def TestLinear1d(_dtype):
"""
_dtype: specify data type of data one of {np.int32, np.float32, np.float64}
"""
in_size = 5
in_data = np.empty([in_size], dtype=_dtype)
out_size = 3
out_data = np.empty([out_size], dtype=_dtype)
weight_size = [out_size, in_size]
weight = np.empty(weight_size, dtype=_dtype)
bias = None #bias = np.zeros([out_size], dtype=_dtype)
v = 0
for i in range(in_size):
in_data[i] = v % 10
v += 1
for r in range(out_size):
for c in range(in_size): # make identity matrix
if (r==c): weight[r][c] = 1
else : weight[r][c] = 0
if bias is not None:
for b in range(out_size): bias[b] = 0
status = Linear1d( out_data # out_size
, in_data # in_size
, weight # out_size x in_size
, bias # out_size
)
if status:
dlr_common.DlrPrint(f"in_data:\n{in_data}", flush=True)
dlr_common.DlrPrint(f"weight:\n{weight}", flush=True)
dlr_common.DlrPrint(f"bias:\n{bias}", flush=True)
dlr_common.DlrPrint(f"out_data:\n{out_data}", flush=True)
def TestDeconvolution2d(_dtype):
"""
_dtype: specify data type of data one of {np.int32, np.float32, np.float64}
"""
in_channel = 2
in_size = 5
in_data = np.empty([in_channel, in_size, in_size], dtype=_dtype)
out_channel = 1
kernel_size = 3
kernel = np.empty([out_channel, in_channel, kernel_size, kernel_size],
dtype=_dtype)
bias = None #bias = np.zeros([out_channel], dtype=_dtype)
stride = 1
padding = 0
status, out_size = GetOutputSizeOfDeconvolution2d(
in_size, kernel_size, stride, padding)
if not status: return
# prepare arrays to pass to C function
out_data = np.empty([out_channel, out_size, out_size], dtype=_dtype)
v = 0
for i in range(in_channel):
for r in range(in_size):
for c in range(in_size):
in_data[i][r][c] = v % 10
v += 1
for o in range(out_channel):
for i in range(in_channel):
for r in range(kernel_size):
for c in range(kernel_size): # make identity matrix
if (r == c): kernel[o][i][r][c] = 1
else: kernel[o][i][r][c] = 0
if bias is not None:
for b in range(out_channel):
bias[b] = 0
status = Deconvolution2d(
out_data # out_channel x out_size x out_size
,
in_data # in_channel x in_size x in_size
,
kernel # out_channel x in_channel x kernel_size x kernel_size
,
bias # out_channel
,
stride,
padding)
if status:
dlr_common.DlrPrint(f"in_data:\n{in_data}", flush=True)
dlr_common.DlrPrint(f"kernel:\n{kernel}", flush=True)
dlr_common.DlrPrint(f"bias:\n{bias}", flush=True)
dlr_common.DlrPrint(f"out_data:\n{out_data}", flush=True)
def TestConcat2d(_dtype):
"""
_dtype: specify data type of data one of {np.int32, np.float32, np.float64}
"""
dims=[0,1]
for dim in dims:
if dim==0:
in_rowsA=3
in_colsA=4
in_rowsB=5
in_colsB=in_colsA
out_rows=in_rowsA+in_rowsB
out_cols=in_colsA
else:
in_rowsA=3
in_colsA=4
in_rowsB=in_rowsA
in_colsB=4
out_rows=in_rowsA
out_cols=in_colsA+in_colsB
in_dataA = np.empty([in_rowsA,in_colsA], dtype=_dtype)
in_dataB = np.empty([in_rowsB,in_colsB], dtype=_dtype)
out_data = np.empty([out_rows,out_cols], dtype=_dtype)
v = 0
for r in range(in_rowsA):
for c in range(in_colsA):
in_dataA[r][c] = v
v += 1
for r in range(in_rowsB):
for c in range(in_colsB):
in_dataB[r][c] = v
v -= 1
print(f"in_dataA={in_dataA.shape}");
print(f"in_dataB={in_dataB.shape}");
print(f"out_data={out_data.shape}");
status = Concat2d( out_data
, in_dataA
, in_dataB
, dim
, rigor=True
, verbose=True)
if status:
dlr_common.DlrPrint(f"in_dataA:\n{in_dataA}")
dlr_common.DlrPrint(f"in_dataB:\n{in_dataB}")
dlr_common.DlrPrint(f"out_data:\n{out_data}")
def TestNorm3dBatch(_dtype):
"""
_dtype: specify data type of data one of {np.int32, np.float32, np.float64}
"""
in_size = [1, 2, 3] # [ in_channel, ....]
