gpu(data_cpu_received[0])
cublas.cublasScopy(h, num_elements, data_gpu_temp.gpudata, 1,
data_gpu.gpudata, 1)
for i in range(1, len(data_cpu_received)):
data_gpu_temp = gpuarray.to_gpu(data_cpu_received[i])
cublas.cublasSaxpy(h, num_elements, 1.0, data_gpu_temp.gpudata,
1, data_gpu.gpudata, 1)
cublas.cublasSscal(h, num_elements, 1.0 / num_samples,
data_gpu.gpudata, 1)
#CPU average
data_cpu = data_cpu_received[0] / num_samples
for i in range(1, len(data_cpu_received)):
data_cpu += data_cpu_received[i] / num_samples
assert np.allclose(data_cpu, data_gpu.get())