in_data = (100 + 100) * np.random.random(size=in_size) - 100
out_data = np.empty(in_size, dtype=_dtype)
running_mean = (100 + 100) * np.random.random(size=(in_size[0])) - 100
running_var = (100 + 100) * np.random.random(size=(in_size[0])) - 100
scale = (10 + 10) * np.random.random(size=(in_size[0])) - 10
bias = (10 + 10) * np.random.random(size=(in_size[0])) - 10
if _dtype is np.int32:
in_data = np.int32(in_data)
running_mean = np.int32(running_mean)
running_var = np.int32(running_var)
scale = np.int32(scale)
bias = np.int32(bias)
epsilon = 1e-5
status = Norm3dBatch(out_data,
in_data,
running_mean=running_mean,
running_var=running_var,
scale=scale,
bias=bias,
epsilon=epsilon,
rigor=True,
verbose=True)
if status:
dlr_common.DlrPrint(f"out_data:\n{out_data}", flush=True)
dlr_common.DlrPrint(f"in_data:\n{in_data}", flush=True)
dlr_common.DlrPrint(f"running_mean:\n{running_mean}", flush=True)
dlr_common.DlrPrint(f"running_var:\n{running_var}", flush=True)
dlr_common.DlrPrint(f"scale:\n{scale}", flush=True)
dlr_common.DlrPrint(f"bias:\n{bias}", flush=True)
dlr_common.DlrPrint(f"epsilon:\n{epsilon}", flush=True)
def TestPooling2dMax(_dtype):
"""
_dtype: specify data type of data one of {np.int32, np.float32, np.float64}
"""
in_channel = 1
in_size = 8
out_channel = in_channel
kernel_size = 2
stride = 1
padding = 0
ceil_mode = False
in_data = np.empty([in_channel, in_size, in_size], dtype=_dtype)
status, out_size = GetOutputSizeOfPooling2dMax(in_size,
kernel_size,
stride,
padding,
rigor=True,
verbose=True)
if not status: return
# prepare arrays to pass to C function
out_data = np.empty([out_channel, out_size, out_size], dtype=_dtype)
v = 0
for i in range(in_channel):
for r in range(in_size):
for c in range(in_size):
in_data[i][r][c] = v
v += 1
status = Pooling2dMax(
out_data # out_channel x out_size x out_size
,
in_data # in_channel x in_size x in_size
,
kernel_size # in_channel x out_channel x kernel_size x kernel_size
,
stride,
padding,
ceil_mode,
rigor=True,
verbose=True)
if status:
dlr_common.DlrPrint(f"in_data:\n{in_data}")
dlr_common.DlrPrint(f"out_data:\n{out_data}")
def TestActivations(_dtype):
"""
_dtype: specify data type of data one of {np.int32, np.float32, np.float64}
"""
funcs = ["ReLu", "LeakyReLu", "Tanh", "Sigmoid"]
for func in funcs:
func_name = "Activation" + func
dims = [1, 2]
for dim in dims:
ndim = np.random.randint(10, size=(dim))
ndim += 1 # prevent 0 in dimension
in_data = (100 + 100) * np.random.random(size=ndim) - 100
if _dtype is np.int32:
in_data = np.int32(in_data)
out_data = np.empty(ndim, dtype=_dtype)
#status = locals()[func_name]( out_data
status = globals()[func_name](out_data,
in_data,
rigor=True,
verbose=True)
if status:
dlr_common.DlrPrint(f"in_data\n{in_data}")
dlr_common.DlrPrint(f"out_data\n{out_data}")
status = LinearNd(
out_data # ndim x out_size
,
in_data # ndim x in_size
,
weight # out_size x in_size
,
bias # out_size
)
if status:
dlr_common.DlrPrint(f"in_data:\n{in_data}", flush=True)
dlr_common.DlrPrint(f"weight:\n{weight}", flush=True)
dlr_common.DlrPrint(f"bias:\n{bias}", flush=True)
dlr_common.DlrPrint(f"out_data:\n{out_data}", flush=True)
#===============================================================================
if __name__ == '__main__':
dlr_common.DlrPrint("Testing LinarNd", flush=True)
dlr_common.DlrPrint("*********************", flush=True)
TestLinearNd(_dtype=np.int32)
TestLinearNd(_dtype=np.float32)
TestLinearNd(_dtype=np.float64)
#===============================================================================
# Revision history:
#
# 2020.09.30: argument order of bias and bias_size changed
# 2020.04.25: Started by Ando Ki ([email protected])
#===============================================================================
for c in range(in_colsB):
in_dataB[r][c] = v
v -= 1
print(f"in_dataA={in_dataA.shape}");
print(f"in_dataB={in_dataB.shape}");
print(f"out_data={out_data.shape}");
status = Concat2d( out_data
, in_dataA
, in_dataB
, dim
, rigor=True
, verbose=True)
if status:
dlr_common.DlrPrint(f"in_dataA:\n{in_dataA}")
dlr_common.DlrPrint(f"in_dataB:\n{in_dataB}")
dlr_common.DlrPrint(f"out_data:\n{out_data}")
if __name__=='__main__':
dlr_common.DlrPrint("Testing Conat2d", flush=True);
dlr_common.DlrPrint("*********************", flush=True)
TestConcat2d(_dtype=np.int32)
#TestConcat2d(_dtype=np.float32)
#TestConcat2d(_dtype=np.float64)
#===============================================================================
# Revision history:
#
# 2020.04.58: Started by Ando Ki ([email protected])
#===============================================================================
status = Pooling2dMax(
out_data # out_channel x out_size x out_size
,
in_data # in_channel x in_size x in_size
,
kernel_size # in_channel x out_channel x kernel_size x kernel_size
,
stride,
padding,
ceil_mode,
rigor=True,
verbose=True)
if status:
dlr_common.DlrPrint(f"in_data:\n{in_data}")
dlr_common.DlrPrint(f"out_data:\n{out_data}")
if __name__ == '__main__':
dlr_common.DlrPrint("Testing Pooling2dMax", flush=True)
dlr_common.DlrPrint("*********************", flush=True)
TestPooling2dMax(_dtype=np.int32)
#TestPooling2dMax(_dtype=np.float32)
#TestPooling2dMax(_dtype=np.float64)
#===============================================================================
# Revision history:
#
# 2020.04.58: Started by Ando Ki ([email protected])
#===============================================================================
dims = [1, 2]
for dim in dims:
ndim = np.random.randint(10, size=(dim))
ndim += 1 # prevent 0 in dimension
in_data = (100 + 100) * np.random.random(size=ndim) - 100
if _dtype is np.int32:
in_data = np.int32(in_data)
out_data = np.empty(ndim, dtype=_dtype)
#status = locals()[func_name]( out_data
status = globals()[func_name](out_data,
in_data,
rigor=True,
verbose=True)
if status:
dlr_common.DlrPrint(f"in_data\n{in_data}")
dlr_common.DlrPrint(f"out_data\n{out_data}")
if __name__ == '__main__':
dlr_common.DlrPrint("Testing Activations", flush=True)
dlr_common.DlrPrint("*********************", flush=True)
#TestActivations(_dtype=np.int32)
TestActivations(_dtype=np.float32)
#TestActivations(_dtype=np.float64)
#===============================================================================
# Revision history:
#
# 2020.04.58: Started by Ando Ki ([email protected])
#===============================================================================
running_var=running_var,
scale=scale,
bias=bias,
epsilon=epsilon,
rigor=True,
verbose=True)
if status:
dlr_common.DlrPrint(f"out_data:\n{out_data}", flush=True)
dlr_common.DlrPrint(f"in_data:\n{in_data}", flush=True)
dlr_common.DlrPrint(f"running_mean:\n{running_mean}", flush=True)
dlr_common.DlrPrint(f"running_var:\n{running_var}", flush=True)
dlr_common.DlrPrint(f"scale:\n{scale}", flush=True)
dlr_common.DlrPrint(f"bias:\n{bias}", flush=True)
dlr_common.DlrPrint(f"epsilon:\n{epsilon}", flush=True)
#===============================================================================
if __name__ == '__main__':
dlr_common.DlrPrint("Testing Norm2dBatch", flush=True)
dlr_common.DlrPrint("*********************", flush=True)
#TestNorm2dBatch(_dtype=np.int32)
TestNorm2dBatch(_dtype=np.float32)
#TestNorm2dBatch(_dtype=np.float64)
#===============================================================================
# Revision history:
#
# 2020.09.30: argument order of bias and bias_size changed
# 2020.04.25: Started by Ando Ki ([email protected])
#===============================================================================
,
in_data # in_channel x in_size x in_size
,
kernel # out_channel x in_channel x kernel_size x kernel_size
,
bias # out_channel
,
stride,
padding)
if status:
dlr_common.DlrPrint(f"in_data:\n{in_data}", flush=True)
dlr_common.DlrPrint(f"kernel:\n{kernel}", flush=True)
dlr_common.DlrPrint(f"bias:\n{bias}", flush=True)
dlr_common.DlrPrint(f"out_data:\n{out_data}", flush=True)
#===============================================================================
if __name__ == '__main__':
dlr_common.DlrPrint("Testing Deconvolution2d", flush=True)
dlr_common.DlrPrint("*********************", flush=True)
#TestDeconvolution2d(_dtype=np.int32)
TestDeconvolution2d(_dtype=np.float32)
#TestDeconvolution2d(_dtype=np.float64)
#===============================================================================
# Revision history:
#
# 2020.09.30: argument order of bias and bias_size changed
# 2020.04.25: Started by Ando Ki ([email protected])
#===============================================================================
